diff --git a/badges/interrogate_badge.svg b/badges/interrogate_badge.svg new file mode 100644 index 0000000..0394f30 --- /dev/null +++ b/badges/interrogate_badge.svg @@ -0,0 +1,58 @@ + + interrogate: 16.2% + + + + + + + + + + + interrogate + interrogate + 16.2% + 16.2% + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/build/build_container.sh b/build/build_container.sh deleted file mode 100755 index 966d0e2..0000000 --- a/build/build_container.sh +++ /dev/null @@ -1,170 +0,0 @@ -#!/bin/bash - -# Default variables - -ARCH_TYPE="gpu" -FORMAT="docker" -OUTPUT_FILE="Dockerfile" -IS_DEVEL="False" -RAPIDS_VERSION="23.08" -CUDA_VERSION="12.0" -UBUNTU_VERSION="20.04" -PYTHON_VERSION="3.10" -DOCKERFILE_DIR=docker/ -RECIPE_ONLY=0 - -# For local hpccm installs -OLD_PATH=$PATH -export PATH=$PATH:$HOME/.local/bin/ - -function print_help() { - echo "Usage: ./build_docker.sh [ARCH_TYPE]" - echo "" - echo "Standard options:" - echo " -h or -help Display this help and exit" - echo " --device ARCH_TYPE: Build a container based on arch type." - echo " Use 'gpu' for GPU container-based architecture." - echo " Use 'cpu' for CPU container-based architecture (default='$ARCH_TYPE')." - echo " --rapids-version RAPIDS_VERSION Defines which version of RAPIDS AI will be used (default='$RAPIDS_VERSION')." - echo " --cuda-version CUDA_VERSION Defines which version of CUDA will be used (default='$CUDA_VERSION')." - echo " --os-version OS_VERSION Defines which version of the container will be used (default='$UBUNTU_VERSION')." - echo " --python-version PYTHON_VERSION Defines which version of the python interpreter will be used (default='$PYTHON_VERSION')." - echo " --format FORMAT Select the container backend for this build." - echo " Use 'docker' for Docker images." - echo " Use 'singularity' for SIF images (default='$FORMAT')." - echo " --recipe-only Print the recipe of the target only. It does not generate an image." - echo " --devel Defines extra packages for development purpose." - echo "" -} - -POSITIONAL_ARGS=() - -while [[ $# -gt 0 ]]; do - case $1 in - -h|--help) - print_help - exit 0 - ;; - --device) - ARCH_TYPE="$2" - shift - shift - ;; - --rapids-version) - RAPIDS_VERSION="$2" - shift - shift - ;; - --cuda-version) - CUDA_VERSION="$2" - shift - shift - ;; - --os-version) - UBUNTU_VERSION="$2" - shift - shift - ;; - --python-version) - PYTHON_VERSION="$2" - shift - shift - ;; - --format) - FORMAT="$2" - shift - shift - ;; - --recipe-only) - RECIPE_ONLY=1 - shift - ;; - --devel) - IS_DEVEL="True" - shift - ;; - -*|--*) - echo "Unknown option $1" - exit 1 - ;; - *) - POSITIONAL_ARGS+=("$1") # save positional arg - shift # past argument - ;; - esac -done - -if [[ "${#POSITIONAL_ARGS[@]}" -gt 1 ]]; then - echo "Invalid number of positional arguments" - exit 1 -elif [[ "${#POSITIONAL_ARGS[@]}" -eq 1 ]]; then - ARCH_TYPE="${POSITIONAL_ARGS[0]}" -fi - -if [[ "$ARCH_TYPE" != "cpu" && "$ARCH_TYPE" != "gpu" ]]; then - echo "Invalid '--device' type. Check -h|--help for further details." - exit 1 -fi - -if [[ "$FORMAT" != "docker" && "$FORMAT" != "singularity" ]]; then - echo "Invalid container backend for '--format'. Check -h|--help for further details." - exit 1 -fi - -function GET_CONTAINER_CMD() { - OLDIFS="$IFS" - IFS=":" - - for P in $PATH; do - if test -f "$P/podman"; then - CONTAINER_CMD=podman - break - else - CONTAINER_CMD=docker - fi - done - - IFS=$OLDIFS - - echo $CONTAINER_CMD -} - -[[ -z "${CONTAINER_CMD}" ]] && CONTAINER_CMD=$(GET_CONTAINER_CMD) - -function FIND_CMD() { - if ! command -v $1 &> /dev/null - then - echo $2 - exit -1 - fi -} - -FIND_CMD hpccm "Binary 'hpccm' could not be found: install HPC container maker first. - Check https://github.com/NVIDIA/hpc-container-maker for more details" - -mkdir -p $DOCKERFILE_DIR - -hpccm --recipe hpccm/build_docker.py \ - --userarg device-target=$ARCH_TYPE \ - devel=$IS_DEVEL \ - rapids-version=$RAPIDS_VERSION \ - cuda-version=$CUDA_VERSION \ - ubuntu-version=$UBUNTU_VERSION \ - python-version=$PYTHON_VERSION \ - --format $FORMAT > $DOCKERFILE_DIR/$OUTPUT_FILE - -if [ $RECIPE_ONLY -eq 1 ]; then - cat $DOCKERFILE_DIR/$OUTPUT_FILE -else - if [[ "$FORMAT" == "docker" ]]; then - FIND_CMD $CONTAINER_CMD "Docker binaries are not found." - $CONTAINER_CMD build $DOCKERFILE_DIR -t dasf:$ARCH_TYPE - else - FIND_CMD singularity "Singularity binaries are not found." - singularity build dasf_$ARCH_TYPE.sif $DOCKERFILE_DIR/$OUTPUT_FILE - fi -fi - -rm -rf $DOCKERFILE_DIR - -export PATH=$OLD_PATH diff --git a/build/hpccm/build_docker.py b/build/hpccm/build_docker.py deleted file mode 100644 index 7628a2f..0000000 --- a/build/hpccm/build_docker.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -HPC Container Maker for DASF core -""" - - -def str2bool(string): - return string.lower() in ['true', '1', 't', 'y', 'yes'] - - -device_target = USERARG.get('device-target', 'gpu') -is_devel = str2bool(USERARG.get('devel', 'False')) - -if is_devel: - # Devel packages always use the latest CUDA version - cuda_version = "11.5" -else: - cuda_version = USERARG.get('cuda-version', '11.2') - -rapidsai_version = USERARG.get('rapids-version', '23.08') -ubuntu_version = USERARG.get('ubuntu-version', '20.04') -python_version = USERARG.get('python-version', '3.10') - -if python_version: - python_version = f"-py{python_version}" - -gpu_image_devel = f"rapidsai/base:{rapidsai_version}-cuda{cuda_version}{python_version}" - -# GPU image needs to be fixed due to dependency matrix -gpu_image = "nvcr.io/nvidia/pytorch:23.06-py3" - -cpu_image = f"ubuntu:{ubuntu_version}" - -if device_target.lower() == "cpu": - Stage0 += baseimage(image=cpu_image) -elif device_target.lower() == "gpu": - # XXX: There is no way to use old GPUs with 11.5 CUDA. - # if is_devel: - # Stage0 += baseimage(image=gpu_image_devel) - # else: - Stage0 += baseimage(image=gpu_image) -else: - raise RuntimeError(f"Device target {device_target} is not known.") - -ubuntu_unified_version = "".join(ubuntu_version.split(".")) - -apt_keys = [ - f"https://developer.download.nvidia.com/compute/cuda/repos/ubuntu{ubuntu_unified_version}/x86_64/3bf863cc.pub", - f"https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu{ubuntu_unified_version}/x86_64/7fa2af80.pub" -] - -packages_list = ["git", "graphviz", "gcc", "python3-dev", "g++", "openssh-client", "wget"] - -if is_devel: - # Install NVIDIA NSight packages - package_list += ["nsight-compute"] - -pip_package_install = "pip3 install --extra-index-url https://test.pypi.org/simple/ XPySom-dask git+https://github.com/discovery-unicamp/dasf-core.git" - -if device_target.lower() == "cpu": - packages_list.extend(["python3-pip"]) - - Stage0 += apt_get(ospackages=packages_list) - - pip_package_install = ("%s jupyterlab" % pip_package_install) - -elif device_target.lower() == "gpu": - if is_devel: - Stage0 += shell(commands=["conda install -n base -c rapidsai git graphviz gcc cxx-compiler openssh wget kvikio -y"]) - else: - Stage0 += apt_get(keys=apt_keys, ospackages=packages_list) - Stage0 += apt_get(ospackages=packages_list) - - pip_package_install = ("%s cupy_xarray" % pip_package_install) - - if is_devel: - pip_package_install = ("%s %s" % (pip_package_install, "git+https://github.com/cupy/cupy.git")) - else: - pip_package_install = ("%s %s" % (pip_package_install, "cupy==13.0.0b1")) - Stage0 += shell(commands=["rm -r /usr/local/lib/python3.10/dist-packages/cupy_cuda12x-12.0.0b3.dist-info"]) # not the best solution but it works - - -Stage0 += shell(commands=["pip3 install pip --upgrade"]) - -Stage0 += shell(commands=[pip_package_install]) - -# TODO: fix numpy issue with version 1.24 and other fixed reqs -Stage0 += shell(commands=["pip install \"numpy<1.24\" bokeh==2.4.3 \"protobuf<=3.20.1\" \"charset-normalizer<3.0\" \"tornado<6.2\""]) - -if is_devel: - Stage0 += shell(commands=["wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh -b && cp /root/miniconda3/bin/conda /usr/bin/conda"]) - - Stage0 += shell(commands=["conda create -n kvikio -c rapidsai"]) - - Stage0 += shell(commands=["conda install -n kvikio -c rapidsai kvikio -y"]) - -Stage0 += workdir(directory='/dasf') - -if is_devel: - Stage0 += label(metadata={'dasf-devel': 'latest'}) -else: - Stage0 += label(metadata={'dasf': 'latest'}) diff --git a/build/start_jupyter_server.sh b/build/start_jupyter_server.sh deleted file mode 100755 index 1f8710c..0000000 --- a/build/start_jupyter_server.sh +++ /dev/null @@ -1,82 +0,0 @@ -#!/bin/bash - -PORT=8891 -TYPE="gpu" -FORMAT="docker" - -function print_help() { - echo "Usage: ./build_docker.sh [ARCH_TYPE]" - echo "" - echo "Standard options:" - echo " -h or -help Display this help and exit" - echo " --device ARCH_TYPE: Build a container based on arch type." - echo " Use 'gpu' for GPU container-based architecture." - echo " Use 'cpu' for CPU container-based architecture (default='$TYPE')." - echo " --port PORT Use the prefered port to start the server (default='$PORT')." - echo " --format FORMAT Select the container backend for this service." - echo " Use 'docker' for Docker images." - echo " Use 'singularity' for SIF images (default='$FORMAT')." - echo "" -} - -while [[ $# -gt 0 ]]; do - case $1 in - -h|--help) - print_help - exit 0 - ;; - --device) - TYPE="$2" - shift - shift - ;; - --format) - FORMAT="$2" - shift - shift - ;; - --port|-p) - PORT="$2" - shift - shift - ;; - *) - break; - esac -done - -function GET_CONTAINER_CMD() { - OLDIFS="$IFS" - IFS=":" - - for P in $PATH; do - if test -f "$P/podman"; then - CONTAINER_CMD=podman - break - else - CONTAINER_CMD=docker - fi - done - - IFS=$OLDIFS - - echo $CONTAINER_CMD -} - -[[ -z "${CONTAINER_CMD}" ]] && CONTAINER_CMD=$(GET_CONTAINER_CMD) - -EXTRA_ARGS=$@ - -if [[ "$FORMAT" == "docker" ]]; then - if [[ "$TYPE" == "gpu" ]]; then - EXTRA_ARGS="$EXTRA_ARGS --gpus all" - fi - - $CONTAINER_CMD run -it --rm -p $PORT:$PORT -e SHELL="/bin/bash" --network=host $EXTRA_ARGS dasf:$TYPE python3 -m jupyterlab --allow-root --ServerApp.port $PORT --no-browser --ServerApp.ip='0.0.0.0' -elif [[ "$FORMAT" == "singularity" ]]; then - if [[ "$TYPE" == "gpu" ]]; then - EXTRA_ARGS="$EXTRA_ARGS --nv" - fi - - singularity exec $EXTRA_ARGS dasf_$TYPE.sif python3 -m jupyterlab --allow-root --ServerApp.port $PORT --no-browser --ServerApp.ip=0.0.0.0 -fi diff --git a/build/vars.sh b/build/vars.sh deleted file mode 100644 index 96307e2..0000000 --- a/build/vars.sh +++ /dev/null @@ -1,8 +0,0 @@ -FORMAT="docker" -OUTPUT_FILE="Dockerfile" -IS_DEVEL="False" -RAPIDS_VERSION="22.08" -CUDA_VERSION="11.2" -UBUNTU_VERSION="20.04" -DOCKERFILE_DIR=docker/ -PORT=8891 diff --git a/dasf/__init__.py b/dasf/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/dasf/datasets/__init__.py b/dasf/datasets/__init__.py deleted file mode 100644 index d795e60..0000000 --- a/dasf/datasets/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from dasf.datasets.base import * # noqa -from dasf.datasets.datasets import * # noqa - -files = [ - # Base Dataset imports - "DatasetType", - "Dataset", - "DatasetArray", - "DatasetZarr", - "DatasetHDF5", - "DatasetXarray", - "DatasetLabeled", - "DatasetDataFrame", - "DatasetParquet", - # Others - "make_blobs", - "make_classification", -] - -__all__ = files diff --git a/dasf/datasets/base.py b/dasf/datasets/base.py deleted file mode 100644 index 897dc97..0000000 --- a/dasf/datasets/base.py +++ /dev/null @@ -1,1475 +0,0 @@ -#!/usr/bin/env python3 - -import json -import os -from numbers import Number - -import dask -import dask.array as da -import dask.dataframe as ddf -import h5py -import numpy as np -import numpy.lib.format -import pandas as pd -import xarray as xr -import zarr - -try: - import cudf - import cupy as cp - - # This is just to enable Xarray Cupy capabilities - import cupy_xarray as cx # noqa - import dask_cudf as dcudf -except ImportError: # pragma: no cover - pass - -try: - import numcodecs - from kvikio.nvcomp_codec import NvCompBatchCodec - from kvikio.zarr import GDSStore -except ImportError: # pragma: no cover - pass - -from pathlib import Path - -from dasf.transforms.base import TargeteredTransform -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import ( - human_readable_size, - is_gds_supported, - is_kvikio_compat_mode, - is_kvikio_supported, - is_nvcomp_codec_supported, -) -from dasf.utils.types import is_array, is_dask_array - - -class Dataset(TargeteredTransform): - """Class representing a generic dataset based on a TargeteredTransform - object. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - *args : type - Additional arguments without keys. - **kwargs : type - Additional keyworkded arguments. - - """ - def __init__(self, - name: str, - download: bool = False, - root: str = None, - *args, - **kwargs): - super().__init__(*args, **kwargs) - - # Dataset internals - self._name = name - self._download = download - self._root = root - self._metadata = {} - self._data = None - self._chunks = None - - self.__set_dataset_cache_dir() - - self.download() - - def __set_dataset_cache_dir(self): - """Generate cached directory in $HOME to store dataset(s). - - """ - self._cache_dir = os.path.abspath(str(Path.home()) + "/.cache/dasf/datasets/") - os.makedirs(self._cache_dir, exist_ok=True) - - if self._root is None: - self._root = self._cache_dir - - def download(self): - """Skeleton of the download method. - - """ - if self._download: - raise NotImplementedError("Function download() needs to be defined") - - def __len__(self) -> int: - """Return internal data length. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return len(self._data) - - def __getitem__(self, idx): - """Generic __getitem__() function based on internal data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return self._data.__getitem__(idx) - - -class DatasetArray(Dataset): - """Class representing an dataset wich is defined as an array of a defined - shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - """ - def __init__(self, - name: str, - download: bool = False, - root: str = None, - chunks="auto"): - - Dataset.__init__(self, name, download, root) - - self._chunks = chunks - - self._root_file = root - - if root is not None: - if not os.path.isfile(root): - raise Exception("Array requires a root=filename.") - - self._root = os.path.dirname(root) - - def __operator_check__(self, other): - assert self._data is not None, "Data is not loaded yet." - if isinstance(other, DatasetArray): - return other._data - return other - - def __repr__(self): - """Return a class representation based on internal array. - - """ - return repr(self._data) - - def __array__(self, dtype=None): - assert self._data is not None, "Data is not loaded yet." - return self._data - - def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): - assert self._data is not None, "Data is not loaded yet." - if method == '__call__': - scalars = [] - - for inp in inputs: - if isinstance(inp, Number): - scalars.append(inp) - elif isinstance(inp, self.__class__): - scalars.append(inp._data) - else: - return NotImplemented - - self.__class__(name=self._name, chunks=self._chunks) - self._data = ufunc(*scalars, **kwargs) - return self - return NotImplemented - - def __check_op_input(self, in_data): - """Return the proper type of data for operation - - >>> Result = DatasetArray + Numpy; or - >>> Result = DatasetArray + DatasetArray - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - """ - if is_array(in_data) or is_dask_array(in_data): - return in_data - if isinstance(in_data, self.__class__): - return in_data._data - raise TypeError("Data is incompatible with Array") - - def __add__(self, other): - """Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data + data - - def __sub__(self, other): - """Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data - data - - def __mul__(self, other): - """Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data * data - - def __div__(self, other): - """Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data / data - - def __copy_attrs_from_data(self): - """Extends metadata to new transformed object (after operations). - - """ - self._metadata["type"] = type(self._data) - - attrs = dir(self._data) - for attr in attrs: - if not attr.startswith("__") and callable(getattr(self._data, attr)): - if not hasattr(self, attr): - self.__dict__[attr] = getattr(self._data, attr) - - def __npy_header(self): - """Read an array header from a filelike object. - - """ - with open(self._root_file, 'rb') as fobj: - version = numpy.lib.format.read_magic(fobj) - func_name = "read_array_header_" + "_".join(str(v) for v in version) - func = getattr(numpy.lib.format, func_name) - return func(fobj) - - def _lazy_load(self, xp, **kwargs): - """Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - """ - npy_shape = self.shape - - local_data = dask.delayed(xp.load)(self._root_file, **kwargs) - - local_data = da.from_delayed(local_data, shape=npy_shape, dtype=xp.float32, meta=xp.array(())) - if isinstance(self._chunks, tuple): - local_data = local_data.rechunk(self._chunks) - - return local_data - - def _load(self, xp, **kwargs): - """Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - """ - - return xp.load(self._root_file, **kwargs) - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._root_file is not None, ("There is no temporary file to " - "inspect") - assert os.path.isfile(self._root_file), ("The root variable should " - "be a NPY file") - - return self.inspect_metadata() - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(cp) - self.__copy_attrs_from_data() - return self - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(np) - self.__copy_attrs_from_data() - return self - - def _load_gpu(self): - """Load data with GPU container (e.g. cupy). - - """ - self._metadata = self._load_meta() - self._data = self._load(cp) - self.__copy_attrs_from_data() - return self - - def _load_cpu(self): - """Load data with CPU container (e.g. numpy). - - """ - self._metadata = self._load_meta() - self._data = self._load(np) - self.__copy_attrs_from_data() - return self - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - @property - def shape(self) -> tuple: - """Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - """ - return self.__npy_header()[0] - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - array_file_size = human_readable_size( - os.path.getsize(self._root_file), - decimal=2 - ) - - npy_shape = self.shape - - return { - "size": array_file_size, - "file": self._root_file, - "shape": npy_shape, - "block": {"chunks": self._chunks}, - } - - -class DatasetZarr(Dataset): - """Class representing an dataset wich is defined as a Zarr array of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - """ - def __init__(self, - name: str, - download: bool = False, - root: str = None, - backend: str = None, - chunks=None): - - Dataset.__init__(self, name, download, root) - - self._backend = backend - self._chunks = chunks - - self._root_file = root - - if root is not None: - if not os.path.isfile(root): - self._root = root - else: - self._root = os.path.dirname(root) - - def _lazy_load(self, xp, **kwargs): - """Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - """ - if (self._backend == "kvikio" and is_kvikio_supported() and - (is_gds_supported() or is_kvikio_compat_mode()) - and is_nvcomp_codec_supported()): - store = GDSStore(self._root_file) - meta = json.loads(store[".zarray"]) - meta["compressor"] = NvCompBatchCodec("lz4").get_config() - store[".zarray"] = json.dumps(meta).encode() - - array = zarr.open_array(store, meta_array=xp.empty(())) - return da.from_zarr(array, chunks=array.chunks).map_blocks(xp.asarray) - - return da.from_zarr(self._root_file, chunks=self._chunks).map_blocks(xp.asarray) - - def _load(self, xp, **kwargs): - """Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - """ - return zarr.open(self._root_file, mode='r', meta_array=xp.empty(())) - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(np) - self.__copy_attrs_from_data() - return self - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(cp) - self.__copy_attrs_from_data() - return self - - def _load_cpu(self): - """Load data with CPU container (e.g. numpy). - - """ - self._metadata = self._load_meta() - self._data = self._load(np) - self.__copy_attrs_from_data() - return self - - def _load_gpu(self): - """Load data with GPU container (e.g. cupy). - - """ - self._metadata = self._load_meta() - self._data = self._load(cp) - self.__copy_attrs_from_data() - return self - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._root_file is not None, "There is no temporary file to inspect" - - return self.inspect_metadata() - - def __read_zarray(self, key): - """Returns the value of ZArray JSON metadata. - - """ - if self._root_file and os.path.isdir(self._root_file): - zarray = os.path.abspath(self._root_file + "/.zarray") - if os.path.exists(zarray): - try: - with open(zarray) as fz: - meta = json.load(fz) - return meta[key] - except Exception: - pass - return None - - @property - def shape(self) -> tuple: - """Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - """ - if not self._data: - shape = self.__read_zarray("shape") - if shape is not None: - return tuple(shape) - return tuple() - - return self._data.shape - - @property - def chunksize(self): - """Returns the chunksize of an array. - - Returns - ------- - tuple - A tuple with the chunksize. - - """ - if not self._data: - chunks = self.__read_zarray("chunks") - if chunks is not None: - return tuple(chunks) - return tuple() - - return self._data.chunksize - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - z = zarr.open(self._root_file, mode='r') - - info = {} - for k, v in z.info_items(): - info[k] = v - - if isinstance(self._chunks, bool) and self._chunks: - self._chunks = info["Chunk shape"] - - if self._chunks is None: - self._chunks = self.chunksize - - return { - "size": human_readable_size( - int(info["No. bytes"].split(' ')[0]) - ), - "compressor": info["Compressor"], - "type": info["Store type"], - "file": self._root_file, - "shape": info["Shape"], - "block": {"chunks": self._chunks}, - } - - def __repr__(self): - """Return a class representation based on internal array. - - """ - return repr(self._data) - - def __check_op_input(self, in_data): - """Return the proper type of data for operation - - >>> Result = DatasetZarr + Numpy; or - >>> Result = DatasetZarr + DatasetZarr - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - """ - if is_array(in_data) or is_dask_array(in_data): - return in_data - elif isinstance(in_data, self.__class__): - return in_data._data - raise TypeError("Data is incompatible with Array") - - def __add__(self, other): - """Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data + data - - def __sub__(self, other): - """Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data - data - - def __mul__(self, other): - """Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data * data - - def __div__(self, other): - """Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - """ - assert self._data is not None, "Data is not loaded yet." - data = self.__check_op_input(other) - return self._data / data - - def __copy_attrs_from_data(self): - """Extends metadata to new transformed object (after operations). - - """ - self._metadata["type"] = type(self._data) - - attrs = dir(self._data) - for attr in attrs: - if not attr.startswith("__") and callable(getattr(self._data, attr)): - if not hasattr(self, attr): - self.__dict__[attr] = getattr(self._data, attr) - - -class DatasetHDF5(Dataset): - """Class representing an dataset wich is defined as a HDF5 dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - dataset_path : str - Relative path of the internal HDF5 dataset (the default is None). - - """ - def __init__(self, - name: str, - download: str = False, - root: str = None, - chunks="auto", - dataset_path: str = None): - - Dataset.__init__(self, name, download, root) - - self._chunks = chunks - - self._root_file = root - - self._dataset_path = dataset_path - - if root is not None: - if not os.path.isfile(root): - raise Exception("HDF5 requires a root=filename.") - - self._root = os.path.dirname(root) - - if dataset_path is None: - raise Exception("HDF5 requires a path.") - - def _lazy_load(self, xp, **kwargs): - """Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - """ - f = h5py.File(self._root_file) - data = f[self._dataset_path] - return da.from_array(data, chunks=self._chunks, meta=xp.array(())) - - def _load(self, xp=None, **kwargs): - """Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) (placeholder). - **kwargs : type - Additional `kwargs` to `xp.load` function. - - """ - f = h5py.File(self._root_file) - return f[self._dataset_path] - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(np) - return self - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data = self._lazy_load(cp) - return self - - def _load_cpu(self): - """Load data with CPU container (e.g. numpy). - - """ - self._metadata = self._load_meta() - self._data = self._load() - return self - - def _load_gpu(self): - """Load data with GPU container (e.g. cupy). - - """ - self._metadata = self._load_meta() - self._data = cp.asarray(self._load()) - return self - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._root_file is not None, "There is no temporary file to inspect" - assert self._dataset_path is not None, "There is no path to fetch data" - - return self.inspect_metadata() - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - f = h5py.File(self._root_file) - data = f[self._dataset_path] - - array_file_size = human_readable_size( - data.size, decimal=2 - ) - - return { - "size": array_file_size, - "file": self._root_file, - "shape": data.shape, - "block": {"chunks": self._chunks}, - } - - -class DatasetXarray(Dataset): - """Class representing an dataset wich is defined as a Xarray dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - data_var : Any - Key (or index) of the internal Xarray dataset (the default is None). - - """ - def __init__(self, - name: str, - download: bool = False, - root: str = None, - chunks=None, - data_var=None): - Dataset.__init__(self, name, download, root) - - self._chunks = chunks - - self._root_file = root - - self._data_var = data_var - - if chunks and not isinstance(chunks, dict): - raise Exception("Chunks should be a dict.") - - if root is not None: - if not os.path.isfile(root): - raise Exception("HDF5 requires a root=filename.") - - self._root = os.path.dirname(root) - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - assert self._chunks is not None, "Lazy operations require chunks" - - if self._data_var: - self._data = xr.open_dataset(self._root_file, - chunks=self._chunks) - else: - self._data = xr.open_dataarray(self._root_file, - chunks=self._chunks) - self._metadata = self._load_meta() - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - assert self._chunks is not None, "Lazy operations require chunks" - - if self._data_var: - self._data = xr.open_dataset(self._root_file, - chunks=self._chunks).as_cupy() - else: - self._data = xr.open_dataarray(self._root_file, - chunks=self._chunks).as_cupy() - self._metadata = self._load_meta() - - def _load_cpu(self): - """Load data with CPU container (e.g. numpy). - - """ - if self._data_var: - self._data = xr.open_dataset(self._root_file) - else: - self._data = xr.open_dataarray(self._root_file) - self._data.load() - self._metadata = self._load_meta() - - def _load_gpu(self): - """Load data with GPU container (e.g. cupy). - - """ - if self._data_var: - self._data = xr.open_dataset(self._root_file).as_cupy() - else: - self._data = xr.open_dataarray(self._root_file).as_cupy() - self._data.load() - self._metadata = self._load_meta() - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._root_file is not None, "There is no temporary file to inspect" - - return self.inspect_metadata() - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - array_file_size = human_readable_size( - os.path.getsize(self._root_file), decimal=2 - ) - - return { - "size": array_file_size, - "file": self._root_file, - "coords": tuple(self._data.coords), - "attrs": self._data.attrs, - "block": {"chunks": self._chunks}, - } - - def __len__(self) -> int: - """Return internal data length. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - if self._data_var: - return len(self._data[self._data_var]) - - return len(self._data) - - def __getitem__(self, idx): - """A __getitem__() function based on internal Xarray data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - # Always slice a DataArray - if self._data_var: - return self._data[self._data_var].data[idx] - - return self._data.data[idx] - - -class DatasetLabeled(Dataset): - """A class representing a labeled dataset. Each item is a 2-element tuple, - where the first element is a array of data and the second element is the - respective label. The items can be accessed from `dataset[x]`. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - Attributes - ---------- - __chunks : type - Description of attribute `__chunks`. - - """ - def __init__(self, - name: str, - download: bool = False, - root: str = None, - chunks="auto"): - - Dataset.__init__(self, name, download, root) - - self._chunks = chunks - - def download(self): - """Download the dataset. - - """ - if hasattr(self, "_train") and hasattr(self._train, "download"): - self._train.download() - - if hasattr(self, "_val") and hasattr(self._val, "download"): - self._val.download() - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data - (train and labels). - - Returns - ------- - dict - A dictionary with metadata information. - - """ - metadata_train = self._train.inspect_metadata() - metadata_val = self._val.inspect_metadata() - - assert ( - metadata_train["shape"] == metadata_val["shape"] - ), "Train and Labels should have same shape: " + str( - metadata_train["shape"] - ) + " != " + str( - metadata_val["shape"] - ) - - return {"train": metadata_train, "labels": metadata_val} - - def _lazy_load(self, xp, **kwargs) -> tuple: - """Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Tuple - A Future object that will return a tuple: (data, label). - - """ - local_data = self._train._lazy_load(xp) - local_labels = self._val._lazy_load(xp) - - return (local_data, local_labels) - - def _load(self, xp, **kwargs) -> tuple: - """Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - Returns - ------- - Tuple - A 2-element tuple: (data, label) - - """ - local_data = self._train._load(xp) - local_labels = self._val._load(xp) - - return (local_data, local_labels) - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._train._root_file is not None, ( - "There is no temporary file to inspect" - ) - assert self._val._root_file is not None, ( - "There is no temporary file to inspect" - ) - assert os.path.isfile(self._train._root_file), ( - "The root variable should be a file" - ) - assert os.path.isfile(self._val._root_file), ( - "The root variable should be a file" - ) - - return self.inspect_metadata() - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data, self._labels = self._lazy_load(cp) - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._metadata = self._load_meta() - self._data, self._labels = self._lazy_load(np) - - def _load_gpu(self): - """Load data with GPU container (e.g. cupy). - - """ - self._metadata = self._load_meta() - self._data, self._labels = self._load(cp) - - def _load_cpu(self): - """Load data with CPU container (e.g. numpy). - - """ - self._metadata = self._load_meta() - self._data, self._labels = self._load(np) - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - def __getitem__(self, idx): - """A __getitem__() function for data and labeled data together. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return (self._data.__getitem__(idx), self._labels.__getitem__(idx)) - - -class DatasetDataFrame(Dataset): - """Class representing an dataset wich is defined as a dataframe. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - """ - def __init__(self, - name: str, - download: bool = True, - root: str = None, - chunks="auto"): - - Dataset.__init__(self, name, download, root) - - self._chunks = chunks - - self._root_file = root - - if root is not None: - if not os.path.isfile(root): - raise Exception("DataFrame requires a root=filename.") - - self._root = os.path.dirname(root) - - def _load_meta(self) -> dict: - """Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - assert self._root_file is not None, ( - "There is no temporary file to inspect" - ) - - return self.inspect_metadata() - - def inspect_metadata(self) -> dict: - """Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - """ - df_file_size = human_readable_size( - os.stat(self._root_file).st_size, decimal=2 - ) - - return { - "size": df_file_size, - "file": self._root_file, - "type": type(self._data), - "shape": self.shape, - "columns": list(self._data.columns), - "block": {"chunks": self._chunks}, - } - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._data = dcudf.read_csv(self._root_file) - self._metadata = self._load_meta() - return self - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._data = ddf.read_csv(self._root_file) - self._metadata = self._load_meta() - return self - - def _load_gpu(self): - """Load data with GPU container (e.g. CuDF). - - """ - self._data = cudf.read_csv(self._root_file) - self._metadata = self._load_meta() - return self - - def _load_cpu(self): - """Load data with CPU container (e.g. pandas). - - """ - self._data = pd.read_csv(self._root_file) - self._metadata = self._load_meta() - return self - - @task_handler - def load(self): - """Placeholder for load function. - - """ - ... - - @property - def shape(self) -> tuple: - """Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return self._data.shape - - def __len__(self) -> int: - """Return internal data length. - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return len(self._data) - - def __getitem__(self, idx): - """A __getitem__() function based on internal dataframe. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - """ - if self._data is None: - raise Exception("Data is not loaded yet") - - return self._data.iloc[idx] - - -class DatasetParquet(DatasetDataFrame): - """Class representing an dataset wich is defined as a Parquet. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - """ - def __init__(self, - name: str, - download: bool = True, - root: str = None, - chunks="auto"): - - DatasetDataFrame.__init__(self, name, download, root, chunks) - - def _lazy_load_gpu(self): - """Load data with GPU container + DASK. (It does not load immediattly) - - """ - self._data = dcudf.read_parquet(self._root_file) - self._metadata = self._load_meta() - return self - - def _lazy_load_cpu(self): - """Load data with CPU container + DASK. (It does not load immediattly) - - """ - self._data = ddf.read_parquet(self._root_file) - self._metadata = self._load_meta() - return self - - def _load_gpu(self): - """Load data with GPU container (e.g. CuDF). - - """ - self._data = cudf.read_parquet(self._root_file) - self._metadata = self._load_meta() - return self - - def _load_cpu(self): - """Load data with CPU container (e.g. pandas). - - """ - self._data = pd.read_parquet(self._root_file) - self._metadata = self._load_meta() - return self diff --git a/dasf/datasets/datasets.py b/dasf/datasets/datasets.py deleted file mode 100644 index ccb1396..0000000 --- a/dasf/datasets/datasets.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.datasets import make_blobs as make_blobs_MCPU -from sklearn.datasets import make_blobs as make_blobs_CPU - -try: - import cupy as cp - from cuml.dask.datasets import make_blobs as make_blobs_MGPU - from cuml.datasets import make_blobs as make_blobs_GPU -except ImportError: # pragma: no cover - pass - -from dask_ml.datasets import make_classification as make_classification_MCPU -from sklearn.datasets import make_classification as make_classification_CPU - -try: - from cuml.dask.datasets import make_classification as make_classification_MGPU - from cuml.datasets import make_classification as make_classification_GPU -except ImportError: # pragma: no cover - pass - -from dask_ml.datasets import make_regression as make_regression_MCPU -from sklearn.datasets import make_regression as make_regression_CPU - -try: - from cuml.dask.datasets import make_regression as make_regression_MGPU - from cuml.datasets import make_regression as make_regression_GPU -except ImportError: # pragma: no cover - pass - -from dasf.utils.funcs import is_dask_gpu_supported, is_dask_supported, is_gpu_supported -from dasf.utils.types import is_cpu_array - - -class make_blobs: - def __new__(cls, **kwargs): - instance = super().__new__(cls) - if kwargs is None: - return instance - else: - return instance(**kwargs) - - def _lazy_make_blobs_cpu(self, **kwargs): - return make_blobs_MCPU(**kwargs) - - def _lazy_make_blobs_gpu(self, **kwargs): - return make_blobs_MGPU(**kwargs) - - def _make_blobs_cpu(self, **kwargs): - return make_blobs_CPU(**kwargs) - - def _make_blobs_gpu(self, **kwargs): - return make_blobs_GPU(**kwargs) - - def __call__(self, **kwargs): - if is_dask_gpu_supported(): - if "centers" in kwargs and is_cpu_array(kwargs["centers"]): - kwargs["centers"] = cp.asarray(kwargs["centers"]) - return self._lazy_make_blobs_gpu(**kwargs) - elif is_dask_supported(): - return self._lazy_make_blobs_cpu(**kwargs) - elif is_gpu_supported(): - if "centers" in kwargs and is_cpu_array(kwargs["centers"]): - kwargs["centers"] = cp.asarray(kwargs["centers"]) - return self._make_blobs_gpu(**kwargs) - else: - return self._make_blobs_cpu(**kwargs) - - -class make_classification: - def __new__(cls, **kwargs): - instance = super().__new__(cls) - if kwargs is None: - return instance - else: - return instance(**kwargs) - - def _lazy_make_classification_cpu(self, **kwargs): - return make_classification_MCPU(**kwargs) - - def _lazy_make_classification_gpu(self, **kwargs): - return make_classification_MGPU(**kwargs) - - def _make_classification_cpu(self, **kwargs): - return make_classification_CPU(**kwargs) - - def _make_classification_gpu(self, **kwargs): - return make_classification_GPU(**kwargs) - - def __call__(self, **kwargs): - if is_dask_gpu_supported(): - return self._lazy_make_classification_gpu(**kwargs) - elif is_dask_supported(): - return self._lazy_make_classification_cpu(**kwargs) - elif is_gpu_supported(): - return self._make_classification_gpu(**kwargs) - else: - return self._make_classification_cpu(**kwargs) - - -class make_regression: - def __new__(cls, **kwargs): - instance = super().__new__(cls) - if kwargs is None: - return instance - else: - return instance(**kwargs) - - def _lazy_make_regression_cpu(self, **kwargs): - return make_regression_MCPU(**kwargs) - - def _lazy_make_regression_gpu(self, **kwargs): - return make_regression_MGPU(**kwargs) - - def _make_regression_cpu(self, **kwargs): - return make_regression_CPU(**kwargs) - - def _make_regression_gpu(self, **kwargs): - return make_regression_GPU(**kwargs) - - def __call__(self, **kwargs): - if is_dask_gpu_supported(): - return self._lazy_make_regression_gpu(**kwargs) - elif is_dask_supported(): - return self._lazy_make_regression_cpu(**kwargs) - elif is_gpu_supported(): - return self._make_regression_gpu(**kwargs) - else: - return self._make_regression_cpu(**kwargs) diff --git a/dasf/datasets/download.py b/dasf/datasets/download.py deleted file mode 100644 index e862c21..0000000 --- a/dasf/datasets/download.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.datasets.base import Dataset -from dasf.utils.funcs import download_file, download_file_from_gdrive - - -class DownloadWget(Dataset): - """Dataset downloadable via wget. - - Parameters - ---------- - url : str - The url to fetch the resource. - filename : str - Name of the file. - root : str - Directory to store the downloaded file. - download : bool - If it the dataset must be downloaded (the default is True). - - """ - def __init__(self, - url: str, - filename: str, - root: str, - download: bool = True): - self.__url = url - self.__filename = filename - - # Set download as false because this class overrides download() - Dataset.__init__(self, name="Download Wget", download=download, root=root) - - def download(self): - """Download the dataset. - - """ - if not self._download or self.__url is None: - return - - if hasattr(self, "download") and self._download is True: - self._root_file = download_file( - self.__url, self.__filename, self._root - ) - - if hasattr(self, "_download_check") and callable(self._download_check): - self._download_check() - - -class DownloadGDrive(Dataset): - """Dataset downloadable via Google Drive. - - Parameters - ---------- - google_file_id : str - Id of the google drive resource. - filename : str - Name of the file. - root : str - Directory to store the downloaded file. - download : bool - If it the dataset must be downloaded (the default is True). - - """ - def __init__(self, - google_file_id: str, - filename: str, - root: str, - download: bool = True): - self.__google_file_id = google_file_id - self.__filename = filename - - # Set download as false because this class overrides download() - Dataset.__init__( - self, name="Download Google Drive", download=download, root=root - ) - - def download(self): - """Download the dataset. - - """ - if not self._download or self.__google_file_id is None: - return - - if hasattr(self, "download") and self._download is True: - self._root_file = download_file_from_gdrive( - self.__google_file_id, self.__filename, self._root - ) - - if hasattr(self, "_download_check") and callable(self._download_check): - self._download_check() diff --git a/dasf/debug/__init__.py b/dasf/debug/__init__.py deleted file mode 100644 index 3268c0c..0000000 --- a/dasf/debug/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.debug.debug import Debug, VisualizeDaskData - -__all__ = ["Debug", "VisualizeDaskData"] diff --git a/dasf/debug/debug.py b/dasf/debug/debug.py deleted file mode 100644 index 21de139..0000000 --- a/dasf/debug/debug.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python3 - -from IPython.core.display import HTML as iHTML -from IPython.core.display import display as idisplay - -from dasf.utils.funcs import is_notebook -from dasf.utils.types import is_dask_array, is_dask_dataframe - - -class Debug: - """Print information about an operator (shape, datatype, etc.), and return - the self object reference. - - Parameters - ---------- - name : str - Name of the operator. - **kwargs : type - Additional keyworkded arguments to `Operator`. - - """ - def display(self, X): - print(is_notebook()) - if (is_dask_array(X) or is_dask_dataframe(X)) and is_notebook(): - idisplay(iHTML(X._repr_html_())) - else: - if hasattr(X, "shape"): - print("Datashape is:", X.shape) - - print("Datatype is:", type(X)) - print("Data content is:", X) - - return X - - -class VisualizeDaskData: - """Visualize DASK data from an operator. - - Parameters - ---------- - filename : str - A path to save the DASK visualization (the default is None). - **kwargs : type - Additional keyworkded arguments to `Operator`. - - """ - def __init__(self, filename: str = None): - self.filename = filename - - def display(self, X): - if not is_dask_array(X) and not is_dask_dataframe(X): - self.logger.warning("This is not a Dask element.") - return X - - if self.filename is not None: - X.visualize(self.filename) - else: - X.visualize() - - return X diff --git a/dasf/feature_extraction/__init__.py b/dasf/feature_extraction/__init__.py deleted file mode 100644 index 2870417..0000000 --- a/dasf/feature_extraction/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.feature_extraction.histogram import Histogram # noqa - -# from dasf.feature_extraction.transform import ConcatenateToDataframe # noqa -from dasf.feature_extraction.transform import ConcatenateToArray # noqa -from dasf.feature_extraction.transform import GetSubDataframe # noqa -from dasf.feature_extraction.transform import GetSubeCubeArray # noqa -from dasf.feature_extraction.transform import SampleDataframe # noqa - -__all__ = [ - "ConcatenateToArray", - # "ConcatenateToDataframe", - "SampleDataframe", - "GetSubeCubeArray", - "GetSubDataframe", - "Histogram", -] diff --git a/dasf/feature_extraction/histogram.py b/dasf/feature_extraction/histogram.py deleted file mode 100644 index 0deff06..0000000 --- a/dasf/feature_extraction/histogram.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 - -import dask.array as da -import numpy as np - -try: - import cupy as cp -except ImportError: # pragma: no cover - pass - -from dasf.transforms.base import TargeteredTransform, Transform - - -class Histogram(TargeteredTransform, Transform): - """Operator to extract the histogram of a data. - - Parameters - ---------- - bins : Optional[int] - Number of bins (the default is None). - range : tuple - 2-element tuple with the lower and upper range of the bins. If not - provided, range is simply (X.min(), X.max()) (the default is None). - normed : bool - If the historgram must be normalized (the default is False). - weights : type - An array of weights, of the same shape as X. Each value in a only - contributes its associated weight towards the bin count - (the default is None). - density : type - If False, the result will contain the number of samples in each bin. - If True, the result is the value of the probability density function - at the bin, normalized such that the integral over the range is 1 - (the default is None). - - Attributes - ---------- - bins - range - normed - weights - density - - """ - def __init__(self, - bins: int = None, - range: tuple = None, - normed: bool = False, - weights=None, - density=None, - *args, - **kwargs): - TargeteredTransform.__init__(self, *args, **kwargs) - - self._bins = bins - self._range = range - self._normed = normed - self._weights = weights - self._density = density - - def __lazy_transform_generic(self, X): - return da.histogram( - X, - bins=self._bins, - range=self._range, - normed=self._normed, - weights=self._weights, - density=self._density, - ) - - def __transform_generic(self, X, xp): - return xp.histogram( - X, - bins=self._bins, - range=self._range, - normed=self._normed, - weights=self._weights, - density=self._density, - ) - - def _lazy_transform_cpu(self, X): - return self.__lazy_transform_generic(X) - - def _lazy_transform_gpu(self, X, **kwargs): - return self.__lazy_transform_generic(X) - - def _transform_cpu(self, X, **kwargs): - return self.__transform_generic(X, np) - - def _transform_gpu(self, X, **kwargs): - return self.__transform_generic(X, cp) diff --git a/dasf/feature_extraction/transform.py b/dasf/feature_extraction/transform.py deleted file mode 100644 index 97629fe..0000000 --- a/dasf/feature_extraction/transform.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np - -try: - import cupy as cp -except ImportError: # pragma: no cover - pass - -from dasf.transforms.base import Transform -from dasf.utils.types import is_dataframe - - -class ConcatenateToArray(Transform): - """Concatenate data from different Arrays into a single array. - - Parameters - ---------- - flatten : bool - If the arrays must be flatten prior concatenating. If `False`, the - arrays must share the shape of last dimansions in order to be - concatenated (the default is False). - - """ - def __init__(self, flatten: bool = False): - self.flatten = flatten - - def __transform_generic(self, xp, **kwargs): - datas = None - for key in kwargs: - if datas is None: - if self.flatten: - flat = kwargs[key].flatten() - datas = xp.asarray([flat]) - else: - data = xp.asarray(kwargs[key]) - datas = xp.expand_dim(data, axis=len(data.shape)) - else: - if self.flatten: - flat = kwargs[key].flatten() - datas = xp.append(datas, xp.asarray([flat]), - axis=0) - else: - data = xp.asarray(kwargs[key]) - datas = xp.append(datas, data, axis=len(data.shape)) - - if self.flatten: - data = xp.transpose(datas) - else: - data = datas - - return data -# return data.rechunk({1: data.shape[1]}) - - def _transform_cpu(self, **kwargs): - return self.__transform_generic(np, **kwargs) - - def _transform_gpu(self, **kwargs): - return self.__transform_generic(cp, **kwargs) - - -class SampleDataframe: - """Return a subset with random samples of the original dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataset. - - """ - def __init__(self, percent: float): - self.__percent = float(percent / 100.0) - - def run(self, X): - """Returns a subset with random samples from the dataset `X`. - - Parameters - ---------- - X : Any - The dataset. - - Returns - ------- - Any - The sampled subset. - - """ - return X.sample(n=int(len(X) * self.__percent)) - - -class GetSubeCubeArray: - """Get a subcube with x% of samples from the original one. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the cube. - - """ - def __init__(self, percent: float): - self.__percent = float(percent / 100.0) - - assert ( - self.__percent > 0 and self.__percent <= 1.0 - ), "Percent must be in [0,1] range." - - def transform(self, X): - i_num, x_num, t_num = X.shape - - i_start_idx = int((i_num - (i_num * self.__percent)) / 2) - i_end_idx = int(i_start_idx + (self.__percent * i_num)) - - x_start_idx = int((x_num - (x_num * self.__percent)) / 2) - x_end_idx = int(x_start_idx + (self.__percent * x_num)) - - t_start_idx = int((t_num - (t_num * self.__percent)) / 2) - t_end_idx = int(t_start_idx + (self.__percent * t_num)) - - return X[i_start_idx:i_end_idx, - x_start_idx:x_end_idx, - t_start_idx:t_end_idx] - - -class GetSubDataframe: - """Get the first x% samples from the dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataframe. - - """ - def __init__(self, percent: float): - self.__percent = float(percent / 100.0) - - def transform(self, X): - new_size = int(len(X) * self.__percent) - - return X.iloc[0:new_size] diff --git a/dasf/ml/__init__.py b/dasf/ml/__init__.py deleted file mode 100644 index e5a0d9b..0000000 --- a/dasf/ml/__init__.py +++ /dev/null @@ -1 +0,0 @@ -#!/usr/bin/env python3 diff --git a/dasf/ml/cluster/__init__.py b/dasf/ml/cluster/__init__.py deleted file mode 100644 index 45b478d..0000000 --- a/dasf/ml/cluster/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.cluster.agglomerative import AgglomerativeClustering # noqa -from dasf.ml.cluster.dbscan import DBSCAN # noqa -from dasf.ml.cluster.kmeans import KMeans # noqa -from dasf.ml.cluster.som import SOM # noqa -from dasf.ml.cluster.spectral import SpectralClustering # noqa - -cluster_methods = [ - "AgglomerativeClustering", - "KMeans", - "DBSCAN", - "SOM", - "SpectralClustering" -] - -# XXX: Import due to CVE-2022-21797 -import joblib # noqa -from packaging import version # noqa - -if version.parse(joblib.__version__) < version.parse("1.2.0"): - # Do not include HDBSCAN while it is not safe - from dasf.ml.cluster.hdbscan import HDBSCAN # noqa - - cluster_methods.append("HDBSCAN") -# End of workaround - -__all__ = cluster_methods diff --git a/dasf/ml/cluster/agglomerative.py b/dasf/ml/cluster/agglomerative.py deleted file mode 100644 index f8774e8..0000000 --- a/dasf/ml/cluster/agglomerative.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python3 - -from sklearn.cluster import ( # noqa - AgglomerativeClustering as AgglomerativeClustering_CPU, -) - -from dasf.ml.cluster.classifier import ClusterClassifier -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml import AgglomerativeClustering as AgglomerativeClustering_GPU -except ImportError: - pass - - -class AgglomerativeClustering(ClusterClassifier): - """ - - Agglomerative Clustering - - Recursively merges the pair of clusters that minimally increases - a given linkage distance. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int or None, default=2 - The number of clusters to find. It must be ``None`` if - ``distance_threshold`` is not ``None``. - - affinity : str or callable, default='euclidean' - Metric used to compute the linkage. Can be "euclidean", "l1", "l2", - "manhattan", "cosine", or "precomputed". - If linkage is "ward", only "euclidean" is accepted. - If "precomputed", a distance matrix (instead of a similarity matrix) - is needed as input for the fit method. - - memory : str or object with the joblib.Memory interface, default=None - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - connectivity : array-like or callable, default=None - Connectivity matrix. Defines for each sample the neighboring - samples following a given structure of the data. - This can be a connectivity matrix itself or a callable that transforms - the data into a connectivity matrix, such as derived from - kneighbors_graph. Default is ``None``, i.e, the - hierarchical clustering algorithm is unstructured. - - compute_full_tree : 'auto' or bool, default='auto' - Stop early the construction of the tree at ``n_clusters``. This is - useful to decrease computation time if the number of clusters is not - small compared to the number of samples. This option is useful only - when specifying a connectivity matrix. Note also that when varying the - number of clusters and using caching, it may be advantageous to compute - the full tree. It must be ``True`` if ``distance_threshold`` is not - ``None``. By default `compute_full_tree` is "auto", which is equivalent - to `True` when `distance_threshold` is not `None` or that `n_clusters` - is inferior to the maximum between 100 or `0.02 * n_samples`. - Otherwise, "auto" is equivalent to `False`. - - linkage : {'ward', 'complete', 'average', 'single'}, default='ward' - Which linkage criterion to use. The linkage criterion determines which - distance to use between sets of observation. The algorithm will merge - the pairs of cluster that minimize this criterion. - - - 'ward' minimizes the variance of the clusters being merged. - - 'average' uses the average of the distances of each observation of - the two sets. - - 'complete' or 'maximum' linkage uses the maximum distances between - all observations of the two sets. - - 'single' uses the minimum of the distances between all observations - of the two sets. - - .. versionadded:: 0.20 - Added the 'single' option - - distance_threshold : float, default=None - The linkage distance threshold above which, clusters will not be - merged. If not ``None``, ``n_clusters`` must be ``None`` and - ``compute_full_tree`` must be ``True``. - - .. versionadded:: 0.21 - - compute_distances : bool, default=False - Computes distances between clusters even if `distance_threshold` is not - used. This can be used to make dendrogram visualization, but introduces - a computational and memory overhead. - - .. versionadded:: 0.24 - - n_neighbors : int, default = 15 - The number of neighbors to compute when connectivity = "knn" - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - Examples - -------- - >>> from dasf.ml.cluster import AgglomerativeClustering - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [4, 2], [4, 4], [4, 0]]) - >>> clustering = AgglomerativeClustering().fit(X) - >>> clustering - AgglomerativeClustering() - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html - - https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering - - """ - def __init__( - self, - n_clusters=2, - affinity="euclidean", - connectivity=None, - linkage="single", - memory=None, - compute_full_tree="auto", - distance_threshold=None, - compute_distances=False, - handle=None, - verbose=False, - n_neighbors=10, - output_type=None, - **kwargs - ): - super().__init__(**kwargs) - - self.n_clusters = n_clusters - self.affinity = affinity - self.connectivity = connectivity - self.linkage = linkage - self.memory = memory - self.compute_full_tree = compute_full_tree - self.distance_threshold = distance_threshold - self.compute_distances = compute_distances - self.handle = handle - self.verbose = verbose - self.n_neighbors = n_neighbors - self.output_type = output_type - - self.__agg_cluster_cpu = AgglomerativeClustering_CPU( - n_clusters=n_clusters, - affinity=affinity, - memory=memory, - connectivity=connectivity, - compute_full_tree=compute_full_tree, - linkage=linkage, - distance_threshold=distance_threshold, - compute_distances=compute_distances, - ) - - if is_gpu_supported(): - if connectivity is None: - connectivity = "knn" - - self.__agg_cluster_gpu = AgglomerativeClustering_GPU( - n_clusters=n_clusters, - affinity=affinity, - linkage=linkage, - handle=handle, - verbose=verbose, - connectivity=connectivity, - n_neighbors=n_neighbors, - output_type=output_type, - ) - - def _fit_cpu(self, X, y=None, convert_dtype=True): - return self.__agg_cluster_cpu.fit(X, y) - - def _fit_gpu(self, X, y=None, convert_dtype=True): - return self.__agg_cluster_gpu.fit(X, y, convert_dtype=convert_dtype) - - def _fit_predict_cpu(self, X, y=None): - return self.__agg_cluster_cpu.fit_predict(X, y) - - def _fit_predict_gpu(self, X, y=None): - return self.__agg_cluster_gpu.fit_predict(X, y) diff --git a/dasf/ml/cluster/classifier.py b/dasf/ml/cluster/classifier.py deleted file mode 100644 index 64541c9..0000000 --- a/dasf/ml/cluster/classifier.py +++ /dev/null @@ -1,20 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.transforms.base import ( - Fit, - FitPredict, - FitTransform, - GetParams, - Predict, - SetParams, - TargeteredTransform, - Transform, -) - - -class ClusterClassifier( - Fit, FitPredict, FitTransform, Predict, - GetParams, SetParams, TargeteredTransform -): - def __init__(self, **kwargs): - TargeteredTransform.__init__(self, **kwargs) diff --git a/dasf/ml/cluster/dbscan.py b/dasf/ml/cluster/dbscan.py deleted file mode 100644 index b8c4b90..0000000 --- a/dasf/ml/cluster/dbscan.py +++ /dev/null @@ -1,188 +0,0 @@ -#!/usr/bin/env python3 - -from sklearn.cluster import DBSCAN as DBSCAN_CPU - -from dasf.ml.cluster.classifier import ClusterClassifier -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml.cluster import DBSCAN as DBSCAN_GPU - from cuml.dask.cluster import DBSCAN as DBSCAN_MGPU -except ImportError: - pass - - -class DBSCAN(ClusterClassifier): - """ - Perform DBSCAN clustering from vector array or distance matrix. - - DBSCAN - Density-Based Spatial Clustering of Applications with Noise. - Finds core samples of high density and expands clusters from them. - Good for data which contains clusters of similar density. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - eps : float, default=0.5 - The maximum distance between two samples for one to be considered - as in the neighborhood of the other. This is not a maximum bound - on the distances of points within a cluster. This is the most - important DBSCAN parameter to choose appropriately for your data set - and distance function. - - min_samples : int, default=5 - The number of samples (or total weight) in a neighborhood for a point - to be considered as a core point. This includes the point itself. - - metric : string, or callable, default='euclidean' - The metric to use when calculating distance between instances in a - feature array. If metric is a string or callable, it must be one of - the options allowed by :func:`sklearn.metrics.pairwise_distances` for - its metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and - must be square. X may be a :term:`Glossary `, in which - case only "nonzero" elements may be considered neighbors for DBSCAN. - - .. versionadded:: 0.17 - metric *precomputed* to accept precomputed sparse matrix. - - metric_params : dict, default=None - Additional keyword arguments for the metric function. - - .. versionadded:: 0.19 - - algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' - The algorithm to be used by the NearestNeighbors module - to compute pointwise distances and find nearest neighbors. - See NearestNeighbors module documentation for details. - - leaf_size : int, default=30 - Leaf size passed to BallTree or cKDTree. This can affect the speed - of the construction and query, as well as the memory required - to store the tree. The optimal value depends - on the nature of the problem. - - p : float, default=None - The power of the Minkowski metric to be used to calculate distance - between points. If None, then ``p=2`` (equivalent to the Euclidean - distance). - - n_jobs : int, default=None - The number of parallel jobs to run. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - calc_core_sample_indices(optional) : boolean, default = True - Indicates whether the indices of the core samples should be calculated. - The the attribute `core_sample_indices_` will not be used, setting this - to False will avoid unnecessary kernel launches. - - - Examples - -------- - >>> from dasf.ml.cluster import DBSCAN - >>> import numpy as np - >>> X = np.array([[1, 2], [2, 2], [2, 3], - ... [8, 7], [8, 8], [25, 80]]) - >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) - >>> clustering - DBSCAN(eps=3, min_samples=2) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering - - See Also - -------- - OPTICS : A similar clustering at multiple values of eps. Our implementation - is optimized for memory usage. - - References - ---------- - Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based - Algorithm for Discovering Clusters in Large Spatial Databases with Noise". - In: Proceedings of the 2nd International Conference on Knowledge Discovery - and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996 - - Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). - DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. - ACM Transactions on Database Systems (TODS), 42(3), 19. - - """ - def __init__( - self, - eps=0.5, - leaf_size=40, - metric="euclidean", - min_samples=5, - p=None, - output_type=None, - calc_core_sample_indices=True, - verbose=False, - **kwargs - ): - super().__init__(**kwargs) - - self.eps = eps - self.leaf_size = leaf_size - self.metric = metric - self.min_samples = min_samples - self.p = p - self.output_type = output_type - self.calc_core_sample_indices = calc_core_sample_indices - self.verbose = verbose - - self.__dbscan_cpu = DBSCAN_CPU( - eps=self.eps, - leaf_size=self.leaf_size, - metric=self.metric, - min_samples=self.min_samples, - p=self.p, - ) - - if is_gpu_supported(): - self.__dbscan_gpu = DBSCAN_GPU( - min_samples=self.min_samples, - output_type=output_type, - calc_core_sample_indices=calc_core_sample_indices, - ) - - try: - self.__dbscan_mgpu = DBSCAN_MGPU( - min_samples=self.min_samples, - output_type=output_type, - calc_core_sample_indices=calc_core_sample_indices, - ) - except ValueError: - self.__dbscan_mgpu = None - - def _lazy_fit_gpu(self, X, y=None, out_dtype="int32"): - if self.__dbscan_mgpu is None: - raise NotImplementedError - return self.__dbscan_mgpu.fit(X=X, out_dtype=out_dtype) - - def _fit_cpu(self, X, y=None, sample_weight=None): - return self.__dbscan_cpu.fit(X=X, y=y, sample_weight=sample_weight) - - def _fit_gpu(self, X, y=None, out_dtype="int32"): - return self.__dbscan_gpu.fit(X=X, out_dtype=out_dtype) - - def _lazy_fit_predict_gpu(self, X, y=None, out_dtype="int32"): - if self.__dbscan_mgpu is None: - raise NotImplementedError - return self.__dbscan_mgpu.fit_predict(X=X, out_dtype=out_dtype) - - def _fit_predict_cpu(self, X, y=None, sample_weight=None): - return self.__dbscan_cpu.fit_predict(X=X, y=y, sample_weight=sample_weight) - - def _fit_predict_gpu(self, X, y=None, out_dtype="int32"): - return self.__dbscan_gpu.fit_predict(X=X, out_dtype=out_dtype) diff --git a/dasf/ml/cluster/hdbscan.py b/dasf/ml/cluster/hdbscan.py deleted file mode 100644 index 325442f..0000000 --- a/dasf/ml/cluster/hdbscan.py +++ /dev/null @@ -1,266 +0,0 @@ -#!/usr/bin/env python3 - -from hdbscan import HDBSCAN as HDBSCAN_CPU - -from dasf.ml.cluster.classifier import ClusterClassifier -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml.cluster import HDBSCAN as HDBSCAN_GPU -except ImportError: - pass - - -class HDBSCAN(ClusterClassifier): - """ - Perform HDBSCAN clustering from vector array or distance matrix. - - HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications - with Noise. Performs DBSCAN over varying epsilon values and integrates - the result to find a clustering that gives the best stability over epsilon. - This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), - and be more robust to parameter selection. - - Parameters - ---------- - min_cluster_size : int, optional (default=5) - The minimum size of clusters; single linkage splits that contain - fewer points than this will be considered points "falling out" of a - cluster rather than a cluster splitting into two new clusters. - - min_samples : int, optional (default=None) - The number of samples in a neighbourhood for a point to be - considered a core point. - - metric : string, or callable, optional (default='euclidean') - The metric to use when calculating distance between instances in a - feature array. If metric is a string or callable, it must be one of - the options allowed by metrics.pairwise.pairwise_distances for its - metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and - must be square. - - p : int, optional (default=None) - p value to use if using the minkowski metric. - - alpha : float, optional (default=1.0) - A distance scaling parameter as used in robust single linkage. - See [3]_ for more information. - - cluster_selection_epsilon: float, optional (default=0.0) - A distance threshold. Clusters below this value will be merged. - See [5]_ for more information. - - algorithm : string, optional (default='best') - Exactly which algorithm to use; hdbscan has variants specialised - for different characteristics of the data. By default this is set - to ``best`` which chooses the "best" algorithm given the nature of - the data. You can force other options if you believe you know - better. Options are: - * ``best`` - * ``generic`` - * ``prims_kdtree`` - * ``prims_balltree`` - * ``boruvka_kdtree`` - * ``boruvka_balltree`` - - leaf_size: int, optional (default=40) - If using a space tree algorithm (kdtree, or balltree) the number - of points ina leaf node of the tree. This does not alter the - resulting clustering, but may have an effect on the runtime - of the algorithm. - - memory : Instance of joblib.Memory or string (optional) - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - approx_min_span_tree : bool, optional (default=True) - Whether to accept an only approximate minimum spanning tree. - For some algorithms this can provide a significant speedup, but - the resulting clustering may be of marginally lower quality. - If you are willing to sacrifice speed for correctness you may want - to explore this; in general this should be left at the default True. - - gen_min_span_tree: bool, optional (default=False) - Whether to generate the minimum spanning tree with regard - to mutual reachability distance for later analysis. - - core_dist_n_jobs : int, optional (default=4) - Number of parallel jobs to run in core distance computations (if - supported by the specific algorithm). For ``core_dist_n_jobs`` - below -1, (n_cpus + 1 + core_dist_n_jobs) are used. - - cluster_selection_method : string, optional (default='eom') - The method used to select clusters from the condensed tree. The - standard approach for HDBSCAN* is to use an Excess of Mass algorithm - to find the most persistent clusters. Alternatively you can instead - select the clusters at the leaves of the tree -- this provides the - most fine grained and homogeneous clusters. Options are: - * ``eom`` - * ``leaf`` - - allow_single_cluster : bool, optional (default=False) - By default HDBSCAN* will not produce a single cluster, setting this - to True will override this and allow single cluster results in - the case that you feel this is a valid result for your dataset. - - prediction_data : boolean, optional - Whether to generate extra cached data for predicting labels or - membership vectors few new unseen points later. If you wish to - persist the clustering object for later re-use you probably want - to set this to True. - (default False) - - match_reference_implementation : bool, optional (default=False) - There exist some interpretational differences between this - HDBSCAN* implementation and the original authors reference - implementation in Java. This can result in very minor differences - in clustering results. Setting this flag to True will, at a some - performance cost, ensure that the clustering results match the - reference implementation. - - connectivity : {'pairwise', 'knn'}, default='knn' - The type of connectivity matrix to compute. - * 'pairwise' will compute the entire fully-connected graph of - pairwise distances between each set of points. This is the fastest - to compute and can be very fast for smaller datasets but requires - O(n^2) space. - - * 'knn' will sparsify the fully-connected connectivity matrix to - save memory and enable much larger inputs. "n_neighbors” will - control the amount of memory used and the graph will be connected - automatically in the event "n_neighbors” was not large enough to - connect it. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - Examples - -------- - >>> from dasf.ml.cluster import HDBSCAN - >>> import numpy as np - >>> X = np.array([[1, 2], [2, 2], [2, 3], - ... [8, 7], [8, 8], [25, 80]]) - >>> clustering = HDBSCAN(min_cluster_size=30, min_samples=2).fit(X) - >>> clustering - HDBSCAN(min_cluster_size=30, min_samples=2) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering - - References - ---------- - - .. [1] Campello, R. J., Moulavi, D., & Sander, J. (2013, April). - Density-based clustering based on hierarchical density estimates. - In Pacific-Asia Conference on Knowledge Discovery and Data Mining - (pp. 160-172). Springer Berlin Heidelberg. - - .. [2] Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015). - Hierarchical density estimates for data clustering, visualization, - and outlier detection. ACM Transactions on Knowledge Discovery - from Data (TKDD), 10(1), 5. - - .. [3] Chaudhuri, K., & Dasgupta, S. (2010). Rates of convergence for the - cluster tree. In Advances in Neural Information Processing Systems - (pp. 343-351). - - .. [4] Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and - Sander, J., 2014. Density-Based Clustering Validation. In SDM - (pp. 839-847). - - .. [5] Malzer, C., & Baum, M. (2019). A Hybrid Approach To Hierarchical - Density-based Cluster Selection. arxiv preprint 1911.02282. - - """ - def __init__( - self, - alpha=1.0, - gen_min_span_tree=False, - leaf_size=40, - metric="euclidean", - min_cluster_size=5, - min_samples=None, - p=None, - algorithm='best', - approx_min_span_tree=True, - core_dist_n_jobs=4, - cluster_selection_method='eom', - allow_single_cluster=False, - prediction_data=False, - match_reference_implementation=False, - connectivity='knn', - output_type=None, - verbose=0, - **kwargs - ): - super().__init__(**kwargs) - - self.alpha = alpha - self.gen_min_span_tree = gen_min_span_tree - self.leaf_size = leaf_size - self.metric = metric - self.min_cluster_size = min_cluster_size - self.min_samples = min_samples - self.p = p - self.algorithm = algorithm - self.approx_min_span_tree = approx_min_span_tree - self.core_dist_n_jobs = core_dist_n_jobs - self.cluster_selection_method = cluster_selection_method - self.allow_single_cluster = allow_single_cluster - self.prediction_data = prediction_data - self.match_reference_implementation = match_reference_implementation - self.connectivity = connectivity - self.output_type = output_type - self.verbose = verbose - - self.__hdbscan_cpu = HDBSCAN_CPU( - alpha=alpha, - gen_min_span_tree=gen_min_span_tree, - leaf_size=leaf_size, - metric=metric, - min_cluster_size=min_cluster_size, - min_samples=min_samples, - p=p, - algorithm=algorithm, - approx_min_span_tree=approx_min_span_tree, - core_dist_n_jobs=core_dist_n_jobs, - cluster_selection_method=cluster_selection_method, - allow_single_cluster=allow_single_cluster, - prediction_data=prediction_data, - match_reference_implementation=match_reference_implementation - ) - - if is_gpu_supported(): - self.__hdbscan_gpu = HDBSCAN_GPU( - alpha=alpha, - gen_min_span_tree=gen_min_span_tree, - metric=metric, - min_cluster_size=min_cluster_size, - min_samples=min_samples, - p=p, - cluster_selection_method=cluster_selection_method, - allow_single_cluster=allow_single_cluster, - verbose=verbose, - connectivity=connectivity, - prediction_data=prediction_data, - output_type=output_type - ) - - def _fit_cpu(self, X, y=None): - return self.__hdbscan_cpu.fit(X=X, y=y) - - def _fit_gpu(self, X, y=None, convert_dtype=True): - return self.__hdbscan_gpu.fit(X=X, y=y, convert_dtype=convert_dtype) - - def _fit_predict_cpu(self, X, y=None): - return self.__hdbscan_cpu.fit_predict(X=X, y=y) - - def _fit_predict_gpu(self, X, y=None): - return self.__hdbscan_gpu.fit_predict(X=X, y=y) diff --git a/dasf/ml/cluster/kmeans.py b/dasf/ml/cluster/kmeans.py deleted file mode 100644 index 35e1bd8..0000000 --- a/dasf/ml/cluster/kmeans.py +++ /dev/null @@ -1,657 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.cluster import KMeans as KMeans_MCPU -from sklearn.cluster import KMeans as KMeans_CPU - -from dasf.ml.cluster.classifier import ClusterClassifier -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml.cluster import KMeans as KMeans_GPU - from cuml.dask.cluster import KMeans as KMeans_MGPU -except ImportError: - pass - - -class KMeans(ClusterClassifier): - """ - K-Means clustering. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - - n_clusters : int, default=8 - The number of clusters to form as well as the number of - centroids to generate. - - init : {'k-means++', 'random'}, callable or array-like of shape (n_clusters, n_features), default='k-means++' - - Method for initialization: - - 'k-means++' : selects initial cluster centers for k-mean - clustering in a smart way to speed up convergence. See section - Notes in k_init for more details. - - 'random': choose `n_clusters` observations (rows) at random from data - for the initial centroids. - - If an array is passed, it should be of shape (n_clusters, n_features) - and gives the initial centers. - - If a callable is passed, it should take arguments X, n_clusters and a - random state and return an initialization. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of - n_init consecutive runs in terms of inertia. - - max_iter : int, default=300 - Maximum number of iterations of the k-means algorithm for a - single run. - - tol : float, default=1e-4 - Relative tolerance with regards to Frobenius norm of the difference - in the cluster centers of two consecutive iterations to declare - convergence. - - precompute_distances : {'auto', True, False}, default='auto' - Precompute distances (faster but takes more memory). - - 'auto' : do not precompute distances if n_samples * n_clusters > 12 - million. This corresponds to about 100MB overhead per job using - double precision. IMPORTANT: This is used only in Dask ML version. - - True : always precompute distances. - - False : never precompute distances. - - verbose : int, default=0 - Verbosity mode. - - random_state : int, RandomState instance or None, default=None - Determines random number generation for centroid initialization. Use - an int to make the randomness deterministic. - See :term:`Glossary `. - - copy_x : bool, default=True - When pre-computing distances it is more numerically accurate to center - the data first. If copy_x is True (default), then the original data is - not modified. If False, the original data is modified, and put back - before the function returns, but small numerical differences may be - introduced by subtracting and then adding the data mean. Note that if - the original data is not C-contiguous, a copy will be made even if - copy_x is False. If the original data is sparse, but not in CSR format, - a copy will be made even if copy_x is False. - - n_jobs : int, default=1 - The number of OpenMP threads to use for the computation. Parallelism is - sample-wise on the main cython loop which assigns each sample to its - closest center. IMPORTANT: This is used only in Dask ML version. - - ``None`` or ``-1`` means using all processors. - - init_max_iter : int, default=None - Number of iterations for init step. - - algorithm : {"auto", "full", "elkan"}, default="full" - K-means algorithm to use. The classical EM-style algorithm is "full". - The "elkan" variation is more efficient on data with well-defined - clusters, by using the triangle inequality. However it's more memory - intensive due to the allocation of an extra array of shape - (n_samples, n_clusters). - - For now "auto" (kept for backward compatibiliy) chooses "elkan" but it - might change in the future for a better heuristic. - - .. versionchanged:: 0.18 - Added Elkan algorithm - - oversampling_factor : int, default=2 - The amount of points to sample in scalable k-means++ initialization - for potential centroids. Increasing this value can lead to better - initial centroids at the cost of memory. The total number of centroids - sampled in scalable k-means++ is oversampling_factor * n_clusters * 8. - - max_samples_per_batch : int, default=32768 - The number of data samples to use for batches of the pairwise distance - computation. This computation is done throughout both fit predict. The - default should suit most cases. The total number of elements in the - batched pairwise distance computation is max_samples_per_batch * - n_clusters. It might become necessary to lower this number when - n_clusters becomes prohibitively large. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - See Also - -------- - MiniBatchKMeans : Alternative online implementation that does incremental - updates of the centers positions using mini-batches. - For large scale learning (say n_samples > 10k) MiniBatchKMeans is - probably much faster than the default batch implementation. - - Notes - ----- - The k-means problem is solved using either Lloyd's or Elkan's algorithm. - - The average complexity is given by O(k n T), where n is the number of - samples and T is the number of iteration. - - The worst case complexity is given by O(n^(k+2/p)) with - n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, - 'How slow is the k-means method?' SoCG2006) - - In practice, the k-means algorithm is very fast (one of the fastest - clustering algorithms available), but it falls in local minima. That's why - it can be useful to restart it several times. - - If the algorithm stops before fully converging (because of ``tol`` or - ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent, - i.e. the ``cluster_centers_`` will not be the means of the points in each - cluster. Also, the estimator will reassign ``labels_`` after the last - iteration to make ``labels_`` consistent with ``predict`` on the training - set. - - Examples - -------- - - >>> from dasf.ml.cluster import KMeans - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [10, 2], [10, 4], [10, 0]]) - >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X) - >>> kmeans.predict([[0, 0], [12, 3]]) - array([1, 0], dtype=int32) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html - - https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html - - https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering - - https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans - - """ - def __init__( - self, - n_clusters=8, - init=None, - n_init=None, - max_iter=300, - tol=0.0001, - verbose=0, - random_state=None, - copy_x=True, - algorithm='full', - oversampling_factor=2.0, - n_jobs=1, - init_max_iter=None, - max_samples_per_batch=32768, - precompute_distances='auto', - output_type=None, - **kwargs - ): - super().__init__(**kwargs) - - self.n_clusters = n_clusters - self.random_state = random_state - self.max_iter = max_iter - self.init = init - self.n_init = n_init - self.tol = tol - self.verbose = verbose - self.copy_x = copy_x - self.algorithm = algorithm - self.oversampling_factor = oversampling_factor - self.n_jobs = n_jobs - self.init_max_iter = init_max_iter - self.max_samples_per_batch = max_samples_per_batch - self.precompute_distances = precompute_distances - self.output_type = output_type - - # Estimator for CPU operations - self.__kmeans_cpu = KMeans_CPU( - n_clusters=n_clusters, - random_state=random_state, - max_iter=max_iter, - init=("k-means++" if init is None else init), - n_init=(10 if n_init is None else n_init), - tol=tol, - verbose=verbose, - copy_x=copy_x, - algorithm=algorithm, - ) - - # Estimator for Dask ML operations - self.__kmeans_mcpu = KMeans_MCPU( - n_clusters=n_clusters, - random_state=random_state, - max_iter=max_iter, - init=("k-means||" if init is None else init), - tol=tol, - oversampling_factor=oversampling_factor, - algorithm=algorithm, - n_jobs=n_jobs, - init_max_iter=init_max_iter, - copy_x=copy_x, - precompute_distances=precompute_distances, - ) - - if is_gpu_supported(): - # Estimator for CuML operations - self.__kmeans_gpu = KMeans_GPU( - n_clusters=n_clusters, - random_state=(1 if random_state is None else random_state), - max_iter=max_iter, - tol=tol, - verbose=verbose, - init=("scalable-k-means++" if init is None else init), - oversampling_factor=oversampling_factor, - max_samples_per_batch=max_samples_per_batch, - ) - - # XXX: KMeans in Multi GPU requires a Client instance, - # skip if not present. - try: - self.__kmeans_mgpu = KMeans_MGPU( - n_clusters=n_clusters, - random_state=(1 if random_state is None else random_state), - max_iter=max_iter, - tol=tol, - verbose=verbose, - init=("scalable-k-means++" if init is None else init), - oversampling_factor=oversampling_factor, - max_samples_per_batch=max_samples_per_batch, - ) - except ValueError: - self.__kmeans_mgpu = None - - def _lazy_fit_cpu(self, X, y=None, sample_weight=None): - """ - Compute Dask k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - self - Fitted estimator. - """ - return self.__kmeans_mcpu.fit(X=X, y=y) - - def _lazy_fit_gpu(self, X, y=None, sample_weight=None): - """ - Compute Dask CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - """ - if self.__kmeans_mgpu is None: - raise NotImplementedError - return self.__kmeans_mgpu.fit(X=X, sample_weight=sample_weight) - - def _fit_cpu(self, X, y=None, sample_weight=None): - """ - Compute Scikit Learn k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - """ - return self.__kmeans_cpu.fit(X=X, y=y, sample_weight=sample_weight) - - def _fit_gpu(self, X, y=None, sample_weight=None): - """ - Compute CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - """ - return self.__kmeans_gpu.fit(X=X, sample_weight=sample_weight) - - def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None): - """ - Compute cluster centers and predict cluster index for each sample using - Dask ML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - local_kmeans = self.__kmeans_mcpu.fit(X=X, y=y) - return local_kmeans.predict(X=X) - - def _lazy_fit_predict_gpu(self, X, y=None, sample_weight=None): - """ - Compute cluster centers and predict cluster index for each sample using - Dask CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - if self.__kmeans_mgpu is None: - raise NotImplementedError - return self.__kmeans_mgpu.fit_predict(X, y, sample_weight) - - def _fit_predict_cpu(self, X, y=None, sample_weight=None): - """ - Compute cluster centers and predict cluster index for each sample using - Scikit Learn. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - return self.__kmeans_cpu.fit_predict(X) - - def _fit_predict_gpu(self, X, y=None, sample_weight=None): - """ - Compute cluster centers and predict cluster index for each sample using - CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - return self.__kmeans_gpu.fit_predict(X=X, sample_weight=sample_weight) - - def _lazy_predict_cpu(self, X, sample_weight=None): - """ - Predict the closest cluster each sample in X belongs to using Dask ML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - return self.__kmeans_mcpu.predict(X) - - def _lazy_predict_gpu(self, X, sample_weight=None): - """ - Predict the closest cluster each sample in X belongs to using Dask - CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - if self.__kmeans_mgpu is None: - raise NotImplementedError - return self.__kmeans_mgpu.predict(X, sample_weight) - - def _predict_cpu(self, X, sample_weight=None): - """ - Predict the closest cluster each sample in X belongs to using Scikit - Learn. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - return self.__kmeans_cpu.predict(X, sample_weight) - - def _predict_gpu(self, X, sample_weight=None): - """ - Predict the closest cluster each sample in X belongs to using CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - return self.__kmeans_gpu.predict(X, sample_weight) - - def _lazy_predict2_cpu(self, X, sample_weight=None): - """ - A block predict using Scikit Learn variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - def __predict(block): - return self._predict_cpu.predict(block, sample_weight=sample_weight) - - return X.map_blocks( - __predict, chunks=(X.chunks[0],), drop_axis=[1], dtype=X.dtype - ) - - def _lazy_predict2_gpu(self, X, sample_weight=None): - """ - A block predict using CuML variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - """ - def __predict(block): - return self._predict_gpu.predict(block, sample_weight=sample_weight) - - return X.map_blocks( - __predict, chunks=(X.chunks[0],), drop_axis=[1], dtype=X.dtype - ) - - def _predict2_cpu(self, X, sample_weight=None): - raise NotImplementedError("Method available only for Dask.") - - def _predict2_gpu(self, X, sample_weight=None): - raise NotImplementedError("Method available only for Dask.") - - @task_handler - def predict2(self, sample_weight=None): - ... diff --git a/dasf/ml/cluster/som.py b/dasf/ml/cluster/som.py deleted file mode 100644 index 3b19c0d..0000000 --- a/dasf/ml/cluster/som.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np -from xpysom_dask import XPySom - -from dasf.ml.cluster.classifier import ClusterClassifier -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import is_gpu_supported - -try: - import cupy as cp -except ImportError: - pass - - -class SOM(ClusterClassifier): - """ - Initializes a Self Organizing Maps. - - A rule of thumb to set the size of the grid for a dimensionality - reduction task is that it should contain 5*sqrt(N) neurons - where N is the number of samples in the dataset to analyze. - - E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 - hence a map 8-by-8 should perform well. - - Parameters - ---------- - x : int - x dimension of the SOM. - - y : int - y dimension of the SOM. - - input_len : int - Number of the elements of the vectors in input. - - sigma : float, default=min(x,y)/2 - Spread of the neighborhood function, needs to be adequate - to the dimensions of the map. - - sigmaN : float, default=0.01 - Spread of the neighborhood function at last iteration. - - learning_rate : float, default=0.5 - initial learning rate. - - learning_rateN : float, default=0.01 - final learning rate - - decay_function : string, default='exponential' - Function that reduces learning_rate and sigma at each iteration. - Possible values: 'exponential', 'linear', 'aymptotic' - - neighborhood_function : string, default='gaussian' - Function that weights the neighborhood of a position in the map. - Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle' - - topology : string, default='rectangular' - Topology of the map. - Possible values: 'rectangular', 'hexagonal' - - activation_distance : string, default='euclidean' - Distance used to activate the map. - Possible values: 'euclidean', 'cosine', 'manhattan' - - random_seed : int, default=None - Random seed to use. - - n_parallel : uint, default=#max_CUDA_threads or 500*#CPUcores - Number of samples to be processed at a time. Setting a too low - value may drastically lower performance due to under-utilization, - setting a too high value increases memory usage without granting - any significant performance benefit. - - xp : numpy or cupy, default=cupy if can be imported else numpy - Use numpy (CPU) or cupy (GPU) for computations. - - std_coeff: float, default=0.5 - Used to calculate gausssian exponent denominator: - d = 2*std_coeff**2*sigma**2 - - compact_support: bool, default=False - Cut the neighbor function to 0 beyond neighbor radius sigma - - Examples - -------- - >>> from dasf.ml.cluster import SOM - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> som = SOM(x=3, y=2, input_len=2, - ... num_epochs=100).fit(X) - >>> som - SOM(x=3, y=2, input_len=2, num_epochs=100) - - """ - def __init__( - self, - x, - y, - input_len, - num_epochs=100, - sigma=0, - sigmaN=1, - learning_rate=0.5, - learning_rateN=0.01, - decay_function="exponential", - neighborhood_function="gaussian", - std_coeff=0.5, - topology="rectangular", - activation_distance="euclidean", - random_seed=None, - n_parallel=0, - compact_support=False, - **kwargs - ): - super().__init__(**kwargs) - - self.x = x - self.y = y - self.input_len = input_len - self.num_epochs = num_epochs - self.sigma = sigma - self.sigmaN = sigmaN - self.learning_rate = learning_rate - self.learning_rateN = learning_rateN - self.decay_function = decay_function - self.neighborhood_function = neighborhood_function - self.std_coeff = std_coeff - self.topology = topology - self.activation_distance = activation_distance - self.random_seed = random_seed - self.n_parallel = n_parallel - self.compact_support = compact_support - - self.__som_cpu = XPySom( - x=self.x, - y=self.y, - input_len=self.input_len, - sigma=self.sigma, - sigmaN=self.sigmaN, - learning_rate=self.learning_rate, - learning_rateN=self.learning_rateN, - decay_function=self.decay_function, - neighborhood_function=self.neighborhood_function, - std_coeff=self.std_coeff, - topology=self.topology, - activation_distance=self.activation_distance, - random_seed=self.random_seed, - n_parallel=self.n_parallel, - compact_support=self.compact_support, - xp=np, - ) - - self.__som_mcpu = XPySom( - x=self.x, - y=self.y, - input_len=self.input_len, - sigma=self.sigma, - sigmaN=self.sigmaN, - learning_rate=self.learning_rate, - learning_rateN=self.learning_rateN, - decay_function=self.decay_function, - neighborhood_function=self.neighborhood_function, - std_coeff=self.std_coeff, - topology=self.topology, - activation_distance=self.activation_distance, - random_seed=self.random_seed, - n_parallel=self.n_parallel, - compact_support=self.compact_support, - xp=np, - use_dask=True, - ) - - if is_gpu_supported(): - self.__som_gpu = XPySom( - x=self.x, - y=self.y, - input_len=self.input_len, - sigma=self.sigma, - sigmaN=self.sigmaN, - learning_rate=self.learning_rate, - learning_rateN=self.learning_rateN, - decay_function=self.decay_function, - neighborhood_function=self.neighborhood_function, - std_coeff=self.std_coeff, - topology=self.topology, - activation_distance=self.activation_distance, - random_seed=self.random_seed, - n_parallel=self.n_parallel, - compact_support=self.compact_support, - xp=cp, - ) - - self.__som_mgpu = XPySom( - x=self.x, - y=self.y, - input_len=self.input_len, - sigma=self.sigma, - sigmaN=self.sigmaN, - learning_rate=self.learning_rate, - learning_rateN=self.learning_rateN, - decay_function=self.decay_function, - neighborhood_function=self.neighborhood_function, - std_coeff=self.std_coeff, - topology=self.topology, - activation_distance=self.activation_distance, - random_seed=self.random_seed, - n_parallel=self.n_parallel, - compact_support=self.compact_support, - xp=cp, - use_dask=True, - ) - - def _lazy_fit_cpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_mcpu - return self.__som_mcpu.train(X, self.num_epochs) - - def _lazy_fit_gpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_mgpu - return self.__som_mgpu.train(X, self.num_epochs) - - def _fit_cpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_cpu - return self.__som_cpu.train(X, self.num_epochs) - - def _fit_gpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_gpu - return self.__som_gpu.train(X, self.num_epochs) - - def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_mcpu - return self.__som_mcpu.train(X, self.num_epochs).predict(X) - - def _lazy_fit_predict_gpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_mgpu - return self.__som_mgpu.train(X, self.num_epochs).predict(X) - - def _fit_predict_cpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_cpu - return self.__som_cpu.train(X, self.num_epochs).predict(X) - - def _fit_predict_gpu(self, X, y=None, sample_weight=None): - self.__som = self.__som_gpu - return self.__som_gpu.train(X, self.num_epochs).predict(X) - - def _lazy_predict_cpu(self, X, sample_weight=None): - return self.__som_mcpu.predict(X) - - def _lazy_predict_gpu(self, X, sample_weight=None): - return self.__som_mgpu.predict(X) - - def _predict_cpu(self, X, sample_weight=None): - return self.__som_cpu.predict(X) - - def _predict_gpu(self, X, sample_weight=None): - return self.__som_gpu.predict(X) - - def _lazy_quantization_error_cpu(self, X): - return self.__som_mcpu.quantization_error(X) - - def _lazy_quantization_error_gpu(self, X): - return self.__som_mgpu.quantization_error(X) - - def _quantization_error_cpu(self, X): - return self.__som_cpu.quantization_error(X) - - def _quantization_error_gpu(self, X): - return self.__som_gpu.quantization_error(X) - - @task_handler - def quantization_error(self, X): - ... diff --git a/dasf/ml/cluster/spectral.py b/dasf/ml/cluster/spectral.py deleted file mode 100644 index ffac4ad..0000000 --- a/dasf/ml/cluster/spectral.py +++ /dev/null @@ -1,270 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.cluster import SpectralClustering as SpectralClustering_MCPU -from sklearn.cluster import SpectralClustering as SpectralClustering_CPU - -from dasf.ml.cluster.classifier import ClusterClassifier - - -class SpectralClustering(ClusterClassifier): - """ - Apply clustering to a projection of the normalized Laplacian. - - In practice Spectral Clustering is very useful when the structure of - the individual clusters is highly non-convex, or more generally when - a measure of the center and spread of the cluster is not a suitable - description of the complete cluster, such as when clusters are - nested circles on the 2D plane. - - If the affinity matrix is the adjacency matrix of a graph, this method - can be used to find normalized graph cuts. - - When calling ``fit``, an affinity matrix is constructed using either - a kernel function such the Gaussian (aka RBF) kernel with Euclidean - distance ``d(X, X)``:: - - np.exp(-gamma * d(X,X) ** 2) - - or a k-nearest neighbors connectivity matrix. - - Alternatively, a user-provided affinity matrix can be specified by - setting ``affinity='precomputed'``. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int, default=8 - The dimension of the projection subspace. - - eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None - The eigenvalue decomposition strategy to use. AMG requires pyamg - to be installed. It can be faster on very large, sparse problems, - but may also lead to instabilities. If None, then ``'arpack'`` is - used. - - n_components : int, default=n_clusters - Number of eigenvectors to use for the spectral embedding - - random_state : int, RandomState instance, default=None - A pseudo random number generator used for the initialization of the - lobpcg eigenvectors decomposition when ``eigen_solver='amg'`` and by - the K-Means initialization. Use an int to make the randomness - deterministic. - See :term:`Glossary `. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of n_init - consecutive runs in terms of inertia. Only used if - ``assign_labels='kmeans'``. - - gamma : float, default=1.0 - Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. - Ignored for ``affinity='nearest_neighbors'``. - - affinity : str or callable, default='rbf' - How to construct the affinity matrix. - - 'nearest_neighbors': construct the affinity matrix by computing a - graph of nearest neighbors. - - 'rbf': construct the affinity matrix using a radial basis function - (RBF) kernel. - - 'precomputed': interpret ``X`` as a precomputed affinity matrix, - where larger values indicate greater similarity between instances. - - 'precomputed_nearest_neighbors': interpret ``X`` as a sparse graph - of precomputed distances, and construct a binary affinity matrix - from the ``n_neighbors`` nearest neighbors of each instance. - - one of the kernels supported by - :func:`~sklearn.metrics.pairwise_kernels`. - - Only kernels that produce similarity scores (non-negative values that - increase with similarity) should be used. This property is not checked - by the clustering algorithm. - - n_neighbors : int, default=10 - Number of neighbors to use when constructing the affinity matrix using - the nearest neighbors method. Ignored for ``affinity='rbf'``. - - eigen_tol : float, default=0.0 - Stopping criterion for eigendecomposition of the Laplacian matrix - when ``eigen_solver='arpack'``. - - assign_labels : {'kmeans', 'discretize'}, default='kmeans' - The strategy for assigning labels in the embedding space. There are two - ways to assign labels after the Laplacian embedding. k-means is a - popular choice, but it can be sensitive to initialization. - Discretization is another approach which is less sensitive to random - initialization. - - degree : float, default=3 - Degree of the polynomial kernel. Ignored by other kernels. - - coef0 : float, default=1 - Zero coefficient for polynomial and sigmoid kernels. - Ignored by other kernels. - - kernel_params : dict of str to any, default=None - Parameters (keyword arguments) and values for kernel passed as - callable object. Ignored by other kernels. - - n_jobs : int, default=None - The number of parallel jobs to run when `affinity='nearest_neighbors'` - or `affinity='precomputed_nearest_neighbors'`. The neighbors search - will be done in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - verbose : bool, default=False - Verbosity mode. - - .. versionadded:: 0.24 - - persist_embedding : bool - Whether to persist the intermediate n_samples x n_components array used - for clustering. - - kmeans_params : dictionary of string to any, optional - Keyword arguments for the KMeans clustering used for the final - clustering. - - Examples - -------- - >>> from dasf.ml.cluster import SpectralClustering - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> clustering = SpectralClustering(n_clusters=2, - ... assign_labels='discretize', - ... random_state=0).fit(X) - >>> clustering - SpectralClustering(assign_labels='discretize', n_clusters=2, - random_state=0) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering - - https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html - - Notes - ----- - A distance matrix for which 0 indicates identical elements and high values - indicate very dissimilar elements can be transformed into an affinity / - similarity matrix that is well-suited for the algorithm by - applying the Gaussian (aka RBF, heat) kernel:: - - np.exp(- dist_matrix ** 2 / (2. * delta ** 2)) - - where ``delta`` is a free parameter representing the width of the Gaussian - kernel. - - An alternative is to take a symmetric version of the k-nearest neighbors - connectivity matrix of the points. - - If the pyamg package is installed, it is used: this greatly - speeds up computation. - - References - ---------- - - - Normalized cuts and image segmentation, 2000 - Jianbo Shi, Jitendra Malik - http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324 - - - A Tutorial on Spectral Clustering, 2007 - Ulrike von Luxburg - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323 - - - Multiclass spectral clustering, 2003 - Stella X. Yu, Jianbo Shi - https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf - - """ - def __init__( - self, - n_clusters=8, - eigen_solver=None, - random_state=None, - n_init=10, - gamma=1.0, - affinity="rbf", - n_neighbors=10, - eigen_tol=0.0, - assign_labels="kmeans", - degree=3, - coef0=1, - kernel_params=None, - n_jobs=None, - n_components=None, - persist_embedding=False, - kmeans_params=None, - verbose=False, - **kwargs - ): - super().__init__(**kwargs) - - self.n_clusters = n_clusters - self.eigen_solver = eigen_solver - self.random_state = random_state - self.n_init = n_init - self.gamma = gamma - self.affinity = affinity - self.n_neighbors = n_neighbors - self.eigen_tol = eigen_tol - self.assign_labels = assign_labels - self.degree = degree - self.coef0 = coef0 - self.kernel_params = kernel_params - self.n_jobs = n_jobs - self.n_components = n_components - self.persist_embedding = persist_embedding - self.kmeans_params = kmeans_params - self.verbose = verbose - - self.__sc_cpu = SpectralClustering_CPU( - n_clusters=n_clusters, - eigen_solver=eigen_solver, - random_state=random_state, - n_init=n_init, - gamma=gamma, - affinity=affinity, - n_neighbors=n_neighbors, - eigen_tol=eigen_tol, - assign_labels=assign_labels, - degree=degree, - coef0=coef0, - kernel_params=kernel_params, - n_jobs=n_jobs, - n_components=n_components, - verbose=verbose - ) - - # If n_components is set to None, use default - n_components = 100 if n_components is None else n_components - - self.__sc_mcpu = SpectralClustering_MCPU( - n_clusters=n_clusters, - eigen_solver=eigen_solver, - random_state=random_state, - n_init=n_init, - gamma=gamma, - affinity=affinity, - n_neighbors=n_neighbors, - eigen_tol=eigen_tol, - assign_labels=assign_labels, - degree=degree, - coef0=coef0, - kernel_params=kernel_params, - n_jobs=n_jobs, - n_components=n_components, - persist_embedding=persist_embedding, - kmeans_params=kmeans_params, - ) - - def _fit_cpu(self, X, y=None, sample_weight=None): - return self.__sc_cpu.fit(X=X, y=y) - - def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None): - return self.__sc_mcpu.fit_predict(X=X) - - def _fit_predict_cpu(self, X, y=None, sample_weight=None): - return self.__sc_cpu.fit_predict(X) diff --git a/dasf/ml/core.py b/dasf/ml/core.py deleted file mode 100644 index 14c4756..0000000 --- a/dasf/ml/core.py +++ /dev/null @@ -1,30 +0,0 @@ -#!/usr/bin/env python3 - -import os -import pickle -from pathlib import Path - - -class MLGeneric: - def __init__(self, name, checkpoint=False, **kwargs): - # Machine Learning Algorithm - self._cached_dir = os.path.abspath( - os.path.join(str(Path.home()), - "/.cache/dasf/ml/")) - os.makedirs(self._cached_dir, exist_ok=True) - - self._tmp = os.path.abspath(os.path.join(self._cached_dir, - name.lower())) - - self.__checkpoint = checkpoint - - def dump(self, model): - if self.get_checkpoint(): - with open(self._tmp, "wb") as fh: - pickle.dump(model, fh) - - def load(self, model): - if self.get_checkpoint() and os.path.exists(self._tmp): - with open(self._tmp, "rb") as fh: - return pickle.load(fh) - return model diff --git a/dasf/ml/decomposition/__init__.py b/dasf/ml/decomposition/__init__.py deleted file mode 100644 index 503984c..0000000 --- a/dasf/ml/decomposition/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.decomposition.pca import PCA - -__all__ = ["PCA"] diff --git a/dasf/ml/decomposition/pca.py b/dasf/ml/decomposition/pca.py deleted file mode 100644 index 82dc1da..0000000 --- a/dasf/ml/decomposition/pca.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.decomposition import PCA as PCA_MCPU -from sklearn.decomposition import PCA as PCA_CPU - -from dasf.transforms.base import Fit, FitTransform, TargeteredTransform -from dasf.utils.funcs import is_dask_supported, is_gpu_supported - -try: - from cuml.dask.decomposition import PCA as PCA_MGPU - from cuml.decomposition import PCA as PCA_GPU -except ImportError: - pass - - -class PCA(Fit, FitTransform, TargeteredTransform): - def __init__( - self, - n_components=None, - copy=True, - whiten=False, - svd_solver="auto", - tol=0.0, - iterated_power="auto", - random_state=None, - *args, - **kwargs, - ): - TargeteredTransform.__init__(self, *args, **kwargs) - - self.__pca_cpu = PCA_CPU( - n_components=n_components, - copy=copy, - whiten=whiten, - svd_solver=svd_solver, - tol=tol, - iterated_power=iterated_power, - random_state=random_state, - ) - - self.__pca_mcpu = PCA_MCPU( - n_components=n_components, - copy=copy, - whiten=whiten, - svd_solver=svd_solver, - tol=tol, - iterated_power=iterated_power, - random_state=random_state, - ) - if is_gpu_supported(): - try: - self.__pca_gpu = PCA_GPU( - n_components=n_components, - copy=copy, - whiten=whiten, - svd_solver=svd_solver, - tol=tol, - iterated_power=iterated_power, - random_state=random_state, - ) - except TypeError: - self.__pca_gpu = None - - # XXX: PCA in Multi GPU requires a Client instance, - # skip if not present. - try: - self.__pca_mgpu = PCA_MGPU( - n_components=n_components, - copy=copy, - whiten=whiten, - svd_solver=svd_solver, - tol=tol, - iterated_power=iterated_power, - random_state=random_state, - ) - except ValueError: - self.__pca_mgpu = None - - def _lazy_fit_cpu(self, X, y=None, sample_weights=None): - return self.__pca_mcpu.fit(X) - - def _lazy_fit_gpu(self, X, y=None, sample_weights=None): - if self.__pca_mgpu is None: - raise NotImplementedError - return self.__pca_mgpu.fit(X) - - def _fit_cpu(self, X, y=None, sample_weights=None): - return self.__pca_cpu.fit(X) - - def _fit_gpu(self, X, y=None, sample_weights=None): - if self.__pca_gpu is None: - raise NotImplementedError - return self.__pca_gpu.fit(X) - - def _lazy_fit_transform_cpu(self, X, y=None, sample_weights=None): - return self.__pca_mcpu.fit_transform(X, y) - - def _lazy_fit_transform_gpu(self, X, y=None, sample_weights=None): - if self.__pca_mgpu is None: - raise NotImplementedError - return self.__pca_mgpu.fit_transform(X, y) - - def _fit_transform_cpu(self, X, y=None, sample_weights=None): - return self.__pca_cpu.fit_transform(X, y) - - def _fit_transform_gpu(self, X, y=None, sample_weights=None): - if self.__pca_gpu is None: - raise NotImplementedError - return self.__pca_gpu.fit_transform(X, y) - - def _lazy_transform_cpu(self, X, y=None, sample_weights=None): - return self.__pca_mcpu.transform(X) - - def _lazy_transform_gpu(self, X, y=None, sample_weights=None): - if self.__pca_mgpu is None: - raise NotImplementedError - return self.__pca_mgpu.transform(X) - - def _transform_cpu(self, X, y=None, sample_weights=None): - return self.__pca_cpu.transform(X) - - def _transform_gpu(self, X, y=None, sample_weights=None): - if self.__pca_gpu is None: - raise NotImplementedError - return self.__pca_gpu.transform(X) - - def _get_covariance_cpu(self): - return self.__pca_cpu.get_covariance() - - def get_covariance(self): - if not is_dask_supported() and not is_gpu_supported(): - return self._get_covariance_cpu() - else: - raise NotImplementedError - - def _get_precision_cpu(self): - return self.__pca_cpu.get_precision() - - def get_precision(self): - if not is_dask_supported() and not is_gpu_supported(): - return self._get_precision_cpu() - else: - raise NotImplementedError diff --git a/dasf/ml/dl/__init__.py b/dasf/ml/dl/__init__.py deleted file mode 100644 index d4c8b02..0000000 --- a/dasf/ml/dl/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.dl.pytorch_lightning import NeuralNetClassifier - -__all__ = ["NeuralNetClassifier"] diff --git a/dasf/ml/dl/clusters/__init__.py b/dasf/ml/dl/clusters/__init__.py deleted file mode 100644 index ef52917..0000000 --- a/dasf/ml/dl/clusters/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.dl.clusters.dask import DaskClusterEnvironment - -__all__ = ["DaskClusterEnvironment"] diff --git a/dasf/ml/dl/clusters/dask.py b/dasf/ml/dl/clusters/dask.py deleted file mode 100644 index 88cfeb9..0000000 --- a/dasf/ml/dl/clusters/dask.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python - -import os - -from pytorch_lightning.plugins.environments import ClusterEnvironment - - -class DaskClusterEnvironment(ClusterEnvironment): - """ - Create a Dask Cluster environment for workers - - metadata -- dictionary containing all data related to workers. - """ - - def __init__(self, metadata=None) -> None: - super().__init__() - - if isinstance(metadata, dict): - self.metadata = metadata - else: - self.metadata = {k.lower(): v for k, v in os.environ.items()} - - self._master_port = 23456 - - def detect(self) -> bool: - if "master" not in self.metadata: - return False - if "world_size" not in self.metadata: - return False - if "global_rank" not in self.metadata: - return False - - return True - - @property - def creates_processes_externally(self) -> bool: - """Return True if the cluster is managed (you don't launch processes - yourself). - """ - return True - - @property - def main_address(self) -> str: - """Return master worker address.""" - return self.metadata["master"] - - @property - def main_port(self) -> int: - """Return master worker port.""" - return self._master_port - - def creates_children(self) -> bool: - """Fork children when generate a cluster.""" - return False - - def world_size(self) -> int: - """Return worker world size.""" - return int(self.metadata["world_size"]) - - def global_rank(self) -> int: - """Return worker global rank.""" - return int(self.metadata["global_rank"]) - - def local_rank(self) -> int: - """Return worker local rank.""" - if "local_rank" in self.metadata: - return int(self.metadata["local_rank"]) - else: - return 0 - - def node_rank(self) -> int: - """Return worker node rank (which is similar to global rank).""" - return int(self.metadata["global_rank"]) - - def set_world_size(self, size: int) -> None: - self.metadata["world_size"] = size - - def set_global_rank(self, rank: int) -> None: - self.metadata["global_rank"] = rank diff --git a/dasf/ml/dl/models/__init__.py b/dasf/ml/dl/models/__init__.py deleted file mode 100644 index ebf3f41..0000000 --- a/dasf/ml/dl/models/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.dl.models.devconvnet import ( - TorchPatchDeConvNet, - TorchPatchDeConvNetSkip, - TorchSectionDeConvNet, - TorchSectionDeConvNetSkip, -) - -__all__ = [ - "TorchPatchDeConvNet", - "TorchPatchDeConvNetSkip", - "TorchSectionDeConvNet", - "TorchSectionDeConvNetSkip", -] diff --git a/dasf/ml/dl/models/devconvnet.py b/dasf/ml/dl/models/devconvnet.py deleted file mode 100644 index 42d0b63..0000000 --- a/dasf/ml/dl/models/devconvnet.py +++ /dev/null @@ -1,1361 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np -import torch -from pytorch_lightning import LightningModule -from torch.nn import ( - BatchNorm2d, - Conv2d, - ConvTranspose2d, - MaxPool2d, - MaxUnpool2d, - ReLU, - Sequential, -) -from torch.nn import functional as F -from torchmetrics import Metric - - -class MyAccuracy(Metric): - def __init__(self, dist_sync_on_step=False): - # call `self.add_state`for every internal state that is needed for the - # metrics computations dist_reduce_fx indicates the function that - # should be used to reduce state from multiple processes - super().__init__(dist_sync_on_step=dist_sync_on_step) - - self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum") - self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") - self.idx = 0 - - def set_idx(self, idx): - self.idx = idx - - def update(self, preds: torch.Tensor, target: torch.Tensor): - # update metric states - pred = preds.detach().max(1)[1].cpu().numpy() - gt = torch.squeeze(target, 1).cpu().numpy() - - assert pred.shape == gt.shape - - np.save("out/pred_" + str(self.idx) + ".npy", pred) - - self.correct += np.sum(pred == gt) - self.total += len(gt.flatten()) - - def __str__(self): - ret = self.compute() - return str(ret) - - def compute(self): - # compute final result - return float(self.correct / self.total) - - -class NNModule(LightningModule): - def __init__( - self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None - ): - super().__init__() - - self.learned_billinear = learned_billinear - self.n_classes = n_classes - self.clip = clip - - if class_weights and isinstance(class_weights, dict): - self.class_weights = torch.tensor( - list(class_weights.values()), requires_grad=False - ) - else: - self.class_weights = None - - self.class_names = list(class_weights.keys()) - - def cross_entropy_loss(self, input, target, weight=None, ignore_index=255): - """ - Use 255 to fill empty values when padding or doing any augmentation operations - like rotation. - """ - target = torch.squeeze(target, dim=1) - - print(input.device) - print(target.device) - print(weight.device) - - loss = F.cross_entropy(input, target, weight, reduction="sum", ignore_index=255) - - return loss - - def configure_optimizers(self): - return torch.optim.Adam(self.parameters(), amsgrad=True) - - def training_step(self, batch, batch_idx): - images, labels = batch - - outputs = self.forward(images) - - self.class_weights = self.class_weights.type_as(labels) - - if self.class_weights.is_cuda: - self.class_weights = self.class_weights.type(torch.cuda.FloatTensor) - else: - self.class_weights = self.class_weights.type(torch.FloatTensor) - - loss = self.cross_entropy_loss( - input=outputs, target=labels, weight=self.class_weights - ) - - # gradient clipping - if self.clip != 0: - torch.nn.utils.clip_grad_norm_(self.parameters(), self.clip) - - return loss - - def test_step(self, test_batch, batch_idx): - images, labels = test_batch - - preds = self(images) - - file_object = open("test.txt", "w") - file_object.write(str(preds.shape)) - file_object.write(str(labels.shape)) - file_object.close() - - self.accuracy.set_idx(batch_idx) - - self.accuracy(preds, labels) - - -class TorchPatchDeConvNet(NNModule): - def __init__( - self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None - ): - super().__init__(n_classes, learned_billinear, clip, class_weights) - - self.unpool = MaxUnpool2d(2, stride=2) - self.conv_block1 = Sequential( - # conv1_1 - Conv2d(1, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv1_2 - Conv2d(64, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool1 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_1 - - # 48*48 - - self.conv_block2 = Sequential( - # conv2_1 - Conv2d(64, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv2_2 - Conv2d(128, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool2 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_2 - - # 24*24 - - self.conv_block3 = Sequential( - # conv3_1 - Conv2d(128, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_2 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_3 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool3 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_3 - - # 12*12 - - self.conv_block4 = Sequential( - # conv4_1 - Conv2d(256, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool4 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_4 - - # 6*6 - - self.conv_block5 = Sequential( - # conv5_1 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool5 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_5 - - # 3*3 - - self.conv_block6 = Sequential( - # fc6 - Conv2d(512, 4096, 3), - # set the filter size and nor padding to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 1*1 - - self.conv_block7 = Sequential( - # fc7 - Conv2d(4096, 4096, 1), - # set the filter size to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.deconv_block8 = Sequential( - # fc6-deconv - ConvTranspose2d(4096, 512, 3, stride=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 3*3 - - self.unpool_block9 = Sequential( - # unpool5 - MaxUnpool2d(2, stride=2), - ) - # usage unpool(output, indices) - - # 6*6 - - self.deconv_block10 = Sequential( - # deconv5_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_3 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block11 = Sequential( - # unpool4 - MaxUnpool2d(2, stride=2), - ) - - # 12*12 - - self.deconv_block12 = Sequential( - # deconv4_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_3 - ConvTranspose2d(512, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block13 = Sequential( - # unpool3 - MaxUnpool2d(2, stride=2), - ) - - # 24*24 - - self.deconv_block14 = Sequential( - # deconv3_1 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_2 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_3 - ConvTranspose2d(256, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block15 = Sequential( - # unpool2 - MaxUnpool2d(2, stride=2), - ) - - # 48*48 - - self.deconv_block16 = Sequential( - # deconv2_1 - ConvTranspose2d(128, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv2_2 - ConvTranspose2d(128, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block17 = Sequential( - # unpool1 - MaxUnpool2d(2, stride=2), - ) - - # 96*96 - - self.deconv_block18 = Sequential( - # deconv1_1 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv1_2 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.seg_score19 = Sequential( - # seg-score - Conv2d(64, self.n_classes, 1), - ) - - if self.learned_billinear: - raise NotImplementedError - - def forward(self, x): - size0 = x.size() - conv1, indices1 = self.conv_block1(x) - size1 = conv1.size() - conv2, indices2 = self.conv_block2(conv1) - size2 = conv2.size() - conv3, indices3 = self.conv_block3(conv2) - size3 = conv3.size() - conv4, indices4 = self.conv_block4(conv3) - size4 = conv4.size() - conv5, indices5 = self.conv_block5(conv4) - - conv6 = self.conv_block6(conv5) - conv7 = self.conv_block7(conv6) - conv8 = self.deconv_block8(conv7) - conv9 = self.unpool(conv8, indices5, output_size=size4) - conv10 = self.deconv_block10(conv9) - conv11 = self.unpool(conv10, indices4, output_size=size3) - conv12 = self.deconv_block12(conv11) - conv13 = self.unpool(conv12, indices3, output_size=size2) - conv14 = self.deconv_block14(conv13) - conv15 = self.unpool(conv14, indices2, output_size=size1) - conv16 = self.deconv_block16(conv15) - conv17 = self.unpool(conv16, indices1, output_size=size0) - conv18 = self.deconv_block18(conv17) - out = self.seg_score19(conv18) - - print(type(out), '- device: ', out.device) - - out = out.type_as(x) - - return out - - def init_vgg16_params(self, vgg16, copy_fc8=True): - blocks = [ - self.conv_block1, - self.conv_block2, - self.conv_block3, - self.conv_block4, - self.conv_block5, - ] - - ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]] - features = list(vgg16.features.children()) - i_layer = 0 - # copy convolutional filters from vgg16 - for idx, conv_block in enumerate(blocks): - for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block): - if isinstance(l1, Conv2d) and isinstance(l2, Conv2d): - if i_layer == 0: - l2.weight.data = ( - ( - l1.weight.data[:, 0, :, :] - + l1.weight.data[:, 1, :, :] - + l1.weight.data[:, 2, :, :] - ) - / 3.0 - ).view(l2.weight.size()) - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - else: - assert l1.weight.size() == l2.weight.size() - assert l1.bias.size() == l2.bias.size() - l2.weight.data = l1.weight.data - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - - def load(self): - """ This is just a no-op load method. """ - return self - - -class TorchPatchDeConvNetSkip(NNModule): - def __init__( - self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None - ): - super().__init__(n_classes, learned_billinear, clip, class_weights) - - self.unpool = MaxUnpool2d(2, stride=2) - self.conv_block1 = Sequential( - # conv1_1 - Conv2d(1, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv1_2 - Conv2d(64, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool1 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_1 - - # 48*48 - - self.conv_block2 = Sequential( - # conv2_1 - Conv2d(64, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv2_2 - Conv2d(128, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool2 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_2 - - # 24*24 - - self.conv_block3 = Sequential( - # conv3_1 - Conv2d(128, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_2 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_3 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool3 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_3 - - # 12*12 - - self.conv_block4 = Sequential( - # conv4_1 - Conv2d(256, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool4 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_4 - - # 6*6 - - self.conv_block5 = Sequential( - # conv5_1 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool5 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_5 - - # 3*3 - - self.conv_block6 = Sequential( - # fc6 - Conv2d(512, 4096, 3), - # set the filter size and nor padding to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 1*1 - - self.conv_block7 = Sequential( - # fc7 - Conv2d(4096, 4096, 1), - # set the filter size to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.deconv_block8 = Sequential( - # fc6-deconv - ConvTranspose2d(4096, 512, 3, stride=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 3*3 - - self.unpool_block9 = Sequential( - # unpool5 - MaxUnpool2d(2, stride=2), - ) - # usage unpool(output, indices) - - # 6*6 - - self.deconv_block10 = Sequential( - # deconv5_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_3 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block11 = Sequential( - # unpool4 - MaxUnpool2d(2, stride=2), - ) - - # 12*12 - - self.deconv_block12 = Sequential( - # deconv4_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_3 - ConvTranspose2d(512, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block13 = Sequential( - # unpool3 - MaxUnpool2d(2, stride=2), - ) - - # 24*24 - - self.deconv_block14 = Sequential( - # deconv3_1 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_2 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_3 - ConvTranspose2d(256, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block15 = Sequential( - # unpool2 - MaxUnpool2d(2, stride=2), - ) - - # 48*48 - - self.deconv_block16 = Sequential( - # deconv2_1 - ConvTranspose2d(128, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv2_2 - ConvTranspose2d(128, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block17 = Sequential( - # unpool1 - MaxUnpool2d(2, stride=2), - ) - - # 96*96 - - self.deconv_block18 = Sequential( - # deconv1_1 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv1_2 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.seg_score19 = Sequential( - # seg-score - Conv2d(64, self.n_classes, 1), - ) - - if self.learned_billinear: - raise NotImplementedError - - def forward(self, x): - size0 = x.size() - conv1, indices1 = self.conv_block1(x) - size1 = conv1.size() - conv2, indices2 = self.conv_block2(conv1) - size2 = conv2.size() - conv3, indices3 = self.conv_block3(conv2) - size3 = conv3.size() - conv4, indices4 = self.conv_block4(conv3) - size4 = conv4.size() - conv5, indices5 = self.conv_block5(conv4) - - conv6 = self.conv_block6(conv5) - conv7 = self.conv_block7(conv6) - conv8 = self.deconv_block8(conv7) + conv5 - conv9 = self.unpool(conv8, indices5, output_size=size4) - conv10 = self.deconv_block10(conv9) + conv4 - conv11 = self.unpool(conv10, indices4, output_size=size3) - conv12 = self.deconv_block12(conv11) + conv3 - conv13 = self.unpool(conv12, indices3, output_size=size2) - conv14 = self.deconv_block14(conv13) + conv2 - conv15 = self.unpool(conv14, indices2, output_size=size1) - conv16 = self.deconv_block16(conv15) + conv1 - conv17 = self.unpool(conv16, indices1, output_size=size0) - conv18 = self.deconv_block18(conv17) - out = self.seg_score19(conv18) - - print(type(out), '- device: ', out.device) - - out = out.type_as(x) - - return out - - def init_vgg16_params(self, vgg16, copy_fc8=True): - blocks = [ - self.conv_block1, - self.conv_block2, - self.conv_block3, - self.conv_block4, - self.conv_block5, - ] - - ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]] - features = list(vgg16.features.children()) - i_layer = 0 - # copy convolutional filters from vgg16 - for idx, conv_block in enumerate(blocks): - for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block): - if isinstance(l1, Conv2d) and isinstance(l2, Conv2d): - if i_layer == 0: - l2.weight.data = ( - ( - l1.weight.data[:, 0, :, :] - + l1.weight.data[:, 1, :, :] - + l1.weight.data[:, 2, :, :] - ) - / 3.0 - ).view(l2.weight.size()) - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - else: - assert l1.weight.size() == l2.weight.size() - assert l1.bias.size() == l2.bias.size() - l2.weight.data = l1.weight.data - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - - def load(self): - """ This is just a no-op load method. """ - return self - - -class TorchSectionDeConvNet(NNModule): - def __init__( - self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=False - ): - super().__init__(n_classes, learned_billinear, clip, class_weights) - - self.unpool = MaxUnpool2d(2, stride=2) - self.conv_block1 = Sequential( - # conv1_1 - Conv2d(1, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv1_2 - Conv2d(64, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool1 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_1 - - # 48*48 - - self.conv_block2 = Sequential( - # conv2_1 - Conv2d(64, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv2_2 - Conv2d(128, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool2 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_2 - - # 24*24 - - self.conv_block3 = Sequential( - # conv3_1 - Conv2d(128, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_2 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_3 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool3 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_3 - - # 12*12 - - self.conv_block4 = Sequential( - # conv4_1 - Conv2d(256, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool4 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_4 - - # 6*6 - - self.conv_block5 = Sequential( - # conv5_1 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool5 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_5 - - # 3*3 - - self.conv_block6 = Sequential( - # fc6 - Conv2d(512, 4096, 3), - # set the filter size and nor padding to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 1*1 - - self.conv_block7 = Sequential( - # fc7 - Conv2d(4096, 4096, 1), - # set the filter size to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.deconv_block8 = Sequential( - # fc6-deconv - ConvTranspose2d(4096, 512, 3, stride=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 3*3 - - self.unpool_block9 = Sequential( - # unpool5 - MaxUnpool2d(2, stride=2), - ) - # usage unpool(output, indices) - - # 6*6 - - self.deconv_block10 = Sequential( - # deconv5_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_3 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block11 = Sequential( - # unpool4 - MaxUnpool2d(2, stride=2), - ) - - # 12*12 - - self.deconv_block12 = Sequential( - # deconv4_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_3 - ConvTranspose2d(512, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block13 = Sequential( - # unpool3 - MaxUnpool2d(2, stride=2), - ) - - # 24*24 - - self.deconv_block14 = Sequential( - # deconv3_1 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_2 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_3 - ConvTranspose2d(256, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block15 = Sequential( - # unpool2 - MaxUnpool2d(2, stride=2), - ) - - # 48*48 - - self.deconv_block16 = Sequential( - # deconv2_1 - ConvTranspose2d(128, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv2_2 - ConvTranspose2d(128, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block17 = Sequential( - # unpool1 - MaxUnpool2d(2, stride=2), - ) - - # 96*96 - - self.deconv_block18 = Sequential( - # deconv1_1 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv1_2 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.seg_score19 = Sequential( - # seg-score - Conv2d(64, self.n_classes, 1), - ) - - if self.learned_billinear: - raise NotImplementedError - - def forward(self, x): - size0 = x.size() - conv1, indices1 = self.conv_block1(x) - size1 = conv1.size() - conv2, indices2 = self.conv_block2(conv1) - size2 = conv2.size() - conv3, indices3 = self.conv_block3(conv2) - size3 = conv3.size() - conv4, indices4 = self.conv_block4(conv3) - size4 = conv4.size() - conv5, indices5 = self.conv_block5(conv4) - - conv6 = self.conv_block6(conv5) - conv7 = self.conv_block7(conv6) - conv8 = self.deconv_block8(conv7) - conv9 = self.unpool(conv8, indices5, output_size=size4) - conv10 = self.deconv_block10(conv9) - conv11 = self.unpool(conv10, indices4, output_size=size3) - conv12 = self.deconv_block12(conv11) - conv13 = self.unpool(conv12, indices3, output_size=size2) - conv14 = self.deconv_block14(conv13) - conv15 = self.unpool(conv14, indices2, output_size=size1) - conv16 = self.deconv_block16(conv15) - conv17 = self.unpool(conv16, indices1, output_size=size0) - conv18 = self.deconv_block18(conv17) - out = self.seg_score19(conv18) - - print(type(out), '- device: ', out.device) - - out = out.type_as(x) - - return out - - def init_vgg16_params(self, vgg16, copy_fc8=True): - blocks = [ - self.conv_block1, - self.conv_block2, - self.conv_block3, - self.conv_block4, - self.conv_block5, - ] - - ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]] - features = list(vgg16.features.children()) - i_layer = 0 - # copy convolutional filters from vgg16 - for idx, conv_block in enumerate(blocks): - for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block): - if isinstance(l1, Conv2d) and isinstance(l2, Conv2d): - if i_layer == 0: - l2.weight.data = ( - ( - l1.weight.data[:, 0, :, :] - + l1.weight.data[:, 1, :, :] - + l1.weight.data[:, 2, :, :] - ) - / 3.0 - ).view(l2.weight.size()) - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - else: - assert l1.weight.size() == l2.weight.size() - assert l1.bias.size() == l2.bias.size() - l2.weight.data = l1.weight.data - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - - def load(self): - """ This is just a no-op load method. """ - return self - - -class TorchSectionDeConvNetSkip(NNModule): - def __init__( - self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None - ): - super().__init__(n_classes, learned_billinear, clip, class_weights) - - self.unpool = MaxUnpool2d(2, stride=2) - self.conv_block1 = Sequential( - # conv1_1 - Conv2d(1, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv1_2 - Conv2d(64, 64, 3, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool1 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_1 - - # 48*48 - - self.conv_block2 = Sequential( - # conv2_1 - Conv2d(64, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv2_2 - Conv2d(128, 128, 3, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool2 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_2 - - # 24*24 - - self.conv_block3 = Sequential( - # conv3_1 - Conv2d(128, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_2 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv3_3 - Conv2d(256, 256, 3, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool3 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_3 - - # 12*12 - - self.conv_block4 = Sequential( - # conv4_1 - Conv2d(256, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv4_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool4 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_4 - - # 6*6 - - self.conv_block5 = Sequential( - # conv5_1 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_2 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # conv5_3 - Conv2d(512, 512, 3, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # pool5 - MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True), - ) - # it returns outputs and pool_indices_5 - - # 3*3 - - self.conv_block6 = Sequential( - # fc6 - Conv2d(512, 4096, 3), - # set the filter size and nor padding to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 1*1 - - self.conv_block7 = Sequential( - # fc7 - Conv2d(4096, 4096, 1), - # set the filter size to make output into 1*1 - BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.deconv_block8 = Sequential( - # fc6-deconv - ConvTranspose2d(4096, 512, 3, stride=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - # 3*3 - - self.unpool_block9 = Sequential( - # unpool5 - MaxUnpool2d(2, stride=2), - ) - # usage unpool(output, indices) - - # 6*6 - - self.deconv_block10 = Sequential( - # deconv5_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv5_3 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block11 = Sequential( - # unpool4 - MaxUnpool2d(2, stride=2), - ) - - # 12*12 - - self.deconv_block12 = Sequential( - # deconv4_1 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_2 - ConvTranspose2d(512, 512, 3, stride=1, padding=1), - BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv4_3 - ConvTranspose2d(512, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block13 = Sequential( - # unpool3 - MaxUnpool2d(2, stride=2), - ) - - # 24*24 - - self.deconv_block14 = Sequential( - # deconv3_1 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_2 - ConvTranspose2d(256, 256, 3, stride=1, padding=1), - BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv3_3 - ConvTranspose2d(256, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block15 = Sequential( - # unpool2 - MaxUnpool2d(2, stride=2), - ) - - # 48*48 - - self.deconv_block16 = Sequential( - # deconv2_1 - ConvTranspose2d(128, 128, 3, stride=1, padding=1), - BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv2_2 - ConvTranspose2d(128, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.unpool_block17 = Sequential( - # unpool1 - MaxUnpool2d(2, stride=2), - ) - - # 96*96 - - self.deconv_block18 = Sequential( - # deconv1_1 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - # deconv1_2 - ConvTranspose2d(64, 64, 3, stride=1, padding=1), - BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True), - ReLU(inplace=True), - ) - - self.seg_score19 = Sequential( - # seg-score - Conv2d(64, self.n_classes, 1), - ) - - if self.learned_billinear: - raise NotImplementedError - - def forward(self, x): - size0 = x.size() - conv1, indices1 = self.conv_block1(x) - size1 = conv1.size() - conv2, indices2 = self.conv_block2(conv1) - size2 = conv2.size() - conv3, indices3 = self.conv_block3(conv2) - size3 = conv3.size() - conv4, indices4 = self.conv_block4(conv3) - size4 = conv4.size() - conv5, indices5 = self.conv_block5(conv4) - - conv6 = self.conv_block6(conv5) - conv7 = self.conv_block7(conv6) - conv8 = self.deconv_block8(conv7) + conv5 - conv9 = self.unpool(conv8, indices5, output_size=size4) - conv10 = self.deconv_block10(conv9) + conv4 - conv11 = self.unpool(conv10, indices4, output_size=size3) - conv12 = self.deconv_block12(conv11) + conv3 - conv13 = self.unpool(conv12, indices3, output_size=size2) - conv14 = self.deconv_block14(conv13) + conv2 - conv15 = self.unpool(conv14, indices2, output_size=size1) - conv16 = self.deconv_block16(conv15) + conv1 - conv17 = self.unpool(conv16, indices1, output_size=size0) - conv18 = self.deconv_block18(conv17) - out = self.seg_score19(conv18) - - print(type(out), '- device: ', out.device) - - out = out.type_as(x) - - return out - - def init_vgg16_params(self, vgg16, copy_fc8=True): - blocks = [ - self.conv_block1, - self.conv_block2, - self.conv_block3, - self.conv_block4, - self.conv_block5, - ] - - ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]] - features = list(vgg16.features.children()) - i_layer = 0 - # copy convolutional filters from vgg16 - for idx, conv_block in enumerate(blocks): - for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block): - if isinstance(l1, Conv2d) and isinstance(l2, Conv2d): - if i_layer == 0: - l2.weight.data = ( - ( - l1.weight.data[:, 0, :, :] - + l1.weight.data[:, 1, :, :] - + l1.weight.data[:, 2, :, :] - ) - / 3.0 - ).view(l2.weight.size()) - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - else: - assert l1.weight.size() == l2.weight.size() - assert l1.bias.size() == l2.bias.size() - l2.weight.data = l1.weight.data - l2.bias.data = l1.bias.data - i_layer = i_layer + 1 - - def load(self): - """ This is just a no-op load method. """ - return self diff --git a/dasf/ml/dl/pytorch_lightning.py b/dasf/ml/dl/pytorch_lightning.py deleted file mode 100644 index 8479264..0000000 --- a/dasf/ml/dl/pytorch_lightning.py +++ /dev/null @@ -1,167 +0,0 @@ -#!/usr/bin/env python3 - -import uuid - -import pytorch_lightning as pl -from dask_pytorch_ddp.results import DaskResultsHandler -from torch.utils.data import DataLoader - -from dasf.ml.dl.clusters import DaskClusterEnvironment -from dasf.transforms.base import Fit -from dasf.utils.funcs import ( - get_dask_gpu_count, - get_dask_running_client, - get_gpu_count, - get_worker_info, - sync_future_loop, -) - - -class TorchDataLoader(pl.LightningDataModule): - def __init__(self, train, val=None, test=None, batch_size=64): - super().__init__() - - self._train = train - self._val = val - self._test = test - - self._batch_size = batch_size - - def prepare_data(self): - if self._train is not None and hasattr(self._train, "download"): - self._train.download() - - if self._val is not None and hasattr(self._val, "download"): - self._val.download() - - if self._test is not None and hasattr(self._test, "download"): - self._test.download() - - def setup(self, stage=None): - if self._train is not None and hasattr(self._train, "load"): - self._train.load() - - if self._val is not None and hasattr(self._val, "load"): - self._val.load() - - if self._test is not None and hasattr(self._test, "load"): - self._test.load() - - def train_dataloader(self): - return DataLoader(self._train, batch_size=self._batch_size) - - def val_dataloader(self): - return DataLoader(self._val, batch_size=self._batch_size) - - def test_dataloader(self): - return DataLoader(self._test, batch_size=self._batch_size) - - -def run_dask_clustered(func, client=None, **kwargs): - if client is None: - client = get_dask_running_client() - - all_workers = get_worker_info(client) - - for worker in all_workers: - # Including worker metadata into kwargs - kwargs['meta'] = worker - - futures = client.submit(func, **kwargs, workers=[worker["worker"]]) - - sync_future_loop(futures) - - -def fit(model, X, y, max_iter, accel, strategy, devices, ngpus, batch_size=32, - plugins=None, meta=None): - - if meta is None: - plugin = DaskClusterEnvironment(metadata=meta) - - nodes = plugin.world_size() - - if plugins is None: - plugins = list() - - plugins.append(plugin) - else: - nodes = 1 - - # Use it for heterogeneous workers. - if ngpus < 0: - ngpus = -1 - - dataloader = TorchDataLoader(train=X, val=y, batch_size=batch_size) - - trainer = pl.Trainer( - max_epochs=max_iter, - accelerator=accel, - strategy=strategy, - gpus=ngpus, - plugins=plugins, - devices=devices, - num_nodes=nodes, - ) - - trainer.fit(model, datamodule=dataloader) - - -class NeuralNetClassifier(Fit): - def __init__(self, model, max_iter=100, batch_size=32): - self._model = model - - self._accel = None - self._strategy = None - self._max_iter = max_iter - self._devices = 0 - self._ngpus = 0 - self._batch_size = batch_size - - self.__trainer = False - self.__handler = DaskResultsHandler(uuid.uuid4().hex) - - def _lazy_fit_generic(self, X, y, accel, ngpus): - self._accel = accel - self._strategy = "ddp" - self._ngpus = self._ndevices = ngpus - - plugins = [DaskClusterEnvironment()] - - run_dask_clustered( - fit, - model=self._model, - X=X, - y=y, - max_iter=self._max_iter, - accel=self._accel, - strategy=self._strategy, - devices=self._ndevices, - ngpus=self._ngpus, - batch_size=self._batch_size, - plugins=plugins, - ) - - def _lazy_fit_gpu(self, X, y=None): - self._lazy_fit_generic(X=X, y=y, accel="gpu", ngpus=get_dask_gpu_count()) - - def _lazy_fit_cpu(self, X, y=None): - self._lazy_fit_generic(X=X, y=y, accel="cpu", ngpus=get_dask_gpu_count()) - - def __fit_generic(self, X, y, accel, ngpus): - self._accel = accel - self._strategy = "dp" - self._ngpus = self._ndevices = ngpus - - dataloader = TorchDataLoader(train=X, val=y, batch_size=self._batch_size) - - self.__trainer = pl.Trainer( - max_epochs=self._max_iter, accelerator=accel, devices=ngpus - ) - - self.__trainer.fit(self._model, datamodule=dataloader) - - def _fit_gpu(self, X, y=None): - self.__fit_generic(X, y, "gpu", get_gpu_count()) - - def _fit_cpu(self, X, y=None): - self.__fit_generic(X, y, "cpu", 0) diff --git a/dasf/ml/inference/__init__.py b/dasf/ml/inference/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/dasf/ml/inference/loader/__init__.py b/dasf/ml/inference/loader/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/dasf/ml/inference/loader/base.py b/dasf/ml/inference/loader/base.py deleted file mode 100644 index 9b726bb..0000000 --- a/dasf/ml/inference/loader/base.py +++ /dev/null @@ -1,102 +0,0 @@ -from dask.distributed import Worker - -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import get_dask_running_client - - -class BaseLoader: - """ - BaseLoader for DL models. When running in a Dask Cluster instantiates a model per worker that will be reused on every subsequent prediction task. - """ - - def __init__(self): - self.model_instances = {} - - def inference(self, model, data): - raise NotImplementedError("Inference must be implemented") - - def load_model(self): - """ - Load Model method is specific for each framework/model. - """ - raise NotImplementedError("Load Model must be implemented") - - def load_model_distributed(self, **kwargs): - """ - Distributed model instantiation - """ - try: - Worker.model = self.load_model(**kwargs) - return "UP" - except: - return "DOWN" - - def _lazy_load(self, **kwargs): - client = get_dask_running_client() - self.model_instances = {} - if client: - worker_addresses = list(client.scheduler_info()["workers"].keys()) - self.model_instances = client.run( - self.load_model_distributed, **kwargs, workers=worker_addresses - ) - - def _load(self, **kwargs): - self.model_instances = {"local": self.load_model(**kwargs)} - - def _lazy_load_cpu(self, **kwargs): - if not (hasattr(self, "device") and self.device): - self.device = "cpu" - self._lazy_load(**kwargs) - - def _lazy_load_gpu(self, **kwargs): - if not (hasattr(self, "device") and self.device): - self.device = "gpu" - self._lazy_load(**kwargs) - - def _load_cpu(self, **kwargs): - if not (hasattr(self, "device") and self.device): - self.device = "cpu" - self._load(**kwargs) - - def _load_gpu(self, **kwargs): - if not (hasattr(self, "device") and self.device): - self.device = "gpu" - self._load(**kwargs) - - @task_handler - def load(self, **kwargs): - ... - - def predict(self, data): - """ - Predict method called on prediction tasks. - """ - if not self.model_instances: - raise RuntimeError( - "Models have not been loaded. load method must be executed beforehand." - ) - if "local" in self.model_instances: - model = self.model_instances["local"] - else: - model = Worker.model - data = self.preprocessing(data) - output = self.inference(model, data) - return self.postprocessing(output) - - def preprocessing(self, data): - """ - Preprocessing stage which is called before inference - """ - return data - - def inference(self, model, data): - """ - Inference method, receives model and input data - """ - raise NotImplementedError("Inference must be implemented") - - def postprocessing(self, data): - """ - Postprocessing stage which is called after inference - """ - return data diff --git a/dasf/ml/inference/loader/torch.py b/dasf/ml/inference/loader/torch.py deleted file mode 100644 index ee1f696..0000000 --- a/dasf/ml/inference/loader/torch.py +++ /dev/null @@ -1,57 +0,0 @@ -import inspect -import os - -import torch - -from .base import BaseLoader - - -class TorchLoader(BaseLoader): - """ - Model Loader for Torch models - """ - - def __init__( - self, model_class_or_file, dtype=torch.float32, checkpoint=None, device=None - ): - """ - model_class_or_file: class or file with model definition - dtype: data type of model input - checkpoint: model chekpoint file - device: device to place model ("cpu" or "gpu") - """ - super().__init__() - self.model_class_or_file = model_class_or_file - self.dtype = dtype - self.checkpoint = checkpoint - self.device = device - - def load_model(self, **kwargs): - device = torch.device("cuda" if self.device == "gpu" else "cpu") - if inspect.isclass(self.model_class_or_file): - model = self.model_class_or_file(**kwargs) - if self.checkpoint: - state_dict = torch.load(self.checkpoint, map_location=device) - state_dict = ( - state_dict["state_dict"] - if "state_dict" in state_dict - else state_dict - ) # In case model was saved by TensorBoard - model.load_state_dict(state_dict) - elif os.path.isfile(self.model_class_or_file): - model = torch.load(self.model_class_or_file) - else: - raise ValueError( - "model_class_or_file must be a model class or path to model file" - ) - model.to(device=device) - model.eval() - return model - - def inference(self, model, data): - data = torch.from_numpy(data) - device = torch.device("cuda" if self.device == "gpu" else "cpu") - data = data.to(device, dtype=self.dtype) - with torch.no_grad(): - output = model(data) - return output.cpu().numpy() if self.device == "gpu" else output.numpy() diff --git a/dasf/ml/mixture/classifier.py b/dasf/ml/mixture/classifier.py deleted file mode 100644 index 630468b..0000000 --- a/dasf/ml/mixture/classifier.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.transforms.base import Fit, FitPredict, FitTransform, GetParams, SetParams - - -class MixtureClassifier(Fit, FitPredict, FitTransform, - GetParams, SetParams): - def fit(self, X, y=None, sample_weight=None): - raise NotImplementedError - - def fit_predict(self, X, y=None, sample_weight=None): - raise NotImplementedError - - def fit_transform(self, X, y=None): - raise NotImplementedError - - def get_params(deep=True): - raise NotImplementedError - - def set_params(**params): - raise NotImplementedError diff --git a/dasf/ml/mixture/gmm.py b/dasf/ml/mixture/gmm.py deleted file mode 100644 index 9d192f4..0000000 --- a/dasf/ml/mixture/gmm.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 - -from sklearn.mixture import GaussianMixture as GaussianMixture_CPU - -from dasf.ml.mixture.classifier import MixtureClassifier - - -class GaussianMixture(MixtureClassifier): - def __init__( - self, - n_components=1, - *, - covariance_type="full", - tol=0.001, - reg_covar=1e-06, - max_iter=100, - n_init=1, - init_params="kmeans", - weights_init=None, - means_init=None, - precisions_init=None, - random_state=None, - warm_start=False, - verbose=0, - verbose_interval=10 - ): - - self.__gmm_cpu = GaussianMixture_CPU( - n_components=n_components, - covariance_type=covariance_type, - tol=tol, - reg_covar=reg_covar, - max_iter=max_iter, - n_init=n_init, - init_params=init_params, - weights_init=weights_init, - means_init=means_init, - precisions_init=precisions_init, - random_state=random_state, - warm_start=warm_start, - verbose=verbose, - verbose_interval=verbose_interval, - ) - - def _fit_cpu(self, X, y=None): - return self.__gmm_cpu.fit(X=X, y=y) - - def _fit_predict_cpu(self, X, y=None): - return self.__gmm_cpu.fit_predict(X=X, y=y) - - def _predict_cpu(self, X, y=None): - return self.__gmm_cpu.predict(X=X) - - def _set_params_cpu(self, **params): - return self.__gmm_cpu.set_params(**params) - - def _get_params_cpu(self, deep=True): - return self.__gmm_cpu.get_params(deep=deep) diff --git a/dasf/ml/model_selection/__init__.py b/dasf/ml/model_selection/__init__.py deleted file mode 100644 index ac8a3b0..0000000 --- a/dasf/ml/model_selection/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.model_selection.split import TrainTestSplit - -__all__ = ["TrainTestSplit"] diff --git a/dasf/ml/model_selection/split.py b/dasf/ml/model_selection/split.py deleted file mode 100644 index ed3ddb3..0000000 --- a/dasf/ml/model_selection/split.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.model_selection import train_test_split as train_test_split_mcpu -from sklearn.model_selection import train_test_split as train_test_split_cpu - -from dasf.transforms import TargeteredTransform, Transform - -try: - from cuml.model_selection import train_test_split as train_test_split_gpu -except ImportError: - pass - - -class train_test_split(TargeteredTransform, Transform): - def __init__( - self, - output="train", - test_size=None, - train_size=None, - random_state=None, - shuffle=None, - blockwise=True, - convert_mixed_types=False, - **kwargs - ): - TargeteredTransform.__init__(self, **kwargs) - - self.output = output - self.test_size = test_size - self.train_size = train_size - self.random_state = random_state - self.shuffle = shuffle - - # Exclusive for Dask operations - self.blockwise = blockwise - - self.convert_mixed_types = convert_mixed_types - - def _lazy_transform_cpu(self, X): - X, y = X - X_train, X_test, y_train, y_test = train_test_split_mcpu( - X, - y, - train_size=self.train_size, - shuffle=self.shuffle, - random_state=self.random_state, - blockwise=self.blockwise, - ) - if self.output == "train": - return X_train, y_train - elif self.output == "test": - return X_test, y_test - - def _lazy_transform_gpu(self, X): - raise NotImplementedError( - "Function train_test_split() is not implemented for Dask and CuML" - ) - - def _transform_cpu(self, X): - X, y = X - X_train, X_test, y_train, y_test = train_test_split_cpu( - X, - y, - train_size=self.train_size, - shuffle=self.shuffle, - random_state=self.random_state, - ) - if self.output == "train": - return X_train, y_train - elif self.output == "test": - return X_test, y_test - - def _transform_gpu(self, X): - X, y = X - X_train, X_test, y_train, y_test = train_test_split_gpu( - X, - y, - train_size=self.train_size, - shuffle=self.shuffle, - random_state=self.random_state, - ) - if self.output == "train": - return X_train, y_train - elif self.output == "test": - return X_test, y_test diff --git a/dasf/ml/neighbors/__init__.py b/dasf/ml/neighbors/__init__.py deleted file mode 100644 index f3e271f..0000000 --- a/dasf/ml/neighbors/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.neighbors.neighbors import NearestNeighbors # noqa - -__all__ = ["NearestNeighbors"] diff --git a/dasf/ml/neighbors/neighbors.py b/dasf/ml/neighbors/neighbors.py deleted file mode 100644 index f3a0c3e..0000000 --- a/dasf/ml/neighbors/neighbors.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python3 - -from sklearn.neighbors import NearestNeighbors as NearestNeighbors_CPU - -from dasf.transforms.base import Fit, GetParams, SetParams -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml.neighbors import NearestNeighbors as NearestNeighbors_GPU -except ImportError: - pass - - -class NearestNeighbors(Fit, GetParams, SetParams): - def __init__(self, n_neighbors=5, radius=1.0, algorithm='auto', - leaf_size=30, metric='minkowski', p=2, - metric_params=None, n_jobs=None, handle=None, verbose=False, - output_type=None, **kwargs): - - self.__nn_cpu = NearestNeighbors_CPU(n_neighbors=n_neighbors, - radius=radius, - algorithm=algorithm, - leaf_size=leaf_size, - metric=metric, p=p, - metric_params=metric_params, - n_jobs=n_jobs, **kwargs) - - if is_gpu_supported(): - self.__nn_gpu = NearestNeighbors_GPU(n_neighbors=n_neighbors, - radius=radius, - algorithm=algorithm, - leaf_size=leaf_size, - metric=metric, p=p, - metric_params=metric_params, - n_jobs=n_jobs, - handle=handle, - verbose=verbose, - output_type=output_type, - **kwargs) - - def _fit_cpu(self, X, y=None, **kwargs): - return self.__nn_cpu.fit(X=X, y=y) - - def _fit_gpu(self, X, y=None, **kwargs): - return self.__nn_gpu.fit(X=X, **kwargs) - - def _get_params_cpu(self, deep=True, **kwargs): - return self.__nn_cpu.get_params(deep=deep) - - def _set_params_cpu(self, **params): - return self.__nn_cpu.set_params(**params) diff --git a/dasf/ml/preprocessing/__init__.py b/dasf/ml/preprocessing/__init__.py deleted file mode 100644 index 3789fea..0000000 --- a/dasf/ml/preprocessing/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.preprocessing.standardscaler import StandardScaler - -__all__ = ["StandardScaler"] diff --git a/dasf/ml/preprocessing/standardscaler.py b/dasf/ml/preprocessing/standardscaler.py deleted file mode 100644 index 33a3773..0000000 --- a/dasf/ml/preprocessing/standardscaler.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 - -from dask_ml.preprocessing import StandardScaler as StandardScaler_MCPU -from sklearn.preprocessing import StandardScaler as StandardScaler_CPU - -from dasf.utils.funcs import is_gpu_supported - -try: - from cuml.preprocessing import StandardScaler as StandardScaler_GPU -except ImportError: - pass - -from dasf.transforms.base import Fit, FitTransform, TargeteredTransform - - -class StandardScaler(Fit, FitTransform, TargeteredTransform): - def __init__(self, copy=True, with_mean=True, with_std=True, **kwargs): - TargeteredTransform.__init__(self, **kwargs) - - self.__std_scaler_cpu = StandardScaler_CPU( - copy=copy, with_mean=with_mean, with_std=with_std - ) - - self.__std_scaler_dask = StandardScaler_MCPU( - copy=copy, with_mean=with_mean, with_std=with_std - ) - - if is_gpu_supported(): - self.__std_scaler_gpu = StandardScaler_GPU( - copy=copy, with_mean=with_mean, with_std=with_std - ) - - def _lazy_fit_cpu(self, X, y=None): - return self.__std_scaler_dask.fit(X=X, y=y) - - def _lazy_fit_gpu(self, X, y=None): - return self.__std_scaler_dask.fit(X=X, y=y) - - def _fit_cpu(self, X, y=None): - return self.__std_scaler_cpu.fit(X=X, y=y) - - def _fit_gpu(self, X, y=None): - return self.__std_scaler_gpu.fit(X=X, y=y) - - def _lazy_fit_transform_cpu(self, X, y=None): - return self.__std_scaler_dask.fit_transform(X=X, y=y) - - def _lazy_fit_transform_gpu(self, X, y=None): - return self.__std_scaler_dask.fit_transform(X=X, y=y) - - def _fit_transform_cpu(self, X, y=None): - return self.__std_scaler_cpu.fit_transform(X=X, y=y) - - def _fit_transform_gpu(self, X, y=None): - ret = self.__std_scaler_gpu.fit(X=X, y=y) - return ret.transform(X=X) - - def _lazy_partial_fit_cpu(self, X, y=None): - return self.__std_scaler_dask.partial_fit(X=X, y=y) - - def _lazy_partial_fit_gpu(self, X, y=None): - return self.__std_scaler_dask.partial_fit(X=X, y=y) - - def _fit_partial_cpu(self, X, y=None): - return self.__std_scaler_cpu.partial_fit(X=X, y=y) - - def _fit_partial_gpu(self, X, y=None): - return self.__std_scaler_gpu.partial_fit(X=X, y=y) - - def _lazy_transform_cpu(self, X, copy=None): - return self.__std_scaler_dask.transform(X=X, copy=copy) - - def _lazy_transform_gpu(self, X, copy=None): - return self.__std_scaler_dask.transform(X=X, copy=copy) - - def _transform_cpu(self, X, copy=None): - return self.__std_scaler_cpu.transform(X=X, copy=copy) - - def _transform_gpu(self, X, copy=None): - return self.__std_scaler_gpu.transform(X=X, copy=copy) - - def _lazy_inverse_transform_cpu(self, X, copy=None): - return self.__std_scaler_dask.inverse_transform(X=X, copy=copy) - - def _lazy_inverse_transform_gpu(self, X, copy=None): - return self.__std_scaler_dask.inverse_transform(X=X, copy=copy) - - def _inverse_transform_cpu(self, X, copy=None): - return self.__std_scaler_cpu.inverse_transform(X=X, copy=copy) - - def _inverse_transform_gpu(self, X, copy=None): - return self.__std_scaler_gpu.inverse_transform(X=X, copy=copy) diff --git a/dasf/ml/svm/__init__.py b/dasf/ml/svm/__init__.py deleted file mode 100644 index 4ecc8f7..0000000 --- a/dasf/ml/svm/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.svm.svm import SVC # noqa -from dasf.ml.svm.svm import SVR # noqa -from dasf.ml.svm.svm import LinearSVC # noqa -from dasf.ml.svm.svm import LinearSVR # noqa - -__all__ = ["SVC", "SVR", "LinearSVC", "LinearSVR"] diff --git a/dasf/ml/svm/svm.py b/dasf/ml/svm/svm.py deleted file mode 100644 index fd21cde..0000000 --- a/dasf/ml/svm/svm.py +++ /dev/null @@ -1,294 +0,0 @@ -#!/usr/bin/env python3 - -from sklearn.svm import SVC as SVC_CPU -from sklearn.svm import SVR as SVR_CPU -from sklearn.svm import LinearSVC as LinearSVC_CPU -from sklearn.svm import LinearSVR as LinearSVR_CPU - -try: - from cuml.svm import SVC as SVC_GPU - from cuml.svm import SVR as SVR_GPU - from cuml.svm import LienarSVC as LinearSVC_GPU - from cuml.svm import LienarSVR as LinearSVR_GPU -except ImportError: - pass - -from dasf.transforms.base import Fit, GetParams, Predict, SetParams -from dasf.utils.funcs import is_gpu_supported - - -class SVC(Fit, Predict, GetParams, SetParams): - def __init__( - self, - C=1.0, - kernel="rbf", - degree=3, - gamma="scale", - coef0=0.0, - shrinking=True, - probability=False, - tol=0.001, - cache_size=200, - class_weight=None, - verbose=False, - max_iter=-1, - decision_function_shape="ovr", - break_ties=False, - nochange_steps=1000, - random_state=None, - ): - - self.__svc_cpu = SVC_CPU( - C=C, - kernel=kernel, - degree=degree, - gamma=gamma, - coef0=coef0, - shrinking=shrinking, - probability=probability, - tol=tol, - cache_size=cache_size, - class_weight=class_weight, - verbose=verbose, - max_iter=max_iter, - decision_function_shape=decision_function_shape, - break_ties=break_ties, - random_state=random_state, - ) - - if is_gpu_supported(): - self.__svc_gpu = SVC_GPU( - C=C, - kernel=kernel, - degree=degree, - gamma=gamma, - coef0=coef0, - tol=tol, - cache_size=cache_size, - class_weight=class_weight, - verbose=verbose, - max_iter=max_iter, - random_state=random_state, - multiclass_strategy=decision_function_shape, - probability=probability, - output_type="input", - ) - - def _fit_cpu(self, X, y, sample_weight=None): - return self.__svc_cpu.fit(X, y, sample_weight) - - def _fit_gpu(self, X, y, sample_weight=None): - return self.__svc_gpu.fit(X, y, sample_weight) - - def _predict_cpu(self, X): - return self.__svc_cpu.predict(X) - - def _predict_gpu(self, X): - return self.__svc_gpu.predict(X) - - def _get_params_cpu(self, deep=True): - return self.__svc_cpu.get_params(deep=deep) - - def _set_params_cpu(self, **params): - return self.__svc_cpu.set_params(**params) - - -class SVR(Fit, Predict): - def __init__( - self, - kernel="rbf", - degree=3, - gamma="scale", - coef0=0.0, - tol=0.001, - C=1.0, - epsilon=0.1, - shrinking=True, - cache_size=200, - verbose=False, - max_iter=-1, - nochange_steps=1000, - ): - - self.__svr_cpu = SVR_CPU( - C=C, - kernel=kernel, - degree=degree, - gamma=gamma, - coef0=coef0, - tol=tol, - epsilon=epsilon, - shrinking=shrinking, - cache_size=cache_size, - verbose=verbose, - max_iter=max_iter, - ) - - if is_gpu_supported(): - self.__svr_gpu = SVR_GPU( - C=C, - kernel=kernel, - degree=degree, - gamma=gamma, - coef0=coef0, - tol=tol, - epsilon=epsilon, - shrinking=shrinking, - cache_size=cache_size, - verbose=verbose, - max_iter=max_iter, - nochange_steps=nochange_steps, - output_type="input", - ) - - def _fit_cpu(self, X, y, sample_weight=None): - return self.__svr_cpu.fit(X, y, sample_weight) - - def _fit_gpu(self, X, y, sample_weight=None): - return self.__svr_gpu.fit(X, y, sample_weight) - - def _predict_cpu(self, X): - return self.__svr_cpu.predict(X) - - def _predict_gpu(self, X): - return self.__svr_gpu.predict(X) - - -class LinearSVC(Fit, Predict, GetParams, SetParams): - def __init__( - self, - epsilon=0.0, - tol=0.0001, - C=1.0, - loss="epsilon_insensitive", - fit_intercept=True, - intercept_scaling=1.0, - dual=True, - verbose=0, - random_state=None, - max_iter=1000, - handle=None, - penalty="l2", - penalized_intercept=False, - linesearch_max_iter=100, - lbfgs_memory=5, - grad_tol=0.0001, - change_tol=1e-05, - multi_class="ovr", - ): - - self.__linear_svc_cpu = LinearSVC_CPU( - epsilon=epsilon, - tol=tol, - C=C, - loss=loss, - fit_intercept=fit_intercept, - intercept_scaling=intercept_scaling, - dual=dual, - verbose=verbose, - random_state=random_state, - max_iter=max_iter, - ) - - if is_gpu_supported(): - self.__linear_svc_gpu = LinearSVC_GPU( - tol=tol, - C=C, - loss=loss, - fit_intercept=fit_intercept, - intercept_scaling=intercept_scaling, - dual=dual, - verbose=verbose, - random_state=random_state, - max_iter=max_iter, - handle=handle, - penalty=penalty, - penalized_intercept=penalized_intercept, - linesearch_max_iter=linesearch_max_iter, - lbfgs_memory=lbfgs_memory, - grad_tol=grad_tol, - change_tol=change_tol, - multi_class=multi_class, - ) - - def _fit_cpu(self, X, y, sample_weight=None): - return self.__linear_svc_cpu.fit(X, y, sample_weight) - - def _fit_gpu(self, X, y, sample_weight=None): - return self.__linear_svc_gpu.fit(X, y, sample_weight) - - def _predict_cpu(self, X): - return self.__linear_svc_cpu.predict(X) - - def _predict_gpu(self, X): - return self.__linear_svc_gpu.predict(X) - - -class LinearSVR(Fit, Predict): - def __init__( - self, - epsilon=0.0, - tol=0.0001, - C=1.0, - loss="epsilon_insensitive", - fit_intercept=True, - intercept_scaling=1.0, - dual=True, - verbose=0, - random_state=None, - max_iter=1000, - handle=None, - penalty="l2", - penalized_intercept=False, - linesearch_max_iter=100, - lbfgs_memory=5, - grad_tol=0.0001, - change_tol=1e-05, - multi_class="ovr", - ): - - self.__linear_svr_cpu = LinearSVR_CPU( - epsilon=epsilon, - tol=tol, - C=C, - loss=loss, - fit_intercept=fit_intercept, - intercept_scaling=intercept_scaling, - dual=dual, - verbose=verbose, - random_state=random_state, - max_iter=max_iter, - ) - - if is_gpu_supported(): - self.__linear_svr_gpu = LinearSVR_GPU( - tol=tol, - C=C, - loss=loss, - fit_intercept=fit_intercept, - intercept_scaling=intercept_scaling, - dual=dual, - verbose=verbose, - random_state=random_state, - max_iter=max_iter, - handle=handle, - penalty=penalty, - penalized_intercept=penalized_intercept, - linesearch_max_iter=linesearch_max_iter, - lbfgs_memory=lbfgs_memory, - grad_tol=grad_tol, - change_tol=change_tol, - multi_class=multi_class, - ) - - def _fit_cpu(self, X, y, sample_weight=None): - return self.__linear_svr_cpu.fit(X, y, sample_weight) - - def _fit_gpu(self, X, y, sample_weight=None): - return self.__linear_svr_gpu.fit(X, y, sample_weight) - - def _predict_cpu(self, X): - return self.__linear_svr_cpu.predict(X) - - def _predict_gpu(self, X): - return self.__linear_svr_gpu.predict(X) diff --git a/dasf/ml/xgboost/__init__.py b/dasf/ml/xgboost/__init__.py deleted file mode 100644 index 6f9053c..0000000 --- a/dasf/ml/xgboost/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.ml.xgboost.xgboost import XGBRegressor # noqa - -__all__ = ["XGBRegressor"] diff --git a/dasf/ml/xgboost/xgboost.py b/dasf/ml/xgboost/xgboost.py deleted file mode 100644 index e63b0a2..0000000 --- a/dasf/ml/xgboost/xgboost.py +++ /dev/null @@ -1,189 +0,0 @@ -#!/usr/bin/env python3 - -import GPUtil -import xgboost as xgb - -from dasf.transforms import Fit, FitPredict, Predict -from dasf.utils.funcs import is_gpu_supported - - -class XGBRegressor(Fit, FitPredict, Predict): - def __init__( - self, - max_depth=None, - max_leaves=None, - max_bin=None, - grow_policy=None, - learning_rate=None, - n_estimators=100, - verbosity=None, - objective=None, - booster=None, - tree_method=None, - n_jobs=None, - gamma=None, - min_child_weight=None, - max_delta_step=None, - subsample=None, - sampling_method=None, - colsample_bytree=None, - colsample_bylevel=None, - colsample_bynode=None, - reg_alpha=None, - reg_lambda=None, - scale_pos_weight=None, - base_score=None, - random_state=None, - num_parallel_tree=None, - monotone_constraints=None, - interaction_constraints=None, - importance_type=None, - gpu_id=None, - validate_parameters=None, - predictor=None, - enable_categorical=False, - max_cat_to_onehot=None, - eval_metric=None, - early_stopping_rounds=None, - callbacks=None, - **kwargs - ): - - self.__xgb_cpu = xgb.XGBRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - max_leaves=max_leaves, - max_bin=max_bin, - grow_policy=grow_policy, - learning_rate=learning_rate, - verbosity=verbosity, - objective=objective, - booster=booster, - tree_method=tree_method, - n_jobs=n_jobs, - gamma=gamma, - min_child_weight=min_child_weight, - max_delta_step=max_delta_step, - subsample=subsample, - sampling_method=sampling_method, - colsample_bytree=colsample_bytree, - colsample_bylevel=colsample_bylevel, - colsample_bynode=colsample_bynode, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - scale_pos_weight=scale_pos_weight, - base_score=base_score, - random_state=random_state, - ) - - self.__xgb_mcpu = xgb.dask.DaskXGBRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - max_leaves=max_leaves, - max_bin=max_bin, - grow_policy=grow_policy, - learning_rate=learning_rate, - verbosity=verbosity, - objective=objective, - booster=booster, - tree_method=tree_method, - n_jobs=n_jobs, - gamma=gamma, - min_child_weight=min_child_weight, - max_delta_step=max_delta_step, - subsample=subsample, - sampling_method=sampling_method, - colsample_bytree=colsample_bytree, - colsample_bylevel=colsample_bylevel, - colsample_bynode=colsample_bynode, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - scale_pos_weight=scale_pos_weight, - base_score=base_score, - random_state=random_state, - ) - - if is_gpu_supported(): - if gpu_id is None: - gpus = GPUtil.getGPUs() - if len(gpus) > 0: - gpu_id = gpus[0].id - - self.__xgb_gpu = xgb.XGBRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - max_leaves=max_leaves, - max_bin=max_bin, - grow_policy=grow_policy, - learning_rate=learning_rate, - verbosity=verbosity, - objective=objective, - booster=booster, - tree_method='gpu_hist', - n_jobs=n_jobs, - gamma=gamma, - min_child_weight=min_child_weight, - max_delta_step=max_delta_step, - subsample=subsample, - sampling_method=sampling_method, - colsample_bytree=colsample_bytree, - colsample_bylevel=colsample_bylevel, - colsample_bynode=colsample_bynode, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - scale_pos_weight=scale_pos_weight, - base_score=base_score, - random_state=random_state, - gpu_id=gpu_id - ) - - self.__xgb_mgpu = xgb.dask.DaskXGBRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - max_leaves=max_leaves, - max_bin=max_bin, - grow_policy=grow_policy, - learning_rate=learning_rate, - verbosity=verbosity, - objective=objective, - booster=booster, - tree_method='gpu_hist', - n_jobs=n_jobs, - gamma=gamma, - min_child_weight=min_child_weight, - max_delta_step=max_delta_step, - subsample=subsample, - sampling_method=sampling_method, - colsample_bytree=colsample_bytree, - colsample_bylevel=colsample_bylevel, - colsample_bynode=colsample_bynode, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - scale_pos_weight=scale_pos_weight, - base_score=base_score, - random_state=random_state, - ) - - def _lazy_fit_cpu(self, X, y=None, sample_weight=None, *args, **kwargs): - return self.__xgb_mcpu.fit(X=X, y=y, *args, **kwargs) - - def _lazy_fit_gpu(self, X, y=None, sample_weight=None, *args, **kwargs): - return self.__xgb_mgpu.fit(X=X, y=y, *args, **kwargs) - - def _fit_cpu(self, X, y=None, sample_weight=None): - return self.__xgb_cpu.fit(X=X, y=y) - - def _fit_gpu(self, X, y=None, sample_weight=None, *args, **kwargs): - return self.__xgb_gpu.fit(X=X, y=y, *args, **kwargs) - - def _lazy_predict_cpu(self, X, sample_weight=None, **kwargs): - return self.__xgb_mcpu.predict(X=X, **kwargs) - - def _lazy_predict_gpu(self, X, sample_weight=None, **kwargs): - return self.__xgb_mgpu.predict(X=X, **kwargs) - - def _predict_cpu(self, X, sample_weight=None, **kwargs): - return self.__xgb_cpu.predict(X=X, **kwargs) - - def _predict_gpu(self, X, sample_weight=None, **kwargs): - return self.__xgb_gpu.predict(X=X, **kwargs) diff --git a/dasf/pipeline/__init__.py b/dasf/pipeline/__init__.py deleted file mode 100644 index cc9da9c..0000000 --- a/dasf/pipeline/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.pipeline.pipeline import Pipeline, PipelinePlugin - -__all__ = [ - "PipelinePlugin", - "Pipeline", -] diff --git a/dasf/pipeline/executors/__init__.py b/dasf/pipeline/executors/__init__.py deleted file mode 100644 index d712276..0000000 --- a/dasf/pipeline/executors/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.pipeline.executors.base import Executor # noqa -from dasf.pipeline.executors.dask import DaskPBSPipelineExecutor # noqa -from dasf.pipeline.executors.dask import DaskPipelineExecutor # noqa -from dasf.pipeline.executors.dask import DaskTasksPipelineExecutor # noqa -from dasf.pipeline.executors.wrapper import LocalExecutor # noqa - -__all__ = [ - "Executor", - "LocalExecutor", - "DaskPipelineExecutor", - "DaskPBSPipelineExecutor", - "DaskTasksPipelineExecutor", -] diff --git a/dasf/pipeline/executors/base.py b/dasf/pipeline/executors/base.py deleted file mode 100644 index 403e5c9..0000000 --- a/dasf/pipeline/executors/base.py +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/env python3 - - -class Executor: - @property - def is_connected(self) -> bool: - return False - - @property - def info(self) -> str: - return "This executor has no info to show." - - def has_dataset(self, key) -> bool: - return False - - def register_dataset(self, **kwargs): - dataset = list(kwargs.values()) - - if len(dataset) != 1: - raise Exception(f"This function requires one dataset only. " - "We found {len(dataset)}.") - - return dataset.pop() - - def get_dataset(self, key): - raise NotImplementedError("This function needs to be specialized for " - "every executor.") - - def register_plugin(self, plugin): - raise Exception("This executor does not accept plugins.") - - def pre_run(self, pipeline): - pass - - def post_run(self, pipeline): - pass - - def execute(self, fn, *args, **kwargs): - ... - - def shutdown(self): - pass diff --git a/dasf/pipeline/executors/dask.py b/dasf/pipeline/executors/dask.py deleted file mode 100644 index 6adc78a..0000000 --- a/dasf/pipeline/executors/dask.py +++ /dev/null @@ -1,311 +0,0 @@ -#!/usr/bin/env python3 - -import os -from typing import Union - -try: - import cupy as cp - import rmm -except ImportError: # pragma: no cover - pass - -from pathlib import Path - -import dask_memusage as dmem -import networkx as nx -from dask.distributed import Client, LocalCluster -from dask_cuda import LocalCUDACluster -from dask_jobqueue import PBSCluster -from distributed.diagnostics.plugin import NannyPlugin, WorkerPlugin - -from dasf.pipeline.executors.base import Executor -from dasf.pipeline.types import TaskExecutorType -from dasf.utils.funcs import ( - get_dask_gpu_count, - get_worker_info, - is_dask_gpu_supported, - is_gpu_supported, -) - - -def setup_dask_protocol(protocol=None): - if protocol is None or protocol == "tcp": - return "tcp://" - - if protocol == "ucx": - return "ucx://" - - raise ValueError(f"Protocol {protocol} is not supported.") - - -class DaskPipelineExecutor(Executor): - """ - A pipeline engine based on dask data flow. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - protocol -- sets the Dask protocol (default TCP) - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - """ - - def __init__( - self, - address=None, - port=8786, - local=False, - use_gpu=False, - profiler=None, - protocol=None, - gpu_allocator="cupy", - cluster_kwargs=None, - client_kwargs=None, - ): - self.address = address - self.port = port - - if not cluster_kwargs: - cluster_kwargs = dict() - - if not client_kwargs: - client_kwargs = dict() - - # If address is not set, consider local - local = local or (address is None and "scheduler_file" not in client_kwargs) - - if address: - address = f"{setup_dask_protocol()}{address}:{port}" - - self.client = Client(address=address) - elif "scheduler_file" in client_kwargs: - self.client = Client(scheduler_file=client_kwargs["scheduler_file"]) - elif local: - if use_gpu: - self.client = Client( - LocalCUDACluster(**cluster_kwargs), **client_kwargs - ) - else: - os.environ["CUDA_VISIBLE_DEVICES"] = "" # This avoids initializing workers on GPU:0 when available - self.client = Client(LocalCluster(**cluster_kwargs), - **client_kwargs) - - # Ask workers for GPUs - if local and not use_gpu: - self.dtype = TaskExecutorType.multi_cpu - else: - # Ask workers for GPUs - if is_dask_gpu_supported(): - self.dtype = TaskExecutorType.multi_gpu - - if gpu_allocator == "cupy": - # Nothing is required yet. - pass - elif gpu_allocator == "rmm" and is_gpu_supported(): - self.client.run(cp.cuda.set_allocator, rmm.rmm_cupy_allocator) - rmm.reinitialize(managed_memory=True) - cp.cuda.set_allocator(rmm.rmm_cupy_allocator) - else: - raise ValueError(f"'{gpu_allocator}' GPU Memory allocator is not " - "known") - else: - self.dtype = TaskExecutorType.multi_cpu - - # Share dtype attribute to client - if not hasattr(self.client, "dtype"): - setattr(self.client, "dtype", self.dtype) - - # Share which is the default backend of a cluster - if not hasattr(self.client, "backend"): - if self.dtype == TaskExecutorType.single_gpu or \ - self.dtype == TaskExecutorType.multi_gpu: - setattr(self.client, "backend", "cupy") - else: - setattr(self.client, "backend", "numpy") - - if profiler == "memusage": - profiler_dir = os.path.abspath( - os.path.join(str(Path.home()), - "/.cache/dasf/profiler/")) - os.makedirs(profiler_dir, exist_ok=True) - - dmem.install( - self.client.cluster.scheduler, - os.path.abspath(profiler_dir + "/dask-memusage"), - ) - - @property - def ngpus(self): - return get_dask_gpu_count() - - @property - def is_connected(self): - if "running" in self.client.status: - return True - return False - - def execute(self, fn, *args, **kwargs): - return fn(*args, **kwargs) - - def register_plugin(self, plugin: Union[WorkerPlugin, - NannyPlugin]): - if isinstance(plugin, WorkerPlugin): - self.client.register_worker_plugin(plugin) - elif isinstance(plugin, NannyPlugin): - self.client.register_worker_plugin(plugin, nanny=True) - - def register_dataset(self, **kwargs): - self.client.publish_dataset(**kwargs) - - def has_dataset(self, key): - return key in self.client.list_datasets() - - def get_dataset(self, key): - return self.client.get_dataset(name=key) - - def shutdown(self, gracefully=True): - if gracefully: - info = get_worker_info(self.client) - - worker_names = [] - for worker in info: - worker_names.append(worker["worker"]) - - if worker_names: - self.client.retire_workers(worker_names, close_workers=True) - else: - self.client.shutdown() - - def close(self): - self.client.close() - - -class DaskTasksPipelineExecutor(DaskPipelineExecutor): - """ - A not centric execution engine based on dask. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - """ - def __init__( - self, - address=None, - port=8786, - local=False, - use_gpu=True, - profiler=None, - protocol=None, - gpu_allocator="cupy", - cluster_kwargs=None, - client_kwargs=None, - ): - - super().__init__( - address=address, - port=port, - local=local, - use_gpu=use_gpu, - profiler=profiler, - protocol=protocol, - gpu_allocator=gpu_allocator, - cluster_kwargs=cluster_kwargs, - client_kwargs=client_kwargs, - ) - - # Ask workers for GPUs - if use_gpu: - if is_dask_gpu_supported(): - self.dtype = TaskExecutorType.single_gpu - else: - self.dtype = TaskExecutorType.single_cpu - else: - self.dtype = TaskExecutorType.single_cpu - - # Share dtype attribute to client - if not hasattr(self.client, "dtype"): - setattr(self.client, "dtype", self.dtype) - - self._tasks_map = dict() - - def pre_run(self, pipeline): - nodes = list(nx.topological_sort(pipeline._dag)) - - # TODO: we need to consider other branches for complex pipelines - dag_paths = nx.all_simple_paths(pipeline._dag, nodes[0], nodes[-1]) - all_paths = [] - for path in dag_paths: - all_paths.append(path) - - workers = get_worker_info(self.client) - - worker_idx = 0 - for path in all_paths: - for node in path: - if node not in self._tasks_map: - self._tasks_map[node] = workers[worker_idx] - - # Increment workers to all new path and repeat if there - # are more paths to assign. - if worker_idx == len(workers): - worker_idx = 0 - else: - worker_idx += 1 - - def post_run(self, pipeline): - pass - - def execute(self, fn, *args, **kwargs): - key = hash(fn) - - worker = self._tasks_map[key]["worker"] - - return self.client.submit(fn, *args, **kwargs, workers=[worker]) - - def register_dataset(self, **kwargs): - self.client.publish_dataset(**kwargs) - - def has_dataset(self, key): - return key in self.client.list_datasets() - - def get_dataset(self, key): - return self.client.get_dataset(name=key) - - def shutdown(self, gracefully=True): - if gracefully: - info = get_worker_info(self.client) - - worker_names = [] - for worker in info: - worker_names.append(worker["worker"]) - - if worker_names: - self.client.retire_workers(worker_names, close_workers=True) - else: - self.client.shutdown() - - def close(self): - self.client.close() - - -class DaskPBSPipelineExecutor(Executor): - def __init__(self, **kwargs): - self.client = Client(PBSCluster(**kwargs)) - - # Ask workers for GPUs - if is_dask_gpu_supported(): - self.dtype = TaskExecutorType.multi_gpu - else: - self.dtype = TaskExecutorType.multi_cpu diff --git a/dasf/pipeline/executors/ray.py b/dasf/pipeline/executors/ray.py deleted file mode 100644 index 2f12dc0..0000000 --- a/dasf/pipeline/executors/ray.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 - -try: - import ray - from ray.util.dask import disable_dask_on_ray, enable_dask_on_ray - - USE_RAY = True -except ImportError: # pragma: no cover - USE_RAY = False - -from dasf.pipeline.executors.base import Executor -from dasf.utils.funcs import get_dask_gpu_count - - -class RayPipelineExecutor(Executor): - """ - A pipeline engine based on ray data flow. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Ray head (default 8786). - local -- kicks off a new local Ray cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Ray - cluster (default False). - """ - - def __init__( - self, - address=None, - port=6379, - local=False, - use_gpu=False, - ray_kwargs=None, - ): - if not USE_RAY: - raise Exception("Ray executor is not support. " - "Check if you have it installed first.") - - self.address = address - self.port = port - - if not ray_kwargs: - ray_kwargs = dict() - - enable_dask_on_ray() - - if address: - address_str = f"ray://{address}:{str(port)}" - - ray.init(address=address_str, **ray_kwargs) - elif local: - ray.init(**ray_kwargs) - - @property - def ngpus(self): - return len(get_dask_gpu_count()) - - @property - def is_connected(self): - return ray.is_initialized() - - def execute(self, fn, *args, **kwargs): - return fn(*args, **kwargs) - - def __del__(self): - disable_dask_on_ray() - - ray.shutdown() diff --git a/dasf/pipeline/executors/wrapper.py b/dasf/pipeline/executors/wrapper.py deleted file mode 100644 index d6b4071..0000000 --- a/dasf/pipeline/executors/wrapper.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np - -try: - import cupy as cp - import rmm -except ImportError: # pragma: no cover - pass - -try: - from jax import jit -except ImportError: # pragma: no cover - pass - -from dasf.pipeline.types import TaskExecutorType -from dasf.utils.funcs import ( - get_backend_supported, - get_gpu_count, - is_gpu_supported, - is_jax_supported, -) - - -class LocalExecutor: - def __init__(self, - use_gpu=None, - backend="numpy", - gpu_allocator="cupy"): - - self.backend = backend - - if use_gpu is None: - if self.ngpus > 0: - self.dtype = TaskExecutorType.single_gpu - else: - self.dtype = TaskExecutorType.single_cpu - elif use_gpu and is_gpu_supported(): - self.dtype = TaskExecutorType.single_gpu - else: - self.dtype = TaskExecutorType.single_cpu - - if gpu_allocator == "rmm" and self.dtype == TaskExecutorType.single_gpu: - rmm.reinitialize(managed_memory=True) - cp.cuda.set_allocator(rmm.rmm_cupy_allocator) - - @property - def ngpus(self) -> int: - return get_gpu_count() - - @property - def is_connected(self) -> bool: - return True - - def pre_run(self, pipeline): - pass - - def post_run(self, pipeline): - pass - - def get_backend(self): - if self.backend == "numpy" and \ - self.dtype == TaskExecutorType.single_gpu: - return eval("cupy") - - return eval("cupy") - - def execute(self, fn, *args, **kwargs): - if get_backend_supported(fn): - kwargs['backend'] = self.get_backend() - - return fn(*args, **kwargs) diff --git a/dasf/pipeline/pipeline.py b/dasf/pipeline/pipeline.py deleted file mode 100644 index 758d3c3..0000000 --- a/dasf/pipeline/pipeline.py +++ /dev/null @@ -1,257 +0,0 @@ -#!/usr/bin/env python3 - -import inspect -from typing import List - -import graphviz -import networkx as nx - -from dasf.utils.logging import init_logging - - -class PipelinePlugin: - def on_pipeline_start(self, fn_keys): - pass - - def on_pipeline_end(self): - pass - - def on_task_start(self, func, params, name): - pass - - def on_task_end(self, func, params, name, ret): - pass - - def on_task_error(self, func, params, name, exception): - pass - - -class Pipeline: - def __init__(self, - name, - executor=None, - verbose=False, - callbacks: List[PipelinePlugin] = None): - from dasf.pipeline.executors.wrapper import LocalExecutor - - self._name = name - self._executor = executor if executor is not None else LocalExecutor() - self._verbose = verbose - - self._dag = nx.DiGraph() - self._dag_table = dict() - self._dag_g = graphviz.Digraph(name, format="png") - - self._logger = init_logging() - self._callbacks = callbacks or [] - - def register_plugin(self, plugin): - if isinstance(plugin, PipelinePlugin): - self._callbacks.append(plugin) - else: - self._executor.register_plugin(plugin) - - def info(self): - print(self._executor.info) - - def execute_callbacks(self, func_name: str, *args, **kwargs): - for callback in self._callbacks: - getattr(callback, func_name)(*args, **kwargs) - - def __add_into_dag(self, obj, func_name, parameters=None, itself=None): - key = hash(obj) - - if key not in self._dag_table: - self._dag.add_node(key) - self._dag_table[key] = dict() - self._dag_table[key]["fn"] = obj - self._dag_table[key]["name"] = func_name - self._dag_table[key]["parameters"] = None - self._dag_table[key]["ret"] = None - - if parameters and isinstance(parameters, dict): - if self._dag_table[key]["parameters"] is None: - self._dag_table[key]["parameters"] = parameters - else: - self._dag_table[key]["parameters"].update(parameters) - - # If we are adding a object which require parameters, - # we need to make sure they are mapped into DAG. - for k, v in parameters.items(): - dep_obj, dep_func_name, _ = self.__inspect_element(v) - self.add(dep_obj) - if not self._dag.has_node(str(key)): - self._dag_g.node(str(key), func_name) - - if not self._dag.has_node(str(hash(dep_obj))): - self._dag_g.node(str(hash(dep_obj)), dep_func_name) - - self._dag.add_edge(hash(dep_obj), key) - - self._dag_g.edge(str(hash(dep_obj)), str(key), label=k) - - def __inspect_element(self, obj): - from dasf.datasets.base import Dataset - from dasf.ml.inference.loader.base import BaseLoader - from dasf.transforms.base import Fit, Transform - - def generate_name(class_name, func_name): - return ("%s.%s" % (class_name, func_name)) - - if inspect.isfunction(obj) and callable(obj): - return (obj, - obj.__qualname__, - None) - elif inspect.ismethod(obj): - return (obj, - generate_name(obj.__self__.__class__.__name__, - obj.__name__), - obj.__self__) - elif issubclass(obj.__class__, Dataset) and hasattr(obj, "load"): - # (Disabled) Register dataset for reusability - # obj = self.__register_dataset(obj) - - return (obj.load, - generate_name(obj.__class__.__name__, - "load"), - obj) - elif issubclass(obj.__class__, Fit) and hasattr(obj, "fit"): - return (obj.fit, - generate_name(obj.__class__.__name__, - "fit"), - obj) - elif issubclass(obj.__class__, BaseLoader) and hasattr(obj, "load"): - return (obj.load, - generate_name(obj.__class__.__name__, - "load"), - obj) - elif issubclass(obj.__class__, Transform) and hasattr(obj, "transform"): - return (obj.transform, - generate_name(obj.__class__.__name__, - "transform"), - obj) - else: - raise ValueError( - f"This object {obj.__class__.__name__} is not a function, " - "method or a transformer object." - ) - - def add(self, obj, **kwargs): - obj, func_name, objref = self.__inspect_element(obj) - self.__add_into_dag(obj, func_name, kwargs, objref) - - return self - - def visualize(self, filename=None): - from dasf.utils.funcs import is_notebook - - if is_notebook(): - return self._dag_g - return self._dag_g.view(filename) - - def __register_dataset(self, dataset): - key = str(hash(dataset.load)) - kwargs = {key: dataset} - - if not self._executor.has_dataset(key): - return self._executor.register_dataset(**kwargs) - - return self._executor.get_dataset(key) - - def __execute(self, func, params, name): - ret = None - - new_params = dict() - if params: - for k, v in params.items(): - dep_obj, *_ = self.__inspect_element(v) - req_key = hash(dep_obj) - - new_params[k] = self._dag_table[req_key]["ret"] - - if len(new_params) > 0: - ret = self._executor.execute(fn=func, **new_params) - else: - ret = self._executor.execute(fn=func) - - return ret - - def get_result_from(self, obj): - _, obj_name, *_ = self.__inspect_element(obj) - - for key in self._dag_table: - if self._dag_table[key]["name"] == obj_name: - if self._dag_table[key]["ret"] is None: - raise Exception("Pipeline was not executed yet.") - return self._dag_table[key]["ret"] - - raise Exception(f"Function {obj_name} was not added into pipeline.") - - def run(self): - if not nx.is_directed_acyclic_graph(self._dag): - raise Exception("Pipeline has not a DAG format.") - - if not hasattr(self._executor, "execute"): - raise Exception( - f"Executor {self._executor.__name__} has not a execute() " - "method." - ) - - if not self._executor.is_connected: - raise Exception("Executor is not connected.") - - fn_keys = list(nx.topological_sort(self._dag)) - - self._logger.info(f"Beginning pipeline run for '{self._name}'") - self.execute_callbacks("on_pipeline_start", fn_keys) - - self._executor.pre_run(self) - - ret = None - failed = False - - for fn_key in fn_keys: - func = self._dag_table[fn_key]["fn"] - params = self._dag_table[fn_key]["parameters"] - name = self._dag_table[fn_key]["name"] - - if not failed: - self._logger.info(f"Task '{name}': Starting task run...") - else: - self._logger.error(f"Task '{name}': Starting task run...") - - try: - if not failed: - # Execute DAG node only if there is no error during the - # execution. Otherwise, skip it. - self.execute_callbacks("on_task_start", func=func, - params=params, name=name) - - result = self.__execute(func, params, name) - self._dag_table[fn_key]["ret"] = result - - self.execute_callbacks("on_task_end", func=func, - params=params, name=name, - ret=result) - - except Exception as e: - self.execute_callbacks("on_task_error", func=func, - params=params, name=name, exception=e) - failed = True - err = str(e) - self._logger.exception(f"Task '{name}': Failed with:\n{err}") - - if not failed: - self._logger.info(f"Task '{name}': Finished task run") - else: - self._logger.error(f"Task '{name}': Finished task run") - - if failed: - self._logger.info(f"Pipeline failed at '{name}'") - else: - self._logger.info("Pipeline run successfully") - - self._executor.post_run(self) - - self.execute_callbacks("on_pipeline_end") - return ret diff --git a/dasf/pipeline/types.py b/dasf/pipeline/types.py deleted file mode 100644 index 96edefc..0000000 --- a/dasf/pipeline/types.py +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env python3 - -from enum import IntEnum, auto - - -class TaskExecutorType(IntEnum): - single_cpu = auto() - multi_cpu = auto() - single_gpu = auto() - multi_gpu = auto() diff --git a/dasf/profile/__init__.py b/dasf/profile/__init__.py deleted file mode 100644 index f69b34c..0000000 --- a/dasf/profile/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from dasf.profile.analysis import TraceAnalyser -from dasf.profile.utils import MultiEventDatabase, register_default_profiler diff --git a/dasf/profile/analysis.py b/dasf/profile/analysis.py deleted file mode 100644 index 1d9b6e1..0000000 --- a/dasf/profile/analysis.py +++ /dev/null @@ -1,336 +0,0 @@ -import argparse -from collections import defaultdict -from pathlib import Path -from typing import List - -import networkx as nx -import numpy as np -import pandas as pd -import tqdm - -from dasf.profile.profiler import EventProfiler -from dasf.profile.utils import MultiEventDatabase - - -class TraceAnalyser: - def __init__(self, database: MultiEventDatabase, process_trace_before: bool = True): - self._database = database - if process_trace_before: - self._database = list(self._database) - - def create_annotated_task_graph(self) -> nx.DiGraph: - graph = nx.DiGraph() - - for event in tqdm.tqdm(self._database, desc="Creating annotated task graph"): - if event.name == "Compute": - name = event.args["name"] - task_key = event.args['key'] - dependencies = event.args['dependencies'] - dependents = event.args['dependents'] - size = event.args['size'] - shape = event.args['shape'] - task_type = event.args['type'] - - # Add the task as a node to the graph and store task information as metadata - graph.add_node( - task_key, - name=name, - size=size, - shape=shape, - type=task_type, - duration=event.duration - ) - - # Add the dependencies as edges to the graph - for dependency in dependencies: - graph.add_edge(dependency, task_key) - - # Add the dependents as edges to the graph - for dependent in dependents: - graph.add_edge(task_key, dependent) - - for node in graph.nodes: - input_data_size = sum([graph.nodes[dependency].get('size', 0) for dependency in graph.predecessors(node)]) - - # Set the input_data_size attribute for the current node - graph.nodes[node]['input_data_size'] = input_data_size - graph.nodes[node]["throughput"] = input_data_size / graph.nodes[node].get("duration", 1) - - return graph - - def per_function_bottleneck(self): - # Create the annotated DAG - graph = self.create_annotated_task_graph() - - # Dictionary to store task durations per thread_id - task_durations = defaultdict(lambda: defaultdict(float)) - # Dictionary to store mean gpu_utilization and gpu_memory_used per task_key - task_resources = defaultdict(lambda: {'gpu_utilization': [], 'gpu_memory_used': []}) - # Dictionaty mapping name to keys - task_name_keys = defaultdict(lambda: defaultdict(list)) - - # Iterate over the traces to calculate task durations per thread_id - for event in tqdm.tqdm(self._database, desc="[function_bottleneck] Analysing traces"): - if event.name == "Compute": - task_key = event.args['name'] - task_duration = event.duration - thread_id = event.thread_id - process_id = event.process_id - task_name_keys[(process_id, thread_id)][task_key].append(event.args['key']) - task_durations[(process_id, thread_id)][task_key] += task_duration - elif event.name == "Resource Usage": - event_timestamp = event.timestamp - gpu_utilization = event.args['gpu_utilization'] - gpu_memory_used = event.args['gpu_memory_used'] - - # Find the corresponding task for the resource event based on timestamp - task_key = None - for task_event in self._database: - if task_event.name == "Compute" and task_event.timestamp <= event_timestamp <= ( - task_event.timestamp + task_event.duration): - task_key = task_event.args['name'] - break - - if task_key is not None: - task_resources[task_key]['gpu_utilization'].append(gpu_utilization) - task_resources[task_key]['gpu_memory_used'].append(gpu_memory_used) - - # Create a list of dictionaries to store data for the DataFrame - data = [] - for (process_id, thread_id), durations in tqdm.tqdm(task_durations.items(), desc="[function_bottleneck] Creating dataframe"): - total_duration = sum(durations.values()) - for task_key, duration in durations.items(): - percentage = (duration / total_duration) * 100 - gpu_utilization_values = task_resources[task_key]['gpu_utilization'] - gpu_memory_used_values = task_resources[task_key]['gpu_memory_used'] - num_tasks = len(task_name_keys[(process_id, thread_id)][task_key]) - mean_data_size = 0 - mean_throughput = 0 - count = 0 - for name_key in task_name_keys[(process_id, thread_id)][task_key]: - mean_data_size += graph.nodes[name_key]["input_data_size"] - mean_throughput += graph.nodes[name_key]["throughput"] - count += 1 - mean_data_size /= count - mean_throughput /= count - - mean_gpu_utilization = sum(gpu_utilization_values) / len(gpu_utilization_values) if len( - gpu_utilization_values) > 0 else 0 - mean_gpu_memory_used = sum(gpu_memory_used_values) / len(gpu_memory_used_values) if len( - gpu_memory_used_values) > 0 else 0 - data.append({ - 'Host': process_id, - "GPU": thread_id.split("-")[-1], - 'Function': task_key, - 'Duration (s)': duration, - 'Percentage of total time (%)': percentage, - 'Mean GPU Utilization (%)': mean_gpu_utilization, - 'Mean GPU Memory Used (GB)': mean_gpu_memory_used / 1e9, - "Mean Data Size (MB)": mean_data_size / 1e6, - "Mean Throughput (MB/s)": mean_throughput/1e6, - "Num Tasks (chunks)": num_tasks, - "Mean Task time (s)": duration / num_tasks - }) - - # Create a Pandas DataFrame from the data list - df = pd.DataFrame(data) - df.set_index(['Host', 'GPU'], append=True) - df.sort_values(by='Duration (s)', ascending=False, inplace=True) - return df - - def per_worker_task_balance(self): - # Dictionary to store the number of tasks per worker at each timestamp - tasks_per_worker = defaultdict(lambda: defaultdict(int)) - - # Find the start and end time - start_time = float('inf') - end_time = float('-inf') - - # Iterate over the traces to calculate the number of tasks per worker at each timestamp - for event in tqdm.tqdm(self._database, desc="[task_balance] Analysing traces"): - if event.name == "Managed Memory": - timestamp = int(event.timestamp) - thread_id = event.thread_id - tasks = event.args['tasks'] - tasks_per_worker[timestamp][thread_id] = tasks - - # Update start and end time - start_time = min(start_time, timestamp) - end_time = max(end_time, timestamp) - - # Shift the linear spacing of 1 second in relation to the start time - timestamps = list(range(0, int(end_time - start_time) + 1)) - - # Calculate the mean number of tasks per thread in each time interval - mean_tasks_per_interval = defaultdict(dict) - - for timestamp in tqdm.tqdm(timestamps, desc="[task_balance] Creating dataframe"): - shifted_timestamp = start_time + timestamp - tasks_per_thread = tasks_per_worker[shifted_timestamp] - for thread_id, tasks in tasks_per_thread.items(): - mean_tasks_per_interval[timestamp][thread_id] = tasks - - # Create a Pandas DataFrame from the mean_tasks_per_interval dictionary - df = pd.DataFrame.from_dict(mean_tasks_per_interval, orient='index') - - df = df.reindex(sorted(df.columns), axis=1) - - # Fill missing values with 0 (if a thread didn't have any tasks in a specific interval) - df.fillna(0, inplace=True) - - # Calculate the mean number of tasks per thread across all intervals - # df['Mean Tasks'] = df.mean(axis=0) - - # Reset the index and rename the column - df.reset_index(inplace=True) - df.rename(columns={'index': 'Time Interval (seconds from begin)'}, inplace=True) - # df["Time Interval"] = df["Time Interval"].apply(lambda x: x + start_time) - - # Print the DataFrame showing the mean number of tasks per thread in each time interval - df.sort_values(by='Time Interval (seconds from begin)', inplace=True) - return df - - def per_task_bottleneck(self): - # Create the annotated DAG - graph = self.create_annotated_task_graph() - # Dictionary to store task durations per thread_id - task_durations = defaultdict(lambda: defaultdict(float)) - # Dictionary to store mean gpu_utilization and gpu_memory_used per task_key - task_resources = defaultdict(lambda: {'gpu_utilization': [], 'gpu_memory_used': []}) - memory_usage_per_task = defaultdict(int) - # Dictionaty mapping name to keys - task_name_keys = defaultdict(lambda: defaultdict(list)) - - # Iterate over the traces to calculate task durations per thread_id - for event in tqdm.tqdm(self._database, desc="[task_bottleneck] Analysing traces"): - if event.name == "Compute": - task_key = event.args['key'] - task_duration = event.duration - thread_id = event.thread_id - process_id = event.process_id - task_name_keys[(process_id, thread_id)][task_key].append(event.args['key']) - task_durations[(process_id, thread_id)][task_key] += task_duration - memory_usage_per_task[task_key] = event.args['size'] - - elif event.name == "Resource Usage": - event_timestamp = event.timestamp - gpu_utilization = event.args['gpu_utilization'] - gpu_memory_used = event.args['gpu_memory_used'] - - # Find the corresponding task for the resource event based on timestamp - task_key = None - for task_event in self._database: - if task_event.name == "Compute" and task_event.timestamp <= event_timestamp <= ( - task_event.timestamp + task_event.duration): - task_key = task_event.args['name'] - break - - if task_key is not None: - task_resources[task_key]['gpu_utilization'].append(gpu_utilization) - task_resources[task_key]['gpu_memory_used'].append(gpu_memory_used) - - # Create a list of dictionaries to store data for the DataFrame - data = [] - for (process_id, thread_id), durations in tqdm.tqdm(task_durations.items(), desc="[task_bottleneck] Creating dataframe"): - total_duration = sum(durations.values()) - for task_key, duration in durations.items(): - percentage = (duration / total_duration) * 100 - gpu_utilization_values = task_resources[task_key]['gpu_utilization'] - gpu_memory_used_values = task_resources[task_key]['gpu_memory_used'] - num_tasks = len(task_name_keys[(process_id, thread_id)][task_key]) - mean_data_size = 0 - mean_throughput = 0 - count = 0 - for name_key in task_name_keys[(process_id, thread_id)][task_key]: - mean_data_size += graph.nodes[name_key]["input_data_size"] - mean_throughput += graph.nodes[name_key]["throughput"] - count += 1 - mean_data_size /= count - mean_throughput /= count - - mean_gpu_utilization = sum(gpu_utilization_values) / len(gpu_utilization_values) if len( - gpu_utilization_values) > 0 else 0 - mean_gpu_memory_used = sum(gpu_memory_used_values) / len(gpu_memory_used_values) if len( - gpu_memory_used_values) > 0 else 0 - data.append({ - 'Host': process_id, - "GPU": thread_id.split("-")[-1], - 'Task Key': task_key, - 'Duration (s)': duration, - 'Percentage of total time (%)': percentage, - 'Memory usage (Mb)': memory_usage_per_task[task_key] / 1e6, - # 'Mean GPU Utilization (%)': mean_gpu_utilization, - # 'Mean GPU Memory Used (GB)': mean_gpu_memory_used / 1e9, - # "Mean Data Size (MB)": mean_data_size / 1e6, - # "Mean throughput (B/s)": mean_throughput, - # "Num Tasks (chunks)": num_tasks, - }) - - # Create a Pandas DataFrame from the data list - df = pd.DataFrame(data) - df.set_index(['Host', 'GPU'], append=True) - df.sort_values(by='Duration (s)', ascending=False, inplace=True) - return df - -valid_analyses = [ - "function_bottleneck", - "task_bottleneck", - "task_balance" -] - -def main(database: MultiEventDatabase, output: str = None, analyses: List[str] = None, head: int = 30): - pd.set_option('display.float_format', lambda x: '%.5f' % x) - pd.set_option('display.max_rows', 100) - pd.set_option('display.max_columns', 100) - pd.set_option('display.width', 1000) - - if analyses is None: - analyses = valid_analyses - - if output is not None: - output = Path(output) - output.mkdir(parents=True, exist_ok=True) - - analyser = TraceAnalyser(database) - if "function_bottleneck" in analyses: - df = analyser.per_function_bottleneck() - if output is not None: - df.to_csv(f"{output}/function_bottleneck.csv") - - print("="*20 + "Function bottleneck" + "="*20) - print(df.head(head)) - print("=" * 80 + "\n") - - if "task_bottleneck" in analyses: - df = analyser.per_task_bottleneck() - if output is not None: - df.to_csv(f"{output}/task_bottleneck.csv") - - print("="*20 + "Task bottleneck" + "="*20) - print(df.head(head)) - print("=" * 80 + "\n") - - if "task_balance" in analyses: - df = analyser.per_worker_task_balance() - if output is not None: - df.to_csv(f"{output}/task_balance.csv") - - print("="*20 + "Task balance" + "="*20) - print(df.head(head)) - print("=" * 80 + "\n") - - print("Analyses finished!") - - -if __name__ == "__main__": - # Argument parser with default help format - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument("-d", "--databases", type=str, nargs="+", help="The databases to analyse",required=True) - parser.add_argument("-o", "--output", type=str, help="The output directory, to save output analysis. If None, print only in screen", required=False) - parser.add_argument("-a", "--analyses", type=str, nargs="+", help="The analyses to perform (if None, perform all)", required=False) - - args = parser.parse_args() - database = MultiEventDatabase( - [EventProfiler(database_file=database) for database in args.databases] - ) - main(database, args.output, args.analyses) \ No newline at end of file diff --git a/dasf/profile/plugins.py b/dasf/profile/plugins.py deleted file mode 100644 index 93e3dff..0000000 --- a/dasf/profile/plugins.py +++ /dev/null @@ -1,150 +0,0 @@ -import os -import socket -import time -from typing import Any - -import nvtx -import pynvml -from dask.distributed.compatibility import PeriodicCallback -from dask.distributed.system_monitor import SystemMonitor -from distributed.diagnostics.plugin import WorkerPlugin -from pynvml import * - -from dasf.profile.profiler import EventProfiler - - -class WorkerTaskPlugin(WorkerPlugin): - def __init__( - self, - name: str = "TracePlugin", - ): - self.name = name - - def setup(self, worker): - self.worker = worker - self.hostname = socket.gethostname() - self.worker_id = f"worker-{self.hostname}-{self.worker.name}" - self.database = EventProfiler( - database_file=f"{self.name}-{self.hostname}.msgpack", - ) - - def transition(self, key, start, finish, *args, **kwargs): - now = time.monotonic() - - if finish == "memory": - # Get the last compute event - startstops = next( - ( - x - for x in reversed(self.worker.state.tasks[key].startstops) - if x["action"] == "compute" - ), - None, - ) - if startstops is not None: - # Add information about the task execution - shape = tuple() - dtype = "unknown" - if hasattr(self.worker.data[key], "shape"): - if isinstance(getattr(self.worker.data[key], "shape"), tuple): - shape = getattr(self.worker.data[key], "shape") - - if hasattr(self.worker.data[key], "dtype"): - dtype = str(getattr(self.worker.data[key], "dtype")) - - task = self.worker.state.tasks[key] - nbytes = task.nbytes or 0 - - self.database.record_complete_event( - name="Compute", - timestamp=now, - # TODO check startstop returning None - duration=startstops["stop"] - startstops["start"], - process_id=self.hostname, - thread_id=self.worker_id, - args={ - "key": key, - "name": "-".join(key.split(",")[0][2:-1].split("-")[:-1]), - "state": finish, - "size": nbytes, - "shape": shape, - "dtype": dtype, - "type": str(type(self.worker.data[key])), - "dependencies": [dep.key for dep in task.dependencies], - "dependents": [dep.key for dep in task.dependents], - }, - ) - - if finish == "memory" or finish == "erred": - # Additionally add the total in-memory tasks - self.database.record_instant_event( - name="Managed Memory", - timestamp=now, - process_id=self.hostname, - thread_id=self.worker_id, - args={ - "key": key, - "state": finish, - "size": self.worker.state.nbytes, - "tasks": len(self.worker.data), - } - ) - -class ResourceMonitor: - def __init__(self, time = 100, autostart: bool = True, name: str = "ResourceMonitor", **monitor_kwargs): - self.time = time - self.name = name - self.hostname = socket.gethostname() - self.database = EventProfiler( - database_file=f"{self.name}-{self.hostname}.msgpack", - ) - self.monitor = SystemMonitor(**monitor_kwargs) - self.callback = PeriodicCallback(self.update, callback_time=self.time) - if autostart: - self.start() - - def __del__(self): - self.stop() - - def update(self): - res = self.monitor.update() - self.database.record_instant_event( - name="Resource Usage", - timestamp=time.monotonic(), - process_id=self.hostname, - thread_id=None, - args=res - ) - return res - - - def start(self): - self.callback.start() - - def stop(self): - self.database.commit() - self.callback.stop() - -class GPUAnnotationPlugin(WorkerPlugin): - def __init__( - self, - name: str = "GPUAnnotationPlugin", - ): - self.name = name - self.gpu_num = None - self.marks = {} - - def setup(self, worker): - self.worker = worker - self.gpu_num = int(os.environ['CUDA_VISIBLE_DEVICES'].split(",")[0]) - print(f"Setting up GPU annotation plugin for worker {self.worker.name}. GPU: {self.gpu_num}") - - def transition(self, key, start, finish, *args, **kwargs): - if finish == "executing": - handle = pynvml.nvmlDeviceGetHandleByIndex(self.gpu_num) - mark = nvtx.start_range(message=key, domain="compute") - self.marks[key] = mark - if start == "executing": - handle = pynvml.nvmlDeviceGetHandleByIndex(self.gpu_num) - nvtx.end_range(self.marks[key]) - del self.marks[key] \ No newline at end of file diff --git a/dasf/profile/profiler.py b/dasf/profile/profiler.py deleted file mode 100644 index 80c347c..0000000 --- a/dasf/profile/profiler.py +++ /dev/null @@ -1,251 +0,0 @@ -import atexit -import fcntl -import os -import uuid -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from pathlib import Path -from queue import SimpleQueue -from typing import Iterable, Union - -import ormsgpack -import portalocker - - -class EventPhases: - COMPLETE = "X" - DURATION_BEGIN = "B" - DURATION_END = "E" - INSTANT = "I" - ASYNC_BEGIN = "b" - ASYNC_INSTANT = "n" - ASYNC_END = "e" - FLOW_BEGIN = "s" - FLOW_STEP = "t" - FLOW_END = "f" - COUNTER = "C" - OBJECT_CREATED = "N" - OBJECT_SNAPSHOT = "O" - OBJECT_DESTROYED = "D" - METADATA = "M" - MARK = "R" - - -class InstantEventScope: - GLOBAL = "g" - PROCESS = "p" - THREAD = "t" - - -@dataclass -class InstantEvent: - name: str - timestamp: float - phase: str = EventPhases.INSTANT - scope: str = InstantEventScope.GLOBAL - process_id: int = 0 - thread_id: int = 0 - args: dict = field(default_factory=dict) - - -@dataclass -class CompleteEvent: - name: str - timestamp: float - duration: float - phase: str = EventPhases.COMPLETE - process_id: int = 0 - thread_id: int = 0 - args: dict = field(default_factory=dict) - - -@dataclass -class DurationBeginEvent: - name: str - timestamp: float - phase: str = EventPhases.DURATION_BEGIN - process_id: int = 0 - thread_id: int = 0 - args: dict = field(default_factory=dict) - - -@dataclass -class DurationEndEvent: - name: str - timestamp: float - phase: str = EventPhases.DURATION_BEGIN - process_id: int = 0 - thread_id: int = 0 - args: dict = field(default_factory=dict) - - -EventTypes = Union[CompleteEvent, InstantEvent, DurationBeginEvent, DurationEndEvent] -event_classes = { - EventPhases.COMPLETE: CompleteEvent, - EventPhases.INSTANT: InstantEvent, - EventPhases.DURATION_BEGIN: DurationBeginEvent, - EventPhases.DURATION_END: DurationEndEvent, -} - - -class EventDatabase(ABC): - def open(self) -> "EventDatabase": - return self - - @abstractmethod - def record(self, event: EventTypes): - raise NotImplementedError - - @abstractmethod - def commit(self): - raise NotImplementedError - - @abstractmethod - def get_traces(self) -> Iterable[EventTypes]: - raise NotImplementedError - - def close(self): - pass - - def __enter__(self): - return self.open() - - def __exit__(self, *args, **kwargs): - self.close() - - -class FileDatabase(EventDatabase): - def __init__( - self, - database_file: str = "traces.msgpack", - commit_threshold: int = 5000, - remove_old_output_file: bool = False, - commit_on_close: bool = True, - lock_timeout: int = 30, - default_byte_size: int = 8, - flush: bool = True, - ): - self.database_file = Path(database_file) - self.commit_threshold = commit_threshold - self.commit_on_close = commit_on_close - self.queue = SimpleQueue() - self.lock_timeout = lock_timeout - self.byte_size = default_byte_size - self.flush = flush - if remove_old_output_file: - self.database_file.unlink(missing_ok=True) - - # Register a function to commit the events when the program exits - atexit.register(self.close) - - def record(self, event: EventTypes): - self.queue.put(event) - if self.queue.qsize() >= self.commit_threshold: - self.commit() - - def commit(self): - # TODO implement async commit - # Create a exclusive lock file to prevent other processes from writing to the file - with portalocker.Lock(self.database_file, mode="ab", timeout=self.lock_timeout) as f: - # Write each event to file - # Always write the size of the event first (8 bytes) then the event data - events = [] - while not self.queue.empty(): - event = self.queue.get() - packed_data = ormsgpack.packb(event) - size = len(packed_data).to_bytes(self.byte_size, byteorder="big") - events.append(size) - events.append(packed_data) - events = b"".join(events) - f.write(events) - - if self.flush: - f.flush() - os.fsync(f.fileno()) - - def get_traces(self) -> Iterable[EventTypes]: - with self.database_file.open("rb") as f: - while True: - chunk = f.read(self.byte_size) - if chunk == b"": - return - size = int.from_bytes(chunk, byteorder="big") - data = f.read(size) - data = ormsgpack.unpackb(data) - data = event_classes[data["phase"]](**data) - yield data - - def close(self): - if self.commit_on_close: - self.commit() - - def __str__(self) -> str: - return f"FileDatabase at {self.database_file}" - - def __repr__(self) -> str: - return f"FileDatabase at {self.database_file}" - - -# Singleton instance of the database -class EventProfiler: - traces_file_prefix = "traces-" - - default_database = FileDatabase - default_database_kwargs = { - "commit_threshold": 1000, - "remove_old_output_file": False, - "commit_on_close": True, - } - - def __init__( - self, - database_file: str = None, - database_creation_kwargs: dict = None, - database: EventDatabase = None, - ): - self.output_file = None - if database is not None: - if database_file is not None: - raise ValueError( - "Cannot specify both output_file and database arguments" - ) - self.database = database - else: - if database_creation_kwargs is None: - database_creation_kwargs = self.default_database_kwargs - if database_file is None: - database_file = f"{self.traces_file_prefix}{str(uuid.uuid4())[:8]}.msgpack" - self.output_file = database_file - self.database = self.default_database( - database_file, **database_creation_kwargs - ) - - def _record(self, event: EventTypes): - self.database.record(event) - - def record_complete_event( - self, - name: str, timestamp: float, duration: float, **kwargs - ): - self._record(CompleteEvent(name, timestamp, duration, **kwargs)) - - def record_instant_event(self, name: str, timestamp: float, **kwargs): - self._record(InstantEvent(name, timestamp, **kwargs)) - - def record_duration_begin_event(self, name: str, timestamp: float, **kwargs): - self._record(DurationBeginEvent(name, timestamp, **kwargs)) - - def record_duration_end_event(self, name: str, timestamp: float, **kwargs): - self._record(DurationEndEvent(name, timestamp, **kwargs)) - - def get_traces(self) -> Iterable[EventTypes]: - return self.database.get_traces() - - def __str__(self): - return f"EventProfiler(database={self.database})" - - def __repr__(self) -> str: - return f"EventProfiler(database={self.database})" - - def commit(self): - self.database.commit() \ No newline at end of file diff --git a/dasf/profile/utils.py b/dasf/profile/utils.py deleted file mode 100644 index e9e7593..0000000 --- a/dasf/profile/utils.py +++ /dev/null @@ -1,47 +0,0 @@ -import atexit -import time -from typing import List - -from dasf.pipeline import Pipeline -from dasf.profile.plugins import GPUAnnotationPlugin, ResourceMonitor, WorkerTaskPlugin -from dasf.profile.profiler import EventDatabase - - -class MultiEventDatabase: - def __init__(self, databases: List[EventDatabase]): - self._databases = databases - - def __iter__(self): - for database in self._databases: - yield from database.get_traces() - - def __str__(self) -> str: - return f"MultiEventDatabase with {len(self._databases)} databases" - - def __repr__(self) -> str: - return str(self) - - -def register_default_profiler(pipeline: Pipeline, name: str = None, enable_nvtx: bool = False, add_time_suffix: bool = True): - if name is None: - name = "default" - - if add_time_suffix: - name += f"-{int(time.time())}" - - worker_plugin = WorkerTaskPlugin(name=f"{name}-TracePlugin") - pipeline.register_plugin(worker_plugin) - print(f"Registered worker plugin: {name}-TracePlugin") - - resource_plugin = ResourceMonitor(name=f"{name}-ResourceMonitor") - print(f"Registered resource plugin: {name}-ResourceMonitor") - - def close(): - resource_plugin.stop() - - if enable_nvtx: - ptx_annotator = GPUAnnotationPlugin() - pipeline.register_plugin(ptx_annotator) - print(f"Registered GPU annotation plugin (NVTX)") - - atexit.register(close) \ No newline at end of file diff --git a/dasf/transforms/__init__.py b/dasf/transforms/__init__.py deleted file mode 100644 index c051865..0000000 --- a/dasf/transforms/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/python3 - -from dasf.transforms.base import MappedTransform # noqa -from dasf.transforms.base import ReductionTransform # noqa -from dasf.transforms.base import TargeteredTransform # noqa -from dasf.transforms.base import Transform # noqa -from dasf.transforms.base import ( # noqa - Fit, - FitPredict, - FitTransform, - GetParams, - Predict, - SetParams, -) -from dasf.transforms.memory import ComputeDaskData, PersistDaskData # noqa -from dasf.transforms.operations import Reshape # noqa -from dasf.transforms.operations import SliceArray, SliceArrayByPercent # noqa -from dasf.transforms.transforms import ArraysToDataFrame # noqa -from dasf.transforms.transforms import ArrayToHDF5 # noqa -from dasf.transforms.transforms import ArrayToZarr # noqa -from dasf.transforms.transforms import Normalize # noqa -from dasf.transforms.transforms import ZarrToArray # noqa - -__all__ = [ - "Fit", - "FitPredict", - "FitTransform", - "Predict", - "GetParams", - "SetParams" - "Transform", - "TargeteredTransform", - "MappedTransform", - "ReductionTransform", - "Normalize", - "ArrayToZarr", - "ArrayToHDF5", - "ZarrToArray", - "ArraysToDataFrame", - "SliceArray", - "SliceArrayByPercent", - "Reshape", - "PersistDaskData", - "ComputeDaskData", -] diff --git a/dasf/transforms/base.py b/dasf/transforms/base.py deleted file mode 100644 index d509ac7..0000000 --- a/dasf/transforms/base.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/python3 - -import inspect -from uuid import uuid4 - -import dask.array as da -import numpy as np - -try: - import cupy as cp - import dask_cudf as dcudf -except ImportError: # pragma: no cover - pass - -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import block_chunk_reduce -from dasf.utils.types import ( - is_dask_array, - is_dask_cpu_dataframe, - is_dask_dataframe, - is_dask_gpu_dataframe, -) - - -class Operator: - def get_uuid(self): - if not hasattr(self, "_uuid"): - self._uuid = uuid4() - return self._uuid - -class Fit(Operator): - def _lazy_fit_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _lazy_fit_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - @task_handler - def fit(self, X, y, sample_weight=None, **kwargs): - ... - - @staticmethod - def fit_from_model(model, X, y, sample_weight=None, **kwargs): - return model.fit(X=X, y=y, sample_weight=sample_weight, **kwargs) - - -class FitPredict(Operator): - def _lazy_fit_predict_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _lazy_fit_predict_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_predict_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_predict_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - @task_handler - def fit_predict(self, X, y=None, **kwargs): - ... - - @staticmethod - def fit_predict_from_model(model, X, y, sample_weight=None, **kwargs): - return model.fit_predict(X=X, y=y, sample_weight=sample_weight, - **kwargs) - - -class FitTransform(Operator): - def _lazy_fit_transform_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _lazy_fit_transform_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_transform_cpu(self, X, y=None, **kwargs): - raise NotImplementedError - - def _fit_transform_gpu(self, X, y=None, **kwargs): - raise NotImplementedError - - @task_handler - def fit_transform(self, X, y=None, **kwargs): - ... - - @staticmethod - def fit_transform_from_model(model, X, y, sample_weight=None, **kwargs): - return model.fit_transform(X=X, y=y, sample_weight=sample_weight, - **kwargs) - - -class Predict(Operator): - def _lazy_predict_cpu(self, X, sample_weight=None, **kwargs): - raise NotImplementedError - - def _lazy_predict_gpu(self, X, sample_weight=None, **kwargs): - raise NotImplementedError - - def _predict_cpu(self, X, sample_weight=None, **kwargs): - raise NotImplementedError - - def _predict_gpu(self, X, sample_weight=None, **kwargs): - raise NotImplementedError - - @task_handler - def predict(self, X, sample_weight=None, **kwargs): - ... - - @staticmethod - def predict_from_model(model, X, sample_weight=None, **kwargs): - if 'sample_weight' not in inspect.signature(model.predict).parameters: - return model.predict(X=X, **kwargs) - return model.predict(X=X, sample_weight=sample_weight, **kwargs) - - -class GetParams(Operator): - def _lazy_get_params_cpu(self, deep=True, **kwargs): - raise NotImplementedError - - def _lazy_get_params_gpu(self, deep=True, **kwargs): - raise NotImplementedError - - def _get_params_cpu(self, deep=True, **kwargs): - raise NotImplementedError - - def _get_params_gpu(self, deep=True, **kwargs): - raise NotImplementedError - - @task_handler - def get_params(self, deep=True, **kwargs): - ... - - -class SetParams(Operator): - def _lazy_set_params_cpu(self, **params): - raise NotImplementedError - - def _lazy_set_params_gpu(self, **params): - raise NotImplementedError - - def _set_params_cpu(self, **params): - raise NotImplementedError - - def _set_params_gpu(self, **params): - raise NotImplementedError - - @task_handler - def set_params(self, **params): - ... - - -class Transform(Operator): - def _lazy_transform_cpu(self, X, **kwargs): - raise NotImplementedError - - def _lazy_transform_gpu(self, X, **kwargs): - raise NotImplementedError - - def _transform_cpu(self, X, **kwargs): - raise NotImplementedError - - def _transform_gpu(self, X, **kwargs): - raise NotImplementedError - - @task_handler - def transform(self, X, **kwargs): - ... - - @staticmethod - def transform_from_model(model, X, **kwargs): - return model.transform(X=X, **kwargs) - - -class TargeteredTransform(Transform): - def __init__(self, run_local=None, run_gpu=None): - super().__init__() - - self._run_local = run_local - self._run_gpu = run_gpu - - -class MappedTransform(Transform): - def __init__( - self, - function, - depth=None, - boundary=None, - trim=True, - output_chunk=None, - drop_axis=None, - new_axis=None, - ): - - self.function = function - self.depth = depth - self.boundary = boundary - self.trim = trim - self.output_chunk = output_chunk - self.drop_axis = drop_axis - self.new_axis = new_axis - - if ( - self.boundary is None - and self.depth is not None - or self.boundary is not None - and self.depth is None - ): - raise Exception("Both boundary and depth should be passed " - "together") - - def __lazy_transform_generic(self, X, xp, **kwargs): - if self.drop_axis is not None or self.new_axis is not None: - drop_axis, new_axis = self.drop_axis, self.new_axis - else: - drop_axis, new_axis = block_chunk_reduce(X, self.output_chunk) - - if self.output_chunk is None: - __output_chunk = X.chunks - else: - __output_chunk = self.output_chunk - - if self.depth and self.boundary: - if self.trim: - new_data = X.map_overlap( - self.function, - **kwargs, - dtype=X.dtype, - depth=self.depth, - boundary=self.boundary, - meta=xp.array(()), - ) - else: - data_blocks = da.overlap.overlap( - X, depth=self.depth, boundary=self.boundary - ) - - new_data = data_blocks.map_blocks( - self.function, - dtype=X.dtype, - drop_axis=drop_axis, - new_axis=new_axis, - chunks=__output_chunk, - meta=xp.array(()), - **kwargs, - ) - else: - if is_dask_array(X): - new_data = X.map_blocks( - self.function, - dtype=X.dtype, - drop_axis=drop_axis, - new_axis=new_axis, - chunks=__output_chunk, - meta=xp.array(()), - **kwargs, - ) - elif is_dask_dataframe(X): - new_data = X.map_partitions(self.function, **kwargs) - - return new_data - - def _lazy_transform_cpu(self, X, **kwargs): - return self.__lazy_transform_generic(X, xp=np, **kwargs) - - def _lazy_transform_gpu(self, X, **kwargs): - return self.__lazy_transform_generic(X, xp=cp, **kwargs) - - def _transform_cpu(self, X, **kwargs): - return self.function(X, **kwargs) - - def _transform_gpu(self, X, **kwargs): - return self.function(X, **kwargs) - - @task_handler - def transform(self, X, **kwargs): - ... - - -class ReductionTransform(Transform): - def __init__(self, output_size, func_aggregate, func_chunk, func_combine=None): - self.output_size = output_size - - self.func_aggregate = func_aggregate - self.func_chunk = func_chunk - self.func_combine = func_combine - - def _operation_aggregate_cpu(self, block, axis=None, keepdims=False): - return self.func_aggregate(block, axis, keepdims, xp=np) - - def _operation_aggregate_gpu(self, block, axis=None, keepdims=False): - return self.func_aggregate(block, axis, keepdims, xp=cp) - - def _operation_combine_cpu(self, block, axis=None, keepdims=False): - return self.func_combine(block, axis, keepdims, xp=np) - - def _operation_combine_gpu(self, block, axis=None, keepdims=False): - return self.func_combine(block, axis, keepdims, xp=cp) - - def _operation_chunk_cpu(self, block, axis=None, keepdims=False): - return self.func_chunk(block, axis, keepdims, xp=np) - - def _operation_chunk_gpu(self, block, axis=None, keepdims=False): - return self.func_chunk(block, axis, keepdims, xp=cp) - - def _lazy_transform_cpu(self, X, *args, **kwargs): - if self.func_combine is not None: - kwargs['combine'] = self._operation_combine_cpu - - if is_dask_cpu_dataframe(X): - return X.reduction(chunk=self._operation_chunk_cpu, - aggregate=self._operation_aggregate_cpu, - meta=self.output_size, - *args, - **kwargs) - else: - return da.reduction(X, - chunk=self._operation_chunk_cpu, - aggregate=self._operation_aggregate_cpu, - dtype=X.dtype, - meta=np.array(self.output_size, - dtype=X.dtype), - *args, - **kwargs) - - def _lazy_transform_gpu(self, X, *args, **kwargs): - if self.func_combine is not None: - kwargs['combine'] = self._operation_combine_gpu - - if is_dask_gpu_dataframe(X): - return X.reduction(chunk=self._operation_chunk_gpu, - aggregate=self._operation_aggregate_gpu, - meta=self.output_size, - *args, - **kwargs) - else: - return da.reduction(X, - chunk=self._operation_chunk_gpu, - aggregate=self._operation_aggregate_gpu, - dtype=X.dtype, - meta=cp.array(self.output_size, - dtype=X.dtype), - *args, - **kwargs) - - def _transform_cpu(self, X, *args, **kwargs): - return self.func_chunk(block=X, xp=np, *args, **kwargs) - - def _transform_gpu(self, X, *args, **kwargs): - return self.func_chunk(block=X, xp=cp, *args, **kwargs) - - @task_handler - def transform(self, X, *args, **kwargs): - ... diff --git a/dasf/transforms/memory.py b/dasf/transforms/memory.py deleted file mode 100644 index 4412baf..0000000 --- a/dasf/transforms/memory.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 - -from dasf.transforms.base import Transform -from dasf.utils.types import is_dask_array, is_dask_dataframe - - -class PersistDaskData(Transform): - """Allow persisting a dask array to memory and return a copy of the object. - It will gather the data blocks from all workers and resembles locally. - """ - def __lazy_transform_generic(self, X): - if is_dask_array(X) or is_dask_dataframe(X): - new_data = X.persist() - else: - new_data = X - - return new_data - - def _lazy_transform_cpu(self, X): - return self.__lazy_transform_generic(X) - - def _lazy_transform_gpu(self, X): - return self.__lazy_transform_generic(X) - - def _transform_cpu(self, X): - # Bypass because the data is local - return X - - def _transform_gpu(self, X): - # Bypass because the data is local - return X - - -class ComputeDaskData(Transform): - """Allow persisting a dask array to memory. It will gather the data blocks - from all workers and resembles locally. - """ - def __lazy_transform_generic(self, X): - if is_dask_array(X) or is_dask_dataframe(X): - new_data = X.compute() - else: - new_data = X - - return new_data - - def _lazy_transform_cpu(self, X): - return self.__lazy_transform_generic(X) - - def _lazy_transform_gpu(self, X): - return self.__lazy_transform_generic(X) - - def _transform_cpu(self, X): - # Bypass because the data is local - return X - - def _transform_gpu(self, X): - # Bypass because the data is local - return X diff --git a/dasf/transforms/operations.py b/dasf/transforms/operations.py deleted file mode 100644 index cf55051..0000000 --- a/dasf/transforms/operations.py +++ /dev/null @@ -1,458 +0,0 @@ -#!/usr/bin/env python3 - -import dask.array as da -import numpy as np -from scipy import stats - -try: - import cupy as cp -except ImportError: # pragma: no cover - pass - -from dasf.ml.inference.loader.base import BaseLoader -from dasf.transforms.base import Fit, ReductionTransform, Transform -from dasf.utils.types import is_array, is_dataframe - - -class Reshape(Fit): - """Get a slice of a cube. An inline slice is a section over the x-axis. - - Parameters - ---------- - iline_index : int - The index of the inline to get. - - """ - def __init__(self, shape: tuple = None): - self.shape = shape - - def fit(self, X, y=None): - if self.shape: - cube_shape = self.shape - elif y is not None and hasattr(y, "shape"): - cube_shape = y.shape - else: - raise Exception("Missing shape input.") - - if is_array(X): - slice_array = X - elif is_dataframe(X): - slice_array = X.values - else: - raise ValueError("X is not a known datatype.") - - return slice_array.reshape(cube_shape) - - -class SliceArray(Transform): - def __init__(self, output_size): - self.x = list(output_size) - - def transform(self, X): - if len(self.x) == 1: - return X[0 : self.x[0]] - elif len(self.x) == 2: - return X[0 : self.x[0], 0 : self.x[1]] - elif len(self.x) == 3: - return X[0 : self.x[0], 0 : self.x[1], 0 : self.x[2]] - else: - raise Exception("The dimmension is not known") - - -class SliceArrayByPercent(Transform): - def __init__(self, x=100.0, y=100.0, z=100.0): - self.x = float(x / 100.0) - self.y = float(y / 100.0) - self.z = float(z / 100.0) - - def transform(self, X): - if self.x > 1 or self.y > 1 or self.z > 1: - raise Exception("Percentages cannot be higher than 100% (1.0)") - - if self.x <= 0 or self.y <= 0 or self.z <= 0: - raise Exception("Percentages cannot be negative or 0") - - if X.ndim == 1: - return X[0 : int(self.x * X.shape[0])] - elif X.ndim == 2: - return X[0 : int(self.x * X.shape[0]), 0 : int(self.y * X.shape[1])] - elif X.ndim == 3: - return X[ - 0 : int(self.x * X.shape[0]), - 0 : int(self.y * X.shape[1]), - 0 : int(self.z * X.shape[2]), - ] - else: - raise Exception("The dimmension is not known") - - -class SliceArrayByPercentile(Transform): - def __init__(self, percentile): - self.p = percentile - - def __internal_chunk_array_positive(self, block, axis=None, keepdims=False, xp=np): - block[block < 0] = 0 - block[block != 0] - return xp.array([xp.percentile(block.flatten(), self.p)]) - - def __internal_aggregate_array_positive(self, block, axis=None, keepdims=False, xp=np): - return xp.array([xp.max(block)]) - - def __internal_chunk_array_negative(self, block, axis=None, keepdims=False, xp=np): - block *= -1 - block[block < 0] = 0 - block[block != 0] - return xp.array([-xp.percentile(block.flatten(), self.p)]) - - def __internal_aggregate_array_negative(self, block, axis=None, keepdims=False, xp=np): - return xp.array([xp.min(block)]) - - def _lazy_transform_cpu(self, X): - positive = ReductionTransform(func_chunk=self.__internal_chunk_array_positive, - func_aggregate=self.__internal_aggregate_array_positive, - output_size=[0]) - - negative = ReductionTransform(func_chunk=self.__internal_chunk_array_negative, - func_aggregate=self.__internal_aggregate_array_negative, - output_size=[0]) - - p = positive._lazy_transform_cpu(X, axis=[0]) - n = negative._lazy_transform_cpu(X, axis=[0]) - - # Unfortunately, we need to compute first. - pos_cutoff = p.compute()[0] - neg_cutoff = n.compute()[0] - - X[X > pos_cutoff] = pos_cutoff - X[X < neg_cutoff] = neg_cutoff - - return X - - def _lazy_transform_gpu(self, X): - positive = ReductionTransform(func_chunk=self.__internal_aggregate_array_positive, - func_aggregate=self.__internal_aggregate_array_positive, - output_size=[0]) - - negative = ReductionTransform(func_chunk=self.__internal_aggregate_array_negative, - func_aggregate=self.__internal_aggregate_array_negative, - output_size=[0]) - - p = positive._lazy_transform_gpu(X) - n = negative._lazy_transform_gpu(X) - - # Unfortunately, we need to compute first. - pos_cutoff = p.compute()[0] - neg_cutoff = n.compute()[0] - - X[X > pos_cutoff] = pos_cutoff - X[X < neg_cutoff] = neg_cutoff - - return X - - def _transform_cpu(self, X): - pos_cutoff = self.__internal_chunk_array_positive(X, xp=np) - neg_cutoff = self.__internal_chunk_array_negative(X, xp=np) - - X[X > pos_cutoff] = pos_cutoff - X[X < neg_cutoff] = neg_cutoff - - return X - - def _transform_gpu(self, X): - pos_cutoff = self.__internal_chunk_array_positive(X, xp=cp) - neg_cutoff = self.__internal_chunk_array_negative(X, xp=cp) - - X[X > pos_cutoff] = pos_cutoff - X[X < neg_cutoff] = neg_cutoff - - return X - - -class ApplyPatchesBase(Transform): - """ - Base Class for ApplyPatches Functionalities - """ - - def __init__(self, function, weight_function, input_size, overlap, offsets): - """ - function: function to be applied to each patch, can be eiter a Python Function or a ModelLoader - weight_function: weight attribution function, must receive a shape and produce a NDArray with the respective weights for each array position - input_size: size of input to the function to be applied, - overlap: dictionary containing overlapping/padding configurations to use with np.pad or dask.overlap.overlap. Its important that for the base patch set the whole "chunk core" is covered by the patches. - offsets: list of offsets for overlapping patches extraction - """ - self._function = function - self._weight_function = weight_function - self._input_size = input_size - self._offsets = offsets if offsets is not None else [] - overlap = overlap if overlap is not None else {} - self._overlap_config = { - "padding": overlap.get("padding", tuple(len(input_size) * [0])), - "boundary": overlap.get("boundary", 0), - } - - def _apply_patches(self, patch_set): - """ - Applies function to each patch in a patch set - - """ - if callable(self._function): - return np.array(list(map(self._function, patch_set))) - if isinstance(self._function, BaseLoader): - return self._function.predict(patch_set) - raise NotImplementedError("Requested Apply Method not supported") - - def _reconstruct_patches(self, patches, index, weights, inner_dim=None): - """ - Rearranges patches to reconstruct area of interest from patches and weights - """ - reconstruct_shape = np.array(self._input_size) * np.array(index) - if weights: - weight = np.zeros(reconstruct_shape) - base_weight = ( - self._weight_function(self._input_size) - if self._weight_function - else np.ones(self._input_size) - ) - else: - weight = None - if inner_dim is not None: - reconstruct_shape = np.append(reconstruct_shape, inner_dim) - reconstruct = np.zeros(reconstruct_shape) - for patch_index, patch in zip(np.ndindex(index), patches): - sl = [ - slice(idx * patch_len, (idx + 1) * patch_len, None) - for idx, patch_len in zip(patch_index, self._input_size) - ] - if weights: - weight[tuple(sl)] = base_weight - if inner_dim is not None: - sl.append(slice(None, None, None)) - reconstruct[tuple(sl)] = patch - return reconstruct, weight - - def _adjust_patches(self, arrays, ref_shape, offset, pad_value=0): - """ - Pads reconstructed_patches with 0s to have same shape as the reference shape from the base patch set - """ - pad_width = [] - sl = [] - ref_shape = list(ref_shape) - arr_shape = list(arrays[0].shape) - if len(offset) < len(ref_shape): - ref_shape = ref_shape[:-1] - arr_shape = arr_shape[:-1] - for idx, lenght, ref in zip(offset, arr_shape, ref_shape): - if idx > 0: - sl.append(slice(0, min(lenght, ref), None)) - pad_width.append((idx, max(ref - lenght - idx, 0))) - else: - sl.append(slice(np.abs(idx), min(lenght, ref - idx), None)) - pad_width.append((0, max(ref - lenght - idx, 0))) - adjusted = [ - np.pad( - arr[tuple([*sl, slice(None, None, None)])], - pad_width=[*pad_width, (0, 0)], - mode="constant", - constant_values=pad_value, - ) - if len(offset) < len(arr.shape) - else np.pad( - arr[tuple(sl)], - pad_width=pad_width, - mode="constant", - constant_values=pad_value, - ) - for arr in arrays - ] - return adjusted - - def _combine_patches(self, results, offsets, indexes): - """ - How results are combined is dependent on what is being combined. - ApplyPatchesWeightedAvg uses Weighted Average - ApplyPatchesVoting uses Voting (hard or soft) - """ - raise NotImplementedError("Combine patches method must be implemented") - - def _extract_patches(self, data, patch_shape): - """ - Patch extraction method. It will be called once for the base patch set and also for the requested offsets (overlapping patch sets) - """ - indexes = tuple(np.array(data.shape) // np.array(patch_shape)) - patches = [] - for patch_index in np.ndindex(indexes): - sl = [ - slice(idx * patch_len, (idx + 1) * patch_len, None) - for idx, patch_len in zip(patch_index, patch_shape) - ] - patches.append(data[tuple(sl)]) - return np.asarray(patches), indexes - - def _operation(self, chunk): - """ - Operation to be performed on each chunk - """ - offsets = list(self._offsets) - base = self._overlap_config["padding"] - offsets.insert(0, tuple([0] * len(base))) - - slices = [ - tuple([slice(i + base, None) for i, base in zip(offset, base)]) - for offset in offsets - ] - results = [] - indexes = [] - for sl in slices: - patch_set, patch_idx = self._extract_patches(chunk[sl], self._input_size) - results.append(self._apply_patches(patch_set)) - indexes.append(patch_idx) - output_slice = tuple( - [slice(0, lenght - 2 * pad) for lenght, pad in zip(chunk.shape, base)] - ) - return self._combine_patches(results, offsets, indexes)[output_slice] - - def _transform(self, X): - if isinstance(self._overlap_config["boundary"], int): - X_overlap = np.pad( - X, - pad_width=[(pad, pad) for pad in self._overlap_config["padding"]], - mode="constant", - constant_values=self._overlap_config["boundary"], - ) - else: - X_overlap = np.pad( - X, - pad_width=[(pad, pad) for pad in self._overlap_config["padding"]], - mode=self._overlap_config["boundary"], - ) - - return self._operation(X_overlap) - - def _lazy_transform(self, X): - X_overlap = da.overlap.overlap( - X, - depth=self._overlap_config["padding"], - boundary=self._overlap_config["boundary"], - ) - - return X_overlap.map_blocks( - self._operation, dtype=X_overlap.dtype, chunks=X.chunks - ) - - def _lazy_transform_cpu(self, X, **kwargs): - return self._lazy_transform(X) - - def _lazy_transform_gpu(self, X, **kwargs): - X = X.map_blocks(cp.asnumpy, dtype=X.dtype, meta=np.array((), dtype=X.dtype)) - return self._lazy_transform(X).map_blocks( - cp.asarray, dtype=X.dtype, meta=cp.array((), dtype=X.dtype) - ) - - def _transform_cpu(self, X, **kwargs): - return self._transform(X) - - def _transform_gpu(self, X, **kwargs): - X = cp.asnumpy(X) - return cp.asarray(self._transform(X)) - - -class ApplyPatchesWeightedAvg(ApplyPatchesBase): - """ - ApplyPatches with Weighted Average combination function. - """ - - def _combine_patches(self, results, offsets, indexes): - reconstructed = [] - weights = [] - for patches, offset, shape in zip(results, offsets, indexes): - reconstruct, weight = self._reconstruct_patches( - patches, shape, weights=True - ) - if len(reconstructed) > 0: - adjusted = self._adjust_patches( - [reconstruct, weight], reconstructed[0].shape, offset - ) - reconstruct = adjusted[0] - weight = adjusted[1] - reconstructed.append(reconstruct) - weights.append(weight) - reconstructed = np.stack(reconstructed, axis=0) - weights = np.stack(weights, axis=0) - return np.sum(reconstructed * weights, axis=0) / np.sum(weights, axis=0) - - -class ApplyPatchesVoting(ApplyPatchesBase): - """ - ApplyPatches with Voting combination function. - """ - - def __init__( - self, - function, - weight_function, - input_size, - overlap, - offsets, - voting, - num_classes, - ): - """ - function: function to be applied to each patch, can be eiter a Python Function or a ModelLoader - weight_function: weight attribution function, must receive a shape and produce a NDArray with the respective weights for each array position - input_size: size of input to the function to be applied, - overlap: dictionary containing overlapping/padding configurations to use with np.pad or dask.overlap.overlap. Its important that for the base patch set the whole "chunk core" is covered by the patches. - offsets: list of offsets for overlapping patches extraction - voting: voting method. "hard" or "soft" - num_classes: number of classes possible - """ - super().__init__(function, weight_function, input_size, overlap, offsets) - self._voting = voting # Types: Hard Voting, Soft Voting - self._num_classes = num_classes - - def _combine_patches(self, results, offsets, indexes): - if self._voting == "hard": - result = self._hard_voting(results, offsets, indexes) - elif self._voting == "soft": - result = self._soft_voting(results, offsets, indexes) - else: - raise ValueError("Invalid Voting Type. Should be either soft or hard.") - return result - - def _hard_voting(self, results, offsets, indexes): - """ - Hard voting combination function - """ - reconstructed = [] - for patches, offset, shape in zip(results, offsets, indexes): - reconstruct, _ = self._reconstruct_patches( - patches, shape, weights=False, inner_dim=self._num_classes - ) - reconstruct = np.argmax(reconstruct, axis=-1).astype(np.float32) - if len(reconstructed) > 0: - adjusted = self._adjust_patches( - [reconstruct], reconstructed[0].shape, offset, pad_value=np.nan - ) - reconstruct = adjusted[0] - reconstructed.append(reconstruct) - reconstructed = np.stack(reconstructed, axis=0) - ret = stats.mode(reconstructed, axis=0, nan_policy="omit", keepdims=False)[0] - return ret - - def _soft_voting(self, results, offsets, indexes): - """ - Soft voting combination function - """ - reconstructed = [] - for patches, offset, shape in zip(results, offsets, indexes): - reconstruct, _ = self._reconstruct_patches( - patches, shape, weights=False, inner_dim=self._num_classes - ) - if len(reconstructed) > 0: - adjusted = self._adjust_patches( - [reconstruct], reconstructed[0].shape, offset - ) - reconstruct = adjusted[0] - reconstructed.append(reconstruct) - reconstructed = np.stack(reconstructed, axis=0) - return np.argmax(np.sum(reconstructed, axis=0), axis=-1) diff --git a/dasf/transforms/transforms.py b/dasf/transforms/transforms.py deleted file mode 100644 index b4d1840..0000000 --- a/dasf/transforms/transforms.py +++ /dev/null @@ -1,347 +0,0 @@ -#!/usr/bin/env python3 - -import math - -import dask -import dask.dataframe as ddf -import h5py -import numpy as np -import pandas as pd -import zarr - -from dasf.transforms.base import Transform -from dasf.utils.types import is_array, is_dask_array, is_dask_gpu_array - -try: - import cudf - import cupy as cp -except ImportError: # pragma: no cover - pass - - -class Normalize(Transform): - def transform(self, X): - return (X - X.mean()) / (X.std(ddof=0)) - - -class ArrayToZarr(Transform): - def __init__(self, chunks=None, save=True, filename=None): - self.chunks = chunks - # TODO: implement the possibility of not saving - self.save = True - self.filename = filename - - @staticmethod - def _convert_filename(url): - if url.endswith(".npy"): - return url.replace(".npy", ".zarr") - return url + ".zarr" - - def _lazy_transform_generic_all(self, data): - if self.filename: - url = self.filename - elif hasattr(data, '_root_file'): - url = data._root_file - else: - raise Exception("Array requires a valid path to convert to Zarr.") - - if data is None: - raise Exception("Dataset needs to be loaded first.") - - url = self._convert_filename(url) - - # XXX: Workaround to avoid error with CuPy and Zarr library - if is_dask_gpu_array(data): - data = data.map_blocks(lambda x: x.get()) - - data.to_zarr(url) - - return url - - def _transform_generic_all(self, data, chunks, **kwargs): - if data is None: - raise Exception("Dataset needs to be loaded first.") - - if not chunks: - raise Exception("Chunks needs to be passed for non lazy arrays.") - - if self.filename: - url = self.filename - else: - raise Exception("Array requires a valid path to convert to Zarr.") - - url = self._convert_filename(url) - - z = zarr.open(store=url, mode='w', shape=data.shape, - chunks=chunks, dtype='i4') - - z = data - - return url - - def _lazy_transform_generic(self, X, **kwargs): - # XXX: Avoid circular dependency - from dasf.datasets.base import DatasetArray, DatasetZarr - - name = None - - if isinstance(X, DatasetArray): - name = X._name - chunks = X._chunks - - if not self.filename and hasattr(X, '_root_file'): - self.filename = X._root_file - - url = self._lazy_transform_generic_all(X._data) - elif is_dask_array(X): - chunks = X.chunks - - url = self._lazy_transform_generic_all(X) - else: - raise Exception("It is not an Array type.") - - return DatasetZarr(name=name, download=False, root=url, chunks=chunks) - - def _transform_generic(self, X, **kwargs): - # XXX: Avoid circular dependency - from dasf.datasets.base import DatasetArray, DatasetZarr - - name = None - url = None - - if hasattr(X, '_chunks') and \ - (X._chunks is not None and X._chunks != 'auto'): - chunks = X._chunks - else: - chunks = self.chunks - - if chunks is None: - raise Exception("Chunks needs to be specified.") - - if isinstance(X, DatasetArray): - name = X._name - - if not self.filename and hasattr(X, '_root_file'): - self.filename = X._root_file - - url = self._transform_generic_all(X._data, chunks) - elif is_array(X): - url = self._transform_generic_all(X, chunks) - else: - raise Exception("It is not an Array type.") - - return DatasetZarr(name=name, download=False, root=url, chunks=chunks) - - def _lazy_transform_gpu(self, X, **kwargs): - return self._lazy_transform_generic(X, **kwargs) - - def _lazy_transform_cpu(self, X, **kwargs): - return self._lazy_transform_generic(X, **kwargs) - - def _transform_gpu(self, X, **kwargs): - return self._transform_generic(X, **kwargs) - - def _transform_cpu(self, X, **kwargs): - return self._transform_generic(X, **kwargs) - - -class ArrayToHDF5(Transform): - def __init__(self, dataset_path, chunks=None, save=True, filename=None): - # Avoid circular dependency - from dasf.datasets.base import DatasetArray, DatasetHDF5 - - self.dataset_path = dataset_path - self.chunks = chunks - # TODO: implement the possibility of not saving - self.save = True - self.filename = filename - - @staticmethod - def _convert_filename(url): - if url.endswith(".npy"): - return url.replace(".npy", ".hdf5") - return url + ".hdf5" - - def _lazy_transform_generic_all(self, data): - if self.filename: - url = self.filename - elif hasattr(data, '_root_file'): - url = data._root_file - else: - raise Exception("Array requires a valid path to convert to HDF5.") - - if data is None: - raise Exception("Dataset needs to be loaded first.") - - url = self._convert_filename(url) - - data.to_hdf5(url, self.dataset_path) - - return url - - def _transform_generic_all(self, data): - if data is None: - raise Exception("Dataset needs to be loaded first.") - - if self.filename: - url = self.filename - else: - raise Exception("Array requires a valid path to convert to Zarr.") - - url = self._convert_filename(url) - - h5f = h5py.File(url, 'w') - h5f.create_dataset(self.dataset_path, data=data) - h5f.close() - - return url - - def _lazy_transform_generic(self, X, **kwargs): - # XXX: Avoid circular dependency - from dasf.datasets.base import DatasetArray, DatasetHDF5 - - name = None - chunks = None - - if isinstance(X, DatasetArray): - name = X._name - chunks = X._chunks - - if not self.filename and hasattr(X, '_root_file'): - self.filename = X._root_file - - url = self._lazy_transform_generic_all(X._data) - elif is_dask_array(X): - chunks = X.chunks - - url = self._lazy_transform_generic_all(X) - else: - raise Exception("It is not an Array type.") - - return DatasetHDF5(name=name, download=False, root=url, chunks=chunks, - dataset_path=self.dataset_path) - - def _transform_generic(self, X, **kwargs): - # XXX: Avoid circular dependency - from dasf.datasets.base import DatasetArray, DatasetHDF5 - - name = None - url = None - - if hasattr(X, '_chunks') and \ - (X._chunks is not None and X._chunks != 'auto'): - chunks = X._chunks - else: - chunks = self.chunks - - if isinstance(X, DatasetArray): - name = X._name - - if not self.filename and hasattr(X, '_root_file'): - self.filename = X._root_file - - url = self._transform_generic_all(X._data) - elif is_array(X): - url = self._transform_generic_all(X) - else: - raise Exception("It is not an Array type.") - - return DatasetHDF5(name=name, download=False, root=url, chunks=chunks, - dataset_path=self.dataset_path) - - def _lazy_transform_gpu(self, X, **kwargs): - return self._lazy_transform_generic(X, **kwargs) - - def _lazy_transform_cpu(self, X, **kwargs): - return self._lazy_transform_generic(X, **kwargs) - - def _transform_gpu(self, X, **kwargs): - return self._transform_generic(X, **kwargs) - - def _transform_cpu(self, X, **kwargs): - return self._transform_generic(X, **kwargs) - - -class ZarrToArray(Transform): - def __init__(self, chunks=None, save=True, filename=None): - self.chunks = chunks - self.save = save - self.filename = filename - - @staticmethod - def _convert_filename(url): - if url.endswith(".zarr"): - return url.replace(".zarr", ".npy") - return url + ".npy" - - def transform(self, X): - # XXX: Avoid circular dependency - from dasf.datasets.base import DatasetZarr - - if issubclass(X.__class__, DatasetZarr): - if self.save: - if self.filename: - url = self.filename - elif hasattr(X, '_root_file'): - url = X._root_file - else: - raise Exception("Array requires a valid path to convert to Array.") - - url = self._convert_filename(url) - - np.save(url, X._data) - - # This is just a place holder - return X._data - else: - raise Exception("Input is not a Zarr dataset.") - - -class ArraysToDataFrame(Transform): - def _build_dataframe(self, data, columns, xp, df): - data = [d.flatten() for d in data] - stacked_data = xp.stack(data, axis=1) - return df.DataFrame( - stacked_data, - columns=columns - ) - - def _lazy_transform(self, xp, df, **kwargs): - X = list(kwargs.values()) - y = list(kwargs.keys()) - assert len(X) == len(y), "Data and labels should have the same length." - - meta = ddf.utils.make_meta([ - (col, data.dtype) - for col, data in zip(y, X) - ], - parent_meta=None if df == pd else cudf.DataFrame - ) - - lazy_dataframe_build = dask.delayed(self._build_dataframe) - data_chunks = [x.to_delayed().ravel() for x in X] - partial_dataframes = [ - ddf.from_delayed(lazy_dataframe_build(data=mapped_chunks, columns=y, xp=xp, df=df), meta=meta) - for mapped_chunks in zip(*data_chunks) - ] - - return ddf.concat(partial_dataframes) - - def _lazy_transform_cpu(self, X=None, **kwargs): - return self._lazy_transform(np, pd, **kwargs) - - def _lazy_transform_gpu(self, X=None, **kwargs): - return self._lazy_transform(cp, cudf, **kwargs) - - def _transform(self, xp, df, **kwargs): - X = list(kwargs.values()) - y = list(kwargs.keys()) - assert len(X) == len(y), "Data and labels should have the same length." - - return self._build_dataframe(data=X, columns=y, xp=xp, df=df) - - def _transform_cpu(self, X=None, **kwargs): - return self._transform(np, pd, **kwargs) - - def _transform_gpu(self, X=None, **kwargs): - return self._transform(cp, cudf, **kwargs) diff --git a/dasf/utils/__init__.py b/dasf/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/dasf/utils/benchmark.py b/dasf/utils/benchmark.py deleted file mode 100644 index baf5895..0000000 --- a/dasf/utils/benchmark.py +++ /dev/null @@ -1,120 +0,0 @@ -#!/usr/bin/env python3 - -import timeit -from time import perf_counter - -try: - import memray - USE_MEMRAY = True -except ImportError: # pragma: no cover - USE_MEMRAY = False - -import cProfile -from pstats import Stats - -try: - from functools import partial - - from memory_profiler import LineProfiler, choose_backend, memory_usage, show_results - - USE_MEM_PROF = True -except ImportError: # pragma: no cover - USE_MEM_PROF = False - - -class TimeBenchmark: - def __init__(self, backend="cprofile"): - self.__backend = backend - - def __enter__(self): - if self.__backend == "cprofile": - self.__pr = cProfile.Profile() - self.__pr.enable() - elif self.__backend == "perf_counter": - self.__start = perf_counter() - self.__end = 0.0 - else: - print("There is no available backend") - return self - - def __exit__(self, *args, **kwargs): - if self.__backend == "cprofile": - self.__pr.disable() - p = Stats(self.__pr) - - p.strip_dirs().sort_stats('cumulative').print_stats(10) - elif self.__backend == "perf_counter": - self.__end = perf_counter() - print("Time spent:", self.__end - self.__start) - - def run(self, function, *args, **kwargs): - if self.__backend == "cprofile": - pr = cProfile.Profile() - pr.enable() - - function(*args, **kwargs) - - pr.disable() - p = Stats(pr) - - p.strip_dirs().sort_stats('cumulative').print_stats(10) - - self.teardown() - elif self.__backend == "timeit": - timeit.repeat("function(*args, **kwargs)", setup="self.setup()") - - self.teardown() - else: - print("There is no available backend") - - -class MemoryBenchmark: - def __init__(self, backend="memray", debug=False, output_file=None, *args, **kwargs): - self.__backend = backend - self.__debug = debug - self.__output_file = output_file - self.__args = args - self.__kwargs = kwargs - - def __enter__(self): - if self.__backend == "memray" and USE_MEMRAY: - self.__memray = memray.Tracker(*self.__args, **self.__kwargs) - - return self.__memray.__enter__() - else: - raise Exception(f"The backend {self.__backend} does not support context " - "manager") - - def __exit__(self, *args, **kwargs): - if self.__backend == "memray" and USE_MEMRAY: - return self.__memray.__exit__(*args, **kwargs) - - def run(self, function, *args, **kwargs): - if self.__backend == "memory_profiler" and USE_MEM_PROF: - if self.__debug: - # profile = LineProfiler(include_children=True) - - get_prof = partial(LineProfiler, backend=choose_backend("psutil")) - show_results_bound = partial( - show_results, precision=4 - ) - - prof = get_prof() - vals = prof(function)(*args, **kwargs) - show_results_bound(prof) - else: - vals = memory_usage((function, args, kwargs), *self.__args, - **self.__kwargs) - - self.teardown() - - return vals - elif self.__backend == "memray" and USE_MEMRAY: - with memray.Tracker(*self.__args, **self.__kwargs): - ret = function(*args, **kwargs) - - self.teardown() - - return ret - else: - print(f"The backend {self.__backend} is not supported") diff --git a/dasf/utils/decorators.py b/dasf/utils/decorators.py deleted file mode 100644 index 3682dbb..0000000 --- a/dasf/utils/decorators.py +++ /dev/null @@ -1,149 +0,0 @@ -""" Implementations of important library decorators. """ -#!/usr/bin/env python3 - -from functools import wraps - -from dasf.utils.funcs import ( - get_dask_running_client, - is_dask_gpu_supported, - is_dask_supported, - is_gpu_supported, -) -from dasf.utils.types import is_dask_array, is_gpu_array - - -def is_forced_local(cls): - """ - Returns if object is forced to run in a CPU. - """ - # pylint: disable=protected-access - if hasattr(cls, "_run_local") and cls._run_local is not None: - # pylint: disable=protected-access - return cls._run_local - return None - - -def is_forced_gpu(cls): - """ - Returns if object is forced to run in a GPU. - """ - # pylint: disable=protected-access - if hasattr(cls, "_run_gpu") and cls._run_gpu is not None: - # pylint: disable=protected-access - return cls._run_gpu - return None - - -def fetch_from_dask(*args, **kwargs) -> tuple: - """ - Fetches to CPU all parameters in a Dask data type. - """ - new_kwargs = {} - new_args = [] - - for key, value in kwargs.items(): - if is_dask_array(value): - new_kwargs[key] = value.compute() - else: - new_kwargs[key] = value - - for value in args: - if is_dask_array(value): - new_args.append(value.compute()) - else: - new_args.append(value) - - return new_args, new_kwargs - - -def fetch_from_gpu(*args, **kwargs) -> tuple: - """ - Fetches to CPU all parameters in a GPU data type. - """ - new_kwargs = {} - new_args = [] - - for key, value in kwargs.items(): - if is_gpu_array(value): - new_kwargs[key] = value.get() - else: - new_kwargs[key] = value - - for value in args: - if is_gpu_array(value): - new_args.append(value.get()) - else: - new_args.append(value) - - return new_args, new_kwargs - - -def fetch_args_from_dask(func): - """ - Fetches to CPU all function parameters in a Dask data type. - """ - def wrapper(*args, **kwargs): - new_args, new_kwargs = fetch_from_dask(*args, **kwargs) - - return func(*new_args, **new_kwargs) - - return wrapper - - -def fetch_args_from_gpu(func): - """ - Fetches to CPU all function parameters in a GPU data type. - """ - def wrapper(*args, **kwargs): - new_args, new_kwargs = fetch_from_gpu(*args, **kwargs) - - return func(*new_args, **new_kwargs) - - return wrapper - - -def task_handler(func): - """ - Returns all mapped functions corresponding to the executor in place. - """ - @wraps(func) - def wrapper(*args, **kwargs): - cls = args[0] - new_args = args[1:] - func_name = func.__name__ - func_type = "" - arch = "cpu" - client = get_dask_running_client() - if client is not None: # Runs task according to current client configuration, i.e, Pipeline Executor - func_type = "_lazy" - arch = "gpu" if getattr(client, "backend", None) == "cupy" else "cpu" - else: - if not is_forced_local(cls) and (is_dask_gpu_supported() or is_dask_supported()): - func_type = "_lazy" - if is_dask_gpu_supported() or is_gpu_supported(): - arch = "gpu" - - if is_forced_local(cls): - func_type = "" - new_args, kwargs = fetch_from_dask(*new_args, **kwargs) - - if is_forced_gpu(cls): - arch = "gpu" - - if arch == "cpu": - new_args, kwargs = fetch_from_gpu(*new_args, **kwargs) - - wrapper_func_attr = f"{func_type}_{func_name}_{arch}" - - if (not hasattr(cls, wrapper_func_attr) and - hasattr(cls, func_name)): - return func(*new_args, **kwargs) - if (not hasattr(cls, wrapper_func_attr) and - not hasattr(cls, func_name)): - raise NotImplementedError( - f"There is no implementation of {wrapper_func_attr} nor " - f"{func_name}" - ) - return getattr(cls, wrapper_func_attr)(*new_args, **kwargs) - - return wrapper diff --git a/dasf/utils/funcs.py b/dasf/utils/funcs.py deleted file mode 100644 index d7ac495..0000000 --- a/dasf/utils/funcs.py +++ /dev/null @@ -1,574 +0,0 @@ -""" Generic and regular functions. """ -#!/usr/bin/env python3 - -import inspect -import os -import threading -import time -from pathlib import Path - -import dask -import dask.delayed as dd -import gdown -import GPUtil -import numpy as np -import pandas -import psutil -from dask.distributed import Client -from distributed.client import FIRST_COMPLETED, wait -from distributed.utils import TimeoutError as DistributedTimeoutError -from IPython import display as disp -from IPython import get_ipython -from ipywidgets import FloatProgress, HBox, Label - -from dasf.pipeline.types import TaskExecutorType - -try: - import cupy as cp - GPU_SUPPORTED = isinstance(cp.__version__, str) -except ImportError: # pragma: no cover - GPU_SUPPORTED = False - -try: - import jax.numpy as jnp - JAX_SUPPORTED = isinstance(jnp.__name__, str) -except ImportError: # pragma: no cover - JAX_SUPPORTED = False - -try: - import kvikio - import kvikio.defaults - KVIKIO_SUPPORTED = True -except ImportError: # pragma: no cover - KVIKIO_SUPPORTED = False - -try: - from kvikio.nvcomp_codec import NvCompBatchCodec - NV_COMP_BATCH_CODEC_SUPPORTED = True -except ImportError: # pragma: no cover - NV_COMP_BATCH_CODEC_SUPPORTED = False - - -def human_readable_size(size, decimal=3) -> str: - """ - converts data size into the proper measurement - """ - for unit in ['B', 'KB', 'MB', 'GB', 'TB']: - if size < 1024.0: - break - size /= 1024.0 - return f"{size:.{decimal}f} {unit}" - - -def get_worker_info(client) -> list: - """ - Returns a list of workers (sorted), and the DNS name for the master host - The master is the 0th worker's host - """ - info = client.scheduler_info() - - if "workers" not in info: - return [] - - workers = info["workers"] - worker_keys = sorted(workers.keys()) - workers_by_host = {} - for key in worker_keys: - worker = workers[key] - host = worker["host"] - workers_by_host.setdefault(host, []).append(key) - - all_workers = [] - - if len(worker_keys) == 0: - return all_workers - - host = workers[worker_keys[0]]["host"] - global_rank = 0 - world_size = len(workers_by_host) - hosts = sorted(workers_by_host.keys()) - for host in hosts: - local_rank = 0 - for worker in workers_by_host[host]: - all_workers.append( - dict( - master=hosts[0], - worker=worker, - nthreads=workers[worker]["nthreads"], - local_rank=0, - global_rank=global_rank, - host=host, - world_size=world_size, - ) - ) - local_rank += 1 - global_rank += 1 - return all_workers - - -def sync_future_loop(futures): - """ - Synchronize all futures submitted to workers. - """ - while True: - if not futures: - break - - try: - result = wait(futures, 0.1, FIRST_COMPLETED) - except DistributedTimeoutError: - continue - - for fut in result.done: - try: - fut.result(timeout=7200) - except Exception as exc: # pylint: disable=broad-except - print(str(exc)) - raise - futures = result.not_done - - -class NotebookProgressBar(threading.Thread): - MIN_CUR = -2 - MIN_TOTAL = -1 - - def __init__(self): - threading.Thread.__init__(self) - - # pylint: disable=disallowed-name - self.bar = None - self.percentage = None - self.data = None - - self.__lock = threading.Lock() - self.__current = self.MIN_CUR - self.__total = self.MIN_TOTAL - self.__error = False - - def show(self): - self.bar = FloatProgress(value=0, min=0, max=100) - self.percentage = Label(value='0 %') - self.data = Label(value='') - box = HBox((self.percentage, self.bar, self.data)) - disp.display(box) - - def set_current(self, current, total): - with self.__lock: - self.__current = current - self.__total = total - - def set_error(self, error): - self.__error = error - - def run(self): - while (not self.__error and self.__current < self.__total): - time.sleep(1) - - if self.__current != self.MIN_CUR and self.__total != self.MIN_TOTAL: - progress = (self.__current / self.__total) * 100 - self.bar.value = progress - self.percentage.value = f"{int(self.bar.value)} %%" - self.data.value = f"{int(self.__current)} / {int(self.__total)}" - - if not self.__error: - self.bar.style.bar_color = '#03c04a' - else: - self.bar.style.bar_color = '#ff0000' - - -def download_file(url, filename=None, directory=None): - """ - Download a generic file and save it. - """ - if directory is not None: - os.makedirs(os.path.dirname(directory), exist_ok=True) - - progressbar = None - - if is_notebook(): - progressbar = NotebookProgressBar() - - def update_notebook_bar(current, total): - progressbar.set_current(current, total) - - try: - if filename and directory: - output = os.path.abspath(os.path.join(directory, filename)) - - if not os.path.exists(output): - if is_notebook(): - # Activate the notebook progress bar - progressbar.show() - progressbar.start() - - gdown.download(url, output=output) # TODO: use pbar=update_notebook_bar - else: - gdown.download(url, output=output) - elif filename: - output = os.path.abspath(os.path.join(os.getcwd(), filename)) - - if not os.path.exists(output): - if is_notebook(): - # Activate the notebook progress bar - progressbar.show() - progressbar.start() - - gdown.download(url, output=output) # TODO: use pbar=update_notebook_bar - else: - gdown.download(url, output=output) - elif directory: - if is_notebook(): - # Activate the notebook progress bar - progressbar.show() - progressbar.start() - - output = \ - os.path.abspath(os.path.join(directory, - gdown.download(url, - bar=update_notebook_bar))) - else: - output = os.path.abspath(os.path.join(directory, gdown.download(url))) - else: - if is_notebook(): - # Activate the notebook progress bar - progressbar.show() - progressbar.start() - - output = \ - os.path.abspath(os.path.join(os.getcwd(), - gdown.download(url, - bar=update_notebook_bar))) - else: - output = os.path.abspath(os.path.join(os.getcwd(), gdown.download(url))) - except Exception as exc: - if progressbar: - progressbar.set_error(True) - - return output - - -def download_file_from_gdrive(file_id, filename=None, directory=None): - """ - Download a file from Google Drive using gdrive file id. - """ - url = f"https://drive.google.com/uc?export=download&confirm=9iBg&id={file_id}" - - return download_file(url, filename=filename, directory=directory) - - -def get_machine_memory_avail(): - """ - Return free memory available from a single machine. - """ - return psutil.virtual_memory().free - - -def set_executor_default(): - """ - Return executor as a CPU (default) instance. - """ - return TaskExecutorType.single_cpu - - -def set_executor_gpu(): - """ - Return executor as a GPU instance. - """ - return TaskExecutorType.single_gpu - - -def is_executor_single(dtype) -> bool: - """ - Return if the executor is a single machine instance. - """ - return dtype in (TaskExecutorType.single_cpu, TaskExecutorType.single_gpu) - - -def is_executor_cluster(dtype) -> bool: - """ - Return if the executor is a cluster instance. - """ - return dtype in (TaskExecutorType.multi_cpu, TaskExecutorType.multi_gpu) - - -def is_executor_cpu(dtype) -> bool: - """ - Return if the executor is a CPU instance. - """ - return dtype in (TaskExecutorType.single_cpu, TaskExecutorType.multi_cpu) - - -def is_executor_gpu(dtype) -> bool: - """ - Return if the executor is a GPU instance. - """ - return dtype in (TaskExecutorType.single_gpu, TaskExecutorType.multi_gpu) - - -def is_gpu_supported() -> bool: - """ - Return if GPU is supported. - """ - return GPU_SUPPORTED and get_gpu_count() >= 1 - - -def is_kvikio_supported() -> bool: - """ - Return if kvikio is supported (installed). - """ - return KVIKIO_SUPPORTED - - -def is_gds_supported() -> bool: - """ - Return if GPU Direct Store is supported. - """ - if is_kvikio_supported(): - props = kvikio.DriverProperties() - return props.is_gds_available - - return False - - -def is_kvikio_compat_mode() -> bool: - """ - Return if Kvikio is running in compatibility mode. - """ - return kvikio.defaults.compat_mode() - - -def is_nvcomp_codec_supported() -> bool: - """ - Return if NVidia Compressor Codecs are supported. - """ - return NV_COMP_BATCH_CODEC_SUPPORTED - - -def is_jax_supported() -> bool: - """ - Return if JAX is supported. - """ - return JAX_SUPPORTED - - -def is_dask_local_supported() -> bool: - """ - Return if Dask is supported locally by the executor. - """ - try: - scheduler = dask.config.get(key="scheduler") - return scheduler is not None - except Exception: - return False - - -def get_dask_running_client(): - """ - Get Dask runner stanza. - """ - try: - return Client.current() - except: - return None - - - -def get_backend_supported(func): - """ - Get backend support. - """ - par = inspect.signature(func) - if "backend" in par.parameters: - return True - return False - - -def is_dask_supported() -> bool: - """ - Return if Dask is supported by the executor. - """ - try: - if is_dask_local_supported(): - return True - - cur = get_dask_running_client() - if hasattr(cur, 'dtype'): - return is_executor_cluster(cur.dtype) - return cur is not None - except Exception: - return False - - -def is_dask_gpu_supported() -> bool: - """ - Return if any node supports GPU. - """ - if is_dask_supported(): - if get_gpu_from_workers(): - return True - elif get_dask_gpu_count() > 0: - return True - - return False - - -def get_gpu_from_workers() -> bool: - try: - cur = get_dask_running_client() - - workers = cur.cluster.scheduler_info["workers"] - - for worker_id, worker_meta in workers.items(): - if 'gpu' in worker_meta: - return True - except Exception: - pass - - return False - - -def get_gpu_count() -> int: - """ - Get single node GPU count. - """ - return len(GPUtil.getGPUs()) - - -def get_dask_gpu_count(fetch=True) -> int: - """ - Get how many GPUs are available in each worker. - """ - # pylint: disable=not-callable - ret = dd(GPUtil.getGPUs)() - if fetch: - return len(ret.compute()) - return ret - - -def block_chunk_reduce(dask_data, output_chunk): - """ - Reduce the chunk according the new output size. - """ - drop_axis = np.array([]) - new_axis = None - - if output_chunk is None or not isinstance(output_chunk, tuple): - return drop_axis.tolist(), new_axis - - data_chunk = dask_data.chunksize - - drop_axis = np.in1d(data_chunk, output_chunk) - new_axis = np.in1d(output_chunk, data_chunk) - - drop_axis = np.where(drop_axis == False) - new_axis = np.where(new_axis == False) - - return drop_axis[0].tolist(), new_axis[0].tolist() - - -def trim_chunk_location(block_info, depth, index=0): - """ - Trim an overlapped chunk to the exact size of the chunk. - """ - if not 'array-location' in block_info[index]: - raise IndexError("Key 'array-location' was not found in block-info.") - - if not 'chunk-location' in block_info[index]: - raise IndexError("Key 'chunk-location' was not found in block-info.") - - loc = block_info[index]['array-location'] - - chunks = block_info[index]['chunk-location'] - - if len(depth) != len(loc) and len(depth) != len(chunks): - raise ValueError(f"Depth {len(depth)}, location {len(loc)} and/or chunks {len(chunks)} do not match.") - - loc_orig = [] - for i in range(0, len(depth)): - loc_orig.append((loc[i][0] - 2 * depth[i] * chunks[i], - loc[i][1] - 2 * depth[i] - (loc[i][0] - 2 * depth[i] * chunks[i]))) - - return loc_orig - - -def return_local_and_gpu(executor, local, gpu): - """ - Return executor type based on passed preferences. - """ - # pylint: disable=too-many-return-statements - if local is not None and gpu is None: - if local is True: - return TaskExecutorType(executor.dtype.value & 2) - if local is False: - return TaskExecutorType(executor.dtype.value | 1) - elif local is None and gpu is not None: - if gpu is True: - return TaskExecutorType((executor.dtype >> 1) + 2) - if gpu is False: - return TaskExecutorType(executor.dtype & 1) - elif local is not None and gpu is not None: - if local is True and gpu is False: - return TaskExecutorType.single_cpu - if local is False and gpu is False: - return TaskExecutorType.multi_cpu - if local is True and gpu is True: - return TaskExecutorType.single_gpu - if local is False and gpu is True: - return TaskExecutorType.multi_gpu - - return executor.dtype - - -def get_dask_mem_usage(profiler): - """ - Get Dask memory usage profile. - """ - profiler_dir = os.path.abspath(str(Path.home()) + "/.cache/dasf/profiler/") - - if profiler == "memusage": - os.makedirs(profiler_dir, exist_ok=True) - - mem = pandas.read_csv(os.path.abspath(profiler_dir + "/dask-memusage")) - - column = mem["max_memory_mb"] - max_index = column.idxmax() - - return mem["max_memory_mb"][max_index] - return 0.0 - - -def is_notebook() -> bool: - """ - Return if the code is being executed in a IPyNotebook. - """ - try: - shell = get_ipython().__class__.__name__ - if shell == "ZMQInteractiveShell": - return True - except NameError: - pass - - return False - - -def weight_gaussian(shape): - """ - Produces a NDArray for a given shape with a Gaussian Distribution in all directions starting from the center - """ - center = np.array(shape) / 2 - distances = np.zeros(shape) - for idx in np.ndindex(shape): - distances[idx] = np.linalg.norm(np.array(idx) - center) - distances = distances / np.max(distances) - return np.exp(-2 * (distances**2)) / (np.sqrt(2 * np.pi) / 2) - - -def weight_radial(shape): - """ - Produces a NDArray for a given shape with a decreasing rate starting from the center - """ - center = np.array(shape) / 2 - distances = np.zeros(shape) - for idx in np.ndindex(shape): - distances[idx] = np.linalg.norm(np.array(idx) - center) - return 1 / (1 + distances) diff --git a/dasf/utils/labels.py b/dasf/utils/labels.py deleted file mode 100644 index 7728cbf..0000000 --- a/dasf/utils/labels.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 - -from threading import Lock - -from dask.base import is_dask_collection -from dask.core import get_dependencies, ishashable, istask -from dask.dot import graphviz_to_file, to_graphviz - -inside_with = Lock() - -g_hash_attrs = dict() -g_func_attrs = dict() -g_data_attrs = dict() - - -class DaskLabel(object): - def __init__(self, start, stop, label=None, color=None): - self.__label = label - self.__color = color - self.__start = start - self.__stop = stop - self.__hash_attrs = g_hash_attrs - self.__func_attrs = g_func_attrs - self.__data_attrs = g_data_attrs - - def start(self, start): - self.__enter(start) - - def stop(self, stop): - self.__exit(stop, None, None, None) - - def __name(self, x): - try: - return str(hash(x)) - except TypeError: - return str(hash(str(x))) - - def __add_item(self, key, tag, label=None, color=None, atype="data"): - if not key in self.__data_attrs: - self.__hash_attrs[key] = dict() - # We use comment as a generic field for tag - self.__hash_attrs[key]["comment"] = tag - self.__hash_attrs[key]["xlabel"] = label - self.__hash_attrs[key]["color"] = color - self.__hash_attrs[key]["type"] = atype - - def __add_func(self, key, tag, label, color): - if not key in self.__func_attrs: - self.__func_attrs[key] = dict() - # We use comment as a generic field for tag - self.__func_attrs[key]["comment"] = tag - if label: - self.__func_attrs[key]["xlabel"] = label - if color: - self.__func_attrs[key]["color"] = color - self.__func_attrs[key]["style"] = "filled" - - def __add_data(self, key, tag, label, color): - if not key in self.__data_attrs: - self.__data_attrs[key] = dict() - # We use comment as a generic field for tag - self.__data_attrs[key]["comment"] = tag - if label: - self.__data_attrs[key]["xlabel"] = label - if color: - self.__data_attrs[key]["color"] = color - self.__data_attrs[key]["style"] = "filled" - - def __generate_hashtable(self, data, delete_dup=False): - if not is_dask_collection(data): - raise Exception("This is not a Dask data: this is %s." % str(type(data))) - else: - dsk = data.dask - - remove = set() - - for k, v in dsk.items(): - k_name = self.__name(k) - if istask(v): - func_name = self.__name((k, "function")) - - if delete_dup and func_name in self.__hash_attrs: - del self.__hash_attrs[func_name] - remove.add(func_name) - elif func_name not in remove: - self.__add_item( - func_name, k, self.__label, self.__color, atype="func" - ) - - for dep in get_dependencies(dsk, k): - dep_name = self.__name(dep) - if delete_dup and dep_name in self.__hash_attrs: - del self.__hash_attrs[dep_name] - remove.add(dep_name) - elif dep_name not in remove: - self.__add_item(dep_name, dep, self.__label, self.__color) - - def __enter(self, dsk): - global inside_with - - inside_with.acquire() - - self.__generate_hashtable(dsk) - - return self - - def __enter__(self): - dsk = eval(self.__start) - - return self.__enter(dsk) - - def __exit(self, dsk, exc_type, exc_val, exc_tb): - global inside_with, g_hash_attrs, g_func_attrs, g_data_attrs - - self.__generate_hashtable(dsk, delete_dup=True) - - for k in self.__hash_attrs: - if self.__hash_attrs[k]["type"] == "data": - self.__add_data( - self.__hash_attrs[k]["comment"], - k, - self.__hash_attrs[k]["xlabel"], - self.__hash_attrs[k]["color"], - ) - elif self.__hash_attrs[k]["type"] == "func": - self.__add_func( - self.__hash_attrs[k]["comment"], - k, - self.__hash_attrs[k]["xlabel"], - self.__hash_attrs[k]["color"], - ) - - g_hash_attrs = {**g_hash_attrs, **self.__hash_attrs} - g_func_attrs = {**g_func_attrs, **self.__func_attrs} - g_data_attrs = {**g_data_attrs, **self.__data_attrs} - - inside_with.release() - - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - dsk = eval(self.__stop) - - return self.__exit(dsk, exc_type, exc_val, exc_tb) - - -def get_attributes(): - global inside_with, g_func_attrs, g_data_attrs - - if inside_with.locked(): - print("WARNING: it cannot reflect all attribute changes.") - - return {"function_attributes": g_func_attrs, "data_attributes": g_data_attrs} diff --git a/dasf/utils/logging.py b/dasf/utils/logging.py deleted file mode 100644 index 2db081b..0000000 --- a/dasf/utils/logging.py +++ /dev/null @@ -1,28 +0,0 @@ -""" Logging helpers for functions. """ -#!/usr/bin/env python3 - -import sys -from logging import INFO, Formatter, Logger, StreamHandler, getLogger - - -def init_logging() -> Logger: - """ - Initialize logger objects to be used by modules. - """ - logger = getLogger("DASF") - - logger.setLevel(INFO) - handler = StreamHandler(sys.stdout) - - if logger.hasHandlers(): - logger.handlers.clear() - else: - formatter = Formatter( - fmt="[%(asctime)s] %(levelname)s - %(message)s", - datefmt="%Y-%m-%d %H:%M:%S%z", - ) - - handler.setFormatter(formatter) - logger.addHandler(handler) - - return logger diff --git a/dasf/utils/types.py b/dasf/utils/types.py deleted file mode 100644 index 433a162..0000000 --- a/dasf/utils/types.py +++ /dev/null @@ -1,187 +0,0 @@ -""" Data types handlers. """ -#!/usr/bin/env python3 - -from typing import Union, get_args - -import dask.array as da -import dask.dataframe as ddf -import numpy as np -import pandas as pd -import xarray as xr -import zarr - -try: - import cudf - import cupy as cp - import dask_cudf as dcudf -except ImportError: # pragma: no cover - pass - -from dasf.utils.funcs import is_gpu_supported - -ArrayCPU = Union[list, np.ndarray, zarr.core.Array] -DataFrameCPU = Union[pd.DataFrame] - -DataCPU = Union[ArrayCPU, DataFrameCPU] - -DaskArray = Union[da.core.Array] -DaskDataFrameCPU = Union[ddf.core.DataFrame] - -XDataArray = Union[xr.DataArray] - -Array = Union[ArrayCPU, DaskArray, XDataArray] -DaskDataFrame = Union[DaskDataFrameCPU] -DataFrame = Union[DataFrameCPU, DaskDataFrameCPU] -DataDask = Union[DaskArray, DaskDataFrameCPU] -try: - ArrayGPU = Union[cp.ndarray] - DataFrameGPU = Union[cudf.DataFrame] - - DataGPU = Union[ArrayGPU, DataFrameGPU] - - DaskDataFrameGPU = Union[dcudf.core.DataFrame] - - Array = Union[Array, ArrayGPU] - DaskDataFrame = Union[DaskDataFrame, DaskDataFrameGPU] - DataFrame = Union[DataFrame, DaskDataFrame, DataFrameGPU] - DataDask = Union[DataDask, DaskDataFrame] -except NameError: # pragma: no cover - pass - - -def is_array(data) -> bool: - """ - Returns if data is a generic array. - """ - return isinstance(data, get_args(Array)) - - -def is_dataframe(data) -> bool: - """ - Returns if data is a generic dataframe. - """ - return isinstance(data, get_args(DataFrame)) - - -def is_cpu_array(data) -> bool: - """ - Returns if data is a CPU arrau like Numpy. - """ - return isinstance(data, get_args(ArrayCPU)) - - -def is_cpu_dataframe(data) -> bool: - """ - Returns if data is a CPU dataframe like Pandas. - """ - return isinstance(data, DataFrameCPU) - - -def is_cpu_datatype(data) -> bool: - """ - Returns if data is a CPU data type. - """ - return isinstance(data, get_args(DataCPU)) - - -def is_gpu_array(data) -> bool: - """ - Returns if data is a GPU array like Cupy. - """ - return is_gpu_supported() and isinstance(data, ArrayGPU) - - -def is_gpu_dataframe(data) -> bool: - """ - Returns if data is a GPU dataframe like Cudf. - """ - return is_gpu_supported() and isinstance(data, DataFrameGPU) - - -def is_gpu_datatype(data) -> bool: - """ - Returns if data is a GPU data type. - """ - return is_gpu_supported() and isinstance(data, get_args(DataGPU)) - - -def is_dask_cpu_array(data) -> bool: - """ - Returns if data is a Dask array with CPU internal array. - """ - if isinstance(data, DaskArray): - # pylint: disable=protected-access - if isinstance(data._meta, get_args(ArrayCPU)): - return True - return False - - -def is_dask_cpu_dataframe(data) -> bool: - """ - Returns if data is a Dask dataframe with CPU internal dataframe. - """ - try: - if is_gpu_supported() and isinstance(data, get_args(DaskDataFrame)): - # pylint: disable=protected-access - if isinstance(data._meta, DataFrameCPU): - return True - elif isinstance(data, DaskDataFrame): - # pylint: disable=protected-access - if isinstance(data._meta, DataFrameCPU): - return True - # We need a Exception here due to Numpy bug. - except TypeError: # pragma: no cover - pass - return False - - -def is_dask_gpu_array(data) -> bool: - """ - Returns if data is a Dask array with GPU internal array. - """ - if is_gpu_supported() and isinstance(data, DaskArray): - # pylint: disable=protected-access - if isinstance(data._meta, ArrayGPU): - return True - return False - - -def is_dask_gpu_dataframe(data) -> bool: - """ - Returns if data is a Dask dataframe with GPU internal dataframe. - """ - if is_gpu_supported() and isinstance(data, get_args(DaskDataFrame)): - # pylint: disable=protected-access - if isinstance(data._meta, DataFrameGPU): - return True - return False - - -def is_dask_array(data) -> bool: - """ - Returns if data is a Dask array. - """ - return isinstance(data, DaskArray) - - -def is_dask_dataframe(data) -> bool: - """ - Returns if data is a Dask dataframe. - """ - if is_gpu_supported(): - return isinstance(data, get_args(DaskDataFrame)) - return isinstance(data, DaskDataFrame) - - -def is_dask(data) -> bool: - """ - Returns if data is a Dask data type. - """ - return isinstance(data, get_args(DataDask)) - - -def is_xarray_array(data) -> bool: - """ - Returns if data is a Xarray. - """ - return isinstance(data, XDataArray) diff --git a/docs/.buildinfo b/docs/.buildinfo deleted file mode 100644 index d4bbda7..0000000 --- a/docs/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. 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-X - - - -16599058 - -PersistDaskData.transform - - - -128329900->16599058 - - -X - - - diff --git a/docs/_images/tutorials_Tutorial_4_18_0.png b/docs/_images/tutorials_Tutorial_4_18_0.png deleted file mode 100644 index a985d6d..0000000 Binary files a/docs/_images/tutorials_Tutorial_4_18_0.png and /dev/null differ diff --git a/docs/_images/tutorials_Tutorial_4_19_0.png b/docs/_images/tutorials_Tutorial_4_19_0.png deleted file mode 100644 index a985d6d..0000000 Binary files a/docs/_images/tutorials_Tutorial_4_19_0.png and /dev/null differ diff --git a/docs/_modules/dasf/datasets/base.html b/docs/_modules/dasf/datasets/base.html deleted file mode 100644 index a6475e1..0000000 --- a/docs/_modules/dasf/datasets/base.html +++ /dev/null @@ -1,1630 +0,0 @@ - - - - - - - - - - dasf.datasets.base — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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  • dasf.datasets.base
    • - - -
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Source code for dasf.datasets.base

-#!/usr/bin/env python3
-
-import os
-import json
-import zarr
-import h5py
-import dask
-
-import numpy as np
-import numpy.lib.format
-import pandas as pd
-import dask.array as da
-import dask.dataframe as ddf
-import xarray as xr
-
-from numbers import Number
-
-try:
-    import cupy as cp
-    import cudf
-    import dask_cudf as dcudf
-    # This is just to enable Xarray Cupy capabilities
-    import cupy_xarray as cx   # noqa
-except ImportError:
-    pass
-
-from pathlib import Path
-
-from dasf.utils.funcs import human_readable_size
-from dasf.utils.decorators import task_handler
-from dasf.utils.types import is_array
-from dasf.utils.types import is_dask_array
-from dasf.transforms.base import TargeteredTransform
-
-
-
[docs]class Dataset(TargeteredTransform):
-    """Class representing a generic dataset based on a TargeteredTransform
-    object.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    *args : type
-        Additional arguments without keys.
-    **kwargs : type
-        Additional keyworkded arguments.
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = False,
-                 root: str = None,
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Dataset internals
-        self._name = name
-        self._download = download
-        self._root = root
-        self._metadata = {}
-        self._data = None
-        self._chunks = None
-
-        self.__set_dataset_cache_dir()
-
-        self.download()
-
-    def __set_dataset_cache_dir(self):
-        """Generate cached directory in $HOME to store dataset(s).
-
-        """
-        self._cache_dir = os.path.abspath(str(Path.home()) + "/.cache/dasf/datasets/")
-        os.makedirs(self._cache_dir, exist_ok=True)
-
-        if self._root is None:
-            self._root = self._cache_dir
-
-
[docs]    def download(self):
-        """Skeleton of the download method.
-
-        """
-        if self._download:
-            raise NotImplementedError("Function download() needs to be defined")

-
-
[docs]    def __len__(self) -> int:
-        """Return internal data length.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return len(self._data)

-
-
[docs]    def __getitem__(self, idx):
-        """Generic __getitem__() function based on internal data.
-
-        Parameters
-        ----------
-        idx : Any
-            Key of the fetched data. It can be an integer or a tuple.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return self._data.__getitem__(idx)

-
-
-
[docs]class DatasetArray(Dataset):
-    """Class representing an dataset wich is defined as an array of a defined
-    shape.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = False,
-                 root: str = None,
-                 chunks="auto"):
-
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-        self._root_file = root
-
-        if root is not None:
-            if not os.path.isfile(root):
-                raise Exception("Array requires a root=filename.")
-
-            self._root = os.path.dirname(root)
-
-
[docs]    def __operator_check__(self, other):
-        assert self._data is not None, "Data is not loaded yet."
-        if isinstance(other, DatasetArray):
-            return other._data
-        return other

-
-
[docs]    def __repr__(self):
-        """Return a class representation based on internal array.
-
-        """
-        return repr(self._data)

-
-
[docs]    def __array__(self, dtype=None):
-        assert self._data is not None, "Data is not loaded yet."
-        return self._data

-
-
[docs]    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
-        assert self._data is not None, "Data is not loaded yet."
-        if method == '__call__':
-            scalars = []
-
-            for inp in inputs:
-                if isinstance(inp, Number):
-                    scalars.append(inp)
-                elif isinstance(inp, self.__class__):
-                    scalars.append(inp._data)
-                else:
-                    return NotImplemented
-
-            self.__class__(name=self._name, chunks=self._chunks)
-            self._data = ufunc(*scalars, **kwargs)
-            return self
-        return NotImplemented

-
-    def __check_op_input(self, in_data):
-        """Return the proper type of data for operation
-
-          >>> Result = DatasetArray + Numpy; or
-          >>> Result = DatasetArray + DatasetArray
-
-        Parameters
-        ----------
-        in_data : Any
-            Input data to be analyzed.
-
-        Returns
-        -------
-        data : Any
-            A data representing the internal array or the class itself.
-
-        """
-        if is_array(in_data) or is_dask_array(in_data):
-            return in_data
-        if isinstance(in_data, self.__class__):
-            return in_data._data
-        raise TypeError("Data is incompatible with Array")
-
-
[docs]    def __add__(self, other):
-        """Internal function of adding two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A sum with two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data + data

-
-
[docs]    def __sub__(self, other):
-        """Internal function of subtracting two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A subtraction of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data - data

-
-
[docs]    def __mul__(self, other):
-        """Internal function of multiplication two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A multiplication of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data * data

-
-
[docs]    def __div__(self, other):
-        """Internal function of division two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A division of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data / data

-
-    def __copy_attrs_from_data(self):
-        """Extends metadata to new transformed object (after operations).
-
-        """
-        self._metadata["type"] = type(self._data)
-
-        attrs = dir(self._data)
-        for attr in attrs:
-            if not attr.startswith("__") and callable(getattr(self._data, attr)):
-                if not hasattr(self, attr):
-                    self.__dict__[attr] = getattr(self._data, attr)
-
-    def __npy_header(self):
-        """Read an array header from a filelike object.
-
-        """
-        with open(self._root_file, 'rb') as fobj:
-            version = numpy.lib.format.read_magic(fobj)
-            func_name = "read_array_header_" + "_".join(str(v) for v in version)
-            func = getattr(numpy.lib.format, func_name)
-            return func(fobj)
-
-
[docs]    def _lazy_load(self, xp, **kwargs):
-        """Lazy load the dataset using an CPU dask container.
-
-        Parameters
-        ----------
-        xp : type
-            Library used to load the file. It must follow numpy library.
-        **kwargs : type
-            Additional keyworkded arguments to the load.
-
-        Returns
-        -------
-        Any
-            The data (or a Future load object, for `_lazy` operations).
-
-        """
-        npy_shape = self.shape
-
-        local_data = dask.delayed(xp.load)(self._root_file, **kwargs)
-
-        local_data = da.from_delayed(local_data, shape=npy_shape, dtype=xp.float32)
-        if isinstance(self._chunks, tuple):
-            local_data = local_data.rechunk(self._chunks)
-
-        return local_data

-
-
[docs]    def _load(self, xp, **kwargs):
-        """Load data using CPU container.
-
-        Parameters
-        ----------
-        xp : Module
-            A module that load data (implement `load` function)
-        **kwargs : type
-            Additional `kwargs` to `xp.load` function.
-
-        """
-
-        return xp.load(self._root_file, **kwargs)

-
-
[docs]    def _load_meta(self) -> dict:
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._root_file is not None, ("There is no temporary file to "
-                                             "inspect")
-        assert os.path.isfile(self._root_file), ("The root variable should "
-                                                 "be a NPY file")
-
-        return self.inspect_metadata()

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(cp)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(np)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. cupy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._load(cp)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. numpy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._load(np)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-    @property
-    def shape(self) -> tuple:
-        """Returns the shape of an array.
-
-        Returns
-        -------
-        tuple
-            A tuple with the shape.
-
-        """
-        return self.__npy_header()[0]
-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        array_file_size = human_readable_size(
-            os.path.getsize(self._root_file),
-            decimal=2
-        )
-
-        npy_shape = self.shape
-
-        return {
-            "size": array_file_size,
-            "file": self._root_file,
-            "shape": npy_shape,
-            "block": {"chunks": self._chunks},
-        }

-
-
-
[docs]class DatasetZarr(Dataset):
-    """Class representing an dataset wich is defined as a Zarr array of a
-    defined shape.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = False,
-                 root: str = None,
-                 chunks=None):
-
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-        self._root_file = root
-
-        if root is not None:
-            if not os.path.isfile(root):
-                self._root = root
-            else:
-                self._root = os.path.dirname(root)
-
-
[docs]    def _lazy_load(self, xp, **kwargs):
-        """Lazy load the dataset using an CPU dask container.
-
-        Parameters
-        ----------
-        xp : type
-            Library used to load the file. It must follow numpy library.
-        **kwargs : type
-            Additional keyworkded arguments to the load.
-
-        Returns
-        -------
-        Any
-            The data (or a Future load object, for `_lazy` operations).
-
-        """
-        return da.from_zarr(self._root_file, chunks=self._chunks).map_blocks(xp.asarray)

-
-
[docs]    def _load(self, xp, **kwargs):
-        """Load data using CPU container.
-
-        Parameters
-        ----------
-        xp : Module
-            A module that load data (implement `load` function)
-        **kwargs : type
-            Additional `kwargs` to `xp.load` function.
-
-        """
-        return zarr.open(self._root_file, mode='r', meta_array=xp.empty(()))

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(np)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(cp)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. numpy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._load(np)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. cupy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._load(cp)
-        self.__copy_attrs_from_data()
-        return self

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-
[docs]    def _load_meta(self) -> dict:
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._root_file is not None, "There is no temporary file to inspect"
-
-        return self.inspect_metadata()

-
-    @property
-    def shape(self) -> tuple:
-        """Returns the shape of an array.
-
-        Returns
-        -------
-        tuple
-            A tuple with the shape.
-
-        """
-        if not self._data:
-            if self._root_file and os.path.isdir(self._root_file):
-                zarray = os.path.abspath(self._root_file + "/.zarray")
-                if os.path.exists(zarray):
-                    try:
-                        with open(zarray) as fz:
-                            meta = json.load(fz)
-                            return tuple(meta["shape"])
-                    except Exception:
-                        pass
-            return tuple()
-
-        return self._data.shape
-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        z = zarr.open(self._root_file, mode='r')
-
-        info = {}
-        for k, v in z.info_items():
-            info[k] = v
-
-        if isinstance(self._chunks, bool) and self._chunks:
-            self._chunks = info["Chunk shape"]
-
-        return {
-            "size": human_readable_size(
-                int(info["No. bytes"].split(' ')[0])
-            ),
-            "compressor": info["Compressor"],
-            "type": info["Store type"],
-            "file": self._root_file,
-            "shape": info["Shape"],
-            "block": {"chunks": self._chunks},
-        }

-
-
[docs]    def __repr__(self):
-        """Return a class representation based on internal array.
-
-        """
-        return repr(self._data)

-
-    def __check_op_input(self, in_data):
-        """Return the proper type of data for operation
-
-          >>> Result = DatasetZarr + Numpy; or
-          >>> Result = DatasetZarr + DatasetZarr
-
-        Parameters
-        ----------
-        in_data : Any
-            Input data to be analyzed.
-
-        Returns
-        -------
-        data : Any
-            A data representing the internal array or the class itself.
-
-        """
-        if is_array(in_data) or is_dask_array(in_data):
-            return in_data
-        elif isinstance(in_data, self.__class__):
-            return in_data._data
-        raise TypeError("Data is incompatible with Array")
-
-
[docs]    def __add__(self, other):
-        """Internal function of adding two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A sum with two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data + data

-
-
[docs]    def __sub__(self, other):
-        """Internal function of subtracting two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A subtraction of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data - data

-
-
[docs]    def __mul__(self, other):
-        """Internal function of multiplication two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A multiplication of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data * data

-
-
[docs]    def __div__(self, other):
-        """Internal function of division two array datasets.
-
-        Parameters
-        ----------
-        other : Any
-            A data representing an array or a DatasetArray.
-
-        Returns
-        -------
-        DatasetArry
-            A division of two arrays.
-
-        """
-        assert self._data is not None, "Data is not loaded yet."
-        data = self.__check_op_input(other)
-        return self._data / data

-
-    def __copy_attrs_from_data(self):
-        """Extends metadata to new transformed object (after operations).
-
-        """
-        self._metadata["type"] = type(self._data)
-
-        attrs = dir(self._data)
-        for attr in attrs:
-            if not attr.startswith("__") and callable(getattr(self._data, attr)):
-                if not hasattr(self, attr):
-                    self.__dict__[attr] = getattr(self._data, attr)

-
-
-
[docs]class DatasetHDF5(Dataset):
-    """Class representing an dataset wich is defined as a HDF5 dataset of a
-    defined shape.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-    dataset_path : str
-        Relative path of the internal HDF5 dataset (the default is None).
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: str = False,
-                 root: str = None,
-                 chunks="auto",
-                 dataset_path: str = None):
-
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-        self._root_file = root
-
-        self._dataset_path = dataset_path
-
-        if root is not None:
-            if not os.path.isfile(root):
-                raise Exception("HDF5 requires a root=filename.")
-
-            self._root = os.path.dirname(root)
-
-        if dataset_path is None:
-            raise Exception("HDF5 requires a path.")
-
-
[docs]    def _lazy_load(self, xp, **kwargs):
-        """Lazy load the dataset using an CPU dask container.
-
-        Parameters
-        ----------
-        xp : type
-            Library used to load the file. It must follow numpy library.
-        **kwargs : type
-            Additional keyworkded arguments to the load.
-
-        Returns
-        -------
-        Any
-            The data (or a Future load object, for `_lazy` operations).
-
-        """
-        f = h5py.File(self._root_file)
-        data = f[self._dataset_path]
-        return da.from_array(data, chunks=self._chunks, meta=xp.array(()))

-
-
[docs]    def _load(self, xp=None, **kwargs):
-        """Load data using CPU container.
-
-        Parameters
-        ----------
-        xp : Module
-            A module that load data (implement `load` function) (placeholder).
-        **kwargs : type
-            Additional `kwargs` to `xp.load` function.
-
-        """
-        f = h5py.File(self._root_file)
-        return f[self._dataset_path]

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(np)
-        return self

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._lazy_load(cp)
-        return self

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. numpy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = self._load()
-        return self

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. cupy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data = cp.asarray(self._load())
-        return self

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-
[docs]    def _load_meta(self) -> dict:
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._root_file is not None, "There is no temporary file to inspect"
-        assert self._dataset_path is not None, "There is no path to fetch data"
-
-        return self.inspect_metadata()

-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        f = h5py.File(self._root_file)
-        data = f[self._dataset_path]
-
-        array_file_size = human_readable_size(
-            data.size, decimal=2
-        )
-
-        return {
-            "size": array_file_size,
-            "file": self._root_file,
-            "shape": data.shape,
-            "block": {"chunks": self._chunks},
-        }

-
-
-
[docs]class DatasetXarray(Dataset):
-    """Class representing an dataset wich is defined as a Xarray dataset of a
-    defined shape.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-    data_var : Any
-        Key (or index) of the internal Xarray dataset (the default is None).
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = False,
-                 root: str = None,
-                 chunks=None,
-                 data_var=None):
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-        self._root_file = root
-
-        self._data_var = data_var
-
-        if chunks and not isinstance(chunks, dict):
-            raise Exception("Chunks should be a dict.")
-
-        if root is not None:
-            if not os.path.isfile(root):
-                raise Exception("HDF5 requires a root=filename.")
-
-            self._root = os.path.dirname(root)
-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        assert self._chunks is not None, "Lazy operations require chunks"
-
-        if self._data_var:
-            self._data = xr.open_dataset(self._root_file,
-                                         chunks=self._chunks)
-        else:
-            self._data = xr.open_dataarray(self._root_file,
-                                           chunks=self._chunks)
-        self._metadata = self._load_meta()

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        assert self._chunks is not None, "Lazy operations require chunks"
-
-        if self._data_var:
-            self._data = xr.open_dataset(self._root_file,
-                                         chunks=self._chunks).as_cupy()
-        else:
-            self._data = xr.open_dataarray(self._root_file,
-                                           chunks=self._chunks).as_cupy()
-        self._metadata = self._load_meta()

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. numpy).
-
-        """
-        if self._data_var:
-            self._data = xr.open_dataset(self._root_file)
-        else:
-            self._data = xr.open_dataarray(self._root_file)
-        self._data.load()
-        self._metadata = self._load_meta()

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. cupy).
-
-        """
-        if self._data_var:
-            self._data = xr.open_dataset(self._root_file).as_cupy()
-        else:
-            self._data = xr.open_dataarray(self._root_file).as_cupy()
-        self._data.load()
-        self._metadata = self._load_meta()

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-
[docs]    def _load_meta(self) -> dict:
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._root_file is not None, "There is no temporary file to inspect"
-
-        return self.inspect_metadata()

-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        array_file_size = human_readable_size(
-            os.path.getsize(self._root_file), decimal=2
-        )
-
-        return {
-            "size": array_file_size,
-            "file": self._root_file,
-            "coords": tuple(self._data.coords),
-            "attrs": self._data.attrs,
-            "block": {"chunks": self._chunks},
-        }

-
-
[docs]    def __len__(self) -> int:
-        """Return internal data length.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        if self._data_var:
-            return len(self._data[self._data_var])
-
-        return len(self._data)

-
-
[docs]    def __getitem__(self, idx):
-        """A __getitem__() function based on internal Xarray data.
-
-        Parameters
-        ----------
-        idx : Any
-            Key of the fetched data. It can be an integer or a tuple.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        # Always slice a DataArray
-        if self._data_var:
-            return self._data[self._data_var].data[idx]
-
-        return self._data.data[idx]

-
-
-
[docs]class DatasetLabeled(Dataset):
-    """A class representing a labeled dataset. Each item is a 2-element tuple,
-    where the first element is a array of data and the second element is the
-    respective label. The items can be accessed from `dataset[x]`.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-
-    Attributes
-    ----------
-    __chunks : type
-        Description of attribute `__chunks`.
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = False,
-                 root: str = None,
-                 chunks="auto"):
-
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-
[docs]    def download(self):
-        """Download the dataset.
-
-        """
-        if hasattr(self, "_train") and hasattr(self._train, "download"):
-            self._train.download()
-
-        if hasattr(self, "_val") and hasattr(self._val, "download"):
-            self._val.download()

-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data
-        (train and labels).
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        metadata_train = self._train.inspect_metadata()
-        metadata_val = self._val.inspect_metadata()
-
-        assert (
-            metadata_train["shape"] == metadata_val["shape"]
-        ), "Train and Labels should have same shape: " + str(
-            metadata_train["shape"]
-        ) + " != " + str(
-            metadata_val["shape"]
-        )
-
-        return {"train": metadata_train, "labels": metadata_val}

-
-
[docs]    def _lazy_load(self, xp, **kwargs) -> tuple:
-        """Lazy load the dataset using an CPU dask container.
-
-        Parameters
-        ----------
-        xp : type
-            Library used to load the file. It must follow numpy library.
-        **kwargs : type
-            Additional keyworkded arguments to the load.
-
-        Returns
-        -------
-        Tuple
-            A Future object that will return a tuple: (data, label).
-
-        """
-        local_data = self._train._lazy_load(xp)
-        local_labels = self._val._lazy_load(xp)
-
-        return (local_data, local_labels)

-
-
[docs]    def _load(self, xp, **kwargs):
-        """Load data using CPU container.
-
-        Parameters
-        ----------
-        xp : Module
-            A module that load data (implement `load` function)
-        **kwargs : type
-            Additional `kwargs` to `xp.load` function.
-
-        Returns
-        -------
-        Tuple
-            A 2-element tuple: (data, label)
-
-        """
-        local_data = self._train._load(xp)
-        local_labels = self._val._load(xp)
-
-        return (local_data, local_labels)

-
-
[docs]    def _load_meta(self) -> dict:
-
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._train._root_file is not None, (
-            "There is no temporary file to inspect"
-        )
-        assert self._val._root_file is not None, (
-            "There is no temporary file to inspect"
-        )
-        assert os.path.isfile(self._train._root_file), (
-            "The root variable should be a file"
-        )
-        assert os.path.isfile(self._val._root_file), (
-            "The root variable should be a file"
-        )
-
-        return self.inspect_metadata()

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data, self._labels = self._lazy_load(cp)

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._metadata = self._load_meta()
-        self._data, self._labels = self._lazy_load(np)

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. cupy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data, self._labels = self._load(cp)

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. numpy).
-
-        """
-        self._metadata = self._load_meta()
-        self._data, self._labels = self._load(np)

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-
[docs]    def __getitem__(self, idx):
-        """A __getitem__() function for data and labeled data together.
-
-        Parameters
-        ----------
-        idx : Any
-            Key of the fetched data. It can be an integer or a tuple.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return (self._data.__getitem__(idx), self._labels.__getitem__(idx))

-
-
-
[docs]class DatasetDataFrame(Dataset):
-    """Class representing an dataset wich is defined as a dataframe.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = True,
-                 root: str = None,
-                 chunks="auto"):
-
-        Dataset.__init__(self, name, download, root)
-
-        self._chunks = chunks
-
-        self._root_file = root
-
-        if root is not None:
-            if not os.path.isfile(root):
-                raise Exception("DataFrame requires a root=filename.")
-
-            self._root = os.path.dirname(root)
-
-
[docs]    def _load_meta(self) -> dict:
-        """Load metadata to inspect.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        assert self._root_file is not None, (
-            "There is no temporary file to inspect"
-        )
-
-        return self.inspect_metadata()

-
-
[docs]    def inspect_metadata(self) -> dict:
-        """Return a dictionary with all metadata information from data.
-
-        Returns
-        -------
-        dict
-            A dictionary with metadata information.
-
-        """
-        df_file_size = human_readable_size(
-            os.stat(self._root_file).st_size, decimal=2
-        )
-
-        return {
-            "size": df_file_size,
-            "file": self._root_file,
-            "type": type(self._data),
-            "shape": self.shape,
-            "columns": list(self._data.columns),
-            "block": {"chunks": self._chunks},
-        }

-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._data = dcudf.read_csv(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._data = ddf.read_csv(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. CuDF).
-
-        """
-        self._data = cudf.read_csv(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. pandas).
-
-        """
-        self._data = pd.read_csv(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    @task_handler
-    def load(self):
-        """Placeholder for load function.
-
-        """
-        ...

-
-    @property
-    def shape(self) -> tuple:
-        """Returns the shape of an array.
-
-        Returns
-        -------
-        tuple
-            A tuple with the shape.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return self._data.shape
-
-
[docs]    def __len__(self) -> int:
-        """Return internal data length.
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return len(self._data)

-
-
[docs]    def __getitem__(self, idx):
-        """A __getitem__() function based on internal dataframe.
-
-        Parameters
-        ----------
-        idx : Any
-            Key of the fetched data. It can be an integer or a tuple.
-
-        """
-        if self._data is None:
-            raise Exception("Data is not loaded yet")
-
-        return self._data.iloc[idx]

-
-
-
[docs]class DatasetParquet(DatasetDataFrame):
-    """Class representing an dataset wich is defined as a Parquet.
-
-    Parameters
-    ----------
-    name : str
-        Symbolic name of the dataset.
-    download : bool
-        If the dataset must be downloaded (the default is False).
-    root : str
-        Root download directory (the default is None).
-    chunks : Any
-        Number of blocks of the array (the default is "auto").
-
-    """
-    def __init__(self,
-                 name: str,
-                 download: bool = True,
-                 root: str = None,
-                 chunks="auto"):
-
-        DatasetDataFrame.__init__(self, name, download, root, chunks)
-
-
[docs]    def _lazy_load_gpu(self):
-        """Load data with GPU container + DASK. (It does not load immediattly)
-
-        """
-        self._data = dcudf.read_parquet(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _lazy_load_cpu(self):
-        """Load data with CPU container + DASK. (It does not load immediattly)
-
-        """
-        self._data = ddf.read_parquet(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _load_gpu(self):
-        """Load data with GPU container (e.g. CuDF).
-
-        """
-        self._data = cudf.read_parquet(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
-
[docs]    def _load_cpu(self):
-        """Load data with CPU container (e.g. pandas).
-
-        """
-        self._data = pd.read_parquet(self._root_file)
-        self._metadata = self._load_meta()
-        return self

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
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-
-
-
- -

Source code for dasf.datasets.datasets

-#!/usr/bin/env python3
-
-from sklearn.datasets import make_blobs as make_blobs_CPU
-from dask_ml.datasets import make_blobs as make_blobs_MCPU
-
-try:
-    import cupy as cp
-
-    from cuml.datasets import make_blobs as make_blobs_GPU
-    from cuml.dask.datasets import make_blobs as make_blobs_MGPU
-except ImportError:
-    pass
-
-from sklearn.datasets import make_classification as make_classification_CPU
-from dask_ml.datasets import make_classification as make_classification_MCPU
-
-try:
-    from cuml.datasets import make_classification as make_classification_GPU
-    from cuml.dask.datasets import make_classification as make_classification_MGPU
-except ImportError:
-    pass
-
-from sklearn.datasets import make_regression as make_regression_CPU
-from dask_ml.datasets import make_regression as make_regression_MCPU
-
-try:
-    from cuml.datasets import make_regression as make_regression_GPU
-    from cuml.dask.datasets import make_regression as make_regression_MGPU
-except ImportError:
-    pass
-
-from dasf.utils.types import is_cpu_array
-from dasf.utils.funcs import is_gpu_supported
-from dasf.utils.funcs import is_dask_supported
-from dasf.utils.funcs import is_dask_gpu_supported
-
-
-
[docs]class make_blobs:
-    def __new__(cls, **kwargs):
-        instance = super().__new__(cls)
-        if kwargs is None:
-            return instance
-        else:
-            return instance(**kwargs)
-
-
[docs]    def _lazy_make_blobs_cpu(self, **kwargs):
-        return make_blobs_MCPU(**kwargs)

-
-
[docs]    def _lazy_make_blobs_gpu(self, **kwargs):
-        return make_blobs_MGPU(**kwargs)

-
-
[docs]    def _make_blobs_cpu(self, **kwargs):
-        return make_blobs_CPU(**kwargs)

-
-
[docs]    def _make_blobs_gpu(self, **kwargs):
-        return make_blobs_GPU(**kwargs)

-
-
[docs]    def __call__(self, **kwargs):
-        if is_dask_gpu_supported():
-            if "centers" in kwargs and is_cpu_array(kwargs["centers"]):
-                kwargs["centers"] = cp.asarray(kwargs["centers"])
-            return self._lazy_make_blobs_gpu(**kwargs)
-        elif is_dask_supported():
-            return self._lazy_make_blobs_cpu(**kwargs)
-        elif is_gpu_supported():
-            if "centers" in kwargs and is_cpu_array(kwargs["centers"]):
-                kwargs["centers"] = cp.asarray(kwargs["centers"])
-            return self._make_blobs_gpu(**kwargs)
-        else:
-            return self._make_blobs_cpu(**kwargs)

-
-
-
[docs]class make_classification:
-    def __new__(cls, **kwargs):
-        instance = super().__new__(cls)
-        if kwargs is None:
-            return instance
-        else:
-            return instance(**kwargs)
-
-
[docs]    def _lazy_make_classification_cpu(self, **kwargs):
-        return make_classification_MCPU(**kwargs)

-
-
[docs]    def _lazy_make_classification_gpu(self, **kwargs):
-        return make_classification_MGPU(**kwargs)

-
-
[docs]    def _make_classification_cpu(self, **kwargs):
-        return make_classification_CPU(**kwargs)

-
-
[docs]    def _make_classification_gpu(self, **kwargs):
-        return make_classification_GPU(**kwargs)

-
-
[docs]    def __call__(self, **kwargs):
-        if is_dask_gpu_supported():
-            return self._lazy_make_classification_gpu(**kwargs)
-        elif is_dask_supported():
-            return self._lazy_make_classification_cpu(**kwargs)
-        elif is_gpu_supported():
-            return self._make_classification_gpu(**kwargs)
-        else:
-            return self._make_classification_cpu(**kwargs)

-
-
-
[docs]class make_regression:
-    def __new__(cls, **kwargs):
-        instance = super().__new__(cls)
-        if kwargs is None:
-            return instance
-        else:
-            return instance(**kwargs)
-
-
[docs]    def _lazy_make_regression_cpu(self, **kwargs):
-        return make_regression_MCPU(**kwargs)

-
-
[docs]    def _lazy_make_regression_gpu(self, **kwargs):
-        return make_regression_MGPU(**kwargs)

-
-
[docs]    def _make_regression_cpu(self, **kwargs):
-        return make_regression_CPU(**kwargs)

-
-
[docs]    def _make_regression_gpu(self, **kwargs):
-        return make_regression_GPU(**kwargs)

-
-
[docs]    def __call__(self, **kwargs):
-        if is_dask_gpu_supported():
-            return self._lazy_make_regression_gpu(**kwargs)
-        elif is_dask_supported():
-            return self._lazy_make_regression_cpu(**kwargs)
-        elif is_gpu_supported():
-            return self._make_regression_gpu(**kwargs)
-        else:
-            return self._make_regression_cpu(**kwargs)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/datasets/download.html b/docs/_modules/dasf/datasets/download.html deleted file mode 100644 index 3c2b267..0000000 --- a/docs/_modules/dasf/datasets/download.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - - - dasf.datasets.download — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.datasets.download

-#!/usr/bin/env python3
-
-from dasf.utils.funcs import download_file
-from dasf.utils.funcs import download_file_from_gdrive
-from dasf.datasets.base import Dataset
-
-
-
[docs]class DownloadWget(Dataset):
-    """Dataset downloadable via wget.
-
-    Parameters
-    ----------
-    url : str
-        The url to fetch the resource.
-    filename : str
-        Name of the file.
-    root : str
-        Directory to store the downloaded file.
-    download : bool
-        If it the dataset must be downloaded (the default is True).
-
-    """
-    def __init__(self,
-                 url: str,
-                 filename: str,
-                 root: str,
-                 download: bool = True):
-        self.__url = url
-        self.__filename = filename
-
-        # Set download as false because this class overrides download()
-        Dataset.__init__(self, name="Download Wget", download=download, root=root)
-
-
[docs]    def download(self):
-        """Download the dataset.
-
-        """
-        if not self._download or self.__url is None:
-            return
-
-        if hasattr(self, "download") and self._download is True:
-            self._root_file = download_file(
-                self.__url, self.__filename, self._root
-            )

-
-
-
[docs]class DownloadGDrive(Dataset):
-    """Dataset downloadable via Google Drive.
-
-    Parameters
-    ----------
-    google_file_id : str
-        Id of the google drive resource.
-    filename : str
-        Name of the file.
-    root : str
-        Directory to store the downloaded file.
-    download : bool
-        If it the dataset must be downloaded (the default is True).
-
-    """
-    def __init__(self,
-                 google_file_id: str,
-                 filename: str,
-                 root: str,
-                 download: bool = True):
-        self.__google_file_id = google_file_id
-        self.__filename = filename
-
-        # Set download as false because this class overrides download()
-        Dataset.__init__(
-            self, name="Download Google Drive", download=download, root=root
-        )
-
-
[docs]    def download(self):
-        """Download the dataset.
-
-        """
-        if not self._download or self.__google_file_id is None:
-            return
-
-        if hasattr(self, "download") and self._download is True:
-            self._root_file = download_file_from_gdrive(
-                self.__google_file_id, self.__filename, self._root
-            )

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/debug/debug.html b/docs/_modules/dasf/debug/debug.html deleted file mode 100644 index 038bde3..0000000 --- a/docs/_modules/dasf/debug/debug.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - - - dasf.debug.debug — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.debug.debug

-#!/usr/bin/env python3
-
-from IPython.core.display import HTML as iHTML
-from IPython.core.display import display as idisplay
-
-from dasf.utils.types import is_dask_array
-from dasf.utils.types import is_dask_dataframe
-
-
-
[docs]class Debug:
-    """Print information about an operator (shape, datatype, etc.), and return
-    the self object reference.
-
-    Parameters
-    ----------
-    name : str
-        Name of the operator.
-    **kwargs : type
-        Additional keyworkded arguments to `Operator`.
-
-    """
-
[docs]    def display(self, X):
-        if hasattr(X, "shape"):
-            print("Datashape is:", X.shape)
-
-        if is_dask_array(X) or is_dask_dataframe(X):
-            idisplay(iHTML(X._repr_html_()))
-        else:
-            print("Datatype is:", type(X))
-            print("Data content is:", X)
-
-        return X

-
-
-
[docs]class VisualizeDaskData:
-    """Visualize DASK data from an operator.
-
-    Parameters
-    ----------
-    filename : str
-        A path to save the DASK visualization (the default is None).
-    **kwargs : type
-        Additional keyworkded arguments to `Operator`.
-
-    """
-    def __init__(self, filename: str = None):
-        self.filename = filename
-
-
[docs]    def display(self, X):
-        if not is_dask_array(X) and not is_dask_dataframe(X):
-            self.logger.warning("This is not a Dask element.")
-            return X
-
-        if self.filename is not None:
-            X.visualize(self.filename)
-        else:
-            X.visualize()
-
-        return X

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/feature_extraction/histogram.html b/docs/_modules/dasf/feature_extraction/histogram.html deleted file mode 100644 index c33e4d1..0000000 --- a/docs/_modules/dasf/feature_extraction/histogram.html +++ /dev/null @@ -1,301 +0,0 @@ - - - - - - - - - - dasf.feature_extraction.histogram — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.feature_extraction.histogram

-#!/usr/bin/env python3
-
-import numpy as np
-import dask.array as da
-
-try:
-    import cupy as cp
-except ImportError:
-    pass
-
-from dasf.transforms.base import Transform
-from dasf.transforms.base import TargeteredTransform
-
-
-
[docs]class Histogram(TargeteredTransform, Transform):
-    """Operator to extract the histogram of a data.
-
-    Parameters
-    ----------
-    bins : Optional[int]
-        Number of bins (the default is None).
-    range : tuple
-        2-element tuple with the lower and upper range of the bins. If not
-        provided, range is simply (X.min(), X.max()) (the default is None).
-    normed : bool
-        If the historgram must be normalized (the default is False).
-    weights : type
-        An array of weights, of the same shape as X. Each value in a only
-        contributes its associated weight towards the bin count
-        (the default is None).
-    density : type
-        If False, the result will contain the number of samples in each bin.
-        If True, the result is the value of the probability density function
-        at the bin, normalized such that the integral over the range is 1
-        (the default is None).
-
-    Attributes
-    ----------
-    bins
-    range
-    normed
-    weights
-    density
-
-    """
-    def __init__(self,
-                 bins: int = None,
-                 range: tuple = None,
-                 normed: bool = False,
-                 weights=None,
-                 density=None,
-                 *args,
-                 **kwargs):
-        TargeteredTransform.__init__(self, *args, **kwargs)
-
-        self._bins = bins
-        self._range = range
-        self._normed = normed
-        self._weights = weights
-        self._density = density
-
-    def __lazy_transform_generic(self, X):
-        return da.histogram(
-            X,
-            bins=self._bins,
-            range=self._range,
-            normed=self._normed,
-            weights=self._weights,
-            density=self._density,
-        )
-
-    def __transform_generic(self, X, xp):
-        return xp.histogram(
-            X,
-            bins=self._bins,
-            range=self._range,
-            normed=self._normed,
-            weights=self._weights,
-            density=self._density,
-        )
-
-
[docs]    def _lazy_transform_cpu(self, X):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _lazy_transform_gpu(self, X, **kwargs):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _transform_cpu(self, X, **kwargs):
-        return self.__transform_generic(X, np)

-
-
[docs]    def _transform_gpu(self, X, **kwargs):
-        return self.__transform_generic(X, cp)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
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- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/feature_extraction/transform.html b/docs/_modules/dasf/feature_extraction/transform.html deleted file mode 100644 index 8b9e62c..0000000 --- a/docs/_modules/dasf/feature_extraction/transform.html +++ /dev/null @@ -1,375 +0,0 @@ - - - - - - - - - - dasf.feature_extraction.transform — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.feature_extraction.transform

-#!/usr/bin/env python3
-
-import numpy as np
-
-try:
-    import cupy as cp
-except ImportError:
-    pass
-
-from dasf.utils.types import is_array
-from dasf.utils.types import is_dataframe
-from dasf.transforms.base import Transform, Fit
-
-
-
[docs]class ConcatenateToArray(Transform):
-    """Concatenate data from different Arrays into a single array.
-
-    Parameters
-    ----------
-    flatten : bool
-        If the arrays must be flatten prior concatenating. If `False`, the
-        arrays must share the shape of last dimansions in order to be
-        concatenated (the default is False).
-
-    """
-    def __init__(self, flatten: bool = False):
-        self.flatten = flatten
-
-    def __transform_generic(self, xp, **kwargs):
-        datas = None
-        for key in kwargs:
-            if datas is None:
-                if self.flatten:
-                    flat = kwargs[key].flatten()
-                    datas = xp.asarray([flat])
-                else:
-                    data = xp.asarray(kwargs[key])
-                    datas = xp.expand_dim(data, axis=len(data.shape))
-            else:
-                if self.flatten:
-                    flat = kwargs[key].flatten()
-                    datas = xp.append(datas, xp.asarray([flat]),
-                                      axis=0)
-                else:
-                    data = xp.asarray(kwargs[key])
-                    datas = xp.append(datas, data, axis=len(data.shape))
-
-        if self.flatten:
-            data = xp.transpose(datas)
-        else:
-            data = datas
-
-        return data
-#        return data.rechunk({1: data.shape[1]})
-
-
[docs]    def _transform_cpu(self, **kwargs):
-        return self.__transform_generic(np, **kwargs)

-
-
[docs]    def _transform_gpu(self, **kwargs):
-        return self.__transform_generic(cp, **kwargs)

-
-
-
[docs]class SampleDataframe:
-    """Return a subset with random samples of the original dataset.
-
-    Parameters
-    ----------
-    percent : float
-        Percentage of the samples to get from the dataset.
-
-    """
-    def __init__(self, percent: float):
-        self.__percent = float(percent / 100.0)
-
-
[docs]    def run(self, X):
-        """Returns a subset with random samples from the dataset `X`.
-
-        Parameters
-        ----------
-        X : Any
-            The dataset.
-
-        Returns
-        -------
-        Any
-            The sampled subset.
-
-        """
-        return X.sample(n=int(len(X) * self.__percent))

-
-
-
[docs]class GetSubeCubeArray:
-    """Get a subcube with x% of samples from the original one.
-
-    Parameters
-    ----------
-    percent : float
-        Percentage of the samples to get from the cube.
-
-    """
-    def __init__(self, percent: float):
-        self.__percent = float(percent / 100.0)
-
-        assert (
-            self.__percent > 0 and self.__percent <= 1.0
-        ), "Percent must be in [0,1] range."
-
-
-
[docs]    def transform(self, X):
-        i_num, x_num, t_num = X.shape
-
-        i_start_idx = int((i_num - (i_num * self.__percent)) / 2)
-        i_end_idx = int(i_start_idx + (self.__percent * i_num))
-
-        x_start_idx = int((x_num - (x_num * self.__percent)) / 2)
-        x_end_idx = int(x_start_idx + (self.__percent * x_num))
-
-        t_start_idx = int((t_num - (t_num * self.__percent)) / 2)
-        t_end_idx = int(t_start_idx + (self.__percent * t_num))
-
-        return X[i_start_idx:i_end_idx,
-                 x_start_idx:x_end_idx,
-                 t_start_idx:t_end_idx]

-
-
-
[docs]class SliceDataframe(Fit):
-    """Get a slice of a cube. An inline slice is a section over the x-axis.
-
-    Parameters
-    ----------
-    iline_index : int
-        The index of the inline to get.
-
-    """
-    def __init__(self, iline_index: int):
-        self.iline_index = iline_index
-
-
[docs]    def fit(self, X, y):
-        cube_shape = y.shape
-
-        if is_array(X):
-            slice_array = X
-        elif is_dataframe(X):
-            slice_array = X.values
-        else:
-            raise ValueError("X is not a known datatype.")
-
-        return slice_array.reshape(cube_shape)

-
-
-
[docs]class GetSubDataframe:
-    """Get the first x% samples from the dataset.
-
-    Parameters
-    ----------
-    percent : float
-        Percentage of the samples to get from the dataframe.
-
-    """
-    def __init__(self, percent: float):
-        self.__percent = float(percent / 100.0)
-
-
[docs]    def transform(self, X):
-        new_size = int(len(X) * self.__percent)
-
-        return X.iloc[0:new_size]

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/agglomerative.html b/docs/_modules/dasf/ml/cluster/agglomerative.html deleted file mode 100644 index 6a53a0a..0000000 --- a/docs/_modules/dasf/ml/cluster/agglomerative.html +++ /dev/null @@ -1,394 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.agglomerative — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.cluster.agglomerative

-#!/usr/bin/env python3
-
-from sklearn.cluster import (
-    AgglomerativeClustering as AgglomerativeClustering_CPU,
-)  # noqa
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-from dasf.utils.funcs import is_gpu_supported
-
-try:
-    from cuml import AgglomerativeClustering as AgglomerativeClustering_GPU
-except ImportError:
-    pass
-
-
-
[docs]class AgglomerativeClustering(ClusterClassifier):
-    """
-
-    Agglomerative Clustering
-
-    Recursively merges the pair of clusters that minimally increases
-    a given linkage distance.
-
-    Read more in the :ref:`User Guide <hierarchical_clustering>`.
-
-    Parameters
-    ----------
-    n_clusters : int or None, default=2
-        The number of clusters to find. It must be ``None`` if
-        ``distance_threshold`` is not ``None``.
-
-    affinity : str or callable, default='euclidean'
-        Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
-        "manhattan", "cosine", or "precomputed".
-        If linkage is "ward", only "euclidean" is accepted.
-        If "precomputed", a distance matrix (instead of a similarity matrix)
-        is needed as input for the fit method.
-
-    memory : str or object with the joblib.Memory interface, default=None
-        Used to cache the output of the computation of the tree.
-        By default, no caching is done. If a string is given, it is the
-        path to the caching directory.
-
-    connectivity : array-like or callable, default=None
-        Connectivity matrix. Defines for each sample the neighboring
-        samples following a given structure of the data.
-        This can be a connectivity matrix itself or a callable that transforms
-        the data into a connectivity matrix, such as derived from
-        kneighbors_graph. Default is ``None``, i.e, the
-        hierarchical clustering algorithm is unstructured.
-
-    compute_full_tree : 'auto' or bool, default='auto'
-        Stop early the construction of the tree at ``n_clusters``. This is
-        useful to decrease computation time if the number of clusters is not
-        small compared to the number of samples. This option is useful only
-        when specifying a connectivity matrix. Note also that when varying the
-        number of clusters and using caching, it may be advantageous to compute
-        the full tree. It must be ``True`` if ``distance_threshold`` is not
-        ``None``. By default `compute_full_tree` is "auto", which is equivalent
-        to `True` when `distance_threshold` is not `None` or that `n_clusters`
-        is inferior to the maximum between 100 or `0.02 * n_samples`.
-        Otherwise, "auto" is equivalent to `False`.
-
-    linkage : {'ward', 'complete', 'average', 'single'}, default='ward'
-        Which linkage criterion to use. The linkage criterion determines which
-        distance to use between sets of observation. The algorithm will merge
-        the pairs of cluster that minimize this criterion.
-
-        - 'ward' minimizes the variance of the clusters being merged.
-        - 'average' uses the average of the distances of each observation of
-          the two sets.
-        - 'complete' or 'maximum' linkage uses the maximum distances between
-          all observations of the two sets.
-        - 'single' uses the minimum of the distances between all observations
-          of the two sets.
-
-        .. versionadded:: 0.20
-            Added the 'single' option
-
-    distance_threshold : float, default=None
-        The linkage distance threshold above which, clusters will not be
-        merged. If not ``None``, ``n_clusters`` must be ``None`` and
-        ``compute_full_tree`` must be ``True``.
-
-        .. versionadded:: 0.21
-
-    compute_distances : bool, default=False
-        Computes distances between clusters even if `distance_threshold` is not
-        used. This can be used to make dendrogram visualization, but introduces
-        a computational and memory overhead.
-
-        .. versionadded:: 0.24
-
-    n_neighbors : int, default = 15
-        The number of neighbors to compute when connectivity = "knn"
-
-    output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
-        Variable to control output type of the results and attributes of the
-        estimator. If None, it'll inherit the output type set at the module
-        level, cuml.global_settings.output_type. See Output Data Type
-        Configuration for more info.
-
-    Examples
-    --------
-    >>> from dasf.ml.cluster import AgglomerativeClustering
-    >>> import numpy as np
-    >>> X = np.array([[1, 2], [1, 4], [1, 0],
-    ...               [4, 2], [4, 4], [4, 0]])
-    >>> clustering = AgglomerativeClustering().fit(X)
-    >>> clustering
-    AgglomerativeClustering()
-
-    For further informations see:
-    - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html
-    - https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering
-
-    """
-    def __init__(
-        self,
-        n_clusters=2,
-        affinity="euclidean",
-        connectivity=None,
-        linkage="single",
-        memory=None,
-        compute_full_tree="auto",
-        distance_threshold=None,
-        compute_distances=False,
-        handle=None,
-        verbose=False,
-        n_neighbors=10,
-        output_type=None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.n_clusters = n_clusters
-        self.affinity = affinity
-        self.connectivity = connectivity
-        self.linkage = linkage
-        self.memory = memory
-        self.compute_full_tree = compute_full_tree
-        self.distance_threshold = distance_threshold
-        self.compute_distances = compute_distances
-        self.handle = handle
-        self.verbose = verbose
-        self.n_neighbors = n_neighbors
-        self.output_type = output_type
-
-        self.__agg_cluster_cpu = AgglomerativeClustering_CPU(
-            n_clusters=n_clusters,
-            affinity=affinity,
-            memory=memory,
-            connectivity=connectivity,
-            compute_full_tree=compute_full_tree,
-            linkage=linkage,
-            distance_threshold=distance_threshold,
-            compute_distances=compute_distances,
-        )
-
-        if is_gpu_supported():
-            if connectivity is None:
-                connectivity = "knn"
-
-            self.__agg_cluster_gpu = AgglomerativeClustering_GPU(
-                n_clusters=n_clusters,
-                affinity=affinity,
-                linkage=linkage,
-                handle=handle,
-                verbose=verbose,
-                connectivity=connectivity,
-                n_neighbors=n_neighbors,
-                output_type=output_type,
-            )
-
-
[docs]    def _fit_cpu(self, X, y=None, convert_dtype=True):
-        return self.__agg_cluster_cpu.fit(X, y)

-
-
[docs]    def _fit_gpu(self, X, y=None, convert_dtype=True):
-        return self.__agg_cluster_gpu.fit(X, y, convert_dtype=convert_dtype)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None):
-        return self.__agg_cluster_cpu.fit_predict(X, y)

-
-
[docs]    def _fit_predict_gpu(self, X, y=None):
-        return self.__agg_cluster_gpu.fit_predict(X, y)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/classifier.html b/docs/_modules/dasf/ml/cluster/classifier.html deleted file mode 100644 index 98f4009..0000000 --- a/docs/_modules/dasf/ml/cluster/classifier.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.classifier — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.cluster.classifier

-#!/usr/bin/env python3
-
-from dasf.transforms.base import Fit
-from dasf.transforms.base import FitPredict
-from dasf.transforms.base import FitTransform
-from dasf.transforms.base import Predict
-from dasf.transforms.base import Transform
-from dasf.transforms.base import TargeteredTransform
-from dasf.transforms.base import GetParams
-from dasf.transforms.base import SetParams
-
-
-
[docs]class ClusterClassifier(
-    Fit, FitPredict, FitTransform, Predict,
-    Transform, GetParams, SetParams,
-    TargeteredTransform
-):
-    def __init__(self, **kwargs):
-        TargeteredTransform.__init__(self, **kwargs)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
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- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/dbscan.html b/docs/_modules/dasf/ml/cluster/dbscan.html deleted file mode 100644 index d837d4c..0000000 --- a/docs/_modules/dasf/ml/cluster/dbscan.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.dbscan — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.cluster.dbscan

-#!/usr/bin/env python3
-
-from sklearn.cluster import DBSCAN as DBSCAN_CPU
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-from dasf.utils.funcs import is_gpu_supported
-
-try:
-    from cuml.cluster import DBSCAN as DBSCAN_GPU
-    from cuml.dask.cluster import DBSCAN as DBSCAN_MGPU
-except ImportError:
-    pass
-
-
-
[docs]class DBSCAN(ClusterClassifier):
-    """
-    Perform DBSCAN clustering from vector array or distance matrix.
-
-    DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
-    Finds core samples of high density and expands clusters from them.
-    Good for data which contains clusters of similar density.
-
-    Read more in the :ref:`User Guide <dbscan>`.
-
-    Parameters
-    ----------
-    eps : float, default=0.5
-        The maximum distance between two samples for one to be considered
-        as in the neighborhood of the other. This is not a maximum bound
-        on the distances of points within a cluster. This is the most
-        important DBSCAN parameter to choose appropriately for your data set
-        and distance function.
-
-    min_samples : int, default=5
-        The number of samples (or total weight) in a neighborhood for a point
-        to be considered as a core point. This includes the point itself.
-
-    metric : string, or callable, default='euclidean'
-        The metric to use when calculating distance between instances in a
-        feature array. If metric is a string or callable, it must be one of
-        the options allowed by :func:`sklearn.metrics.pairwise_distances` for
-        its metric parameter.
-        If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square. X may be a :term:`Glossary <sparse graph>`, in which
-        case only "nonzero" elements may be considered neighbors for DBSCAN.
-
-        .. versionadded:: 0.17
-           metric *precomputed* to accept precomputed sparse matrix.
-
-    metric_params : dict, default=None
-        Additional keyword arguments for the metric function.
-
-        .. versionadded:: 0.19
-
-    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
-        The algorithm to be used by the NearestNeighbors module
-        to compute pointwise distances and find nearest neighbors.
-        See NearestNeighbors module documentation for details.
-
-    leaf_size : int, default=30
-        Leaf size passed to BallTree or cKDTree. This can affect the speed
-        of the construction and query, as well as the memory required
-        to store the tree. The optimal value depends
-        on the nature of the problem.
-
-    p : float, default=None
-        The power of the Minkowski metric to be used to calculate distance
-        between points. If None, then ``p=2`` (equivalent to the Euclidean
-        distance).
-
-    n_jobs : int, default=None
-        The number of parallel jobs to run.
-        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
-        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
-        for more details.
-
-    output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
-        Variable to control output type of the results and attributes of the
-        estimator. If None, it'll inherit the output type set at the module
-        level, cuml.global_settings.output_type. See Output Data Type
-        Configuration for more info.
-
-    calc_core_sample_indices(optional) : boolean, default = True
-        Indicates whether the indices of the core samples should be calculated.
-        The the attribute `core_sample_indices_` will not be used, setting this
-        to False will avoid unnecessary kernel launches.
-
-
-    Examples
-    --------
-    >>> from dasf.ml.cluster import DBSCAN
-    >>> import numpy as np
-    >>> X = np.array([[1, 2], [2, 2], [2, 3],
-    ...               [8, 7], [8, 8], [25, 80]])
-    >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
-    >>> clustering
-    DBSCAN(eps=3, min_samples=2)
-
-    For further informations see:
-    - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN
-    - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan
-    - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering
-
-    See Also
-    --------
-    OPTICS : A similar clustering at multiple values of eps. Our implementation
-        is optimized for memory usage.
-
-    References
-    ----------
-    Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
-    Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
-    In: Proceedings of the 2nd International Conference on Knowledge Discovery
-    and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
-
-    Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
-    DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
-    ACM Transactions on Database Systems (TODS), 42(3), 19.
-
-    """
-    def __init__(
-        self,
-        eps=0.5,
-        leaf_size=40,
-        metric="euclidean",
-        min_samples=5,
-        p=None,
-        output_type=None,
-        calc_core_sample_indices=True,
-        verbose=False,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.eps = eps
-        self.leaf_size = leaf_size
-        self.metric = metric
-        self.min_samples = min_samples
-        self.p = p
-        self.output_type = output_type
-        self.calc_core_sample_indices = calc_core_sample_indices
-        self.verbose = verbose
-
-        self.__dbscan_cpu = DBSCAN_CPU(
-            eps=self.eps,
-            leaf_size=self.leaf_size,
-            metric=self.metric,
-            min_samples=self.min_samples,
-            p=self.p,
-        )
-
-        if is_gpu_supported():
-            self.__dbscan_gpu = DBSCAN_GPU(
-                min_samples=self.min_samples,
-                output_type=output_type,
-                calc_core_sample_indices=calc_core_sample_indices,
-            )
-
-            try:
-                self.__dbscan_mgpu = DBSCAN_MGPU(
-                    min_samples=self.min_samples,
-                    output_type=output_type,
-                    calc_core_sample_indices=calc_core_sample_indices,
-                )
-            except ValueError:
-                self.__dbscan_mgpu = None
-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, out_dtype="int32"):
-        if self.__dbscan_mgpu is None:
-            raise NotImplementedError
-        return self.__dbscan_mgpu.fit(X=X, out_dtype=out_dtype)

-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weight=None):
-        return self.__dbscan_cpu.fit(X=X, y=y, sample_weight=sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y=None, out_dtype="int32"):
-        return self.__dbscan_gpu.fit(X=X, out_dtype=out_dtype)

-
-
[docs]    def _lazy_fit_predict_gpu(self, X, y=None, out_dtype="int32"):
-        if self.__dbscan_mgpu is None:
-            raise NotImplementedError
-        return self.__dbscan_mgpu.fit_predict(X=X, out_dtype=out_dtype)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None, sample_weight=None):
-        return self.__dbscan_cpu.fit_predict(X=X, y=y, sample_weight=sample_weight)

-
-
[docs]    def _fit_predict_gpu(self, X, y=None, out_dtype="int32"):
-        return self.__dbscan_gpu.fit_predict(X=X, out_dtype=out_dtype)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/hdbscan.html b/docs/_modules/dasf/ml/cluster/hdbscan.html deleted file mode 100644 index 4e2e942..0000000 --- a/docs/_modules/dasf/ml/cluster/hdbscan.html +++ /dev/null @@ -1,474 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.hdbscan — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.cluster.hdbscan

-#!/usr/bin/env python3
-
-from hdbscan import HDBSCAN as HDBSCAN_CPU
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-from dasf.utils.funcs import is_gpu_supported
-
-try:
-    from cuml.cluster import HDBSCAN as HDBSCAN_GPU
-except ImportError:
-    pass
-
-
-
[docs]class HDBSCAN(ClusterClassifier):
-    """
-    Perform HDBSCAN clustering from vector array or distance matrix.
-
-    HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications
-    with Noise. Performs DBSCAN over varying epsilon values and integrates
-    the result to find a clustering that gives the best stability over epsilon.
-    This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN),
-    and be more robust to parameter selection.
-
-    Parameters
-    ----------
-    min_cluster_size : int, optional (default=5)
-        The minimum size of clusters; single linkage splits that contain
-        fewer points than this will be considered points "falling out" of a
-        cluster rather than a cluster splitting into two new clusters.
-
-    min_samples : int, optional (default=None)
-        The number of samples in a neighbourhood for a point to be
-        considered a core point.
-
-    metric : string, or callable, optional (default='euclidean')
-        The metric to use when calculating distance between instances in a
-        feature array. If metric is a string or callable, it must be one of
-        the options allowed by metrics.pairwise.pairwise_distances for its
-        metric parameter.
-        If metric is "precomputed", X is assumed to be a distance matrix and
-        must be square.
-
-    p : int, optional (default=None)
-        p value to use if using the minkowski metric.
-
-    alpha : float, optional (default=1.0)
-        A distance scaling parameter as used in robust single linkage.
-        See [3]_ for more information.
-
-    cluster_selection_epsilon: float, optional (default=0.0)
-                A distance threshold. Clusters below this value will be merged.
-        See [5]_ for more information.
-
-    algorithm : string, optional (default='best')
-        Exactly which algorithm to use; hdbscan has variants specialised
-        for different characteristics of the data. By default this is set
-        to ``best`` which chooses the "best" algorithm given the nature of
-        the data. You can force other options if you believe you know
-        better. Options are:
-            * ``best``
-            * ``generic``
-            * ``prims_kdtree``
-            * ``prims_balltree``
-            * ``boruvka_kdtree``
-            * ``boruvka_balltree``
-
-    leaf_size: int, optional (default=40)
-        If using a space tree algorithm (kdtree, or balltree) the number
-        of points ina leaf node of the tree. This does not alter the
-        resulting clustering, but may have an effect on the runtime
-        of the algorithm.
-
-    memory : Instance of joblib.Memory or string (optional)
-        Used to cache the output of the computation of the tree.
-        By default, no caching is done. If a string is given, it is the
-        path to the caching directory.
-
-    approx_min_span_tree : bool, optional (default=True)
-        Whether to accept an only approximate minimum spanning tree.
-        For some algorithms this can provide a significant speedup, but
-        the resulting clustering may be of marginally lower quality.
-        If you are willing to sacrifice speed for correctness you may want
-        to explore this; in general this should be left at the default True.
-
-    gen_min_span_tree: bool, optional (default=False)
-        Whether to generate the minimum spanning tree with regard
-        to mutual reachability distance for later analysis.
-
-    core_dist_n_jobs : int, optional (default=4)
-        Number of parallel jobs to run in core distance computations (if
-        supported by the specific algorithm). For ``core_dist_n_jobs``
-        below -1, (n_cpus + 1 + core_dist_n_jobs) are used.
-
-    cluster_selection_method : string, optional (default='eom')
-        The method used to select clusters from the condensed tree. The
-        standard approach for HDBSCAN* is to use an Excess of Mass algorithm
-        to find the most persistent clusters. Alternatively you can instead
-        select the clusters at the leaves of the tree -- this provides the
-        most fine grained and homogeneous clusters. Options are:
-            * ``eom``
-            * ``leaf``
-
-    allow_single_cluster : bool, optional (default=False)
-        By default HDBSCAN* will not produce a single cluster, setting this
-        to True will override this and allow single cluster results in
-        the case that you feel this is a valid result for your dataset.
-
-    prediction_data : boolean, optional
-        Whether to generate extra cached data for predicting labels or
-        membership vectors few new unseen points later. If you wish to
-        persist the clustering object for later re-use you probably want
-        to set this to True.
-        (default False)
-
-    match_reference_implementation : bool, optional (default=False)
-        There exist some interpretational differences between this
-        HDBSCAN* implementation and the original authors reference
-        implementation in Java. This can result in very minor differences
-        in clustering results. Setting this flag to True will, at a some
-        performance cost, ensure that the clustering results match the
-        reference implementation.
-
-    connectivity : {'pairwise', 'knn'}, default='knn'
-        The type of connectivity matrix to compute.
-            * 'pairwise' will compute the entire fully-connected graph of
-            pairwise distances between each set of points. This is the fastest
-            to compute and can be very fast for smaller datasets but requires
-            O(n^2) space.
-
-            * 'knn' will sparsify the fully-connected connectivity matrix to
-            save memory and enable much larger inputs. "n_neighbors” will
-            control the amount of memory used and the graph will be connected
-            automatically in the event "n_neighbors” was not large enough to
-            connect it.
-
-    output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
-        Variable to control output type of the results and attributes of the
-        estimator. If None, it'll inherit the output type set at the module
-        level, cuml.global_settings.output_type. See Output Data Type
-        Configuration for more info.
-
-    Examples
-    --------
-    >>> from dasf.ml.cluster import HDBSCAN
-    >>> import numpy as np
-    >>> X = np.array([[1, 2], [2, 2], [2, 3],
-    ...               [8, 7], [8, 8], [25, 80]])
-    >>> clustering = HDBSCAN(min_cluster_size=30, min_samples=2).fit(X)
-    >>> clustering
-    HDBSCAN(min_cluster_size=30, min_samples=2)
-
-    For further informations see:
-    - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN
-    - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan
-    - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering
-
-    References
-    ----------
-
-    .. [1] Campello, R. J., Moulavi, D., & Sander, J. (2013, April).
-       Density-based clustering based on hierarchical density estimates.
-       In Pacific-Asia Conference on Knowledge Discovery and Data Mining
-       (pp. 160-172). Springer Berlin Heidelberg.
-
-    .. [2] Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015).
-       Hierarchical density estimates for data clustering, visualization,
-       and outlier detection. ACM Transactions on Knowledge Discovery
-       from Data (TKDD), 10(1), 5.
-
-    .. [3] Chaudhuri, K., & Dasgupta, S. (2010). Rates of convergence for the
-       cluster tree. In Advances in Neural Information Processing Systems
-       (pp. 343-351).
-
-    .. [4] Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and
-       Sander, J., 2014. Density-Based Clustering Validation. In SDM
-       (pp. 839-847).
-
-    .. [5] Malzer, C., & Baum, M. (2019). A Hybrid Approach To Hierarchical
-           Density-based Cluster Selection. arxiv preprint 1911.02282.
-
-    """
-    def __init__(
-        self,
-        alpha=1.0,
-        gen_min_span_tree=False,
-        leaf_size=40,
-        metric="euclidean",
-        min_cluster_size=5,
-        min_samples=None,
-        p=None,
-        algorithm='best',
-        approx_min_span_tree=True,
-        core_dist_n_jobs=4,
-        cluster_selection_method='eom',
-        allow_single_cluster=False,
-        prediction_data=False,
-        match_reference_implementation=False,
-        connectivity='knn',
-        output_type=None,
-        verbose=0,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.alpha = alpha
-        self.gen_min_span_tree = gen_min_span_tree
-        self.leaf_size = leaf_size
-        self.metric = metric
-        self.min_cluster_size = min_cluster_size
-        self.min_samples = min_samples
-        self.p = p
-        self.algorithm = algorithm
-        self.approx_min_span_tree = approx_min_span_tree
-        self.core_dist_n_jobs = core_dist_n_jobs
-        self.cluster_selection_method = cluster_selection_method
-        self.allow_single_cluster = allow_single_cluster
-        self.prediction_data = prediction_data
-        self.match_reference_implementation = match_reference_implementation
-        self.connectivity = connectivity
-        self.output_type = output_type
-        self.verbose = verbose
-
-        self.__hdbscan_cpu = HDBSCAN_CPU(
-            alpha=alpha,
-            gen_min_span_tree=gen_min_span_tree,
-            leaf_size=leaf_size,
-            metric=metric,
-            min_cluster_size=min_cluster_size,
-            min_samples=min_samples,
-            p=p,
-            algorithm=algorithm,
-            approx_min_span_tree=approx_min_span_tree,
-            core_dist_n_jobs=core_dist_n_jobs,
-            cluster_selection_method=cluster_selection_method,
-            allow_single_cluster=allow_single_cluster,
-            prediction_data=prediction_data,
-            match_reference_implementation=match_reference_implementation
-        )
-
-        if is_gpu_supported():
-            self.__hdbscan_gpu = HDBSCAN_GPU(
-                alpha=alpha,
-                gen_min_span_tree=gen_min_span_tree,
-                metric=metric,
-                min_cluster_size=min_cluster_size,
-                min_samples=min_samples,
-                p=p,
-                cluster_selection_method=cluster_selection_method,
-                allow_single_cluster=allow_single_cluster,
-                verbose=verbose,
-                connectivity=connectivity,
-                output_type=output_type
-            )
-
-
[docs]    def _fit_cpu(self, X, y=None):
-        return self.__hdbscan_cpu.fit(X=X, y=y)

-
-
[docs]    def _fit_gpu(self, X, y=None, convert_dtype=True):
-        return self.__hdbscan_gpu.fit(X=X, y=y, convert_dtype=convert_dtype)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None):
-        return self.__hdbscan_cpu.fit_predict(X=X, y=y)

-
-
[docs]    def _fit_predict_gpu(self, X, y=None):
-        return self.__hdbscan_gpu.fit_predict(X=X, y=y)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/kmeans.html b/docs/_modules/dasf/ml/cluster/kmeans.html deleted file mode 100644 index a8edef8..0000000 --- a/docs/_modules/dasf/ml/cluster/kmeans.html +++ /dev/null @@ -1,866 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.kmeans — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.cluster.kmeans

-#!/usr/bin/env python3
-
-from sklearn.cluster import KMeans as KMeans_CPU
-from dask_ml.cluster import KMeans as KMeans_MCPU
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-from dasf.utils.funcs import is_gpu_supported
-from dasf.utils.decorators import task_handler
-
-try:
-    from cuml.cluster import KMeans as KMeans_GPU
-    from cuml.dask.cluster import KMeans as KMeans_MGPU
-except ImportError:
-    pass
-
-
-
[docs]class KMeans(ClusterClassifier):
-    """
-    K-Means clustering.
-
-    Read more in the :ref:`User Guide <k_means>`.
-
-    Parameters
-    ----------
-
-    n_clusters : int, default=8
-        The number of clusters to form as well as the number of
-        centroids to generate.
-
-    init : {'k-means++', 'random'}, callable or array-like of shape (n_clusters, n_features), default='k-means++'
-
-        Method for initialization:
-
-        'k-means++' : selects initial cluster centers for k-mean
-        clustering in a smart way to speed up convergence. See section
-        Notes in k_init for more details.
-
-        'random': choose `n_clusters` observations (rows) at random from data
-        for the initial centroids.
-
-        If an array is passed, it should be of shape (n_clusters, n_features)
-        and gives the initial centers.
-
-        If a callable is passed, it should take arguments X, n_clusters and a
-        random state and return an initialization.
-
-    n_init : int, default=10
-        Number of time the k-means algorithm will be run with different
-        centroid seeds. The final results will be the best output of
-        n_init consecutive runs in terms of inertia.
-
-    max_iter : int, default=300
-        Maximum number of iterations of the k-means algorithm for a
-        single run.
-
-    tol : float, default=1e-4
-        Relative tolerance with regards to Frobenius norm of the difference
-        in the cluster centers of two consecutive iterations to declare
-        convergence.
-
-    precompute_distances : {'auto', True, False}, default='auto'
-        Precompute distances (faster but takes more memory).
-
-        'auto' : do not precompute distances if n_samples * n_clusters > 12
-        million. This corresponds to about 100MB overhead per job using
-        double precision. IMPORTANT: This is used only in Dask ML version.
-
-        True : always precompute distances.
-
-        False : never precompute distances.
-
-    verbose : int, default=0
-        Verbosity mode.
-
-    random_state : int, RandomState instance or None, default=None
-        Determines random number generation for centroid initialization. Use
-        an int to make the randomness deterministic.
-        See :term:`Glossary <random_state>`.
-
-    copy_x : bool, default=True
-        When pre-computing distances it is more numerically accurate to center
-        the data first. If copy_x is True (default), then the original data is
-        not modified. If False, the original data is modified, and put back
-        before the function returns, but small numerical differences may be
-        introduced by subtracting and then adding the data mean. Note that if
-        the original data is not C-contiguous, a copy will be made even if
-        copy_x is False. If the original data is sparse, but not in CSR format,
-        a copy will be made even if copy_x is False.
-
-    n_jobs : int, default=1
-        The number of OpenMP threads to use for the computation. Parallelism is
-        sample-wise on the main cython loop which assigns each sample to its
-        closest center. IMPORTANT: This is used only in Dask ML version.
-
-        ``None`` or ``-1`` means using all processors.
-
-    init_max_iter : int, default=None
-        Number of iterations for init step.
-
-    algorithm : {"auto", "full", "elkan"}, default="full"
-        K-means algorithm to use. The classical EM-style algorithm is "full".
-        The "elkan" variation is more efficient on data with well-defined
-        clusters, by using the triangle inequality. However it's more memory
-        intensive due to the allocation of an extra array of shape
-        (n_samples, n_clusters).
-
-        For now "auto" (kept for backward compatibiliy) chooses "elkan" but it
-        might change in the future for a better heuristic.
-
-        .. versionchanged:: 0.18
-            Added Elkan algorithm
-
-    oversampling_factor : int, default=2
-        The amount of points to sample in scalable k-means++ initialization
-        for potential centroids. Increasing this value can lead to better
-        initial centroids at the cost of memory. The total number of centroids
-        sampled in scalable k-means++ is oversampling_factor * n_clusters * 8.
-
-    max_samples_per_batch : int, default=32768
-        The number of data samples to use for batches of the pairwise distance
-        computation. This computation is done throughout both fit predict. The
-        default should suit most cases. The total number of elements in the
-        batched pairwise distance computation is max_samples_per_batch *
-        n_clusters. It might become necessary to lower this number when
-        n_clusters becomes prohibitively large.
-
-    output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None
-        Variable to control output type of the results and attributes of the
-        estimator. If None, it'll inherit the output type set at the module
-        level, cuml.global_settings.output_type. See Output Data Type
-        Configuration for more info.
-
-    See Also
-    --------
-    MiniBatchKMeans : Alternative online implementation that does incremental
-        updates of the centers positions using mini-batches.
-        For large scale learning (say n_samples > 10k) MiniBatchKMeans is
-        probably much faster than the default batch implementation.
-
-    Notes
-    -----
-    The k-means problem is solved using either Lloyd's or Elkan's algorithm.
-
-    The average complexity is given by O(k n T), where n is the number of
-    samples and T is the number of iteration.
-
-    The worst case complexity is given by O(n^(k+2/p)) with
-    n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii,
-    'How slow is the k-means method?' SoCG2006)
-
-    In practice, the k-means algorithm is very fast (one of the fastest
-    clustering algorithms available), but it falls in local minima. That's why
-    it can be useful to restart it several times.
-
-    If the algorithm stops before fully converging (because of ``tol`` or
-    ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent,
-    i.e. the ``cluster_centers_`` will not be the means of the points in each
-    cluster. Also, the estimator will reassign ``labels_`` after the last
-    iteration to make ``labels_`` consistent with ``predict`` on the training
-    set.
-
-    Examples
-    --------
-
-    >>> from dasf.ml.cluster import KMeans
-    >>> import numpy as np
-    >>> X = np.array([[1, 2], [1, 4], [1, 0],
-    ...               [10, 2], [10, 4], [10, 0]])
-    >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
-    >>> kmeans.predict([[0, 0], [12, 3]])
-    array([1, 0], dtype=int32)
-
-    For further informations see:
-    - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
-    - https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html
-    - https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering
-    - https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans
-
-    """
-    def __init__(
-        self,
-        n_clusters=8,
-        init=None,
-        n_init=None,
-        max_iter=300,
-        tol=0.0001,
-        verbose=0,
-        random_state=None,
-        copy_x=True,
-        algorithm='full',
-        oversampling_factor=2.0,
-        n_jobs=1,
-        init_max_iter=None,
-        max_samples_per_batch=32768,
-        precompute_distances='auto',
-        output_type=None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.n_clusters = n_clusters
-        self.random_state = random_state
-        self.max_iter = max_iter
-        self.init = init
-        self.n_init = n_init
-        self.tol = tol
-        self.verbose = verbose
-        self.copy_x = copy_x
-        self.algorithm = algorithm
-        self.oversampling_factor = oversampling_factor
-        self.n_jobs = n_jobs
-        self.init_max_iter = init_max_iter
-        self.max_samples_per_batch = max_samples_per_batch
-        self.precompute_distances = precompute_distances
-        self.output_type = output_type
-
-        # Estimator for CPU operations
-        self.__kmeans_cpu = KMeans_CPU(
-            n_clusters=n_clusters,
-            random_state=random_state,
-            max_iter=max_iter,
-            init=("k-means++" if init is None else init),
-            n_init=(10 if n_init is None else n_init),
-            tol=tol,
-            verbose=verbose,
-            copy_x=copy_x,
-            algorithm=algorithm,
-        )
-
-        # Estimator for Dask ML operations
-        self.__kmeans_mcpu = KMeans_MCPU(
-            n_clusters=n_clusters,
-            random_state=random_state,
-            max_iter=max_iter,
-            init=("k-means||" if init is None else init),
-            tol=tol,
-            oversampling_factor=oversampling_factor,
-            algorithm=algorithm,
-            n_jobs=n_jobs,
-            init_max_iter=init_max_iter,
-            copy_x=copy_x,
-            precompute_distances=precompute_distances,
-        )
-
-        if is_gpu_supported():
-            # Estimator for CuML operations
-            self.__kmeans_gpu = KMeans_GPU(
-                n_clusters=n_clusters,
-                random_state=(1 if random_state is None else random_state),
-                max_iter=max_iter,
-                tol=tol,
-                verbose=verbose,
-                init=("scalable-k-means++" if init is None else init),
-                oversampling_factor=oversampling_factor,
-                max_samples_per_batch=max_samples_per_batch,
-            )
-
-            # XXX: KMeans in Multi GPU requires a Client instance,
-            # skip if not present.
-            try:
-                self.__kmeans_mgpu = KMeans_MGPU(
-                    n_clusters=n_clusters,
-                    random_state=(1 if random_state is None else random_state),
-                    max_iter=max_iter,
-                    tol=tol,
-                    verbose=verbose,
-                    init=("scalable-k-means++" if init is None else init),
-                    oversampling_factor=oversampling_factor,
-                    max_samples_per_batch=max_samples_per_batch,
-                )
-            except ValueError:
-                self.__kmeans_mgpu = None
-
-
[docs]    def _lazy_fit_cpu(self, X, y=None, sample_weight=None):
-        """
-        Compute Dask k-means clustering.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training instances to cluster. It must be noted that the data
-            will be converted to C ordering, which will cause a memory
-            copy if the given data is not C-contiguous.
-            If a sparse matrix is passed, a copy will be made if it&apos;s not in
-            CSR format.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : Ignored
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        self
-            Fitted estimator.
-        """
-        return self.__kmeans_mcpu.fit(X=X, y=y)

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, sample_weight=None):
-        """
-        Compute Dask CuML k-means clustering.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training instances to cluster. It must be noted that the data
-            will be converted to C ordering, which will cause a memory
-            copy if the given data is not C-contiguous.
-            If a sparse matrix is passed, a copy will be made if it&apos;s not in
-            CSR format.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        self
-            Fitted estimator.
-        """
-        if self.__kmeans_mgpu is None:
-            raise NotImplementedError
-        return self.__kmeans_mgpu.fit(X=X, sample_weight=sample_weight)

-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weight=None):
-        """
-        Compute Scikit Learn k-means clustering.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training instances to cluster. It must be noted that the data
-            will be converted to C ordering, which will cause a memory
-            copy if the given data is not C-contiguous.
-            If a sparse matrix is passed, a copy will be made if it&apos;s not in
-            CSR format.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        self
-            Fitted estimator.
-        """
-        return self.__kmeans_cpu.fit(X=X, y=y, sample_weight=sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y=None, sample_weight=None):
-        """
-        Compute CuML k-means clustering.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training instances to cluster. It must be noted that the data
-            will be converted to C ordering, which will cause a memory
-            copy if the given data is not C-contiguous.
-            If a sparse matrix is passed, a copy will be made if it&apos;s not in
-            CSR format.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        self
-            Fitted estimator.
-        """
-        return self.__kmeans_gpu.fit(X=X, sample_weight=sample_weight)

-
-
[docs]    def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None):
-        """
-        Compute cluster centers and predict cluster index for each sample using
-        Dask ML.
-
-        Convenience method; equivalent to calling fit(X) followed by
-        predict(X).
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to transform.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : Ignored
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        local_kmeans = self.__kmeans_mcpu.fit(X=X, y=y)
-        return local_kmeans.predict(X=X)

-
-
[docs]    def _lazy_fit_predict_gpu(self, X, y=None, sample_weight=None):
-        """
-        Compute cluster centers and predict cluster index for each sample using
-        Dask CuML.
-
-        Convenience method; equivalent to calling fit(X) followed by
-        predict(X).
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to transform.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        if self.__kmeans_mgpu is None:
-            raise NotImplementedError
-        return self.__kmeans_mgpu.fit_predict(X, y, sample_weight)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None, sample_weight=None):
-        """
-        Compute cluster centers and predict cluster index for each sample using
-        Scikit Learn.
-
-        Convenience method; equivalent to calling fit(X) followed by
-        predict(X).
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to transform.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : Ignored
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return self.__kmeans_cpu.fit_predict(X)

-
-
[docs]    def _fit_predict_gpu(self, X, y=None, sample_weight=None):
-        """
-        Compute cluster centers and predict cluster index for each sample using
-        CuML.
-
-        Convenience method; equivalent to calling fit(X) followed by
-        predict(X).
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to transform.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return self.__kmeans_gpu.fit_predict(X=X, sample_weight=sample_weight)

-
-
[docs]    def _lazy_predict_cpu(self, X, sample_weight=None):
-        """
-        Predict the closest cluster each sample in X belongs to using Dask ML.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : Ignored
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return self.__kmeans_mcpu.predict(X)

-
-
[docs]    def _lazy_predict_gpu(self, X, sample_weight=None):
-        """
-        Predict the closest cluster each sample in X belongs to using Dask
-        CuML.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        if self.__kmeans_mgpu is None:
-            raise NotImplementedError
-        return self.__kmeans_mgpu.predict(X, sample_weight)

-
-
[docs]    def _predict_cpu(self, X, sample_weight=None):
-        """
-        Predict the closest cluster each sample in X belongs to using Scikit
-        Learn.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return self.__kmeans_cpu.predict(X, sample_weight)

-
-
[docs]    def _predict_gpu(self, X, sample_weight=None):
-        """
-        Predict the closest cluster each sample in X belongs to using CuML.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return self.__kmeans_gpu.predict(X, sample_weight)

-
-
[docs]    def _lazy_predict2_cpu(self, X, sample_weight=None):
-        """
-        A block predict using Scikit Learn variant but for Dask.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        def __predict(block):
-            return self._predict_cpu.predict(block, sample_weight=sample_weight)
-
-        return X.map_blocks(
-            __predict, chunks=(X.chunks[0],), drop_axis=[1], dtype=X.dtype
-        )

-
-
[docs]    def _lazy_predict2_gpu(self, X, sample_weight=None):
-        """
-        A block predict using CuML variant but for Dask.
-
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        def __predict(block):
-            return self._predict_gpu.predict(block, sample_weight=sample_weight)
-
-        return X.map_blocks(
-            __predict, chunks=(X.chunks[0],), drop_axis=[1], dtype=X.dtype
-        )

-
-
[docs]    def _predict2_cpu(self, X, sample_weight=None):
-        raise NotImplementedError("Method available only for Dask.")

-
-
[docs]    def _predict2_gpu(self, X, sample_weight=None):
-        raise NotImplementedError("Method available only for Dask.")

-
-
[docs]    @task_handler
-    def predict2(self, sample_weight=None):
-        ...

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/som.html b/docs/_modules/dasf/ml/cluster/som.html deleted file mode 100644 index bf274f9..0000000 --- a/docs/_modules/dasf/ml/cluster/som.html +++ /dev/null @@ -1,484 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.som — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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  • dasf.ml.cluster.som
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- -

Source code for dasf.ml.cluster.som

-#!/usr/bin/env python3
-
-import numpy as np
-
-from xpysom_dask import XPySom
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-from dasf.utils.funcs import is_gpu_supported
-from dasf.utils.decorators import task_handler
-
-try:
-    import cupy as cp
-except ImportError:
-    pass
-
-
-
[docs]class SOM(ClusterClassifier):
-    """
-    Initializes a Self Organizing Maps.
-
-    A rule of thumb to set the size of the grid for a dimensionality
-    reduction task is that it should contain 5*sqrt(N) neurons
-    where N is the number of samples in the dataset to analyze.
-
-    E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23
-    hence a map 8-by-8 should perform well.
-
-    Parameters
-    ----------
-    x : int
-        x dimension of the SOM.
-
-    y : int
-        y dimension of the SOM.
-
-    input_len : int
-        Number of the elements of the vectors in input.
-
-    sigma : float, default=min(x,y)/2
-        Spread of the neighborhood function, needs to be adequate
-        to the dimensions of the map.
-
-    sigmaN : float, default=0.01
-        Spread of the neighborhood function at last iteration.
-
-    learning_rate : float, default=0.5
-        initial learning rate.
-
-    learning_rateN : float, default=0.01
-        final learning rate
-
-    decay_function : string, default='exponential'
-        Function that reduces learning_rate and sigma at each iteration.
-        Possible values: 'exponential', 'linear', 'aymptotic'
-
-    neighborhood_function : string, default='gaussian'
-        Function that weights the neighborhood of a position in the map.
-        Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle'
-
-    topology : string, default='rectangular'
-        Topology of the map.
-        Possible values: 'rectangular', 'hexagonal'
-
-    activation_distance : string, default='euclidean'
-        Distance used to activate the map.
-        Possible values: 'euclidean', 'cosine', 'manhattan'
-
-    random_seed : int, default=None
-        Random seed to use.
-
-    n_parallel : uint, default=#max_CUDA_threads or 500*#CPUcores
-        Number of samples to be processed at a time. Setting a too low
-        value may drastically lower performance due to under-utilization,
-        setting a too high value increases memory usage without granting 
-        any significant performance benefit.
-
-    xp : numpy or cupy, default=cupy if can be imported else numpy
-        Use numpy (CPU) or cupy (GPU) for computations.
-
-    std_coeff: float, default=0.5
-        Used to calculate gausssian exponent denominator:
-        d = 2*std_coeff**2*sigma**2
-
-    compact_support: bool, default=False
-        Cut the neighbor function to 0 beyond neighbor radius sigma
-
-    Examples
-    --------
-    >>> from dasf.ml.cluster import SOM
-    >>> import numpy as np
-    >>> X = np.array([[1, 1], [2, 1], [1, 0],
-    ...               [4, 7], [3, 5], [3, 6]])
-    >>> som = SOM(x=3, y=2, input_len=2,
-    ...           num_epochs=100).fit(X)
-    >>> som
-    SOM(x=3, y=2, input_len=2, num_epochs=100)
-
-    """
-    def __init__(
-        self,
-        x,
-        y,
-        input_len,
-        num_epochs=100,
-        sigma=0,
-        sigmaN=1,
-        learning_rate=0.5,
-        learning_rateN=0.01,
-        decay_function="exponential",
-        neighborhood_function="gaussian",
-        std_coeff=0.5,
-        topology="rectangular",
-        activation_distance="euclidean",
-        random_seed=None,
-        n_parallel=0,
-        compact_support=False,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.x = x
-        self.y = y
-        self.input_len = input_len
-        self.num_epochs = num_epochs
-        self.sigma = sigma
-        self.sigmaN = sigmaN
-        self.learning_rate = learning_rate
-        self.learning_rateN = learning_rateN
-        self.decay_function = decay_function
-        self.neighborhood_function = neighborhood_function
-        self.std_coeff = std_coeff
-        self.topology = topology
-        self.activation_distance = activation_distance
-        self.random_seed = random_seed
-        self.n_parallel = n_parallel
-        self.compact_support = compact_support
-
-        self.__som_cpu = XPySom(
-            x=self.x,
-            y=self.y,
-            input_len=self.input_len,
-            sigma=self.sigma,
-            sigmaN=self.sigmaN,
-            learning_rate=self.learning_rate,
-            learning_rateN=self.learning_rateN,
-            decay_function=self.decay_function,
-            neighborhood_function=self.neighborhood_function,
-            std_coeff=self.std_coeff,
-            topology=self.topology,
-            activation_distance=self.activation_distance,
-            random_seed=self.random_seed,
-            n_parallel=self.n_parallel,
-            compact_support=self.compact_support,
-            xp=np,
-        )
-
-        self.__som_mcpu = XPySom(
-            x=self.x,
-            y=self.y,
-            input_len=self.input_len,
-            sigma=self.sigma,
-            sigmaN=self.sigmaN,
-            learning_rate=self.learning_rate,
-            learning_rateN=self.learning_rateN,
-            decay_function=self.decay_function,
-            neighborhood_function=self.neighborhood_function,
-            std_coeff=self.std_coeff,
-            topology=self.topology,
-            activation_distance=self.activation_distance,
-            random_seed=self.random_seed,
-            n_parallel=self.n_parallel,
-            compact_support=self.compact_support,
-            xp=np,
-            use_dask=True,
-        )
-
-        if is_gpu_supported():
-            self.__som_gpu = XPySom(
-                x=self.x,
-                y=self.y,
-                input_len=self.input_len,
-                sigma=self.sigma,
-                sigmaN=self.sigmaN,
-                learning_rate=self.learning_rate,
-                learning_rateN=self.learning_rateN,
-                decay_function=self.decay_function,
-                neighborhood_function=self.neighborhood_function,
-                std_coeff=self.std_coeff,
-                topology=self.topology,
-                activation_distance=self.activation_distance,
-                random_seed=self.random_seed,
-                n_parallel=self.n_parallel,
-                compact_support=self.compact_support,
-                xp=cp,
-            )
-
-            self.__som_mgpu = XPySom(
-                x=self.x,
-                y=self.y,
-                input_len=self.input_len,
-                sigma=self.sigma,
-                sigmaN=self.sigmaN,
-                learning_rate=self.learning_rate,
-                learning_rateN=self.learning_rateN,
-                decay_function=self.decay_function,
-                neighborhood_function=self.neighborhood_function,
-                std_coeff=self.std_coeff,
-                topology=self.topology,
-                activation_distance=self.activation_distance,
-                random_seed=self.random_seed,
-                n_parallel=self.n_parallel,
-                compact_support=self.compact_support,
-                xp=cp,
-                use_dask=True,
-            )
-
-
[docs]    def _lazy_fit_cpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_mcpu
-        return self.__som_mcpu.train(X, self.num_epochs)

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_mgpu
-        return self.__som_mgpu.train(X, self.num_epochs)

-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_cpu
-        return self.__som_cpu.train(X, self.num_epochs)

-
-
[docs]    def _fit_gpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_gpu
-        return self.__som_gpu.train(X, self.num_epochs)

-
-
[docs]    def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_mcpu
-        return self.__som_mcpu.train(X, self.num_epochs).predict(X)

-
-
[docs]    def _lazy_fit_predict_gpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_mgpu
-        return self.__som_mgpu.train(X, self.num_epochs).predict(X)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_cpu
-        return self.__som_cpu.train(X, self.num_epochs).predict(X)

-
-
[docs]    def _fit_predict_gpu(self, X, y=None, sample_weight=None):
-        self.__som = self.__som_gpu
-        return self.__som_gpu.train(X, self.num_epochs).predict(X)

-
-
[docs]    def _lazy_predict_cpu(self, X, sample_weight=None):
-        return self.__som_mcpu.predict(X)

-
-
[docs]    def _lazy_predict_gpu(self, X, sample_weight=None):
-        return self.__som_mgpu.predict(X)

-
-
[docs]    def _predict_cpu(self, X, sample_weight=None):
-        return self.__som_cpu.predict(X)

-
-
[docs]    def _predict_gpu(self, X, sample_weight=None):
-        return self.__som_gpu.predict(X)

-
-
[docs]    def _lazy_quantization_error_cpu(self, X):
-        return self.__som_mcpu.quantization_error(X)

-
-
[docs]    def _lazy_quantization_error_gpu(self, X):
-        return self.__som_mgpu.quantization_error(X)

-
-
[docs]    def _quantization_error_cpu(self, X):
-        return self.__som_cpu.quantization_error(X)

-
-
[docs]    def _quantization_error_gpu(self, X):
-        return self.__som_gpu.quantization_error(X)

-
-
[docs]    @task_handler
-    def quantization_error(self, X):
-        ...

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/cluster/spectral.html b/docs/_modules/dasf/ml/cluster/spectral.html deleted file mode 100644 index 8f053e9..0000000 --- a/docs/_modules/dasf/ml/cluster/spectral.html +++ /dev/null @@ -1,479 +0,0 @@ - - - - - - - - - - dasf.ml.cluster.spectral — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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  • dasf.ml.cluster.spectral
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    • - -
- - -
-
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-
- -

Source code for dasf.ml.cluster.spectral

-#!/usr/bin/env python3
-
-from sklearn.cluster import SpectralClustering as SpectralClustering_CPU
-from dask_ml.cluster import SpectralClustering as SpectralClustering_MCPU
-
-from dasf.ml.cluster.classifier import ClusterClassifier
-
-
-
[docs]class SpectralClustering(ClusterClassifier):
-    """
-    Apply clustering to a projection of the normalized Laplacian.
-
-    In practice Spectral Clustering is very useful when the structure of
-    the individual clusters is highly non-convex, or more generally when
-    a measure of the center and spread of the cluster is not a suitable
-    description of the complete cluster, such as when clusters are
-    nested circles on the 2D plane.
-
-    If the affinity matrix is the adjacency matrix of a graph, this method
-    can be used to find normalized graph cuts.
-
-    When calling ``fit``, an affinity matrix is constructed using either
-    a kernel function such the Gaussian (aka RBF) kernel with Euclidean
-    distance ``d(X, X)``::
-
-            np.exp(-gamma * d(X,X) ** 2)
-
-    or a k-nearest neighbors connectivity matrix.
-
-    Alternatively, a user-provided affinity matrix can be specified by
-    setting ``affinity='precomputed'``.
-
-    Read more in the :ref:`User Guide <spectral_clustering>`.
-
-    Parameters
-    ----------
-    n_clusters : int, default=8
-        The dimension of the projection subspace.
-
-    eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None
-        The eigenvalue decomposition strategy to use. AMG requires pyamg
-        to be installed. It can be faster on very large, sparse problems,
-        but may also lead to instabilities. If None, then ``'arpack'`` is
-        used.
-
-    n_components : int, default=n_clusters
-        Number of eigenvectors to use for the spectral embedding
-
-    random_state : int, RandomState instance, default=None
-        A pseudo random number generator used for the initialization of the
-        lobpcg eigenvectors decomposition when ``eigen_solver='amg'`` and by
-        the K-Means initialization. Use an int to make the randomness
-        deterministic.
-        See :term:`Glossary <random_state>`.
-
-    n_init : int, default=10
-        Number of time the k-means algorithm will be run with different
-        centroid seeds. The final results will be the best output of n_init
-        consecutive runs in terms of inertia. Only used if
-        ``assign_labels='kmeans'``.
-
-    gamma : float, default=1.0
-        Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels.
-        Ignored for ``affinity='nearest_neighbors'``.
-
-    affinity : str or callable, default='rbf'
-        How to construct the affinity matrix.
-         - 'nearest_neighbors': construct the affinity matrix by computing a
-           graph of nearest neighbors.
-         - 'rbf': construct the affinity matrix using a radial basis function
-           (RBF) kernel.
-         - 'precomputed': interpret ``X`` as a precomputed affinity matrix,
-           where larger values indicate greater similarity between instances.
-         - 'precomputed_nearest_neighbors': interpret ``X`` as a sparse graph
-           of precomputed distances, and construct a binary affinity matrix
-           from the ``n_neighbors`` nearest neighbors of each instance.
-         - one of the kernels supported by
-           :func:`~sklearn.metrics.pairwise_kernels`.
-
-        Only kernels that produce similarity scores (non-negative values that
-        increase with similarity) should be used. This property is not checked
-        by the clustering algorithm.
-
-    n_neighbors : int, default=10
-        Number of neighbors to use when constructing the affinity matrix using
-        the nearest neighbors method. Ignored for ``affinity='rbf'``.
-
-    eigen_tol : float, default=0.0
-        Stopping criterion for eigendecomposition of the Laplacian matrix
-        when ``eigen_solver='arpack'``.
-
-    assign_labels : {'kmeans', 'discretize'}, default='kmeans'
-        The strategy for assigning labels in the embedding space. There are two
-        ways to assign labels after the Laplacian embedding. k-means is a
-        popular choice, but it can be sensitive to initialization.
-        Discretization is another approach which is less sensitive to random
-        initialization.
-
-    degree : float, default=3
-        Degree of the polynomial kernel. Ignored by other kernels.
-
-    coef0 : float, default=1
-        Zero coefficient for polynomial and sigmoid kernels.
-        Ignored by other kernels.
-
-    kernel_params : dict of str to any, default=None
-        Parameters (keyword arguments) and values for kernel passed as
-        callable object. Ignored by other kernels.
-
-    n_jobs : int, default=None
-        The number of parallel jobs to run when `affinity='nearest_neighbors'`
-        or `affinity='precomputed_nearest_neighbors'`. The neighbors search
-        will be done in parallel.
-        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
-        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
-        for more details.
-
-    verbose : bool, default=False
-        Verbosity mode.
-
-        .. versionadded:: 0.24
-
-    persist_embedding : bool
-        Whether to persist the intermediate n_samples x n_components array used
-        for clustering.
-
-    kmeans_params : dictionary of string to any, optional
-        Keyword arguments for the KMeans clustering used for the final
-        clustering.
-
-    Examples
-    --------
-    >>> from dasf.ml.cluster import SpectralClustering
-    >>> import numpy as np
-    >>> X = np.array([[1, 1], [2, 1], [1, 0],
-    ...               [4, 7], [3, 5], [3, 6]])
-    >>> clustering = SpectralClustering(n_clusters=2,
-    ...         assign_labels='discretize',
-    ...         random_state=0).fit(X)
-    >>> clustering
-    SpectralClustering(assign_labels='discretize', n_clusters=2,
-        random_state=0)
-
-    For further informations see:
-    - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering
-    - https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html
-
-    Notes
-    -----
-    A distance matrix for which 0 indicates identical elements and high values
-    indicate very dissimilar elements can be transformed into an affinity /
-    similarity matrix that is well-suited for the algorithm by
-    applying the Gaussian (aka RBF, heat) kernel::
-
-        np.exp(- dist_matrix ** 2 / (2. * delta ** 2))
-
-    where ``delta`` is a free parameter representing the width of the Gaussian
-    kernel.
-
-    An alternative is to take a symmetric version of the k-nearest neighbors
-    connectivity matrix of the points.
-
-    If the pyamg package is installed, it is used: this greatly
-    speeds up computation.
-
-    References
-    ----------
-
-    - Normalized cuts and image segmentation, 2000
-      Jianbo Shi, Jitendra Malik
-      http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
-
-    - A Tutorial on Spectral Clustering, 2007
-      Ulrike von Luxburg
-      http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
-
-    - Multiclass spectral clustering, 2003
-      Stella X. Yu, Jianbo Shi
-      https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf
-
-    """
-    def __init__(
-        self,
-        n_clusters=8,
-        eigen_solver=None,
-        random_state=None,
-        n_init=10,
-        gamma=1.0,
-        affinity="rbf",
-        n_neighbors=10,
-        eigen_tol=0.0,
-        assign_labels="kmeans",
-        degree=3,
-        coef0=1,
-        kernel_params=None,
-        n_jobs=None,
-        n_components=None,
-        persist_embedding=False,
-        kmeans_params=None,
-        verbose=False,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.n_clusters = n_clusters
-        self.eigen_solver = eigen_solver
-        self.random_state = random_state
-        self.n_init = n_init
-        self.gamma = gamma
-        self.affinity = affinity
-        self.n_neighbors = n_neighbors
-        self.eigen_tol = eigen_tol
-        self.assign_labels = assign_labels
-        self.degree = degree
-        self.coef0 = coef0
-        self.kernel_params = kernel_params
-        self.n_jobs = n_jobs
-        self.n_components = n_components
-        self.persist_embedding = persist_embedding
-        self.kmeans_params = kmeans_params
-        self.verbose = verbose
-
-        self.__sc_cpu = SpectralClustering_CPU(
-            n_clusters=n_clusters,
-            eigen_solver=eigen_solver,
-            random_state=random_state,
-            n_init=n_init,
-            gamma=gamma,
-            affinity=affinity,
-            n_neighbors=n_neighbors,
-            eigen_tol=eigen_tol,
-            assign_labels=assign_labels,
-            degree=degree,
-            coef0=coef0,
-            kernel_params=kernel_params,
-            n_jobs=n_jobs,
-            n_components=n_components,
-            verbose=verbose
-        )
-
-        # If n_components is set to None, use default
-        n_components = 100 if n_components is None else n_components
-
-        self.__sc_mcpu = SpectralClustering_MCPU(
-            n_clusters=n_clusters,
-            eigen_solver=eigen_solver,
-            random_state=random_state,
-            n_init=n_init,
-            gamma=gamma,
-            affinity=affinity,
-            n_neighbors=n_neighbors,
-            eigen_tol=eigen_tol,
-            assign_labels=assign_labels,
-            degree=degree,
-            coef0=coef0,
-            kernel_params=kernel_params,
-            n_jobs=n_jobs,
-            n_components=n_components,
-            persist_embedding=persist_embedding,
-            kmeans_params=kmeans_params,
-        )
-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weight=None):
-        return self.__sc_cpu.fit(X=X, y=y)

-
-
[docs]    def _lazy_fit_predict_cpu(self, X, y=None, sample_weight=None):
-        return self.__sc_mcpu.fit_predict(X=X)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None, sample_weight=None):
-        return self.__sc_cpu.fit_predict(X)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/core.html b/docs/_modules/dasf/ml/core.html deleted file mode 100644 index 9c073f3..0000000 --- a/docs/_modules/dasf/ml/core.html +++ /dev/null @@ -1,240 +0,0 @@ - - - - - - - - - - dasf.ml.core — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.core

-#!/usr/bin/env python3
-
-import os
-import pickle
-
-from pathlib import Path
-
-
-
[docs]class MLGeneric:
-    def __init__(self, name, checkpoint=False, **kwargs):
-        # Machine Learning Algorithm
-        self._cached_dir = os.path.abspath(
-            os.path.join(str(Path.home()),
-                         "/.cache/dasf/ml/"))
-        os.makedirs(self._cached_dir, exist_ok=True)
-
-        self._tmp = os.path.abspath(os.path.join(self._cached_dir,
-                                                 name.lower()))
-
-        self.__checkpoint = checkpoint
-
-
[docs]    def dump(self, model):
-        if self.get_checkpoint():
-            with open(self._tmp, "wb") as fh:
-                pickle.dump(model, fh)

-
-
[docs]    def load(self, model):
-        if self.get_checkpoint() and os.path.exists(self._tmp):
-            with open(self._tmp, "rb") as fh:
-                return pickle.load(fh)
-        return model

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/decomposition/pca.html b/docs/_modules/dasf/ml/decomposition/pca.html deleted file mode 100644 index 2d6cca9..0000000 --- a/docs/_modules/dasf/ml/decomposition/pca.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - dasf.ml.decomposition.pca — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.ml.decomposition.pca

-#!/usr/bin/env python3
-
-from sklearn.decomposition import PCA as PCA_CPU
-from dask_ml.decomposition import PCA as PCA_MCPU
-
-from dasf.utils.funcs import is_dask_supported
-from dasf.utils.funcs import is_gpu_supported
-from dasf.transforms.base import Fit, FitTransform
-from dasf.transforms.base import TargeteredTransform
-
-try:
-    from cuml.decomposition import PCA as PCA_GPU
-    from cuml.dask.decomposition import PCA as PCA_MGPU
-except ImportError:
-    pass
-
-
-
[docs]class PCA(Fit, FitTransform, TargeteredTransform):
-    def __init__(
-        self,
-        n_components=None,
-        copy=True,
-        whiten=False,
-        svd_solver="auto",
-        tol=0.0,
-        iterated_power="auto",
-        random_state=None,
-        *args,
-        **kwargs,
-    ):
-        TargeteredTransform.__init__(self, *args, **kwargs)
-
-        self.__pca_cpu = PCA_CPU(
-            n_components=n_components,
-            copy=copy,
-            whiten=whiten,
-            svd_solver=svd_solver,
-            tol=tol,
-            iterated_power=iterated_power,
-            random_state=random_state,
-        )
-
-        self.__pca_mcpu = PCA_MCPU(
-            n_components=n_components,
-            copy=copy,
-            whiten=whiten,
-            svd_solver=svd_solver,
-            tol=tol,
-            iterated_power=iterated_power,
-            random_state=random_state,
-        )
-        if is_gpu_supported():
-            try:
-                self.__pca_gpu = PCA_GPU(
-                    n_components=n_components,
-                    copy=copy,
-                    whiten=whiten,
-                    svd_solver=svd_solver,
-                    tol=tol,
-                    iterated_power=iterated_power,
-                    random_state=random_state,
-                )
-            except TypeError:
-                self.__pca_gpu = None
-
-            # XXX: PCA in Multi GPU requires a Client instance,
-            # skip if not present.
-            try:
-                self.__pca_mgpu = PCA_MGPU(
-                    n_components=n_components,
-                    copy=copy,
-                    whiten=whiten,
-                    svd_solver=svd_solver,
-                    tol=tol,
-                    iterated_power=iterated_power,
-                    random_state=random_state,
-                )
-            except ValueError:
-                self.__pca_mgpu = None
-
-
[docs]    def _lazy_fit_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_mcpu.fit(X)

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_mgpu is None:
-            raise NotImplementedError
-        return self.__pca_mgpu.fit(X)

-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_cpu.fit(X)

-
-
[docs]    def _fit_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_gpu is None:
-            raise NotImplementedError
-        return self.__pca_gpu.fit(X)

-
-
[docs]    def _lazy_fit_transform_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_mcpu.fit_transform(X, y)

-
-
[docs]    def _lazy_fit_transform_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_mgpu is None:
-            raise NotImplementedError
-        return self.__pca_mgpu.fit_transform(X, y)

-
-
[docs]    def _fit_transform_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_cpu.fit_transform(X, y)

-
-
[docs]    def _fit_transform_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_gpu is None:
-            raise NotImplementedError
-        return self.__pca_gpu.fit_transform(X, y)

-
-
[docs]    def _lazy_transform_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_mcpu.transform(X)

-
-
[docs]    def _lazy_transform_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_mgpu is None:
-            raise NotImplementedError
-        return self.__pca_mgpu.transform(X)

-
-
[docs]    def _transform_cpu(self, X, y=None, sample_weights=None):
-        return self.__pca_cpu.transform(X)

-
-
[docs]    def _transform_gpu(self, X, y=None, sample_weights=None):
-        if self.__pca_gpu is None:
-            raise NotImplementedError
-        return self.__pca_gpu.transform(X)

-
-
[docs]    def _get_covariance_cpu(self):
-        return self.__pca_cpu.get_covariance()

-
-
[docs]    def get_covariance(self):
-        if not is_dask_supported() and not is_gpu_supported():
-            return self._get_covariance_cpu()
-        else:
-            raise NotImplementedError

-
-
[docs]    def _get_precision_cpu(self):
-        return self.__pca_cpu.get_precision()

-
-
[docs]    def get_precision(self):
-        if not is_dask_supported() and not is_gpu_supported():
-            return self._get_precision_cpu()
-        else:
-            raise NotImplementedError

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/dl/clusters/dask.html b/docs/_modules/dasf/ml/dl/clusters/dask.html deleted file mode 100644 index a307a6c..0000000 --- a/docs/_modules/dasf/ml/dl/clusters/dask.html +++ /dev/null @@ -1,288 +0,0 @@ - - - - - - - - - - dasf.ml.dl.clusters.dask — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.dl.clusters.dask

-#!/usr/bin/env python
-
-import os
-
-from pytorch_lightning.plugins.environments import ClusterEnvironment
-
-
-
[docs]class DaskClusterEnvironment(ClusterEnvironment):
-    """
-    Create a Dask Cluster environment for workers
-
-    metadata -- dictionary containing all data related to workers.
-    """
-
-    def __init__(self, metadata=None) -> None:
-        super().__init__()
-
-        if isinstance(metadata, dict):
-            self.metadata = metadata
-        else:
-            self.metadata = {k.lower(): v for k, v in os.environ.items()}
-
-        self._master_port = 23456
-
-
[docs]    def detect(self) -> bool:
-        if "master" not in self.metadata:
-            return False
-        if "world_size" not in self.metadata:
-            return False
-        if "global_rank" not in self.metadata:
-            return False
-
-        return True

-
-    @property
-    def creates_processes_externally(self) -> bool:
-        """Return True if the cluster is managed (you don't launch processes
-        yourself).
-        """
-        return True
-
-    @property
-    def main_address(self) -> str:
-        """Return master worker address."""
-        return self.metadata["master"]
-
-    @property
-    def main_port(self) -> int:
-        """Return master worker port."""
-        return self._master_port
-
-
[docs]    def creates_children(self) -> bool:
-        """Fork children when generate a cluster."""
-        return False

-
-
[docs]    def world_size(self) -> int:
-        """Return worker world size."""
-        return int(self.metadata["world_size"])

-
-
[docs]    def global_rank(self) -> int:
-        """Return worker global rank."""
-        return int(self.metadata["global_rank"])

-
-
[docs]    def local_rank(self) -> int:
-        """Return worker local rank."""
-        if "local_rank" in self.metadata:
-            return int(self.metadata["local_rank"])
-        else:
-            return 0

-
-
[docs]    def node_rank(self) -> int:
-        """Return worker node rank (which is similar to global rank)."""
-        return int(self.metadata["global_rank"])

-
-
[docs]    def set_world_size(self, size: int) -> None:
-        self.metadata["world_size"] = size

-
-
[docs]    def set_global_rank(self, rank: int) -> None:
-        self.metadata["global_rank"] = rank

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/dl/models/devconvnet.html b/docs/_modules/dasf/ml/dl/models/devconvnet.html deleted file mode 100644 index e092ff0..0000000 --- a/docs/_modules/dasf/ml/dl/models/devconvnet.html +++ /dev/null @@ -1,1541 +0,0 @@ - - - - - - - - - - dasf.ml.dl.models.devconvnet — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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  • dasf.ml.dl.models.devconvnet
    • - - -
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Source code for dasf.ml.dl.models.devconvnet

-#!/usr/bin/env python3
-
-import torch
-import numpy as np
-
-from torch.nn import MaxUnpool2d
-from torch.nn import MaxPool2d, ConvTranspose2d
-from torch.nn import Sequential, Conv2d
-from torch.nn import BatchNorm2d, ReLU
-
-from torch.nn import functional as F
-
-from torchmetrics import Metric
-
-from pytorch_lightning import LightningModule
-
-
-
[docs]class MyAccuracy(Metric):
-    def __init__(self, dist_sync_on_step=False):
-        # call `self.add_state`for every internal state that is needed for the
-        # metrics computations dist_reduce_fx indicates the function that
-        # should be used to reduce state from multiple processes
-        super().__init__(dist_sync_on_step=dist_sync_on_step)
-
-        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
-        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
-        self.idx = 0
-
-
[docs]    def set_idx(self, idx):
-        self.idx = idx

-
-
[docs]    def update(self, preds: torch.Tensor, target: torch.Tensor):
-        # update metric states
-        pred = preds.detach().max(1)[1].cpu().numpy()
-        gt = torch.squeeze(target, 1).cpu().numpy()
-
-        assert pred.shape == gt.shape
-
-        np.save("out/pred_" + str(self.idx) + ".npy", pred)
-
-        self.correct += np.sum(pred == gt)
-        self.total += len(gt.flatten())

-
-
[docs]    def __str__(self):
-        ret = self.compute()
-        return str(ret)

-
-
[docs]    def compute(self):
-        # compute final result
-        return float(self.correct / self.total)

-
-
-
[docs]class NNModule(LightningModule):
-    def __init__(
-        self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None
-    ):
-        super().__init__()
-
-        self.learned_billinear = learned_billinear
-        self.n_classes = n_classes
-        self.clip = clip
-
-        if class_weights and isinstance(class_weights, dict):
-            self.class_weights = torch.tensor(
-                list(class_weights.values()), requires_grad=False
-            )
-        else:
-            self.class_weights = None
-
-        self.class_names = list(class_weights.keys())
-
-
[docs]    def cross_entropy_loss(self, input, target, weight=None, ignore_index=255):
-        """
-        Use 255 to fill empty values when padding or doing any augmentation operations
-        like rotation.
-        """
-        target = torch.squeeze(target, dim=1)
-        loss = F.cross_entropy(input, target, weight, reduction="sum", ignore_index=255)
-
-        return loss

-
-
[docs]    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), amsgrad=True)

-
-
[docs]    def training_step(self, batch, batch_idx):
-        images, labels = batch
-
-        outputs = self.forward(images)
-
-        loss = self.cross_entropy_loss(
-            input=outputs, target=labels, weight=self.class_weights
-        )
-
-        # gradient clipping
-        if self.clip != 0:
-            torch.nn.utils.clip_grad_norm_(self.parameters(), self.clip)
-
-        return loss

-
-
[docs]    def test_step(self, test_batch, batch_idx):
-        images, labels = test_batch
-
-        preds = self(images)
-
-        file_object = open("test.txt", "w")
-        file_object.write(str(preds.shape))
-        file_object.write(str(labels.shape))
-        file_object.close()
-
-        self.accuracy.set_idx(batch_idx)
-
-        self.accuracy(preds, labels)

-
-
-
[docs]class TorchPatchDeConvNet(NNModule):
-    def __init__(
-        self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None
-    ):
-        super().__init__(n_classes, learned_billinear, clip, class_weights)
-
-        self.unpool = MaxUnpool2d(2, stride=2)
-        self.conv_block1 = Sequential(
-            # conv1_1
-            Conv2d(1, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv1_2
-            Conv2d(64, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool1
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_1
-
-        # 48*48
-
-        self.conv_block2 = Sequential(
-            # conv2_1
-            Conv2d(64, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv2_2
-            Conv2d(128, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool2
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_2
-
-        # 24*24
-
-        self.conv_block3 = Sequential(
-            # conv3_1
-            Conv2d(128, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_2
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_3
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool3
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_3
-
-        # 12*12
-
-        self.conv_block4 = Sequential(
-            # conv4_1
-            Conv2d(256, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool4
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_4
-
-        # 6*6
-
-        self.conv_block5 = Sequential(
-            # conv5_1
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool5
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_5
-
-        # 3*3
-
-        self.conv_block6 = Sequential(
-            # fc6
-            Conv2d(512, 4096, 3),
-            # set the filter size and nor padding to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 1*1
-
-        self.conv_block7 = Sequential(
-            # fc7
-            Conv2d(4096, 4096, 1),
-            # set the filter size to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.deconv_block8 = Sequential(
-            # fc6-deconv
-            ConvTranspose2d(4096, 512, 3, stride=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 3*3
-
-        self.unpool_block9 = Sequential(
-            # unpool5
-            MaxUnpool2d(2, stride=2),
-        )
-        # usage unpool(output, indices)
-
-        # 6*6
-
-        self.deconv_block10 = Sequential(
-            # deconv5_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_3
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block11 = Sequential(
-            # unpool4
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 12*12
-
-        self.deconv_block12 = Sequential(
-            # deconv4_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_3
-            ConvTranspose2d(512, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block13 = Sequential(
-            # unpool3
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 24*24
-
-        self.deconv_block14 = Sequential(
-            # deconv3_1
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_2
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_3
-            ConvTranspose2d(256, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block15 = Sequential(
-            # unpool2
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 48*48
-
-        self.deconv_block16 = Sequential(
-            # deconv2_1
-            ConvTranspose2d(128, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv2_2
-            ConvTranspose2d(128, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block17 = Sequential(
-            # unpool1
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 96*96
-
-        self.deconv_block18 = Sequential(
-            # deconv1_1
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv1_2
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.seg_score19 = Sequential(
-            # seg-score
-            Conv2d(64, self.n_classes, 1),
-        )
-
-        if self.learned_billinear:
-            raise NotImplementedError
-
-
[docs]    def forward(self, x):
-        size0 = x.size()
-        conv1, indices1 = self.conv_block1(x)
-        size1 = conv1.size()
-        conv2, indices2 = self.conv_block2(conv1)
-        size2 = conv2.size()
-        conv3, indices3 = self.conv_block3(conv2)
-        size3 = conv3.size()
-        conv4, indices4 = self.conv_block4(conv3)
-        size4 = conv4.size()
-        conv5, indices5 = self.conv_block5(conv4)
-
-        conv6 = self.conv_block6(conv5)
-        conv7 = self.conv_block7(conv6)
-        conv8 = self.deconv_block8(conv7)
-        conv9 = self.unpool(conv8, indices5, output_size=size4)
-        conv10 = self.deconv_block10(conv9)
-        conv11 = self.unpool(conv10, indices4, output_size=size3)
-        conv12 = self.deconv_block12(conv11)
-        conv13 = self.unpool(conv12, indices3, output_size=size2)
-        conv14 = self.deconv_block14(conv13)
-        conv15 = self.unpool(conv14, indices2, output_size=size1)
-        conv16 = self.deconv_block16(conv15)
-        conv17 = self.unpool(conv16, indices1, output_size=size0)
-        conv18 = self.deconv_block18(conv17)
-        out = self.seg_score19(conv18)
-
-        return out

-
-
[docs]    def init_vgg16_params(self, vgg16, copy_fc8=True):
-        blocks = [
-            self.conv_block1,
-            self.conv_block2,
-            self.conv_block3,
-            self.conv_block4,
-            self.conv_block5,
-        ]
-
-        ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
-        features = list(vgg16.features.children())
-        i_layer = 0
-        # copy convolutional filters from vgg16
-        for idx, conv_block in enumerate(blocks):
-            for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
-                if isinstance(l1, Conv2d) and isinstance(l2, Conv2d):
-                    if i_layer == 0:
-                        l2.weight.data = (
-                            (
-                                l1.weight.data[:, 0, :, :]
-                                + l1.weight.data[:, 1, :, :]
-                                + l1.weight.data[:, 2, :, :]
-                            )
-                            / 3.0
-                        ).view(l2.weight.size())
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1
-                    else:
-                        assert l1.weight.size() == l2.weight.size()
-                        assert l1.bias.size() == l2.bias.size()
-                        l2.weight.data = l1.weight.data
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1

-
-
[docs]    def load(self):
-        """ This is just a no-op load method. """
-        return self

-
-
-
[docs]class TorchPatchDeConvNetSkip(NNModule):
-    def __init__(
-        self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None
-    ):
-        super().__init__(n_classes, learned_billinear, clip, class_weights)
-
-        self.unpool = MaxUnpool2d(2, stride=2)
-        self.conv_block1 = Sequential(
-            # conv1_1
-            Conv2d(1, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv1_2
-            Conv2d(64, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool1
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_1
-
-        # 48*48
-
-        self.conv_block2 = Sequential(
-            # conv2_1
-            Conv2d(64, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv2_2
-            Conv2d(128, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool2
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_2
-
-        # 24*24
-
-        self.conv_block3 = Sequential(
-            # conv3_1
-            Conv2d(128, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_2
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_3
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool3
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_3
-
-        # 12*12
-
-        self.conv_block4 = Sequential(
-            # conv4_1
-            Conv2d(256, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool4
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_4
-
-        # 6*6
-
-        self.conv_block5 = Sequential(
-            # conv5_1
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool5
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_5
-
-        # 3*3
-
-        self.conv_block6 = Sequential(
-            # fc6
-            Conv2d(512, 4096, 3),
-            # set the filter size and nor padding to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 1*1
-
-        self.conv_block7 = Sequential(
-            # fc7
-            Conv2d(4096, 4096, 1),
-            # set the filter size to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.deconv_block8 = Sequential(
-            # fc6-deconv
-            ConvTranspose2d(4096, 512, 3, stride=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 3*3
-
-        self.unpool_block9 = Sequential(
-            # unpool5
-            MaxUnpool2d(2, stride=2),
-        )
-        # usage unpool(output, indices)
-
-        # 6*6
-
-        self.deconv_block10 = Sequential(
-            # deconv5_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_3
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block11 = Sequential(
-            # unpool4
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 12*12
-
-        self.deconv_block12 = Sequential(
-            # deconv4_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_3
-            ConvTranspose2d(512, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block13 = Sequential(
-            # unpool3
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 24*24
-
-        self.deconv_block14 = Sequential(
-            # deconv3_1
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_2
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_3
-            ConvTranspose2d(256, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block15 = Sequential(
-            # unpool2
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 48*48
-
-        self.deconv_block16 = Sequential(
-            # deconv2_1
-            ConvTranspose2d(128, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv2_2
-            ConvTranspose2d(128, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block17 = Sequential(
-            # unpool1
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 96*96
-
-        self.deconv_block18 = Sequential(
-            # deconv1_1
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv1_2
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.seg_score19 = Sequential(
-            # seg-score
-            Conv2d(64, self.n_classes, 1),
-        )
-
-        if self.learned_billinear:
-            raise NotImplementedError
-
-
[docs]    def forward(self, x):
-        size0 = x.size()
-        conv1, indices1 = self.conv_block1(x)
-        size1 = conv1.size()
-        conv2, indices2 = self.conv_block2(conv1)
-        size2 = conv2.size()
-        conv3, indices3 = self.conv_block3(conv2)
-        size3 = conv3.size()
-        conv4, indices4 = self.conv_block4(conv3)
-        size4 = conv4.size()
-        conv5, indices5 = self.conv_block5(conv4)
-
-        conv6 = self.conv_block6(conv5)
-        conv7 = self.conv_block7(conv6)
-        conv8 = self.deconv_block8(conv7) + conv5
-        conv9 = self.unpool(conv8, indices5, output_size=size4)
-        conv10 = self.deconv_block10(conv9) + conv4
-        conv11 = self.unpool(conv10, indices4, output_size=size3)
-        conv12 = self.deconv_block12(conv11) + conv3
-        conv13 = self.unpool(conv12, indices3, output_size=size2)
-        conv14 = self.deconv_block14(conv13) + conv2
-        conv15 = self.unpool(conv14, indices2, output_size=size1)
-        conv16 = self.deconv_block16(conv15) + conv1
-        conv17 = self.unpool(conv16, indices1, output_size=size0)
-        conv18 = self.deconv_block18(conv17)
-        out = self.seg_score19(conv18)
-
-        return out

-
-
[docs]    def init_vgg16_params(self, vgg16, copy_fc8=True):
-        blocks = [
-            self.conv_block1,
-            self.conv_block2,
-            self.conv_block3,
-            self.conv_block4,
-            self.conv_block5,
-        ]
-
-        ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
-        features = list(vgg16.features.children())
-        i_layer = 0
-        # copy convolutional filters from vgg16
-        for idx, conv_block in enumerate(blocks):
-            for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
-                if isinstance(l1, Conv2d) and isinstance(l2, Conv2d):
-                    if i_layer == 0:
-                        l2.weight.data = (
-                            (
-                                l1.weight.data[:, 0, :, :]
-                                + l1.weight.data[:, 1, :, :]
-                                + l1.weight.data[:, 2, :, :]
-                            )
-                            / 3.0
-                        ).view(l2.weight.size())
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1
-                    else:
-                        assert l1.weight.size() == l2.weight.size()
-                        assert l1.bias.size() == l2.bias.size()
-                        l2.weight.data = l1.weight.data
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1

-
-
[docs]    def load(self):
-        """ This is just a no-op load method. """
-        return self

-
-
-
[docs]class TorchSectionDeConvNet(NNModule):
-    def __init__(
-        self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=False
-    ):
-        super().__init__(n_classes, learned_billinear, clip, class_weights)
-
-        self.unpool = MaxUnpool2d(2, stride=2)
-        self.conv_block1 = Sequential(
-            # conv1_1
-            Conv2d(1, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv1_2
-            Conv2d(64, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool1
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_1
-
-        # 48*48
-
-        self.conv_block2 = Sequential(
-            # conv2_1
-            Conv2d(64, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv2_2
-            Conv2d(128, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool2
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_2
-
-        # 24*24
-
-        self.conv_block3 = Sequential(
-            # conv3_1
-            Conv2d(128, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_2
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_3
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool3
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_3
-
-        # 12*12
-
-        self.conv_block4 = Sequential(
-            # conv4_1
-            Conv2d(256, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool4
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_4
-
-        # 6*6
-
-        self.conv_block5 = Sequential(
-            # conv5_1
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool5
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_5
-
-        # 3*3
-
-        self.conv_block6 = Sequential(
-            # fc6
-            Conv2d(512, 4096, 3),
-            # set the filter size and nor padding to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 1*1
-
-        self.conv_block7 = Sequential(
-            # fc7
-            Conv2d(4096, 4096, 1),
-            # set the filter size to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.deconv_block8 = Sequential(
-            # fc6-deconv
-            ConvTranspose2d(4096, 512, 3, stride=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 3*3
-
-        self.unpool_block9 = Sequential(
-            # unpool5
-            MaxUnpool2d(2, stride=2),
-        )
-        # usage unpool(output, indices)
-
-        # 6*6
-
-        self.deconv_block10 = Sequential(
-            # deconv5_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_3
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block11 = Sequential(
-            # unpool4
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 12*12
-
-        self.deconv_block12 = Sequential(
-            # deconv4_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_3
-            ConvTranspose2d(512, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block13 = Sequential(
-            # unpool3
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 24*24
-
-        self.deconv_block14 = Sequential(
-            # deconv3_1
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_2
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_3
-            ConvTranspose2d(256, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block15 = Sequential(
-            # unpool2
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 48*48
-
-        self.deconv_block16 = Sequential(
-            # deconv2_1
-            ConvTranspose2d(128, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv2_2
-            ConvTranspose2d(128, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block17 = Sequential(
-            # unpool1
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 96*96
-
-        self.deconv_block18 = Sequential(
-            # deconv1_1
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv1_2
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.seg_score19 = Sequential(
-            # seg-score
-            Conv2d(64, self.n_classes, 1),
-        )
-
-        if self.learned_billinear:
-            raise NotImplementedError
-
-
[docs]    def forward(self, x):
-        size0 = x.size()
-        conv1, indices1 = self.conv_block1(x)
-        size1 = conv1.size()
-        conv2, indices2 = self.conv_block2(conv1)
-        size2 = conv2.size()
-        conv3, indices3 = self.conv_block3(conv2)
-        size3 = conv3.size()
-        conv4, indices4 = self.conv_block4(conv3)
-        size4 = conv4.size()
-        conv5, indices5 = self.conv_block5(conv4)
-
-        conv6 = self.conv_block6(conv5)
-        conv7 = self.conv_block7(conv6)
-        conv8 = self.deconv_block8(conv7)
-        conv9 = self.unpool(conv8, indices5, output_size=size4)
-        conv10 = self.deconv_block10(conv9)
-        conv11 = self.unpool(conv10, indices4, output_size=size3)
-        conv12 = self.deconv_block12(conv11)
-        conv13 = self.unpool(conv12, indices3, output_size=size2)
-        conv14 = self.deconv_block14(conv13)
-        conv15 = self.unpool(conv14, indices2, output_size=size1)
-        conv16 = self.deconv_block16(conv15)
-        conv17 = self.unpool(conv16, indices1, output_size=size0)
-        conv18 = self.deconv_block18(conv17)
-        out = self.seg_score19(conv18)
-
-        return out

-
-
[docs]    def init_vgg16_params(self, vgg16, copy_fc8=True):
-        blocks = [
-            self.conv_block1,
-            self.conv_block2,
-            self.conv_block3,
-            self.conv_block4,
-            self.conv_block5,
-        ]
-
-        ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
-        features = list(vgg16.features.children())
-        i_layer = 0
-        # copy convolutional filters from vgg16
-        for idx, conv_block in enumerate(blocks):
-            for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
-                if isinstance(l1, Conv2d) and isinstance(l2, Conv2d):
-                    if i_layer == 0:
-                        l2.weight.data = (
-                            (
-                                l1.weight.data[:, 0, :, :]
-                                + l1.weight.data[:, 1, :, :]
-                                + l1.weight.data[:, 2, :, :]
-                            )
-                            / 3.0
-                        ).view(l2.weight.size())
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1
-                    else:
-                        assert l1.weight.size() == l2.weight.size()
-                        assert l1.bias.size() == l2.bias.size()
-                        l2.weight.data = l1.weight.data
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1

-
-
[docs]    def load(self):
-        """ This is just a no-op load method. """
-        return self

-
-
-
[docs]class TorchSectionDeConvNetSkip(NNModule):
-    def __init__(
-        self, n_classes=4, learned_billinear=False, clip=0.1, class_weights=None
-    ):
-        super().__init__(n_classes, learned_billinear, clip, class_weights)
-
-        self.unpool = MaxUnpool2d(2, stride=2)
-        self.conv_block1 = Sequential(
-            # conv1_1
-            Conv2d(1, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv1_2
-            Conv2d(64, 64, 3, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool1
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_1
-
-        # 48*48
-
-        self.conv_block2 = Sequential(
-            # conv2_1
-            Conv2d(64, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv2_2
-            Conv2d(128, 128, 3, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool2
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_2
-
-        # 24*24
-
-        self.conv_block3 = Sequential(
-            # conv3_1
-            Conv2d(128, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_2
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv3_3
-            Conv2d(256, 256, 3, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool3
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_3
-
-        # 12*12
-
-        self.conv_block4 = Sequential(
-            # conv4_1
-            Conv2d(256, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv4_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool4
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_4
-
-        # 6*6
-
-        self.conv_block5 = Sequential(
-            # conv5_1
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_2
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # conv5_3
-            Conv2d(512, 512, 3, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # pool5
-            MaxPool2d(2, stride=2, return_indices=True, ceil_mode=True),
-        )
-        # it returns outputs and pool_indices_5
-
-        # 3*3
-
-        self.conv_block6 = Sequential(
-            # fc6
-            Conv2d(512, 4096, 3),
-            # set the filter size and nor padding to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 1*1
-
-        self.conv_block7 = Sequential(
-            # fc7
-            Conv2d(4096, 4096, 1),
-            # set the filter size to make output into 1*1
-            BatchNorm2d(4096, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.deconv_block8 = Sequential(
-            # fc6-deconv
-            ConvTranspose2d(4096, 512, 3, stride=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        # 3*3
-
-        self.unpool_block9 = Sequential(
-            # unpool5
-            MaxUnpool2d(2, stride=2),
-        )
-        # usage unpool(output, indices)
-
-        # 6*6
-
-        self.deconv_block10 = Sequential(
-            # deconv5_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv5_3
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block11 = Sequential(
-            # unpool4
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 12*12
-
-        self.deconv_block12 = Sequential(
-            # deconv4_1
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_2
-            ConvTranspose2d(512, 512, 3, stride=1, padding=1),
-            BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv4_3
-            ConvTranspose2d(512, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block13 = Sequential(
-            # unpool3
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 24*24
-
-        self.deconv_block14 = Sequential(
-            # deconv3_1
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_2
-            ConvTranspose2d(256, 256, 3, stride=1, padding=1),
-            BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv3_3
-            ConvTranspose2d(256, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block15 = Sequential(
-            # unpool2
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 48*48
-
-        self.deconv_block16 = Sequential(
-            # deconv2_1
-            ConvTranspose2d(128, 128, 3, stride=1, padding=1),
-            BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv2_2
-            ConvTranspose2d(128, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.unpool_block17 = Sequential(
-            # unpool1
-            MaxUnpool2d(2, stride=2),
-        )
-
-        # 96*96
-
-        self.deconv_block18 = Sequential(
-            # deconv1_1
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-            # deconv1_2
-            ConvTranspose2d(64, 64, 3, stride=1, padding=1),
-            BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
-            ReLU(inplace=True),
-        )
-
-        self.seg_score19 = Sequential(
-            # seg-score
-            Conv2d(64, self.n_classes, 1),
-        )
-
-        if self.learned_billinear:
-            raise NotImplementedError
-
-
[docs]    def forward(self, x):
-        size0 = x.size()
-        conv1, indices1 = self.conv_block1(x)
-        size1 = conv1.size()
-        conv2, indices2 = self.conv_block2(conv1)
-        size2 = conv2.size()
-        conv3, indices3 = self.conv_block3(conv2)
-        size3 = conv3.size()
-        conv4, indices4 = self.conv_block4(conv3)
-        size4 = conv4.size()
-        conv5, indices5 = self.conv_block5(conv4)
-
-        conv6 = self.conv_block6(conv5)
-        conv7 = self.conv_block7(conv6)
-        conv8 = self.deconv_block8(conv7) + conv5
-        conv9 = self.unpool(conv8, indices5, output_size=size4)
-        conv10 = self.deconv_block10(conv9) + conv4
-        conv11 = self.unpool(conv10, indices4, output_size=size3)
-        conv12 = self.deconv_block12(conv11) + conv3
-        conv13 = self.unpool(conv12, indices3, output_size=size2)
-        conv14 = self.deconv_block14(conv13) + conv2
-        conv15 = self.unpool(conv14, indices2, output_size=size1)
-        conv16 = self.deconv_block16(conv15) + conv1
-        conv17 = self.unpool(conv16, indices1, output_size=size0)
-        conv18 = self.deconv_block18(conv17)
-        out = self.seg_score19(conv18)
-
-        return out

-
-
[docs]    def init_vgg16_params(self, vgg16, copy_fc8=True):
-        blocks = [
-            self.conv_block1,
-            self.conv_block2,
-            self.conv_block3,
-            self.conv_block4,
-            self.conv_block5,
-        ]
-
-        ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
-        features = list(vgg16.features.children())
-        i_layer = 0
-        # copy convolutional filters from vgg16
-        for idx, conv_block in enumerate(blocks):
-            for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
-                if isinstance(l1, Conv2d) and isinstance(l2, Conv2d):
-                    if i_layer == 0:
-                        l2.weight.data = (
-                            (
-                                l1.weight.data[:, 0, :, :]
-                                + l1.weight.data[:, 1, :, :]
-                                + l1.weight.data[:, 2, :, :]
-                            )
-                            / 3.0
-                        ).view(l2.weight.size())
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1
-                    else:
-                        assert l1.weight.size() == l2.weight.size()
-                        assert l1.bias.size() == l2.bias.size()
-                        l2.weight.data = l1.weight.data
-                        l2.bias.data = l1.bias.data
-                        i_layer = i_layer + 1

-
-
[docs]    def load(self):
-        """ This is just a no-op load method. """
-        return self

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/dl/pytorch_lightning.html b/docs/_modules/dasf/ml/dl/pytorch_lightning.html deleted file mode 100644 index 534647e..0000000 --- a/docs/_modules/dasf/ml/dl/pytorch_lightning.html +++ /dev/null @@ -1,372 +0,0 @@ - - - - - - - - - - dasf.ml.dl.pytorch_lightning — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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- -
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- -
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    • - -
  • dasf.ml.dl.pytorch_lightning
    • - - -
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- - -
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- -

Source code for dasf.ml.dl.pytorch_lightning

-#!/usr/bin/env python3
-
-import uuid
-
-from torch.utils.data import DataLoader
-
-import pytorch_lightning as pl
-
-from dask_pytorch_ddp.results import DaskResultsHandler
-
-from dasf.ml.dl.clusters import DaskClusterEnvironment
-from dasf.utils.funcs import get_gpu_count
-from dasf.utils.funcs import get_dask_gpu_count
-from dasf.utils.funcs import get_worker_info
-from dasf.utils.funcs import get_dask_running_client
-from dasf.utils.funcs import sync_future_loop
-from dasf.transforms.base import Fit
-
-
-
[docs]class TorchDataLoader(pl.LightningDataModule):
-    def __init__(self, train, val=None, test=None, batch_size=64):
-        super().__init__()
-
-        self._train = train
-        self._val = val
-        self._test = test
-
-        self._batch_size = batch_size
-
-
[docs]    def prepare_data(self):
-        if self._train is not None and hasattr(self._train, "download"):
-            self._train.download()
-
-        if self._val is not None and hasattr(self._val, "download"):
-            self._val.download()
-
-        if self._test is not None and hasattr(self._test, "download"):
-            self._test.download()

-
-
[docs]    def setup(self, stage=None):
-        if self._train is not None and hasattr(self._train, "load"):
-            self._train.load()
-
-        if self._val is not None and hasattr(self._val, "load"):
-            self._val.load()
-
-        if self._test is not None and hasattr(self._test, "load"):
-            self._test.load()

-
-
[docs]    def train_dataloader(self):
-        return DataLoader(self._train, batch_size=self._batch_size)

-
-
[docs]    def val_dataloader(self):
-        return DataLoader(self._val, batch_size=self._batch_size)

-
-
[docs]    def test_dataloader(self):
-        return DataLoader(self._test, batch_size=self._batch_size)

-
-
-
[docs]def run_dask_clustered(func, client=None, **kwargs):
-    if client is None:
-        client = get_dask_running_client()
-
-    all_workers = get_worker_info(client)
-
-    for worker in all_workers:
-        futures = client.submit(func, **kwargs, workers=[worker["worker"]])
-
-    sync_future_loop(futures)

-
-
-
[docs]def fit(
-    model, X, y, max_iter, accel, strategy, devices, ngpus, batch_size=32, plugins=None
-):
-
-    # Variable world_size is based on the number of Dask workers
-    if plugins is not None and isinstance(plugins, list):
-        nodes = 1
-        for plugin in plugins:
-            if isinstance(plugin, DaskClusterEnvironment):
-                nodes = plugin.world_size()
-                break
-    else:
-        nodes = 1
-
-    # Use it for heterogeneous workers.
-    if ngpus < 0:
-        ngpus = -1
-
-    dataloader = TorchDataLoader(train=X, val=y, batch_size=batch_size)
-
-    trainer = pl.Trainer(
-        max_epochs=max_iter,
-        accelerator=accel,
-        strategy=strategy,
-        gpus=ngpus,
-        plugins=plugins,
-        devices=devices,
-        num_nodes=nodes,
-    )
-
-    trainer.fit(model, datamodule=dataloader)

-
-
-
[docs]class NeuralNetClassifier(Fit):
-    def __init__(self, model, max_iter=100, batch_size=32):
-        self._model = model
-
-        self._accel = None
-        self._strategy = None
-        self._max_iter = max_iter
-        self._devices = 0
-        self._ngpus = 0
-        self._batch_size = batch_size
-
-        self.__trainer = False
-        self.__handler = DaskResultsHandler(uuid.uuid4().hex)
-
-
[docs]    def _lazy_fit_generic(self, X, y, accel, ngpus):
-        self._accel = accel
-        self._strategy = "ddp"
-        self._ngpus = self._ndevices = ngpus
-
-        plugins = [DaskClusterEnvironment()]
-
-        run_dask_clustered(
-            fit,
-            model=self._model,
-            X=X,
-            y=y,
-            max_iter=self._max_iter,
-            accel=self._accel,
-            strategy=self._strategy,
-            devices=self._ndevices,
-            ngpus=self._ngpus,
-            batch_size=self._batch_size,
-            plugins=plugins,
-        )

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None):
-        self._lazy_fit_generic(X=X, y=y, accel="gpu", ngpus=len(get_dask_gpu_count()))

-
-
[docs]    def _lazy_fit_cpu(self, X, y=None):
-        self._lazy_fit_generic(X=X, y=y, accel="cpu", ngpus=len(get_dask_gpu_count()))

-
-    def __fit_generic(self, X, y, accel, ngpus):
-        self._accel = accel
-        self._strategy = "dp"
-        self._ngpus = self._ndevices = ngpus
-
-        dataloader = TorchDataLoader(train=X, val=y, batch_size=self._batch_size)
-
-        self.__trainer = pl.Trainer(
-            max_epochs=self._max_iter, accelerator=accel, gpus=ngpus
-        )
-
-        self.__trainer.fit(self._model, datamodule=dataloader)
-
-
[docs]    def _fit_gpu(self, X, y=None):
-        self.__fit_generic(X, y, "gpu", len(get_gpu_count()))

-
-
[docs]    def _fit_cpu(self, X, y=None):
-        self.__fit_generic(X, y, "cpu", 0)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/mixture/classifier.html b/docs/_modules/dasf/ml/mixture/classifier.html deleted file mode 100644 index b569b62..0000000 --- a/docs/_modules/dasf/ml/mixture/classifier.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - - - dasf.ml.mixture.classifier — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.mixture.classifier

-#!/usr/bin/env python3
-
-from dasf.transforms.base import Fit
-from dasf.transforms.base import FitPredict
-from dasf.transforms.base import FitTransform
-from dasf.transforms.base import GetParams
-from dasf.transforms.base import SetParams
-
-
-
[docs]class MixtureClassifier(Fit, FitPredict, FitTransform,
-                        GetParams, SetParams):
-
[docs]    def fit(self, X, y=None, sample_weight=None):
-        raise NotImplementedError

-
-
[docs]    def fit_predict(self, X, y=None, sample_weight=None):
-        raise NotImplementedError

-
-
[docs]    def fit_transform(self, X, y=None):
-        raise NotImplementedError

-
-
[docs]    def get_params(deep=True):
-        raise NotImplementedError

-
-
[docs]    def set_params(**params):
-        raise NotImplementedError

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/mixture/gmm.html b/docs/_modules/dasf/ml/mixture/gmm.html deleted file mode 100644 index c231296..0000000 --- a/docs/_modules/dasf/ml/mixture/gmm.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - - - - dasf.ml.mixture.gmm — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.mixture.gmm

-#!/usr/bin/env python3
-
-from sklearn.mixture import GaussianMixture as GaussianMixture_CPU
-
-from dasf.ml.mixture.classifier import MixtureClassifier
-
-
-
[docs]class GaussianMixture(MixtureClassifier):
-    def __init__(
-        self,
-        n_components=1,
-        *,
-        covariance_type="full",
-        tol=0.001,
-        reg_covar=1e-06,
-        max_iter=100,
-        n_init=1,
-        init_params="kmeans",
-        weights_init=None,
-        means_init=None,
-        precisions_init=None,
-        random_state=None,
-        warm_start=False,
-        verbose=0,
-        verbose_interval=10
-    ):
-
-        self.__gmm_cpu = GaussianMixture_CPU(
-            n_components=n_components,
-            covariance_type=covariance_type,
-            tol=tol,
-            reg_covar=reg_covar,
-            max_iter=max_iter,
-            n_init=n_init,
-            init_params=init_params,
-            weights_init=weights_init,
-            means_init=means_init,
-            precisions_init=precisions_init,
-            random_state=random_state,
-            warm_start=warm_start,
-            verbose=verbose,
-            verbose_interval=verbose_interval,
-        )
-
-
[docs]    def _fit_cpu(self, X, y=None):
-        return self.__gmm_cpu.fit(X=X, y=y)

-
-
[docs]    def _fit_predict_cpu(self, X, y=None):
-        return self.__gmm_cpu.fit_predict(X=X, y=y)

-
-
[docs]    def _predict_cpu(self, X, y=None):
-        return self.__gmm_cpu.predict(X=X)

-
-
[docs]    def _set_params_cpu(self, **params):
-        return self.__gmm_cpu.set_params(**params)

-
-
[docs]    def _get_params_cpu(self, deep=True):
-        return self.__gmm_cpu.get_params(deep=deep)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/model_selection/split.html b/docs/_modules/dasf/ml/model_selection/split.html deleted file mode 100644 index f77d6bb..0000000 --- a/docs/_modules/dasf/ml/model_selection/split.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - - - dasf.ml.model_selection.split — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.model_selection.split

-#!/usr/bin/env python3
-
-from sklearn.model_selection import train_test_split as train_test_split_cpu
-from dask_ml.model_selection import train_test_split as train_test_split_mcpu
-
-from dasf.transforms import TargeteredTransform, Transform
-
-try:
-    from cuml.model_selection import train_test_split as train_test_split_gpu
-except ImportError:
-    pass
-
-
-class train_test_split(TargeteredTransform, Transform):
-    def __init__(
-        self,
-        output="train",
-        test_size=None,
-        train_size=None,
-        random_state=None,
-        shuffle=None,
-        blockwise=True,
-        convert_mixed_types=False,
-        **kwargs
-    ):
-        TargeteredTransform.__init__(self, **kwargs)
-
-        self.output = output
-        self.test_size = test_size
-        self.train_size = train_size
-        self.random_state = random_state
-        self.shuffle = shuffle
-
-        # Exclusive for Dask operations
-        self.blockwise = blockwise
-
-        self.convert_mixed_types = convert_mixed_types
-
-
[docs]    def _lazy_transform_cpu(self, X):
-        X, y = X
-        X_train, X_test, y_train, y_test = train_test_split_mcpu(
-            X,
-            y,
-            train_size=self.train_size,
-            shuffle=self.shuffle,
-            random_state=self.random_state,
-            blockwise=self.blockwise,
-        )
-        if self.output == "train":
-            return X_train, y_train
-        elif self.output == "test":
-            return X_test, y_test

-
-
[docs]    def _lazy_transform_gpu(self, X):
-        raise NotImplementedError(
-            "Function train_test_split() is not implemented for Dask and CuML"
-        )

-
-
[docs]    def _transform_cpu(self, X):
-        X, y = X
-        X_train, X_test, y_train, y_test = train_test_split_cpu(
-            X,
-            y,
-            train_size=self.train_size,
-            shuffle=self.shuffle,
-            random_state=self.random_state,
-        )
-        if self.output == "train":
-            return X_train, y_train
-        elif self.output == "test":
-            return X_test, y_test

-
-
[docs]    def _transform_gpu(self, X):
-        X, y = X
-        X_train, X_test, y_train, y_test = train_test_split_gpu(
-            X,
-            y,
-            train_size=self.train_size,
-            shuffle=self.shuffle,
-            random_state=self.random_state,
-        )
-        if self.output == "train":
-            return X_train, y_train
-        elif self.output == "test":
-            return X_test, y_test

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/neighbors/neighbors.html b/docs/_modules/dasf/ml/neighbors/neighbors.html deleted file mode 100644 index ec1be29..0000000 --- a/docs/_modules/dasf/ml/neighbors/neighbors.html +++ /dev/null @@ -1,262 +0,0 @@ - - - - - - - - - - dasf.ml.neighbors.neighbors — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.neighbors.neighbors

-#!/usr/bin/env python3
-
-from sklearn.neighbors import NearestNeighbors as NearestNeighbors_CPU
-
-from dasf.utils.funcs import is_gpu_supported
-from dasf.transforms.base import Fit
-from dasf.transforms.base import GetParams
-from dasf.transforms.base import SetParams
-
-try:
-    from cuml.neighbors import NearestNeighbors as NearestNeighbors_GPU
-except ImportError:
-    pass
-
-
-
[docs]class NearestNeighbors(Fit, GetParams, SetParams):
-    def __init__(self, n_neighbors=5, radius=1.0, algorithm='auto',
-                 leaf_size=30, metric='minkowski', p=2,
-                 metric_params=None, n_jobs=None, handle=None, verbose=False,
-                 output_type=None, **kwargs):
-
-        self.__nn_cpu = NearestNeighbors_CPU(n_neighbors=n_neighbors,
-                                             radius=radius,
-                                             algorithm=algorithm,
-                                             leaf_size=leaf_size,
-                                             metric=metric, p=p,
-                                             metric_params=metric_params,
-                                             n_jobs=n_jobs, **kwargs)
-
-        if is_gpu_supported():
-            self.__nn_gpu = NearestNeighbors_GPU(n_neighbors=n_neighbors,
-                                                 radius=radius,
-                                                 algorithm=algorithm,
-                                                 leaf_size=leaf_size,
-                                                 metric=metric, p=p,
-                                                 metric_params=metric_params,
-                                                 n_jobs=n_jobs,
-                                                 handle=handle,
-                                                 verbose=verbose,
-                                                 output_type=output_type,
-                                                 **kwargs)
-
-
[docs]    def _fit_cpu(self, X, y=None, **kwargs):
-        return self.__nn_cpu.fit(X=X, y=y)

-
-
[docs]    def _fit_gpu(self, X, y=None, **kwargs):
-        return self.__nn_gpu.fit(X=X, **kwargs)

-
-
[docs]    def _get_params_cpu(self, deep=True, **kwargs):
-        return self.__nn_cpu.get_params(deep=deep)

-
-
[docs]    def _set_params_cpu(self, **params):
-        return self.__nn_cpu.set_params(**params)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/preprocessing/standardscaler.html b/docs/_modules/dasf/ml/preprocessing/standardscaler.html deleted file mode 100644 index 1ac7d3f..0000000 --- a/docs/_modules/dasf/ml/preprocessing/standardscaler.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - - - - dasf.ml.preprocessing.standardscaler — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.preprocessing.standardscaler

-#!/usr/bin/env python3
-
-from sklearn.preprocessing import StandardScaler as StandardScaler_CPU
-from dask_ml.preprocessing import StandardScaler as StandardScaler_MCPU
-
-from dasf.utils.funcs import is_gpu_supported
-
-try:
-    from cuml.preprocessing import StandardScaler as StandardScaler_GPU
-except ImportError:
-    pass
-
-from dasf.transforms.base import Fit
-from dasf.transforms.base import Transform
-from dasf.transforms.base import FitTransform
-
-
-
[docs]class StantardScaler(Fit, Transform, FitTransform):
-    def __init__(self, copy=True, with_mean=True, with_std=True):
-
-        self.__std_scaler_cpu = StandardScaler_CPU(
-            copy=copy, with_mean=with_mean, with_std=with_std
-        )
-
-        self.__std_scaler_dask = StandardScaler_MCPU(
-            copy=copy, with_mean=with_mean, with_std=with_std
-        )
-
-        if is_gpu_supported():
-            self.__std_scaler_gpu = StandardScaler_GPU(
-                copy=copy, with_mean=with_mean, with_std=with_std
-            )
-
-
[docs]    def _lazy_fit_cpu(self, X, y=None):
-        return self.__std_scaler_dask.fit(X=X, y=y)

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None):
-        return self.__std_scaler_dask.fit(X=X, y=y)

-
-
[docs]    def _fit_cpu(self, X, y=None):
-        return self.__std_scaler_cpu.fit(X=X, y=y)

-
-
[docs]    def _fit_gpu(self, X, y=None):
-        return self.__std_scaler_gpu.fit(X=X, y=y)

-
-
[docs]    def _lazy_fit_transform_cpu(self, X, y=None):
-        return self.__std_scaler_dask.fit_transform(X=X, y=y)

-
-
[docs]    def _lazy_fit_transform_gpu(self, X, y=None):
-        return self.__std_scaler_dask.fit_transform(X=X, y=y)

-
-
[docs]    def _fit_transform_cpu(self, X, y=None):
-        return self.__std_scaler_cpu.fit_transform(X=X, y=y)

-
-
[docs]    def _fit_transform_gpu(self, X, y=None):
-        ret = self.__std_scaler_gpu.fit(X=X, y=y)
-        return ret.transform(X=X)

-
-
[docs]    def _lazy_partial_fit_cpu(self, X, y=None):
-        return self.__std_scaler_dask.partial_fit(X=X, y=y)

-
-
[docs]    def _lazy_partial_fit_gpu(self, X, y=None):
-        return self.__std_scaler_dask.partial_fit(X=X, y=y)

-
-
[docs]    def _fit_partial_cpu(self, X, y=None):
-        return self.__std_scaler_cpu.partial_fit(X=X, y=y)

-
-
[docs]    def _fit_partial_gpu(self, X, y=None):
-        return self.__std_scaler_gpu.partial_fit(X=X, y=y)

-
-
[docs]    def _lazy_transform_cpu(self, X, copy=None):
-        return self.__std_scaler_dask.transform(X=X, copy=copy)

-
-
[docs]    def _lazy_transform_gpu(self, X, copy=None):
-        return self.__std_scaler_dask.transform(X=X, copy=copy)

-
-
[docs]    def _transform_cpu(self, X, copy=None):
-        return self.__std_scaler_cpu.transform(X=X, copy=copy)

-
-
[docs]    def _transform_gpu(self, X, copy=None):
-        return self.__std_scaler_gpu.transform(X=X, copy=copy)

-
-
[docs]    def _lazy_inverse_transform_cpu(self, X, copy=None):
-        return self.__std_scaler_dask.inverse_transform(X=X, copy=copy)

-
-
[docs]    def _lazy_inverse_transform_gpu(self, X, copy=None):
-        return self.__std_scaler_dask.inverse_transform(X=X, copy=copy)

-
-
[docs]    def _inverse_transform_cpu(self, X, copy=None):
-        return self.__std_scaler_cpu.inverse_transform(X=X, copy=copy)

-
-
[docs]    def _inverse_transform_gpu(self, X, copy=None):
-        return self.__std_scaler_gpu.inverse_transform(X=X, copy=copy)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/svm/svm.html b/docs/_modules/dasf/ml/svm/svm.html deleted file mode 100644 index c7cc249..0000000 --- a/docs/_modules/dasf/ml/svm/svm.html +++ /dev/null @@ -1,506 +0,0 @@ - - - - - - - - - - dasf.ml.svm.svm — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for dasf.ml.svm.svm

-#!/usr/bin/env python3
-
-from sklearn.svm import SVC as SVC_CPU
-from sklearn.svm import SVR as SVR_CPU
-from sklearn.svm import LinearSVC as LinearSVC_CPU
-from sklearn.svm import LinearSVR as LinearSVR_CPU
-
-try:
-    from cuml.svm import SVC as SVC_GPU
-    from cuml.svm import SVR as SVR_GPU
-    from cuml.svm import LienarSVC as LinearSVC_GPU
-    from cuml.svm import LienarSVR as LinearSVR_GPU
-except ImportError:
-    pass
-
-from dasf.utils.funcs import is_gpu_supported
-from dasf.transforms.base import Fit
-from dasf.transforms.base import Predict
-from dasf.transforms.base import GetParams
-from dasf.transforms.base import SetParams
-
-
-
[docs]class SVC(Fit, Predict, GetParams, SetParams):
-    def __init__(
-        self,
-        C=1.0,
-        kernel="rbf",
-        degree=3,
-        gamma="scale",
-        coef0=0.0,
-        shrinking=True,
-        probability=False,
-        tol=0.001,
-        cache_size=200,
-        class_weight=None,
-        verbose=False,
-        max_iter=-1,
-        decision_function_shape="ovr",
-        break_ties=False,
-        nochange_steps=1000,
-        random_state=None,
-    ):
-
-        self.__svc_cpu = SVC_CPU(
-            C=C,
-            kernel=kernel,
-            degree=degree,
-            gamma=gamma,
-            coef0=coef0,
-            shrinking=shrinking,
-            probability=probability,
-            tol=tol,
-            cache_size=cache_size,
-            class_weight=class_weight,
-            verbose=verbose,
-            max_iter=max_iter,
-            decision_function_shape=decision_function_shape,
-            break_ties=break_ties,
-            random_state=random_state,
-        )
-
-        if is_gpu_supported():
-            self.__svc_gpu = SVC_GPU(
-                C=C,
-                kernel=kernel,
-                degree=degree,
-                gamma=gamma,
-                coef0=coef0,
-                tol=tol,
-                cache_size=cache_size,
-                class_weight=class_weight,
-                verbose=verbose,
-                max_iter=max_iter,
-                random_state=random_state,
-                multiclass_strategy=decision_function_shape,
-                probability=probability,
-                output_type="input",
-            )
-
-
[docs]    def _fit_cpu(self, X, y, sample_weight=None):
-        return self.__svc_cpu.fit(X, y, sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y, sample_weight=None):
-        return self.__svc_gpu.fit(X, y, sample_weight)

-
-
[docs]    def _predict_cpu(self, X):
-        return self.__svc_cpu.predict(X)

-
-
[docs]    def _predict_gpu(self, X):
-        return self.__svc_gpu.predict(X)

-
-
[docs]    def _get_params_cpu(self, deep=True):
-        return self.__svc_cpu.get_params(deep=deep)

-
-
[docs]    def _set_params_cpu(self, **params):
-        return self.__svc_cpu.set_params(**params)

-
-
-
[docs]class SVR(Fit, Predict):
-    def __init__(
-        self,
-        kernel="rbf",
-        degree=3,
-        gamma="scale",
-        coef0=0.0,
-        tol=0.001,
-        C=1.0,
-        epsilon=0.1,
-        shrinking=True,
-        cache_size=200,
-        verbose=False,
-        max_iter=-1,
-        nochange_steps=1000,
-    ):
-
-        self.__svr_cpu = SVR_CPU(
-            C=C,
-            kernel=kernel,
-            degree=degree,
-            gamma=gamma,
-            coef0=coef0,
-            tol=tol,
-            epsilon=epsilon,
-            shrinking=shrinking,
-            cache_size=cache_size,
-            verbose=verbose,
-            max_iter=max_iter,
-        )
-
-        if is_gpu_supported():
-            self.__svr_gpu = SVR_GPU(
-                C=C,
-                kernel=kernel,
-                degree=degree,
-                gamma=gamma,
-                coef0=coef0,
-                tol=tol,
-                epsilon=epsilon,
-                shrinking=shrinking,
-                cache_size=cache_size,
-                verbose=verbose,
-                max_iter=max_iter,
-                nochange_steps=nochange_steps,
-                output_type="input",
-            )
-
-
[docs]    def _fit_cpu(self, X, y, sample_weight=None):
-        return self.__svr_cpu.fit(X, y, sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y, sample_weight=None):
-        return self.__svr_gpu.fit(X, y, sample_weight)

-
-
[docs]    def _predict_cpu(self, X):
-        return self.__svr_cpu.predict(X)

-
-
[docs]    def _predict_gpu(self, X):
-        return self.__svr_gpu.predict(X)

-
-
-
[docs]class LinearSVC(Fit, Predict, GetParams, SetParams):
-    def __init__(
-        self,
-        epsilon=0.0,
-        tol=0.0001,
-        C=1.0,
-        loss="epsilon_insensitive",
-        fit_intercept=True,
-        intercept_scaling=1.0,
-        dual=True,
-        verbose=0,
-        random_state=None,
-        max_iter=1000,
-        handle=None,
-        penalty="l2",
-        penalized_intercept=False,
-        linesearch_max_iter=100,
-        lbfgs_memory=5,
-        grad_tol=0.0001,
-        change_tol=1e-05,
-        multi_class="ovr",
-    ):
-
-        self.__linear_svc_cpu = LinearSVC_CPU(
-            epsilon=epsilon,
-            tol=tol,
-            C=C,
-            loss=loss,
-            fit_intercept=fit_intercept,
-            intercept_scaling=intercept_scaling,
-            dual=dual,
-            verbose=verbose,
-            random_state=random_state,
-            max_iter=max_iter,
-        )
-
-        if is_gpu_supported():
-            self.__linear_svc_gpu = LinearSVC_GPU(
-                tol=tol,
-                C=C,
-                loss=loss,
-                fit_intercept=fit_intercept,
-                intercept_scaling=intercept_scaling,
-                dual=dual,
-                verbose=verbose,
-                random_state=random_state,
-                max_iter=max_iter,
-                handle=handle,
-                penalty=penalty,
-                penalized_intercept=penalized_intercept,
-                linesearch_max_iter=linesearch_max_iter,
-                lbfgs_memory=lbfgs_memory,
-                grad_tol=grad_tol,
-                change_tol=change_tol,
-                multi_class=multi_class,
-            )
-
-
[docs]    def _fit_cpu(self, X, y, sample_weight=None):
-        return self.__linear_svc_cpu.fit(X, y, sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y, sample_weight=None):
-        return self.__linear_svc_gpu.fit(X, y, sample_weight)

-
-
[docs]    def _predict_cpu(self, X):
-        return self.__linear_svc_cpu.predict(X)

-
-
[docs]    def _predict_gpu(self, X):
-        return self.__linear_svc_gpu.predict(X)

-
-
-
[docs]class LinearSVR(Fit, Predict):
-    def __init__(
-        self,
-        epsilon=0.0,
-        tol=0.0001,
-        C=1.0,
-        loss="epsilon_insensitive",
-        fit_intercept=True,
-        intercept_scaling=1.0,
-        dual=True,
-        verbose=0,
-        random_state=None,
-        max_iter=1000,
-        handle=None,
-        penalty="l2",
-        penalized_intercept=False,
-        linesearch_max_iter=100,
-        lbfgs_memory=5,
-        grad_tol=0.0001,
-        change_tol=1e-05,
-        multi_class="ovr",
-    ):
-
-        self.__linear_svr_cpu = LinearSVR_CPU(
-            epsilon=epsilon,
-            tol=tol,
-            C=C,
-            loss=loss,
-            fit_intercept=fit_intercept,
-            intercept_scaling=intercept_scaling,
-            dual=dual,
-            verbose=verbose,
-            random_state=random_state,
-            max_iter=max_iter,
-        )
-
-        if is_gpu_supported():
-            self.__linear_svr_gpu = LinearSVR_GPU(
-                tol=tol,
-                C=C,
-                loss=loss,
-                fit_intercept=fit_intercept,
-                intercept_scaling=intercept_scaling,
-                dual=dual,
-                verbose=verbose,
-                random_state=random_state,
-                max_iter=max_iter,
-                handle=handle,
-                penalty=penalty,
-                penalized_intercept=penalized_intercept,
-                linesearch_max_iter=linesearch_max_iter,
-                lbfgs_memory=lbfgs_memory,
-                grad_tol=grad_tol,
-                change_tol=change_tol,
-                multi_class=multi_class,
-            )
-
-
[docs]    def _fit_cpu(self, X, y, sample_weight=None):
-        return self.__linear_svr_cpu.fit(X, y, sample_weight)

-
-
[docs]    def _fit_gpu(self, X, y, sample_weight=None):
-        return self.__linear_svr_gpu.fit(X, y, sample_weight)

-
-
[docs]    def _predict_cpu(self, X):
-        return self.__linear_svr_cpu.predict(X)

-
-
[docs]    def _predict_gpu(self, X):
-        return self.__linear_svr_gpu.predict(X)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/ml/xgboost/xgboost.html b/docs/_modules/dasf/ml/xgboost/xgboost.html deleted file mode 100644 index dbebddc..0000000 --- a/docs/_modules/dasf/ml/xgboost/xgboost.html +++ /dev/null @@ -1,405 +0,0 @@ - - - - - - - - - - dasf.ml.xgboost.xgboost — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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    • - - -
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    • - -
- - -
-
-
-
- -

Source code for dasf.ml.xgboost.xgboost

-#!/usr/bin/env python3
-
-import GPUtil
-
-import xgboost as xgb
-
-from dasf.transforms import Fit
-from dasf.transforms import Predict
-from dasf.transforms import FitPredict
-
-from dasf.utils.funcs import is_gpu_supported
-
-
-
[docs]class XGBoost(Fit, FitPredict, Predict):
-    def __init__(
-        self,
-        max_depth=None,
-        max_leaves=None,
-        max_bin=None,
-        grow_policy=None,
-        learning_rate=None,
-        n_estimators=100,
-        verbosity=None,
-        objective=None,
-        booster=None,
-        tree_method=None,
-        n_jobs=None,
-        gamma=None,
-        min_child_weight=None,
-        max_delta_step=None,
-        subsample=None,
-        sampling_method=None,
-        colsample_bytree=None,
-        colsample_bylevel=None,
-        colsample_bynode=None,
-        reg_alpha=None,
-        reg_lambda=None,
-        scale_pos_weight=None,
-        base_score=None,
-        random_state=None,
-        num_parallel_tree=None,
-        monotone_constraints=None,
-        interaction_constraints=None,
-        importance_type=None,
-        gpu_id=None,
-        validate_parameters=None,
-        predictor=None,
-        enable_categorical=False,
-        max_cat_to_onehot=None,
-        eval_metric=None,
-        early_stopping_rounds=None,
-        callbacks=None,
-        **kwargs
-    ):
-
-        self.__xgb_cpu = xgb.XGBRegressor(
-            n_estimators=n_estimators,
-            max_depth=max_depth,
-            max_leaves=max_leaves,
-            max_bin=max_bin,
-            grow_policy=grow_policy,
-            learning_rate=learning_rate,
-            verbosity=verbosity,
-            objective=objective,
-            booster=booster,
-            tree_method=tree_method,
-            n_jobs=n_jobs,
-            gamma=gamma,
-            min_child_weight=min_child_weight,
-            max_delta_step=max_delta_step,
-            subsample=subsample,
-            sampling_method=sampling_method,
-            colsample_bytree=colsample_bytree,
-            colsample_bylevel=colsample_bylevel,
-            colsample_bynode=colsample_bynode,
-            reg_alpha=reg_alpha,
-            reg_lambda=reg_lambda,
-            scale_pos_weight=scale_pos_weight,
-            base_score=base_score,
-            random_state=random_state,
-        )
-
-        self.__xgb_mcpu = xgb.dask.DaskXGBClassifier(
-            n_estimators=n_estimators,
-            max_depth=max_depth,
-            max_leaves=max_leaves,
-            max_bin=max_bin,
-            grow_policy=grow_policy,
-            learning_rate=learning_rate,
-            verbosity=verbosity,
-            objective=objective,
-            booster=booster,
-            tree_method=tree_method,
-            n_jobs=n_jobs,
-            gamma=gamma,
-            min_child_weight=min_child_weight,
-            max_delta_step=max_delta_step,
-            subsample=subsample,
-            sampling_method=sampling_method,
-            colsample_bytree=colsample_bytree,
-            colsample_bylevel=colsample_bylevel,
-            colsample_bynode=colsample_bynode,
-            reg_alpha=reg_alpha,
-            reg_lambda=reg_lambda,
-            scale_pos_weight=scale_pos_weight,
-            base_score=base_score,
-            random_state=random_state,
-        )
-
-        if is_gpu_supported():
-            if gpu_id is None:
-                gpus = GPUtil.getGPUs()
-                if len(gpus) > 0:
-                    gpu_id = gpus[0].id
-
-            self.__xgb_gpu = xgb.XGBRegressor(
-                n_estimators=n_estimators,
-                max_depth=max_depth,
-                max_leaves=max_leaves,
-                max_bin=max_bin,
-                grow_policy=grow_policy,
-                learning_rate=learning_rate,
-                verbosity=verbosity,
-                objective=objective,
-                booster=booster,
-                tree_method='gpu_hist',
-                n_jobs=n_jobs,
-                gamma=gamma,
-                min_child_weight=min_child_weight,
-                max_delta_step=max_delta_step,
-                subsample=subsample,
-                sampling_method=sampling_method,
-                colsample_bytree=colsample_bytree,
-                colsample_bylevel=colsample_bylevel,
-                colsample_bynode=colsample_bynode,
-                reg_alpha=reg_alpha,
-                reg_lambda=reg_lambda,
-                scale_pos_weight=scale_pos_weight,
-                base_score=base_score,
-                random_state=random_state,
-                gpu_id=gpu_id
-            )
-
-            self.__xgb_mgpu = xgb.dask.DaskXGBClassifier(
-                n_estimators=n_estimators,
-                max_depth=max_depth,
-                max_leaves=max_leaves,
-                max_bin=max_bin,
-                grow_policy=grow_policy,
-                learning_rate=learning_rate,
-                verbosity=verbosity,
-                objective=objective,
-                booster=booster,
-                tree_method=tree_method,
-                n_jobs=n_jobs,
-                gamma=gamma,
-                min_child_weight=min_child_weight,
-                max_delta_step=max_delta_step,
-                subsample=subsample,
-                sampling_method=sampling_method,
-                colsample_bytree=colsample_bytree,
-                colsample_bylevel=colsample_bylevel,
-                colsample_bynode=colsample_bynode,
-                reg_alpha=reg_alpha,
-                reg_lambda=reg_lambda,
-                scale_pos_weight=scale_pos_weight,
-                base_score=base_score,
-                random_state=random_state,
-            )
-
-            self.__xgb_gpu.set_param({'predictor': 'gpu_predictor'})
-            self.__xgb_mgpu.set_param({'predictor': 'gpu_predictor'})
-
-
[docs]    def _lazy_fit_cpu(self, X, y=None, sample_weight=None):
-        return self.__xgb_mcpu.fit(X=X, y=y)

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, sample_weight=None):
-        return self.__xgb_mgpu.fit(X=X, y=y)

-
-
[docs]    def _fit_cpu(self, X, y=None, sample_weight=None):
-        return self.__xgb_cpu.fit(X=X, y=y)

-
-
[docs]    def _fit_gpu(self, X, y=None, sample_weight=None):
-        return self.__xgb_gpu.fit(X=X, y=y)

-
-
[docs]    def _lazy_predict_cpu(self, X, sample_weight=None, **kwargs):
-        return self.__kmeans_mcpu.predict(data=X, **kwargs)

-
-
[docs]    def _lazy_predict_gpu(self, X, sample_weight=None, **kwargs):
-        return self.__kmeans_mgpu.predict(data=X, **kwargs)

-
-
[docs]    def _predict_cpu(self, X, sample_weight=None, **kwargs):
-        return self.__kmeans_cpu.predict(X, **kwargs)

-
-
[docs]    def _predict_gpu(self, X, sample_weight=None, **kwargs):
-        return self.__kmeans_gpu.predict(X, **kwargs)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/pipeline/executors/base.html b/docs/_modules/dasf/pipeline/executors/base.html deleted file mode 100644 index 41dd6e5..0000000 --- a/docs/_modules/dasf/pipeline/executors/base.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - - - - - dasf.pipeline.executors.base — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-
-
-
- -

Source code for dasf.pipeline.executors.base

-#!/usr/bin/env python3
-
-
-
[docs]class Executor:
-    @property
-    def ngpus(self) -> int:
-        return 0
-
-    @property
-    def is_connected(self) -> bool:
-        return False
-
-
[docs]    def pre_run(self, pipeline):
-        pass

-
-
[docs]    def post_run(self, pipeline):
-        pass

-
-
[docs]    def execute(self, fn, *args, **kwargs):
-        ...

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/pipeline/executors/dask.html b/docs/_modules/dasf/pipeline/executors/dask.html deleted file mode 100644 index 8ada758..0000000 --- a/docs/_modules/dasf/pipeline/executors/dask.html +++ /dev/null @@ -1,411 +0,0 @@ - - - - - - - - - - dasf.pipeline.executors.dask — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.pipeline.executors.dask
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.pipeline.executors.dask

-#!/usr/bin/env python3
-
-import os
-
-import networkx as nx
-
-import dask_memusage as dmem
-
-from pathlib import Path
-
-from dask.distributed import Client, LocalCluster
-from dask_cuda import LocalCUDACluster
-
-from dask_jobqueue import PBSCluster
-
-from dasf.pipeline.types import TaskExecutorType
-from dasf.pipeline.executors.base import Executor
-from dasf.utils.funcs import is_dask_gpu_supported
-from dasf.utils.funcs import get_dask_gpu_count
-from dasf.utils.funcs import get_worker_info
-
-
-
[docs]class DaskPipelineExecutor(Executor):
-    """
-    A pipeline engine based on dask data flow.
-
-    Keyword arguments:
-    address -- address of the Dask scheduler (default None).
-    port -- port of the Dask scheduler (default 8786).
-    local -- kicks off a new local Dask cluster (default False).
-    use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask
-                cluster (default False).
-    profiler -- sets a Dask profiler.
-    cluster_kwargs -- extra Dask parameters like memory, processes, etc.
-    client_kwargs -- extra Client parameters.
-    """
-
-    def __init__(
-        self,
-        address=None,
-        port=8786,
-        local=False,
-        use_gpu=False,
-        profiler=None,
-        cluster_kwargs=None,
-        client_kwargs=None,
-    ):
-        self.address = address
-        self.port = port
-
-        if not cluster_kwargs:
-            cluster_kwargs = dict()
-
-        if not client_kwargs:
-            client_kwargs = dict()
-
-        # If address is not set, consider local
-        local = local or (address is None and "scheduler_file" not in client_kwargs)
-
-        if address:
-            self.client = Client(address=f"{address}:{port}")
-        elif "scheduler_file" in client_kwargs:
-            self.client = Client(scheduler_file=client_kwargs["scheduler_file"])
-        elif local:
-            if use_gpu:
-                self.client = Client(
-                    LocalCUDACluster(**cluster_kwargs), **client_kwargs
-                )
-            else:
-                self.client = Client(LocalCluster(**cluster_kwargs),
-                                     **client_kwargs)
-
-        # Ask workers for GPUs
-        if local and not use_gpu:
-            self.dtype = TaskExecutorType.multi_cpu
-        else:
-            # Ask workers for GPUs
-            if is_dask_gpu_supported():
-                self.dtype = TaskExecutorType.multi_gpu
-            else:
-                self.dtype = TaskExecutorType.multi_cpu
-
-        # Share dtype attribute to client
-        if not hasattr(self.client, "dtype"):
-            setattr(self.client, "dtype", self.dtype)
-
-        if profiler == "memusage":
-            profiler_dir = os.path.abspath(
-                os.path.join(str(Path.home()),
-                             "/.cache/dasf/profiler/"))
-            os.makedirs(profiler_dir, exist_ok=True)
-
-            dmem.install(
-                self.client.cluster.scheduler,
-                os.path.abspath(profiler_dir + "/dask-memusage"),
-            )
-
-    @property
-    def ngpus(self):
-        return len(get_dask_gpu_count())
-
-    @property
-    def is_connected(self):
-        if "running" in self.client.status:
-            return True
-        return False
-
-
[docs]    def execute(self, fn, *args, **kwargs):
-        return fn(*args, **kwargs)

-
-
-
[docs]class DaskTasksPipelineExecutor(DaskPipelineExecutor):
-    """
-    A not centric execution engine based on dask.
-
-    Keyword arguments:
-    address -- address of the Dask scheduler (default None).
-    port -- port of the Dask scheduler (default 8786).
-    local -- kicks off a new local Dask cluster (default False).
-    use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask
-                cluster (default False).
-    profiler -- sets a Dask profiler.
-    cluster_kwargs -- extra Dask parameters like memory, processes, etc.
-    client_kwargs -- extra Client parameters.
-    """
-    def __init__(
-        self,
-        address=None,
-        port=8786,
-        local=False,
-        use_gpu=False,
-        profiler=None,
-        cluster_kwargs=None,
-        client_kwargs=None,
-    ):
-
-        super().__init__(
-            address=address,
-            port=port,
-            local=local,
-            use_gpu=use_gpu,
-            profiler=profiler,
-            cluster_kwargs=cluster_kwargs,
-            client_kwargs=client_kwargs,
-        )
-
-        # Ask workers for GPUs
-        if is_dask_gpu_supported():
-            self.dtype = TaskExecutorType.single_gpu
-        else:
-            self.dtype = TaskExecutorType.single_cpu
-
-        # Share dtype attribute to client
-        if not hasattr(self.client, "dtype"):
-            setattr(self.client, "dtype", self.dtype)
-
-        self._tasks_map = dict()
-
-
[docs]    def pre_run(self, pipeline):
-        nodes = list(nx.topological_sort(pipeline._dag))
-
-        # TODO: we need to consider other branches for complex pipelines
-        dag_paths = nx.all_simple_paths(pipeline._dag, nodes[0], nodes[-1])
-        all_paths = []
-        for path in dag_paths:
-            all_paths.append(path)
-
-        workers = get_worker_info(self.client)
-
-        worker_idx = 0
-        for path in all_paths:
-            for node in path:
-                if node not in self._tasks_map:
-                    self._tasks_map[node] = workers[worker_idx]
-
-            # Increment workers to all new path and repeat if there
-            # are more paths to assign.
-            if worker_idx == len(workers):
-                worker_idx = 0
-            else:
-                worker_idx += 1

-
-
[docs]    def post_run(self, pipeline):
-        pass

-
-
[docs]    def execute(self, fn, *args, **kwargs):
-        key = hash(fn)
-
-        worker = self._tasks_map[key]["worker"]
-
-        return self.client.submit(fn, *args, **kwargs, workers=[worker])

-
-
-
[docs]class DaskPBSPipelineExecutor(Executor):
-    def __init__(self, **kwargs):
-        self.client = Client(PBSCluster(**kwargs))
-
-        # Ask workers for GPUs
-        if is_dask_gpu_supported():
-            self.dtype = TaskExecutorType.multi_gpu
-        else:
-            self.dtype = TaskExecutorType.multi_cpu

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/pipeline/executors/ray.html b/docs/_modules/dasf/pipeline/executors/ray.html deleted file mode 100644 index 857c8bd..0000000 --- a/docs/_modules/dasf/pipeline/executors/ray.html +++ /dev/null @@ -1,175 +0,0 @@ - - - - - - dasf.pipeline.executors.ray — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.pipeline.executors.ray

-#!/usr/bin/env python3
-
-try:
-    import ray
-    
-    from ray.util.dask import ray_dask_get
-    from ray.util.dask import enable_dask_on_ray
-    from ray.util.dask import disable_dask_on_ray
-
-    USE_RAY=True
-except ImportError:
-    USE_RAY=False
-
-from dasf.pipeline.executors.base import Executor
-from dasf.utils.funcs import get_dask_gpu_count
-
-
[docs]class RayPipelineExecutor(Executor):
-    """
-    A pipeline engine based on ray data flow.
-
-    Keyword arguments:
-    address -- address of the Dask scheduler (default None).
-    port -- port of the Ray head (default 8786).
-    local -- kicks off a new local Ray cluster (default False).
-    use_gpu -- in conjunction with `local`, it kicks off a local CUDA Ray
-                cluster (default False).
-    """
-
-    def __init__(
-        self,
-        address=None,
-        port=6379,
-        local=False,
-        use_gpu=False,
-        ray_kwargs=None,
-    ):
-    
-        self.address = address
-        self.port = port
-
-        if not ray_kwargs:
-            ray_kwargs = dict()
-
-        enable_dask_on_ray()
-
-        if address:
-            address_str = f"ray://{address}:{str(port)}"
-
-            ray.init(address=address, **ray_kwargs)
-        elif local:
-            ray.init(**ray_kwargs)
-
-    @property
-    def ngpus(self):
-        return len(get_dask_gpu_count())
-
-    @property
-    def is_connected(self):
-        return ray.is_initialized()            
-
-
[docs]    def execute(self, fn, *args, **kwargs):
-        return fn(*args, **kwargs)

-
-
[docs]    def __del__(self):
-        disable_dask_on_ray()
-
-        ray.shutdown()

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/pipeline/pipeline.html b/docs/_modules/dasf/pipeline/pipeline.html deleted file mode 100644 index 3afeecd..0000000 --- a/docs/_modules/dasf/pipeline/pipeline.html +++ /dev/null @@ -1,408 +0,0 @@ - - - - - - - - - - dasf.pipeline.pipeline — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.pipeline.pipeline
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.pipeline.pipeline

-#!/usr/bin/env python3
-
-import inspect
-import graphviz
-
-import networkx as nx
-
-from dasf.utils.logging import init_logging
-
-
-
[docs]class Pipeline:
-    def __init__(self, name, executor=None, verbose=False):
-        self._name = name
-        self._executor = executor
-        self._verbose = verbose
-
-        self._dag = nx.DiGraph()
-        self._dag_table = dict()
-        self._dag_g = graphviz.Digraph(name, format="png")
-
-        self._logger = init_logging()
-
-    def __add_into_dag(self, obj, func_name, parameters=None, itself=None):
-        key = hash(obj)
-
-        if key not in self._dag_table:
-            self._dag.add_node(key)
-            self._dag_table[key] = dict()
-            self._dag_table[key]["fn"] = obj
-            self._dag_table[key]["name"] = func_name
-            self._dag_table[key]["parameters"] = None
-            self._dag_table[key]["ret"] = None
-
-        if parameters and isinstance(parameters, dict):
-            if self._dag_table[key]["parameters"] is None:
-                self._dag_table[key]["parameters"] = parameters
-            else:
-                self._dag_table[key]["parameters"].update(parameters)
-
-            # If we are adding a object which require parameters,
-            # we need to make sure they are mapped into DAG.
-            for k, v in parameters.items():
-                dep_obj, dep_func_name, _ = self.__inspect_element(v)
-                self.add(dep_obj)
-                if not self._dag.has_node(str(key)):
-                    self._dag_g.node(str(key), func_name)
-
-                if not self._dag.has_node(str(hash(dep_obj))):
-                    self._dag_g.node(str(hash(dep_obj)), dep_func_name)
-
-                self._dag.add_edge(hash(dep_obj), key)
-
-                self._dag_g.edge(str(hash(dep_obj)), str(key), label=k)
-
-    def __inspect_element(self, obj):
-        from dasf.datasets.base import Dataset
-        from dasf.transforms.base import Transform, Fit
-
-        def generate_name(class_name, func_name):
-            return ("%s.%s" % (class_name, func_name))
-
-        if inspect.isfunction(obj) and callable(obj):
-            return (obj,
-                    obj.__qualname__,
-                    None)
-        elif inspect.ismethod(obj):
-            return (obj,
-                    generate_name(obj.__self__.__class__.__name__,
-                                  obj.__name__),
-                    obj.__self__)
-        elif issubclass(obj.__class__, Transform) and hasattr(obj, "transform"):
-            return (obj.transform,
-                    generate_name(obj.__class__.__name__,
-                                  "transform"),
-                    obj)
-        elif issubclass(obj.__class__, Dataset) and hasattr(obj, "load"):
-            return (obj.load,
-                    generate_name(obj.__class__.__name__,
-                                  "load"),
-                    obj)
-        elif issubclass(obj.__class__, Fit) and hasattr(obj, "fit"):
-            return (obj.fit,
-                    generate_name(obj.__class__.__name__,
-                                  "fit"),
-                    obj)
-        else:
-            raise ValueError(
-                f"This object {obj.__class__.__name__} is not a function, "
-                "method or a transformer object."
-            )
-
-
[docs]    def add(self, obj, **kwargs):
-        obj, func_name, objref = self.__inspect_element(obj)
-        self.__add_into_dag(obj, func_name, kwargs, objref)
-
-        return self

-
-
[docs]    def visualize(self, filename=None):
-        from dasf.utils.funcs import is_notebook
-
-        if is_notebook():
-            return self._dag_g
-        return self._dag_g.view(filename)

-
-    def __execute(self, func, params, name):
-        ret = None
-
-        new_params = dict()
-        if params:
-            for k, v in params.items():
-                dep_obj, *_ = self.__inspect_element(v)
-                req_key = hash(dep_obj)
-
-                new_params[k] = self._dag_table[req_key]["ret"]
-
-        if len(new_params) > 0:
-            if self._executor:
-                ret = self._executor.execute(fn=func, **new_params)
-            else:
-                ret = func(**new_params)
-        else:
-            if self._executor:
-                ret = self._executor.execute(fn=func)
-            else:
-                ret = func()
-
-        return ret
-
-
[docs]    def get_result_from(self, obj):
-        _, obj_name, *_ = self.__inspect_element(obj)
-
-        for key in self._dag_table:
-            if self._dag_table[key]["name"] == obj_name:
-                if self._dag_table[key]["ret"] is None:
-                    raise Exception("Pipeline was not executed yet.")
-                return self._dag_table[key]["ret"]
-
-        raise Exception(f"Function {obj_name} was not added into pipeline.")

-
-
[docs]    def run(self):
-        if not nx.is_directed_acyclic_graph(self._dag):
-            raise Exception("Pipeline has not a DAG format.")
-
-        if self._executor and not hasattr(self._executor, "execute"):
-            raise Exception(
-                f"Executor {self._executor.__name__} has not a execute() "
-                "method."
-            )
-
-        if self._executor:
-            if not self._executor.is_connected:
-                raise Exception("Executor is not connected.")
-
-        fn_keys = list(nx.topological_sort(self._dag))
-
-        self._logger.info(f"Beginning pipeline run for '{self._name}'")
-
-        if self._executor:
-            self._executor.pre_run(self)
-
-        ret = None
-        failed = False
-
-        for fn_key in fn_keys:
-            func = self._dag_table[fn_key]["fn"]
-            params = self._dag_table[fn_key]["parameters"]
-            name = self._dag_table[fn_key]["name"]
-
-            if not failed:
-                self._logger.info(f"Task '{name}': Starting task run...")
-            else:
-                self._logger.error(f"Task '{name}': Starting task run...")
-
-            try:
-                if not failed:
-                    # Execute DAG node only if there is no error during the
-                    # execution. Otherwise, skip it.
-                    self._dag_table[fn_key]["ret"] = self.__execute(func,
-                                                                    params,
-                                                                    name)
-            except Exception as e:
-                failed = True
-                err = str(e)
-                self._logger.exception(f"Task '{name}': Failed with:\n{err}")
-
-            if not failed:
-                self._logger.info(f"Task '{name}': Finished task run")
-            else:
-                self._logger.error(f"Task '{name}': Finished task run")
-
-        if failed:
-            self._logger.info(f"Pipeline failed at '{name}'")
-        else:
-            self._logger.info("Pipeline run successfully")
-
-        if self._executor:
-            self._executor.post_run(self)
-
-        return ret

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/pipeline/types.html b/docs/_modules/dasf/pipeline/types.html deleted file mode 100644 index b65d00c..0000000 --- a/docs/_modules/dasf/pipeline/types.html +++ /dev/null @@ -1,219 +0,0 @@ - - - - - - - - - - dasf.pipeline.types — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.pipeline.types
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.pipeline.types

-#!/usr/bin/env python3
-
-from enum import IntEnum, auto
-
-
-
[docs]class TaskExecutorType(IntEnum):
-    single_cpu = auto()
-    multi_cpu = auto()
-    single_gpu = auto()
-    multi_gpu = auto()

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/profile/database.html b/docs/_modules/dasf/profile/database.html deleted file mode 100644 index eaa6901..0000000 --- a/docs/_modules/dasf/profile/database.html +++ /dev/null @@ -1,170 +0,0 @@ - - - - - - dasf.profile.database — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.profile.database

-import json
-
-from dataclasses import dataclass, asdict, field
-from pathlib import Path
-from typing import List
-
-
-
[docs]@dataclass
-class TraceEvent:
-    # Mandadory options
-    name: str  # name: name of the event, (displayed in Trace Viewer).
-    phase: str  # ph: the event type (single character).
-    timestamp: float  # ts: tracing clock timestamp (microsecond granularity).
-    process_id: str  # pid: the process identifier.
-    thread_id: str  # tid: the thread identifier.
-
-    # Global options
-    category: List[str] = None  # cat: event categoies.
-    data: dict = None  # args: dictionary of arguments provided for the event.
-    thread_timestamp: float = None  # tts: thread clock timestamp (microsecond granularity).
-    color_name: str = None  # cname: color name for the event
-
-    # Duration event fields (X)
-    duration: float = None          # dur: tracing clock duration of complete events (microsecond granularity).
-    thread_duration: float = None   # tdur: the thread clock duration of complete events (microsecond granularity).

-
-
-
[docs]class TraceDatabase:
-
[docs]    def add_trace_event(self, trace: TraceEvent):
-        raise NotImplementedError

-
-
[docs]    def commit(self):
-        raise NotImplementedError

-
-
[docs]    def get_traces(self) -> List[TraceEvent]:
-        raise NotImplementedError

-
-
-
[docs]class SingleFileTraceDatabase(TraceDatabase):
-    def __init__(self, path: Path, encoder: callable = json.dumps, decoder: callable = json.loads):
-        self._path = Path(path)
-        self._encoder = encoder
-        self._decoder = decoder
-
-    # Note, this process is not process-safe!
-
[docs]    def add_trace_event(self, trace: TraceEvent) -> int:  # returns the record id
-        obj = f"trace: {self._encoder(asdict(trace))}\n"
-        with self._path.open("a") as f:
-            f.write(obj)

-
-
[docs]    def get_traces(self) -> List[TraceEvent]:
-        traces = []
-        if not self._path.exists():
-            return traces
-        with self._path.open("r") as f:
-            for line in f:
-                if not line:
-                    continue
-                if line.startswith("trace: "):
-                    trace = TraceEvent(**self._decoder(line[7:]))
-                    traces.append(trace)
-        return traces

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/profile/event.html b/docs/_modules/dasf/profile/event.html deleted file mode 100644 index 42d751d..0000000 --- a/docs/_modules/dasf/profile/event.html +++ /dev/null @@ -1,331 +0,0 @@ - - - - - - dasf.profile.event — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.profile.event

-import time
-import threading
-import os
-import json
-from pathlib import Path
-from typing import List, Optional
-from dasf.profile.database import TraceEvent, TraceDatabase, SingleFileTraceDatabase
-
-
-
[docs]class Singleton(type):
-    _instances = {}
-
-
[docs]    def __call__(cls, *args, **kwargs):
-        if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]

-
-
-
[docs]class TraceDatabase(metaclass=Singleton):
-    db_name: str = "traces.txt"
-
-    def __init__(self, database: TraceDatabase = None):
-        self._database = database or SingleFileTraceDatabase(self.db_name)
-
-    @property
-    def database(self) -> TraceDatabase:
-        return self._database

-
-
-
[docs]def get_time_ms():
-    return time.time() * 1000

-
-
-
[docs]def add_trace_duration_begin(
-    name: str,
-    process_id: str,
-    thread_id: str,
-    category: List[str] = None,
-    timestamp: float = None,
-    thread_timestamp: float = None,
-    data: dict = None,
-):
-    event = TraceEvent(
-        name=name,
-        category=category,
-        phase="B",
-        timestamp=get_time_ms(),
-        process_id=process_id,
-        thread_id=thread_id,
-        data=data,
-        thread_timestamp=thread_timestamp,
-        color_name=None,
-    )
-    TraceDatabase().database.add_trace_event(event)

-
-
-
[docs]def add_trace_duration_end(
-    name: str,
-    process_id: str,
-    thread_id: str,
-    category: List[str] = None,
-    timestamp: float = None,
-    thread_timestamp: float = None,
-    data: dict = None,
-):
-    event = TraceEvent(
-        name=name,
-        category=category,
-        phase="E",
-        timestamp=get_time_ms(),
-        process_id=process_id,
-        thread_id=thread_id,
-        data=data,
-        thread_timestamp=thread_timestamp,
-        color_name=None,
-    )
-    TraceDatabase().database.add_trace_event(event)

-
-
-
[docs]def add_trace_complete(
-    name: str,
-    process_id: str,
-    thread_id: str,
-    timestamp: float,
-    duration: float,
-    thread_timestamp: float = None,
-    thread_duration: float = None,
-    category: List[str] = None,
-    data: dict = None,
-):
-    if thread_timestamp is not None or thread_duration is not None:
-        if thread_timestamp is None or thread_duration is None:
-            raise ValueError(
-                "initial_thread_timestamp and thread_duration must be set together"
-            )
-    event = TraceEvent(
-        name=name,
-        category=category,
-        phase="X",
-        timestamp=timestamp,
-        duration=duration,
-        process_id=process_id,
-        thread_id=thread_id,
-        data=data,
-        thread_timestamp=thread_timestamp,
-        thread_duration=thread_duration,
-        color_name=None,
-    )
-    TraceDatabase().database.add_trace_event(event)

-
-
-
[docs]def get_traces() -> List[TraceEvent]:
-    return TraceDatabase().database.get_traces()

-
-
-
[docs]def to_chrome_event_format(
-    trace_events: List[TraceEvent],
-    trace_options: dict = None,
-    format_kwargs: dict = None,
-) -> str:
-    traces = []
-    pids = set()
-    tids = set()
-    # stack_frames = []
-    for trace in trace_events:
-        if isinstance(trace, TraceEvent):
-            pids.add(trace.process_id)
-            tids.add((trace.process_id, trace.thread_id))
-
-    pids = list(pids)
-    tids = list(tids)
-
-    for trace in trace_events:
-        if isinstance(trace, TraceEvent):
-            t = {
-                "name": trace.name,
-                "ph": trace.phase,
-                "cat": ",".join(trace.category) if trace.category else "default",
-                "ts": trace.timestamp * 1e6,
-                "pid": pids.index(trace.process_id),
-                "tid": tids.index((trace.process_id, trace.thread_id)),
-            }
-
-            if trace.data is not None:
-                t["args"] = trace.data
-            if trace.thread_timestamp is not None:
-                t["tts"] = trace.thread_timestamp
-            if trace.color_name is not None:
-                t["cname"] = trace.color_name
-            if trace.duration is not None:
-                t["dur"] = trace.duration * 1e6
-            if trace.thread_duration is not None:
-                t["tdur"] = trace.thread_duration
-            # pids.add(trace.process_id)
-            # threads.add((trace.process_id, trace.thread_id))
-            traces.append(t)
-
-    # print(f"PIDS: {pids}")
-    for pid in pids:
-        traces.append(
-            {
-                "name": "process_name",
-                "ph": "M",
-                "pid": pids.index(pid),
-                "args": {"name": pid},
-            }
-        )
-
-    for pid, tid in tids:
-        traces.append(
-            {
-                "name": "thread_name",
-                "ph": "M",
-                "pid": pids.index(pid),
-                "tid": tids.index((pid, tid)),
-                "args": {"name": tid},
-            }
-        )
-
-    traces = {
-        "traceEvents": traces,
-        # "stackFrames": stack_frames
-    }
-
-    if trace_options is not None:
-        traces.update(trace_options)
-
-    format_kwargs = format_kwargs or {}
-    return json.dumps(traces, **format_kwargs)

-
-
-
[docs]class Profile:
-    def __init__(
-        self,
-        trace_file: str = "traces.txt",
-        remove_old_trace_file: bool = True,
-        processed_filename: Optional[str] = "profile.json",
-        process_trace_options: dict = None,
-        process_trace_kwargs: dict = None,
-    ):
-        self.trace_file = Path(trace_file)
-        self.processed_filename = Path(processed_filename)
-        self.remove_old_trace_file = remove_old_trace_file
-        self.process_trace_options = process_trace_options
-        self.process_trace_kwargs = process_trace_kwargs
-
-
[docs]    def __enter__(self):
-        if self.remove_old_trace_file:
-            self.trace_file.unlink(missing_ok=True)
-        db = SingleFileTraceDatabase(self.trace_file)
-        TraceDatabase(db)

-
-
[docs]    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self.processed_filename is not None:
-            # print("Processing traces...")
-            traces = get_traces()
-            with self.processed_filename.open("w") as f:
-                f.write(
-                    to_chrome_event_format(
-                        traces, self.process_trace_options, self.process_trace_kwargs
-                    )
-                )
-            print(f"Chrome trace file written to {self.processed_filename}")

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/profile/plugins/dasf.html b/docs/_modules/dasf/profile/plugins/dasf.html deleted file mode 100644 index 45c689a..0000000 --- a/docs/_modules/dasf/profile/plugins/dasf.html +++ /dev/null @@ -1,136 +0,0 @@ - - - - - - dasf.profile.plugins.dasf — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.profile.plugins.dasf

-import time
-import socket
-from distributed.diagnostics.plugin import WorkerPlugin, SchedulerPlugin
-from dasf.profile.event import add_trace_complete
-from dasf.pipeline import PipelinePlugin
-
-
-
[docs]class PipelineTaskTimer(PipelinePlugin):
-    def __init__(self):
-        self.start_times = dict()
-        self.hostname = socket.gethostname()
-
-
[docs]    def on_task_start(self, func, params, name):
-        self.start_times[name] = time.time()

-        # print(f"Pipeline Task Timer Start: {name}: {self.start_times[name]}")
-
-
[docs]    def on_task_end(self, func, params, name, ret):
-        duration = time.time() - self.start_times[name]
-        # print(f"Pipeline Task Timer End: {name}: {duration}")
-        add_trace_complete(
-            name=name,
-            process_id="dasf-core",
-            thread_id="core",
-            timestamp=self.start_times[name],
-            duration=duration,
-            category=["dasf", "task time"],
-            data={"task": name},
-        )

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/profile/plugins/dask.html b/docs/_modules/dasf/profile/plugins/dask.html deleted file mode 100644 index 003f0d0..0000000 --- a/docs/_modules/dasf/profile/plugins/dask.html +++ /dev/null @@ -1,167 +0,0 @@ - - - - - - dasf.profile.plugins.dask — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.profile.plugins.dask

-import time
-import socket
-from distributed.diagnostics.plugin import WorkerPlugin, SchedulerPlugin
-from dasf.profile.event import add_trace_complete
-
-
-
[docs]class TaskTimePlugin(WorkerPlugin):
-
[docs]    def setup(self, worker):
-        self.worker = worker
-        self.start_times = dict()
-        self.transfer_times = dict()
-        self.waiting_time = dict()
-        self.hostname = socket.gethostname()

-
-
[docs]    def transition(self, key, start, finish, *args, **kwargs):
-        if finish == "executing": # start execting     
-            self.start_times[key] = time.time()
-            if key in self.waiting_time: 
-                duration = time.time() - self.waiting_time[key]
-                add_trace_complete(
-                    name="Waiting",
-                    process_id=self.hostname,
-                    thread_id=f"worker-{self.worker.name}",
-                    timestamp=self.waiting_time[key],
-                    duration=duration,
-                    category=["worker", "dask", "waiting"],
-                    data={"from": start, "to": finish, "key": key},
-                )
-            
-        elif (start == "executing" or start == "long-running") and key in self.start_times: # end executing
-            if key in self.start_times:
-                duration = time.time() - self.start_times[key]
-                add_trace_complete(
-                    name="Processing",
-                    process_id=self.hostname,
-                    thread_id=f"worker-{self.worker.name}",
-                    timestamp=self.start_times[key],
-                    duration=duration,
-                    category=["worker", "dask", "processing"],
-                    data={"from": start, "to": finish, "key": key},
-                )
-        if finish == "fetch": # start transfer
-            self.transfer_times[key] = time.time()
-        elif start == "flight" and finish == "memory":
-            if key in self.transfer_times:
-                duration = time.time() - self.transfer_times[key]
-                add_trace_complete(
-                    name="Transfering",
-                    process_id=self.hostname,
-                    thread_id=f"worker-{self.worker.name}",
-                    timestamp=self.transfer_times[key],
-                    duration=duration,
-                    category=["worker", "dask", "transfering"],
-                    data={"from": start, "to": finish, "key": key},
-                )
-            
-        if finish == "waiting":
-            self.waiting_time[key] = time.time()    

-        
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/transforms/base.html b/docs/_modules/dasf/transforms/base.html deleted file mode 100644 index 16c21fe..0000000 --- a/docs/_modules/dasf/transforms/base.html +++ /dev/null @@ -1,456 +0,0 @@ - - - - - - - - - - dasf.transforms.base — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.transforms.base
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.transforms.base

-#!/usr/bin/python3
-
-import inspect
-import dask.array as da
-
-from dasf.utils.decorators import task_handler
-from dasf.utils.types import is_dask_array
-from dasf.utils.types import is_dask_dataframe
-from dasf.utils.funcs import block_chunk_reduce
-
-
-
[docs]class Fit:
-
[docs]    def _lazy_fit_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_fit_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def fit(self, X, y, sample_weight=None, **kwargs):
-        ...

-
-
[docs]    @staticmethod
-    def fit_from_model(model, X, y, sample_weight=None, **kwargs):
-        return model.fit(X=X, y=y, sample_weight=sample_weight, **kwargs)

-
-
-
[docs]class FitPredict:
-
[docs]    def _lazy_fit_predict_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_fit_predict_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_predict_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_predict_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def fit_predict(self, X, y=None, **kwargs):
-        ...

-
-
[docs]    @staticmethod
-    def fit_predict_from_model(model, X, y, sample_weight=None, **kwargs):
-        return model.fit_predict(X=X, y=y, sample_weight=sample_weight,
-                                 **kwargs)

-
-
-
[docs]class FitTransform:
-
[docs]    def _lazy_fit_transform_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_fit_transform_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_transform_cpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _fit_transform_gpu(self, X, y=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def fit_transform(self, X, y=None, **kwargs):
-        ...

-
-
[docs]    @staticmethod
-    def fit_transform_from_model(model, X, y, sample_weight=None, **kwargs):
-        return model.fit_transform(X=X, y=y, sample_weight=sample_weight,
-                                   **kwargs)

-
-
-
[docs]class Predict:
-
[docs]    def _lazy_predict_cpu(self, X, sample_weight=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_predict_gpu(self, X, sample_weight=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _predict_cpu(self, X, sample_weight=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _predict_gpu(self, X, sample_weight=None, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def predict(self, X, sample_weight=None, **kwargs):
-        ...

-
-
[docs]    @staticmethod
-    def predict_from_model(model, X, sample_weight=None, **kwargs):
-        if 'sample_weight' not in inspect.signature(model.predict).parameters:
-            return model.predict(X=X, **kwargs)
-        return model.predict(X=X, sample_weight=sample_weight, **kwargs)

-
-
-
[docs]class GetParams:
-
[docs]    def _lazy_get_params_cpu(self, deep=True, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_get_params_gpu(self, deep=True, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _get_params_cpu(self, deep=True, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _get_params_gpu(self, deep=True, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def get_params(self, deep=True, **kwargs):
-        ...

-
-
-
[docs]class SetParams:
-
[docs]    def _lazy_set_params_cpu(self, **params):
-        raise NotImplementedError

-
-
[docs]    def _lazy_set_params_gpu(self, **params):
-        raise NotImplementedError

-
-
[docs]    def _set_params_cpu(self, **params):
-        raise NotImplementedError

-
-
[docs]    def _set_params_gpu(self, **params):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def set_params(self, **params):
-        ...

-
-
-
[docs]class Transform:
-
[docs]    def _lazy_transform_cpu(self, X, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _lazy_transform_gpu(self, X, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _transform_cpu(self, X, **kwargs):
-        raise NotImplementedError

-
-
[docs]    def _transform_gpu(self, X, **kwargs):
-        raise NotImplementedError

-
-
[docs]    @task_handler
-    def transform(self, X, **kwargs):
-        ...

-
-
[docs]    @staticmethod
-    def transform_from_model(model, X, **kwargs):
-        return model.transform(X=X, **kwargs)

-
-
-
[docs]class TargeteredTransform:
-    def __init__(self, run_local=None, run_gpu=None):
-        super().__init__()
-
-        self._run_local = run_local
-        self._run_gpu = run_gpu

-
-
-
[docs]class MappedTransform(Transform):
-    def __init__(
-        self,
-        function,
-        depth=None,
-        boundary=None,
-        trim=True,
-        output_chunk=None,
-    ):
-
-        self.function = function
-        self.depth = depth
-        self.boundary = boundary
-        self.trim = trim
-        self.output_chunk = output_chunk
-
-        if (
-            self.boundary is None
-            and self.depth is not None
-            or self.boundary is not None
-            and self.depth is None
-        ):
-            raise Exception("Both boundary and depth should be passed "
-                            "together")
-
-    def __lazy_transform_generic(self, X, **kwargs):
-        drop_axis, new_axis = block_chunk_reduce(X, self.output_chunk)
-
-        if self.depth and self.boundary:
-            if self.trim:
-                new_data = X.map_overlap(
-                    self.function,
-                    **kwargs,
-                    dtype=X.dtype,
-                    depth=self.depth,
-                    boundary=self.boundary,
-                )
-            else:
-                data_blocks = da.overlap.overlap(
-                    X, depth=self.depth, boundary=self.boundary
-                )
-
-                new_data = data_blocks.map_blocks(
-                    self.function,
-                    dtype=X.dtype,
-                    drop_axis=drop_axis,
-                    new_axis=new_axis,
-                    **kwargs,
-                )
-        else:
-            if is_dask_array(X):
-                new_data = X.map_blocks(
-                    self.function,
-                    dtype=X.dtype,
-                    drop_axis=drop_axis,
-                    new_axis=new_axis,
-                    **kwargs,
-                )
-            elif is_dask_dataframe(X):
-                new_data = X.map_partitions(self.function, **kwargs)
-
-        return new_data
-
-
[docs]    def _lazy_transform_cpu(self, X, **kwargs):
-        return self.__lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _lazy_transform_gpu(self, X, **kwargs):
-        return self.__lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _transform_cpu(self, X, **kwargs):
-        return self.function(X, **kwargs)

-
-
[docs]    def _transform_gpu(self, X, **kwargs):
-        return self.function(X, **kwargs)

-
-
[docs]    @task_handler
-    def transform(self, X, **kwargs):
-        ...

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/transforms/memory.html b/docs/_modules/dasf/transforms/memory.html deleted file mode 100644 index f601ad8..0000000 --- a/docs/_modules/dasf/transforms/memory.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - - - dasf.transforms.memory — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
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    • - -
  • Module code »
    • - -
  • dasf.transforms.memory
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.transforms.memory

-#!/usr/bin/env python3
-
-from dasf.utils.types import is_dask_array
-from dasf.utils.types import is_dask_dataframe
-from dasf.transforms.base import Transform
-
-
-
[docs]class PersistDaskData(Transform):
-    """Allow persisting a dask array to memory and return a copy of the object.
-    It will gather the data blocks from all workers and resembles locally.
-    """
-    def __lazy_transform_generic(self, X):
-        if is_dask_array(X) or is_dask_dataframe(X):
-            new_data = X.persist()
-        else:
-            new_data = X
-
-        return new_data
-
-
[docs]    def _lazy_transform_cpu(self, X):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _lazy_transform_gpu(self, X):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _transform_cpu(self, X):
-        # Bypass because the data is local
-        return X

-
-
[docs]    def _transform_gpu(self, X):
-        # Bypass because the data is local
-        return X

-
-
-
[docs]class LoadDaskData(Transform):
-    """Allow persisting a dask array to memory. It will gather the data blocks
-    from all workers and resembles locally.
-    """
-    def __lazy_transform_generic(self, X):
-        if is_dask_array(X) or is_dask_dataframe(X):
-            new_data = X.compute()
-        else:
-            new_data = X
-
-        return new_data
-
-
[docs]    def _lazy_transform_cpu(self, X):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _lazy_transform_gpu(self, X):
-        return self.__lazy_transform_generic(X)

-
-
[docs]    def _transform_cpu(self, X):
-        # Bypass because the data is local
-        return X

-
-
[docs]    def _transform_gpu(self, X):
-        # Bypass because the data is local
-        return X

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/transforms/operations.html b/docs/_modules/dasf/transforms/operations.html deleted file mode 100644 index 48341cc..0000000 --- a/docs/_modules/dasf/transforms/operations.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - - - dasf.transforms.operations — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
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    • - -
  • Module code »
    • - -
  • dasf.transforms.operations
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.transforms.operations

-#!/usr/bin/env python3
-
-from dasf.transforms.base import Transform
-
-
-
[docs]class Reshape:
-    """Reshape data with a new shape.
-
-    Parameters
-    ----------
-    shape : tuple
-        The new shape of the data.
-
-    """
-    def __init__(self, shape: tuple):
-        self.shape = shape
-
-
[docs]    def run(self, X):
-        print(X.shape)
-        return X.reshape(self.shape)

-
-
-
[docs]class SliceArray:
-    def __init__(self, output_size):
-        self.x = list(output_size)
-
-
[docs]    def transform(self, X):
-        if len(self.x) == 1:
-            return X[0:self.x[0]]
-        elif len(self.x) == 2:
-            return X[0:self.x[0], 0:self.x[1]]
-        elif len(self.x) == 3:
-            return X[0:self.x[0], 0:self.x[0], 0:self.x[0]]
-        else:
-            raise Exception("The dimmension is not known")

-
-
-
[docs]class SliceArrayByPercent(Transform):
-    def __init__(self, x=100.0, y=100.0, z=100.0):
-        self.x = float(x / 100.0)
-        self.y = float(y / 100.0)
-        self.z = float(z / 100.0)
-
-
[docs]    def transform(self, X):
-        if self.x > 1 or self.y > 1 or self.z > 1:
-            raise Exception("Percentages cannot be higher than 100% (1.0)")
-
-        if X.ndim == 1:
-            return X[0:int(self.x * X.shape[0])]
-        elif X.ndim == 2:
-            return X[0:int(self.x * X.shape[0]), 0:int(self.y * X.shape[1])]
-        elif X.ndim == 3:
-            return X[
-                0:int(self.x * X.shape[0]),
-                0:int(self.y * X.shape[1]),
-                0:int(self.z * X.shape[2]),
-            ]
-        else:
-            raise Exception("The dimmension is not known")

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/transforms/transforms.html b/docs/_modules/dasf/transforms/transforms.html deleted file mode 100644 index 4e8e0ea..0000000 --- a/docs/_modules/dasf/transforms/transforms.html +++ /dev/null @@ -1,540 +0,0 @@ - - - - - - - - - - dasf.transforms.transforms — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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    • - -
  • Module code »
    • - -
  • dasf.transforms.transforms
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.transforms.transforms

-#!/usr/bin/env python3
-
-import h5py
-import math
-import zarr
-
-import numpy as np
-import pandas as pd
-
-from dasf.utils.types import is_array
-from dasf.utils.types import is_dask_array
-from dasf.transforms.base import Transform
-
-try:
-    import cupy as cp
-    import cudf
-except ImportError:
-    pass
-
-
-
[docs]class Normalize(Transform):
-
[docs]    def transform(self, X):
-        return (X - X.mean()) / (X.std(ddof=0))

-
-
-
[docs]class ArrayToZarr(Transform):
-    def __init__(self, chunks=None, save=True, filename=None):
-        self.chunks = chunks
-        # TODO: implement the possibility of not saving
-        self.save = True
-        self.filename = filename
-
-
[docs]    @staticmethod
-    def _convert_filename(url):
-        if url.endswith(".npy"):
-            return url.replace(".npy", ".zarr")
-        return url + ".zarr"

-
-
[docs]    def _lazy_transform_generic_all(self, data):
-        if self.filename:
-            url = self.filename
-        elif hasattr(data, '_root_file'):
-            url = data._root_file
-        else:
-            raise Exception("Array requires a valid path to convert to Zarr.")
-
-        if data is None:
-            raise Exception("Dataset needs to be loaded first.")
-
-        url = self._convert_filename(url)
-
-        data.to_zarr(url)
-
-        return url

-
-
[docs]    def _transform_generic_all(self, data, chunks, **kwargs):
-        if data is None:
-            raise Exception("Dataset needs to be loaded first.")
-
-        if not chunks:
-            raise Exception("Chunks needs to be passed for non lazy arrays.")
-
-        if self.filename:
-            url = self.filename
-        else:
-            raise Exception("Array requires a valid path to convert to Zarr.")
-
-        url = self._convert_filename(url)
-
-        z = zarr.open(store=url, mode='w', shape=data.shape,
-                      chunks=chunks, dtype='i4')
-
-        z = data
-
-        return url

-
-
[docs]    def _lazy_transform_generic(self, X, **kwargs):
-        # XXX: Avoid circular dependency
-        from dasf.datasets.base import DatasetArray
-        from dasf.datasets.base import DatasetZarr
-
-        name = None
-
-        if isinstance(X, DatasetArray):
-            name = X._name
-            chunks = X._chunks
-
-            if not self.filename and hasattr(X, '_root_file'):
-                self.filename = X._root_file
-
-            url = self._lazy_transform_generic_all(X._data)
-        elif is_dask_array(X):
-            chunks = X.chunks
-
-            url = self._lazy_transform_generic_all(X)
-        else:
-            raise Exception("It is not an Array type.")
-
-        return DatasetZarr(name=name, download=False, root=url, chunks=chunks)

-
-
[docs]    def _transform_generic(self, X, **kwargs):
-        # XXX: Avoid circular dependency
-        from dasf.datasets.base import DatasetArray
-        from dasf.datasets.base import DatasetZarr
-
-        name = None
-        url = None
-
-        if hasattr(X, '_chunks') and \
-           (X._chunks is not None and X._chunks != 'auto'):
-            chunks = X._chunks
-        else:
-            chunks = self.chunks
-
-        if chunks is None:
-            raise Exception("Chunks needs to be specified.")
-
-        if isinstance(X, DatasetArray):
-            name = X._name
-
-            if not self.filename and hasattr(X, '_root_file'):
-                self.filename = X._root_file
-
-            url = self._transform_generic_all(X._data, chunks)
-        elif is_array(X):
-            url = self._transform_generic_all(X, chunks)
-        else:
-            raise Exception("It is not an Array type.")
-
-        return DatasetZarr(name=name, download=False, root=url, chunks=chunks)

-
-
[docs]    def _lazy_transform_gpu(self, X, **kwargs):
-        return self._lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _lazy_transform_cpu(self, X, **kwargs):
-        return self._lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _transform_gpu(self, X, **kwargs):
-        return self._transform_generic(X, **kwargs)

-
-
[docs]    def _transform_cpu(self, X, **kwargs):
-        return self._transform_generic(X, **kwargs)

-
-
-
[docs]class ArrayToHDF5(Transform):
-    def __init__(self, dataset_path, chunks=None, save=True, filename=None):
-        # Avoid circular dependency
-        from dasf.datasets.base import DatasetArray
-        from dasf.datasets.base import DatasetHDF5
-
-        self.dataset_path = dataset_path
-        self.chunks = chunks
-        # TODO: implement the possibility of not saving
-        self.save = True
-        self.filename = filename
-
-
[docs]    @staticmethod
-    def _convert_filename(url):
-        if url.endswith(".npy"):
-            return url.replace(".npy", ".hdf5")
-        return url + ".hdf5"

-
-
[docs]    def _lazy_transform_generic_all(self, data):
-        if self.filename:
-            url = self.filename
-        elif hasattr(data, '_root_file'):
-            url = data._root_file
-        else:
-            raise Exception("Array requires a valid path to convert to HDF5.")
-
-        if data is None:
-            raise Exception("Dataset needs to be loaded first.")
-
-        url = self._convert_filename(url)
-
-        data.to_hdf5(url, self.dataset_path)
-
-        return url

-
-
[docs]    def _transform_generic_all(self, data):
-        if data is None:
-            raise Exception("Dataset needs to be loaded first.")
-
-        if self.filename:
-            url = self.filename
-        else:
-            raise Exception("Array requires a valid path to convert to Zarr.")
-
-        url = self._convert_filename(url)
-
-        h5f = h5py.File(url, 'w')
-        h5f.create_dataset(self.dataset_path, data=data)
-        h5f.close()
-
-        return url

-
-
[docs]    def _lazy_transform_generic(self, X, **kwargs):
-        # XXX: Avoid circular dependency
-        from dasf.datasets.base import DatasetArray
-        from dasf.datasets.base import DatasetHDF5
-
-        name = None
-        chunks = None
-
-        if isinstance(X, DatasetArray):
-            name = X._name
-            chunks = X._chunks
-
-            if not self.filename and hasattr(X, '_root_file'):
-                self.filename = X._root_file
-
-            url = self._lazy_transform_generic_all(X._data)
-        elif is_dask_array(X):
-            chunks = X.chunks
-
-            url = self._lazy_transform_generic_all(X)
-        else:
-            raise Exception("It is not an Array type.")
-
-        return DatasetHDF5(name=name, download=False, root=url, chunks=chunks,
-                           dataset_path=self.dataset_path)

-
-
[docs]    def _transform_generic(self, X, **kwargs):
-        # XXX: Avoid circular dependency
-        from dasf.datasets.base import DatasetArray
-        from dasf.datasets.base import DatasetHDF5
-
-        name = None
-        url = None
-
-        if hasattr(X, '_chunks') and \
-           (X._chunks is not None and X._chunks != 'auto'):
-            chunks = X._chunks
-        else:
-            chunks = self.chunks
-
-        if isinstance(X, DatasetArray):
-            name = X._name
-
-            if not self.filename and hasattr(X, '_root_file'):
-                self.filename = X._root_file
-
-            url = self._transform_generic_all(X._data)
-        elif is_array(X):
-            url = self._transform_generic_all(X)
-        else:
-            raise Exception("It is not an Array type.")
-
-        return DatasetHDF5(name=name, download=False, root=url, chunks=chunks,
-                           dataset_path=self.dataset_path)

-
-
[docs]    def _lazy_transform_gpu(self, X, **kwargs):
-        return self._lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _lazy_transform_cpu(self, X, **kwargs):
-        return self._lazy_transform_generic(X, **kwargs)

-
-
[docs]    def _transform_gpu(self, X, **kwargs):
-        return self._transform_generic(X, **kwargs)

-
-
[docs]    def _transform_cpu(self, X, **kwargs):
-        return self._transform_generic(X, **kwargs)

-
-
-
[docs]class ArraysToDataFrame(Transform):
-    def __transform_generic(self, X, y):
-        assert len(X) == len(y), "Data and labels should have the same length."
-
-        dfs = None
-        for i, x in enumerate(X):
-            if is_array(x):
-                # Dask has some facilities to convert to DataFrame
-                if is_dask_array(x):
-                    new_chunk = math.prod(x.chunksize)
-                    flat = x.flatten().rechunk(new_chunk)
-
-                    if dfs is None:
-                        dfs = flat.to_dask_dataframe(columns=[y[i]])
-                    else:
-                        dfs = dfs.join(flat.to_dask_dataframe(columns=[y[i]]))
-                else:
-                    flat = x.flatten()
-
-                    if dfs is None:
-                        dfs = list()
-                    dfs.append(flat)
-            else:
-                raise Exception("This is not an array. This is a '%s'."
-                                % str(type(x)))
-
-        return dfs
-
-
[docs]    def _lazy_transform_cpu(self, X=None, **kwargs):
-        X = list(kwargs.values())
-        y = list(kwargs.keys())
-
-        return self.__transform_generic(X, y)

-
-
[docs]    def _lazy_transform_gpu(self, X=None, **kwargs):
-        X = list(kwargs.values())
-        y = list(kwargs.keys())
-
-        return self.__transform_generic(X, y)

-
-
[docs]    def _transform_gpu(self, X=None, **kwargs):
-        X = list(kwargs.values())
-        y = list(kwargs.keys())
-
-        dfs = self.__transform_generic(X, y)
-
-        if is_array(dfs) and not is_dask_array(dfs):
-            datas = cp.stack(dfs, axis=-1)
-            datas = cudf.DataFrame(datas, columns=y)
-        else:
-            datas = dfs
-
-        return datas

-
-
[docs]    def _transform_cpu(self, X=None, **kwargs):
-        X = list(kwargs.values())
-        y = list(kwargs.keys())
-
-        dfs = self.__transform_generic(X, y)
-
-        if is_array(dfs) and not is_dask_array(dfs):
-            datas = np.stack(dfs, axis=-1)
-            datas = pd.DataFrame(datas, columns=y)
-        else:
-            datas = dfs
-
-        return datas

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/benchmark.html b/docs/_modules/dasf/utils/benchmark.html deleted file mode 100644 index 462b32a..0000000 --- a/docs/_modules/dasf/utils/benchmark.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - dasf.utils.benchmark — DASF 1.0 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for dasf.utils.benchmark

-#!/usr/bin/env python3
-
-import timeit
-
-from time import perf_counter
-
-try:
-    import memray
-    USE_MEMRAY = True
-except ImportError:
-    USE_MEMRAY = False
-
-import cProfile
-from pstats import Stats
-
-try:
-    from functools import partial
-    from memory_profiler import show_results, LineProfiler
-    from memory_profiler import memory_usage, choose_backend
-
-    USE_MEM_PROF = True
-except ImportError:
-    USE_MEM_PROF = False
-
-
-
[docs]class TimeBenchmark:
-    def __init__(self, backend="cprofile"):
-        self.__backend = backend
-
-
[docs]    def __enter__(self):
-        if self.__backend == "cprofile":
-            self.__pr = cProfile.Profile()
-            self.__pr.enable()
-        elif self.__backend == "perf_counter":
-            self.__start = perf_counter()
-            self.__end = 0.0
-        else:
-            print("There is no available backend")
-        return self

-
-
[docs]    def __exit__(self, *args, **kwargs):
-        if self.__backend == "cprofile":
-            self.__pr.disable()
-            p = Stats(self.__pr)
-
-            p.strip_dirs().sort_stats('cumulative').print_stats(10)
-        elif self.__backend == "perf_counter":
-            self.__end = perf_counter()
-            print("Time spent:", self.__end - self.__start)

-
-
[docs]    def run(self, function, *args, **kwargs):
-        if self.__backend == "cprofile":
-            pr = cProfile.Profile()
-            pr.enable()
-
-            function(*args, **kwargs)
-
-            pr.disable()
-            p = Stats(pr)
-
-            p.strip_dirs().sort_stats('cumulative').print_stats(10)
-
-            self.teardown()
-        elif self.__backend == "timeit":
-            timeit.repeat("function(*args, **kwargs)", setup="self.setup()")
-
-            self.teardown()
-        else:
-            print("There is no available backend")

-
-
-
[docs]class MemoryBenchmark:
-    def __init__(self, backend="memray", debug=False, output_file=None, *args, **kwargs):
-        self.__backend = backend
-        self.__debug = debug
-        self.__output_file = output_file
-        self.__args = args
-        self.__kwargs = kwargs
-
-
[docs]    def __enter__(self):
-        if self.__backend == "memray" and USE_MEMRAY:
-            self.__memray = memray.Tracker(*self.__args, **self.__kwargs)
-
-            return self.__memray.__enter__()
-        else:
-            raise Exception(f"The backend {self.__backend} does not support context "
-                            "manager")

-
-
[docs]    def __exit__(self, *args, **kwargs):
-        if self.__backend == "memray" and USE_MEMRAY:
-            return self.__memray.__exit__(*args, **kwargs)

-
-
[docs]    def run(self, function, *args, **kwargs):
-        if self.__backend == "memory_profiler" and USE_MEM_PROF:
-            if self.__debug:
-                # profile = LineProfiler(include_children=True)
-
-                get_prof = partial(LineProfiler, backend=choose_backend("psutil"))
-                show_results_bound = partial(
-                    show_results, precision=4
-                )
-
-                prof = get_prof()
-                vals = prof(function)(*args, **kwargs)
-                show_results_bound(prof)
-            else:
-                vals = memory_usage((function, args, kwargs), *self.__args,
-                                    **self.__kwargs)
-
-            self.teardown()
-
-            return vals
-        elif self.__backend == "memray" and USE_MEMRAY:
-            with memray.Tracker(*self.__args, **self.__kwargs):
-                ret = function(*args, **kwargs)
-
-            self.teardown()
-
-            return ret
-        else:
-            print(f"The backend {self.__backend} is not supported")

-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/decorators.html b/docs/_modules/dasf/utils/decorators.html deleted file mode 100644 index 31ff6f6..0000000 --- a/docs/_modules/dasf/utils/decorators.html +++ /dev/null @@ -1,352 +0,0 @@ - - - - - - - - - - dasf.utils.decorators — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.utils.decorators
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.utils.decorators

-""" Implementations of important library decorators. """
-#!/usr/bin/env python3
-
-from functools import wraps
-
-from dasf.utils.types import is_dask_array
-from dasf.utils.types import is_gpu_array
-from dasf.utils.funcs import is_gpu_supported
-from dasf.utils.funcs import is_dask_supported
-from dasf.utils.funcs import is_dask_gpu_supported
-
-
-
[docs]def is_forced_local(cls):
-    """
-    Returns if object is forced to run in a CPU.
-    """
-    # pylint: disable=protected-access
-    if hasattr(cls, "_run_local") and cls._run_local is not None:
-        # pylint: disable=protected-access
-        return cls._run_local
-    return None

-
-
-
[docs]def is_forced_gpu(cls):
-    """
-    Returns if object is forced to run in a GPU.
-    """
-    # pylint: disable=protected-access
-    if hasattr(cls, "_run_gpu") and cls._run_gpu is not None:
-        # pylint: disable=protected-access
-        return cls._run_gpu
-    return None

-
-
-
[docs]def fetch_from_dask(*args, **kwargs) -> tuple:
-    """
-    Fetches to CPU all parameters in a Dask data type.
-    """
-    new_kwargs = {}
-    new_args = []
-
-    for key, value in kwargs.items():
-        if is_dask_array(value):
-            new_kwargs[key] = value.compute()
-        else:
-            new_kwargs[key] = value
-
-    for value in args:
-        if is_dask_array(value):
-            new_args.append(value.compute())
-        else:
-            new_args.append(value)
-
-    return new_args, new_kwargs

-
-
-
[docs]def fetch_from_gpu(*args, **kwargs) -> tuple:
-    """
-    Fetches to CPU all parameters in a GPU data type.
-    """
-    new_kwargs = {}
-    new_args = []
-
-    for key, value in kwargs.items():
-        if is_gpu_array(value):
-            new_kwargs[key] = value.get()
-        else:
-            new_kwargs[key] = value
-
-    for value in args:
-        if is_gpu_array(value):
-            new_args.append(value.get())
-        else:
-            new_args.append(value)
-
-    return new_args, new_kwargs

-
-
-
[docs]def fetch_args_from_dask(func):
-    """
-    Fetches to CPU all function parameters in a Dask data type.
-    """
-    def wrapper(*args, **kwargs):
-        new_args, new_kwargs = fetch_from_dask(*args, **kwargs)
-
-        return func(*new_args, **new_kwargs)
-
-    return wrapper

-
-
-
[docs]def fetch_args_from_gpu(func):
-    """
-    Fetches to CPU all function parameters in a GPU data type.
-    """
-    def wrapper(*args, **kwargs):
-        new_args, new_kwargs = fetch_from_gpu(*args, **kwargs)
-
-        return func(*new_args, **new_kwargs)
-
-    return wrapper

-
-
-
[docs]def task_handler(func):
-    """
-    Returns all mapped functions corresponding to the executor in place.
-    """
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        cls = args[0]
-        new_args = args[1:]
-        func_name = func.__name__
-        func_type = ""
-        arch = "cpu"
-
-        if is_forced_local(cls):
-            new_args, kwargs = fetch_from_dask(*new_args, **kwargs)
-
-        if not is_forced_local(cls) and (is_dask_gpu_supported() or is_dask_supported()):
-            func_type = "_lazy"
-
-        if is_forced_gpu(cls) is not None:
-            if is_forced_gpu(cls):
-                arch = "gpu"
-            else:
-                new_args, kwargs = fetch_from_gpu(*new_args, **kwargs)
-                arch = "cpu"
-        elif is_dask_gpu_supported() or is_gpu_supported():
-            arch = "gpu"
-
-        wrapper_func_attr = f"{func_type}_{func_name}_{arch}"
-
-        if (not hasattr(cls, wrapper_func_attr) and
-           hasattr(cls, func_name)):
-            return func(*new_args, **kwargs)
-        if (not hasattr(cls, wrapper_func_attr) and
-              not hasattr(cls, func_name)):
-            raise NotImplementedError(
-                f"There is no implementation of {wrapper_func_attr} nor "
-                "{func_name}"
-            )
-        return getattr(cls, wrapper_func_attr)(*new_args, **kwargs)
-
-    return wrapper

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/funcs.html b/docs/_modules/dasf/utils/funcs.html deleted file mode 100644 index d768eab..0000000 --- a/docs/_modules/dasf/utils/funcs.html +++ /dev/null @@ -1,656 +0,0 @@ - - - - - - - - - - dasf.utils.funcs — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.utils.funcs
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.utils.funcs

-""" Generic and regular functions. """
-#!/usr/bin/env python3
-
-import os
-import time
-import threading
-
-from pathlib import Path
-
-import wget
-import pandas
-import psutil
-import GPUtil
-import numpy as np
-
-import dask
-import dask.delayed as dd
-from dask.distributed import Client
-
-from distributed.client import wait, FIRST_COMPLETED
-from distributed.utils import TimeoutError as DistributedTimeoutError
-
-from dasf.pipeline.types import TaskExecutorType
-
-from IPython import display as disp
-from ipywidgets import HBox, FloatProgress, Label
-
-try:
-    import cupy as cp
-    GPU_SUPPORTED = isinstance(cp.__version__, str)
-except ImportError:
-    GPU_SUPPORTED = False
-
-
-
[docs]def human_readable_size(size, decimal=3) -> str:
-    """
-    converts data size into the proper measurement
-    """
-    for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
-        if size < 1024.0:
-            break
-        size /= 1024.0
-    return f"{size:.{decimal}f} {unit}"

-
-
-
[docs]def get_full_qualname(obj) -> str:
-    """
-    Return fully qualified name of objects.
-    """
-    klass = obj.__class__
-    module = klass.__module__
-    if module == "builtins":
-        return klass.__qualname__
-    return module + "." + klass.__qualname__

-
-
-
[docs]def get_worker_info(client) -> list:
-    """
-    Returns a list of workers (sorted), and the DNS name for the master host
-    The master is the 0th worker's host
-    """
-    workers = client.scheduler_info()["workers"]
-    worker_keys = sorted(workers.keys())
-    workers_by_host = {}
-    for key in worker_keys:
-        worker = workers[key]
-        host = worker["host"]
-        workers_by_host.setdefault(host, []).append(key)
-    host = workers[worker_keys[0]]["host"]
-    all_workers = []
-    global_rank = 0
-    world_size = len(workers_by_host)
-    hosts = sorted(workers_by_host.keys())
-    for host in hosts:
-        local_rank = 0
-        for worker in workers_by_host[host]:
-            all_workers.append(
-                dict(
-                    master=hosts[0],
-                    worker=worker,
-                    nthreads=workers[worker]["nthreads"],
-                    local_rank=0,
-                    global_rank=global_rank,
-                    host=host,
-                    world_size=world_size,
-                )
-            )
-            local_rank += 1
-            global_rank += 1
-    return all_workers

-
-
-
[docs]def sync_future_loop(futures):
-    """
-    Synchronize all futures submitted to workers.
-    """
-    while True:
-        if not futures:
-            break
-
-        try:
-            result = wait(futures, 0.1, FIRST_COMPLETED)
-        except DistributedTimeoutError:
-            continue
-
-        for fut in result.done:
-            try:
-                fut.result(timeout=7200)
-            except Exception as exc:  # pylint: disable=broad-except
-                print(str(exc))
-                raise
-        futures = result.not_done

-
-
-
[docs]class NotebookProgressBar(threading.Thread):
-    MIN_CUR = -2
-    MIN_TOTAL = -1
-
-    def __init__(self):
-        threading.Thread.__init__(self)
-
-        # pylint: disable=disallowed-name
-        self.bar = None
-        self.percentage = None
-        self.data = None
-
-        self.__lock = threading.Lock()
-        self.__current = self.MIN_CUR
-        self.__total = self.MIN_TOTAL
-        self.__error = False
-
-
[docs]    def show(self):
-        self.bar = FloatProgress(value=0, min=0, max=100)
-        self.percentage = Label(value='0 %')
-        self.data = Label(value='')
-        box = HBox((self.percentage, self.bar, self.data))
-        disp.display(box)

-
-
[docs]    def set_current(self, current, total):
-        with self.__lock:
-            self.__current = current
-            self.__total = total

-
-
[docs]    def set_error(self, error):
-        self.__error = error

-
-
[docs]    def run(self):
-        while (not self.__error and self.__current < self.__total):
-            time.sleep(1)
-
-            if self.__current != self.MIN_CUR and self.__total != self.MIN_TOTAL:
-                progress = (self.__current / self.__total) * 100
-                self.bar.value = progress
-                self.percentage.value = f"{int(self.bar.value)} %%"
-                self.data.value = f"{int(self.__current)} / {int(self.__total)}"
-
-        if not self.__error:
-            self.bar.style.bar_color = '#03c04a'
-        else:
-            self.bar.style.bar_color = '#ff0000'

-
-
-
[docs]def download_file(url, filename=None, directory=None):
-    """
-    Download a generic file and save it.
-    """
-    if directory is not None:
-        os.makedirs(os.path.dirname(directory), exist_ok=True)
-
-    progressbar = None
-
-    if is_notebook():
-        progressbar = NotebookProgressBar()
-
-        def update_notebook_bar(current, total):
-            progressbar.set_current(current, total)
-
-    try:
-        if filename and directory:
-            output = os.path.abspath(os.path.join(directory, filename))
-
-            if not os.path.exists(output):
-                if is_notebook():
-                    # Activate the notebook progress bar
-                    progressbar.show()
-                    progressbar.start()
-
-                    wget.download(url, out=output, bar=update_notebook_bar)
-                else:
-                    wget.download(url, out=output)
-        elif filename:
-            output = os.path.abspath(os.path.join(os.getcwd(), filename))
-
-            if not os.path.exists(output):
-                if is_notebook():
-                    # Activate the notebook progress bar
-                    progressbar.show()
-                    progressbar.start()
-
-                    wget.download(url, out=output, bar=update_notebook_bar)
-                else:
-                    wget.download(url, out=output)
-        elif directory:
-            if is_notebook():
-                # Activate the notebook progress bar
-                progressbar.show()
-                progressbar.start()
-
-                output = \
-                    os.path.abspath(os.path.join(directory,
-                                                 wget.download(url,
-                                                               bar=update_notebook_bar)))
-            else:
-                output = os.path.abspath(os.path.join(directory, wget.download(url)))
-        else:
-            if is_notebook():
-                # Activate the notebook progress bar
-                progressbar.show()
-                progressbar.start()
-
-                output = \
-                    os.path.abspath(os.path.join(os.getcwd(),
-                                                 wget.download(url,
-                                                               bar=update_notebook_bar)))
-            else:
-                output = os.path.abspath(os.path.join(os.getcwd(), wget.download(url)))
-    except Exception as exc:
-        if progressbar:
-            progressbar.set_error(True)
-
-    return output

-
-
-
[docs]def download_file_from_gdrive(file_id, filename=None, directory=None):
-    """
-    Download a file from Google Drive using gdrive file id.
-    """
-    url = f"https://drive.google.com/uc?export=download&confirm=9iBg&id={file_id}"
-
-    return download_file(url, filename=filename, directory=directory)

-
-
-
[docs]def get_machine_memory_avail():
-    """
-    Return free memory available from a single machine.
-    """
-    return psutil.virtual_memory().free

-
-
-
[docs]def set_executor_default():
-    """
-    Return executor as a CPU (default) instance.
-    """
-    return TaskExecutorType.single_cpu

-
-
-
[docs]def set_executor_gpu():
-    """
-    Return executor as a GPU instance.
-    """
-    return TaskExecutorType.single_gpu

-
-
-
[docs]def is_executor_single(dtype) -> bool:
-    """
-    Return if the executor is a single machine instance.
-    """
-    return dtype in (TaskExecutorType.single_cpu, TaskExecutorType.single_gpu)

-
-
-
[docs]def is_executor_cluster(dtype) -> bool:
-    """
-    Return if the executor is a cluster instance.
-    """
-    return dtype in (TaskExecutorType.multi_cpu, TaskExecutorType.multi_gpu)

-
-
-
[docs]def is_executor_cpu(dtype) -> bool:
-    """
-    Return if the executor is a CPU instance.
-    """
-    return dtype in (TaskExecutorType.single_cpu, TaskExecutorType.multi_cpu)

-
-
-
[docs]def is_executor_gpu(dtype) -> bool:
-    """
-    Return if the executor is a GPU instance.
-    """
-    return dtype in (TaskExecutorType.single_gpu, TaskExecutorType.multi_gpu)

-
-
-
[docs]def is_gpu_supported() -> bool:
-    """
-    Return if GPU is supported.
-    """
-    return GPU_SUPPORTED

-
-
-
[docs]def is_dask_local_supported() -> bool:
-    """
-    Return if Dask is supported locally by the executor.
-    """
-    try:
-        scheduler = dask.config.get(key="scheduler")
-        return scheduler is not None
-    except Exception:
-        return False

-
-
-
[docs]def get_dask_running_client():
-    """
-    Get Dask runner stanza.
-    """
-    return Client.current()

-
-
-
[docs]def is_dask_supported() -> bool:
-    """
-    Return if Dask is supported by the executor.
-    """
-    try:
-        if is_dask_local_supported():
-            return True
-
-        cur = get_dask_running_client()
-        if hasattr(cur, 'dtype'):
-            return is_executor_cluster(cur.dtype)
-        return cur is not None
-    except Exception:
-        return False

-
-
-
[docs]def is_dask_gpu_supported() -> bool:
-    """
-    Return if any node supports GPU.
-    """
-    if is_dask_supported():
-        if len(get_dask_gpu_count()) > 0:
-            return True
-
-    return False

-
-
-
[docs]def get_gpu_count() -> int:
-    """
-    Get single node GPU count.
-    """
-    return GPUtil.getGPUs()

-
-
-
[docs]def get_dask_gpu_count(fetch=True) -> int:
-    """
-    Get how many GPUs are available in each worker.
-    """
-    # pylint: disable=not-callable
-    ret = dd(GPUtil.getGPUs)()
-    if fetch:
-        return ret.compute()
-    return ret

-
-
-
[docs]def block_chunk_reduce(dask_data, output_chunk):
-    """
-    Reduce the chunk according the new output size.
-    """
-    drop_axis = np.array([])
-    new_axis = None
-
-    if output_chunk is None or not isinstance(output_chunk, tuple):
-        return drop_axis.tolist(), new_axis
-
-    data_chunk_range = len(dask_data.chunksize)
-    output_chunk_range = len(output_chunk)
-
-    data_indexes = np.arange(data_chunk_range)
-    output_indexes = np.arange(output_chunk_range)
-
-    if data_chunk_range > output_chunk_range:
-        inter = np.intersect1d(data_indexes, output_indexes)
-
-        drop_axis = np.delete(data_indexes, inter)
-    elif data_chunk_range < output_chunk_range:
-        inter = np.intersect1d(data_indexes, output_indexes)
-
-        new_axis = np.delete(output_chunk_range, inter).tolist()
-
-    return drop_axis.tolist(), new_axis

-
-
-
[docs]def return_local_and_gpu(executor, local, gpu):
-    """
-    Return executor type based on passed preferences.
-    """
-    # pylint: disable=too-many-return-statements
-    if local is not None and gpu is None:
-        if local is True:
-            return TaskExecutorType(executor.dtype.value & 2)
-        if local is False:
-            return TaskExecutorType(executor.dtype.value | 1)
-    elif local is None and gpu is not None:
-        if gpu is True:
-            return TaskExecutorType((executor.dtype >> 1) + 2)
-        if gpu is False:
-            return TaskExecutorType(executor.dtype & 1)
-    elif local is not None and gpu is not None:
-        if local is True and gpu is False:
-            return TaskExecutorType.single_cpu
-        if local is False and gpu is False:
-            return TaskExecutorType.multi_cpu
-        if local is True and gpu is True:
-            return TaskExecutorType.single_gpu
-        if local is False and gpu is True:
-            return TaskExecutorType.multi_gpu
-
-    return executor.dtype

-
-
-
[docs]def get_dask_mem_usage(profiler):
-    """
-    Get Dask memory usage profile.
-    """
-    profiler_dir = os.path.abspath(str(Path.home()) + "/.cache/dasf/profiler/")
-
-    if profiler == "memusage":
-        os.makedirs(profiler_dir, exist_ok=True)
-
-        mem = pandas.read_csv(os.path.abspath(profiler_dir + "/dask-memusage"))
-
-        column = mem["max_memory_mb"]
-        max_index = column.idxmax()
-
-        return mem["max_memory_mb"][max_index]
-    return 0.0

-
-
-
[docs]def is_notebook() -> bool:
-    """
-    Return if the code is being executed in a IPyNotebook.
-    """
-    try:
-        shell = get_ipython().__class__.__name__
-        if shell == "ZMQInteractiveShell":
-            return True
-    except NameError:
-        pass
-
-    return False

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/labels.html b/docs/_modules/dasf/utils/labels.html deleted file mode 100644 index 259dd46..0000000 --- a/docs/_modules/dasf/utils/labels.html +++ /dev/null @@ -1,362 +0,0 @@ - - - - - - - - - - dasf.utils.labels — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.utils.labels
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.utils.labels

-#!/usr/bin/env python3
-
-from threading import Lock
-
-from dask.core import get_dependencies, ishashable, istask
-from dask.base import is_dask_collection
-from dask.dot import to_graphviz, graphviz_to_file
-
-inside_with = Lock()
-
-g_hash_attrs = dict()
-g_func_attrs = dict()
-g_data_attrs = dict()
-
-
-
[docs]class DaskLabel(object):
-    def __init__(self, start, stop, label=None, color=None):
-        self.__label = label
-        self.__color = color
-        self.__start = start
-        self.__stop = stop
-        self.__hash_attrs = g_hash_attrs
-        self.__func_attrs = g_func_attrs
-        self.__data_attrs = g_data_attrs
-
-
[docs]    def start(self, start):
-        self.__enter(start)

-
-
[docs]    def stop(self, stop):
-        self.__exit(stop, None, None, None)

-
-    def __name(self, x):
-        try:
-            return str(hash(x))
-        except TypeError:
-            return str(hash(str(x)))
-
-    def __add_item(self, key, tag, label=None, color=None, atype="data"):
-        if not key in self.__data_attrs:
-            self.__hash_attrs[key] = dict()
-            # We use comment as a generic field for tag
-            self.__hash_attrs[key]["comment"] = tag
-            self.__hash_attrs[key]["xlabel"] = label
-            self.__hash_attrs[key]["color"] = color
-            self.__hash_attrs[key]["type"] = atype
-
-    def __add_func(self, key, tag, label, color):
-        if not key in self.__func_attrs:
-            self.__func_attrs[key] = dict()
-            # We use comment as a generic field for tag
-            self.__func_attrs[key]["comment"] = tag
-            if label:
-                self.__func_attrs[key]["xlabel"] = label
-            if color:
-                self.__func_attrs[key]["color"] = color
-                self.__func_attrs[key]["style"] = "filled"
-
-    def __add_data(self, key, tag, label, color):
-        if not key in self.__data_attrs:
-            self.__data_attrs[key] = dict()
-            # We use comment as a generic field for tag
-            self.__data_attrs[key]["comment"] = tag
-            if label:
-                self.__data_attrs[key]["xlabel"] = label
-            if color:
-                self.__data_attrs[key]["color"] = color
-                self.__data_attrs[key]["style"] = "filled"
-
-    def __generate_hashtable(self, data, delete_dup=False):
-        if not is_dask_collection(data):
-            raise Exception("This is not a Dask data: this is %s." % str(type(data)))
-        else:
-            dsk = data.dask
-
-        remove = set()
-
-        for k, v in dsk.items():
-            k_name = self.__name(k)
-            if istask(v):
-                func_name = self.__name((k, "function"))
-
-                if delete_dup and func_name in self.__hash_attrs:
-                    del self.__hash_attrs[func_name]
-                    remove.add(func_name)
-                elif func_name not in remove:
-                    self.__add_item(
-                        func_name, k, self.__label, self.__color, atype="func"
-                    )
-
-                for dep in get_dependencies(dsk, k):
-                    dep_name = self.__name(dep)
-                    if delete_dup and dep_name in self.__hash_attrs:
-                        del self.__hash_attrs[dep_name]
-                        remove.add(dep_name)
-                    elif dep_name not in remove:
-                        self.__add_item(dep_name, dep, self.__label, self.__color)
-
-    def __enter(self, dsk):
-        global inside_with
-
-        inside_with.acquire()
-
-        self.__generate_hashtable(dsk)
-
-        return self
-
-
[docs]    def __enter__(self):
-        dsk = eval(self.__start)
-
-        return self.__enter(dsk)

-
-    def __exit(self, dsk, exc_type, exc_val, exc_tb):
-        global inside_with, g_hash_attrs, g_func_attrs, g_data_attrs
-
-        self.__generate_hashtable(dsk, delete_dup=True)
-
-        for k in self.__hash_attrs:
-            if self.__hash_attrs[k]["type"] == "data":
-                self.__add_data(
-                    self.__hash_attrs[k]["comment"],
-                    k,
-                    self.__hash_attrs[k]["xlabel"],
-                    self.__hash_attrs[k]["color"],
-                )
-            elif self.__hash_attrs[k]["type"] == "func":
-                self.__add_func(
-                    self.__hash_attrs[k]["comment"],
-                    k,
-                    self.__hash_attrs[k]["xlabel"],
-                    self.__hash_attrs[k]["color"],
-                )
-
-        g_hash_attrs = {**g_hash_attrs, **self.__hash_attrs}
-        g_func_attrs = {**g_func_attrs, **self.__func_attrs}
-        g_data_attrs = {**g_data_attrs, **self.__data_attrs}
-
-        inside_with.release()
-
-        return self
-
-
[docs]    def __exit__(self, exc_type, exc_val, exc_tb):
-        dsk = eval(self.__stop)
-
-        return self.__exit(dsk, exc_type, exc_val, exc_tb)

-
-
-
[docs]def get_attributes():
-    global inside_with, g_func_attrs, g_data_attrs
-
-    if inside_with.locked():
-        print("WARNING: it cannot reflect all attribute changes.")
-
-    return {"function_attributes": g_func_attrs, "data_attributes": g_data_attrs}

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/logging.html b/docs/_modules/dasf/utils/logging.html deleted file mode 100644 index a7c0c2f..0000000 --- a/docs/_modules/dasf/utils/logging.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - - - dasf.utils.logging — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • dasf.utils.logging
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for dasf.utils.logging

-""" Logging helpers for functions. """
-#!/usr/bin/env python3
-
-import sys
-
-from logging import INFO, Formatter, Logger, StreamHandler, getLogger
-
-
-
[docs]def init_logging() -> Logger:
-    """
-    Initialize logger objects to be used by modules.
-    """
-    logger = getLogger("DASF")
-
-    logger.setLevel(INFO)
-    handler = StreamHandler(sys.stdout)
-
-    if logger.hasHandlers():
-        logger.handlers.clear()
-    else:
-        formatter = Formatter(
-            fmt="[%(asctime)s] %(levelname)s - %(message)s",
-            datefmt="%Y-%m-%d %H:%M:%S%z",
-        )
-
-        handler.setFormatter(formatter)
-        logger.addHandler(handler)
-
-    return logger

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/dasf/utils/types.html b/docs/_modules/dasf/utils/types.html deleted file mode 100644 index b5e3687..0000000 --- a/docs/_modules/dasf/utils/types.html +++ /dev/null @@ -1,396 +0,0 @@ - - - - - - - - - - dasf.utils.types — DASF 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- - -
-
-
-
- -

Source code for dasf.utils.types

-""" Data types handlers. """
-#!/usr/bin/env python3
-
-from typing import Union, get_args
-
-import numpy as np
-import pandas as pd
-import dask.array as da
-import dask.dataframe as ddf
-import xarray as xr
-
-try:
-    import cupy as cp
-    import cudf
-    import dask_cudf as dcudf
-except ImportError:
-    pass
-
-from dasf.utils.funcs import is_gpu_supported
-
-
-ArrayCPU = Union[list, np.ndarray]
-DataFrameCPU = Union[pd.DataFrame]
-
-DataCPU = Union[ArrayCPU, DataFrameCPU]
-
-DaskArray = Union[da.core.Array]
-DaskDataFrameCPU = Union[ddf.core.DataFrame]
-
-XDataArray = Union[xr.DataArray]
-
-Array = Union[ArrayCPU, DaskArray, XDataArray]
-DaskDataFrame = Union[DaskDataFrameCPU]
-DataFrame = Union[DataFrameCPU, DaskDataFrameCPU]
-DataDask = Union[DaskArray, DaskDataFrameCPU]
-try:
-    ArrayGPU = Union[cp.ndarray]
-    DataFrameGPU = Union[cudf.DataFrame]
-
-    DataGPU = Union[ArrayGPU, DataFrameGPU]
-
-    DaskDataFrameGPU = Union[dcudf.core.DataFrame]
-
-    Array = Union[Array, ArrayGPU]
-    DaskDataFrame = Union[DaskDataFrame, DaskDataFrameGPU]
-    DataFrame = Union[DataFrame, DaskDataFrame, DataFrameGPU]
-    DataDask = Union[DataDask, DaskDataFrame]
-except NameError:
-    pass
-
-
-
[docs]def is_array(data) -> bool:
-    """
-    Returns if data is a generic array.
-    """
-    return isinstance(data, get_args(Array))

-
-
-
[docs]def is_dataframe(data) -> bool:
-    """
-    Returns if data is a generic dataframe.
-    """
-    return isinstance(data, get_args(DataFrame))

-
-
-
[docs]def is_cpu_array(data) -> bool:
-    """
-    Returns if data is a CPU arrau like Numpy.
-    """
-    return isinstance(data, get_args(ArrayCPU))

-
-
-
[docs]def is_cpu_dataframe(data) -> bool:
-    """
-    Returns if data is a CPU dataframe like Pandas.
-    """
-    return isinstance(data, DataFrameCPU)

-
-
-
[docs]def is_cpu_datatype(data) -> bool:
-    """
-    Returns if data is a CPU data type.
-    """
-    return isinstance(data, get_args(DataCPU))

-
-
-
[docs]def is_gpu_array(data) -> bool:
-    """
-    Returns if data is a GPU array like Cupy.
-    """
-    return is_gpu_supported() and isinstance(data, ArrayGPU)

-
-
-
[docs]def is_gpu_dataframe(data) -> bool:
-    """
-    Returns if data is a GPU dataframe like Cudf.
-    """
-    return is_gpu_supported() and isinstance(data, DataFrameGPU)

-
-
-
[docs]def is_gpu_datatype(data) -> bool:
-    """
-    Returns if data is a GPU data type.
-    """
-    return is_gpu_supported() and isinstance(data, get_args(DataGPU))

-
-
-
[docs]def is_dask_cpu_array(data) -> bool:
-    """
-    Returns if data is a Dask array with CPU internal array.
-    """
-    if isinstance(data, DaskArray):
-        # pylint: disable=protected-access
-        if isinstance(data._meta, get_args(ArrayCPU)):
-            return True
-    return False

-
-
-
[docs]def is_dask_cpu_dataframe(data) -> bool:
-    """
-    Returns if data is a Dask dataframe with CPU internal dataframe.
-    """
-    try:
-        if is_gpu_supported() and isinstance(data, get_args(DaskDataFrame)):
-            # pylint: disable=protected-access
-            if isinstance(data._meta, DataFrameCPU):
-                return True
-        elif isinstance(data, DaskDataFrame):
-            # pylint: disable=protected-access
-            if isinstance(data._meta, DataFrameCPU):
-                return True
-    # We need a Exception here due to Numpy bug.
-    except TypeError:
-        pass
-    return False

-
-
-
[docs]def is_dask_gpu_array(data) -> bool:
-    """
-    Returns if data is a Dask array with GPU internal array.
-    """
-    if is_gpu_supported() and isinstance(data, DaskArray):
-        # pylint: disable=protected-access
-        if isinstance(data._meta, ArrayGPU):
-            return True
-    return False

-
-
-
[docs]def is_dask_gpu_dataframe(data) -> bool:
-    """
-    Returns if data is a Dask dataframe with GPU internal dataframe.
-    """
-    if is_gpu_supported() and isinstance(data, get_args(DaskDataFrame)):
-        # pylint: disable=protected-access
-        if isinstance(data._meta, DataFrameGPU):
-            return True
-    return False

-
-
-
[docs]def is_dask_array(data) -> bool:
-    """
-    Returns if data is a Dask array.
-    """
-    return isinstance(data, DaskArray)

-
-
-
[docs]def is_dask_dataframe(data) -> bool:
-    """
-    Returns if data is a Dask dataframe.
-    """
-    if is_gpu_supported():
-        return isinstance(data, get_args(DaskDataFrame))
-    return isinstance(data, DaskDataFrame)

-
-
-
[docs]def is_dask(data) -> bool:
-    """
-    Returns if data is a Dask data type.
-    """
-    return isinstance(data, get_args(DataDask))

-
-
-
[docs]def is_xarray_array(data) -> bool:
-    """
-    Returns if data is a Xarray.
-    """
-    return isinstance(data, XDataArray)

-
- -
- -
-
- -
- -
-

- © Copyright 2022, UNICAMP. - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
-
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/index.html b/docs/_modules/index.html deleted file mode 100644 index d59c265..0000000 --- a/docs/_modules/index.html +++ /dev/null @@ -1,150 +0,0 @@ - - - - - - Overview: module code — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Overview: module code
    • -
  • -
    • -
-
-
-
-
- -

All modules for which code is available

- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_sources/api.rst.txt b/docs/_sources/api.rst.txt deleted file mode 100644 index 2c7a2c0..0000000 --- a/docs/_sources/api.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -DASF API Reference ------------------------ - -.. toctree:: - :maxdepth: 2 - - autoapi/dasf/index diff --git a/docs/_sources/autoapi/dasf/datasets/base/index.rst.txt b/docs/_sources/autoapi/dasf/datasets/base/index.rst.txt deleted file mode 100644 index d574631..0000000 --- a/docs/_sources/autoapi/dasf/datasets/base/index.rst.txt +++ /dev/null @@ -1,1035 +0,0 @@ -:py:mod:`dasf.datasets.base` -============================ - -.. py:module:: dasf.datasets.base - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.datasets.base.Dataset - dasf.datasets.base.DatasetArray - dasf.datasets.base.DatasetZarr - dasf.datasets.base.DatasetHDF5 - dasf.datasets.base.DatasetXarray - dasf.datasets.base.DatasetLabeled - dasf.datasets.base.DatasetDataFrame - dasf.datasets.base.DatasetParquet - - - - -.. py:class:: Dataset(name, download = False, root = None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.TargeteredTransform` - - Class representing a generic dataset based on a TargeteredTransform - object. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - *args : type - Additional arguments without keys. - **kwargs : type - Additional keyworkded arguments. - - - .. py:method:: __set_dataset_cache_dir() - - Generate cached directory in $HOME to store dataset(s). - - - - - .. py:method:: download() - - Skeleton of the download method. - - - - - .. py:method:: __len__() - - Return internal data length. - - - - - .. py:method:: __getitem__(idx) - - Generic __getitem__() function based on internal data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetArray(name, download = False, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as an array of a defined - shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: __operator_check__(other) - - - .. py:method:: __repr__() - - Return a class representation based on internal array. - - - - - .. py:method:: __array__(dtype=None) - - - .. py:method:: __array_ufunc__(ufunc, method, *inputs, **kwargs) - - - .. py:method:: __check_op_input(in_data) - - Return the proper type of data for operation - - >>> Result = DatasetArray + Numpy; or - >>> Result = DatasetArray + DatasetArray - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - - - .. py:method:: __add__(other) - - Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - - - .. py:method:: __sub__(other) - - Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - - - .. py:method:: __mul__(other) - - Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - - - .. py:method:: __div__(other) - - Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - - - .. py:method:: __copy_attrs_from_data() - - Extends metadata to new transformed object (after operations). - - - - - .. py:method:: __npy_header() - - Read an array header from a filelike object. - - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - -.. py:class:: DatasetZarr(name, download = False, root = None, chunks=None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a Zarr array of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: __repr__() - - Return a class representation based on internal array. - - - - - .. py:method:: __check_op_input(in_data) - - Return the proper type of data for operation - - >>> Result = DatasetZarr + Numpy; or - >>> Result = DatasetZarr + DatasetZarr - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - - - .. py:method:: __add__(other) - - Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - - - .. py:method:: __sub__(other) - - Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - - - .. py:method:: __mul__(other) - - Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - - - .. py:method:: __div__(other) - - Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - - - .. py:method:: __copy_attrs_from_data() - - Extends metadata to new transformed object (after operations). - - - - - -.. py:class:: DatasetHDF5(name, download = False, root = None, chunks='auto', dataset_path = None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a HDF5 dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - dataset_path : str - Relative path of the internal HDF5 dataset (the default is None). - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp=None, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) (placeholder). - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - -.. py:class:: DatasetXarray(name, download = False, root = None, chunks=None, data_var=None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a Xarray dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - data_var : Any - Key (or index) of the internal Xarray dataset (the default is None). - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: __len__() - - Return internal data length. - - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function based on internal Xarray data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetLabeled(name, download = False, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - A class representing a labeled dataset. Each item is a 2-element tuple, - where the first element is a array of data and the second element is the - respective label. The items can be accessed from `dataset[x]`. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - Attributes - ---------- - __chunks : type - Description of attribute `__chunks`. - - - .. py:method:: download() - - Download the dataset. - - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data - (train and labels). - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Tuple - A Future object that will return a tuple: (data, label). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - Returns - ------- - Tuple - A 2-element tuple: (data, label) - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function for data and labeled data together. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetDataFrame(name, download = True, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a dataframe. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. CuDF). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. pandas). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: __len__() - - Return internal data length. - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function based on internal dataframe. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetParquet(name, download = True, root = None, chunks='auto') - - - Bases: :py:obj:`DatasetDataFrame` - - Class representing an dataset wich is defined as a Parquet. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. CuDF). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. pandas). - - - - - diff --git a/docs/_sources/autoapi/dasf/datasets/blobs/index.rst.txt b/docs/_sources/autoapi/dasf/datasets/blobs/index.rst.txt deleted file mode 100644 index b6e17e2..0000000 --- a/docs/_sources/autoapi/dasf/datasets/blobs/index.rst.txt +++ /dev/null @@ -1,44 +0,0 @@ -:py:mod:`dasf.datasets.blobs` -============================= - -.. py:module:: dasf.datasets.blobs - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.datasets.blobs.make_blobs - - - - -.. py:class:: make_blobs - - Singleton class used to generate isotropic Gaussian blobs for clustering. - It automatically selects the implementation based on hardware and available - libraries and return a container suitable for it (cupy, numpy, cupy+dask or - numpy+dask). - - The class implements `__call__` being a callable object. - - .. py:method:: _lazy_make_blobs_cpu(**kwargs) - - - .. py:method:: _lazy_make_blobs_gpu(**kwargs) - - - .. py:method:: _make_blobs_cpu(**kwargs) - - - .. py:method:: _make_blobs_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/datasets/datasets/index.rst.txt b/docs/_sources/autoapi/dasf/datasets/datasets/index.rst.txt deleted file mode 100644 index 69c3d5f..0000000 --- a/docs/_sources/autoapi/dasf/datasets/datasets/index.rst.txt +++ /dev/null @@ -1,78 +0,0 @@ -:py:mod:`dasf.datasets.datasets` -================================ - -.. py:module:: dasf.datasets.datasets - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.datasets.datasets.make_blobs - dasf.datasets.datasets.make_classification - dasf.datasets.datasets.make_regression - - - - -.. py:class:: make_blobs - - - .. py:method:: _lazy_make_blobs_cpu(**kwargs) - - - .. py:method:: _lazy_make_blobs_gpu(**kwargs) - - - .. py:method:: _make_blobs_cpu(**kwargs) - - - .. py:method:: _make_blobs_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - -.. py:class:: make_classification - - - .. py:method:: _lazy_make_classification_cpu(**kwargs) - - - .. py:method:: _lazy_make_classification_gpu(**kwargs) - - - .. py:method:: _make_classification_cpu(**kwargs) - - - .. py:method:: _make_classification_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - -.. py:class:: make_regression - - - .. py:method:: _lazy_make_regression_cpu(**kwargs) - - - .. py:method:: _lazy_make_regression_gpu(**kwargs) - - - .. py:method:: _make_regression_cpu(**kwargs) - - - .. py:method:: _make_regression_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/datasets/download/index.rst.txt b/docs/_sources/autoapi/dasf/datasets/download/index.rst.txt deleted file mode 100644 index 1e3cd76..0000000 --- a/docs/_sources/autoapi/dasf/datasets/download/index.rst.txt +++ /dev/null @@ -1,74 +0,0 @@ -:py:mod:`dasf.datasets.download` -================================ - -.. py:module:: dasf.datasets.download - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.datasets.download.DownloadWget - dasf.datasets.download.DownloadGDrive - - - - -.. py:class:: DownloadWget(url, filename, root, download = True) - - - Bases: :py:obj:`dasf.datasets.base.Dataset` - - Dataset downloadable via wget. - - Parameters - ---------- - url : str - The url to fetch the resource. - filename : str - Name of the file. - root : str - Directory to store the downloaded file. - download : bool - If it the dataset must be downloaded (the default is True). - - - .. py:method:: download() - - Download the dataset. - - - - - -.. py:class:: DownloadGDrive(google_file_id, filename, root, download = True) - - - Bases: :py:obj:`dasf.datasets.base.Dataset` - - Dataset downloadable via Google Drive. - - Parameters - ---------- - google_file_id : str - Id of the google drive resource. - filename : str - Name of the file. - root : str - Directory to store the downloaded file. - download : bool - If it the dataset must be downloaded (the default is True). - - - .. py:method:: download() - - Download the dataset. - - - - - diff --git a/docs/_sources/autoapi/dasf/datasets/index.rst.txt b/docs/_sources/autoapi/dasf/datasets/index.rst.txt deleted file mode 100644 index 3c6fc2e..0000000 --- a/docs/_sources/autoapi/dasf/datasets/index.rst.txt +++ /dev/null @@ -1,1086 +0,0 @@ -:py:mod:`dasf.datasets` -======================= - -.. py:module:: dasf.datasets - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - base/index.rst - datasets/index.rst - download/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.datasets.Dataset - dasf.datasets.DatasetArray - dasf.datasets.DatasetZarr - dasf.datasets.DatasetHDF5 - dasf.datasets.DatasetXarray - dasf.datasets.DatasetLabeled - dasf.datasets.DatasetDataFrame - dasf.datasets.DatasetParquet - dasf.datasets.make_blobs - dasf.datasets.make_classification - - - - -.. py:class:: Dataset(name, download = False, root = None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.TargeteredTransform` - - Class representing a generic dataset based on a TargeteredTransform - object. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - *args : type - Additional arguments without keys. - **kwargs : type - Additional keyworkded arguments. - - - .. py:method:: __set_dataset_cache_dir() - - Generate cached directory in $HOME to store dataset(s). - - - - - .. py:method:: download() - - Skeleton of the download method. - - - - - .. py:method:: __len__() - - Return internal data length. - - - - - .. py:method:: __getitem__(idx) - - Generic __getitem__() function based on internal data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetArray(name, download = False, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as an array of a defined - shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: __operator_check__(other) - - - .. py:method:: __repr__() - - Return a class representation based on internal array. - - - - - .. py:method:: __array__(dtype=None) - - - .. py:method:: __array_ufunc__(ufunc, method, *inputs, **kwargs) - - - .. py:method:: __check_op_input(in_data) - - Return the proper type of data for operation - - >>> Result = DatasetArray + Numpy; or - >>> Result = DatasetArray + DatasetArray - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - - - .. py:method:: __add__(other) - - Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - - - .. py:method:: __sub__(other) - - Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - - - .. py:method:: __mul__(other) - - Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - - - .. py:method:: __div__(other) - - Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - - - .. py:method:: __copy_attrs_from_data() - - Extends metadata to new transformed object (after operations). - - - - - .. py:method:: __npy_header() - - Read an array header from a filelike object. - - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - -.. py:class:: DatasetZarr(name, download = False, root = None, chunks=None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a Zarr array of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: __repr__() - - Return a class representation based on internal array. - - - - - .. py:method:: __check_op_input(in_data) - - Return the proper type of data for operation - - >>> Result = DatasetZarr + Numpy; or - >>> Result = DatasetZarr + DatasetZarr - - Parameters - ---------- - in_data : Any - Input data to be analyzed. - - Returns - ------- - data : Any - A data representing the internal array or the class itself. - - - - .. py:method:: __add__(other) - - Internal function of adding two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A sum with two arrays. - - - - .. py:method:: __sub__(other) - - Internal function of subtracting two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A subtraction of two arrays. - - - - .. py:method:: __mul__(other) - - Internal function of multiplication two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A multiplication of two arrays. - - - - .. py:method:: __div__(other) - - Internal function of division two array datasets. - - Parameters - ---------- - other : Any - A data representing an array or a DatasetArray. - - Returns - ------- - DatasetArry - A division of two arrays. - - - - .. py:method:: __copy_attrs_from_data() - - Extends metadata to new transformed object (after operations). - - - - - -.. py:class:: DatasetHDF5(name, download = False, root = None, chunks='auto', dataset_path = None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a HDF5 dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - dataset_path : str - Relative path of the internal HDF5 dataset (the default is None). - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Any - The data (or a Future load object, for `_lazy` operations). - - - - .. py:method:: _load(xp=None, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) (placeholder). - **kwargs : type - Additional `kwargs` to `xp.load` function. - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - -.. py:class:: DatasetXarray(name, download = False, root = None, chunks=None, data_var=None) - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a Xarray dataset of a - defined shape. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - data_var : Any - Key (or index) of the internal Xarray dataset (the default is None). - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: __len__() - - Return internal data length. - - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function based on internal Xarray data. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetLabeled(name, download = False, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - A class representing a labeled dataset. Each item is a 2-element tuple, - where the first element is a array of data and the second element is the - respective label. The items can be accessed from `dataset[x]`. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - Attributes - ---------- - __chunks : type - Description of attribute `__chunks`. - - - .. py:method:: download() - - Download the dataset. - - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data - (train and labels). - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load(xp, **kwargs) - - Lazy load the dataset using an CPU dask container. - - Parameters - ---------- - xp : type - Library used to load the file. It must follow numpy library. - **kwargs : type - Additional keyworkded arguments to the load. - - Returns - ------- - Tuple - A Future object that will return a tuple: (data, label). - - - - .. py:method:: _load(xp, **kwargs) - - Load data using CPU container. - - Parameters - ---------- - xp : Module - A module that load data (implement `load` function) - **kwargs : type - Additional `kwargs` to `xp.load` function. - - Returns - ------- - Tuple - A 2-element tuple: (data, label) - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. cupy). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. numpy). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function for data and labeled data together. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetDataFrame(name, download = True, root = None, chunks='auto') - - - Bases: :py:obj:`Dataset` - - Class representing an dataset wich is defined as a dataframe. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:property:: shape - :type: tuple - - Returns the shape of an array. - - Returns - ------- - tuple - A tuple with the shape. - - - - .. py:method:: _load_meta() - - Load metadata to inspect. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: inspect_metadata() - - Return a dictionary with all metadata information from data. - - Returns - ------- - dict - A dictionary with metadata information. - - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. CuDF). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. pandas). - - - - - .. py:method:: load() - - Placeholder for load function. - - - - - .. py:method:: __len__() - - Return internal data length. - - - - .. py:method:: __getitem__(idx) - - A __getitem__() function based on internal dataframe. - - Parameters - ---------- - idx : Any - Key of the fetched data. It can be an integer or a tuple. - - - - -.. py:class:: DatasetParquet(name, download = True, root = None, chunks='auto') - - - Bases: :py:obj:`DatasetDataFrame` - - Class representing an dataset wich is defined as a Parquet. - - Parameters - ---------- - name : str - Symbolic name of the dataset. - download : bool - If the dataset must be downloaded (the default is False). - root : str - Root download directory (the default is None). - chunks : Any - Number of blocks of the array (the default is "auto"). - - - .. py:method:: _lazy_load_gpu() - - Load data with GPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _lazy_load_cpu() - - Load data with CPU container + DASK. (It does not load immediattly) - - - - - .. py:method:: _load_gpu() - - Load data with GPU container (e.g. CuDF). - - - - - .. py:method:: _load_cpu() - - Load data with CPU container (e.g. pandas). - - - - - -.. py:class:: make_blobs - - - .. py:method:: _lazy_make_blobs_cpu(**kwargs) - - - .. py:method:: _lazy_make_blobs_gpu(**kwargs) - - - .. py:method:: _make_blobs_cpu(**kwargs) - - - .. py:method:: _make_blobs_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - -.. py:class:: make_classification - - - .. py:method:: _lazy_make_classification_cpu(**kwargs) - - - .. py:method:: _lazy_make_classification_gpu(**kwargs) - - - .. py:method:: _make_classification_cpu(**kwargs) - - - .. py:method:: _make_classification_gpu(**kwargs) - - - .. py:method:: __call__(**kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/debug/debug/index.rst.txt b/docs/_sources/autoapi/dasf/debug/debug/index.rst.txt deleted file mode 100644 index 0f238b5..0000000 --- a/docs/_sources/autoapi/dasf/debug/debug/index.rst.txt +++ /dev/null @@ -1,55 +0,0 @@ -:py:mod:`dasf.debug.debug` -========================== - -.. py:module:: dasf.debug.debug - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.debug.debug.Debug - dasf.debug.debug.VisualizeDaskData - - - - -.. py:class:: Debug - - - Print information about an operator (shape, datatype, etc.), and return - the self object reference. - - Parameters - ---------- - name : str - Name of the operator. - **kwargs : type - Additional keyworkded arguments to `Operator`. - - - .. py:method:: display(X) - - - -.. py:class:: VisualizeDaskData(filename = None) - - - Visualize DASK data from an operator. - - Parameters - ---------- - filename : str - A path to save the DASK visualization (the default is None). - **kwargs : type - Additional keyworkded arguments to `Operator`. - - - .. py:method:: display(X) - - - diff --git a/docs/_sources/autoapi/dasf/debug/index.rst.txt b/docs/_sources/autoapi/dasf/debug/index.rst.txt deleted file mode 100644 index c3275fc..0000000 --- a/docs/_sources/autoapi/dasf/debug/index.rst.txt +++ /dev/null @@ -1,64 +0,0 @@ -:py:mod:`dasf.debug` -==================== - -.. py:module:: dasf.debug - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - debug/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.debug.Debug - dasf.debug.VisualizeDaskData - - - - -.. py:class:: Debug - - - Print information about an operator (shape, datatype, etc.), and return - the self object reference. - - Parameters - ---------- - name : str - Name of the operator. - **kwargs : type - Additional keyworkded arguments to `Operator`. - - - .. py:method:: display(X) - - - -.. py:class:: VisualizeDaskData(filename = None) - - - Visualize DASK data from an operator. - - Parameters - ---------- - filename : str - A path to save the DASK visualization (the default is None). - **kwargs : type - Additional keyworkded arguments to `Operator`. - - - .. py:method:: display(X) - - - diff --git a/docs/_sources/autoapi/dasf/feature_extraction/histogram/index.rst.txt b/docs/_sources/autoapi/dasf/feature_extraction/histogram/index.rst.txt deleted file mode 100644 index d6a82b7..0000000 --- a/docs/_sources/autoapi/dasf/feature_extraction/histogram/index.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -:py:mod:`dasf.feature_extraction.histogram` -=========================================== - -.. py:module:: dasf.feature_extraction.histogram - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.feature_extraction.histogram.Histogram - - - - -.. py:class:: Histogram(bins = None, range = None, normed = False, weights=None, density=None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.TargeteredTransform`, :py:obj:`dasf.transforms.base.Transform` - - Operator to extract the histogram of a data. - - Parameters - ---------- - bins : Optional[int] - Number of bins (the default is None). - range : tuple - 2-element tuple with the lower and upper range of the bins. If not - provided, range is simply (X.min(), X.max()) (the default is None). - normed : bool - If the historgram must be normalized (the default is False). - weights : type - An array of weights, of the same shape as X. Each value in a only - contributes its associated weight towards the bin count - (the default is None). - density : type - If False, the result will contain the number of samples in each bin. - If True, the result is the value of the probability density function - at the bin, normalized such that the integral over the range is 1 - (the default is None). - - Attributes - ---------- - bins - range - normed - weights - density - - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: __transform_generic(X, xp) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/feature_extraction/index.rst.txt b/docs/_sources/autoapi/dasf/feature_extraction/index.rst.txt deleted file mode 100644 index 89a9d7b..0000000 --- a/docs/_sources/autoapi/dasf/feature_extraction/index.rst.txt +++ /dev/null @@ -1,188 +0,0 @@ -:py:mod:`dasf.feature_extraction` -================================= - -.. py:module:: dasf.feature_extraction - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - histogram/index.rst - transform/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.feature_extraction.ConcatenateToArray - dasf.feature_extraction.SampleDataframe - dasf.feature_extraction.GetSubeCubeArray - dasf.feature_extraction.SliceDataframe - dasf.feature_extraction.GetSubDataframe - dasf.feature_extraction.Histogram - - - - -.. py:class:: ConcatenateToArray(flatten = False) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Concatenate data from different Arrays into a single array. - - Parameters - ---------- - flatten : bool - If the arrays must be flatten prior concatenating. If `False`, the - arrays must share the shape of last dimansions in order to be - concatenated (the default is False). - - - .. py:method:: __transform_generic(xp, **kwargs) - - - .. py:method:: _transform_cpu(**kwargs) - - - .. py:method:: _transform_gpu(**kwargs) - - - -.. py:class:: SampleDataframe(percent) - - - Return a subset with random samples of the original dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataset. - - - .. py:method:: run(X) - - Returns a subset with random samples from the dataset `X`. - - Parameters - ---------- - X : Any - The dataset. - - Returns - ------- - Any - The sampled subset. - - - - -.. py:class:: GetSubeCubeArray(percent) - - - Get a subcube with x% of samples from the original one. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the cube. - - - .. py:method:: transform(X) - - - -.. py:class:: SliceDataframe(iline_index) - - - Bases: :py:obj:`dasf.transforms.base.Fit` - - Get a slice of a cube. An inline slice is a section over the x-axis. - - Parameters - ---------- - iline_index : int - The index of the inline to get. - - - .. py:method:: fit(X, y) - - - -.. py:class:: GetSubDataframe(percent) - - - Get the first x% samples from the dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataframe. - - - .. py:method:: transform(X) - - - -.. py:class:: Histogram(bins = None, range = None, normed = False, weights=None, density=None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.TargeteredTransform`, :py:obj:`dasf.transforms.base.Transform` - - Operator to extract the histogram of a data. - - Parameters - ---------- - bins : Optional[int] - Number of bins (the default is None). - range : tuple - 2-element tuple with the lower and upper range of the bins. If not - provided, range is simply (X.min(), X.max()) (the default is None). - normed : bool - If the historgram must be normalized (the default is False). - weights : type - An array of weights, of the same shape as X. Each value in a only - contributes its associated weight towards the bin count - (the default is None). - density : type - If False, the result will contain the number of samples in each bin. - If True, the result is the value of the probability density function - at the bin, normalized such that the integral over the range is 1 - (the default is None). - - Attributes - ---------- - bins - range - normed - weights - density - - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: __transform_generic(X, xp) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/feature_extraction/transform/index.rst.txt b/docs/_sources/autoapi/dasf/feature_extraction/transform/index.rst.txt deleted file mode 100644 index 23f185b..0000000 --- a/docs/_sources/autoapi/dasf/feature_extraction/transform/index.rst.txt +++ /dev/null @@ -1,123 +0,0 @@ -:py:mod:`dasf.feature_extraction.transform` -=========================================== - -.. py:module:: dasf.feature_extraction.transform - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.feature_extraction.transform.ConcatenateToArray - dasf.feature_extraction.transform.SampleDataframe - dasf.feature_extraction.transform.GetSubeCubeArray - dasf.feature_extraction.transform.SliceDataframe - dasf.feature_extraction.transform.GetSubDataframe - - - - -.. py:class:: ConcatenateToArray(flatten = False) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Concatenate data from different Arrays into a single array. - - Parameters - ---------- - flatten : bool - If the arrays must be flatten prior concatenating. If `False`, the - arrays must share the shape of last dimansions in order to be - concatenated (the default is False). - - - .. py:method:: __transform_generic(xp, **kwargs) - - - .. py:method:: _transform_cpu(**kwargs) - - - .. py:method:: _transform_gpu(**kwargs) - - - -.. py:class:: SampleDataframe(percent) - - - Return a subset with random samples of the original dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataset. - - - .. py:method:: run(X) - - Returns a subset with random samples from the dataset `X`. - - Parameters - ---------- - X : Any - The dataset. - - Returns - ------- - Any - The sampled subset. - - - - -.. py:class:: GetSubeCubeArray(percent) - - - Get a subcube with x% of samples from the original one. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the cube. - - - .. py:method:: transform(X) - - - -.. py:class:: SliceDataframe(iline_index) - - - Bases: :py:obj:`dasf.transforms.base.Fit` - - Get a slice of a cube. An inline slice is a section over the x-axis. - - Parameters - ---------- - iline_index : int - The index of the inline to get. - - - .. py:method:: fit(X, y) - - - -.. py:class:: GetSubDataframe(percent) - - - Get the first x% samples from the dataset. - - Parameters - ---------- - percent : float - Percentage of the samples to get from the dataframe. - - - .. py:method:: transform(X) - - - diff --git a/docs/_sources/autoapi/dasf/index.rst.txt b/docs/_sources/autoapi/dasf/index.rst.txt deleted file mode 100644 index 2c47f49..0000000 --- a/docs/_sources/autoapi/dasf/index.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -:py:mod:`dasf` -============== - -.. py:module:: dasf - - -Subpackages ------------ -.. toctree:: - :titlesonly: - :maxdepth: 3 - - datasets/index.rst - debug/index.rst - feature_extraction/index.rst - ml/index.rst - pipeline/index.rst - profile/index.rst - transforms/index.rst - utils/index.rst - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/agglomerative/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/agglomerative/index.rst.txt deleted file mode 100644 index 4cb4ec7..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/agglomerative/index.rst.txt +++ /dev/null @@ -1,136 +0,0 @@ -:py:mod:`dasf.ml.cluster.agglomerative` -======================================= - -.. py:module:: dasf.ml.cluster.agglomerative - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.agglomerative.AgglomerativeClustering - - - - -.. py:class:: AgglomerativeClustering(n_clusters=2, affinity='euclidean', connectivity=None, linkage='single', memory=None, compute_full_tree='auto', distance_threshold=None, compute_distances=False, handle=None, verbose=False, n_neighbors=10, output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Agglomerative Clustering - - Recursively merges the pair of clusters that minimally increases - a given linkage distance. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int or None, default=2 - The number of clusters to find. It must be ``None`` if - ``distance_threshold`` is not ``None``. - - affinity : str or callable, default='euclidean' - Metric used to compute the linkage. Can be "euclidean", "l1", "l2", - "manhattan", "cosine", or "precomputed". - If linkage is "ward", only "euclidean" is accepted. - If "precomputed", a distance matrix (instead of a similarity matrix) - is needed as input for the fit method. - - memory : str or object with the joblib.Memory interface, default=None - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - connectivity : array-like or callable, default=None - Connectivity matrix. Defines for each sample the neighboring - samples following a given structure of the data. - This can be a connectivity matrix itself or a callable that transforms - the data into a connectivity matrix, such as derived from - kneighbors_graph. Default is ``None``, i.e, the - hierarchical clustering algorithm is unstructured. - - compute_full_tree : 'auto' or bool, default='auto' - Stop early the construction of the tree at ``n_clusters``. This is - useful to decrease computation time if the number of clusters is not - small compared to the number of samples. This option is useful only - when specifying a connectivity matrix. Note also that when varying the - number of clusters and using caching, it may be advantageous to compute - the full tree. It must be ``True`` if ``distance_threshold`` is not - ``None``. By default `compute_full_tree` is "auto", which is equivalent - to `True` when `distance_threshold` is not `None` or that `n_clusters` - is inferior to the maximum between 100 or `0.02 * n_samples`. - Otherwise, "auto" is equivalent to `False`. - - linkage : {'ward', 'complete', 'average', 'single'}, default='ward' - Which linkage criterion to use. The linkage criterion determines which - distance to use between sets of observation. The algorithm will merge - the pairs of cluster that minimize this criterion. - - - 'ward' minimizes the variance of the clusters being merged. - - 'average' uses the average of the distances of each observation of - the two sets. - - 'complete' or 'maximum' linkage uses the maximum distances between - all observations of the two sets. - - 'single' uses the minimum of the distances between all observations - of the two sets. - - .. versionadded:: 0.20 - Added the 'single' option - - distance_threshold : float, default=None - The linkage distance threshold above which, clusters will not be - merged. If not ``None``, ``n_clusters`` must be ``None`` and - ``compute_full_tree`` must be ``True``. - - .. versionadded:: 0.21 - - compute_distances : bool, default=False - Computes distances between clusters even if `distance_threshold` is not - used. This can be used to make dendrogram visualization, but introduces - a computational and memory overhead. - - .. versionadded:: 0.24 - - n_neighbors : int, default = 15 - The number of neighbors to compute when connectivity = "knn" - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - Examples - -------- - >>> from dasf.ml.cluster import AgglomerativeClustering - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [4, 2], [4, 4], [4, 0]]) - >>> clustering = AgglomerativeClustering().fit(X) - >>> clustering - AgglomerativeClustering() - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html - - https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering - - - .. py:method:: _fit_cpu(X, y=None, convert_dtype=True) - - - .. py:method:: _fit_gpu(X, y=None, convert_dtype=True) - - - .. py:method:: _fit_predict_cpu(X, y=None) - - - .. py:method:: _fit_predict_gpu(X, y=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/classifier/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/classifier/index.rst.txt deleted file mode 100644 index 77e28e0..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/classifier/index.rst.txt +++ /dev/null @@ -1,25 +0,0 @@ -:py:mod:`dasf.ml.cluster.classifier` -==================================== - -.. py:module:: dasf.ml.cluster.classifier - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.classifier.ClusterClassifier - - - - -.. py:class:: ClusterClassifier(**kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitPredict`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.Predict`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams`, :py:obj:`dasf.transforms.base.TargeteredTransform` - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/dbscan/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/dbscan/index.rst.txt deleted file mode 100644 index 569a34b..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/dbscan/index.rst.txt +++ /dev/null @@ -1,147 +0,0 @@ -:py:mod:`dasf.ml.cluster.dbscan` -================================ - -.. py:module:: dasf.ml.cluster.dbscan - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.dbscan.DBSCAN - - - - -.. py:class:: DBSCAN(eps=0.5, leaf_size=40, metric='euclidean', min_samples=5, p=None, output_type=None, calc_core_sample_indices=True, verbose=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Perform DBSCAN clustering from vector array or distance matrix. - - DBSCAN - Density-Based Spatial Clustering of Applications with Noise. - Finds core samples of high density and expands clusters from them. - Good for data which contains clusters of similar density. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - eps : float, default=0.5 - The maximum distance between two samples for one to be considered - as in the neighborhood of the other. This is not a maximum bound - on the distances of points within a cluster. This is the most - important DBSCAN parameter to choose appropriately for your data set - and distance function. - - min_samples : int, default=5 - The number of samples (or total weight) in a neighborhood for a point - to be considered as a core point. This includes the point itself. - - metric : string, or callable, default='euclidean' - The metric to use when calculating distance between instances in a - feature array. If metric is a string or callable, it must be one of - the options allowed by :func:`sklearn.metrics.pairwise_distances` for - its metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and - must be square. X may be a :term:`Glossary `, in which - case only "nonzero" elements may be considered neighbors for DBSCAN. - - .. versionadded:: 0.17 - metric *precomputed* to accept precomputed sparse matrix. - - metric_params : dict, default=None - Additional keyword arguments for the metric function. - - .. versionadded:: 0.19 - - algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' - The algorithm to be used by the NearestNeighbors module - to compute pointwise distances and find nearest neighbors. - See NearestNeighbors module documentation for details. - - leaf_size : int, default=30 - Leaf size passed to BallTree or cKDTree. This can affect the speed - of the construction and query, as well as the memory required - to store the tree. The optimal value depends - on the nature of the problem. - - p : float, default=None - The power of the Minkowski metric to be used to calculate distance - between points. If None, then ``p=2`` (equivalent to the Euclidean - distance). - - n_jobs : int, default=None - The number of parallel jobs to run. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - calc_core_sample_indices(optional) : boolean, default = True - Indicates whether the indices of the core samples should be calculated. - The the attribute `core_sample_indices_` will not be used, setting this - to False will avoid unnecessary kernel launches. - - - Examples - -------- - >>> from dasf.ml.cluster import DBSCAN - >>> import numpy as np - >>> X = np.array([[1, 2], [2, 2], [2, 3], - ... [8, 7], [8, 8], [25, 80]]) - >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) - >>> clustering - DBSCAN(eps=3, min_samples=2) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering - - See Also - -------- - OPTICS : A similar clustering at multiple values of eps. Our implementation - is optimized for memory usage. - - References - ---------- - Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based - Algorithm for Discovering Clusters in Large Spatial Databases with Noise". - In: Proceedings of the 2nd International Conference on Knowledge Discovery - and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996 - - Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). - DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. - ACM Transactions on Database Systems (TODS), 42(3), 19. - - - .. py:method:: _lazy_fit_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_gpu(X, y=None, out_dtype='int32') - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/hdbscan/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/hdbscan/index.rst.txt deleted file mode 100644 index 6f6a5da..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/hdbscan/index.rst.txt +++ /dev/null @@ -1,203 +0,0 @@ -:py:mod:`dasf.ml.cluster.hdbscan` -================================= - -.. py:module:: dasf.ml.cluster.hdbscan - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.hdbscan.HDBSCAN - - - - -.. py:class:: HDBSCAN(alpha=1.0, gen_min_span_tree=False, leaf_size=40, metric='euclidean', min_cluster_size=5, min_samples=None, p=None, algorithm='best', approx_min_span_tree=True, core_dist_n_jobs=4, cluster_selection_method='eom', allow_single_cluster=False, prediction_data=False, match_reference_implementation=False, connectivity='knn', output_type=None, verbose=0, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Perform HDBSCAN clustering from vector array or distance matrix. - - HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications - with Noise. Performs DBSCAN over varying epsilon values and integrates - the result to find a clustering that gives the best stability over epsilon. - This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), - and be more robust to parameter selection. - - Parameters - ---------- - min_cluster_size : int, optional (default=5) - The minimum size of clusters; single linkage splits that contain - fewer points than this will be considered points "falling out" of a - cluster rather than a cluster splitting into two new clusters. - - min_samples : int, optional (default=None) - The number of samples in a neighbourhood for a point to be - considered a core point. - - metric : string, or callable, optional (default='euclidean') - The metric to use when calculating distance between instances in a - feature array. If metric is a string or callable, it must be one of - the options allowed by metrics.pairwise.pairwise_distances for its - metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and - must be square. - - p : int, optional (default=None) - p value to use if using the minkowski metric. - - alpha : float, optional (default=1.0) - A distance scaling parameter as used in robust single linkage. - See [3]_ for more information. - - cluster_selection_epsilon: float, optional (default=0.0) - A distance threshold. Clusters below this value will be merged. - See [5]_ for more information. - - algorithm : string, optional (default='best') - Exactly which algorithm to use; hdbscan has variants specialised - for different characteristics of the data. By default this is set - to ``best`` which chooses the "best" algorithm given the nature of - the data. You can force other options if you believe you know - better. Options are: - * ``best`` - * ``generic`` - * ``prims_kdtree`` - * ``prims_balltree`` - * ``boruvka_kdtree`` - * ``boruvka_balltree`` - - leaf_size: int, optional (default=40) - If using a space tree algorithm (kdtree, or balltree) the number - of points ina leaf node of the tree. This does not alter the - resulting clustering, but may have an effect on the runtime - of the algorithm. - - memory : Instance of joblib.Memory or string (optional) - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - approx_min_span_tree : bool, optional (default=True) - Whether to accept an only approximate minimum spanning tree. - For some algorithms this can provide a significant speedup, but - the resulting clustering may be of marginally lower quality. - If you are willing to sacrifice speed for correctness you may want - to explore this; in general this should be left at the default True. - - gen_min_span_tree: bool, optional (default=False) - Whether to generate the minimum spanning tree with regard - to mutual reachability distance for later analysis. - - core_dist_n_jobs : int, optional (default=4) - Number of parallel jobs to run in core distance computations (if - supported by the specific algorithm). For ``core_dist_n_jobs`` - below -1, (n_cpus + 1 + core_dist_n_jobs) are used. - - cluster_selection_method : string, optional (default='eom') - The method used to select clusters from the condensed tree. The - standard approach for HDBSCAN* is to use an Excess of Mass algorithm - to find the most persistent clusters. Alternatively you can instead - select the clusters at the leaves of the tree -- this provides the - most fine grained and homogeneous clusters. Options are: - * ``eom`` - * ``leaf`` - - allow_single_cluster : bool, optional (default=False) - By default HDBSCAN* will not produce a single cluster, setting this - to True will override this and allow single cluster results in - the case that you feel this is a valid result for your dataset. - - prediction_data : boolean, optional - Whether to generate extra cached data for predicting labels or - membership vectors few new unseen points later. If you wish to - persist the clustering object for later re-use you probably want - to set this to True. - (default False) - - match_reference_implementation : bool, optional (default=False) - There exist some interpretational differences between this - HDBSCAN* implementation and the original authors reference - implementation in Java. This can result in very minor differences - in clustering results. Setting this flag to True will, at a some - performance cost, ensure that the clustering results match the - reference implementation. - - connectivity : {'pairwise', 'knn'}, default='knn' - The type of connectivity matrix to compute. - * 'pairwise' will compute the entire fully-connected graph of - pairwise distances between each set of points. This is the fastest - to compute and can be very fast for smaller datasets but requires - O(n^2) space. - - * 'knn' will sparsify the fully-connected connectivity matrix to - save memory and enable much larger inputs. "n_neighbors” will - control the amount of memory used and the graph will be connected - automatically in the event "n_neighbors” was not large enough to - connect it. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - Examples - -------- - >>> from dasf.ml.cluster import HDBSCAN - >>> import numpy as np - >>> X = np.array([[1, 2], [2, 2], [2, 3], - ... [8, 7], [8, 8], [25, 80]]) - >>> clustering = HDBSCAN(min_cluster_size=30, min_samples=2).fit(X) - >>> clustering - HDBSCAN(min_cluster_size=30, min_samples=2) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering - - References - ---------- - - .. [1] Campello, R. J., Moulavi, D., & Sander, J. (2013, April). - Density-based clustering based on hierarchical density estimates. - In Pacific-Asia Conference on Knowledge Discovery and Data Mining - (pp. 160-172). Springer Berlin Heidelberg. - - .. [2] Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015). - Hierarchical density estimates for data clustering, visualization, - and outlier detection. ACM Transactions on Knowledge Discovery - from Data (TKDD), 10(1), 5. - - .. [3] Chaudhuri, K., & Dasgupta, S. (2010). Rates of convergence for the - cluster tree. In Advances in Neural Information Processing Systems - (pp. 343-351). - - .. [4] Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and - Sander, J., 2014. Density-Based Clustering Validation. In SDM - (pp. 839-847). - - .. [5] Malzer, C., & Baum, M. (2019). A Hybrid Approach To Hierarchical - Density-based Cluster Selection. arxiv preprint 1911.02282. - - - .. py:method:: _fit_cpu(X, y=None) - - - .. py:method:: _fit_gpu(X, y=None, convert_dtype=True) - - - .. py:method:: _fit_predict_cpu(X, y=None) - - - .. py:method:: _fit_predict_gpu(X, y=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/index.rst.txt deleted file mode 100644 index 52feec9..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/index.rst.txt +++ /dev/null @@ -1,1128 +0,0 @@ -:py:mod:`dasf.ml.cluster` -========================= - -.. py:module:: dasf.ml.cluster - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - agglomerative/index.rst - classifier/index.rst - dbscan/index.rst - hdbscan/index.rst - kmeans/index.rst - som/index.rst - spectral/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.AgglomerativeClustering - dasf.ml.cluster.KMeans - dasf.ml.cluster.DBSCAN - dasf.ml.cluster.SOM - dasf.ml.cluster.SpectralClustering - - - - -.. py:class:: AgglomerativeClustering(n_clusters=2, affinity='euclidean', connectivity=None, linkage='single', memory=None, compute_full_tree='auto', distance_threshold=None, compute_distances=False, handle=None, verbose=False, n_neighbors=10, output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Agglomerative Clustering - - Recursively merges the pair of clusters that minimally increases - a given linkage distance. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int or None, default=2 - The number of clusters to find. It must be ``None`` if - ``distance_threshold`` is not ``None``. - - affinity : str or callable, default='euclidean' - Metric used to compute the linkage. Can be "euclidean", "l1", "l2", - "manhattan", "cosine", or "precomputed". - If linkage is "ward", only "euclidean" is accepted. - If "precomputed", a distance matrix (instead of a similarity matrix) - is needed as input for the fit method. - - memory : str or object with the joblib.Memory interface, default=None - Used to cache the output of the computation of the tree. - By default, no caching is done. If a string is given, it is the - path to the caching directory. - - connectivity : array-like or callable, default=None - Connectivity matrix. Defines for each sample the neighboring - samples following a given structure of the data. - This can be a connectivity matrix itself or a callable that transforms - the data into a connectivity matrix, such as derived from - kneighbors_graph. Default is ``None``, i.e, the - hierarchical clustering algorithm is unstructured. - - compute_full_tree : 'auto' or bool, default='auto' - Stop early the construction of the tree at ``n_clusters``. This is - useful to decrease computation time if the number of clusters is not - small compared to the number of samples. This option is useful only - when specifying a connectivity matrix. Note also that when varying the - number of clusters and using caching, it may be advantageous to compute - the full tree. It must be ``True`` if ``distance_threshold`` is not - ``None``. By default `compute_full_tree` is "auto", which is equivalent - to `True` when `distance_threshold` is not `None` or that `n_clusters` - is inferior to the maximum between 100 or `0.02 * n_samples`. - Otherwise, "auto" is equivalent to `False`. - - linkage : {'ward', 'complete', 'average', 'single'}, default='ward' - Which linkage criterion to use. The linkage criterion determines which - distance to use between sets of observation. The algorithm will merge - the pairs of cluster that minimize this criterion. - - - 'ward' minimizes the variance of the clusters being merged. - - 'average' uses the average of the distances of each observation of - the two sets. - - 'complete' or 'maximum' linkage uses the maximum distances between - all observations of the two sets. - - 'single' uses the minimum of the distances between all observations - of the two sets. - - .. versionadded:: 0.20 - Added the 'single' option - - distance_threshold : float, default=None - The linkage distance threshold above which, clusters will not be - merged. If not ``None``, ``n_clusters`` must be ``None`` and - ``compute_full_tree`` must be ``True``. - - .. versionadded:: 0.21 - - compute_distances : bool, default=False - Computes distances between clusters even if `distance_threshold` is not - used. This can be used to make dendrogram visualization, but introduces - a computational and memory overhead. - - .. versionadded:: 0.24 - - n_neighbors : int, default = 15 - The number of neighbors to compute when connectivity = "knn" - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - Examples - -------- - >>> from dasf.ml.cluster import AgglomerativeClustering - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [4, 2], [4, 4], [4, 0]]) - >>> clustering = AgglomerativeClustering().fit(X) - >>> clustering - AgglomerativeClustering() - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html - - https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering - - - .. py:method:: _fit_cpu(X, y=None, convert_dtype=True) - - - .. py:method:: _fit_gpu(X, y=None, convert_dtype=True) - - - .. py:method:: _fit_predict_cpu(X, y=None) - - - .. py:method:: _fit_predict_gpu(X, y=None) - - - -.. py:class:: KMeans(n_clusters=8, init=None, n_init=None, max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='full', oversampling_factor=2.0, n_jobs=1, init_max_iter=None, max_samples_per_batch=32768, precompute_distances='auto', output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - K-Means clustering. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - - n_clusters : int, default=8 - The number of clusters to form as well as the number of - centroids to generate. - - init : {'k-means++', 'random'}, callable or array-like of shape (n_clusters, n_features), default='k-means++' - - Method for initialization: - - 'k-means++' : selects initial cluster centers for k-mean - clustering in a smart way to speed up convergence. See section - Notes in k_init for more details. - - 'random': choose `n_clusters` observations (rows) at random from data - for the initial centroids. - - If an array is passed, it should be of shape (n_clusters, n_features) - and gives the initial centers. - - If a callable is passed, it should take arguments X, n_clusters and a - random state and return an initialization. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of - n_init consecutive runs in terms of inertia. - - max_iter : int, default=300 - Maximum number of iterations of the k-means algorithm for a - single run. - - tol : float, default=1e-4 - Relative tolerance with regards to Frobenius norm of the difference - in the cluster centers of two consecutive iterations to declare - convergence. - - precompute_distances : {'auto', True, False}, default='auto' - Precompute distances (faster but takes more memory). - - 'auto' : do not precompute distances if n_samples * n_clusters > 12 - million. This corresponds to about 100MB overhead per job using - double precision. IMPORTANT: This is used only in Dask ML version. - - True : always precompute distances. - - False : never precompute distances. - - verbose : int, default=0 - Verbosity mode. - - random_state : int, RandomState instance or None, default=None - Determines random number generation for centroid initialization. Use - an int to make the randomness deterministic. - See :term:`Glossary `. - - copy_x : bool, default=True - When pre-computing distances it is more numerically accurate to center - the data first. If copy_x is True (default), then the original data is - not modified. If False, the original data is modified, and put back - before the function returns, but small numerical differences may be - introduced by subtracting and then adding the data mean. Note that if - the original data is not C-contiguous, a copy will be made even if - copy_x is False. If the original data is sparse, but not in CSR format, - a copy will be made even if copy_x is False. - - n_jobs : int, default=1 - The number of OpenMP threads to use for the computation. Parallelism is - sample-wise on the main cython loop which assigns each sample to its - closest center. IMPORTANT: This is used only in Dask ML version. - - ``None`` or ``-1`` means using all processors. - - init_max_iter : int, default=None - Number of iterations for init step. - - algorithm : {"auto", "full", "elkan"}, default="full" - K-means algorithm to use. The classical EM-style algorithm is "full". - The "elkan" variation is more efficient on data with well-defined - clusters, by using the triangle inequality. However it's more memory - intensive due to the allocation of an extra array of shape - (n_samples, n_clusters). - - For now "auto" (kept for backward compatibiliy) chooses "elkan" but it - might change in the future for a better heuristic. - - .. versionchanged:: 0.18 - Added Elkan algorithm - - oversampling_factor : int, default=2 - The amount of points to sample in scalable k-means++ initialization - for potential centroids. Increasing this value can lead to better - initial centroids at the cost of memory. The total number of centroids - sampled in scalable k-means++ is oversampling_factor * n_clusters * 8. - - max_samples_per_batch : int, default=32768 - The number of data samples to use for batches of the pairwise distance - computation. This computation is done throughout both fit predict. The - default should suit most cases. The total number of elements in the - batched pairwise distance computation is max_samples_per_batch * - n_clusters. It might become necessary to lower this number when - n_clusters becomes prohibitively large. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - See Also - -------- - MiniBatchKMeans : Alternative online implementation that does incremental - updates of the centers positions using mini-batches. - For large scale learning (say n_samples > 10k) MiniBatchKMeans is - probably much faster than the default batch implementation. - - Notes - ----- - The k-means problem is solved using either Lloyd's or Elkan's algorithm. - - The average complexity is given by O(k n T), where n is the number of - samples and T is the number of iteration. - - The worst case complexity is given by O(n^(k+2/p)) with - n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, - 'How slow is the k-means method?' SoCG2006) - - In practice, the k-means algorithm is very fast (one of the fastest - clustering algorithms available), but it falls in local minima. That's why - it can be useful to restart it several times. - - If the algorithm stops before fully converging (because of ``tol`` or - ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent, - i.e. the ``cluster_centers_`` will not be the means of the points in each - cluster. Also, the estimator will reassign ``labels_`` after the last - iteration to make ``labels_`` consistent with ``predict`` on the training - set. - - Examples - -------- - - >>> from dasf.ml.cluster import KMeans - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [10, 2], [10, 4], [10, 0]]) - >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X) - >>> kmeans.predict([[0, 0], [12, 3]]) - array([1, 0], dtype=int32) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html - - https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html - - https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering - - https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans - - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None) - - Compute Dask k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None) - - Compute Dask CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - Compute Scikit Learn k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None) - - Compute CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Dask ML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Dask CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Scikit Learn. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _fit_predict_gpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Dask ML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Dask - CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict_cpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Scikit - Learn. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict_gpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict2_cpu(X, sample_weight=None) - - A block predict using Scikit Learn variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict2_gpu(X, sample_weight=None) - - A block predict using CuML variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict2_cpu(X, sample_weight=None) - :abstractmethod: - - - .. py:method:: _predict2_gpu(X, sample_weight=None) - :abstractmethod: - - - .. py:method:: predict2(sample_weight=None) - - - -.. py:class:: DBSCAN(eps=0.5, leaf_size=40, metric='euclidean', min_samples=5, p=None, output_type=None, calc_core_sample_indices=True, verbose=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Perform DBSCAN clustering from vector array or distance matrix. - - DBSCAN - Density-Based Spatial Clustering of Applications with Noise. - Finds core samples of high density and expands clusters from them. - Good for data which contains clusters of similar density. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - eps : float, default=0.5 - The maximum distance between two samples for one to be considered - as in the neighborhood of the other. This is not a maximum bound - on the distances of points within a cluster. This is the most - important DBSCAN parameter to choose appropriately for your data set - and distance function. - - min_samples : int, default=5 - The number of samples (or total weight) in a neighborhood for a point - to be considered as a core point. This includes the point itself. - - metric : string, or callable, default='euclidean' - The metric to use when calculating distance between instances in a - feature array. If metric is a string or callable, it must be one of - the options allowed by :func:`sklearn.metrics.pairwise_distances` for - its metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and - must be square. X may be a :term:`Glossary `, in which - case only "nonzero" elements may be considered neighbors for DBSCAN. - - .. versionadded:: 0.17 - metric *precomputed* to accept precomputed sparse matrix. - - metric_params : dict, default=None - Additional keyword arguments for the metric function. - - .. versionadded:: 0.19 - - algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' - The algorithm to be used by the NearestNeighbors module - to compute pointwise distances and find nearest neighbors. - See NearestNeighbors module documentation for details. - - leaf_size : int, default=30 - Leaf size passed to BallTree or cKDTree. This can affect the speed - of the construction and query, as well as the memory required - to store the tree. The optimal value depends - on the nature of the problem. - - p : float, default=None - The power of the Minkowski metric to be used to calculate distance - between points. If None, then ``p=2`` (equivalent to the Euclidean - distance). - - n_jobs : int, default=None - The number of parallel jobs to run. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - calc_core_sample_indices(optional) : boolean, default = True - Indicates whether the indices of the core samples should be calculated. - The the attribute `core_sample_indices_` will not be used, setting this - to False will avoid unnecessary kernel launches. - - - Examples - -------- - >>> from dasf.ml.cluster import DBSCAN - >>> import numpy as np - >>> X = np.array([[1, 2], [2, 2], [2, 3], - ... [8, 7], [8, 8], [25, 80]]) - >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) - >>> clustering - DBSCAN(eps=3, min_samples=2) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan - - https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering - - See Also - -------- - OPTICS : A similar clustering at multiple values of eps. Our implementation - is optimized for memory usage. - - References - ---------- - Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based - Algorithm for Discovering Clusters in Large Spatial Databases with Noise". - In: Proceedings of the 2nd International Conference on Knowledge Discovery - and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996 - - Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). - DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. - ACM Transactions on Database Systems (TODS), 42(3), 19. - - - .. py:method:: _lazy_fit_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, out_dtype='int32') - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_gpu(X, y=None, out_dtype='int32') - - - -.. py:class:: SOM(x, y, input_len, num_epochs=100, sigma=0, sigmaN=1, learning_rate=0.5, learning_rateN=0.01, decay_function='exponential', neighborhood_function='gaussian', std_coeff=0.5, topology='rectangular', activation_distance='euclidean', random_seed=None, n_parallel=0, compact_support=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Initializes a Self Organizing Maps. - - A rule of thumb to set the size of the grid for a dimensionality - reduction task is that it should contain 5*sqrt(N) neurons - where N is the number of samples in the dataset to analyze. - - E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 - hence a map 8-by-8 should perform well. - - Parameters - ---------- - x : int - x dimension of the SOM. - - y : int - y dimension of the SOM. - - input_len : int - Number of the elements of the vectors in input. - - sigma : float, default=min(x,y)/2 - Spread of the neighborhood function, needs to be adequate - to the dimensions of the map. - - sigmaN : float, default=0.01 - Spread of the neighborhood function at last iteration. - - learning_rate : float, default=0.5 - initial learning rate. - - learning_rateN : float, default=0.01 - final learning rate - - decay_function : string, default='exponential' - Function that reduces learning_rate and sigma at each iteration. - Possible values: 'exponential', 'linear', 'aymptotic' - - neighborhood_function : string, default='gaussian' - Function that weights the neighborhood of a position in the map. - Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle' - - topology : string, default='rectangular' - Topology of the map. - Possible values: 'rectangular', 'hexagonal' - - activation_distance : string, default='euclidean' - Distance used to activate the map. - Possible values: 'euclidean', 'cosine', 'manhattan' - - random_seed : int, default=None - Random seed to use. - - n_parallel : uint, default=#max_CUDA_threads or 500*#CPUcores - Number of samples to be processed at a time. Setting a too low - value may drastically lower performance due to under-utilization, - setting a too high value increases memory usage without granting - any significant performance benefit. - - xp : numpy or cupy, default=cupy if can be imported else numpy - Use numpy (CPU) or cupy (GPU) for computations. - - std_coeff: float, default=0.5 - Used to calculate gausssian exponent denominator: - d = 2*std_coeff**2*sigma**2 - - compact_support: bool, default=False - Cut the neighbor function to 0 beyond neighbor radius sigma - - Examples - -------- - >>> from dasf.ml.cluster import SOM - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> som = SOM(x=3, y=2, input_len=2, - ... num_epochs=100).fit(X) - >>> som - SOM(x=3, y=2, input_len=2, num_epochs=100) - - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None) - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None) - - - .. py:method:: _predict_cpu(X, sample_weight=None) - - - .. py:method:: _predict_gpu(X, sample_weight=None) - - - .. py:method:: _lazy_quantization_error_cpu(X) - - - .. py:method:: _lazy_quantization_error_gpu(X) - - - .. py:method:: _quantization_error_cpu(X) - - - .. py:method:: _quantization_error_gpu(X) - - - .. py:method:: quantization_error(X) - - - -.. py:class:: SpectralClustering(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, n_components=None, persist_embedding=False, kmeans_params=None, verbose=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Apply clustering to a projection of the normalized Laplacian. - - In practice Spectral Clustering is very useful when the structure of - the individual clusters is highly non-convex, or more generally when - a measure of the center and spread of the cluster is not a suitable - description of the complete cluster, such as when clusters are - nested circles on the 2D plane. - - If the affinity matrix is the adjacency matrix of a graph, this method - can be used to find normalized graph cuts. - - When calling ``fit``, an affinity matrix is constructed using either - a kernel function such the Gaussian (aka RBF) kernel with Euclidean - distance ``d(X, X)``:: - - np.exp(-gamma * d(X,X) ** 2) - - or a k-nearest neighbors connectivity matrix. - - Alternatively, a user-provided affinity matrix can be specified by - setting ``affinity='precomputed'``. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int, default=8 - The dimension of the projection subspace. - - eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None - The eigenvalue decomposition strategy to use. AMG requires pyamg - to be installed. It can be faster on very large, sparse problems, - but may also lead to instabilities. If None, then ``'arpack'`` is - used. - - n_components : int, default=n_clusters - Number of eigenvectors to use for the spectral embedding - - random_state : int, RandomState instance, default=None - A pseudo random number generator used for the initialization of the - lobpcg eigenvectors decomposition when ``eigen_solver='amg'`` and by - the K-Means initialization. Use an int to make the randomness - deterministic. - See :term:`Glossary `. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of n_init - consecutive runs in terms of inertia. Only used if - ``assign_labels='kmeans'``. - - gamma : float, default=1.0 - Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. - Ignored for ``affinity='nearest_neighbors'``. - - affinity : str or callable, default='rbf' - How to construct the affinity matrix. - - 'nearest_neighbors': construct the affinity matrix by computing a - graph of nearest neighbors. - - 'rbf': construct the affinity matrix using a radial basis function - (RBF) kernel. - - 'precomputed': interpret ``X`` as a precomputed affinity matrix, - where larger values indicate greater similarity between instances. - - 'precomputed_nearest_neighbors': interpret ``X`` as a sparse graph - of precomputed distances, and construct a binary affinity matrix - from the ``n_neighbors`` nearest neighbors of each instance. - - one of the kernels supported by - :func:`~sklearn.metrics.pairwise_kernels`. - - Only kernels that produce similarity scores (non-negative values that - increase with similarity) should be used. This property is not checked - by the clustering algorithm. - - n_neighbors : int, default=10 - Number of neighbors to use when constructing the affinity matrix using - the nearest neighbors method. Ignored for ``affinity='rbf'``. - - eigen_tol : float, default=0.0 - Stopping criterion for eigendecomposition of the Laplacian matrix - when ``eigen_solver='arpack'``. - - assign_labels : {'kmeans', 'discretize'}, default='kmeans' - The strategy for assigning labels in the embedding space. There are two - ways to assign labels after the Laplacian embedding. k-means is a - popular choice, but it can be sensitive to initialization. - Discretization is another approach which is less sensitive to random - initialization. - - degree : float, default=3 - Degree of the polynomial kernel. Ignored by other kernels. - - coef0 : float, default=1 - Zero coefficient for polynomial and sigmoid kernels. - Ignored by other kernels. - - kernel_params : dict of str to any, default=None - Parameters (keyword arguments) and values for kernel passed as - callable object. Ignored by other kernels. - - n_jobs : int, default=None - The number of parallel jobs to run when `affinity='nearest_neighbors'` - or `affinity='precomputed_nearest_neighbors'`. The neighbors search - will be done in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - verbose : bool, default=False - Verbosity mode. - - .. versionadded:: 0.24 - - persist_embedding : bool - Whether to persist the intermediate n_samples x n_components array used - for clustering. - - kmeans_params : dictionary of string to any, optional - Keyword arguments for the KMeans clustering used for the final - clustering. - - Examples - -------- - >>> from dasf.ml.cluster import SpectralClustering - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> clustering = SpectralClustering(n_clusters=2, - ... assign_labels='discretize', - ... random_state=0).fit(X) - >>> clustering - SpectralClustering(assign_labels='discretize', n_clusters=2, - random_state=0) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering - - https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html - - Notes - ----- - A distance matrix for which 0 indicates identical elements and high values - indicate very dissimilar elements can be transformed into an affinity / - similarity matrix that is well-suited for the algorithm by - applying the Gaussian (aka RBF, heat) kernel:: - - np.exp(- dist_matrix ** 2 / (2. * delta ** 2)) - - where ``delta`` is a free parameter representing the width of the Gaussian - kernel. - - An alternative is to take a symmetric version of the k-nearest neighbors - connectivity matrix of the points. - - If the pyamg package is installed, it is used: this greatly - speeds up computation. - - References - ---------- - - - Normalized cuts and image segmentation, 2000 - Jianbo Shi, Jitendra Malik - http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324 - - - A Tutorial on Spectral Clustering, 2007 - Ulrike von Luxburg - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323 - - - Multiclass spectral clustering, 2003 - Stella X. Yu, Jianbo Shi - https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/kmeans/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/kmeans/index.rst.txt deleted file mode 100644 index 0ee75be..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/kmeans/index.rst.txt +++ /dev/null @@ -1,541 +0,0 @@ -:py:mod:`dasf.ml.cluster.kmeans` -================================ - -.. py:module:: dasf.ml.cluster.kmeans - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.kmeans.KMeans - - - - -.. py:class:: KMeans(n_clusters=8, init=None, n_init=None, max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='full', oversampling_factor=2.0, n_jobs=1, init_max_iter=None, max_samples_per_batch=32768, precompute_distances='auto', output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - K-Means clustering. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - - n_clusters : int, default=8 - The number of clusters to form as well as the number of - centroids to generate. - - init : {'k-means++', 'random'}, callable or array-like of shape (n_clusters, n_features), default='k-means++' - - Method for initialization: - - 'k-means++' : selects initial cluster centers for k-mean - clustering in a smart way to speed up convergence. See section - Notes in k_init for more details. - - 'random': choose `n_clusters` observations (rows) at random from data - for the initial centroids. - - If an array is passed, it should be of shape (n_clusters, n_features) - and gives the initial centers. - - If a callable is passed, it should take arguments X, n_clusters and a - random state and return an initialization. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of - n_init consecutive runs in terms of inertia. - - max_iter : int, default=300 - Maximum number of iterations of the k-means algorithm for a - single run. - - tol : float, default=1e-4 - Relative tolerance with regards to Frobenius norm of the difference - in the cluster centers of two consecutive iterations to declare - convergence. - - precompute_distances : {'auto', True, False}, default='auto' - Precompute distances (faster but takes more memory). - - 'auto' : do not precompute distances if n_samples * n_clusters > 12 - million. This corresponds to about 100MB overhead per job using - double precision. IMPORTANT: This is used only in Dask ML version. - - True : always precompute distances. - - False : never precompute distances. - - verbose : int, default=0 - Verbosity mode. - - random_state : int, RandomState instance or None, default=None - Determines random number generation for centroid initialization. Use - an int to make the randomness deterministic. - See :term:`Glossary `. - - copy_x : bool, default=True - When pre-computing distances it is more numerically accurate to center - the data first. If copy_x is True (default), then the original data is - not modified. If False, the original data is modified, and put back - before the function returns, but small numerical differences may be - introduced by subtracting and then adding the data mean. Note that if - the original data is not C-contiguous, a copy will be made even if - copy_x is False. If the original data is sparse, but not in CSR format, - a copy will be made even if copy_x is False. - - n_jobs : int, default=1 - The number of OpenMP threads to use for the computation. Parallelism is - sample-wise on the main cython loop which assigns each sample to its - closest center. IMPORTANT: This is used only in Dask ML version. - - ``None`` or ``-1`` means using all processors. - - init_max_iter : int, default=None - Number of iterations for init step. - - algorithm : {"auto", "full", "elkan"}, default="full" - K-means algorithm to use. The classical EM-style algorithm is "full". - The "elkan" variation is more efficient on data with well-defined - clusters, by using the triangle inequality. However it's more memory - intensive due to the allocation of an extra array of shape - (n_samples, n_clusters). - - For now "auto" (kept for backward compatibiliy) chooses "elkan" but it - might change in the future for a better heuristic. - - .. versionchanged:: 0.18 - Added Elkan algorithm - - oversampling_factor : int, default=2 - The amount of points to sample in scalable k-means++ initialization - for potential centroids. Increasing this value can lead to better - initial centroids at the cost of memory. The total number of centroids - sampled in scalable k-means++ is oversampling_factor * n_clusters * 8. - - max_samples_per_batch : int, default=32768 - The number of data samples to use for batches of the pairwise distance - computation. This computation is done throughout both fit predict. The - default should suit most cases. The total number of elements in the - batched pairwise distance computation is max_samples_per_batch * - n_clusters. It might become necessary to lower this number when - n_clusters becomes prohibitively large. - - output_type : {'input', 'cudf', 'cupy', 'numpy', 'numba'}, default=None - Variable to control output type of the results and attributes of the - estimator. If None, it'll inherit the output type set at the module - level, cuml.global_settings.output_type. See Output Data Type - Configuration for more info. - - See Also - -------- - MiniBatchKMeans : Alternative online implementation that does incremental - updates of the centers positions using mini-batches. - For large scale learning (say n_samples > 10k) MiniBatchKMeans is - probably much faster than the default batch implementation. - - Notes - ----- - The k-means problem is solved using either Lloyd's or Elkan's algorithm. - - The average complexity is given by O(k n T), where n is the number of - samples and T is the number of iteration. - - The worst case complexity is given by O(n^(k+2/p)) with - n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, - 'How slow is the k-means method?' SoCG2006) - - In practice, the k-means algorithm is very fast (one of the fastest - clustering algorithms available), but it falls in local minima. That's why - it can be useful to restart it several times. - - If the algorithm stops before fully converging (because of ``tol`` or - ``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent, - i.e. the ``cluster_centers_`` will not be the means of the points in each - cluster. Also, the estimator will reassign ``labels_`` after the last - iteration to make ``labels_`` consistent with ``predict`` on the training - set. - - Examples - -------- - - >>> from dasf.ml.cluster import KMeans - >>> import numpy as np - >>> X = np.array([[1, 2], [1, 4], [1, 0], - ... [10, 2], [10, 4], [10, 0]]) - >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X) - >>> kmeans.predict([[0, 0], [12, 3]]) - array([1, 0], dtype=int32) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html - - https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html - - https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering - - https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans - - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None) - - Compute Dask k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None) - - Compute Dask CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - Compute Scikit Learn k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None) - - Compute CuML k-means clustering. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - Training instances to cluster. It must be noted that the data - will be converted to C ordering, which will cause a memory - copy if the given data is not C-contiguous. - If a sparse matrix is passed, a copy will be made if it's not in - CSR format. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - self - Fitted estimator. - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Dask ML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Dask CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - Scikit Learn. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _fit_predict_gpu(X, y=None, sample_weight=None) - - Compute cluster centers and predict cluster index for each sample using - CuML. - - Convenience method; equivalent to calling fit(X) followed by - predict(X). - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to transform. - - y : Ignored - Not used, present here for API consistency by convention. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Dask ML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : Ignored - Not used, present here for API consistency by convention. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Dask - CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict_cpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using Scikit - Learn. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict_gpu(X, sample_weight=None) - - Predict the closest cluster each sample in X belongs to using CuML. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict2_cpu(X, sample_weight=None) - - A block predict using Scikit Learn variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _lazy_predict2_gpu(X, sample_weight=None) - - A block predict using CuML variant but for Dask. - - In the vector quantization literature, `cluster_centers_` is called - the code book and each value returned by `predict` is the index of - the closest code in the code book. - - Parameters - ---------- - X : {array-like, sparse matrix} of shape (n_samples, n_features) - New data to predict. - - sample_weight : array-like of shape (n_samples,), default=None - The weights for each observation in X. If None, all observations - are assigned equal weight. - - Returns - ------- - labels : ndarray of shape (n_samples,) - Index of the cluster each sample belongs to. - - - .. py:method:: _predict2_cpu(X, sample_weight=None) - :abstractmethod: - - - .. py:method:: _predict2_gpu(X, sample_weight=None) - :abstractmethod: - - - .. py:method:: predict2(sample_weight=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/som/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/som/index.rst.txt deleted file mode 100644 index c4a7d41..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/som/index.rst.txt +++ /dev/null @@ -1,156 +0,0 @@ -:py:mod:`dasf.ml.cluster.som` -============================= - -.. py:module:: dasf.ml.cluster.som - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.som.SOM - - - - -.. py:class:: SOM(x, y, input_len, num_epochs=100, sigma=0, sigmaN=1, learning_rate=0.5, learning_rateN=0.01, decay_function='exponential', neighborhood_function='gaussian', std_coeff=0.5, topology='rectangular', activation_distance='euclidean', random_seed=None, n_parallel=0, compact_support=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Initializes a Self Organizing Maps. - - A rule of thumb to set the size of the grid for a dimensionality - reduction task is that it should contain 5*sqrt(N) neurons - where N is the number of samples in the dataset to analyze. - - E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 - hence a map 8-by-8 should perform well. - - Parameters - ---------- - x : int - x dimension of the SOM. - - y : int - y dimension of the SOM. - - input_len : int - Number of the elements of the vectors in input. - - sigma : float, default=min(x,y)/2 - Spread of the neighborhood function, needs to be adequate - to the dimensions of the map. - - sigmaN : float, default=0.01 - Spread of the neighborhood function at last iteration. - - learning_rate : float, default=0.5 - initial learning rate. - - learning_rateN : float, default=0.01 - final learning rate - - decay_function : string, default='exponential' - Function that reduces learning_rate and sigma at each iteration. - Possible values: 'exponential', 'linear', 'aymptotic' - - neighborhood_function : string, default='gaussian' - Function that weights the neighborhood of a position in the map. - Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle' - - topology : string, default='rectangular' - Topology of the map. - Possible values: 'rectangular', 'hexagonal' - - activation_distance : string, default='euclidean' - Distance used to activate the map. - Possible values: 'euclidean', 'cosine', 'manhattan' - - random_seed : int, default=None - Random seed to use. - - n_parallel : uint, default=#max_CUDA_threads or 500*#CPUcores - Number of samples to be processed at a time. Setting a too low - value may drastically lower performance due to under-utilization, - setting a too high value increases memory usage without granting - any significant performance benefit. - - xp : numpy or cupy, default=cupy if can be imported else numpy - Use numpy (CPU) or cupy (GPU) for computations. - - std_coeff: float, default=0.5 - Used to calculate gausssian exponent denominator: - d = 2*std_coeff**2*sigma**2 - - compact_support: bool, default=False - Cut the neighbor function to 0 beyond neighbor radius sigma - - Examples - -------- - >>> from dasf.ml.cluster import SOM - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> som = SOM(x=3, y=2, input_len=2, - ... num_epochs=100).fit(X) - >>> som - SOM(x=3, y=2, input_len=2, num_epochs=100) - - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_gpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None) - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None) - - - .. py:method:: _predict_cpu(X, sample_weight=None) - - - .. py:method:: _predict_gpu(X, sample_weight=None) - - - .. py:method:: _lazy_quantization_error_cpu(X) - - - .. py:method:: _lazy_quantization_error_gpu(X) - - - .. py:method:: _quantization_error_cpu(X) - - - .. py:method:: _quantization_error_gpu(X) - - - .. py:method:: quantization_error(X) - - - diff --git a/docs/_sources/autoapi/dasf/ml/cluster/spectral/index.rst.txt b/docs/_sources/autoapi/dasf/ml/cluster/spectral/index.rst.txt deleted file mode 100644 index 892c9c8..0000000 --- a/docs/_sources/autoapi/dasf/ml/cluster/spectral/index.rst.txt +++ /dev/null @@ -1,205 +0,0 @@ -:py:mod:`dasf.ml.cluster.spectral` -================================== - -.. py:module:: dasf.ml.cluster.spectral - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.cluster.spectral.SpectralClustering - - - - -.. py:class:: SpectralClustering(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, n_components=None, persist_embedding=False, kmeans_params=None, verbose=False, **kwargs) - - - Bases: :py:obj:`dasf.ml.cluster.classifier.ClusterClassifier` - - Apply clustering to a projection of the normalized Laplacian. - - In practice Spectral Clustering is very useful when the structure of - the individual clusters is highly non-convex, or more generally when - a measure of the center and spread of the cluster is not a suitable - description of the complete cluster, such as when clusters are - nested circles on the 2D plane. - - If the affinity matrix is the adjacency matrix of a graph, this method - can be used to find normalized graph cuts. - - When calling ``fit``, an affinity matrix is constructed using either - a kernel function such the Gaussian (aka RBF) kernel with Euclidean - distance ``d(X, X)``:: - - np.exp(-gamma * d(X,X) ** 2) - - or a k-nearest neighbors connectivity matrix. - - Alternatively, a user-provided affinity matrix can be specified by - setting ``affinity='precomputed'``. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - n_clusters : int, default=8 - The dimension of the projection subspace. - - eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None - The eigenvalue decomposition strategy to use. AMG requires pyamg - to be installed. It can be faster on very large, sparse problems, - but may also lead to instabilities. If None, then ``'arpack'`` is - used. - - n_components : int, default=n_clusters - Number of eigenvectors to use for the spectral embedding - - random_state : int, RandomState instance, default=None - A pseudo random number generator used for the initialization of the - lobpcg eigenvectors decomposition when ``eigen_solver='amg'`` and by - the K-Means initialization. Use an int to make the randomness - deterministic. - See :term:`Glossary `. - - n_init : int, default=10 - Number of time the k-means algorithm will be run with different - centroid seeds. The final results will be the best output of n_init - consecutive runs in terms of inertia. Only used if - ``assign_labels='kmeans'``. - - gamma : float, default=1.0 - Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. - Ignored for ``affinity='nearest_neighbors'``. - - affinity : str or callable, default='rbf' - How to construct the affinity matrix. - - 'nearest_neighbors': construct the affinity matrix by computing a - graph of nearest neighbors. - - 'rbf': construct the affinity matrix using a radial basis function - (RBF) kernel. - - 'precomputed': interpret ``X`` as a precomputed affinity matrix, - where larger values indicate greater similarity between instances. - - 'precomputed_nearest_neighbors': interpret ``X`` as a sparse graph - of precomputed distances, and construct a binary affinity matrix - from the ``n_neighbors`` nearest neighbors of each instance. - - one of the kernels supported by - :func:`~sklearn.metrics.pairwise_kernels`. - - Only kernels that produce similarity scores (non-negative values that - increase with similarity) should be used. This property is not checked - by the clustering algorithm. - - n_neighbors : int, default=10 - Number of neighbors to use when constructing the affinity matrix using - the nearest neighbors method. Ignored for ``affinity='rbf'``. - - eigen_tol : float, default=0.0 - Stopping criterion for eigendecomposition of the Laplacian matrix - when ``eigen_solver='arpack'``. - - assign_labels : {'kmeans', 'discretize'}, default='kmeans' - The strategy for assigning labels in the embedding space. There are two - ways to assign labels after the Laplacian embedding. k-means is a - popular choice, but it can be sensitive to initialization. - Discretization is another approach which is less sensitive to random - initialization. - - degree : float, default=3 - Degree of the polynomial kernel. Ignored by other kernels. - - coef0 : float, default=1 - Zero coefficient for polynomial and sigmoid kernels. - Ignored by other kernels. - - kernel_params : dict of str to any, default=None - Parameters (keyword arguments) and values for kernel passed as - callable object. Ignored by other kernels. - - n_jobs : int, default=None - The number of parallel jobs to run when `affinity='nearest_neighbors'` - or `affinity='precomputed_nearest_neighbors'`. The neighbors search - will be done in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - - verbose : bool, default=False - Verbosity mode. - - .. versionadded:: 0.24 - - persist_embedding : bool - Whether to persist the intermediate n_samples x n_components array used - for clustering. - - kmeans_params : dictionary of string to any, optional - Keyword arguments for the KMeans clustering used for the final - clustering. - - Examples - -------- - >>> from dasf.ml.cluster import SpectralClustering - >>> import numpy as np - >>> X = np.array([[1, 1], [2, 1], [1, 0], - ... [4, 7], [3, 5], [3, 6]]) - >>> clustering = SpectralClustering(n_clusters=2, - ... assign_labels='discretize', - ... random_state=0).fit(X) - >>> clustering - SpectralClustering(assign_labels='discretize', n_clusters=2, - random_state=0) - - For further informations see: - - https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering - - https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html - - Notes - ----- - A distance matrix for which 0 indicates identical elements and high values - indicate very dissimilar elements can be transformed into an affinity / - similarity matrix that is well-suited for the algorithm by - applying the Gaussian (aka RBF, heat) kernel:: - - np.exp(- dist_matrix ** 2 / (2. * delta ** 2)) - - where ``delta`` is a free parameter representing the width of the Gaussian - kernel. - - An alternative is to take a symmetric version of the k-nearest neighbors - connectivity matrix of the points. - - If the pyamg package is installed, it is used: this greatly - speeds up computation. - - References - ---------- - - - Normalized cuts and image segmentation, 2000 - Jianbo Shi, Jitendra Malik - http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324 - - - A Tutorial on Spectral Clustering, 2007 - Ulrike von Luxburg - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323 - - - Multiclass spectral clustering, 2003 - Stella X. Yu, Jianbo Shi - https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_predict_cpu(X, y=None, sample_weight=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/core/index.rst.txt b/docs/_sources/autoapi/dasf/ml/core/index.rst.txt deleted file mode 100644 index a0bc6b3..0000000 --- a/docs/_sources/autoapi/dasf/ml/core/index.rst.txt +++ /dev/null @@ -1,29 +0,0 @@ -:py:mod:`dasf.ml.core` -====================== - -.. py:module:: dasf.ml.core - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.core.MLGeneric - - - - -.. py:class:: MLGeneric(name, checkpoint=False, **kwargs) - - - .. py:method:: dump(model) - - - .. py:method:: load(model) - - - diff --git a/docs/_sources/autoapi/dasf/ml/decomposition/index.rst.txt b/docs/_sources/autoapi/dasf/ml/decomposition/index.rst.txt deleted file mode 100644 index d246b6b..0000000 --- a/docs/_sources/autoapi/dasf/ml/decomposition/index.rst.txt +++ /dev/null @@ -1,82 +0,0 @@ -:py:mod:`dasf.ml.decomposition` -=============================== - -.. py:module:: dasf.ml.decomposition - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - pca/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.decomposition.PCA - - - - -.. py:class:: PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.TargeteredTransform` - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _get_covariance_cpu() - - - .. py:method:: get_covariance() - - - .. py:method:: _get_precision_cpu() - - - .. py:method:: get_precision() - - - diff --git a/docs/_sources/autoapi/dasf/ml/decomposition/pca/index.rst.txt b/docs/_sources/autoapi/dasf/ml/decomposition/pca/index.rst.txt deleted file mode 100644 index 5b19d26..0000000 --- a/docs/_sources/autoapi/dasf/ml/decomposition/pca/index.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -:py:mod:`dasf.ml.decomposition.pca` -=================================== - -.. py:module:: dasf.ml.decomposition.pca - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.decomposition.pca.PCA - - - - -.. py:class:: PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None, *args, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.TargeteredTransform` - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _fit_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _lazy_transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _transform_cpu(X, y=None, sample_weights=None) - - - .. py:method:: _transform_gpu(X, y=None, sample_weights=None) - - - .. py:method:: _get_covariance_cpu() - - - .. py:method:: get_covariance() - - - .. py:method:: _get_precision_cpu() - - - .. py:method:: get_precision() - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/clusters/dask/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/clusters/dask/index.rst.txt deleted file mode 100644 index 4a0c147..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/clusters/dask/index.rst.txt +++ /dev/null @@ -1,84 +0,0 @@ -:py:mod:`dasf.ml.dl.clusters.dask` -================================== - -.. py:module:: dasf.ml.dl.clusters.dask - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.clusters.dask.DaskClusterEnvironment - - - - -.. py:class:: DaskClusterEnvironment(metadata=None) - - - Bases: :py:obj:`pytorch_lightning.plugins.environments.ClusterEnvironment` - - Create a Dask Cluster environment for workers - - metadata -- dictionary containing all data related to workers. - - .. py:property:: creates_processes_externally - :type: bool - - Return True if the cluster is managed (you don't launch processes - yourself). - - - .. py:property:: main_address - :type: str - - Return master worker address. - - - .. py:property:: main_port - :type: int - - Return master worker port. - - - .. py:method:: detect() - - Detects the environment settings corresponding to this cluster and returns ``True`` if they match. - - - .. py:method:: creates_children() - - Fork children when generate a cluster. - - - .. py:method:: world_size() - - Return worker world size. - - - .. py:method:: global_rank() - - Return worker global rank. - - - .. py:method:: local_rank() - - Return worker local rank. - - - .. py:method:: node_rank() - - Return worker node rank (which is similar to global rank). - - - .. py:method:: set_world_size(size) - - - .. py:method:: set_global_rank(rank) - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/clusters/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/clusters/index.rst.txt deleted file mode 100644 index 12c72f3..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/clusters/index.rst.txt +++ /dev/null @@ -1,93 +0,0 @@ -:py:mod:`dasf.ml.dl.clusters` -============================= - -.. py:module:: dasf.ml.dl.clusters - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - dask/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.clusters.DaskClusterEnvironment - - - - -.. py:class:: DaskClusterEnvironment(metadata=None) - - - Bases: :py:obj:`pytorch_lightning.plugins.environments.ClusterEnvironment` - - Create a Dask Cluster environment for workers - - metadata -- dictionary containing all data related to workers. - - .. py:property:: creates_processes_externally - :type: bool - - Return True if the cluster is managed (you don't launch processes - yourself). - - - .. py:property:: main_address - :type: str - - Return master worker address. - - - .. py:property:: main_port - :type: int - - Return master worker port. - - - .. py:method:: detect() - - Detects the environment settings corresponding to this cluster and returns ``True`` if they match. - - - .. py:method:: creates_children() - - Fork children when generate a cluster. - - - .. py:method:: world_size() - - Return worker world size. - - - .. py:method:: global_rank() - - Return worker global rank. - - - .. py:method:: local_rank() - - Return worker local rank. - - - .. py:method:: node_rank() - - Return worker node rank (which is similar to global rank). - - - .. py:method:: set_world_size(size) - - - .. py:method:: set_global_rank(rank) - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/index.rst.txt deleted file mode 100644 index 482bced..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/index.rst.txt +++ /dev/null @@ -1,62 +0,0 @@ -:py:mod:`dasf.ml.dl` -==================== - -.. py:module:: dasf.ml.dl - - -Subpackages ------------ -.. toctree:: - :titlesonly: - :maxdepth: 3 - - clusters/index.rst - models/index.rst - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - pytorch_lightning/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.NeuralNetClassifier - - - - -.. py:class:: NeuralNetClassifier(model, max_iter=100, batch_size=32) - - - Bases: :py:obj:`dasf.transforms.base.Fit` - - .. py:method:: _lazy_fit_generic(X, y, accel, ngpus) - - - .. py:method:: _lazy_fit_gpu(X, y=None) - - - .. py:method:: _lazy_fit_cpu(X, y=None) - - - .. py:method:: __fit_generic(X, y, accel, ngpus) - - - .. py:method:: _fit_gpu(X, y=None) - - - .. py:method:: _fit_cpu(X, y=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/models/devconvnet/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/models/devconvnet/index.rst.txt deleted file mode 100644 index bcf5ecc..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/models/devconvnet/index.rst.txt +++ /dev/null @@ -1,588 +0,0 @@ -:py:mod:`dasf.ml.dl.models.devconvnet` -====================================== - -.. py:module:: dasf.ml.dl.models.devconvnet - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.models.devconvnet.MyAccuracy - dasf.ml.dl.models.devconvnet.NNModule - dasf.ml.dl.models.devconvnet.TorchPatchDeConvNet - dasf.ml.dl.models.devconvnet.TorchPatchDeConvNetSkip - dasf.ml.dl.models.devconvnet.TorchSectionDeConvNet - dasf.ml.dl.models.devconvnet.TorchSectionDeConvNetSkip - - - - -.. py:class:: MyAccuracy(dist_sync_on_step=False) - - - Bases: :py:obj:`torchmetrics.Metric` - - Base class for all metrics present in the Metrics API. - - Implements ``add_state()``, ``forward()``, ``reset()`` and a few other things to - handle distributed synchronization and per-step metric computation. - - Override ``update()`` and ``compute()`` functions to implement your own metric. Use - ``add_state()`` to register metric state variables which keep track of state on each - call of ``update()`` and are synchronized across processes when ``compute()`` is called. - - Note: - Metric state variables can either be :class:`~torch.Tensor` or an empty list which can we used - to store :class:`~torch.Tensor`. - - Note: - Different metrics only override ``update()`` and not ``forward()``. A call to ``update()`` - is valid, but it won't return the metric value at the current step. A call to ``forward()`` - automatically calls ``update()`` and also returns the metric value at the current step. - - Args: - kwargs: additional keyword arguments, see :ref:`Metric kwargs` for more info. - - - compute_on_cpu: If metric state should be stored on CPU during computations. Only works - for list states. - - dist_sync_on_step: If metric state should synchronize on ``forward()``. Default is ``False`` - - process_group: The process group on which the synchronization is called. Default is the world. - - dist_sync_fn: function that performs the allgather option on the metric state. Default is an - custom implementation that calls ``torch.distributed.all_gather`` internally. - - distributed_available_fn: function that checks if the distributed backend is available. - Defaults to a check of ``torch.distributed.is_available()`` and ``torch.distributed.is_initialized()``. - - sync_on_compute: If metric state should synchronize when ``compute`` is called. Default is ``True``- - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: set_idx(idx) - - - .. py:method:: update(preds, target) - - Override this method to update the state variables of your metric class. - - - .. py:method:: __str__() - - Return str(self). - - - .. py:method:: compute() - - Override this method to compute the final metric value from state variables synchronized across the - distributed backend. - - - -.. py:class:: NNModule(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`pytorch_lightning.LightningModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: cross_entropy_loss(input, target, weight=None, ignore_index=255) - - Use 255 to fill empty values when padding or doing any augmentation operations - like rotation. - - - .. py:method:: configure_optimizers() - - Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you'd need - one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only - works in the manual optimization mode. - - Return: - Any of these 6 options. - - - **Single optimizer**. - - **List or Tuple** of optimizers. - - **Two lists** - The first list has multiple optimizers, and the second has multiple LR schedulers - (or multiple ``lr_scheduler_config``). - - **Dictionary**, with an ``"optimizer"`` key, and (optionally) a ``"lr_scheduler"`` - key whose value is a single LR scheduler or ``lr_scheduler_config``. - - **None** - Fit will run without any optimizer. - - The ``lr_scheduler_config`` is a dictionary which contains the scheduler and its associated configuration. - The default configuration is shown below. - - .. code-block:: python - - lr_scheduler_config = { - # REQUIRED: The scheduler instance - "scheduler": lr_scheduler, - # The unit of the scheduler's step size, could also be 'step'. - # 'epoch' updates the scheduler on epoch end whereas 'step' - # updates it after a optimizer update. - "interval": "epoch", - # How many epochs/steps should pass between calls to - # `scheduler.step()`. 1 corresponds to updating the learning - # rate after every epoch/step. - "frequency": 1, - # Metric to to monitor for schedulers like `ReduceLROnPlateau` - "monitor": "val_loss", - # If set to `True`, will enforce that the value specified 'monitor' - # is available when the scheduler is updated, thus stopping - # training if not found. If set to `False`, it will only produce a warning - "strict": True, - # If using the `LearningRateMonitor` callback to monitor the - # learning rate progress, this keyword can be used to specify - # a custom logged name - "name": None, - } - - When there are schedulers in which the ``.step()`` method is conditioned on a value, such as the - :class:`torch.optim.lr_scheduler.ReduceLROnPlateau` scheduler, Lightning requires that the - ``lr_scheduler_config`` contains the keyword ``"monitor"`` set to the metric name that the scheduler - should be conditioned on. - - .. testcode:: - - # The ReduceLROnPlateau scheduler requires a monitor - def configure_optimizers(self): - optimizer = Adam(...) - return { - "optimizer": optimizer, - "lr_scheduler": { - "scheduler": ReduceLROnPlateau(optimizer, ...), - "monitor": "metric_to_track", - "frequency": "indicates how often the metric is updated" - # If "monitor" references validation metrics, then "frequency" should be set to a - # multiple of "trainer.check_val_every_n_epoch". - }, - } - - - # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler - def configure_optimizers(self): - optimizer1 = Adam(...) - optimizer2 = SGD(...) - scheduler1 = ReduceLROnPlateau(optimizer1, ...) - scheduler2 = LambdaLR(optimizer2, ...) - return ( - { - "optimizer": optimizer1, - "lr_scheduler": { - "scheduler": scheduler1, - "monitor": "metric_to_track", - }, - }, - {"optimizer": optimizer2, "lr_scheduler": scheduler2}, - ) - - Metrics can be made available to monitor by simply logging it using - ``self.log('metric_to_track', metric_val)`` in your :class:`~pytorch_lightning.core.module.LightningModule`. - - Note: - Some things to know: - - - Lightning calls ``.backward()`` and ``.step()`` automatically in case of automatic optimization. - - If a learning rate scheduler is specified in ``configure_optimizers()`` with key - ``"interval"`` (default "epoch") in the scheduler configuration, Lightning will call - the scheduler's ``.step()`` method automatically in case of automatic optimization. - - If you use 16-bit precision (``precision=16``), Lightning will automatically handle the optimizer. - - If you use :class:`torch.optim.LBFGS`, Lightning handles the closure function automatically for you. - - If you use multiple optimizers, you will have to switch to 'manual optimization' mode and step them - yourself. - - If you need to control how often the optimizer steps, override the :meth:`optimizer_step` hook. - - - .. py:method:: training_step(batch, batch_idx) - - Here you compute and return the training loss and some additional metrics for e.g. the progress bar or - logger. - - Args: - batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]): - The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. - batch_idx (``int``): Integer displaying index of this batch - - Return: - Any of. - - - :class:`~torch.Tensor` - The loss tensor - - ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'`` - - ``None`` - Training will skip to the next batch. This is only for automatic optimization. - This is not supported for multi-GPU, TPU, IPU, or DeepSpeed. - - In this step you'd normally do the forward pass and calculate the loss for a batch. - You can also do fancier things like multiple forward passes or something model specific. - - Example:: - - def training_step(self, batch, batch_idx): - x, y, z = batch - out = self.encoder(x) - loss = self.loss(out, x) - return loss - - To use multiple optimizers, you can switch to 'manual optimization' and control their stepping: - - .. code-block:: python - - def __init__(self): - super().__init__() - self.automatic_optimization = False - - - # Multiple optimizers (e.g.: GANs) - def training_step(self, batch, batch_idx): - opt1, opt2 = self.optimizers() - - # do training_step with encoder - ... - opt1.step() - # do training_step with decoder - ... - opt2.step() - - Note: - When ``accumulate_grad_batches`` > 1, the loss returned here will be automatically - normalized by ``accumulate_grad_batches`` internally. - - - .. py:method:: test_step(test_batch, batch_idx) - - Operates on a single batch of data from the test set. In this step you'd normally generate examples or - calculate anything of interest such as accuracy. - - Args: - batch: The output of your :class:`~torch.utils.data.DataLoader`. - batch_idx: The index of this batch. - dataloader_id: The index of the dataloader that produced this batch. - (only if multiple test dataloaders used). - - Return: - Any of. - - - Any object or value - - ``None`` - Testing will skip to the next batch - - .. code-block:: python - - # if you have one test dataloader: - def test_step(self, batch, batch_idx): - ... - - - # if you have multiple test dataloaders: - def test_step(self, batch, batch_idx, dataloader_idx=0): - ... - - Examples:: - - # CASE 1: A single test dataset - def test_step(self, batch, batch_idx): - x, y = batch - - # implement your own - out = self(x) - loss = self.loss(out, y) - - # log 6 example images - # or generated text... or whatever - sample_imgs = x[:6] - grid = torchvision.utils.make_grid(sample_imgs) - self.logger.experiment.add_image('example_images', grid, 0) - - # calculate acc - labels_hat = torch.argmax(out, dim=1) - test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) - - # log the outputs! - self.log_dict({'test_loss': loss, 'test_acc': test_acc}) - - If you pass in multiple test dataloaders, :meth:`test_step` will have an additional argument. We recommend - setting the default value of 0 so that you can quickly switch between single and multiple dataloaders. - - .. code-block:: python - - # CASE 2: multiple test dataloaders - def test_step(self, batch, batch_idx, dataloader_idx=0): - # dataloader_idx tells you which dataset this is. - ... - - Note: - If you don't need to test you don't need to implement this method. - - Note: - When the :meth:`test_step` is called, the model has been put in eval mode and - PyTorch gradients have been disabled. At the end of the test epoch, the model goes back - to training mode and gradients are enabled. - - - -.. py:class:: TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/models/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/models/index.rst.txt deleted file mode 100644 index c3879fb..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/models/index.rst.txt +++ /dev/null @@ -1,267 +0,0 @@ -:py:mod:`dasf.ml.dl.models` -=========================== - -.. py:module:: dasf.ml.dl.models - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - devconvnet/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.models.TorchPatchDeConvNet - dasf.ml.dl.models.TorchPatchDeConvNetSkip - dasf.ml.dl.models.TorchSectionDeConvNet - dasf.ml.dl.models.TorchSectionDeConvNetSkip - - - - -.. py:class:: TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - -.. py:class:: TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None) - - - Bases: :py:obj:`NNModule` - - Base class for all neural network modules. - - Your models should also subclass this class. - - Modules can also contain other Modules, allowing to nest them in - a tree structure. You can assign the submodules as regular attributes:: - - import torch.nn as nn - import torch.nn.functional as F - - class Model(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5) - self.conv2 = nn.Conv2d(20, 20, 5) - - def forward(self, x): - x = F.relu(self.conv1(x)) - return F.relu(self.conv2(x)) - - Submodules assigned in this way will be registered, and will have their - parameters converted too when you call :meth:`to`, etc. - - .. note:: - As per the example above, an ``__init__()`` call to the parent class - must be made before assignment on the child. - - :ivar training: Boolean represents whether this module is in training or - evaluation mode. - :vartype training: bool - - Initializes internal Module state, shared by both nn.Module and ScriptModule. - - .. py:method:: forward(x) - - Same as :meth:`torch.nn.Module.forward`. - - Args: - *args: Whatever you decide to pass into the forward method. - **kwargs: Keyword arguments are also possible. - - Return: - Your model's output - - - .. py:method:: init_vgg16_params(vgg16, copy_fc8=True) - - - .. py:method:: load() - - This is just a no-op load method. - - - diff --git a/docs/_sources/autoapi/dasf/ml/dl/pytorch_lightning/index.rst.txt b/docs/_sources/autoapi/dasf/ml/dl/pytorch_lightning/index.rst.txt deleted file mode 100644 index 11cf70f..0000000 --- a/docs/_sources/autoapi/dasf/ml/dl/pytorch_lightning/index.rst.txt +++ /dev/null @@ -1,266 +0,0 @@ -:py:mod:`dasf.ml.dl.pytorch_lightning` -====================================== - -.. py:module:: dasf.ml.dl.pytorch_lightning - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.pytorch_lightning.TorchDataLoader - dasf.ml.dl.pytorch_lightning.NeuralNetClassifier - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.ml.dl.pytorch_lightning.run_dask_clustered - dasf.ml.dl.pytorch_lightning.fit - - - -.. py:class:: TorchDataLoader(train, val=None, test=None, batch_size=64) - - - Bases: :py:obj:`pytorch_lightning.LightningDataModule` - - A DataModule standardizes the training, val, test splits, data preparation and transforms. The main - advantage is consistent data splits, data preparation and transforms across models. - - Example:: - - class MyDataModule(LightningDataModule): - def __init__(self): - super().__init__() - def prepare_data(self): - # download, split, etc... - # only called on 1 GPU/TPU in distributed - def setup(self, stage): - # make assignments here (val/train/test split) - # called on every process in DDP - def train_dataloader(self): - train_split = Dataset(...) - return DataLoader(train_split) - def val_dataloader(self): - val_split = Dataset(...) - return DataLoader(val_split) - def test_dataloader(self): - test_split = Dataset(...) - return DataLoader(test_split) - def teardown(self): - # clean up after fit or test - # called on every process in DDP - - Attributes: - prepare_data_per_node: - If True, each LOCAL_RANK=0 will call prepare data. - Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data. - allow_zero_length_dataloader_with_multiple_devices: - If True, dataloader with zero length within local rank is allowed. - Default value is False. - - .. py:method:: prepare_data() - - Use this to download and prepare data. Downloading and saving data with multiple processes (distributed - settings) will result in corrupted data. Lightning ensures this method is called only within a single - process, so you can safely add your downloading logic within. - - .. warning:: DO NOT set state to the model (use ``setup`` instead) - since this is NOT called on every device - - Example:: - - def prepare_data(self): - # good - download_data() - tokenize() - etc() - - # bad - self.split = data_split - self.some_state = some_other_state() - - In a distributed environment, ``prepare_data`` can be called in two ways - (using :ref:`prepare_data_per_node`) - - 1. Once per node. This is the default and is only called on LOCAL_RANK=0. - 2. Once in total. Only called on GLOBAL_RANK=0. - - Example:: - - # DEFAULT - # called once per node on LOCAL_RANK=0 of that node - class LitDataModule(LightningDataModule): - def __init__(self): - super().__init__() - self.prepare_data_per_node = True - - - # call on GLOBAL_RANK=0 (great for shared file systems) - class LitDataModule(LightningDataModule): - def __init__(self): - super().__init__() - self.prepare_data_per_node = False - - This is called before requesting the dataloaders: - - .. code-block:: python - - model.prepare_data() - initialize_distributed() - model.setup(stage) - model.train_dataloader() - model.val_dataloader() - model.test_dataloader() - model.predict_dataloader() - - - .. py:method:: setup(stage=None) - - Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when - you need to build models dynamically or adjust something about them. This hook is called on every process - when using DDP. - - Args: - stage: either ``'fit'``, ``'validate'``, ``'test'``, or ``'predict'`` - - Example:: - - class LitModel(...): - def __init__(self): - self.l1 = None - - def prepare_data(self): - download_data() - tokenize() - - # don't do this - self.something = else - - def setup(self, stage): - data = load_data(...) - self.l1 = nn.Linear(28, data.num_classes) - - - .. py:method:: train_dataloader() - - An iterable or collection of iterables specifying training samples. - - For more information about multiple dataloaders, see this :ref:`section `. - - The dataloader you return will not be reloaded unless you set - :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to - a positive integer. - - For data processing use the following pattern: - - - download in :meth:`prepare_data` - - process and split in :meth:`setup` - - However, the above are only necessary for distributed processing. - - .. warning:: do not assign state in prepare_data - - - :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit` - - :meth:`prepare_data` - - :meth:`setup` - - Note: - Lightning tries to add the correct sampler for distributed and arbitrary hardware. - There is no need to set it yourself. - - - .. py:method:: val_dataloader() - - An iterable or collection of iterables specifying validation samples. - - For more information about multiple dataloaders, see this :ref:`section `. - - The dataloader you return will not be reloaded unless you set - :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to - a positive integer. - - It's recommended that all data downloads and preparation happen in :meth:`prepare_data`. - - - :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit` - - :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate` - - :meth:`prepare_data` - - :meth:`setup` - - Note: - Lightning tries to add the correct sampler for distributed and arbitrary hardware - There is no need to set it yourself. - - Note: - If you don't need a validation dataset and a :meth:`validation_step`, you don't need to - implement this method. - - - .. py:method:: test_dataloader() - - An iterable or collection of iterables specifying test samples. - - For more information about multiple dataloaders, see this :ref:`section `. - - For data processing use the following pattern: - - - download in :meth:`prepare_data` - - process and split in :meth:`setup` - - However, the above are only necessary for distributed processing. - - .. warning:: do not assign state in prepare_data - - - - :meth:`~pytorch_lightning.trainer.trainer.Trainer.test` - - :meth:`prepare_data` - - :meth:`setup` - - Note: - Lightning tries to add the correct sampler for distributed and arbitrary hardware. - There is no need to set it yourself. - - Note: - If you don't need a test dataset and a :meth:`test_step`, you don't need to implement - this method. - - - -.. py:function:: run_dask_clustered(func, client=None, **kwargs) - - -.. py:function:: fit(model, X, y, max_iter, accel, strategy, devices, ngpus, batch_size=32, plugins=None) - - -.. py:class:: NeuralNetClassifier(model, max_iter=100, batch_size=32) - - - Bases: :py:obj:`dasf.transforms.base.Fit` - - .. py:method:: _lazy_fit_generic(X, y, accel, ngpus) - - - .. py:method:: _lazy_fit_gpu(X, y=None) - - - .. py:method:: _lazy_fit_cpu(X, y=None) - - - .. py:method:: __fit_generic(X, y, accel, ngpus) - - - .. py:method:: _fit_gpu(X, y=None) - - - .. py:method:: _fit_cpu(X, y=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/index.rst.txt b/docs/_sources/autoapi/dasf/ml/index.rst.txt deleted file mode 100644 index 931caaf..0000000 --- a/docs/_sources/autoapi/dasf/ml/index.rst.txt +++ /dev/null @@ -1,31 +0,0 @@ -:py:mod:`dasf.ml` -================= - -.. py:module:: dasf.ml - - -Subpackages ------------ -.. toctree:: - :titlesonly: - :maxdepth: 3 - - cluster/index.rst - decomposition/index.rst - dl/index.rst - model_selection/index.rst - neighbors/index.rst - preprocessing/index.rst - svm/index.rst - xgboost/index.rst - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - core/index.rst - - diff --git a/docs/_sources/autoapi/dasf/ml/mixture/classifier/index.rst.txt b/docs/_sources/autoapi/dasf/ml/mixture/classifier/index.rst.txt deleted file mode 100644 index 8afd00d..0000000 --- a/docs/_sources/autoapi/dasf/ml/mixture/classifier/index.rst.txt +++ /dev/null @@ -1,45 +0,0 @@ -:py:mod:`dasf.ml.mixture.classifier` -==================================== - -.. py:module:: dasf.ml.mixture.classifier - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.mixture.classifier.MixtureClassifier - - - - -.. py:class:: MixtureClassifier - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitPredict`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: fit(X, y=None, sample_weight=None) - :abstractmethod: - - - .. py:method:: fit_predict(X, y=None, sample_weight=None) - :abstractmethod: - - - .. py:method:: fit_transform(X, y=None) - :abstractmethod: - - - .. py:method:: get_params() - :abstractmethod: - - - .. py:method:: set_params() - :abstractmethod: - - - diff --git a/docs/_sources/autoapi/dasf/ml/mixture/gmm/index.rst.txt b/docs/_sources/autoapi/dasf/ml/mixture/gmm/index.rst.txt deleted file mode 100644 index 93b0f2d..0000000 --- a/docs/_sources/autoapi/dasf/ml/mixture/gmm/index.rst.txt +++ /dev/null @@ -1,40 +0,0 @@ -:py:mod:`dasf.ml.mixture.gmm` -============================= - -.. py:module:: dasf.ml.mixture.gmm - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.mixture.gmm.GaussianMixture - - - - -.. py:class:: GaussianMixture(n_components=1, *, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10) - - - Bases: :py:obj:`dasf.ml.mixture.classifier.MixtureClassifier` - - .. py:method:: _fit_cpu(X, y=None) - - - .. py:method:: _fit_predict_cpu(X, y=None) - - - .. py:method:: _predict_cpu(X, y=None) - - - .. py:method:: _set_params_cpu(**params) - - - .. py:method:: _get_params_cpu(deep=True) - - - diff --git a/docs/_sources/autoapi/dasf/ml/model_selection/index.rst.txt b/docs/_sources/autoapi/dasf/ml/model_selection/index.rst.txt deleted file mode 100644 index c237f87..0000000 --- a/docs/_sources/autoapi/dasf/ml/model_selection/index.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -:py:mod:`dasf.ml.model_selection` -================================= - -.. py:module:: dasf.ml.model_selection - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - split/index.rst - - diff --git a/docs/_sources/autoapi/dasf/ml/model_selection/split/index.rst.txt b/docs/_sources/autoapi/dasf/ml/model_selection/split/index.rst.txt deleted file mode 100644 index eea6289..0000000 --- a/docs/_sources/autoapi/dasf/ml/model_selection/split/index.rst.txt +++ /dev/null @@ -1,38 +0,0 @@ -:py:mod:`dasf.ml.model_selection.split` -======================================= - -.. py:module:: dasf.ml.model_selection.split - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.model_selection.split.train_test_split - - - - -.. py:class:: train_test_split(output='train', test_size=None, train_size=None, random_state=None, shuffle=None, blockwise=True, convert_mixed_types=False, **kwargs) - - - Bases: :py:obj:`dasf.transforms.TargeteredTransform`, :py:obj:`dasf.transforms.Transform` - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X) - :abstractmethod: - - - .. py:method:: _transform_cpu(X) - - - .. py:method:: _transform_gpu(X) - - - diff --git a/docs/_sources/autoapi/dasf/ml/neighbors/index.rst.txt b/docs/_sources/autoapi/dasf/ml/neighbors/index.rst.txt deleted file mode 100644 index a30497c..0000000 --- a/docs/_sources/autoapi/dasf/ml/neighbors/index.rst.txt +++ /dev/null @@ -1,46 +0,0 @@ -:py:mod:`dasf.ml.neighbors` -=========================== - -.. py:module:: dasf.ml.neighbors - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - neighbors/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.neighbors.NearestNeighbors - - - - -.. py:class:: NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, verbose=False, output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y=None, **kwargs) - - - .. py:method:: _fit_gpu(X, y=None, **kwargs) - - - .. py:method:: _get_params_cpu(deep=True, **kwargs) - - - .. py:method:: _set_params_cpu(**params) - - - diff --git a/docs/_sources/autoapi/dasf/ml/neighbors/neighbors/index.rst.txt b/docs/_sources/autoapi/dasf/ml/neighbors/neighbors/index.rst.txt deleted file mode 100644 index 21eb93b..0000000 --- a/docs/_sources/autoapi/dasf/ml/neighbors/neighbors/index.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -:py:mod:`dasf.ml.neighbors.neighbors` -===================================== - -.. py:module:: dasf.ml.neighbors.neighbors - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.neighbors.neighbors.NearestNeighbors - - - - -.. py:class:: NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, verbose=False, output_type=None, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y=None, **kwargs) - - - .. py:method:: _fit_gpu(X, y=None, **kwargs) - - - .. py:method:: _get_params_cpu(deep=True, **kwargs) - - - .. py:method:: _set_params_cpu(**params) - - - diff --git a/docs/_sources/autoapi/dasf/ml/preprocessing/index.rst.txt b/docs/_sources/autoapi/dasf/ml/preprocessing/index.rst.txt deleted file mode 100644 index 66630cb..0000000 --- a/docs/_sources/autoapi/dasf/ml/preprocessing/index.rst.txt +++ /dev/null @@ -1,94 +0,0 @@ -:py:mod:`dasf.ml.preprocessing` -=============================== - -.. py:module:: dasf.ml.preprocessing - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - standardscaler/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.preprocessing.StantardScaler - - - - -.. py:class:: StantardScaler(copy=True, with_mean=True, with_std=True, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.TargeteredTransform` - - .. py:method:: _lazy_fit_cpu(X, y=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None) - - - .. py:method:: _fit_cpu(X, y=None) - - - .. py:method:: _fit_gpu(X, y=None) - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None) - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None) - - - .. py:method:: _fit_transform_cpu(X, y=None) - - - .. py:method:: _fit_transform_gpu(X, y=None) - - - .. py:method:: _lazy_partial_fit_cpu(X, y=None) - - - .. py:method:: _lazy_partial_fit_gpu(X, y=None) - - - .. py:method:: _fit_partial_cpu(X, y=None) - - - .. py:method:: _fit_partial_gpu(X, y=None) - - - .. py:method:: _lazy_transform_cpu(X, copy=None) - - - .. py:method:: _lazy_transform_gpu(X, copy=None) - - - .. py:method:: _transform_cpu(X, copy=None) - - - .. py:method:: _transform_gpu(X, copy=None) - - - .. py:method:: _lazy_inverse_transform_cpu(X, copy=None) - - - .. py:method:: _lazy_inverse_transform_gpu(X, copy=None) - - - .. py:method:: _inverse_transform_cpu(X, copy=None) - - - .. py:method:: _inverse_transform_gpu(X, copy=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/preprocessing/standardscaler/index.rst.txt b/docs/_sources/autoapi/dasf/ml/preprocessing/standardscaler/index.rst.txt deleted file mode 100644 index 7a8bdda..0000000 --- a/docs/_sources/autoapi/dasf/ml/preprocessing/standardscaler/index.rst.txt +++ /dev/null @@ -1,85 +0,0 @@ -:py:mod:`dasf.ml.preprocessing.standardscaler` -============================================== - -.. py:module:: dasf.ml.preprocessing.standardscaler - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.preprocessing.standardscaler.StantardScaler - - - - -.. py:class:: StantardScaler(copy=True, with_mean=True, with_std=True, **kwargs) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.FitTransform`, :py:obj:`dasf.transforms.base.TargeteredTransform` - - .. py:method:: _lazy_fit_cpu(X, y=None) - - - .. py:method:: _lazy_fit_gpu(X, y=None) - - - .. py:method:: _fit_cpu(X, y=None) - - - .. py:method:: _fit_gpu(X, y=None) - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None) - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None) - - - .. py:method:: _fit_transform_cpu(X, y=None) - - - .. py:method:: _fit_transform_gpu(X, y=None) - - - .. py:method:: _lazy_partial_fit_cpu(X, y=None) - - - .. py:method:: _lazy_partial_fit_gpu(X, y=None) - - - .. py:method:: _fit_partial_cpu(X, y=None) - - - .. py:method:: _fit_partial_gpu(X, y=None) - - - .. py:method:: _lazy_transform_cpu(X, copy=None) - - - .. py:method:: _lazy_transform_gpu(X, copy=None) - - - .. py:method:: _transform_cpu(X, copy=None) - - - .. py:method:: _transform_gpu(X, copy=None) - - - .. py:method:: _lazy_inverse_transform_cpu(X, copy=None) - - - .. py:method:: _lazy_inverse_transform_gpu(X, copy=None) - - - .. py:method:: _inverse_transform_cpu(X, copy=None) - - - .. py:method:: _inverse_transform_gpu(X, copy=None) - - - diff --git a/docs/_sources/autoapi/dasf/ml/svm/index.rst.txt b/docs/_sources/autoapi/dasf/ml/svm/index.rst.txt deleted file mode 100644 index aa4d2f9..0000000 --- a/docs/_sources/autoapi/dasf/ml/svm/index.rst.txt +++ /dev/null @@ -1,109 +0,0 @@ -:py:mod:`dasf.ml.svm` -===================== - -.. py:module:: dasf.ml.svm - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - svm/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.svm.SVC - dasf.ml.svm.SVR - dasf.ml.svm.LinearSVC - dasf.ml.svm.LinearSVR - - - - -.. py:class:: SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, nochange_steps=1000, random_state=None) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - .. py:method:: _get_params_cpu(deep=True) - - - .. py:method:: _set_params_cpu(**params) - - - -.. py:class:: SVR(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, nochange_steps=1000) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - -.. py:class:: LinearSVC(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr') - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - -.. py:class:: LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr') - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - diff --git a/docs/_sources/autoapi/dasf/ml/svm/svm/index.rst.txt b/docs/_sources/autoapi/dasf/ml/svm/svm/index.rst.txt deleted file mode 100644 index 92a987a..0000000 --- a/docs/_sources/autoapi/dasf/ml/svm/svm/index.rst.txt +++ /dev/null @@ -1,100 +0,0 @@ -:py:mod:`dasf.ml.svm.svm` -========================= - -.. py:module:: dasf.ml.svm.svm - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.svm.svm.SVC - dasf.ml.svm.svm.SVR - dasf.ml.svm.svm.LinearSVC - dasf.ml.svm.svm.LinearSVR - - - - -.. py:class:: SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, nochange_steps=1000, random_state=None) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - .. py:method:: _get_params_cpu(deep=True) - - - .. py:method:: _set_params_cpu(**params) - - - -.. py:class:: SVR(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, nochange_steps=1000) - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - -.. py:class:: LinearSVC(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr') - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict`, :py:obj:`dasf.transforms.base.GetParams`, :py:obj:`dasf.transforms.base.SetParams` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - -.. py:class:: LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr') - - - Bases: :py:obj:`dasf.transforms.base.Fit`, :py:obj:`dasf.transforms.base.Predict` - - .. py:method:: _fit_cpu(X, y, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y, sample_weight=None) - - - .. py:method:: _predict_cpu(X) - - - .. py:method:: _predict_gpu(X) - - - diff --git a/docs/_sources/autoapi/dasf/ml/xgboost/index.rst.txt b/docs/_sources/autoapi/dasf/ml/xgboost/index.rst.txt deleted file mode 100644 index 5afcef8..0000000 --- a/docs/_sources/autoapi/dasf/ml/xgboost/index.rst.txt +++ /dev/null @@ -1,58 +0,0 @@ -:py:mod:`dasf.ml.xgboost` -========================= - -.. py:module:: dasf.ml.xgboost - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - xgboost/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.xgboost.XGBRegressor - - - - -.. py:class:: XGBRegressor(max_depth=None, max_leaves=None, max_bin=None, grow_policy=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, sampling_method=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs) - - - Bases: :py:obj:`dasf.transforms.Fit`, :py:obj:`dasf.transforms.FitPredict`, :py:obj:`dasf.transforms.Predict` - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _predict_cpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _predict_gpu(X, sample_weight=None, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/ml/xgboost/xgboost/index.rst.txt b/docs/_sources/autoapi/dasf/ml/xgboost/xgboost/index.rst.txt deleted file mode 100644 index e73acfd..0000000 --- a/docs/_sources/autoapi/dasf/ml/xgboost/xgboost/index.rst.txt +++ /dev/null @@ -1,49 +0,0 @@ -:py:mod:`dasf.ml.xgboost.xgboost` -================================= - -.. py:module:: dasf.ml.xgboost.xgboost - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.ml.xgboost.xgboost.XGBRegressor - - - - -.. py:class:: XGBRegressor(max_depth=None, max_leaves=None, max_bin=None, grow_policy=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, sampling_method=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs) - - - Bases: :py:obj:`dasf.transforms.Fit`, :py:obj:`dasf.transforms.FitPredict`, :py:obj:`dasf.transforms.Predict` - - .. py:method:: _lazy_fit_cpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _lazy_fit_gpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _fit_cpu(X, y=None, sample_weight=None) - - - .. py:method:: _fit_gpu(X, y=None, sample_weight=None, *args, **kwargs) - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _predict_cpu(X, sample_weight=None, **kwargs) - - - .. py:method:: _predict_gpu(X, sample_weight=None, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/executors/base/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/executors/base/index.rst.txt deleted file mode 100644 index 315f73b..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/executors/base/index.rst.txt +++ /dev/null @@ -1,40 +0,0 @@ -:py:mod:`dasf.pipeline.executors.base` -====================================== - -.. py:module:: dasf.pipeline.executors.base - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.base.Executor - - - - -.. py:class:: Executor - - - .. py:property:: ngpus - :type: int - - - .. py:property:: is_connected - :type: bool - - - .. py:method:: pre_run(pipeline) - - - .. py:method:: post_run(pipeline) - - - .. py:method:: execute(fn, *args, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/executors/dask/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/executors/dask/index.rst.txt deleted file mode 100644 index fb4a4bc..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/executors/dask/index.rst.txt +++ /dev/null @@ -1,119 +0,0 @@ -:py:mod:`dasf.pipeline.executors.dask` -====================================== - -.. py:module:: dasf.pipeline.executors.dask - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.dask.DaskPipelineExecutor - dasf.pipeline.executors.dask.DaskTasksPipelineExecutor - dasf.pipeline.executors.dask.DaskPBSPipelineExecutor - - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.dask.GPU_SUPPORTED - - -.. py:data:: GPU_SUPPORTED - - - -.. py:class:: DaskPipelineExecutor(address=None, port=8786, local=False, use_gpu=False, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None) - - - Bases: :py:obj:`dasf.pipeline.executors.base.Executor` - - A pipeline engine based on dask data flow. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - - .. py:property:: ngpus - - - .. py:property:: is_connected - - - .. py:method:: execute(fn, *args, **kwargs) - - - .. py:method:: register_dataset(**kwargs) - - - .. py:method:: has_dataset(key) - - - .. py:method:: get_dataset(key) - - - .. py:method:: shutdown(gracefully=True) - - - -.. py:class:: DaskTasksPipelineExecutor(address=None, port=8786, local=False, use_gpu=True, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None) - - - Bases: :py:obj:`DaskPipelineExecutor` - - A not centric execution engine based on dask. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - - .. py:method:: pre_run(pipeline) - - - .. py:method:: post_run(pipeline) - - - .. py:method:: execute(fn, *args, **kwargs) - - - .. py:method:: register_dataset(**kwargs) - - - .. py:method:: has_dataset(key) - - - .. py:method:: get_dataset(key) - - - .. py:method:: shutdown(gracefully=True) - - - -.. py:class:: DaskPBSPipelineExecutor(**kwargs) - - - Bases: :py:obj:`dasf.pipeline.executors.base.Executor` - - diff --git a/docs/_sources/autoapi/dasf/pipeline/executors/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/executors/index.rst.txt deleted file mode 100644 index 4f1cc8f..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/executors/index.rst.txt +++ /dev/null @@ -1,141 +0,0 @@ -:py:mod:`dasf.pipeline.executors` -================================= - -.. py:module:: dasf.pipeline.executors - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - base/index.rst - dask/index.rst - ray/index.rst - wrapper/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.Executor - dasf.pipeline.executors.DaskPipelineExecutor - dasf.pipeline.executors.DaskPBSPipelineExecutor - dasf.pipeline.executors.DaskTasksPipelineExecutor - - - - -.. py:class:: Executor - - - .. py:property:: ngpus - :type: int - - - .. py:property:: is_connected - :type: bool - - - .. py:method:: pre_run(pipeline) - - - .. py:method:: post_run(pipeline) - - - .. py:method:: execute(fn, *args, **kwargs) - - - -.. py:class:: DaskPipelineExecutor(address=None, port=8786, local=False, use_gpu=False, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None) - - - Bases: :py:obj:`dasf.pipeline.executors.base.Executor` - - A pipeline engine based on dask data flow. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - - .. py:property:: ngpus - - - .. py:property:: is_connected - - - .. py:method:: execute(fn, *args, **kwargs) - - - .. py:method:: register_dataset(**kwargs) - - - .. py:method:: has_dataset(key) - - - .. py:method:: get_dataset(key) - - - .. py:method:: shutdown(gracefully=True) - - - -.. py:class:: DaskPBSPipelineExecutor(**kwargs) - - - Bases: :py:obj:`dasf.pipeline.executors.base.Executor` - - -.. py:class:: DaskTasksPipelineExecutor(address=None, port=8786, local=False, use_gpu=True, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None) - - - Bases: :py:obj:`DaskPipelineExecutor` - - A not centric execution engine based on dask. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Dask scheduler (default 8786). - local -- kicks off a new local Dask cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Dask - cluster (default False). - profiler -- sets a Dask profiler. - gpu_allocator -- sets which is the memory allocator for GPU (default cupy). - cluster_kwargs -- extra Dask parameters like memory, processes, etc. - client_kwargs -- extra Client parameters. - - .. py:method:: pre_run(pipeline) - - - .. py:method:: post_run(pipeline) - - - .. py:method:: execute(fn, *args, **kwargs) - - - .. py:method:: register_dataset(**kwargs) - - - .. py:method:: has_dataset(key) - - - .. py:method:: get_dataset(key) - - - .. py:method:: shutdown(gracefully=True) - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/executors/ray/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/executors/ray/index.rst.txt deleted file mode 100644 index 4e0923e..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/executors/ray/index.rst.txt +++ /dev/null @@ -1,59 +0,0 @@ -:py:mod:`dasf.pipeline.executors.ray` -===================================== - -.. py:module:: dasf.pipeline.executors.ray - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.ray.RayPipelineExecutor - - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.ray.USE_RAY - - -.. py:data:: USE_RAY - :value: True - - - -.. py:class:: RayPipelineExecutor(address=None, port=6379, local=False, use_gpu=False, ray_kwargs=None) - - - Bases: :py:obj:`dasf.pipeline.executors.base.Executor` - - A pipeline engine based on ray data flow. - - Keyword arguments: - address -- address of the Dask scheduler (default None). - port -- port of the Ray head (default 8786). - local -- kicks off a new local Ray cluster (default False). - use_gpu -- in conjunction with `local`, it kicks off a local CUDA Ray - cluster (default False). - - .. py:property:: ngpus - - - .. py:property:: is_connected - - - .. py:method:: execute(fn, *args, **kwargs) - - - .. py:method:: __del__() - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/executors/wrapper/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/executors/wrapper/index.rst.txt deleted file mode 100644 index 6960047..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/executors/wrapper/index.rst.txt +++ /dev/null @@ -1,28 +0,0 @@ -:py:mod:`dasf.pipeline.executors.wrapper` -========================================= - -.. py:module:: dasf.pipeline.executors.wrapper - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.executors.wrapper.PrefectPipelineExecutor - - - - -.. py:class:: PrefectPipelineExecutor - - - Bases: :py:obj:`prefect.executors.local.LocalExecutor` - - .. py:property:: dtype - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/index.rst.txt deleted file mode 100644 index 645d658..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/index.rst.txt +++ /dev/null @@ -1,92 +0,0 @@ -:py:mod:`dasf.pipeline` -======================= - -.. py:module:: dasf.pipeline - - -Subpackages ------------ -.. toctree:: - :titlesonly: - :maxdepth: 3 - - executors/index.rst - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - pipeline/index.rst - types/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.Pipeline - dasf.pipeline.PipelinePlugin - - - - -.. py:class:: Pipeline(name, executor=None, verbose=False, callbacks = None) - - - .. py:method:: register_plugin(plugin) - - - .. py:method:: execute_callbacks(func_name, *args, **kwargs) - - - .. py:method:: __add_into_dag(obj, func_name, parameters=None, itself=None) - - - .. py:method:: __inspect_element(obj) - - - .. py:method:: add(obj, **kwargs) - - - .. py:method:: visualize(filename=None) - - - .. py:method:: __register_dataset(dataset) - - - .. py:method:: __execute(func, params, name) - - - .. py:method:: get_result_from(obj) - - - .. py:method:: run() - - - -.. py:class:: PipelinePlugin - - - .. py:method:: on_pipeline_start(fn_keys) - - - .. py:method:: on_pipeline_end() - - - .. py:method:: on_task_start(func, params, name) - - - .. py:method:: on_task_end(func, params, name, ret) - - - .. py:method:: on_task_error(func, params, name, exception) - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/pipeline/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/pipeline/index.rst.txt deleted file mode 100644 index a069995..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/pipeline/index.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -:py:mod:`dasf.pipeline.pipeline` -================================ - -.. py:module:: dasf.pipeline.pipeline - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.pipeline.PipelinePlugin - dasf.pipeline.pipeline.Pipeline - - - - -.. py:class:: PipelinePlugin - - - .. py:method:: on_pipeline_start(fn_keys) - - - .. py:method:: on_pipeline_end() - - - .. py:method:: on_task_start(func, params, name) - - - .. py:method:: on_task_end(func, params, name, ret) - - - .. py:method:: on_task_error(func, params, name, exception) - - - -.. py:class:: Pipeline(name, executor=None, verbose=False, callbacks = None) - - - .. py:method:: register_plugin(plugin) - - - .. py:method:: execute_callbacks(func_name, *args, **kwargs) - - - .. py:method:: __add_into_dag(obj, func_name, parameters=None, itself=None) - - - .. py:method:: __inspect_element(obj) - - - .. py:method:: add(obj, **kwargs) - - - .. py:method:: visualize(filename=None) - - - .. py:method:: __register_dataset(dataset) - - - .. py:method:: __execute(func, params, name) - - - .. py:method:: get_result_from(obj) - - - .. py:method:: run() - - - diff --git a/docs/_sources/autoapi/dasf/pipeline/types/index.rst.txt b/docs/_sources/autoapi/dasf/pipeline/types/index.rst.txt deleted file mode 100644 index 2f19ce1..0000000 --- a/docs/_sources/autoapi/dasf/pipeline/types/index.rst.txt +++ /dev/null @@ -1,45 +0,0 @@ -:py:mod:`dasf.pipeline.types` -============================= - -.. py:module:: dasf.pipeline.types - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.pipeline.types.TaskExecutorType - - - - -.. py:class:: TaskExecutorType - - - Bases: :py:obj:`enum.IntEnum` - - Enum where members are also (and must be) ints - - Initialize self. See help(type(self)) for accurate signature. - - .. py:attribute:: single_cpu - - - - .. py:attribute:: multi_cpu - - - - .. py:attribute:: single_gpu - - - - .. py:attribute:: multi_gpu - - - - diff --git a/docs/_sources/autoapi/dasf/profile/database/index.rst.txt b/docs/_sources/autoapi/dasf/profile/database/index.rst.txt deleted file mode 100644 index 9a35707..0000000 --- a/docs/_sources/autoapi/dasf/profile/database/index.rst.txt +++ /dev/null @@ -1,108 +0,0 @@ -:py:mod:`dasf.profile.database` -=============================== - -.. py:module:: dasf.profile.database - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.profile.database.TraceEvent - dasf.profile.database.TraceDatabase - dasf.profile.database.SingleFileTraceDatabase - - - - -.. py:class:: TraceEvent - - - .. py:attribute:: name - :type: str - - - - .. py:attribute:: phase - :type: str - - - - .. py:attribute:: timestamp - :type: float - - - - .. py:attribute:: process_id - :type: str - - - - .. py:attribute:: thread_id - :type: str - - - - .. py:attribute:: category - :type: List[str] - - - - .. py:attribute:: data - :type: dict - - - - .. py:attribute:: thread_timestamp - :type: float - - - - .. py:attribute:: color_name - :type: str - - - - .. py:attribute:: duration - :type: float - - - - .. py:attribute:: thread_duration - :type: float - - - - -.. py:class:: TraceDatabase - - - .. py:method:: add_trace_event(trace) - :abstractmethod: - - - .. py:method:: commit() - :abstractmethod: - - - .. py:method:: get_traces() - :abstractmethod: - - - -.. py:class:: SingleFileTraceDatabase(path, encoder = json.dumps, decoder = json.loads) - - - Bases: :py:obj:`TraceDatabase` - - .. py:method:: add_trace_event(trace) - - - .. py:method:: get_traces() - - - diff --git a/docs/_sources/autoapi/dasf/profile/event/index.rst.txt b/docs/_sources/autoapi/dasf/profile/event/index.rst.txt deleted file mode 100644 index 8944349..0000000 --- a/docs/_sources/autoapi/dasf/profile/event/index.rst.txt +++ /dev/null @@ -1,91 +0,0 @@ -:py:mod:`dasf.profile.event` -============================ - -.. py:module:: dasf.profile.event - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.profile.event.Singleton - dasf.profile.event.TraceDatabase - dasf.profile.event.Profile - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.profile.event.get_time_ms - dasf.profile.event.add_trace_duration_begin - dasf.profile.event.add_trace_duration_end - dasf.profile.event.add_trace_complete - dasf.profile.event.get_traces - dasf.profile.event.to_chrome_event_format - - - -.. py:class:: Singleton - - - Bases: :py:obj:`type` - - .. py:attribute:: _instances - - - - .. py:method:: __call__(*args, **kwargs) - - Call self as a function. - - - -.. py:class:: TraceDatabase(database = None) - - - .. py:property:: database - :type: TraceDatabase - - - .. py:attribute:: db_name - :type: str - :value: 'traces.txt' - - - - -.. py:function:: get_time_ms() - - -.. py:function:: add_trace_duration_begin(name, process_id, thread_id, category = None, timestamp = None, thread_timestamp = None, data = None) - - -.. py:function:: add_trace_duration_end(name, process_id, thread_id, category = None, timestamp = None, thread_timestamp = None, data = None) - - -.. py:function:: add_trace_complete(name, process_id, thread_id, timestamp, duration, thread_timestamp = None, thread_duration = None, category = None, data = None) - - -.. py:function:: get_traces() - - -.. py:function:: to_chrome_event_format(trace_events, trace_options = None, format_kwargs = None) - - -.. py:class:: Profile(trace_file = 'traces.txt', remove_old_trace_file = True, processed_filename = 'profile.json', process_trace_options = None, process_trace_kwargs = None) - - - .. py:method:: __enter__() - - - .. py:method:: __exit__(exc_type, exc_val, exc_tb) - - - diff --git a/docs/_sources/autoapi/dasf/profile/index.rst.txt b/docs/_sources/autoapi/dasf/profile/index.rst.txt deleted file mode 100644 index b581935..0000000 --- a/docs/_sources/autoapi/dasf/profile/index.rst.txt +++ /dev/null @@ -1,25 +0,0 @@ -:py:mod:`dasf.profile` -====================== - -.. py:module:: dasf.profile - - -Subpackages ------------ -.. toctree:: - :titlesonly: - :maxdepth: 3 - - plugins/index.rst - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - database/index.rst - event/index.rst - - diff --git a/docs/_sources/autoapi/dasf/profile/plugins/dasf/index.rst.txt b/docs/_sources/autoapi/dasf/profile/plugins/dasf/index.rst.txt deleted file mode 100644 index 8f97051..0000000 --- a/docs/_sources/autoapi/dasf/profile/plugins/dasf/index.rst.txt +++ /dev/null @@ -1,31 +0,0 @@ -:py:mod:`dasf.profile.plugins.dasf` -=================================== - -.. py:module:: dasf.profile.plugins.dasf - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.profile.plugins.dasf.PipelineTaskTimer - - - - -.. py:class:: PipelineTaskTimer - - - Bases: :py:obj:`dasf.pipeline.PipelinePlugin` - - .. py:method:: on_task_start(func, params, name) - - - .. py:method:: on_task_end(func, params, name, ret) - - - diff --git a/docs/_sources/autoapi/dasf/profile/plugins/dask/index.rst.txt b/docs/_sources/autoapi/dasf/profile/plugins/dask/index.rst.txt deleted file mode 100644 index 7319069..0000000 --- a/docs/_sources/autoapi/dasf/profile/plugins/dask/index.rst.txt +++ /dev/null @@ -1,92 +0,0 @@ -:py:mod:`dasf.profile.plugins.dask` -=================================== - -.. py:module:: dasf.profile.plugins.dask - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.profile.plugins.dask.TaskTimePlugin - - - - -.. py:class:: TaskTimePlugin - - - Bases: :py:obj:`distributed.diagnostics.plugin.WorkerPlugin` - - Interface to extend the Worker - - A worker plugin enables custom code to run at different stages of the Workers' - lifecycle. - - A plugin enables custom code to run at each of step of a Workers's life. Whenever such - an event happens, the corresponding method on this class will be called. Note that the - user code always runs within the Worker's main thread. - - To implement a plugin implement some of the methods of this class and register - the plugin to your client in order to have it attached to every existing and - future workers with ``Client.register_worker_plugin``. - - Examples - -------- - >>> class ErrorLogger(WorkerPlugin): - ... def __init__(self, logger): - ... self.logger = logger - ... - ... def setup(self, worker): - ... self.worker = worker - ... - ... def transition(self, key, start, finish, *args, **kwargs): - ... if finish == 'error': - ... ts = self.worker.tasks[key] - ... exc_info = (type(ts.exception), ts.exception, ts.traceback) - ... self.logger.error( - ... "Error during computation of '%s'.", key, - ... exc_info=exc_info - ... ) - - >>> import logging - >>> plugin = ErrorLogger(logging) - >>> client.register_worker_plugin(plugin) # doctest: +SKIP - - .. py:method:: setup(worker) - - Run when the plugin is attached to a worker. This happens when the plugin is registered - and attached to existing workers, or when a worker is created after the plugin has been - registered. - - - .. py:method:: transition(key, start, finish, *args, **kwargs) - - Throughout the lifecycle of a task (see :doc:`Worker State - `), Workers are instructed by the scheduler to compute - certain tasks, resulting in transitions in the state of each task. The - Worker owning the task is then notified of this state transition. - - Whenever a task changes its state, this method will be called. - - .. warning:: - - This is an advanced feature and the transition mechanism and details - of task states are subject to change without deprecation cycle. - - Parameters - ---------- - key : string - start : string - Start state of the transition. - One of waiting, ready, executing, long-running, memory, error. - finish : string - Final state of the transition. - kwargs : More options passed when transitioning - - - diff --git a/docs/_sources/autoapi/dasf/profile/plugins/index.rst.txt b/docs/_sources/autoapi/dasf/profile/plugins/index.rst.txt deleted file mode 100644 index 48678c3..0000000 --- a/docs/_sources/autoapi/dasf/profile/plugins/index.rst.txt +++ /dev/null @@ -1,17 +0,0 @@ -:py:mod:`dasf.profile.plugins` -============================== - -.. py:module:: dasf.profile.plugins - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - dasf/index.rst - dask/index.rst - resource_monitor/index.rst - - diff --git a/docs/_sources/autoapi/dasf/profile/plugins/resource_monitor/index.rst.txt b/docs/_sources/autoapi/dasf/profile/plugins/resource_monitor/index.rst.txt deleted file mode 100644 index 8721226..0000000 --- a/docs/_sources/autoapi/dasf/profile/plugins/resource_monitor/index.rst.txt +++ /dev/null @@ -1,73 +0,0 @@ -:py:mod:`dasf.profile.plugins.resource_monitor` -=============================================== - -.. py:module:: dasf.profile.plugins.resource_monitor - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.profile.plugins.resource_monitor.Format - dasf.profile.plugins.resource_monitor.ResourceMonitor - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.profile.plugins.resource_monitor.run_continuously - - - -.. py:class:: Format - - - .. py:method:: temperature(value) - :staticmethod: - - - .. py:method:: byte_value(value) - :staticmethod: - - - .. py:method:: percent(value) - :staticmethod: - - - -.. py:function:: run_continuously(scheduler, interval=1) - - Continuously run, while executing pending jobs at each - elapsed time interval. - @return cease_continuous_run: threading. Event which can - be set to cease continuous run. Please note that it is - *intended behavior that run_continuously() does not run - missed jobs*. For example, if you've registered a job that - should run every minute and you set a continuous run - interval of one hour then your job won't be run 60 times - at each interval but only once. - - -.. py:class:: ResourceMonitor(path = None, monitor_interval=0.1, verbose = False) - - - Bases: :py:obj:`dasf.pipeline.PipelinePlugin` - - .. py:method:: get_info(event_list, verbose = False) - :staticmethod: - - - .. py:method:: on_pipeline_start(fn_keys) - - - .. py:method:: on_pipeline_end() - - - diff --git a/docs/_sources/autoapi/dasf/transforms/base/index.rst.txt b/docs/_sources/autoapi/dasf/transforms/base/index.rst.txt deleted file mode 100644 index a3d9428..0000000 --- a/docs/_sources/autoapi/dasf/transforms/base/index.rst.txt +++ /dev/null @@ -1,238 +0,0 @@ -:py:mod:`dasf.transforms.base` -============================== - -.. py:module:: dasf.transforms.base - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.transforms.base.Fit - dasf.transforms.base.FitPredict - dasf.transforms.base.FitTransform - dasf.transforms.base.Predict - dasf.transforms.base.GetParams - dasf.transforms.base.SetParams - dasf.transforms.base.Transform - dasf.transforms.base.TargeteredTransform - dasf.transforms.base.MappedTransform - - - - -.. py:class:: Fit - - - .. py:method:: _lazy_fit_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit(X, y, sample_weight=None, **kwargs) - - - .. py:method:: fit_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: FitPredict - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_predict_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_predict_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit_predict(X, y=None, **kwargs) - - - .. py:method:: fit_predict_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: FitTransform - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_transform_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_transform_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit_transform(X, y=None, **kwargs) - - - .. py:method:: fit_transform_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: Predict - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _predict_cpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _predict_gpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: predict(X, sample_weight=None, **kwargs) - - - .. py:method:: predict_from_model(model, X, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: GetParams - - - .. py:method:: _lazy_get_params_cpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_get_params_gpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _get_params_cpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _get_params_gpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: get_params(deep=True, **kwargs) - - - -.. py:class:: SetParams - - - .. py:method:: _lazy_set_params_cpu(**params) - :abstractmethod: - - - .. py:method:: _lazy_set_params_gpu(**params) - :abstractmethod: - - - .. py:method:: _set_params_cpu(**params) - :abstractmethod: - - - .. py:method:: _set_params_gpu(**params) - :abstractmethod: - - - .. py:method:: set_params(**params) - - - -.. py:class:: Transform - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - :abstractmethod: - - - .. py:method:: _transform_cpu(X, **kwargs) - :abstractmethod: - - - .. py:method:: _transform_gpu(X, **kwargs) - :abstractmethod: - - - .. py:method:: transform(X, **kwargs) - - - .. py:method:: transform_from_model(model, X, **kwargs) - :staticmethod: - - - -.. py:class:: TargeteredTransform(run_local=None, run_gpu=None) - - - Bases: :py:obj:`Transform` - - -.. py:class:: MappedTransform(function, depth=None, boundary=None, trim=True, output_chunk=None, drop_axis=None, new_axis=None) - - - Bases: :py:obj:`Transform` - - .. py:method:: __lazy_transform_generic(X, xp, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: transform(X, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/transforms/index.rst.txt b/docs/_sources/autoapi/dasf/transforms/index.rst.txt deleted file mode 100644 index af5b945..0000000 --- a/docs/_sources/autoapi/dasf/transforms/index.rst.txt +++ /dev/null @@ -1,398 +0,0 @@ -:py:mod:`dasf.transforms` -========================= - -.. py:module:: dasf.transforms - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - base/index.rst - memory/index.rst - operations/index.rst - transforms/index.rst - - -Package Contents ----------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.transforms.ArraysToDataFrame - dasf.transforms.ArrayToZarr - dasf.transforms.ArrayToHDF5 - dasf.transforms.ZarrToArray - dasf.transforms.Normalize - dasf.transforms.SliceArray - dasf.transforms.SliceArrayByPercent - dasf.transforms.Reshape - dasf.transforms.PersistDaskData - dasf.transforms.ComputeDaskData - dasf.transforms.Fit - dasf.transforms.FitPredict - dasf.transforms.FitTransform - dasf.transforms.Predict - dasf.transforms.GetParams - dasf.transforms.TargeteredTransform - dasf.transforms.MappedTransform - - - - -.. py:class:: ArraysToDataFrame - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: __transform_generic(X, y) - - - .. py:method:: _lazy_transform_cpu(X=None, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X=None, **kwargs) - - - .. py:method:: _transform_gpu(X=None, **kwargs) - - - .. py:method:: _transform_cpu(X=None, **kwargs) - - - -.. py:class:: ArrayToZarr(chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: _lazy_transform_generic_all(data) - - - .. py:method:: _transform_generic_all(data, chunks, **kwargs) - - - .. py:method:: _lazy_transform_generic(X, **kwargs) - - - .. py:method:: _transform_generic(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - -.. py:class:: ArrayToHDF5(dataset_path, chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: _lazy_transform_generic_all(data) - - - .. py:method:: _transform_generic_all(data) - - - .. py:method:: _lazy_transform_generic(X, **kwargs) - - - .. py:method:: _transform_generic(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - -.. py:class:: ZarrToArray(chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: transform(X) - - - -.. py:class:: Normalize - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: transform(X) - - - -.. py:class:: SliceArray(output_size) - - - .. py:method:: transform(X) - - - -.. py:class:: SliceArrayByPercent(x=100.0, y=100.0, z=100.0) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: transform(X) - - - -.. py:class:: Reshape(shape) - - - Reshape data with a new shape. - - Parameters - ---------- - shape : tuple - The new shape of the data. - - - .. py:method:: run(X) - - - -.. py:class:: PersistDaskData - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Allow persisting a dask array to memory and return a copy of the object. - It will gather the data blocks from all workers and resembles locally. - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X) - - - .. py:method:: _transform_cpu(X) - - - .. py:method:: _transform_gpu(X) - - - -.. py:class:: ComputeDaskData - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Allow persisting a dask array to memory. It will gather the data blocks - from all workers and resembles locally. - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X) - - - .. py:method:: _transform_cpu(X) - - - .. py:method:: _transform_gpu(X) - - - -.. py:class:: Fit - - - .. py:method:: _lazy_fit_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit(X, y, sample_weight=None, **kwargs) - - - .. py:method:: fit_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: FitPredict - - - .. py:method:: _lazy_fit_predict_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_predict_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_predict_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_predict_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit_predict(X, y=None, **kwargs) - - - .. py:method:: fit_predict_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: FitTransform - - - .. py:method:: _lazy_fit_transform_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_fit_transform_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_transform_cpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: _fit_transform_gpu(X, y=None, **kwargs) - :abstractmethod: - - - .. py:method:: fit_transform(X, y=None, **kwargs) - - - .. py:method:: fit_transform_from_model(model, X, y, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: Predict - - - .. py:method:: _lazy_predict_cpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_predict_gpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _predict_cpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: _predict_gpu(X, sample_weight=None, **kwargs) - :abstractmethod: - - - .. py:method:: predict(X, sample_weight=None, **kwargs) - - - .. py:method:: predict_from_model(model, X, sample_weight=None, **kwargs) - :staticmethod: - - - -.. py:class:: GetParams - - - .. py:method:: _lazy_get_params_cpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _lazy_get_params_gpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _get_params_cpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: _get_params_gpu(deep=True, **kwargs) - :abstractmethod: - - - .. py:method:: get_params(deep=True, **kwargs) - - - -.. py:class:: TargeteredTransform(run_local=None, run_gpu=None) - - - Bases: :py:obj:`Transform` - - -.. py:class:: MappedTransform(function, depth=None, boundary=None, trim=True, output_chunk=None, drop_axis=None, new_axis=None) - - - Bases: :py:obj:`Transform` - - .. py:method:: __lazy_transform_generic(X, xp, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: transform(X, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/transforms/memory/index.rst.txt b/docs/_sources/autoapi/dasf/transforms/memory/index.rst.txt deleted file mode 100644 index d2d4a10..0000000 --- a/docs/_sources/autoapi/dasf/transforms/memory/index.rst.txt +++ /dev/null @@ -1,68 +0,0 @@ -:py:mod:`dasf.transforms.memory` -================================ - -.. py:module:: dasf.transforms.memory - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.transforms.memory.PersistDaskData - dasf.transforms.memory.ComputeDaskData - - - - -.. py:class:: PersistDaskData - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Allow persisting a dask array to memory and return a copy of the object. - It will gather the data blocks from all workers and resembles locally. - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X) - - - .. py:method:: _transform_cpu(X) - - - .. py:method:: _transform_gpu(X) - - - -.. py:class:: ComputeDaskData - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - Allow persisting a dask array to memory. It will gather the data blocks - from all workers and resembles locally. - - .. py:method:: __lazy_transform_generic(X) - - - .. py:method:: _lazy_transform_cpu(X) - - - .. py:method:: _lazy_transform_gpu(X) - - - .. py:method:: _transform_cpu(X) - - - .. py:method:: _transform_gpu(X) - - - diff --git a/docs/_sources/autoapi/dasf/transforms/operations/index.rst.txt b/docs/_sources/autoapi/dasf/transforms/operations/index.rst.txt deleted file mode 100644 index c019622..0000000 --- a/docs/_sources/autoapi/dasf/transforms/operations/index.rst.txt +++ /dev/null @@ -1,52 +0,0 @@ -:py:mod:`dasf.transforms.operations` -==================================== - -.. py:module:: dasf.transforms.operations - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.transforms.operations.Reshape - dasf.transforms.operations.SliceArray - dasf.transforms.operations.SliceArrayByPercent - - - - -.. py:class:: Reshape(shape) - - - Reshape data with a new shape. - - Parameters - ---------- - shape : tuple - The new shape of the data. - - - .. py:method:: run(X) - - - -.. py:class:: SliceArray(output_size) - - - .. py:method:: transform(X) - - - -.. py:class:: SliceArrayByPercent(x=100.0, y=100.0, z=100.0) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: transform(X) - - - diff --git a/docs/_sources/autoapi/dasf/transforms/transforms/index.rst.txt b/docs/_sources/autoapi/dasf/transforms/transforms/index.rst.txt deleted file mode 100644 index e5f32a3..0000000 --- a/docs/_sources/autoapi/dasf/transforms/transforms/index.rst.txt +++ /dev/null @@ -1,134 +0,0 @@ -:py:mod:`dasf.transforms.transforms` -==================================== - -.. py:module:: dasf.transforms.transforms - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.transforms.transforms.Normalize - dasf.transforms.transforms.ArrayToZarr - dasf.transforms.transforms.ArrayToHDF5 - dasf.transforms.transforms.ZarrToArray - dasf.transforms.transforms.ArraysToDataFrame - - - - -.. py:class:: Normalize - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: transform(X) - - - -.. py:class:: ArrayToZarr(chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: _lazy_transform_generic_all(data) - - - .. py:method:: _transform_generic_all(data, chunks, **kwargs) - - - .. py:method:: _lazy_transform_generic(X, **kwargs) - - - .. py:method:: _transform_generic(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - -.. py:class:: ArrayToHDF5(dataset_path, chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: _lazy_transform_generic_all(data) - - - .. py:method:: _transform_generic_all(data) - - - .. py:method:: _lazy_transform_generic(X, **kwargs) - - - .. py:method:: _transform_generic(X, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X, **kwargs) - - - .. py:method:: _lazy_transform_cpu(X, **kwargs) - - - .. py:method:: _transform_gpu(X, **kwargs) - - - .. py:method:: _transform_cpu(X, **kwargs) - - - -.. py:class:: ZarrToArray(chunks=None, save=True, filename=None) - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: _convert_filename(url) - :staticmethod: - - - .. py:method:: transform(X) - - - -.. py:class:: ArraysToDataFrame - - - Bases: :py:obj:`dasf.transforms.base.Transform` - - .. py:method:: __transform_generic(X, y) - - - .. py:method:: _lazy_transform_cpu(X=None, **kwargs) - - - .. py:method:: _lazy_transform_gpu(X=None, **kwargs) - - - .. py:method:: _transform_gpu(X=None, **kwargs) - - - .. py:method:: _transform_cpu(X=None, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/utils/benchmark/index.rst.txt b/docs/_sources/autoapi/dasf/utils/benchmark/index.rst.txt deleted file mode 100644 index 793fded..0000000 --- a/docs/_sources/autoapi/dasf/utils/benchmark/index.rst.txt +++ /dev/null @@ -1,65 +0,0 @@ -:py:mod:`dasf.utils.benchmark` -============================== - -.. py:module:: dasf.utils.benchmark - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.utils.benchmark.TimeBenchmark - dasf.utils.benchmark.MemoryBenchmark - - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.benchmark.USE_MEMRAY - dasf.utils.benchmark.USE_MEM_PROF - - -.. py:data:: USE_MEMRAY - :value: True - - - -.. py:data:: USE_MEM_PROF - :value: True - - - -.. py:class:: TimeBenchmark(backend='cprofile') - - - .. py:method:: __enter__() - - - .. py:method:: __exit__(*args, **kwargs) - - - .. py:method:: run(function, *args, **kwargs) - - - -.. py:class:: MemoryBenchmark(backend='memray', debug=False, output_file=None, *args, **kwargs) - - - .. py:method:: __enter__() - - - .. py:method:: __exit__(*args, **kwargs) - - - .. py:method:: run(function, *args, **kwargs) - - - diff --git a/docs/_sources/autoapi/dasf/utils/decorators/index.rst.txt b/docs/_sources/autoapi/dasf/utils/decorators/index.rst.txt deleted file mode 100644 index 576ca59..0000000 --- a/docs/_sources/autoapi/dasf/utils/decorators/index.rst.txt +++ /dev/null @@ -1,65 +0,0 @@ -:py:mod:`dasf.utils.decorators` -=============================== - -.. py:module:: dasf.utils.decorators - -.. autoapi-nested-parse:: - - Implementations of important library decorators. - - - -Module Contents ---------------- - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.decorators.is_forced_local - dasf.utils.decorators.is_forced_gpu - dasf.utils.decorators.fetch_from_dask - dasf.utils.decorators.fetch_from_gpu - dasf.utils.decorators.fetch_args_from_dask - dasf.utils.decorators.fetch_args_from_gpu - dasf.utils.decorators.task_handler - - - -.. py:function:: is_forced_local(cls) - - Returns if object is forced to run in a CPU. - - -.. py:function:: is_forced_gpu(cls) - - Returns if object is forced to run in a GPU. - - -.. py:function:: fetch_from_dask(*args, **kwargs) - - Fetches to CPU all parameters in a Dask data type. - - -.. py:function:: fetch_from_gpu(*args, **kwargs) - - Fetches to CPU all parameters in a GPU data type. - - -.. py:function:: fetch_args_from_dask(func) - - Fetches to CPU all function parameters in a Dask data type. - - -.. py:function:: fetch_args_from_gpu(func) - - Fetches to CPU all function parameters in a GPU data type. - - -.. py:function:: task_handler(func) - - Returns all mapped functions corresponding to the executor in place. - - diff --git a/docs/_sources/autoapi/dasf/utils/funcs/index.rst.txt b/docs/_sources/autoapi/dasf/utils/funcs/index.rst.txt deleted file mode 100644 index a637145..0000000 --- a/docs/_sources/autoapi/dasf/utils/funcs/index.rst.txt +++ /dev/null @@ -1,250 +0,0 @@ -:py:mod:`dasf.utils.funcs` -========================== - -.. py:module:: dasf.utils.funcs - -.. autoapi-nested-parse:: - - Generic and regular functions. - - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.utils.funcs.NotebookProgressBar - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.funcs.human_readable_size - dasf.utils.funcs.get_full_qualname - dasf.utils.funcs.get_worker_info - dasf.utils.funcs.sync_future_loop - dasf.utils.funcs.download_file - dasf.utils.funcs.download_file_from_gdrive - dasf.utils.funcs.get_machine_memory_avail - dasf.utils.funcs.set_executor_default - dasf.utils.funcs.set_executor_gpu - dasf.utils.funcs.is_executor_single - dasf.utils.funcs.is_executor_cluster - dasf.utils.funcs.is_executor_cpu - dasf.utils.funcs.is_executor_gpu - dasf.utils.funcs.is_gpu_supported - dasf.utils.funcs.is_dask_local_supported - dasf.utils.funcs.get_dask_running_client - dasf.utils.funcs.is_dask_supported - dasf.utils.funcs.is_dask_gpu_supported - dasf.utils.funcs.get_gpu_count - dasf.utils.funcs.get_dask_gpu_count - dasf.utils.funcs.block_chunk_reduce - dasf.utils.funcs.return_local_and_gpu - dasf.utils.funcs.get_dask_mem_usage - dasf.utils.funcs.is_notebook - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.funcs.GPU_SUPPORTED - - -.. py:data:: GPU_SUPPORTED - - - -.. py:function:: human_readable_size(size, decimal=3) - - converts data size into the proper measurement - - -.. py:function:: get_full_qualname(obj) - - Return fully qualified name of objects. - - -.. py:function:: get_worker_info(client) - - Returns a list of workers (sorted), and the DNS name for the master host - The master is the 0th worker's host - - -.. py:function:: sync_future_loop(futures) - - Synchronize all futures submitted to workers. - - -.. py:class:: NotebookProgressBar - - - Bases: :py:obj:`threading.Thread` - - A class that represents a thread of control. - - This class can be safely subclassed in a limited fashion. There are two ways - to specify the activity: by passing a callable object to the constructor, or - by overriding the run() method in a subclass. - - - This constructor should always be called with keyword arguments. Arguments are: - - *group* should be None; reserved for future extension when a ThreadGroup - class is implemented. - - *target* is the callable object to be invoked by the run() - method. Defaults to None, meaning nothing is called. - - *name* is the thread name. By default, a unique name is constructed of - the form "Thread-N" where N is a small decimal number. - - *args* is the argument tuple for the target invocation. Defaults to (). - - *kwargs* is a dictionary of keyword arguments for the target - invocation. Defaults to {}. - - If a subclass overrides the constructor, it must make sure to invoke - the base class constructor (Thread.__init__()) before doing anything - else to the thread. - - - .. py:attribute:: MIN_CUR - - - - .. py:attribute:: MIN_TOTAL - - - - .. py:method:: show() - - - .. py:method:: set_current(current, total) - - - .. py:method:: set_error(error) - - - .. py:method:: run() - - Method representing the thread's activity. - - You may override this method in a subclass. The standard run() method - invokes the callable object passed to the object's constructor as the - target argument, if any, with sequential and keyword arguments taken - from the args and kwargs arguments, respectively. - - - - -.. py:function:: download_file(url, filename=None, directory=None) - - Download a generic file and save it. - - -.. py:function:: download_file_from_gdrive(file_id, filename=None, directory=None) - - Download a file from Google Drive using gdrive file id. - - -.. py:function:: get_machine_memory_avail() - - Return free memory available from a single machine. - - -.. py:function:: set_executor_default() - - Return executor as a CPU (default) instance. - - -.. py:function:: set_executor_gpu() - - Return executor as a GPU instance. - - -.. py:function:: is_executor_single(dtype) - - Return if the executor is a single machine instance. - - -.. py:function:: is_executor_cluster(dtype) - - Return if the executor is a cluster instance. - - -.. py:function:: is_executor_cpu(dtype) - - Return if the executor is a CPU instance. - - -.. py:function:: is_executor_gpu(dtype) - - Return if the executor is a GPU instance. - - -.. py:function:: is_gpu_supported() - - Return if GPU is supported. - - -.. py:function:: is_dask_local_supported() - - Return if Dask is supported locally by the executor. - - -.. py:function:: get_dask_running_client() - - Get Dask runner stanza. - - -.. py:function:: is_dask_supported() - - Return if Dask is supported by the executor. - - -.. py:function:: is_dask_gpu_supported() - - Return if any node supports GPU. - - -.. py:function:: get_gpu_count() - - Get single node GPU count. - - -.. py:function:: get_dask_gpu_count(fetch=True) - - Get how many GPUs are available in each worker. - - -.. py:function:: block_chunk_reduce(dask_data, output_chunk) - - Reduce the chunk according the new output size. - - -.. py:function:: return_local_and_gpu(executor, local, gpu) - - Return executor type based on passed preferences. - - -.. py:function:: get_dask_mem_usage(profiler) - - Get Dask memory usage profile. - - -.. py:function:: is_notebook() - - Return if the code is being executed in a IPyNotebook. - - diff --git a/docs/_sources/autoapi/dasf/utils/index.rst.txt b/docs/_sources/autoapi/dasf/utils/index.rst.txt deleted file mode 100644 index aa1bd79..0000000 --- a/docs/_sources/autoapi/dasf/utils/index.rst.txt +++ /dev/null @@ -1,20 +0,0 @@ -:py:mod:`dasf.utils` -==================== - -.. py:module:: dasf.utils - - -Submodules ----------- -.. toctree:: - :titlesonly: - :maxdepth: 1 - - benchmark/index.rst - decorators/index.rst - funcs/index.rst - labels/index.rst - logging/index.rst - types/index.rst - - diff --git a/docs/_sources/autoapi/dasf/utils/labels/index.rst.txt b/docs/_sources/autoapi/dasf/utils/labels/index.rst.txt deleted file mode 100644 index 7c56fc1..0000000 --- a/docs/_sources/autoapi/dasf/utils/labels/index.rst.txt +++ /dev/null @@ -1,96 +0,0 @@ -:py:mod:`dasf.utils.labels` -=========================== - -.. py:module:: dasf.utils.labels - - -Module Contents ---------------- - -Classes -~~~~~~~ - -.. autoapisummary:: - - dasf.utils.labels.DaskLabel - - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.labels.get_attributes - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.labels.inside_with - dasf.utils.labels.g_hash_attrs - dasf.utils.labels.g_func_attrs - dasf.utils.labels.g_data_attrs - - -.. py:data:: inside_with - - - -.. py:data:: g_hash_attrs - - - -.. py:data:: g_func_attrs - - - -.. py:data:: g_data_attrs - - - -.. py:class:: DaskLabel(start, stop, label=None, color=None) - - - Bases: :py:obj:`object` - - .. py:method:: start(start) - - - .. py:method:: stop(stop) - - - .. py:method:: __name(x) - - - .. py:method:: __add_item(key, tag, label=None, color=None, atype='data') - - - .. py:method:: __add_func(key, tag, label, color) - - - .. py:method:: __add_data(key, tag, label, color) - - - .. py:method:: __generate_hashtable(data, delete_dup=False) - - - .. py:method:: __enter(dsk) - - - .. py:method:: __enter__() - - - .. py:method:: __exit(dsk, exc_type, exc_val, exc_tb) - - - .. py:method:: __exit__(exc_type, exc_val, exc_tb) - - - -.. py:function:: get_attributes() - - diff --git a/docs/_sources/autoapi/dasf/utils/logging/index.rst.txt b/docs/_sources/autoapi/dasf/utils/logging/index.rst.txt deleted file mode 100644 index 88958f0..0000000 --- a/docs/_sources/autoapi/dasf/utils/logging/index.rst.txt +++ /dev/null @@ -1,29 +0,0 @@ -:py:mod:`dasf.utils.logging` -============================ - -.. py:module:: dasf.utils.logging - -.. autoapi-nested-parse:: - - Logging helpers for functions. - - - -Module Contents ---------------- - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.logging.init_logging - - - -.. py:function:: init_logging() - - Initialize logger objects to be used by modules. - - diff --git a/docs/_sources/autoapi/dasf/utils/types/index.rst.txt b/docs/_sources/autoapi/dasf/utils/types/index.rst.txt deleted file mode 100644 index a9e732d..0000000 --- a/docs/_sources/autoapi/dasf/utils/types/index.rst.txt +++ /dev/null @@ -1,181 +0,0 @@ -:py:mod:`dasf.utils.types` -========================== - -.. py:module:: dasf.utils.types - -.. autoapi-nested-parse:: - - Data types handlers. - - - -Module Contents ---------------- - - -Functions -~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.types.is_array - dasf.utils.types.is_dataframe - dasf.utils.types.is_cpu_array - dasf.utils.types.is_cpu_dataframe - dasf.utils.types.is_cpu_datatype - dasf.utils.types.is_gpu_array - dasf.utils.types.is_gpu_dataframe - dasf.utils.types.is_gpu_datatype - dasf.utils.types.is_dask_cpu_array - dasf.utils.types.is_dask_cpu_dataframe - dasf.utils.types.is_dask_gpu_array - dasf.utils.types.is_dask_gpu_dataframe - dasf.utils.types.is_dask_array - dasf.utils.types.is_dask_dataframe - dasf.utils.types.is_dask - dasf.utils.types.is_xarray_array - - - -Attributes -~~~~~~~~~~ - -.. autoapisummary:: - - dasf.utils.types.ArrayCPU - dasf.utils.types.DataFrameCPU - dasf.utils.types.DataCPU - dasf.utils.types.DaskArray - dasf.utils.types.DaskDataFrameCPU - dasf.utils.types.XDataArray - dasf.utils.types.Array - dasf.utils.types.DaskDataFrame - dasf.utils.types.DataFrame - dasf.utils.types.DataDask - dasf.utils.types.ArrayGPU - - -.. py:data:: ArrayCPU - - - -.. py:data:: DataFrameCPU - - - -.. py:data:: DataCPU - - - -.. py:data:: DaskArray - - - -.. py:data:: DaskDataFrameCPU - - - -.. py:data:: XDataArray - - - -.. py:data:: Array - - - -.. py:data:: DaskDataFrame - - - -.. py:data:: DataFrame - - - -.. py:data:: DataDask - - - -.. py:data:: ArrayGPU - - - -.. py:function:: is_array(data) - - Returns if data is a generic array. - - -.. py:function:: is_dataframe(data) - - Returns if data is a generic dataframe. - - -.. py:function:: is_cpu_array(data) - - Returns if data is a CPU arrau like Numpy. - - -.. py:function:: is_cpu_dataframe(data) - - Returns if data is a CPU dataframe like Pandas. - - -.. py:function:: is_cpu_datatype(data) - - Returns if data is a CPU data type. - - -.. py:function:: is_gpu_array(data) - - Returns if data is a GPU array like Cupy. - - -.. py:function:: is_gpu_dataframe(data) - - Returns if data is a GPU dataframe like Cudf. - - -.. py:function:: is_gpu_datatype(data) - - Returns if data is a GPU data type. - - -.. py:function:: is_dask_cpu_array(data) - - Returns if data is a Dask array with CPU internal array. - - -.. py:function:: is_dask_cpu_dataframe(data) - - Returns if data is a Dask dataframe with CPU internal dataframe. - - -.. py:function:: is_dask_gpu_array(data) - - Returns if data is a Dask array with GPU internal array. - - -.. py:function:: is_dask_gpu_dataframe(data) - - Returns if data is a Dask dataframe with GPU internal dataframe. - - -.. py:function:: is_dask_array(data) - - Returns if data is a Dask array. - - -.. py:function:: is_dask_dataframe(data) - - Returns if data is a Dask dataframe. - - -.. py:function:: is_dask(data) - - Returns if data is a Dask data type. - - -.. py:function:: is_xarray_array(data) - - Returns if data is a Xarray. - - diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt deleted file mode 100644 index db20367..0000000 --- a/docs/_sources/index.rst.txt +++ /dev/null @@ -1,32 +0,0 @@ -===================================================== -Welcome to DASF Documentation! -===================================================== - - -DASF is an Accelerated and Scalable Framework ------------------------------------------------ - -DASF is a generic framework specialized in acceleration and scaling common -techniques for Machine Learning. DASF uses most methods and function from the -most common libraries to increase the speed up of most algorithms. Part of this -is to use Dask data to scale computation and RAPIDS AI algorithms to extend the -support to GPUs as well. - -Contents ---------------- - -.. toctree:: - :maxdepth: 2 - - principles - installation - overview - tutorials - api - -Indices and tables -+++++++++++++++++++ - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_sources/installation.rst.txt b/docs/_sources/installation.rst.txt deleted file mode 100644 index 40219c9..0000000 --- a/docs/_sources/installation.rst.txt +++ /dev/null @@ -1,62 +0,0 @@ -.. _installation: - -========================== -Installation Guide -========================== - -The installation can be done using `conda` or `docker`. - -Using Docker --------------- - -To install DASF using docker, you must in the go to the `build/` directory and -execute the command below directory according to your build type: `cpu` or -`gpu`. - -.. code-block:: bash - - ./build_docker.sh - - -The `dasf` image will be created and be ready to use. Once it is ready, you -can start a jupyter instance by executing the command: - -.. code-block:: bash - - ./start_jupyter_server.sh - - -Using Conda -------------- - -If you just want to create a base Conda environment for DASF, you need to -create it, using the respective YAML file based on architecture: for CPUs -or GPUs. The environment name is always `dasf`. - - -.. code-block:: bash - - conda env create -f build/conda/{cpu,gpu}/environment.yml - - -Development version --------------------- - -To install this development version, all you need to do is run `pip` from the -root project directory (the same where `pyproject.toml` lives). - -.. code-block:: bash - - python -m pip install -e . - -Testing --------- - -If you have a working environment with DASF installed, you can execute the all -the test set. Make sure you have all development packages installed such as -**pytest**, **parameterized** and **mock**. To run, you need to execute -`pytest` from the `tests/` directory. - -.. code-block:: bash - - pytest tests/ diff --git a/docs/_sources/overview.rst.txt b/docs/_sources/overview.rst.txt deleted file mode 100644 index a902390..0000000 --- a/docs/_sources/overview.rst.txt +++ /dev/null @@ -1,31 +0,0 @@ -.. _overview: - -========================== -Overview -========================== - -DASF offers a wide range of Machine learning algorithms. Below, a table of -implemented algorithms and the respective infra-structure. - -Implemented Machine Learning Algorithms ------------------------------------------ - -The table below is a list of supported machine learning algorithms by DASF framework. - -+--------------------------+---------+---------+---------------+---------------+ -| **ML Algorithm** | **CPU** | **GPU** | **Multi-CPU** | **Multi-GPU** | -+==========================+=========+=========+===============+===============+ -| K-Means | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| SOM | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| Agglomerative Clustering | ✓ | ✓ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| DBSCAN | ✓ | ✓ | ✗ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| HDBSCAN | ✓ | ✓ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| Gaussian Mixture Models | ✓ | ✗ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| PCA | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ diff --git a/docs/_sources/principles.rst.txt b/docs/_sources/principles.rst.txt deleted file mode 100644 index 1eaa755..0000000 --- a/docs/_sources/principles.rst.txt +++ /dev/null @@ -1,24 +0,0 @@ -.. _principles: - -========================== -Principles -========================== - -The growth in the use of machine learning techniques has led to the emergence of a significant number of frameworks, libraries and tools in recent times. Depending on the technique used or the purpose of the project, there will possibly be a way to develop something using what already exists. With the further growth of deep learning techniques, more of these facilities become available. - -One of the problems with these deep learning techniques is the use of data in batch format. So a large piece of data is subdivided into smaller pieces and iterated during epoch training. Today, there are no tools that process data distributedly on demand in full machine learning pipelines. There are also no tools that still use the maximum computational power using GPUs, for example. - -Taking advantage of this niche space to be explored, the DASF was created whose recursive acronym is DASF is an Accelerated and Scalable Framework. The project seeks to fill this gap in creating machine learning pipelines using large volumes of data without dividing them into batches. - -So that this was also possible, a series of libraries were gathered that could compose the framework, composing most of the functionalities proposed by it. Such tools will be specified in the next sections. - -DASF as a Simple API ----------------------- - -DASF tries to enable a simple API for the user to use. The idea is to make the user's life easier when using the framework. We believe that the user should not have to worry about the details of the framework, but rather focus on the problem to be solved. The framework should be transparent to the user. - -In order to simplify the learning-curve some concepts were created to facilitate the use of the framework. The main ones are: - -* **Standard API**: We try to follow the same API as scikit-learn, so that the user does not have to learn a new API to use the framework. This is a very popular API and is widely used in the community. So, operations in DASF usually implement the same methods as scikit-learn, such as fit, predict, transform, etc. -* **Extensibility to new devices**: DASF allows simple extensibility to be used in new devices (e.g., GPU) by implementing a simple interface. This allows the user to use the framework in different devices without having to worry about the details of the implementation. -* **Extensibility to scale**: For multi-node scalability we use the DASK construct graphs under the hood. This allows the user to use the framework in a distributed way without having to worry about the details of the implementation. \ No newline at end of file diff --git a/docs/_sources/tutorials.rst.txt b/docs/_sources/tutorials.rst.txt deleted file mode 100644 index f354361..0000000 --- a/docs/_sources/tutorials.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -.. _tutorials: - -========================== -Tutorials -========================== - -Tutorials using DASF. - -.. toctree:: - :maxdepth: 2 - - tutorials/Tutorial_1.ipynb - tutorials/Tutorial_2.ipynb - tutorials/Tutorial_3.ipynb - tutorials/Tutorial_4.ipynb diff --git a/docs/_sources/tutorials/Tutorial_1.ipynb.txt b/docs/_sources/tutorials/Tutorial_1.ipynb.txt deleted file mode 100644 index de77cbd..0000000 --- a/docs/_sources/tutorials/Tutorial_1.ipynb.txt +++ /dev/null @@ -1,289 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "981cafbc-1031-4827-b9a5-77ae10b4fa92", - "metadata": {}, - "source": [ - "### Tutorial 1 - A Quick Demo\n", - "\n", - "In this first tutorial, we want to present some basics of DASF framework and how you can use it to manage your machine learning algorithms in a multi architecture environemnt like single machines, clusteres and GPUs.\n", - "\n", - "If you are familiar with all the [scikit-learn](https://scikit-learn.org/stable/index.html) API, DASF has the same methodology of function notations. The only difference of DASF is that this framework is directly associated with the host environment. If you are using a clustered environment with [Dask](https://www.dask.org/) for example, you will use the optimized functions for that environment type. If you are running your code in a single GPU host environment, your code will have the specific optimizations for that type and so on so forth.\n", - "\n", - "Let's try our first example with some basic clustering algorithm. First, create a simple dataset using `make_blobs` function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9a10b3d6-4d4a-498a-a03e-036679fea3fe", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import make_blobs\n", - "\n", - "n_samples = 500000\n", - "n_bins = 3\n", - "\n", - "# Generate 3 blobs with 2 classes where the second blob contains\n", - "# half positive samples and half negative samples. Probability in this\n", - "# blob is therefore 0.5.\n", - "centers = [(-6, -6), (0, 0), (9, 1)]\n", - "X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "id": "d5d5931b-08a2-402c-acc1-6378b63e1308", - "metadata": {}, - "source": [ - "Notice that we are using the same code available in scikit-learn tutorials and demos.\n", - "\n", - "To have a better view of the data distribution, we can plot the generated dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6d8b9470-6079-4b4c-8ad0-639719057ca0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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trrn12KimwlFLe/U6bl3xZ/gSG3ngp08wPj7+e3+9/tCJlRVBuIyJa+jC5Wzr1s1077qHQ4M72dh6bpOtqqocGtyJpjLMlq23veaxRqORucgs9pRMbUMriqKgKoCkkldS6CQjKWkGa7EWh66B4cketLIVp9qISXHjIIBFrmIuHmFw5ijtlRsJz0TQFe3YrR5KxRSZeBSfro311R/gyMxj7H30V1RUejFbzLTUdbKkfhOh8TduABbeGaJYEYTLkAjREt4LAoEAd352Bw/89AkePzlMi2cdFoODeHqesfgxtNUR7vzsjvM2uAKUVJkEUwBE8kF6c48TK8yglXSokkpYHsSvrqRQKGAs+DEpLiLpWUpFSBPFolahlWyMTPQRjcYwlZzUO1YhSRJJeY64PEODbQ17J3+BseClRb4VX8aHSa9htLeb/tGfUlVVzeE9PedtABbeOaJYEYTLzDsZoiUIF9v69evx+/089ujjPPf4vxCaiKNSwldn433Lrsbv95+3OO9c24jFZCNti/B08J+IZ8JoZBeNpWswaR1MyycYKOzCTiMOpYFsokBBiWNR/bhpZkrtxs9KLKqfRGkKElbSmjQn8k9h11XSW3yEjBJjOjZGh/5mllpvRtWVKOTiVPicaDI2jo4+yTPDD2M4k6fjZw3ccMP14mTh90QUK4JwGXmnQrQE4WI536XL2dlZRnvmCJg2csW65bgsfnJyiqEXuvnai99FQcavrjqnOD/2m4MMnh4DRcfs9B4cxQY8ukby8j5MeQ8O6qigAx1mbGoVFPUUyJElgQEHEUaY4QheOtBjxkUjRtVBpDhEb/FhhqUXkKUshUKBbL5Ad/o/0Wl1GAwGZmUr2qKF2tImprNnmM/3cs+3n+CR+3fyhb/+NLfeeutFfpXfe0SxIgiXkXciREsQLoazqyPde3rIZWRMFj3rrlxGQ0M9v/n5MwsF+Kpzf69rbJ3c/8SPCNPPjh23UuVqIBKJEAnnUAebMU2sYKjwEpW6JTSbtjOb62NU2UOJIk7qUCiRo5d2bkWHCdCQZg4v7Xho5QyPMcoufCylQJqUOsM8/YyzF1QVm1pDA9upYzOKKpMoTpAoTpItpLDpvVippEW9AYNexzbbnfT2vcDf/92/AYiC5R0mihVBuEy8EyFagvBuk2WZffv2cd+PfsP0YAolbUCLkRJ5TndNkDPM0qDfzC1XvbYAn5iYpEl7NUbJSnf/i9hzLQydCiLl7OgKdtZKf4qq6pkpHKNHeYwa1nCF5v9GQSak9JFihmGe5yT3s4KPYcFHkTxOAuix4qCefh4nxCkM2DDjxSXV41XbcFBPgQw+lmCnCj0WbNRgZoj5Yj85JYtfX0VeSlIsKMRn8zRpr2NwRsO//v3PWLly5TknDKIZ/u0RxYogXCbeiRAtQXi3nF1J2f30Afa+dAhN1EejdSPtrm24rVVkilFG44fomv0NRleCUGcIh8OBwWBAq9VSKpWYCYbxWFrIp1PsO/RLAnkNLqUFXdGOJBswqE48dDDNcVqKN+GiCQf15EliJYKDWlQU+vgNQV7GQwsFkmjQUyRLiQIWfLRyI/VsQ4eBYfVZ8qRp4QaGeY48cQqkkMkBKi4ayZMko8wRKpyhpMtg1rip1q5GVUs0FCW6Twzx85/9nP/1tf8lmuHfIaJYEYTLxNsN0RKEd0tXVxf3//gxijNe5mZBmveySvsp6nKbkCIyVqOPakcHfs0SZoNRRoPH+enPf4jf0ozdaaOpvZ6mpkaUogolDbko5NMlKFmwUktOzmDGjR4rSSZx08hyPsE4exhnPxZ8uGhERcFDKzYqMeDAjBc3zWjRk2SKeQYx4aCebZhxoqASZgAHtdipwkcHs5yimnVoKKFQQoOCg1pyRCiqeZLaMRrM63EZqlFVFSMu5lJ9PHn/f7Js+W/Z98wJ0Qz/DhDFiiBcJs6GaHU92M2S2k3nvRR0NkRr4/WvDdH6XYnla+Gt2L9/P9/92g8xBZcTsCylZ+gxquSNBLTbKBTyFDIFhhKjOD1WphPDpLIJciQZyR4gpySxpSuIziWYHJ5Fo5MgNU8qm0CWCziVRkySlwIFdFjQoCNBkAqWkWWeIjkc1FHNGnLECHGKWU6wjDvRY0WlhAknRpys4JOc4BfECSKTQgK0GFApocWEFhO1bOI4v2CM3bRxE0XyqJTQYUJCywzHQJNlmfYWUqkUcr5IvpTGqtYQnc7xw3/4Bdc1f/41PWbtVes5OPAkv7r7UdEMf4FEsSIIl5G3E6L1el6vGBHL18Jb1dXVxbe/9k/ke+vYWvlxSlKObC6Ps9hGRs5jMTix6PxkCjHOTO1hVNmLVfKyQv0EJTVLjWYFM+pxZgtnUKaK2JwWYvERJuWjmEs+LJIPWcmhUKJIlhwJ0oQIcAURhhZXVPLEyREjyghummnnNkKcREUhzRwlCiSwUMVaQCJED41sp0SRNCH0WInQjw4LLuoZ5jkSBKlmLVr0JJhkmGdJMMUVxb9GjdhJq3kMWiMliqAvkc8UiPQYqVm6fPF9Go/HCQYnmAmGKcl2euIR/tH0T/z//vZvxHvqTYhiRRAuI+cL0bKZ3KRyUYYi3Wgqw+eEaL2RNypGZmdnRZaL8JaMj4/zqx8/RjZoY3PlR/A76wkmT1BSClilCqz4KcoZDFoteeJMKt00sp0G9UrijBNhiDrtBhqlKzgjP8VI4UW06SUE5UOE5D5a2UFRzaPHSoJJBniKcfYSYZhefo2Depq5jjwJVBRMuMkRo5UbUJHRYcRDC9McLRcrk5Qo4KKBGY5TxWoGeYoY4xiwYqMGB7W4acSMj1mOc4ZHUIE5TiOTppnrcBfb0Eh6jJKDTDFMQcoyb+jHbvRTqaziZPcZHA4HqVSK3qMDqGkLbnMLBp2JVl2Sw0/8jLtyP+ET/+394j31BkSxIgiXmbMhWvv3HeDovmeQ8wp6i4aN13eyZettF1SovFGw3IuP/xO5bIGlpltFlst73O9yie/1HrNv3wHkGRcVVgmrzgNAMHMKFZV+9Qmm6EZRikhZDZlSGCtVtHEzJQqUpDxFKcO0fByHtpZqaTUjpT0cS/+apH6cnJpnjh5ARUFlmm406PCznAauQkJDmhBj7KZAmlo2YMKJgowWEzHGkNCQJwGAATsACiWyREgxQz+PM003bdxEkilmOIIRO1mimHBRw3qyRIkxSpEMRpx0cBuqIpGWwmQ1UTQaDVMcZibfR4EiikbD1GiYl158Ca1ixqU2UetfGA1QKpWotrUyz3JcsVXiPfUmRLEiCJehQCBAIBDgjg+99Q+bNwuW+/FzX2V+WuaD798islzeo36XS3xv9Jjq6mqO7euj3Xct0dkXyMhRiopMX3gXZslFQc1Sra7HQzPR0jD7+T5mKhjmOTToSakzOC0VFHRRDueeJF6aIqtEiDNJURtDq3rIkaCLH2LCQys34qUNNy3YqSbEKSpZQZJpxti7uDVZJsccPVjwUclK8iQokaeETI4oJtwkmSZNCBtVVLCctXyOGY5ylHsYZx81rMNLO1qMzHGaKQ4RJ4gROwPspJIVGFUbWXWeeekMYWUAp9LIHGdIEcVdbGG0dw6XqRJvtY2hwSFSsQyqKjGjHGfKGeR9Kz5Nz+y0eE+9AVGsCMJlTK/Xo9frkWWZVCr1pkWLLMu88MJLlKY9rwngAigpRTRFE1VKKxMTk7hcrtc8h8hyubz9LuMa3uwxt338GuScgtNSQUtdJ6O93eSKKUbi3biVVkDlNA/jpgUrftw0UclKVECDAR1mhtMvE2UED820aW5Fr5oYL+5nOnmENGGijGLCg5sm7NQCKnniKMho0DNFNylmiTLAPr6HCTdRRkkT4hr+H0y4iTGKm1YAZjhKkYUVmyTTAKzkU5TIoUGHBi0a9IQ5Q4RhJCSsVNDO7cxynEn2M8hOJqWDIKnISga7UksVa4lwhixx4ozjK3Uil2SkopPxwSmMGhtWXQ1aSU+f8gS6eDUvPXEQ71IH3Xt7uOHGKDabTbyvXkUUK4JwGbvQM+RXpofu29VFu3orPbpe6uvrcDqdi/eTS3lKRZVqSyszwTBLlpTQarWv+bkiy+XScqGXc36XcQ0X8phH7nuOrJwipURZGtjM/p7/h4NDj+CW2/BIrWSlKDk1zii7UJAxYCNDGB1mcsQoUSRFiEauppGrkZUMBZLYqKJRvZ4CSY7zM/TYCHAFPtox40FCQ4oZJjjALMexUUMrN5MjgQZtOVb/DD08QCPbyRHDzwo06EgyxQQHUZBZxkcZ5jmyRJiimwkOUMFSNvFXpAlRooCdaorkOM59SGioZxsO6vCorWhUDRMcJsogPTyAEQcVdJJilgGewEIlFrUCk9qB2eDCZvDSX9wJxjzb7J8lnAzSe/gl5oJ7yaZlrHaTaGZ/FVGsCMJl6kLPkF95v1r7djxqHJ+6jKmeNNOjR1m2tp2amhoA9FojWp2GIln0RROl0vmLFZHlcmm40GL1bDGze/fetzyu4Y1GPCiKwurAtTx9ehB9IMnR/mexSB4mQsPoshXosaFTLSzhalQgxQxDPMswz2PAxjq+iBY9QfbhJICLZkZ4gRhjJJmmQBILPrx04qOTGOP46MRGFVb86DCiUGSOHow4KZGjnycokMKMCx1m8qQ4zN308QhumqlkBRa8hDnDKC+xgk+yhDuIM4IeM2a8qCg0sB0dRkw4yTIPwBw9hDhJC+/DTg1xxgCJIjI+OskQJk+COrZQwzq06JhniBAn6eVhalmPq1BPlllSmilqtKuZiJ0mI8fJFWUSoTwNxWuwZOyveR+/ExECl3MMgShWBOEydKFnyLIs/9fclRU3UVKKHOnbi0k20mzfwGRkkJ4j/VitVpxOJzqtnpa6Tk50d7HUduV5C5XfR5aL8NZdSLHq9/sXi5l8RuZw1xHaPVsIJYJUOl97xi5JEo3O1Rza9RS3vf9W9Hr9a0Y8lEolIpEI09MzhCYjKEWVaE7LwHgPuYSCGnHiK6wkSYwA26jnChRkAEy4MWDHTg1B9jHBPiR0DPE0VayihwewU0M1a6kGZLKEOMEgTy4myB7m36hkJY1sx00zx7gHmQxmfMikCbAFL50UyRNnDDNeMsxhoQIPLUQYYJCncBBAh5la1gMqFioJcRoPrRTJoUVfvsSkY6EgyTHM8zioo5HtRBmmRAErfkw4iTCKQhE7dUQZIsUMEioOGqhhHTFGOcOjGBUngdJGnHIbupwXnWrESw0znMKQ89Lds4er17+fW1f8GYcGd/KTu+7nwOZDTPSHf+cIgfdCDIEoVgThEnKhZz4XOtDw/l89iG62dfF+Z4uR0d5umu2bqPW0MhCKEgxO4HQ6KZVKtFau4VnpQaoMFWg028957rea5XI5n8ldyi6kWP3Bd+7BZDZgy7TS4rkBo9ZKJF9NYizBo8l7uGrtDjpq/qs35WwGSG//MMflY/yvL/1vVm7uIBqOs9TjXvz+4OlhIqEkhqILn6MWp8NFMp9mZjxNu/0KZkpDhPPDyGqROXqZ5SRuWvCzFC1GbFSylf/BI/wpfTyCjyXoMJElSjPXUskaZNLoMQMSdqqxUsUsx7FSiQk3JWR6+TU1bGCWUzTzPrJECLCNFm5ApUSGedK0EWecefoZ5UVK5KllE04aCLKPHHFO8SBFMpSQSRCkSAaAHDEKpFEoolAixihxgizlw2jQIaHBjBsJLRGG6eMRVErUsQUPLXhoJkecCQ4wwUG8LKGSlRTI4C+uBFVLiF6ijJElTIoZ6liHIR5g95EncF3pp9a8ghdeeIKJ/m5uWfn53ylC4HfpUboUXVLFyne+8x2++tWv8ld/9VfcddddF/twBOFd81bOfC50oGGjaxUP7nqSj6798Dn3WxrYzODYPZyM7GSF5ybc5hrGB3tRFIXZiXkGIwfJaGcZ4XkeOphked2VbznL5d04k/tDLoTerFjtqNjCC48+gbfGxMeu+zMkSaJUKjHijOAoNDOTP7XwgWj1U+kMMDk5uZgBolWc+E1tNBVu5th/HqFn6DRm90GsmQC5mEQ6psFVbEVVYXo2yPTcGDPSMQySDbemiTO5A7iUFpZyPU4akMkww3HO8BhuWulgB2lm0GGiji1Us46j/Bg/y2nielQUjFjJEiVNCBcNOGkgxAlCnMRKBQYcaDFwmH/DTSs6zJhw0sQ1aNGRYh4FGTdNqCg4qKeETIR+QpygmRuY5BBFMiSZooErcNNEljjj7GWePlQUvHSQZIocMfIkMeHCST0yGXLEMOLAgJ0wfeSJsZo/ZikfJc5IeSVIoo6tSGgZ5Xns1DHHKUyqG4U8Bux4aUdTLtjipRFGpnvRJPLstj+OK7mURmk7aamX9up16LQLv+cXGiEwNDTEfT/6LZXpLWxZcetlHUNwyRQrXV1d3H333axcufJiH4ogvKve6pnPhQ40NOoslAoSFoPjnNsrnQGuWruD3UeeYD40jDlXw3RsiOnIGCnNDCVbhBtb/pRIfoJg6RDZ1CgebcUFZ7ns37+f+3/6GFK4inbf6/99Xq/YeLMi5L2wpP12XEixOjExSZWyhpI8QUkpotPq0Wq1VNX7mOqZZnnFjczPDdMbPIAJJ71HBzDna6mpaGHX3A9Z1baFFYEr6KzZyMhYP3uOPsXNNf83Nr2eaD5NXA6jLVpw0YKqSJzgYdAY6Q49QoW8mlo2Uc06DNgxYqOadYzyIgM8RQ3rGGM3Vnz46ESlAEjYqERFQY8ZHW6ijKDDRI4Y/TyBi0aqWYuf5aSZZZ5+ZLIoFJnmMC3cSI4YWaIUyWDGiw4TJtykmaaJ7UhIOKijlwdRKNHGLfhYigkHRYoYsLCMjzDOXgZ4nL38AxV0YMJFJasJcZIYYxRIMcNRSsjkSRBhEAf1NHAVKaaIMYYVPz460KLHRztpZpngEClmmOYQrdyMj06s+HHRgAE7CiXG8i9ypvAoew49wzW+Thr8KzhZ7EEu5ReLlTeLEDj7Hnnw3keZ6cvTXGkkX8iyNLB58fLf5RZDcEkUK6lUik9+8pP8+Mc/5lvf+tYb3jefz5PP5xf/nEgkft+HJwi/N7/L7owLHWiYL2bQGlQyhde+Rzpq1uOy+jl85gVe2P9LUkqYdZXbWVbfQVv1h6n1tKLV6Dg0uJNZ634+99d30tra+qY7TR599HHu/9HjGDKV+N0abNIwKxqvIODrWPz7vPoavEavsmxDCy0tTUxOTnNkbw+5dBGTVceqzR2sXbdm8We/V5a03443K1bPTiv2W1qZKAbP+ZCrr69jevQoU9Eh6k1rGQo+i0dpp5Qy4/fWczLyJEVrGL9zLS+efJChiT7i4RwzuT6em70LX34VplwVXtpx0QjAIE8R4jT5Uoqa0hoqWE6BFHkS6DCjoKBQpInrmOEYgzxNmB4auBpQSDOPhIYiecbZg4smnAQokkeDjhFeoIrVVLCUGGP4WIKDOqpYS5ECs5zAgBUX9dioYpaTDPE0EQbJk6REAYUi1aylQIo2bmaEF3FSj4sm5ukjxQwAEhqsVFLNWlw00cd/EqYDDTq0PEiCCSY4gAkXbpqoZQNGnPTwIFWsYo5eShTw0YmPJWjQlvt1JJbyYeIEyzuSVrOWz5MgiJ1azLhQKKJFRzu3MKecZjDzDNmoQsw2i9amQa89t5n99SIEzr5HStMenMGttFiWYZKNjPZ2Mzh27uW/yymG4JIoVv78z/+cHTt2cP31179psfKd73yHb37zm+/SkQnC79erl/NLpdLiDhytVnveM58LH2h4hOVbGhma6WZ5YOtr7lfpDBAwr8dtOsrG5Vvx2qoYnRxkcLQPjUaipX4Jyxq2EBof5kj3Uerr6xd//qud/QdycF8c78zVODRV5GIp9gztY697Jzds/ihb22895xr85ubbmI6MMjJ5hqd/s5dUNkqdbQUB81r0kpVgepZ9Dz1DwfEzlqxtZMOVqzh1YJgm9bo/6GTdNytWS6USSlFdaBLVnfsh53Q6Wba2nZ4j/YQjc0wzxu6JZyimNeyb+TlJwzgNgQDPdT2MQ26iwXg9jkIWm24ZI4m9TKoPEeBK/KxghmNMcIhpjpInhZNaAlxFgQRTHGaCA1jw4aGZStbgppEKltHLQxiwk2aWDPO4acFHO17aseInS5gkkxTJE+IkMlm0GJjmCAVS5IiiQYcFLxUso0CaKMMUyBCihyP8BB1mmrkOFy0USDLBfmY5QYZ5aliPgoxCiRAn8dJOGzejx0KCSSY5RD+PYqeWWtbhYykKMhnmkEmTYY52dtDCTcgk0WDESgW1bKJEgTQh7NSiQYOKgoSGEjIGHBhx4KYJmSRxxlAokiWCFl15OKIGRVXwqR0MsJNCEg4HH2f11o7FgvOVXh0h8MqTn9VLrmVXcD+V2mU4LT6a7Zs4Gdl5zuW/8z3HpeqiFyv3338/R44coaur64Lu/5WvfIUvfelLi39OJBKL/4gKwuXklcv5iURiccCZUlTR6CSq6n3U19edszvDYrEA5x9oWCzJyKU8keQMj3X/mLH4MZqURsZHdhKam+b9G75Ilath8ecXi0VePvUkOWOI6VkN8REJd2k9asZItpjg8NBxXrI9hckBB/9hjsMvncZo0b/mksvZfyCVwQask1pscj2VllZ0JhNyKcuJ8MM89Oy/kUwkKE66CbCNifSL9AwcwZCppFV7I/O5NN70VmpS6zFZTKjaAlVqGwH1JiZyBxg/eJiJvl3okhVced3KP+hk3TcrVrVaLZIWRtIHWdLe9JrH19TUYLVaeWL3rxgZOYo+P4LX2EiDeyUm7WpODD1DrbSe5Y0fwGOt4szUaby045Sa6VefZoID5Imiw4qDGkrILOPDRBkixCm8tLKcO1EooqIyzwCn+BX15d4NDQaSTJEnySo+STu30cODJAhSzxZK5Ms7Z55gggMs56NYqcBGJRp05EmQYZ4cMVSK2PCTYppBnqZIFjfNLOEOfHQgIVEgRQNX0cd/coZHGOIZEkyiw8QS7qCT28uTlo1kCFPBUqboYpIujLhwEcBOHS6aOMN/MsZuNOiJMIABGxIpCiRJMomHhQC8FFOYcVMiR44EMmnCnKZIFh9LiDGMCQ8qMgXSi/OMNOjQqHpSzFIkwwH+hVxqnnTXNHKhyBWrbjlnF9erIwReefKjKAoanURBzgEL75EVnpuYDy1c/jv7PJdLDMFFLVaCwSB/9Vd/xTPPPIPJZLqgxxiNxkv+RRWEC3F2OT8dL3Do2NFzBpwV5Bxjx8Y4daSfrGaGAf3C7oyNV69aLBTODjT8j4OHUfIS85F5guF+QpEp3DTR6rkG52gtSw1pBoNH+cHU33LFyltYVr+VVC7KmdBBhpTnMGt9VKWvwJNeQUkGi85DRjtHMHeU4NQQtskaqsxrMFesx1VhpevBM+dccnn00ccZ3BfHOqmlmNRT1BXJFOO4jXbsJh+bNV8gGg3yi+f/nmr9KnR6LbOhPq6pvJKtNR/npem70WYdrNH/EXrFwnTuOE5tPQHPMnQ6HZ5MA/tjIeLqSVbqttN7dACbzXZOmB1cXkvab9cbTd9OJpP0x/dwKvo0yuBGgtOjtNR1ntOvoKoqY9FTbOi4jkDhWrxKJ1XOJg6GHqRJu51W6QbGp/vR15pIynNoSyYMkpFq1iKTJs4UFSwhziRGrFSzhiD7aOJaOrh9cetukTxeOiiR5wT34aVjcUUlQCvN3ECJArVs4Dj3EWQfjVyDmQoSTJYbWB1kiSChQUKLGffi7iE7VhJMkGGOMKfxs4IAV5BmFpUiFvzoMKEgE+AKUswyyovkiOKknnq2kCfOwgWgHDJpDFho41bC9DHDcRq4EgMWFPL4WUGBFDFGsOJfXKHRYWWGo3hox4KXGKMYsFMijwY9WoxEGKSCZVjwMspLvMx32cbfYsFHliglZLQYGWMXwzyPhzYqWIKPDpwlJ4NHDzEd/jfet/kOOmrWvyZC4NW9TP/VozSFz16LJElIkkTAvI6h4DNcuURGq9FdNjEEF7VY6e7uJhQKsW7dusXbSqUSu3fv5l/+5V/I5/PnzXkQhPcCo9FITk7Tc/IUrfr3UetvXfzQSSQSmNI1ZNNaYtIw9ppqmgo30/Xg8XMKhenpaf79+w+RHXegyzkohA2s5NPUGlYhyQW8+UoUWcbubCKkOcG+4d8QsZ3A63Wz6c5Wen9kQO33M5ebpi/XhaRqSJVCxJQgqBJrpM9Rqa4kX5inMGomHi+yds0HmEic4IGfPkEwGOT+Hz1O1fRNeOVlGHQuVAUy6TDx7BFcxhqkvIGAsp3J4gkKxRxzqWmsaoC50TQ7Z+/ldOkp2tX3U6FvYzJ/HKPiwq1tolAooNfrcVi8+KOrGI3uoa6tk2w6u7jV+tUulyXtt+v1pm+PBM+w7+QzxORJOj1X0lS4FrvewWjvkcV+hfbqdTx9+H5S6ix/sun/JTyRYqpnDq+tlql4HwHNtdiUGuajU/Rlj5PMxpGKBkrEseGniWsY52VauZFT3E+eOKPsppJVOAgwRw9p5jDhxE0zGrR4aSdNmH4ew4iDKlaVQ95G8NCKiybauYUzPM48A2SZR4NU3oUTo5IVABTJkWSKAilUSsDC50OCSWxU0cCVVLISLXrSzBJnAisVmPFgwkkt6xlnLxr0OKhDjxUFGQmJInlkspjxoEWPnTr0mNFiIEMYAzZ0GPCzjGmO0Mz1aNCRI4oBK4M8zSQHCbANFZUsUSx40GJimGfJEmU1nybFLG6aiDHKi3ydDm7HRQNJpghzmnH24WMJTgJo0aGqCg5NLS7lo0Six9h95AmclgrG5nrPiRA4Xy/T2R6lycggtZ6Ff18sOjelokKhmOPk+N4LjiG42C5qsXLddddx8uTJc277kz/5Ezo7O/m7v/s7UagI72l6vR6dvchUYpCrWr+4WKhkshlmp2YxKC6aXQ2cij6Iz2FjReAKltdvOyfwbfeTR9hc+XFal2zk5499n4BxE9e5/gqAUHaQeGKSjoY1xNJzGHUGrF6FjddW8ME7PojRaOTH//ILwtEgUrGSeuVqNOiZ5SQqWjKE8amdmPFQUjK4dY2o+Sy9RwfYcMUWBvsO85O77keXrsCvWYbf0gpFLbmkjJUK5nIDjGd68GgaMWhs6BUraSWBlWqW8AG8ShuhdA8JIhjxk9OmySgRXDSg0xjJZzOoqoqcL2IoeMiXZIJz/TQ41jATDJ53FMDlsqT9Tnj19O1oOE7P8GkCNav4k/V3oWT09BzpR0lrWWZ6P0OJl/nNrp9QW/84A/Furlh5B1WuBsxSnOnRo4zP95LL51CyRmRVwSkFiBQGkEtZVDWHm2bsUg1zah9aDOix0MB20oQYZCer+WMSBNFiwEsbHtrKTaN6jDjo4FbmGUCDBi16PLQuFh52qnHRRBu3cIZHGeI5nNTiJECaECacGLCiQY9KG/P0E+YMRdLM048WI3ZqsVKFHgs6DBjKz59kChMuJDTosSAhYcCCBj1JJjDjRYMeHabF/00QRAIsVKDDRIkcMUZRKWHAhhEXoKBBgwEbDuqoYxMjvMg8Z7BRjaG8/XqSg2QI087NuGlhhBdo4CpkUiSZ5jS/wYiTPHHiBHFQh50anNRjwEqJAtPREUpSFnPGxVw2w7/L/5NVW9vOiRA4Xy/TK3uUBkJR3OYaQrkR4tIsz5y+B3119E1jCC4VF7VYsdvtLF++/JzbrFYrXq/3NbcLwnuNLMvISQmtM8ep6NM0WbYwn5hkcnacQkbBqLMwnz9DXh+hlPdQLMnotPrzBr6dOHmcaCLMKtMnyGazFHIyBrWC+eIUQ5peOurXMhQOkc4ZuOefH+bE/hESmXnGz8zRIn+Epeqd2PEzzwB1bMTPcmY4zl6+hw0/KNA3bmBZwxa0WQsn+g8zON5DYkKD2aAjnJrGZ8yjljTkCnkkVYuNOjLEKCgZoqUpQEORHEUKjPMyKgo2qtCgp0iOVDZKSVNEpzGhqlAoyFDSotOaUaUSesnM8PxxbJlmShWp14wC+ENM1n3l9O1f/vJXmDMN3L7qLxcKX9fCv6dnzvQz0t9FJl5iPp9gznAMt91LlasR+K8PtK79x4gkZ0ipc/iNy9CUNGSVGHlNAo/UiqNUR15NkWKaLFHm6adEgQqWMcILZJjDgB0tOvRYKJBERUXFSIEUWgy4aUShSIxxJCQqWUGaEPMMIrFQrNuoRKGIg3oqWMYcp5nhOG3cghY9oFLLBvKkOMMjZIiUA+RU1PL2Yy1GiuRxEkAmTY4YFnwky5emVCBPgkm60WNCjwUjCyt1SaYokUOLCR0GbFRTIk+UocUdSiqlcuGkLqbzesp5L/v5JwqkiDCEDgMeWlnL57DiZ5CdpJmjg1tJMMUsp8qjAdzUs5USLxFgG1Z8JJhAixEHtTioI6vGiMkjKGkj05FB/uzGD7Nq1apzBpier5fpbI9SMDjB9PggPdnHMHfMs+VjVWzZ+oHLolCBS6DBVhD+UOXzecx6B5tXbOXgycfp7n+CSmUN5nw1Cgojxb2EiicwmPVkcqnFLahnA98e2PUEH1r5AYrFIpPj02hKBpS8kYJaAkmHKsnYqCYSGWQkP0qmCHEF8DhJDpo50HcCQ6yeWnUzCgUyhEkyg5MAUxwhQbDc2FiDnSrkfIr+iWPEpFFK4SyqqrJE+gB6m8xE/DR1iavQYUAvWdBJRkpqCQsVzKjHmOYoZtxo0RPgKpzUkWCCWU4BMMtJGtlOSSlS1OaQ5QKSqsVscFBSigRLB3FQQ0qd4XT8eWx5J9PT0zQ0LDQMv9Vk3feiviOjtPvOzV5JpVLEQ2mculoCNX48eTf9pUcpzUg8E30Ii9FOR816ampqqK6fxDHhZTJ3iEplCWnCGMw6tIobY9ZOUZNGLalEGMZLOw7qyBEnQRA9FpLM4MOJjSoACqQpkkOHET1mVFTMeDDjIUQP4+yjkaux4MNFlhxx+vgN47yMBh1OGvHSjo8ORniRJJPUsA4TbnLECLKXIPuoZA1WfJRQiDBAHVuw4MOAjTxxjDiIMYoZL3OcIk+83HczRhu3oMNAlhgZ5kgzhw4TFSxjjL2YcZMgiAknElpksszRg51qJLSASoEMWgwoKGSJLA5p1GOmklW4aSRdHmgYY5R6tmDEgcI4CSZo4QYs+IESBqyLl8jm6S9vv45TJIcFHxb8aKQSqWIDP7nrAZ55ZDdmvWMxa6ihoZ7uytOv6WVyOp04HA6S+idYutLNX37lS7S0tFyE39Lf3SVXrLz00ksX+xAE4V1xdtlWq9Fh0BuQbCaypSAzci8GjYVa0wq2GD/NYHY3pyMPEk3NYvbYiMfjTIxNMzMeoSc/Quh0kfBMlEK+yLw6TEQzzFzpDKqqUCSPDj36hA1J0ZGUwozMnCYZTZItlGiR1qGoMhI6IgyRZIo4QcbZQwPbqWEDKaaoZg1FNUsyM0VOfZyQdBK3qRq3pZ56VwMD0/8vI7zAEj4EUomkNMl46SAzHGeOHjLMI6FDg44Qx8kTZyWfYJaTTHKYWY4zyi4c1JMoTWFUXRhNJuRint78o8yrwyy17sBicLEn+c8Ycg4eeXqerVdsxeIwXHCy7nvV+foV4vH4YthbbWW5HypTIFSqYl3bLXQd2ceuI4/jsvrx2WpJzGfYVPthTsw/SaTYg0XxUedrZmYuSLFUAEXHLIeRybCUDwNgwoWpvCIRYYBq1pRn5bjRYyHFTLkh1sIpHsJLOxZ8AAzzPFa8tLGjHJR2hCmO0sz1FMmhUsJeLpSdNDDNEQZ5BlDLzbYabFQv9rQM8FQ5DM7DUu7EihcTHrJEUVGZ5QSznEJCSxPXUqRAlCHauRUHBQCiDBPkAH38hgzh8gygUabopkCKKEPM008tmzjCj3HTQokCCYIEOUCUYWQyFEiRZIoMc4wARhz4WEIbt2DBS7G8UiOhxUUTMmk8tBMniAYtZjzUsIEieTKEcdFEhjAmXExlDxEvJjH1NpKXPGxe9pH/yhqqPM2KLU2c3H/onF6mV6ZPf+qzH7jsChW4BIsVQfhDcXbZ9r7/8xheeQ1XN/85cqnA8NAgFrUKh8mLqqpUSO3E9Ms5M9WNmjXRe3SAqbkIBsm0kH5ZaqM/Mkw+n+Wk5kGaNNfQzPswSx4SSpDh0i56eQw3zczQg65oR6t6yMuTOKknwxw54tipoYKljLEHNy20cQsqJbLMYcCGBS8myYFGNZBXk4RyvSS0cxTj7bQbbqBXfoqEOoVBsRKmDws+HNQt9gtMcxSFAnpsJJlkD99hJX9EHRuYpIvT/AYfS9ArFpLMYsm7mVS7iEsTNBg30Gl/H+PF/dS4WrHpvQyn9zNzcg9rNi9l24fWsGbttbS2ti6+vn9Icfzn61cIBidQ05ZzGrczxShavYamhmYiE2lOzr5Az/h+ruj4IEpRpdLSwlrDrewc/mfUgobKmaWkUimsSjU59pTn12wuZ4fkMWDDTi1FCqSZZZaT+FmOFv1if0eeFOPsI0OIAFtJMUM1a5innyPcwxh7cdNMnjg2KnFSX96iHEdCQkXFST0emilSoEQemQxB9hFjnBJ5athAjBE06OnjUSIM08jVWPET5gyDPEWaEEmmaOJaGtjOHD0MsJN5+vGzAgM2Ekwyxi5mOIaXdoZ4Fge11LOZInkc1GDATpQhcsTQoidPghIFHNTTwo0kGCdDGAe1LOHDlMiVt3EvhOMlmMBKFXGC1LGJKlYQY5QCaRzUMc1RatmEBg1e2skSRkKDCSclZOJMkCtmqDR0kEtHcFv91LibF7OGTu4/xB2fvoHxsSBH9z2DnFcuOH36UiaKFUG4iDZsWMe/ZO+ltnATAAadEY+7gmQoh6qqzGb7KepTdLqu5vTQbvIaD9Z8AI3lFEur1jAx300uG6dPfoIkc9QrW6lWNuCXlqGTjGgVM2toZpJDnOA+UsxyFV+hWMgyw2nyZDChx4wbCxUUSBKmj3Z2YKWCKCPkiFNCxijZKJLHRwfVrGZOc5x5ZYCZ6SYc+jqqWEmQQ8QJ0sS11LGpPD/FDkAHt6HDTJCXaeRaQpzkID+gkSsJcZIM84yxZ2G7p6RBpxqxqdVUSstw6P0cy/+SnCHMtupP4tE2MilvYs75MpIlwomDg3Tv6kdv0lDXvnDm/nam1F5uXt2voCgKM8EwbnPLYqGiqirj2W5amjvxenwsX9fB1O7TPH/sQTy2KqJykMnoED3xnUxke1FKJcZzR5DVDA4CdHA7ndyOjapyUJsePVb0WJBJ4SLANN3IpOjkdjQs5IVMcpAEk+Vi+Tjj7ENBponrGOY5wgyQZAaFIu3cgkweF01EGCRDBA1aVErosKBSokASmTzzDGDBS4IgaUIYsLOBP2OCA5zifmY5QQmZHFFSzFLLBvwsx0cnRTJ4aMFFA7OcYJoji9OVtRjw0EaJPDaq8NBejumvpYb11LGVUV5ihmOLu5428udUs56j/Dtt3IKflRznXqIMUc069JjIEmGWk7hoIs44SaZp51ZMuKnAhkwamTQhehnkaVq4AT0W9FhJE8JDK8f5OQkmqFRXcGb2AKVMhHzhe5iMJlrqOllSv4nQ+DDjY0Hu/NhHueND752CXRQrgnARVVZW0tzahDSQZSDUhdtcg6TXMF8aYii0F0VTxG52kQhHGFJ7cVpXkLFmyJqmaPAv4YXhR5lPJ7AWa/GxgjZuIcYoGTWMVfUjISGhw4gLHVZAy4zmOCWlQIEUQzzLCj6JERc5YpTIUyRLgTSneIBJDqJQZJIuKtXl2NVa/CzFTi2SqiVY7CZbyNNcuI4adSMFcrhpoZ1byRCmSBYvnWjQEmGQeraQZIIiWZbwwfIlp32kCaPDhE6jJanO4dEGSKqz6DVGktIE+eIcrRWr2ey7Da8pwFxiArlQoDRTwdHgST62/aM4LRX0jL3Mrx57CptUydYVN7K0vv0PJo7/ldkrqwPXohRVDLqF/CpVVTkZ2UnRGmZp/UJPT01NDRs3bSQ2sZ9x8zN0s5fRySksJT8VLAMFdKqVCQ5hwoEOA25a0GEC5tFjwYyPXh4kT5wWbsBJPT38mi7+rRyYJmHAjo1q0oSY5igRBimQYpKD5Iijx4wZJ3mSzHCcMH3IZIkwwAjP08kHKTGHHiuglnesHUcmA6iYcDPBAVLMME8/Htow4sBOLVWsKoeuxSlRYIL9WPHjoJYYo+ix0Mz1mPFipYoYQ2jQcZAfoMNGlgRj7MaIAz9LsFKBk4XCvMTC2JcckfJk50EM2GjgajRItHITAzxJiB5sVJYvi00zykvkSS42BZeQMWLDRiUFUkhomChfUnLRQJq58krSfgZ5hkpWUM82ThT/A33aQkPheiwaSzlO/17qagMc3dd3TtbQ2RE1l3PBIooVQbiIjEYjNfWVeIw1WIpWZoJDJDJxYlIMrWTDo2nCofoI5g4Ty8R5Lv59DBYNNZ4W+vYfwZdfhSVbT5QDtHAjLhrKDYwnmKQLHcbyiPsibpqYZwBFKWGjejHrIcE4PjrIEWWcfYTpI8EURux4aKWOLUCJGY4zwktUsRodBvJKGrmQQ6aIrOapZDkxxso9B1kKJMtTZVvRoCdDmAxz1LCeIZ6hnVupZQOnuL98fA3ktVEcGi92tQY7tVzl/AsqDK0ks1HcBitekx9VVQklxsgoady6VgKuEK1Vq0lkI0xMBtlo+xM8aiu5ySmczVXnzCV6L8fxn81e+dXdj/LEyT4SGQOyJBEuGhnPdlO0hrlq7Y5zElDRF6kNVFEyZgiPpQkUr6FZuR4rfnLEmeUEEQbRYSXI/nLx2YEeMwpFZvg1QV5GixEJDQ1ciYSGo9yDjWpMuMqN2n4qWIKHNmrZwBy9hDlDniSgRYuBGtbTxLW4aSZDmGPcy2keJsUszVyLnRpKFJiimxhj5IgxTXc5dE2PBS8H+Wfs1NDC+zBgx4q/XAAEsFLJHL3EGKGCpeXLOSP4WUZlOewtTYggB5jlJEYcpJnGhIsiWUaZY4ojNHEtWozUsIECKby0M8pukuVU3DH2YMaJjRpUFGY4hg4jVvxo0aNBi5tmwpyml4e5iqWoLJQtVirIk2QJH2SGYxzlnvL7t4SLBiQ0tHIjOsyoFGkwbEOOaagMLFmM0z899AyVNhgcHOT48ZPvmYGfolgRhN+zN+qd+K/l+zPcuuI6amqi7H3hEDXWJei0DrKZHJH4PMPqAZxKA05dHVk1iC7txh734C9uJFGeo6IgE2OMLBFUQIeJPAlMOHHSUN4NcZoIIwzwFF46KJBihBcJchAJcBIoT4Ntx0ols5xkgv00cBUb+UvG2c0YL5NmlhRz+FmGo7z8P0UXMhncNGHBSwNXEWGQKMNYqcRBLRnCWPChUEImjRkvSaYBUKQi2qIZs1TNpHIMPTaGw0eQTHYkJELTc9hsNiLZSZJKCJumErPVwLxOi4SGY8MvoUm5WVl5MwADodhieNwfQhz/+Pg4g4PDZOUU49nTjGWmUDJ6OmrXs2LZRpbW33ZOoaKqKoeHnmW82Mv0mTSd8kdo092KpmBFJlMORbOQI8Y4e7FTxxRHmOIwKioxxlCQy9uTVaY5Qju3oVDEx1KyzDPNEZq4Gj8r0ZSHBDqoJUeMYZ5fLFQ7uJ0qVlIkixEnCkVW8yf08lB58OFpzLgXZueUtz4rFLFRjRk3ZjxY8RNlhCjDNHMDKio5YuWtwR5yRKlgGTFGiTKCHnP5y0KaOeY5wyDPkCNCBUuoZQM+lqBQLBfa80QZppeHaeF92KhEJoudGpJMo8OInWoqWIYBC3mSRBnBio92bsNDCzrMJAjioJ4RnucMj9HDAyzhDmQMC/9dKGGjBjPjuGhELr8mcUbRY2Ws3DuUYBK1JJHIRAknJrD5l7HCcxMjY4fpH97NT/7pVxCufM8M/BTFiiD8npwd0/5GZzayLLNq1Qq6XvgVhwZ3kp20EJ8oUaFWgKzHXHIxLT1PVo1SI62nno30pB9mMHmAtcrnqGQ1dmqZ4gh54mjQY8CGi0ZUFDKEKZAmyTQJxkkTQoseMz7G2I2EDhcN5AliwYu+HJ2+EAvuw8cSJjnIFIfx0k4zNzBBFylC1LOFLGHsVNPMNeix0Muv0WOjhEyRLA5qCdGDFmP5THWOKCOE6eNlvsc8A6go2KnGrbbhpI6oOkKCaZJMclT9BbPZU3g1LbjkeqL9o9iqwGTWUWlo5mDyXiRvlF889116R4/gUOs4pHmIVsdm3OYaZoJDi+Fxl1Mc/1ttDn7lNOrlno+yeYmbSe8Izx36DbHUPNXuptcUKi8ef5j+0EE8pgA+Wcta251kkgUktGSJUSCBFgMr+CQgUSSLGR8THCBHlDo2U8MG0oQAidM8zAnuI8kUbpqRSVPLBlq5mQJJXDTiogkFGS0GGriKcV7GjBs71eRJkCWKBgMuGrBRYBV/DGgYYw9p5pFJY8JJBZ00cx0VrCBPjEm6iDFaXll0c4KfU8UqGtmOj3WYcKEgI5PjJL9kkoM0877FgYjz9NPLb7DgoUSeZq6jge1o0AISWcKkCeOjgxPcxzRHaOTq8olAAD0mAmyjhk04qcNOdfk9N8ksxxniGVSux4gdmSxxxqhkNSlmylu1tVSwlCI55hlkksMUSGHFT4wxYgyRYIJ6rqSGdWSYo4rVFLIlTuefIzo5RsDXiSRpMBW9RCayeBs3sGXFre+ZgZ+iWBGE34NXfnic78xm+451pFIZuvf0kMvITIXGeGlwF/lhL23qDlDs5EkSkk6RkqbolG7Hq2klXBzEgIsiBRwEcFCLkzoseBlnH5v5KxzUIpNZjPwGsFPFDEdp4mrq2cYYu8gwhw4zNayngk6cBBhnD5McZp4zqKho0C2eDZ7ifhrZjp1qLHgwYKWW9TRzPW6aytsxF7Zu2qgkzBlsVFIqbwtNMk2Yfvr4T2TS6DGjQUsdm9FhIsYoSrlboZ1bMWInwzxm3KSUOaalo7QaN+DQNSGhY//8zxnO7KeluJVa+zYktQULfsKhISZi99Du3oZR0p0THnepx/FfSIF7vsecnbT7ymnUAV8HAecKHnvpPn7x3D9w86ZPEvAtIZWL0h8+xGBs/0IEe9aGUVODRtKRl+NoUMkQxk0TVhb6npq5jh4epJZWcsSY5wwWfEzRRY4oRlzUsI4efo0eAy4aSTBR3vo7jBEHGnRkCKNSIk+ChX4TJ1WsRo+ZLBG8tGHESZEseqw4qWMZHybNLDOcoIIltLODOjZTx2YUihTJUMN6RniBAXZSxRpk0vTzJDkSrOTj5T4b8LOMKlbRy8PEGMWKH5AYZzdajORJ4yRAE9dhxF7OT5HRoCXFLCZc+FlOiFNEGaWK1QTZj50a6tmGDiOp8irLwnuzHgse4gSZ5QTL+Ch6zOgwkyOKhzbSzKHByAgvEaKHFNO4aMSMmywRimSZ4wwNXEkNaymQWNzWbaMSraJnILGLU9Nr8JhqUJCx4GN149XvqYGfolgRhHfY6314wMKZzVMH7+ObX/onHGYfxqKHQlYhGs0znQ2TK06hx8EMp8qx5c3UsgGDamdM3cdEuSmxQIphni3HlDdiwk2YPuY4jRlveQk8TpoQThqIMEiOOBUsAVT8rECPlWmOYKeaerbhoQWZTHk7aIFGrsFJoDz7ZRdj7MKClypWMs7L2KmmnVtJMIGTBvRYqGAZPTyECQdWKpnmKFoM6DDTy8Plx1XRyqcWI8bnGSDGGAUSTNNNE9fjZwUVdDLLSTLMEeBKktoR5tU+ipEUg+k9RHITNKpXo083MT43iYJKjbGBJfYb6cvt5MTs03TWbTgn5fZSjuN/swL39ZbuXzlp99UfTrW1tXxsxxf41csxumL/wZi2ltm5CShpCE1Gcems5LJzVCq1zMbHoGQsN5kuXFrLMA+oaDGiwUCEIaxUEmOYLPO0swMtBnLEmKIbfTk5NkQPEhIuGtGgwcHCh2iGNBISRhwUSKJBjwZdOQ/IjAUfRXKYcKHHgoqymC5rxlXe6rsaJ/WoKBTJosWIDiNt3MI8A8QZoZ5tzHGaECd4iUHq2Uwbt2DGg48lGHEzy0lk0gzxLHrMtHMbIU7RwQ4seMq9IjISGozYcVDLDMfx0s4IL5ZzZdYxxWGW8zFqWI+uPEcozRxpQgsjCqhZfM9oys9lowYPrWSJcooHUFGJM06eBFr0hDlNmnlM2NFhwkZVeQXpIB5asFCBBOgxU8NGZjnFzqHvc13bp5EMRbzm6sXm6rPT2PVa42Ko5OWywvhKolgRhHfYG314JBIJQqMpMlMmKpzNLHfdRDxeIJGbRy48yjwDeGhlJZ/ErHEhSRoGS88yyHNYqWAJd5AjyjyD5IhxhHsIsI0YI7hppZ/HmeEolayiQJIieebpZ55+qliJl3Z0mDDjwU4tUYYIsp9aNhJnjGGeo51bseJHgxY/y9CgXcg/wUyYPvTYKJCkmrWoKIv5EVnmUZAXtjXTS4yx8rwVHSf4D+KM0cjVBLgCO1WY8aCi4KWdKbrp5zF8LKGKVcikGeWl8pltkYw0i0XyMpmKcbL4OGk5TgNXEtBsx6nWg6phpnSS0cxJlIKWZufVjGcOkzIGF4uV3zWO/93Ia3mzAvf1lu5fPWn3fFwuF9tX3c7z0z9gamKGwrgXj9SCP1LEYrJzMvkoUn4ctbTQ25Qjip/lWPChQbu45XYhhXgYFYXmckFZzzZyRJmkCyt+AlzBBAdw0UCGMBISeqxYysME04QWc0lAokSeEnm06DDhAVRgod/KgA1QSTNHitnyjpyFgYZaDJTIIaFBhxEVFS16/CznFL+iklVUsop6NpMlyjRH2cs/YKWCJNOUyKECJrxo0SNB+RKqpRyvL6NBV/7eQlLt2cubC/OMEgQ5wCi7sOKjjk04yg21OsyY8RJjhCD7F3+Ho4wywWECbEaDrpyIm0ImQ4op6ti0EL5IjjB9hDlDhnm8tNHOreixMEcPUcbwY8WInTgTBNhGI9dwRP0xA9MnSJlHWdW2kvnUNL3jBxia6KNUVNDqNIvTty/1FcbzEcWKILyDXu/D4+zZzdHT+xiePcUq/Z141QCWdIBEchaplCGgXoWLZoLsp55toDSRJcwYe2nkKtrYgUyaEV7EQwt5UkQY4Hn+JwWSNLK9PPI+yRi7CNGLnRoc1NDKjQvbUcvnoxZ8aDHSyHa6+SlJJsoTaitp5Oryikc/WWLoMFAiTy2bmOYYgzxZDucqMMdpimTKfQxxvLTSyQdIMEUPD3Cah8tbWDXUs5UVfAKpPE03zRkseHFQTycfZJaTaDGgRY8JF1YqSDOHGSMyKbSqlhrWESlOYCSDHitnlKcoKXm8tFPDejKEmUkPks1n8FnaSRdOUCzJaDW6txzH/7tckvldvVGB+0ZL9/l8nlxGRqcxLM6OOp+J6SCH95zCq3ZiUGVCchfJQphivECRLDpGMVFBgTQmXIthfpN0MchOguzDhJ0SMmbci4mqIzxPlmh5+7EbB7XUsgEXDYDCBAepKvdmqJQWN9LLpLHiR0VZ7HExYF0YmkmBfHlLc54UQV4mxUw5qySMQgEDNsy40GNFUy51ciQBtZzHolAiv1ggGbCWf24WA1ZMOEmUf+d1mPCzHB1GDFjJMFfeLWRdLMYLJMkRRY8JEy6cNOJnORlCRBgiyMtUsQodZorkCXOaEL3MchwJDXkSFMkxxi5UitSzGS1GghzAgpcA2+jgA8xytDx1OloemOgkR6yccNtCLZuY5CCTHKKBq0gxTZg+aljDaTwMJ/eTyk9QNVvBo/P3oEv7aDLfgEXnJiNHy9ub76GuNoC+4dJcYXw9olgRhHfQq2PPZ+Pji2c3RbnIicF9VBRX0WS7gkhxmJnoFNHiNBp1oe21g9vIMk8fj7KVLzHGy1jxU8dWVFTCnCHJFHqsWPHhoQWFImlmcVBDlBHq2EQdmzjFA1SxGkt56duADS0G8iQwYC0vb7spkuUwd6MpN9v28yhmvExxmBFeLKfYRjHhQoepPHRuIWHUTg3zDJDgAAoygzyFTI4CyXJfgoSuPOG2gavw0lZOJ02UQ7/myztFFhoM5+jBhBsrPpJML26RNahVpArT5KQMWTWGl/byTJUKcuUz59P8lgDbSBEiV4yjKRRJpGOcGN/DZPL0W4rjf/UlGZPeRiwe4sD9p97x3RQXsjpyvqX78fFx9uzeS3fXEfryYXzO6sUz51c2054+fZoHn/9X5LwesGGmARsGPBSIM8YcfcwzgJVKPLSSI06GOWY5QS8PAxqcNKBFt/DakuAwP6KK1XjLlyQqWEaY02SIUM9WooyQZo4cMaxU4KJhcaZPkTxajExyCNCQI0GQ/TgJIKHFhIcSMnOcZoCnyo2lWwiVQ95sVCOTxkUDRXKLA//yJMszdLzllNy1FEgzxRHcNFMggRU/Pjqx4CNDmCAvM8ZeYgxhxEoFy5jhBE1ci0IRgBJ5skQpkMJDO7M8jJN6fLSjYxUWKhllN7VsooJOxtjLFF04qGMJHwQkogxhxIVMhgkOYsRJiTwxRqhjI2Z89PAAdqpp4QZ0GEgRIsQppjnCNEfx0s4cJ/GzghjDpAlho5o4YxTJocVAXDOBVevi2JkD3NTwJdZVvv+c36lm+yZORJ5i74l7+MT7rrlsVlVAFCuC8I56Zex532QXe44+uXh2o8fMQGEMj9LJRKp3YTBcwYCqlqiQVpBQp1BRaOVmuvj/OMT/V+7huI4gLyOTJUMYA1acBLBTvbirYoinqWETJXJM0U07tyKhJU4QDy0YsJXPFBcSHUBajCGvZCUeWjCVh9BNc5wpfoWTBpbxYbSYSDFLlAHC9COhxYyTAZ5enNvSzi1kidLDg8hkqWAJVirRYaFIjlFeIMEEMxyjijVYqUCDnhmOEGEQP8sw4kAmvbhjqUCqHFKXx4IPCQ1pdR6FIhr0ZIlgwombZqpZyzgvM8YemrmOHDGiuSGmI/tZYfOx8eZlFxw1/spLMg2BpZwOHnzFUrqEMpfjx3f9Ev+335ndFOeb63M+r1y6P3bs2GIxtdHzSVJjeioKdYz2HmFw7B6uWruDjpr1HDt2jKd2PkE8H6KdW2nhhvKqhJsccaIM4STABIdIMYOEFgteTvEAYc5goQIVBReNOKhBJoNCiVlOMM5edJjwsYQ4Y4uXdzLly0bNXI8FD8M8R4xRkkzjpZ1x9jLGHvLEcVBPhEFSzJAjyhLuwEoFEYbo5wlK5GnhJqY4TCVryDKPgzqSTJAlgpNGkkyWQwWNRBhCAopkWc5HGeYFbFRTIEk9W2nmfeSIoKJio4p6tuIgwBA7meVUOctloehu4SZAJckMSaZRKBHiFDFGMONlhmNoMCCTokCKQ/wLa/hTJjlIE9fSys3IZJimGyt+VrKGGMMc4+cc5d/Ll0K9GHER5GWauJZGtpdXn8x46cDPMqz46eEBaliPnWpmOYGDBqbows9yIgwyxLOYcaIYfLS4NhCcG8KUqobK1/4eedRWbNJ5vnGJE8WKILyDzuamPH/vs6TDJarym1jhX1jaz8gJrJKHKlaRz2WIMklRzeOmCRuVFCmQYrq84uEgxAkUijipL2eUzJMrz0tZOEPNIqGlQJI4QXp4CAtuciSY5CAqCnOcopP3Lw6PUymhxVg+a30SHx1UsZoiORRKWPCiwYgNP/MMABqs+HHTRBPX0MdviTNezmA5hotGbFQxzxDj7MWAjVV8Bg+tzHICFwGcNCye1WaYY5KDi1N7bVQTZXgxAj3KKBYq8NKGhxbiTJAmhEwWCz5KnKKJ6zDhYJ5+7NQgkyVLlHq2kCbEDMeoZi3TUjeZUoTP/uXH6ezsvOD/hmcvyTg8Xh7be+9rltLHE4c5NbWL+37xS776P7/ytn9nzjfX53zONgfPzs6e09+SSCQ4lDyKKe9je8X/xano07zU/SiT4zOc2BVkKP8yPjpYwh24CKBQRIeJBJNUs5YmrlvsOnLRWB4o2I0NPyWcBNi6uLKy0HviZgkf5Di/YIinFwuZOBOM8zJTdGHGQ5RhVEq0chNj7Kabu1EoUkKmipW0cTN6zCSYIEQvY7xMlBF8dBJnHJDYxF8S4hRO6qlkJX38liAv46COE9xHlggadKio5EmQZBIfS2nlFnRYCXEKPWZMOGhgOwYs5IhiwFpeZUzSwJXMcYp5hhhjN35WcJz7OM0jOKhbCEAkTYqp8jRlK1aqqGdzect1itP8lkF2sptv08L11LCBJBNEGSHOGBI6ZjgCSBixE+IkaWbx0YkWPV7aWM6dlMgvNtBL6JDJ0swNzHCMWU4Spp8Yw4BEggkijJAkiISEBj1IOZL5CE2GLczkzqCbUfBa6zDoTBSKOaLZKSRrhq0tNxLsP4osiwZbQfiDtXXrZn5x94Mw3c61ze9bmH6slMim85QUlZg8iV+7FFnJEmaAAllG1T1IQIkSsxwnyjBr+FOijGDEQQmZAimc1OGkHh1mShSIMUqcIEYcrOdzpAgxyUFO81tAQ5YwkxzETg1F8oCKikKQfZSQqWQVdmqZ5wxFCsQZL2/dvIZufsRcOSq8UG4EtFGNh1ZSzFLDJhrZjodmenkYlRLVbKSBKwnRg4tGaliLEQc1rCPJNB7Wl3ccjWHCiRk3c+TJMM8EB8oNlzls1FAgiYkELgLkSXKS+8kRZSt/i4dW+vgtk3Sxhj+lSJYYo1Szll4eYpyXQS+jl13semk3dXV1F9Qge/aSjNuwkr3HnqIyv3Gx2Dyr2b4J3YSTp+5/kDs/NvS2J9i+eq7P+S4FvbI5uKur+5z+FqfTybK17fQc6ad76jSx4gQnwod5+dizFJUiGeZYw2dxUE+BNGbcRBlFQsJJI1p0VLOGMzzKWj6Ll1bmGUCHBRPO8gRlE146yluAVQzY2MCfkSbEKX5FGzcTYwQf7bhpwkMbOaJMcIB5zmDGgwUfUYapZCVNXIcWHWY8eGilg9sZ4Al6+DUZomjR01TOJZmjh2behw0/dWxmjN3kSeKjk1o2AZBmlkm6KFGggiV4aSPDHDJpCuWCREIizRx5kuWmXgNnG3od1DPGXrSYUDmFTKq8MymLFkM5s2ieFJPUcxWr+BRmPEhoF5OeVVQGeQotBibYj4SGLFGyRDFiw00TTgLl7eBadBiY5hgJJriev0dCgxYDMlmC7GOOHiiXIVmijLEHL22Y8VAghQYdeozUshE3LWQIE88PMpI/giJZsBrcxOQJYvEgFrMNk8lI9ZIKAg0dxOUZjue7RYOtIPyhGh8fZ/fuvcxMzONL6nn6yC/QaXUYNTYKORlKMKXpolZaiwkXRmz46AA0yCTJECVELybc2Kkjzjij7KKOTbhowIgLKz6KFMo7ctYzySFUFCz4qWQVfpZTQi6PtU+XPwAi1LEZCz5STDPEszRxDRa8xBkvB7gFiDCIATt6zNSykQF2okFPvDxJNs44WeaJ0I+EhkF2UssmEkxjxk01a0kzu9gsefa6fz1b6OYnzNOPkwBa9EQZxoCNEgWO8GNijNHObQzyNLOcwEMbhvKgvIVhc91UsBQ71UhItLGDKCOM8iIdvJ8MYVLMEGOMvBRjueH9zJS6ufdfHqbvcBCjRf+mDbJnL8lMR0bRpX2vKVRgoX9khecmguED7N3z8tsuVuDcuT4bW8/9maqqLjYHr99wEz/+/i9f099SU1PDVPIM3QefR406acnfSlQZp4jMKC+Wi9U0KWYYZTcT7MeAnUGexkFd+RJchtP8Z7lQWWi8PDs/yoQTLU/jLa/EuWlCQqKercxyglF20cjV5SGHmcVE2Ro2EOY0Y+zFTjVG7CgoxBmnge1YcKMpT2huYwdxgkxymHx5R1KEYVRUHNSUhwZ6MGCnlo04qFu89FTFGqpYS4jjRBhghuNo0JAnhaZcUGjKlxYlNFAuM3RYFrcL26lGh5EccVbxaVbwKSRUEkyW4wBK9PEIQzzDDMdpZHt5sk+GIjlq2cAM3dipxUFduc8liAELFSylkpVIaMp/n1oauRYbVQzwFF38iDB9aNAToR8bVTSXRwYkmURBJs4YDupx08QMxzGh0sKNWPERYwQDFqqVzZjwEWOS+vQW7JYmksUwc4kwLo8NzaQWSaMho5tG7xcNtoLwB2n//v088NMnKM64sOcbqNKswirXkpFj5NQ4OtVMnWEdg8Xn6C8+iUttI8gB+nmKIlmK5MkRI04QL2308gB5kiTLc3oWwq5SFEhTYiFpdGHQYAk/y5niMEv5EFoMVLOWEnnyxDCUJ7qOsRuF0mLIVSWry6scI+XYci8GZlBRSDNbjuuPMcNRjNgpkCTFLBUsw89K9JgxYCNPnATjmPFgwk2G+fKZpkKBFCZc2KmllZs4wS+YYD/VrC0vdyeZ4RgxxrDiJ80MDurRomeMXSiUkNDgppk2bibK8GIzoQ4jNaxjmBfIMI8GHfP0I5PFrHExmTtKpW4lrfJmlnIlmXiCQ/cfe8MGWaPRiEavMjJ5hnXmz7xuw6tcylNrWUbP4SHkT779pfSzc30e+OkTPH5ymBbPuoWclVyUoUj3YnNwZWXleftbZuPjHB3YzVLrTfgNaxifGKFUVPHSRogTaNCVi5QD2KmmgatwUl9uZO0pr1YkCNNLFWvIMU+WCB5aaOEGbPjJk2SaI5zifhq4Cj9LMeJAQoOVCgzYKZHHTlU5GbkRN03UsK48TDBGLTeTJsQYuzDhwoSrXEAsvM42qtGiQyaPDgtO6sgwz16+S4YIeRL46ECPvbxi4UBCiwYdNvxQDrQL0YMRB6AyTz/HuQ8TznKm0FbMuBfzXHLEUSiVdyElqGUTbeygSBqVEhp0KBSQkFjFp4kzxggv0ML7yDJPmjARBpBJo8NEkkls+Mt9LjJ6zCSZIsrwYuE/x2lq2cgq/rjcID+32NO1lA+zlA+jopAnVk7sXYoeK5N0kWBisb9slBdZzxcokiVLHANW6tjGMM8yI/dinwpg0tajM3kpRMMY9JVMnorTnfstWz9T8bZ+Z99tolgRhLchk8nQ29vL7l17+O29L1CTvQKfrplEqJtiSaLC0E5Ok2Qyd4ysFMUpVeIoNXBafQyZNA7qaeQqnASY5DAT7MPFleVeklXosbCXv2eKwxRIUlFuuMuTIMowOWLlMzyVUV6iji2E6S0n2EIFy6hnK0mm0GCgimWYcTNPP3FGUakrj6jvxEoVSSYXdwqdHRZnpaLcXNhLI9upZGU5ldROhEECXIVMllFeIEQvZlzlf+T1yGTKKbQeDFgoIjPBfuY4Xf6QKGLATj3bysPqapnhKHGC1LONRraXZ6wMoaIQogeZLAAKJfTlY9WgRYOeGKMYsJEsTeNlCVW6DjQpK6MnIqglCbN2BZFwHz+56/7zNsjq9XqWbWjh5Sd6MVtc5/1vrqoq0ewUdYEAGTn0ji2lr1+/Hr/fz/59Bzi67xnkvILeomHj9Z2LzcGyLJ+3v6V3/AC6tI9lvhsYGw6SU+NY8GKnHi0GRnkJFYVGrqKFG8gyj40qTLgIsI0ufsg8/fhZTg0bGOUlathAJx/Az7Jy3oiGKtYwzDOMsQsdJjKEyZGgQIYRnsOEC5CwU42nvPMLVKpZTR+PYMSOATsRhpjgIB3cRjVrygV1hnH2lncRRUkywQgvkmSSSlbSyR308xjVrMOKr5yJshA2l2CyPHzTgZUqTnI/Turw0bnYH2WnhllOcpJfEWAbblqIMEiWCAol6tjMDMdo5KryzCMdJtxoMZIjBkiYgAau5DA/WpyyXCCJykJhaKOKGY5hwY+KjI0q7NRgwomEjgxzDLITgBCnsFFFgCvo5dfYqUWDDicBMoTKYXk5VBQSBKljEzrMKOSpZTMhTjHBQXr5Ncv5GGnm0KInRwQTDoK8jI9O7KUqjBk7qmQinUsQzB9hPNlD5D90zI1/lStv3HBZDDcUxYog/A7279/P/b96kL1PHSE+l6OQl3HpAqz3bCI6m8CaDzAnnaFSXkG0NIZBcWOlBmPBjEMJYKAPD220cRM2qspNpGnW8UVcNBIrL387aaSWzdioJkIfwzwHLFzJrmMLHdxWjrbvo0CaCQ6gUizPIEnhZzkSmvLMkf7yrqI0EYax4qeZ65krz+6RADNu0oSws5oJDlJCZpRd5TH3VYvDDYtkFl+LYZ7DzzJmOcFxfsYq/og0cwtR4BjIESfIfoZ5HgNmtvG3aDAwxWGyzJNihhTTTHCApXyEJXyIEV5gotyI66WdFDPlFN4YPTyATBaZDAkmmKefNLNkmCfCAJZySNcSPkAmFyY5myKnMxGobKNQzKEkzByZOsV99/2Sr371tQ2yV165jZ/Y72cscpJqS8drLslMRgaRrBnsbhOy8Z1dSg8EAgQCAe740PmD6M7X31IsyQxN9NFgvA65VKBYkgnJ/SSYJcgBEsyUk1lvp41bkcvJsQvBZ/pyU+sSQvQioWGGI+XdXFUkmcBLO3osSEhI5UsPEQaZ4jDzDKJFj4vG8lTl1sVVhBP8glZuoprVmPEAEnpsxBjGRwfTHKGG9WjRQTnV1kUTXlpRKDHOPvSYWcmnaOI6kkwgk6GaVQS4ihwRskSw4C0XFFHSzKHHTIkcFSyhnivKl1EWsllW88eMs4dx9i5udc4wR5pZGriSMGfw0o6NSrLMk2WeEkVkMkhoKJBCh4Uccfby3XIRAh7aqWQ5flZykv8gxAnauRUbldjLq5YKxfIYCwUfnbhpYYTnqWQVKjBHD8v4KFr05cyjCky4CNNPniQBrsCMlzM8Qh2bqWcrVnzlLftXlYdOxtFhwoiTJNN08UMseNCoOpSUTCYTwma1s7X60+hlO6ZRma4H5y6L4YaiWBGEt+hnP/sZP/6HhzCEm6jLvZ+VtBFWR0jmQhyYfBiP1MwK0wc5WvglxwsPsEzzIVxSAwU1Q6o4W26addHI1TgJYC5Hfzupp51bKCEjkyoP/RtczFFQUcsrCRIg4aIBOzWE6ecUDzHLcTLMUaKAmQrShHESYI5p9Fjo5HZKyOSJMcpeguznCD/BQxuznITyjooCGY5xL9McoY4tBLiCLBFq2USWMFnC5Qh0GyZcxBkjxTReWkmUd+84aQA0qBRIE2KYZ8uFRwd2apjiMM1ch4c2JtjHCC9SIM1B/pk6NuFnBVoMDLETN41Y8HGIH6CiIJOmklUYsBPmNEacFEiWw8oamGeQKlZhltxUaDpJSdNMhQbwOaupdNXjs9cSmRjnyV/dw513fvQ1PSctLS3c8rEr2fXTXXhmA3gsta/ZTbF0TRtH5v+TjVvfWhruhdLr9a/7vK/ub5mMDBIMDSBlmohLBfrm9zBT6sErtaJTTWiQsJSzTiKcQYMeYzkUbZ5+AJzUUUEnkxxilpOLTbgKReboYzkfw0VgMa3YzwoO8gNKyDSynaV8CAktVvzYqKKVGxnlRfp5vNwXFSTGCMe5F7m8iy1LhAhD+FnCwqDEPJN00cT1mPHSy29Yyh3lSc4FJjm8OKtKAqzlqcdxJsqhbTHyJOnnKVw0EeCq8o45DRYqSRAsb5euQ4OeIC8DMMMxPLSRIkyJAilC1LKxvE1bRoeREvnySkeWFJOoKHSwAxNOZHKEOMVJ7i/30BSZ5ThajNSxCQ1aYowyRTcJJljFZwhxAh1GLPjKO/cWel80aLFRtbhqOsNxSuTLRUsfeRLlgLksJlw0cA3THGWablw0oMVAPVv//+z9d5ik+Xne934qdVfnnMN0T09OO5tnAzZjkUgkEswSSVOyLNH0kXhsnyPJ4diySSsc+UgWRZoUAQkkIRBEXIRdAJvDbJidnZynp3OO1bG6K/mP+k0JpAlQFAECh5gHF65rp9GNrZ56632f3/Pc9/c24mWJsEa7iS7YsCBSiGou69BdeVBNpEtqY9AjD/yEk0Pf/IEPN7zVrNyqW+XbI9X/5Ndff/11v/NP/sjO9Q/ra7jXyvymrsrbXFt83YHCHsOZV131tP74Q2pjHUZzJ5Sr1hG5XbRQbsE1I161w7vU6JBQISZp0XX9HgExCVVabVo076oNC6q0OOBjJSz4sJed9u+c9kmbFlRqtsO7JDXKy5h3Jdzwltzjl9XosiVVSomt1+9N/4cxx62aCqC2VNCrzNkwZ48P2O391kzbtqZah1YHbFqQMiYnI2ddpRa1uhUUJNWbdNKiG/o9ok6Ps/69nLRaPWp0WHRdjU4tDkiqddBP2LQc/i4Sxryu2T5tDjrn09rdYd4lKWPu9DftCeC8bWsaDOjzqEkngsjzMfX6im6KwiXV+WaxSLlcOuvSxS9rrx6wp/E+zVW98muV31Yg+3N/7WeNXvqXlodPiW5vKuSIJiI6dzbr7t7jytzrfy4a7nfz+vxWfcu/efkF81MpS4sp7fm82eyYudxV7YXbJDWadEKDAeWqtTsqImrBNZWaSg6zKi0KCtZMWjWl2T59Hi4FYo542Wv+iQHv0eF2BbmgrRoNjeUhacul6UEEeVkD3mPOJa/4dSkj8vISqgLnpUlexit+TbN9anWbcdqaGXV6ZWwpV63H/W7qUFaMabbPhBOl3yVjM+ilmrXYLyPtjE/qcV9JBN7ioA5HbVqybNiaadXaTHlHhzv0epcN8xZdkbNlxIt2e18pJVy4rosN1pJJJ/U45oCPWTdTmnpc9kUXfU6jfhFRKaMy1kw6oVytJnv1eUS9flERQ160w0Ou+3r4nTcC4K7MmmlQpkq724I2J+KGZ0srtF2elA1k21kXNegPV0vEpBO63esR/7MVo5YNa5DVUNglvpr3zasfd1vz+1TVJuXzeffseq8vnbnmhedf9DM/+9M/kA6hW83Krfqhrm+HVN+xo8fIyNj/7etf//o3VC7t8dDALzh74zUNiQGxSFwkQia/aX/0w6ZzZw1mXpKNZhyO/JSt/IpLvmTDvE3LstISKmyYsyUla8uqCQURBTllaiTVB2bIKbu9V7MDqrRKqFStTcaGCW/K2LTPB3W6W1yZbesy1nW6S7V2N3zDpHe0yWg0oNm+YPN8291+2ZyLhjyrSrtOd6rWad20ZcMlK3PROVFWOmUWuSnrJYz+omvq3Y4zcrb1uN+Y44775yXseaMBN3zTiFe0OKBLvzKVEirlZXW7xw3Pusd/Zd2sMccNeI8KTd72mwFut1uFJmNek5UWldBklwoNutxjyZCMNUf9orRla2aUFRrUF/qKpN9cxuzKOS+v/oGyWIXVulkvPPOaD3/kQ2Kx2B9rVHt7e/2Nv/tT/vB3v2pzKmNH3VF1VU02tle8MvoHfy4a7vfi+rz//mM++tef9I//u99QnzsoXZl1Y/Fl9Xo0x3Zry95m1gWNdtu0LGdTVFyLg9JSgY3SbdOCVVNGvWrOZV3u0e8x1Vo02SOpQbdjLvqcq74sbVGNTqumg7C2VZVWORmbFkKicNK2NdtWlamxYtxO79HtHptB/Lph3h4fMOeiG57VYEC72+3Vb8mQcW9IqJIyas20vJyCgm73mnDCkOd0OWbVhDo7NNgpIuqyL8rLaXdHicWTsS6mrJRSnLYsHnKu9vuIiIjrvqFBv5QxU0475w8c8DE522ISylRLS7nuaTPOO+gngwOnWs6WORc02WOHB+XlNNlt0LN2e1/Qy3SHMMIiJympXk7GuhkZG/b6UWtmrJjQYEBORqNdKjWr0y0iJqFSRlqb24x6VZ0dMtYkNdqyKmVcQqU3/O8WXPOw/1FMTFOwkW9YUKZCf/Yh64UFg3OndW7ucfHGKUuZEVevXfL2Pxt34e1Bdzx48AdOx3KrWblVP7T17VJuv/x/ft7I7CftbT3mzoH/8PXXP/WGV1477V1Vv1LMDMkXxKNJ0UhMTXmz1Oa0umif7sI9LuW+qCrXptleq2aDBTOvWqs1Mya8pUqbdkfFlEmokrZozbRK2RB8dkONTr0eUqbKkhsiIlJGXfEV9fqUq9PjATU6VGpCxKwLltywxwdkrBn0dTkZ9fqsmTHipRBk2O6ar+nzqB73a3VIVMKss/b6kGmn3PCsnZ7U7nbzLgcM+bZytVLGSnk8a6asmhBXbr+PqtPjsi9KqFSpSYPdIgpmXTDsRdtWDHhPoNGWSWoIOSwZvd7lrN8z4UTIa+ky44I2R9ToDBkzMdvWLBiUkValxQ7vMuY4CqISej3obn/HhjmLBjXba2fhcTcKz3on/3Eb8yu+9tmXPPvsnXb277F3326Pf+CB0k36jwteXzD6pwhe+d4FHX6nFOYXv/IvLWUmpYcbdSR3uLF1Vj4esZQbtjPyhKRaW1bt9Lghz0tImnZat2OqtNgwr1aPCo1iyl32lDq9OtwuKm7dnGYHFOSVqbbfj1k1bs4llVpMOy2uQrkG5WptSVk2bNNSAM8VHWXF66yYTVWuWosD+jzuhm8Y8ryohH0+otk+GRu2rWkOOU+XfcG8K3o9GETflKu134dd8iXDXg56mmJ6+LTTASrYVLLRZ21ZdEOdHo12i4gGe/VxGZtq9chYV5DX73FzLhj3prM+ZdwJrQ5osNOqiYAAmFOn14xT1oxrc1SZKtvhgJCz7aI/steHzLpowTVN9spYs2JMNFy3KRM2zFoyrEG/nZ6wZtopnzDqVfX6FGRlrMvYVK7OoK/btOBuf9sln3PVU5rsCZ+hqKwNq8ZNOanT3UGLM6dau0ot1sxIqlcWqdaRv8Ng7usykbTPP/dx3XX77C37kA2zulb7nfjM2R84HcutZuVW/VDWt0u5nUmNKqxW2rn+YW0rA7pq9qurqwOddbu9+OKLNtYy0ttrItGIbD4NGsq7zUcmLeavq9UlL2tLyg3Pm3FBgz4H/aRytcp8wbSzzvmUcW+o1qGgYMY53e63ZlbKmFVTdnlPwEcVtSrLRkx5R5kaBRltDpVC2raD1bJGp20rIiL2+pBFgxZc8bL/VbkaactqdBhzXJPdOt0VYFP1tq3Ly0qosMND5lyyaUGfh5z2ScNestt7Ja1acC2M/5td8SWblsM4/4xRr9rnI2HcfkpCuTLVdnjIouumnJbUULKRrhqXkxVTLmfbiklTThNUEgkVJp2QtalSCwrWTMtYlw7Y9m73iIqbdlqDfq0Oy9lWo1PakrTlYNsupsBkbCjLtUotDLuyMGrwzJTjz59w9J79/t4//Nvuuuuu7yh4/V4GHX6nFOZa3Z568ZNOzr7qseYP2kivac/frjba683N37VSmNFgr4KcpLpApT1VsnW3OmzTvG3rWh2SlzXjnB0eUK7WqnFZaTmZbwnzK6izw5q3rJoUV6ZOj6w1a6ZUaNLmiCmnbJgHVVrkbCtXIy4ZEpLLZKX1e8w1z6hSZ68fNeuCbavq7AihgmU2LRr0dSmjYS3VY8jzmuy1adGY1y25YcJbylRrts9Oj1t0zZyL+jyi1UFbVsy5pEJTsEqnzDoXBKx1pelh0c5/u2XD+jzmdf8/I14quePq9Tnq57XYb9WUWRdMeSdcw42S6lRqEldhxZg2h4LF+UkJlZYM2rQkL+eqL1s1LSutzZHApmmSVOear2l3R2DZFF15KaPSlu3xfpUa1et3yec12GnCW7asGfempHptjpaszzPOiYiW2DJRcdF8QrxQazU3Z2LznM7C/W5rfq9oMiOVGHSo935Hog9+26Tv71fdalZu1Q9lfbuU24ujb0ist3i49z9zfe5tY2PjpWalorxKXUWrzPq2hdUpjbXNlucm1Zd1qUrUa6saMLN2w2L+RslyeMFn7fEBHe4UwbCXbFgI1MkdcnLq9Zp13ohXAnp7r5RROduhKSkGGK6bVa4G9LjfpLclNYSbaUxEVE5OmSoNBsy5WIJobUnpcrcqLQHMXeGyp1RrCzTP8hAiV1eyTJar1eaIcW/Y60d1utuIlyy5rk6vdTMmbVpw1aBvKhJz59zwrHa3WzUpbUGtLpUalaktCRWLv+/LanQqV2vKKVVaTXrbBX8UBJQtWh3S6qB0CJJLmTDuTY126vWu0LBcNueCUa+ATnfZ4SEFOWumihhyUfMuq9GpXo/9PuqKL6vQLBIEqN2Ze8xMnnP8mQumZv57//bT/2fpJv0nBa/faerx3TiRfrvrM5VKuXT6uj2J97kefUc6u+Lq2ss6t++X1Ki8UGfLWuAUF8XNxcyc66q1WzYkLxscZ8MmnbBs1KrxILytVq7GsmHkA7iwVkHBujkzztuwqNUhE04oyGp2oCRGrddv0XUREeUajHtTlVYZ68pUigTnT9aWuLLSKmrDgoSkvG0bZlVp0+agWWdd9RV9nhCXdMOz0lIqNarSpMf9gTBbEBW3YV5SvQXXXPeMjrAOupnrk1BlxEvGvC6uwlt+w6YlbQGkWKUZMSsmNNopqQ6x0KDXl5gpW1bDoWDVW/4PzfYH9P6KfFiJFaGI+WDnb5BQodkBE95ATJ0eM87asKBcnS0r1kzZ5QPWTBjxspTRoO/ab5cnVesoZTglNZSmLkf9gmXDxrwWMP1FPdemeVPeKU2Qig1QXC4gBdrTh/RGHzU/Ny/WtKr7cLNYLAbfNun7+1W3mpVb9UNX3y7l9qYFtL/iSdFoVENFp+mxQfv354qahrKkgb69hs4NWVjeb3fPbRZTZ81uXtdasUtL9Q6F7bhzm5+xFLlhtTCl093aHdVowLpZC65qc0SZSnOuBOjaOzrcqcVB531Gkz26HQuCvoVw81sKN+REeBBUI2/LiqiEWGCaRMTFJUs38BWj0pb0e8x9ftWM0yUUfpsjmuwNLopllZoUFMLIeFqZmhJYqyCvw1F1ekx4yzv+TdijVyjSdzc02aPXA5YM2ulxCRUlu3NCZemGnZO1ZsoJv6XeTjPOWjGuzW0u+pyoMgnV+jxkn4/I2jTvsjHHw8OozoJrlo3q8YDb/aK4ZFhbPR/4FW263KtGh20btq2IKdNot1odpWlNg34rxkx5x6Yltbo0pPe6+Nbb/pd/9L/47d/57f/bmuc7TT32d937Fz6RfqcU5rGxcYX1Sn3NB5kqODX7tKiYpsheddkBne6x4IqYpBYHjHpNXs4hPxlylNYtGTLppKxNi66bcUG5KnFJVTqkQmzBTcx8zjaE667Gpnnj3lCrR17epqXABilagLetSGq0EBrsPg+JSZSa4JuI+4zNAGPLaLJLjU4x5dbNmHa6tB6ddcGnvE9MmbLw51YHJTVIatDuSEj5Xguwt2Lg53VPm/R2iAhQsjYXVzl9kmpNOmHOZTd8w7zLkhqljFoPAvM5F5Wp0uFOM07bslJaJdXo1OH2gAIY9I6PWzFuzXSpcZp3VUKFXg+o1OKar1gxrts9qnVots+Q5531B/b4UQd8TMa6NS3a3eGKL+lxv7ikTYvBCRWVtiRlJMQSPKbZHmlLCqjVadZFneHfsWzEnIuBFZMXjURNOS0SoTbSaSZ7yfrSnPJCTuPGAalUSl1d3Z+a9P39rFvNyq36oatvl3KbyW3JZfMq48Wvl8WT8tmCXC5XOm08cOg9Llz7NVdWX3A4fp/ejj1Gp64aWV9SE2t3IV+0EFcVWhUU9HlEg52i4sa9CYEO222/DyNi2ZA5l6wY12iXiIgJb1k2ZFDBLu8ND/t61bqMe0PWliptJpzQ4Y4Q5pYXE5MPu+6IqFGvqQgiyZgyFZrDKThbIn9WarLgumb75WVVabFsyFqg2d4MOUyo1BJO55Va9HvcvCtGvGSX94ZgukrnfEpChRqdQSyYVqFRVExBQVy5Lve66HMu+ZyEpE53O+8P5YNep8luDaHBW3LdmDdUaXXIT4hJmnXWrPPWTZt3VbPdWh1RocmIV0w7rcs9oZGLiymXUCknHQLgis3OtnWNdul0h5xsMEbPiueq/d7HP2NrO6s62iSZqCqteebn5//UqQdFFP9f9ET67a7PXC5nemxeQ8WAlcy0fJ7W7GGFRFYuk1Oer7ffh73g/+O6Z+z0hMue0uqgQ37apoUA1yvi3ov5OgdsBGfVpJPKwyqjUnMpQ6csgNxWTdrhYbPO2bJqnw8pyBn3hjUTWh1BzIZFs86LiGuxP9CGKyRUBL5Lcc23YsIO71Jnh7SlkoA8qc6ci6UgxAb9dntfyam2bdV2sCRvmNXudlExsy6GfKjbVWox4jWDvm7FhHgQ2BYndYfCtK9ZXIUxr7rqq4Y8Lyttzbx6PeZdLLn0Rrxg1YyUYeVqVWjUZK8u99jpCWnLkuqNe92A99rj/ca9qYAJJywbKenKmu1TpwdRcRWl93fSCclwOLh5vSbV2+ndcrYsum7aGQX5YvaViEN+So2OQIJZQEGXuwz6phEvqdMnoVJcmQjFeIrCS0a9Jp1fkc8UVEQaxVWoK6uwPFTw1uwpB+/Yo7Oz848lfd9qVm7VrfpLrm+XcpuIlYvFozYyxa9vZ9OiiUipUYFdPYccPXyX58992h/dWLW39lFlNVWmVk56afllU7lzmmL7lBWq5PN5jXYpU23TohEvi0vqco89PiBnu5QA22SfIc+55mlZabWBLFvUdjQ66GNqdcnaVKXFaEieXTZs2Av2+ygEYmjEiknzLltwVY9jomK2QprrlrVggbxu3iV1euXlSpqSvKykBmumDfqGDfOu+JK8bCn1tUaXjHVTTqrVZY8PatQXbpgRaSs63SWpXibEAyQC2TYrbdWkSs22rVu3YMO8Sk063WPWOf0eExUz7ZRJJ+3wLjs8HFYb8wE33iFnq3RjTgQRZlH7kgwU3PttWZGxIanWtjUxSTPOiSvX52E7PGzbipRxNdotGSmKTPOzvvb7r7i7+0Meu/cxlbVl3vj0W94efMa7d/7tb4vi/4ueSL/d9ZnL5eSzBWXxpMsrz2u2T2Nit8XskOnCeb0e0RDpt7fwQWd8MqwLIsFB9apytWKB7bFqQjHYb0pUTLd7g6j7eimFOxdw9EmNbvimNdO63KNCo1nn5GUkNRrwpGln3PBNOdmAnk8XBc3eHaYeKQkVKoIIfMQromIWDepwl4iCdbNh8pZRo8uKCevmHPbTuh2zZkbGujp9RrxsxEtWTDvl44GKe926aYOe0e4Oy4YsGdTsgFYH7fSE9aCvabQrrHgimuy2bcO0M0F/U6fDXRrstGXNmhkLrmvQZ68Pha+nTHnHaZ/QHDgxlZr1e0RCuS3L4sp0u0+dHqum7PdjGu2UUGneFcNeVKnRET8XJqf5gCu4rFqHvIxOd6Fg1nlTToVmasqi67odM+Wk62ZtWbVsSEyZSSc12m3YS3hJlVYd7giJ5ycsFK7atqEh0i8RrZKM1qhNNkpGY/paek0uDbrwzlVVVVWlpO8fhAyhW83Krfqhq2+XchuPJQx07zN88aT+6nssbU7q3Nn8x5qVQqGgpqncz//dD5qfX3Dypd+STZOp3rAcm9S7frdjlf+512f+QHW0WTq/ZNGgNdM2LeoNCPmMTdshhC2mzJzLNi1pd0RSo2od5l2QljLuNbPOaXebGh3mXbFmWkzCgCeNe9O6OR3uUKXNomuGvRjygyKqtCtTY8mQMa+bcVaLA/b7qG3rEirMu+qM3zMXXDdlak15x7AXgy1yQo12LfbrCquG0z5h06Jj/q5EINXmZDToN++K3d4rJqEgaTMk6RaBdjHTTge2xLpVk3Iy4sosG7RmyooxFRrc8GwY/9eYdV65ahFxSXUB6V8UeiY12OU9lo2Y8JYlN1z2Rbu830oQjLY4YMtKmLyc0eF2+3xEUp1VE1JGVWpVrl6lJpuWLOSvOjf5su3jm37lY7+mr+Wgc+dPmxpcktqZKumZ/mT9RU6k3+76jMViovGIze1VE6lLmiI7VVSVWU1Vmi1cdKHwWTu9W5+HZGw46w9sW0XBkiExCXlZBfkgNs5ZdMOAJ+3zYWumvOU3RMS0OqRctZQJ8y7ZtGSnJwJgrakUa5C3rUqbI35WuXqDvi6CCW9bdsOQ53S624pxM85ZN2POZRPetMNDVoyb8rZeD4ort2nJullpK2acDlqmttJ72GiXWNC6wLwLprwdsrOO2uEhWekQfPmOjA1pSxrssG1FXLKkL4mIyMmAfo+GSIqoaYtmnJUPuUk1Oh30E7rdq0KThCQiIXrgm676qpxt1dpUa3PN1yQ1WjdTCgldMe6Uf+Nxv1aKFehxvwFPWjNpwgnQ4pAaHc7597I2g43/dY369bjXlvWwZqsz7k3TzqjSokaHdkc12WveJQuuqtVj1Ctyti0ZUpC1KaXFoWJUQazJnPN6ym5XU1FvO59SKBR0Ne5ybXbJ6OiY4UIx6fv7PVXhVrNyq35I69ul3B7oPebayMe9OPpx3U0Denr2ln6mUCh44+pX5RqnPPLoe0xMTIltV5mZmnH2/Dn51QqFSMErqd8wkbskL2Pbpv0+GqyIBS0OhDXNSondsOiGQd/Q4z6NdrvuGeOOa7TL3f4LceVmXbRk0Lg3NNsvrtKIl2xZVa3dvMsmvCUrLWsr/PuK7qFRr1gyaMOCBVf1epcu98iHRmnLqkYDytUY8ap512xJmXHWpmVd7lKvrxSeWK5an0flZQ15QYXGEk49KqbJfld8xQ3Pu83PiYghLy+DiGHftGpSnR1WzUiq0+EODXaq1WnaGcuGveU3ZG26y9/WYr+CnBUTNkyo0BhyUrYNeI8Jb2pxUEKVpEbjjrvgMz7jo5IaNNkVLMzXpcJUodu94XRd1FBEJUTFxNWUkP0Z69qztzk/+UUff/Yf+ZUP/7qOph02ZzJ/THz9J+sveiL9067PWCymvafZ0NlhE+sXxLI3pDZn5PN525F1Y4XjTvmEfT6k1SEH/bi3/baNwEYpU2PdrA53GvailCEt9mt3VM62BgMlDslVX5HUICIioUKlRiNeCm6WrLSUdrfJ2Qran52GvWTcG6q02OdDIYvnjDXTciG/5yZ8sFqbXd4rIuqqr5pxTrVWMUkRMSkjtq3b5T3iylz2JfX6tDpkziXXPa1BvyZ7dLhDRMSkt93wnHp9jvhrmuxxysfFlNm2IWJBkz3KVJbyfIoBAsSCW+0ml6bNUatGrZrU7jZdjgV7/qSoGKIqNerxgGEv2LQY1lPrFlwrHR6O+FkxZWZdMOR5b/nXKjQqV6vH/TbMWTJszYwIJVJ1MW28ypaUdncEgX5EXIU2h3Q5psoXrJrU60EpI5aN6nafY/6ey77gos8qKNjlfbrcbcmQiz4jZcQeP6I8W20k+rLJ2Jv2Rd4nEi1OBSORiPpkh9fO/3tdD6/9pYEP/6y61azcqh/K+k4pt5GaDTc2vyhRe8zEaoVUpsH0/JiTg8+ZTJ9X1RTzD/7OGXWRHnVVTS7dmLa6GFOZ61dVGFCnT6eHLBk26hVDXnSbvx6mDPkglC06DtZMmnVWo532+bA5F62Z1O0+Xe4huBwiYio1S6qzZFidHl0+GpJyL0tbtmrKhrnArFiTsanDnToc1eaoGWfBqgkrRvV7TFzSshGrJsODKem6p9XoBLu82xE/V5o0THrbtDN2eMhhP2PGOYsGtTkStAmVkmo02OWyL9iS0uIAClZNuuYZK8bFlRv2ki0pOzxsjx9RpiLoaBIqNWu0y1VfUa5WeRD7VoRTfXENkZcIqc+LBr3qf7MlFazM3TrcIS+nVreUESf9rmqt9vsxV3wpcGkKiFgzHay2W5IabFkJLpYNd/lbNgsLXr34BXcfeJe+zt3OTV0xOdxv//59f2zyRrGpHVz8i51Iv931uRGf8srab5pcv6LTPXbnP6g5sldBxkjkNaOF15zxe5rsEVMuKiaCVofV6jbsxdIDOmtTt3ttWrQuq1qrev1iygx4j2Z7zLvsmmck1dvtA3LSFl0PTfBrKtQH59eEES/pdo9mBy24osu9IcDwqya8oMk+Gau63WfNlCptYkGkvWLKjLNqdIWpR9Q+H9JoQI0u133NoGdD0GWRF9PmiHmXDXhCTJld3uuSzxv0TXEVOgNocM1cyPQpl1RXEqlSkLUlbysA7Kq12GfQM1rs0eUu6+ZUaDLtpKSGAGprkrVp1ZQxx1VqsWFRk73WTKnWpskeEFOm3VFlISV6zHEjXtbigDUzKjSU4IY3P3NbVnS403n/XlKDnC3LRrQ5HBrHVtvWdDtmzgVZaXf5O675iks+F3hOHaq0mnNZRMSqSWthglmjQ5PdEiqt5Kec3fiiye1z9jTfqyK9ZSO75HLqRZPJV/2dn/tvfyCcQPwANCu//uu/7vOf/7zLly+rqKhw//33+8f/+B/bu3fvn/3Dt+pW/QXq26XcfvBv3aV3x0eMjow5dfwbJsem3bg+rL6iw719H3T5xjk1i9WWNhasFtbYanDUByUKtWp0KFMTmA23leyhKcNhsnFdlTb1+qwYU6PTljV7fUCFerPOqdWt2zFlAU0eD6fNRdfkZAOye4d+j6rUJC1l07Ixr7nsSzYtSlvV6351doTckXWVmhz1C1JGTDuj2X5VmpWrUemIFePh+1+Vs22nxxz1i+r1ycuo1aPdHYY8Z8gLdnpciwPmXAjcjKeNejWcWvPSVgKQq1tchawNNboc8XPq9Fh03aIb8rZd97ROd8rLWjOjXLVe7zLllDGv63FfGNkX1OiwYqKEfV80KGOjlD6dkzbjAgTI3P3W7bFqwqzzVk2CTSkRMTPOWDaiTHUQ9BZ3/xnrtq2LRyrsLXzIVP6Uf/eNX9PXftDk2ri11KaWrkY7d/aXJiyFQsFb15/5rqD4/7Trc7NiRVnruoHFx+zJfFRb5JCySJVoJKE3/y6XPOWqryrIWzOrWrsJJ0RE7fVhMWUBLlhs05aClblcrXlXtTtqyinT3lGm0qjX9HrATo+LSphzQbl6t3nUiJcMeyFM7oZFxbU5atoZM86acU5MmXmXVGqVMlLKqkmoMOjriNjlfTYt2jBnv48qpmufU6U1zHBGtLvTnMsu+qwex/R5RMpICFiMKXqViuGeM856y7+UtRWypNZc87Q9PqCY0B0PB4Bo6WennNJoV/C9ZYvvu0oVmqyaMuxFZWqMeFmrQzrcoVpH0EI1KFejSqthz+twuwf8vwz6huu+rlKLeZfNOKvNIb3ul1ApKmHCW1JGzOnR71Ed7ihpuRoMqNCow+1SRpz3hzrdrTOkXd9ci132JfMuh8iAwaBZGVCjW0KlZUN6PWjJkFpdQaS8blfkPSoKjW5kn5OOzji3+SVTq41qamvU7ohq6dvjvvvu+wtdw9/N+r43Ky+99JJf/uVfdvfdd8tms/7hP/yHnnzySRcvXlRVVfX9fnm36q94fSfo13333eeuuwf97//rb3t87y/Y03GHL7z5G0bGb8hnqCq0yuY31GS6NUX2ytrWaLd1cyED6GUt9uv3KCKu+7oVo5YCK6E6WHqhUrO0FdPO6PWgGh3K1crZDgGGlVZMqrfDLk+aca5k/UwZMeNsoORmrZjQ7rYw8q4OOS5jyIuJ6/OolDFzLmryQUXuRVplgGYV5NXq0mSvDXNWTcnZQl65WpVaZG16y78q0UNX/QMRUQ36ww23RdqSaWdKePxd3mOHhyRUiIir12fAk5YNueG5sELaq1xNQL3XaXfYVV+1YkqFWjlZ5DXot+QGoiHH6EfU6Qnj9EhYbWy55msy0nrca84lKaMlJ8WIl8TEbVpUq1ut7qBjKIbZjXrNhgXRQkyZqmL43/KYqey0xmSf+fxlT7/1Se1XBhw4sF9VXbnh1OnvKor/T16fX/riUxbOl+tofY/4SpvtjRWFHOXRSpEoe/Lvs1QYNOhZETF3+CVrpkw44YR/rVorIpbcsOCqZntLD7ZF1zTZraDgrN8z6jWd7tTpLttWrZiwbCSEBi7odJdxrzvhN8WUq9PtDaMlfVBn4PpctG3OJRmbetyvWptGu7zqn2h10A4Pe90/1e9xcWXSlpWplrWlTmtYH9JktyEvqLNDVMy62dKqioKUUTPOhaaqOHGp0WXZsAVXDXuxlOwcUwYh3fkVG+b0ecQ5n7JqxiVfkLUhY0OXew14Up0eG0HPMuUdOzykx33mXbFqyqS3LRlxu19EwU5PWHDNFV+26KpeD+j3uA2zFlxTp0evB0sQxSVD5l21y3ugJATO2nKbny8lkZertm1NxoZ1c9bMaHObPg/Ly5l2Oji8VuWkTTmtVq+0Zff7r53x+0a9anfhvZrstmJCfc1d2ts7rCVG3HvPXV6f+Ix73rPnB0KrcrO+783KM88888f+/IlPfEJra6uTJ0966KGHvk+v6lb9sNWflnI7OjrqX/6Lf+XUS6Oq4gueevmT4htNdhbeLZlrVabKG9v/Wq9eqcK4DncGVPmsMW/ocb8WhwKcbNpOGXMuBd5B0bmTsSkqasOCtJSMjRAp3xDSaaNiEnIy0hbt9SMhkydi1YRF19zwnBqddnpChzu97Tf1ur/kvGnQHzgkZ8Kqpky72wx5QVSZWPhvXi6sDaLq7JCzZc10qbm46TLK2dZkr22r2t0hZRgRXe610+OqtYede6Vux2yYFxUz4L0a9IuKWTUZEnEj+jwaaLgpFRptWw2n1WotDrroC675ij1+pASX27Roy1oJ8V6m2qwLKjSpt0NcUq3OIGz8uGP+bum1vOPjIfH3mrhyB/2kVgdlAr23QHg9RafWnIvWQ9J0d+RuHdlDMuk12zXLsvVT3pw64fh0RnNnjXufOOSnfvonvuuI8pvX5pk3rqjc6NFS2ysbi1nYWradS9vOFTkponl1uV6VGiU1aXebLf3K1FoxZtY5i27I2tTioKy0MrWiohZcNuVUwLfHg+PkXvMuiwZLfLEh3WnRdTlZbW6Tl9XlPhQM+roOdxjwpKQ6sy6Ydla1NnkZ4960Yjw8/LvV6DDheOAFRaWlxIMGZs6lsCppVKsrAPyiAfB2obSiWTVtzZRlIyER+lAQBL9b2pKsTXV6TXnHWb8vL2enx2xZMe4taUsGvNcVXzbmNW0OaXFQyqhme0IMRVxClU59Ot1pxCvGHA9wvSFLhg36umb7lak26rgpJ806b95l5epKsMcylcpUBY1VUnMg8t4E8A15Xs62So2a7THppAFP6vOoOZdMO63Po7YCO+jmtKrL3crV6nKPOReNeFmHuwx7wUWfs99HVGqx30e87v/rtE864MeLGUqrE/b13ya9uuq5U59TffgvL6TzP7a+783Kn6xUKgUaGxv/1P99a2vL1tZW6c8rKyt/Ka/rVv1w1YkTJ3z6d77synMZuwrvN7503r7tByUyzdaycyo0q9Gpyb5AWE1ZNmLJoKu+Jq5Ck30Ksrat2bSoXp+tMD0pDq1jarSLSbrk8/o8EjQtRVh/RFRWWsaSORfDiiMbmp2CWRdNO2XAe+z2PhGMe0uFhlLWSpVW7W63JRUapRWt6lRpK6Xu3gTKFV0DNwIErCChSpW2YLessmXFgisyNsMYO27WOWVqxSSsm3HFU+KSWh3U4oAN82p0aHFAQjIkSrep0RVEiWsy1nW43aBvSlsuwcOSgZxaXBldc8nn7PCQpHrzLrviSyIiIR9lW7UO1drCeL8Ix+vzsHM+7bh/os+jmuzW5W7Lhk05Y9I7qrTaMCehwpZVcy5aNqrXu1S4ZNZFK8bU6lAer9ZU2COSSEtt3JDbrPa+jr9uZWNebUtBbHzV5z/5DYlE4rvesGxtbUmvZ+U2Y5bWVlXkmzXVdNnYXLO2VlwF5nLbwbFSHXQVk8Fldlifh6Qte8n/oiBnwLvNuuCSz4mrUKHeHo8qU2PNtNf9cwUFGZsqNZYYPDfDCVsdBCmjqjRbN2und+v3qJRh86646sua7bHfj0kZC86zK077pJiEbvcGy+6SJTdUarZpITQ5Z0qgsw63lz4PKSPIy9iwZkpezoaZIP1uk5cNfrHewFIprk36PWbVpNf9c0OeV6VZvX4tDhjyXGi07tLnYTPOqdNtt/cHge6aVZOS6gNK8FELrnrbb4Umu161TgUFp3wihHvu0OKAWj1qddsw54qntLutBK+Lisna0u6oG551zK+a9LZZF+3xfhlpaStWjJdWy6NeFVcuptmCqzrdpdlei66HROeo3d5v1tnw/327GWdMORmawqLG6oLPWDerxX4bmVkvDX7KevmYjcRl//Tn/+EPjFblZv1ANSuFQsGv/uqvevDBBx06dOhP/Z5f//Vf9z/9T//TX/Iru1U/THWTUNqYusvtdTUmU4Ma87vcXfvXjG1dU8jHbVlXpSAmLq5CxpRrng4skstqdRv1ilYHxFXI2Q4IqEZ7vM+b/pW0JeXqA+9iLoj3Wkw7o93tIVm4yEfZsiJt2QV/GE65xZzben3aHZEyKhUyRHIyZl0Ie/U2KSMK8qJh5bHgmm2romIhgbY8gNZOhayfBhWawsqnuXTDL8hrc9S2NVc8ZSPQdXO2tDui3+MlncG0U077d1rsV65Wg51yMvIysjaUq9NgpxUTstIqNAV9waZqbVaMKVdrwlt6PajFftd8zWVfQsGCq5aMuMN/FqzNtZrsFgkTgG2rMtbV6NRsj4i4GecsuCovq9FeK8a12GfFuFnnRJWJS6rUpMvdKjSo1OS6b4grVx1pVZ1vsxabtRa97kj8J1TkmoqovsbDUulBjxx4wMmhb35PMlXKy8tl8mlzS9NaxTXWdopEImqSjRL5JYVsxPL2RFgPZkTETDmjTre8nAXX5GXExFVoNeeSZcNWTarRpS8wdhIqlKkK12NjEGYW9R/FtUFRa9XpLmNek1ClWrsRLzvspzWGqIdRr+p0lwN+XI3OMC3csMNDrvmq8/5Q2nJ4fw+Yd1VSozHHVWm1z4fczMOad8WoV60YN+a4Hv+NWt0WXFWgxEyp1eOqL2t18Fu0Ws22rNiyqsPtZl2waUG5aguumXfZuLe02G+nx1RqNuIlOz0pLycmIqFSQd6Ca+EgsK5crVkXStPCeZetmlSQc8hPO+JnbFo056KomB73m3XWkOf1e0LOVmADFwKVdtmkEyIStqSMez0kshc1M13uUatLMRdrzpYlqyYd9ddV67BhwaJr6uywYlSTfcFleL9lw1ZMutN/rl6vVZPO+D0T3rQQvSITWdesXSY2b/dA+7d9/n4/6weqWfkv/8v/0tmzZ7366qvf9nv+/t//+371V3+19OeVlRU9PT1/GS/vVv0Vqu+UlHv8+BtyU41u3/OEl6+/ZnLlqoOJj0pn1m1lN1Rpk5ayZkatHS74I3FJSfV63K/D7aq1W3LddV9XpVWj3WISlqwXdRCBqtrmNglJaUvmXZUyZs2kmHJd7gmsz5NWTWhzWLXOUjDfZV9So92cCzI2g0V3rw4TFlzV79HQ8MRVaZeWkrZs2bBrnhZXZsl1cRWWDNkwJ6lRlfaQ+bLfsiFxSdXaZKyrCHH0Q16walIscG0rtajSqs0hETFd7nbOvzfjXJgUbQbseTFQraiRabbkRtDTUJBTkBVTFkTAp62btccHbFkL35O3ZkrairgyK8aLcDQDkupR1CHEVYgps+CaWIC/XfOMcrUBm14MlEtqDFoVJStrTFlYs03ZtGDZsB0eFI1F1Ce6bNTcUJvrsr/ifZa3JyymBtVVtshnC/L5/PcsUyWRSCiry1s2IhNdK309EomIRWOi8TKx7aJNdsuaer2SahTkLBpUpbUEAFwzo9NdGu1UplpM0pzzTvm32h0x4Emd7rJiQrd71ei0YV6VVouua3FAQcGU01qCzigr7WZGVbEB6tDlHuVq5WVUajbvsnXT9vmI677hii9rcVCrQ97xuxZds8eP6Ha/CDI2dGjSZG9YAWXNueiCP9TmSJg8rqvRbsuaUa9ZNaXLvbLS4splbasLD+gKdxv2sryMgmJgxKIhEVH9HtPmiC1rIuKBctsUGrxqlZrMuihrQ1RZyaVXpsoBPy6mzKZlK8akLZaotU32BOv2mFaHzbssa0O1dhsWZGwGS/68BVetmdBgZ/j8boX4gFmXfD7Qfpdd8SUJlfo9olZPSWe26Hp4LzYlVARhfJekOhsWS1lJMWUO+gmF6JYzhT+wt/F+H9r3931t7B/bSJ/7gYDA/cn6gWlWfuVXfsVTTz3l5Zdf1t3d/W2/r7y8/AfyL/JW/f9H/VlJuYODgz7zb59SN3a/V8feMr80a3l9lsq4VHpBvJAknN6X3LBtzaLrDvhxfR4JN+RLGuw04N0u+pyrviKhSl7OnIuqtRnwXhPeVBSL7pS2ZIeHPee/s25ayph5FzXabdmwejtsWTHpLeXqRUXk5UTEgsDusBpd0lK6PWjIPzLquP0+Em7yGQVZ6+bMOW/ES7LShr2syW49wTmUtqLJgCmnRSXU6zPhTXV6lam26IZzPqVGhzaH1epSHtYyb/stOz1uwJPK1ejziLSl4Ih6W5d7RcVExeRkRERVaTPvskknbFu3bc2KMUMh46fODpd9yaLrkuo02KXBTjFlRrxswpuqtClXU6L33syUiSkLgsuYmkAErdAgJmnC23K2bJgPD+waW1aUqw1NT7HF2jAnY13KqD2598rmC6azow4lPyqXzIlHkwr5gnRmXTRZZKF8rzJVMpmMzGpERVPe0OKLKjYatFXuFolElCUTNlbSBj1n2jtiyuzxIyXB9LZ1ERFpS7asGvCEI/6aaadUalauVrN9xr1u3OshKqHfqFfU6ioJUreshFiHMtc9bd2MPg+JKleuxloAoaWM6nZMQS5oK5pC9lBLEOmu6XG/i/7IWb+vTq9Ni6q1qdAsH2YOaSnrAajYZI+UUctGDHneulm1ulVotOi6Ua/KydjnQyo1Bi1WQsGytPUwJSkLuUL1tm1osd+MMyVB+7CXzLts0TVT3hFzTDHXqEyllgB+61Cu2rwrKjTY4WG1ukREbVu330fMOueiz9rtvQF5Xwh4gXFd7jHpbVFlKjQqyFs1oUqbcW+EKU7OmOMyNrQ7qtsDVo0779MiEgpyDvqYFgdLWrObGUg3mTF5FxAJzVaxAV8yqNHucNDqkihUqo60q6yoFYvE1eq05sp35Xr9btf3vVkpFAp+5Vd+xRe+8AUvvvii/v7+7/dLulV/RevPSso9dKzPO69cMX15y0DlQW2xg5IVHc7knjOxfFlP7JhEtEJUmYXcdRsWrBjT7V61ui26XhKoLhkETfYoV+Ok35GQ1OsBne6RsSZt2ZxLKIgHt0+RRHlEn0dtW3PdMyIiVkwEkNb7wjh9XUbauNd1u09CdQilO2fORSsmnPMpS25oDZyTrC0LrlkLJ9tc+HNchWEvhATnmlKg34oxORl1uoN76EIQ9z2iw12WXNfmiFrdBjwREOgvI6rX/SKi2twmZdyMM+KSGu0KOPxiZk+VVoO+bsppa6ZNOyOuvCRALMgbCnbQejtVaVGhUcaGbvep0WEs2GtrdctIh5VbWTH/xHJItz0va1O1Vl3uDiLSRHC2FCdOjXYFhH/azSDINbNBE5OzWVjVEm1VyMRFclWWsym5yhWRclJbs7p3/wfa8fciU2Vra0tFotbDd9zvjbPPOz79L7Qv36GlbJet3LrL2ReMOi4n4z7/Tx3utGpcuXrV2q0YddYfqNeryR7LhsJ73RMEzTn7fTRg2d/S7T4Ndpr0thlntTpoyQ2rJs0F0e1u75NUzDDqdLcFV0ockoQKMeU2zGnQpyAfODwN0lKiorK2XPTZ0qrmoI9JqjPllIw10YCca7JXQV6z/RZc02iPsUAiTqrTaJeomGptdnhYvMSYiSoExs+aGYuuqdWtXp9xr3vT/6EsOPKGvaDN4eAs67VpXk7GRsgjKmrGinquuZD03WCnglwQ4+6TlhKTsMPD5lw04W0VmkJDEbVuSVK9vJx1MzYtueqrxrwaQhLjej1oh4dUaAwhnldc8zUdjmowoNkBUTHLhvR5xLYVlZpL0MWb9uyUUQ0GDHsx8Fq2iwGGoqWwxXwhoyd6j5mFi06svWKrclNFsuYHIgvoT9b3vVn55V/+ZZ/61Kd86UtfUlNTY3p6GtTV1amoqPgzfvpW3ar/uPqzknK/ceaTfuef/JGHB37OzrZyyUy5uspmdZXNDqw/ZGT0mrb8USLk8ku2ranTa8IJ+30EEetmLRuRs2XTkm0bomKa7TfuLWVqZGy66qmQPhtVDC08YdtaaCwOKygY8aK4CpsWRMQMeNJhP2PDbLhpZsRUmPK2RYNueN6qcXFJTfaq0aEgb9pZI15Sr0+5Oi32u8PfUKHBhnkjXjbtTBhPX7Fuzi7vccDHZK277hvGHFeh2VjII+r37lKgW61uCRUKCga8x5pp62YsGlRvh6i4tEUR8ZBsvKzLner0hXH2Uya8JSJql/ep1iKpQYOdlgy64blwg94fbrJTlgwpUyUqpsOdUkZ9039rl/fZMCdn24ZZRA14svj+e1W724MWaFulJvX6LLgaRMCdQSgcF1WpgElvWjOlwYARL3vTv1Seq7Nt1WJ+0H3p/0o8F1delqEqq7mtWzaXEY8lvieZKuXl5dKZdea27Uo+4mrNcZdWPufc1rZonOX4rLLtZhVaZKwrAgUTgdMTUaPHrPPaHLFpqZTiGw1gtoK8MlV2esJ5ny6Raau0ueYZC67KSktbVq3DI/5HNYE1UqVVrS43PCcuGaZ+Ue2OmnVWylhoagoqNKjVLS2lTk8Q3V4TV1ZaN+Zta7JPiwPiyoL6ZE5joOyWq9bqsDkX1ehUpdWAJ133jBEv6feYgrzipK1c2oq5IEi/GTNRocmW5VJ4YJvDjvpF5ao12e2UT9i0oMGAZTekLcvYlJM14iVpS/b5sCotFg3K2pKzZUtKTHkQzT4f7NVRUbEAijxuwpsioia9bcuqpDrb1u3wrtIBoF5vgMrdbsizbnhOmyNWjNrnw95xwphXtbszSICLNOiESsNesGFBQqUZ5zTZo8GACW+KSQTAYoOCglQuZWl73GZyTj6XNzw44vz5844dO1a69r7T2vwvq77vzcpv/uZvgkceeeSPff0Tn/iEX/iFX/jLf0G36q9kHT/+xndMyk3EkiqX9mhPHBLvjhq+eNLOmmIuy8GWhw3NnDW+/Yb2/O22rAYIVAeKo/FiGOEeNeFrcy5YNRXEqeXK1TriZzXaHZgPL1k1YcC7DXjSqknP+QchHK5et3tVaZVUbz1wGYY8q9l+maD/uEkcXTdrxAsO+JgqraoCgOumo6fJbi0OqNaq17tExGwF1023+2xaFpeUUCErbacnQm5RJvBVIuFBVqPV4QDsahJXhoIy1baty9nSbK8hL2h1wLwrYdJzyUE/JSFp1jk3PCsvbzVkAFVpddjP2uVJ867YMKdctW73GfRs+D132LIqL6NGZ4mvEZfU7ZiTfseEN+3yHuVqRYI9ukgEnrVu2h1+SUHBZriJ9zimSqsxx60Yl7ak3VEFBRPelDIqpsyyIV3uklCtRrsNc2ac91zhv9e4PaB5q03dVsLIG6+KxaN2du81tX7VE7/w3ctUyWQyjh8/bmRkyPaVnCfa/p6mxj2uRF81tPy2TC4tUdhwW9lPmN8eMeFN62ZLD/c52ya9HbQqSeXqlAUwWXGqtFvGuoK8imAPL1MjbcWs8xr0i0nYsmLDgk3zpp3WZI+MTZsWbFmR1OS8T4uKG1KrRpecjFGvKQtTlYRKm5YN+rp18w77GWtmgjh725YJtTp1OyZvu2TpjwStU0x5oEAXp3Md7jTvsiWDutxjxCuWjeh2r3J1Jp1wzdM2zEuosmJMh7s02xdoxWuBvLvfsuHwOe60x/td9TVLBuVDyGB1WFuOec1hP63L3aISlo2Iiqmzw7Y11TpUBqt1zpaKMG2q0mrS2+ZctmLMbu8PSdYXbEsZ8N5AFJ4RlwyNX1aHuywbsWbGpsUQ6NhrxCtWSqGgayo0uO4ZKyYlVEgZExGXl1Wp2bIhjQZ0urPkPFw1YSM6p7d7p5Htab11t/ncv/u6zs7O0r3z263N/zLr+96sFAqF7/dLuFV/xSuTyTh9/LKBxif/1KTcbC7jxvgVe2oeMTO+4MC9d7s+8knnFp9xuPG9mit22FP9iDPLT1kq3JAoVKnTLy9r2bAZ5xzyk2FPnLVlxaZlRGxYkLOtSqsme0TDqeZuf9uscyadlJWWUK1Ck90+oNOdAavdZFGPPX5UyrBxb5Wolhnr4srFlduyKqZclVYxZWp0WDakTI15l+32AdFg583aDMGEo6adseBqsF3fUKE5iPmKeSUzzkiotMePiIpbNR5WUGtiOkXFrZhQqTXYhSNhGrRsy1oAZZ2QsWkhsCZuQsGGvCgfOB21OhzyE7LSRfCaMQuuIRK0EwMWDYpLanObRjsV5G1Zs21VvT7N9kmoUqFJl7skg/PktH9bwq+f9xk526ad1my/hCqNBpSpMu2My54y7bRqnWp16vOoG76p14N6PRgeckdlbVtwzTVfc8M3laePuS/+N1XHmqxvLzr11tcs1Z7yoerb/8LX7k2N1ctff8OZN69qTB8WTW44sfiHljen5TcT2nP3yGfy1rPflC0B/XbL2XbdM+LBZl5MpY5KqlOhXrm6YOudkzKsSrucbZuWREXV6TXsxW+xw++11wd1O+Z1/9wlnzPvsraQETTjrHmXVWkVV27GGRdVhJyqoq6i2NSsmfKOGRdUa7UU9Ehd7jHtHe2Oqtcf9DVFlkiFRhFRKaPq9YqIGvOqtFUzzqjQ5JxPh9+rXsqo675ecsAVrczdcjJWTajRZd1MSaidUFVy1N20UJer1etB1z1tNLBgytWUNEAxSWnL1s0Gt15VIACftOBqaNbPOuUTQVCfEBEVl9TvEctG7PFB1VoNe6lEsF03YzokLBdXRkUoXqvDLvhDG+YNeUFELPBoLgSReqT0OSxODnuCzuyao37emhlDnrdqUsIDIpGYbGHLZnROY2KHk4t/pL4t6YN3/RdOjH7F7//e71saz3zbtflP/tIHvuv2/O9U3/dm5Vbdqu91bW1tyaTzqpMNf+zr2VxGJrclm8vIZfNqy1rls2nN1V0euuMDXn7nqxZmb+ituFNdTaOKpeYirTUyrbGwV1y5vJxlw/KypeZg1YSECkn1YsoCPKrXsiFR5QoBajXjvGWjXvNPVWhSo0ODndbNystacC3oL4qo9FWTxryu1UFbVoJ9eSNkidxuxZhm+0o31VyY0iTVhX35nBUTZl0w6W2Vmg14t7ikWRdKDcywFy0Z0miXiKghz9m2Yc20ZSPq9QaHR9FlcjNPJ23JlNMWXXfVV6ybk7as090WXQ/J08WpRrkaB/yYMa+HbJnJUiL0ulk5W2p0l6zRMWUa7NTlbkXxay7wPnJyMurtkNRg1jlxZSLipdfV6qBNS/b6oDWTqgLO/ILPaLJHowG7vNe2Dd3uUa0dBTd8U5UWOzwkZys8ZuoRCQ/kimJq8OZZ0Vy5eLZKPp1yuPUx2da9XvrqSUeOHPlPPoF+q8Zqa6lRZ/pBd9f9rBNzf+TE3B/ZkXvYQPRxVbEW0VjCVPZ8AIvVWTVZcrcUOTZ3OOsPRCVMOimmTLMK8UAkThm3GcixQ563bd0L/kdLBlWolwpxBDf/bo74Od3uddVXvOVfyUir0qjXQ3Z5j6xNcy6adyWE8d2mTK2sTeOOm3NJk11ytg15QZXWUsp2XIV2d1ozUxK1FuRd81Vpy/b7aGk6WRNgh7W6dLlbs30BtrhuziWLrquzw2P+kQs+bciLGuyUtelaSIguOoKuS0jqdHeJGbRiLHyep3W5R1K9dkctG3LRZ0POUky11pBvNC5jQ9a2lAkT3lSvV5d7bFow5nUpo/o8otcDzvp98y6rt0NEJISB0mjAqqlAuS4CFuMqwuRzSwFXfDk4EGu1OqLXQzrdoYBlI9bNaLDTZV8QlVCmxqpTqjQb87qkBrFCwpyL5lzWGdlvYutFHzj6P2iv36FhusvTn37G+w/9LccOf+D/tjZ/6/oz3xN7/neqW83KrforX+Xl5RLJqLWNJTCTGnVx9A2D45flsnmRaMHU8qDqwrC2yg6RSER/8xE19ze5OP66q6NftpncNBI7rjWxn2xeS3ZfyU457EVXfUWnu21JldY3KaNWjJXC+qLirvqylDE1urQEy+2aGSNe1u0e5LU6pCBrwfXAC9mwaUGTfS749xoNaLRLQqV5leE8FVemRk0IMIuFk23WhoiohCqFcAKccdYOD+pVJEQXwVoZnYGAOe2siGhpRVPE/3dZMmjKKVNOiYip16cyZKfcfLCsm9HuiBpdtq0rU6XPQyWMV71+TSGcLqFGwWuWjRgMFu/ig26rBKCbcdqorAFPqtcrKhbcKMUguoioRdfU6FJvhwlvqbfTWrAdx4JgcdLb1k3bsmqnJ9Trc9VXjHsz5DSNiEuUQg+rdchK2+1H1Ooy7o1g+x5VrQ0RTXbpco9ThQuenfgN+zvudcfBB/X07FVb+4ivnPvX/8n25W/VWN1x4HH/7pv/m331j2iu7pJIJdTmesXzFa5GniZDJBpRiG2Zyr3jDn/Tdpg49HvclHesmAjE4phVE5YN6XBHKawxKmrbpos+57qviYhqcdA+H9LioDGvK8g56/d0ukdSnWXDkhpUabVuOqQiH1SmUsa6VoesmzPuTUsG5YK7qugUe9QOD8nLmfSWaWeMOo6ISz4fHEldYQrUFLxZaTs9pl6vpWA3TihTaUC9HdrdFlaAxQVns/0mnbBsVExcs32uetqGWSnDGuzQ5V55GXV22LRo0tt2e58Od5hz0Zjjut2r2zGrJiTVS9jvXv8Pp33Ckuu63BUmnCtWTUlbtG4OEZ3uKqWV93qXbSkLroUojFqzzuv1IMjbLmEfa3VJGZW1UQLRFYNPp/V7QsqIen1GvaJCgxrtFl0z57ItKaumjHtDXFKnO73lX8nZ1u72YJ3eNO51U06JRSIq647oqd+jvb5PNpcxPj8osdLhSM8jf+ra/Htlz/9OdatZuVV/5SuRSDh6/z4nPnNSRNSrp58WX2/WX/GkyniDjeySic3f8c7SVxxLfthLzx63tDFtavOSTUsqkknJiqRY5bZkNG56cdWWNZVaStkq53zKeZ/WbK+CggVXjHlDVEK/RzTbY8hLZpy11wft8xFxSRlrbnge+WAFLgoOoxJhB94TMkBmZW2KSWoIbIwiq6RFRhqsm7VuXlyFQpg8xJQb8pw+j4ZR/fngtDkkbTk4jcaDlmNRs30WXbdpyX4f1ukeMTFV2jXbq0qbOReMBtR4l2PGva5SU2BozAeyacSAd1s25IR/bcOigqxrvmrRHkn14pJmnLNm2kEfs9OT1k1LW5JQFWi1DS75nDGv2endtm2UxuL5IHTcMG+X98tYs2HRigmJwL0pPkTWSwTShKrAIVkJOoBhy0ZMejtwVooW0BpdosrU6rRuTkRMk91WTQWKa3HlV6NDXLnuxG1S0WEtPe8rhRr+RezL36qxSmfWbW5sSm/kDU4PenPu8ypyrcoitfojj6gua5aWciPzosHc8875tD6PmHXObHi/L3vKkhvu9DdV63DW74f3+KOqNEsZM+a4BVdVarXPB1VqDkneCxr063avZaPOh3VLs32lxm/dnA1zzvikMtWlCU8x5PIROz1uw4KLPqfN4aAZ2SMuaUtKo91O+V1pqQA/61GnR1bavOLncI8P6HJvKf271SEzzmq2105PqtUppkxW2rpZGZv6PeGar3rFr8vYsC1l0YoeD+j1oEpNMtbU2aHDna57xjVfc9jPmnUxIAieNOtccIfNKFcjqc5BP+mCP3TG7+v1QNDcbJlzyYKrDvqYPo/Iy8jLy9mWMmrOZSf8pmKmETd8U41OM85pd0fQf23Ly9iypsyqBVeNOS4f0rGz1tzhl2yad8OzwRH3oB73IWLaKRNO2LQY1pyntTpk27oF10w4oclu/R41FzvlzNJXlG1FfOKZX7O0OWV05rr6WI9PvfjP7Oo54EDvMW11/6Ep+V7Z879T3WpWbtUPRd1//zEvfOV/97mX3nE4+WOOtL6vdGIoFAoWF5Y8m/sXLg6/ZXf7XUbWTinfbtJeuENFWZWN8int8XHTmYuqtCrIm3RCpcbSqXHSSZsWjXsTbFp00Md0OyZry3VP63Kv2/2SMlUo2FJm24YyNZIaS2jwSKDU1uq2aqr0EI+I2bYWSKOV5l1SkJeXF1NuwVUZG/K2xVXqdr8rnsILqkIOy34/plKTqIQlg7ak5GzL2lamSkFBrR67fcCqCRVaRZBQqU63DQviLhnyXNC4zFtwxbLRYP9tBxFRzQ5YcM2mZVU61OnW7qhWhxQtoJeCJfWgdMhHytkOVOA1u7zPjDOGvKBSi36PqdYqLWXCCbPO2elxdbpMOe1mKnOlxrDqarXhsnK1+jwmqca6eStGLbqqRpcRLwf0ecJlX1CjQ1TMmmmjjmsMJ/cmu016x7pZFZrEwwMlL6t2c7fJ8SWvnv2aD9//N+VyOZVltf9J9uU/qbGanZk3N7OgdmteVZyt3IZDnrS/8FH5TEYkE5WMtLlNv4R6l3zeuhllqlGcaJ33Kc32aXNEQU63Y676sg3zGvQrKIT07pRme0ITXKS/bpgHW1Ili2ybIw77WasmpC2HHKh7Lbhm3HFlqi24rMPt9vmodoed9QdaHXKbv15KCG7Qp0yVFeNq9WiyT693ydvS5kgIVoy44RsmvaPb/YY8b8WoLvca94YeDwR2Sl3IzYrK2pQyJmVUuRqrJg14dwlG2OdREUVxfIv9JVjibu+3bCjQcsf0B1bQulltjgRq7Xm1uu3ypLQl1zwtJx04K2u63FtcswTkfxEwOB1WurEQiDhvwJMy1uVkzDgrbSlA8O6yad6mpVKcQFH7tREyjk7ZtOiqp9TqNeeyltAA3tQg1euT1GjVeIlXVNTTvCMnrdcDWiJ7bZZNWc3Mq8n2q0nU2Jileuugfek7VSVrNa70GL446vrIJzx0xwfs7fwPGpXvhT3/O9WtZuVW/VBUb2+vHftbDR/PSGaazK9OKIsnbWfTZldGrG4subf1J5xa/ayZmXOOlv+MfZXvlc2nzWxckxf10Xv/K199+dNm3NDuNif9tjGv6XCXHR6StWmnJ2RsWjdn3iUrxsWUueE5eVk9HgjCvjJEwsh2QqvDJfhazracjIw1y4ZCBtBpG+ZExV3xZQ36SxqSHd4lK23BZRsWRVCmRlRMlTZZaYOeVac3TFyqg+Ng1rY1jfbYsmzRoIRqGWvaHLJlWTHYLxcaoJxqncrCGPuSLxj3VtjZV8lYL0Hgmu3R5rZS1ktRMEuPB5SrCb9LUWzc6U7jXlMfHlzFYL24LRs2LWqwK+D1BwOcrLj+abInPMzytqybd7mYc2JWMZgxX1pndbhDo50BENddcnUsuGbWBft8RLkaI14qjds3zJl1XrdjJYFnjY7gDskFp8xFUXEb0Xkd+bu8eeLTyuZ7lccrTaUvSvVcNzMzo7q6+j/6Wv1WjVUqlXL1zLCO8oNW86M28vPqC3063BnWIJXF1VghLydnrx+1ZtKEk5YNSaiUDY6avIxTPi5nO7hdPmDeFTFJCVVWTaKg010a9MtKl/RXxWyo1RJuvl6fNVOBsJoOYXpFuGAhJP/2eEC3+yy6qkqLOZf0ebhERC5GP1xXqdl139BoQLvbLRsWV27ZcCkyodsxM8563j8UU6bbMdPOqdCow1HlqmVshNDNm1zZ1hAcWLQiV2oREdPjAc32yts266KkelVaSrqtMjWu+hryGg3YtgGa7XczBb24JizGXdTpddQvhNVPQY/7zLnohucgcHIrpC0FFP5EcN8VHYRZabPOiyk374qstGrtEiqtmTHlpDWzWhwIoLqyAJ674LqnJdVbNeG8T4ccroO63BXcWo/ZMG/VtNv9EopZThPeUhVpMpx/RYejIoWYka1nHSn7iK6yeyxlpkRiOfmVlLt6ftbQxutefuer6qtaSxOW74U9/zvVrWblVv1QVCaTsTKT8fg9H1aZrTY9NiifLYgmImJNm1p0OdT1kLnBS+aXZzQlu8wVLojEItq7Wq1sFzHxO2vvtr66YcjzoNFujXbZtmLbmnUzqnWURK1Lbhj0TSNeFlMWdvDFU1dOWtqSDXNaHbbshmmntTsqa8u8K+ZdLY3gi2yWiGVDRryiwx0lwNklX7Dkuk5363SvCo3WzRjzujkXZG25mSk06aRtq8rUqNauSkvJGrxtNdAu4zYtydqSsalcrYQKa2ZLK5Emu21YVKZWrU7N9mnQLy9v2jsGfcNOj+t0Z6Bxrktb1qA/PASrJFSUQhcXDarTIx7SposTpZmwlulWq1eHO/R52JoZk9426rgVo9JWbFv1kP9OTMKGOdkAzcvYtMPDIiXqb0FSfSlnBRZcFhVXp8cd/pYKdeZcct3T5l1UrlaZqtLEa92cQV8356Ju9xpZOWUg8phMIW5jolxb526X1l+RWSz3W//s9/9czolv1VitTGcV1ivd3flhL4z+rvHFy+r02TSvzeHS679p7U2EadqoN+TR4rBty2LK7fUhCUkZ66VMpl4PmPC2ORf0ekBeTl1wkVAEvW0EYXYxn+cVOzxkw7yMdVlbytWot1O5WnHlOtzuhm+63S9qsidMwC4E5H6r8mCJrtBgyTAKtq1oDI1s0dESI7RjiwZDk11l05I7/KI6fS75olqdtq1rtFvKqC2rQeNSzIcqxgMMlgTSCZXiypFXpU3U1ZIl+WYsRM6WtEVZW9bNKVMtZ9uWlLwciojFnOK0KWtDTHkQwG5aMyuhOrBhZlVpCdddREHBiokQePrl0iRktw9otq8k6h3xigpN1s1YNW2XJ826YNGgCk0GPacQdGY9jgUg3bIJbxnyvIJciMJoVKXVikkzYRW0YbbY9OTLRAsRHZE7XYp+VmWuXWv2sHxyXWNbnexqzOp2xsLqhMMt77Uwe8PFsTe01fUqFAoGF0+654nvnj3/z6pbzcqt+qGom6fV9uYevc177d+fk8vlRCIRLz17XFPtgFwhK5PJ6is/5lD/faLRmM3tNYsrU9ZW1px8ddjaxpq4pBue1+dh+/1Yyba5bs5Fn1OvX5Vm1To0GnDFU1JGtdgP4pKBhMlNmFytLt3uddq/NexlDfotBRFki33WzYmKaXW4FJJ4w3NmnZcyZt2cfT6qxQGTTgQrY0FMuQYD5l0qUULnXdHtPvV2qNJqSyoAvzZtWbFmVtpSCcldCLyWLcuyNuVt27IqoQoLej2o012SatToAp3uct3ThrygxX4NdoqIm3ZKt2M63ImIWRdERNXrM+dSwNtvholAWlbamqmSG+WMT7riyyWmRauDanRaMyllzEV/FPJdVk05KWPDff5rtTrDAzD/LRbSKmtmlKlRr88lnxeVsGFBlRaxkCtzzTPGvKFBv7gKq6ac95mwQltRpc1i4SyFqES0UnSlwan8NxTaV/3c/f+DkbmLfy7nxE2N1VufPqFi8rCGil2aK7odaXvCublvanF7KUemMQREFnNwkuLh723TvBb71WjX7j1ytrQ5LC+rXLWo8oC6/wPb1jXbq9kBiwatm5PUoEJ9gOXVmPSOCW8pyGu0S40OOVmzzmmwU0XIZSrIh+siGqzkDaq1hWal6JMpNiExUQk5W+E11ZWSvSNBvLwWVif1gX4bk7RuTq1e416Xs6XRbpNO6nKvuApbUqU4gJyMcnXBgl9k5CTCZ69MLZTyqdJSGgxo0G/McV3uVaUlxBQUM4CGvQiy0jYsqdIcXERVIVLiLevmS9O/4rpxKuhoNmxasm4+5HzdZmcQyi66bj40y1AXhL/FA0/cJV8y5HlrpjXoC9DJOT0eUK9Pi/0qNIZ/PmDIC657Rpsj4V6TseSGZ/19TfZqtidMBc+5s/yvSRTiljOj+qMPqW0rL2muJremxTerLKbm9Dbn9VbcaXDsGx7ct+2doedE2+bdd/+P/kVvzf/RdatZuVU/FPUnHUGxWEwsFrO9vS2fLSiLJ2ULW7Zzacl4jUSszOLatNHpa6LblerzA6Lpeku5NywbUWeH/T4qY8N1T6vRYZf3ygXR680d+A7vUq3Dmhlx1ca9rtm+Um7JtFOqdVg3o81tASKVdskXlKkUETXvojo7NNtnzaR4CEAcc9yU02p1abZXiwNmnFWtXbdjqrWbdtagr4cpzD3a3Oa8Txt3XCaITtOWgjV4l5QX5GWsmbLoegBctcvalLZs06K8vMkwjr7ZiFBQrk4kuHQo6Pe4JTdc8zU7PKxah1Evi4iESdKQah2mnLbXjypXo8GAWeelLYdJVKNxb0qo0qBPKiQk93lYvR1hZN6m2z0W3bDshos+a9OKhKQ6PVJGNNsXroRCMD5vG/GSNVNhilAoOak63KFMtYSkEa9acFWFJpsW5MPpe8uqtGX9HtXvsaDZGZYy6oTfUqVSf2G/ikj9f5Jz4v77j3nj2d92/fyG+xoOgt7KO8SVq9KsIG/WOZsWJDWIS4bJ3qxZ52VsqtOjUpPd3mfQNy25rlZPyKiZ0uEOmxZNOGGnd4sophdPOqnRLjcDJ4twvQt2eLgUwVAMLCwGRlaF9UpULDzIb1KE52xbk1ApLyMuGTRAexQCZC0tpUKjtCXpwEO5GUTZFd7TdXOqtdsK4aEXfdayITHlmuw255Kzfk+HOxVTyldkbVox5bIvmvKOVkec9SmrpsLq9nZ5GVVaTAQicpUWWdumndFop7ycq74qKqrPoxCyqyasmTTlpJQxcZWue0a9HcEpVczKWjHuqi/r95hy1TYsWHYDEXt9UIv9ylTa7QNmnDXsRTXaS9bzdTMi4T8pYwa8117vM+o1LQ7Y6QnzLtswr0KjCo2i4vb5sBUTVk3p8UBwE9Ub8B7rZl3zTDETK9Kns26vwnZONJ3X296vra2tdA22dDQZGUtZWp81kxqVyzO/MeWps/9KsmvVT/7SB/5SwXC3mpVb9UNR3+oI2t91b0lcG4vFROMR25m0yvJa6/lZ5TUxG9trRqevqcp2aa3aZTY1qkLBZmROhSYx5batmfCWPg9rddiY11TrQF6ZWiNe9rbfDi6eMvPOW3ZDUoPb/ZKCrCVD9viAGWe86V+AcnU6HLXT45rssW7WuDeNeEmDARnpcBLeFzJvttTbYdJJfR7W4fYgus247uuO+oUwDr+m1QG3+0WXfN4Nz0qEDJ5KzQZ9w7xL+j1qIXBRKIoQYdu6ejukpSy4IqHKET8ja8OGbIkXkZNVzDsqV6vbNc9oc5tNC3IyouLmXQrunEozTouKqdQiHwTCRXhZozVTihlDh62alJUO+pM9osE+XTwBr4mJO+Sngvj2LZ3uElNmyHM2LehwR+nBOOZ1S4aUa5CTtWXVET8nqVGlpiCqnFOp2VG/WApCXDMtrkKdXhvmTDsjbUmNThUaJQu1ZpzyeM8/ENkuMzY27tChg39u50Rvb6+f+hs/4r9583/10ty2Q7l3i+SLuUNLkUEHIj9mLTJtIz8rVRiTUIFIEGueU67WnAtqdRryvC0pGZuIIKLRgAYDIhhzXNaGZnvt91FXPGXORTs8FFaJx1Xr0OFOS26YdCL8bEyxMS2qMm6uSGZdUKfXvMvBlltQUJDUYMZZl31Rp7uljIkq/l00hnVRq0MqNUkZValZk90lgvNlT1l0PfBIigDCGefVaDfqNSsmdLlHXNKIl8IqpcERP6fFITlpU94x5rjLvuSgn7DguknvmHbGdd8s5fXs8C6Lrul0p3Vzhr2gXn9w4W0a9HUb5nW737JBvR4Mh5VMaB4atDgoZcQFn1GhMTRxGf0e0eqgmPKQZVW8pmeckTKuQbm0qRJvKCutTLWCrLgqWVsGPKTNERQsuSEvK6lOIei0dniXy74Y3vc1bY6Yc1GlVlUBMTBdOO9q6iWtZXvEKwqUb8vn86LRKKipqdHQWmMjHbdWNmxi9ZL18lHHfuJd3vXQj//wEWxv1a36y6r77z/m5Euf8Nb1Z9yzq4jdj8Vi2nuaTZyfMJk5q6I+ZjU6YW55THS7UmvVrmJ+xuaMrcKa+dyQFgdDcvErqrXp8YB5lyTVl1Y9w16y6Lp6O1Ro1uVuy4Ysuu6SL5h3RX8Qv93MBRnxilYH9HtUpRYtDihTqVqbFgeMetWN4MDJ2LBqys1JwU1Xx4B3BwdPo2ueVqU14PNXrZsOAsm99vmQK75i1GvmXSnh1Ps9ptsx494w5EXLhpWrU6PDpkVjjls1IS9nw4i1wNdIGZOX02igBMtbNydrS51eFRrMOFMKWKOgXj+huTnj9zQaCLkonYgY9rw5l+zwkL0+aNoZZ/xbfX66pDdpsV9MmSnvKMgHgmrxpH/zwdbtmIiYa552M9iuWptOdxv2om2ranTqciw4hDokQwJvUoNDfsq4162ZElehVncpUXjRoDGv2+kxHbHb9Obud7bA2Mp5tzX8iOmxQfv35/6TnBPHjh3zk3/rfZ755ElDka/b3t6Wi29K5UasJyYNxB42uXXWbOZaENomLLhq1oUwJSiu0ygm8eZlLbqhqRTYuK2ALasiorrcK6ZMxoYbXrBqUrN9Jrylwx0WXNRklxnnzbmo3yMWXLFiTE3g0ox4xZppA5406S2DnlWhXkKlBjvV6y9pfZLqlKuXs2XbqnHHRcQd9lNSRiwbUq9fUoPpEMD3oL8fPnsv27Rkw6yjft49ftm4113zNekgWu90pxpdWh3UbK8KTfo8ok6Piz5vyZC0JdU6An4+Z12TFZOGPK9Wt7v9sm0rRrwSSMhl0pZRnLIMe1G7w9octWLChjmblpSrlbMVoihWSxymQ34yiNeLLJS8bTHlarTrdp9TPm7Linr9jvp5BUU9VYWTMtad9NtBP1Ypa1ODARsWzblUmh5GxVWEWIMZZ4OA/MOmvCOp3mE/ZcQrxhx3Kv1p/YUHVJTXujD1kopUn6q6CtXVlSoqqqzn5h04ute+fXt9+exZf/snf9bP/OzPfBfvyv/xdatZuVU/NNXb2+snf+kD/vB3v+or524YaLxTdbLBRnzKO9tflMttev+9f825qyecGv6ao4mfBhMrl4xn3rFUKMbTtzgQaKnnDXhSNNiJi2jsrJxti67b58Ma7DTuuAoNUkbcTKMd8bJJJ+RkQobHuHI1muwJWSeXAm8kSRgF7/SEJTesm9XpTkuGrJsTVyEvY7f3hjVMsYUpNkSPhp+OSmooWTBrdOp1v053GPeW3d5ny4ou9ypXrVyttGXDXjbuzWAJjQVbcIs1UyjesAtyQTg4FoSMiSDua1KpRaUmK8aMe1OTXZLqtTlqyHOu+pqkWru8R/GUWATPbVvR5mhYsVwz77JOdxvxslXjutwTiMBxq6ZKwKs1U7at27YanEiL1sw47Gf1uL8kRM3LueJLUkatm7FsyGmfkLZsxYQm+0rk1bQllZpsWpRQUQpKLOopdphzQXN0t8ZIn1xiS1/0QVdmv2BfzWPykYJcLie1MadQlimdWv9j64Mf/BEjF+Y0LN/haN8jKhJ1rp2/YSZ6Sio7JplrCg/7dSkjZl2StalCvYK8Kq3a3RYyfZbMOV8KsYyKmnNeTFnAx5dLGQ4n+k1T3jHkhVLQY5e7Ndotqd6YN2yYk1Bp2mnLRksrxd3ep9sxVdqc8m8U5HS4U0zCUnjPZl0QV27Tkia7dLtPm6PGvSYqqtVhKSfNulBiuLQ7Yt5FKWMl4FuFpjDdWtbqkF4PGvGyMrt1OWbLsnVzut1X0ond5hcsuG7Yi3Z4l8bAeylTHVY29U76LUuGZG1qdUhSnXo7Qr7OsErNLvmSG76pUrNpp5SpDhEYyRJZOiKqzo6StbtSU/isjKvTq1KLLSnskFAlK22X94ZGqzPopjbU6FaQc8onwrRkQ1xSQUGNdpNOGvemOt0i4hYNmvMfMAttjqjX57pn1OjQ7xFxSQsum9g67cDKj5ovP+tM6in1iwMi0bxCIqOiKW9f7b1ODn1TWdeyB9/1ke/W7fjPXbealVv1Q1V33XWX1tZWrx9/w6nj35DZyku0Rj38N7qMXJy1sDGqvb3DN258zub6qpb1g8a3TlkqjKjQrFqbKi0a9JdEqfmAfLoJpJp0UqUmu7zHqkkrpox7S5M99vtouMGfNe2MJddMeFuLg1aMqtcnLxMcE8sa7ZKXDRbi4k3nki9osNuQl7TYZ810yX66aUFBIbBYcsqDhbmYu1MrZcyUkyJiCgph5bEcxJq75WWkjFs2pMFOtXrcCAGKU96xZFC5esX8kRYb5pWrF1VmzZSMTY26S+C0674urtywl4K1uMhd2bbmki9qc1izvXrc72ZSb07WhDdMOqnf4zI2jTmuSosmu827JCYhY90lXyhpDxZcEVMuKi4vF0LtXnbKxw36hgoNomIiErI2pIyHtdJRTfaWgudSRlzyWWnLIT+nXIVGMQnxYEEtRhoUU6fL1VgtTKqP9EqWVWrUTzZuevmGSOOKly99zgtnP6ulr9L//P/+Z3+uILhvbbCfuzKqs63bpeunxdK9tqKLRiOvy8bSMrktfZ4QQbU2u7xfzpYV4/o9FuzCnVKGRSVsmrdtw5g3QyTE6zYtiYgoVxtYPM02LXnFrwXNSaWcbU32hGtp1Iyzpp0O7/suB30sCGrPmQ8OoAWDlo2p1GTNpP1+PIDfup3yiZD3tEudXkuGzbkkbdmGhQAsLE7s8vKSakuTk1aH7PVB+UB9nXHWppRpJx3w45rsKq2QRr2qWruketvWbFnVYKcjfi44ozZDQGZdoL7epaBgJoQ1FjOwmsUklKnW4oA5Fwx7Xo2uwFhZDY69okMrIi4qqlKTKi3iyqSM2jCvSosqbbasmnMhTKhGxSXt91GbFhRTxoqurZikfo+actJVXzXpbT3uD/DDolYsL2PRoG3rbvimqLhWh0w6qVxduI/kQmxEnZiEbvfL2XJ669PKMkmLsQmtZjVn94pkSa+t+IMX/4n22wt+5e/94l/66udb61azcqt+6Kq3t1dvb6+P/tgfjz0fHR315ae+4pkvPW+tMONK9mlnsp8RyZbb4wN6PWjcG5bccNBPq7fDUggM3P6WmPdpZ+z3YRQCH+S6bvfa5f3hgVeu2V5tjlgz5TX/VMpQyRab1KBcrXmXAzirv2SfjIqLShj1ik3zut1nzBshRO6KSi0KcuHUlZWWCkGFQ6adKfEciuK9uIyNwHI5LyIS7MwtWh22w8MyQSDZaKcmeywbct0zKrUryJpz2f/F3n+HW3qf533vZ7Xde+9l9vQGYNB7IwB2sImkmmWHtuKayM7JiZ0r55zYSS7ZSWzLcSzZkmVaEiWKFEmTRC9Ex6AMBtP73jOze+919fPH+s0KKZGSJVGiTM2Da/6YtmfthXe97/N7nvv+3i322+bR8LC5HBKn6y0aNOE9CZVysrZ7rNhgZGxptrcIKstKuuRZ66ZRuNFvmHXZ8/rc55yvS1lXptaaKVe9YcI7ctLBNlqhXr8a3RZdQcSIN6yZ0BYcVeUa5GXNuWDNlFLVmuzU7xHdbpO0Kiup293GvOV9/96CS5rtlZMJDqJyEcsqtChTa8GQQop1IRyyPLLXRm5BSbTS0MK7crE5Je/V2lH5sNu6bmMj88cOgvveBvu85rW0Cye+qTm/X3tsrwrNTq8+4apXwrTvA9rdZNOCS5426Dm97pFQUjzVF/KARuTl3OSvFoXYu3zcTh+TD/qTTQsa7QrTqjVVmr8r9yqhRrcN8xYMgstedNXLcrI2LUhZ1+VWKyZsmBdVqlqHOWdNOGrWWVkZ006AvLR1S9odssNHjHsnUI5nrJkJ9vp13e605LIRrxGmiQ12qtUnaanIFCqoacpkbFgN/JxrFuVOt6rUEj5Pb4Qk9ZaiU6jFPhPeV+udoG+qsWWxaOuu0KKQDXYluOFqxZTatGTLQuAlJYs2+nWzqnXI2CyyjvKyqrRaMW7IC+r0SlkpovvzssrClIy8Tre74mXLRl30lB0+HKZiJUpUqdblvG9I23KDv2LY6xZc8pZ/KWvLRgiSzBet1AV20ph3xHKd6iLtcuWr5hLvSWW21FaXaWyvVla+paWl5Yd2D/6T1PVm5Xr9pa1EIlHUD6TTaVeuXDF0csqOyvu09D9odjDpSuotEQl5eYOetR5ElROOBiR+XJkGWxaKjprCCWvTqLe8418HZsolM/65en163KdSY3AONWmyS4lKKZtB4LegyU5EzLsoaaXIgVh0xbwLNi0g76InbFmWsmLUmxrtUq4urCwqXPGKNTMmHZVQbsBj4sqMO2LCEQkVdvukRtvlgsNkzZQ1Uy56InArGgIRs161dqd9WcaU3R4XV2bUW9bN6nZHcD5NueQp8y6qN6DNDcoUQiTL1Vs1Yd6g7R5VocmykeLDYsCjyjVKWTHoeVe9qkqHuEo5mcD7uGjDr4Um6RHVOuRkzDnvoietmhCTsGZSqxt1ulWjHYGrEVOqziVPGfKcRrtNOabVft3uCgGTJ1RpV6fPSV8WU65MjSptIoQRfCny5pzTYr+aSJeFzBUr6WlXIy/bsGgqesbutfvs67nd/pt36ejowJ8sCO73N9jHjh3ztd/7hu9880mrsxmpSEpFvjUwU+8JYY0rruX5DHstJDEnZaUsuWrDnF736HO/mBJxZZrsMeOEiLictJR1bQ6EVdxZZWpCaOWyuDI1Idiwy+2qtNkwH1J8Cg6zfrfqda+UdaPeMuecU35blfbQxGaUq7XXZ5SptW7GKb9r0DMmHBERVaEJ+UDKLXBiJhzRar8e94a15LR55015X8qqlLUwpUshqlqHCi3KNcjYdMZXlKgsOtwyNiy6KmmtaNfftBgSwC9osV+JKuvmZGy66lUXPSll1aLLmu1XEQIyryVU52VtWjLuiC3LFlxSrkGVtuIUp/Dnm0w5YckVLQ6YdkraunVzKjTJ2JKXDwnUcXGlBjxm1GFzzikJa9srXgrTqFHVOox5W0TEfp/XbK910+ZccNJvKVEVwiMb1OnXbJ89PmUlckU2vuTOvk8qy9ZbLxnz8AP3e+bsv/tzzQH6fnW9Wblef6lrZGTE4cNve/25I46/c155ttmN2/rFNhPaY+0u5L+jUmsR5paVst0HVQZ7cmH6cUmNThvmipk9444ER0Oj7T5YzE+Zcswpv63FvnDqq9DpdlOO2zJvI5Blr42l6/RYNWHcERlJo9605KpyjVrsVa5BtS4rxsID+NnAMCl4NCa9a955Ax7T5iYlKmyYk5Oy16fFlKnSos1NytVZN2vQs2adU6LauCP2+7wa3Ua9YdgbStVot8ON/lqRMDrluHO+WeRb5IIj6n7/P1VaLRs27j1XvapSm2UjqrRZM2nSMX0e0OeBwOkoE5VQZ5uIqGGvhVVMvWmn1OiwI0y6CjEBG0rVanNTMYpgw7xa3brcoU5PcE4UnE0py7rcYc754FBKetu/coOflVCpTK0q7fb4lKQVCy7ZNG/Ao0FjsGbJsBVj1s3a5WPiuVKNkR0uesZY/l3JyLI6vVoq+/Xt6iw2KvzpguCuNdj33nuve++910ufeMk/+lv/u8TVXapS/S74dpjKXXDVq+r06nKHJcMuezE0B42a7bbXZ4qQsnmX7PIxORlJyxrtDtqMwqrvWlM675JWBzTaJSrmgm/ZtKjH3VrdoE6faScMeVG/B+zyOHJWjFs2okydpGVDXrRhVqMdEFxoD9vmYTt9zIv+3y56Ro0uK0Y126fHPSKiLnlGg35lGhUiHXZqtd8OH3LRE076slnndLnTijEZmxZdteiqCo3KNVo2ptUNSlSFwM5WKetWjVs3LY8Fl6wY0+EOCSUygfybkzXtlGXDBjwWrrbzcjIh3PJ2eVk5OWO+IiJij08Y924x8Twvp81BqyZMeN+qCTV6NNmpVresdMD1dwf9y2qYjJ4SVVK0Vp/wm8Gav6uop2tx0JZFdXpVaS++h7X67PRxlzxl0LMO+imdbgshok1aHdCbu8vk5tuOTz7rlpZPiORicrncn3sO0Per683K9fpLW0eOHPHV//C03HSTssWb7N+6TXNtlzMnXzG+dk5ZospKfsI2j2p3k2P+o2Z7NNsrLxtcAQWibNKKuDJrpmUkXfYde/2EGp3a3BhWEBntDgXXwjO63RkezhXGvSsqZtZZVVo02xsYDmUSyjXYbty7ksG5stdnNNsjZVWNLo12qNHljN+1aUG3u9XotGVFPKxJ8tKiYsa9q0SVOv3iSsM0ZFJUTIkqfR4w75KkFa0OSlmz4JKrXtPldimrut2hXJ0ytXb6qEa7rJtRoUk0OHHe9W+CILagCZhxJjhFPqiQwDtn2bAytfo8IG1d0nJgdsRtWdbqhuCwWDLrrDnndblds722LBWzeVZNqbfNdh+0ZNiGeQMecS0sLiulXH3Q86wW6a1DnjfgESf8ljO+asAjBjxaTM8uVavBgA21LnlafUD2v+2XpK2H7JglKetWIsOSsQW3Rf+GE9H/6PaWT+qtPmT44rjW1tYibIs/XRBcOv3/rC9nZubsabvTzFCtejtUBrFn2mZx9Zi1JStl06JtHpYN3psVYyHNt2CTLYiwa2yYkbEe1olVgXPSotd9DvvnYfI2I65EpbZAHS4LCeHrlo2q0qbFgbAeyodgwcJEqtaBIq34Nn/HliWj3nTEr9hVDCtc1mBA0or9Pu8GPycm7pxv6HSLQ37euLfMOmfVlDq90tbt8rgV48a87bgv6nS7RjuUqZWTtWbGaV+WtGzdtDVTtqyIihdTxPNhijPhXaumvO2f2+HDStXYMG/RZWmbmuxRo0OtfoOelbQibS1o15KmHLPoikY7dbs36GS2G/ayo35NU2j4anTrcY9pJ415W7tDVk2o0loEzJVrtGbKmLdlpZzzn6StK1WlTo8WBxSS02824YgydUEMXRMmZIXg1QIscG8AGq6JiplxWpO9qrTYzC3qyt3lcmrGpeW39LfuFYvF/txzgL5fXW9WrtdfyhoZGfHV//C0ppXb3Lz3Ea+88Ka2ugGNVR0mR6eNbJ03sV44RW7zsJN+W1RMp9uDtbZA66wII9wtyy56IlgW0xptD06SAvW1IOwsnM5a3VhEkF9LOS5Rqd2hInK7gD3vCt6aggVx2YisjGb7NdjhtN+Rsu7azr5Wl50+6rxvO+OrcrKSlnS5NVBNF60YN+O03T6JvBqdStVKhxyea4C0Rjtc9Yom+1z0tDnng7i4IAqMKbFiQkK5EpXq9MpKSlpSrsmqCWkb1kzpdGto5iqCpuU0uOo1Jap0uNmqMSWqiiLXnKy5QLQt12DGGRd8C3ntDmm1X4kaScsWXbZhwaJBUQlVWpSoUKklCJTTRrwhKi6uXFRcLGh/YkrU6dNkj/pA0a23XVypBUNBE1OmVK28jFN+V7l6RDTZadqpQpJ1pNJA7e36yz5laPMNmc2UjtqdOuu3uzSzaHR07HuaFf74QXDXpoDHD5+X3sqJleRduHTetsSjFiJJtbq1u8Ulz9jmYds8IiqKkrBCSKjQVFx/FMBhE7Iy1k1bMKjFPlnpIAwfFzMTvt8C6K9aexGjX6qmuPJZdFmrg8YdCWGQHY74NyKiGuxQqjo07HllavS616T3Q3jhDq1ucNl3XPCkdXOBDFumxX77fFYsCMLnXAj5W+shOXzZjDM2LarWqkRl+Gx8tagxKnGnlDVbFs04U8xDGnVYXi58ngY02B4OHFPGvSuuwg1+1pRjhrwgKykips+DmsPndtwRDbZpC1OaK14y6i31BjTZocc9QSScUaPLoKdVaSs2h832iysNAviCBuqip1TrCM65erkA4Rv1pjVTVkzatOBmf0Ov+1zwhCnHbVo0FvRrh3xBnV5xZeZcEBFXpU3aukQA7o15J6wEh3W6Q06m4GzMbuqMH3Js5UtuO3SnWCz2554D9P3qerNyvX7s67tPotceCocPvy033eS2Ax+UTqeLFNu5zWGDG2/YHXncYP5FzfabdsqE99zo53SHjJ68/Pc8XAuUz22a7LHgkm0+ULQYXht/52UlrRZFi5c9H06fl+zwYds9JqZUpRajDodT47gVY3rdZ7uPmvS/KFfvpN+SUGa7D6nSJmXVuCMhvGynce+ENOeqYLvsUa1T0qoyDcFttCxP2OsX5LgRMRlJ1TqUqtFkp3nnLbkaJjVLSkOy7TVQVcE+XVo8/UXErYUpS5fbJZTLy9nmQTzsgm/ZMG/CUbW6g1ulXIkqMaUiosFplVRZtElHi6fZdjcFZ0ZpIOpm1OkRCYPzSq1GvGnEYRUatDkkocK6GfX6JZRbMmLGaRvmpayJiup1vyteNuu0HvdYNaZevwgqNOr1oLRNnW63ZdG0kzI27PBhbWU7VZVWOZ96ylL8qpryRrlIWiQSUV/eUeStxGKx4nX5hz0Afv81+91TwIGGR1WV1VtcnfHOyXGvb76gPNunV8I2H3DON4rZUGXqRMTCai5t2bA6faq02gi4+wqVGu0y71IxwHDFuoxNtXrU6JKV9q7/O6yRmiRUWDYiZU2dXqMOu+Dbtiyp0W2bD2iwXdq6UYeNeVer/TrcEpK7F4oW94JDJWqvT9k0Zz0EUaZt6nOfQg5P2lJA09frl7ZeRPaTD3ThpA3zYaXS4VZ/y6yzLnpaTtqmBXGletxj0lHTTrvqNRsWgguoVMqqq14z45RdHtfqoIyNgOvvKDJqctKqQ4RDQVvSaJePa3PQJc+6y39X1E2tGC+GLBZYSB807ohxb9kwq96AGp0hODTpjK/ococytdI2rZky7ZQty7Z5xEVPSdlQGVZ0cy6KK3Wjv2rGaTNOm3XehvmQ1ZWVUB7CQ2MyUvKyZpyxalKHm5Wrk5GUlZbNJWXX0rKlSa3tzT+SHKDvV9eblev1Y1u//ySaKIu68a7dbr31ZscPnzfQ8GgRDHeNYnt54x2VuRY97nQl/3rxwRdTosUB1yihBbdNwTFRqdW6GSUqVWu3YFC1zuB6uGDBoGyw1+YDsL0ihBle9bIqbXrcq1KLNVNy0vb7XJjKbDnut0Qw4Z3izbjPfTrdoTqMircsq9TqqlcMelZGyk4fsWioKCpNW7dpPvwoCHAjhKlPRE5GOjhuruUBFUi0ZWp0abFfVDTE0l/Q5S4xFXJytiwrBMAt2Qjo9AGPqtMbQu82i7bRnT4uZcOYt8w6bdYZtQED/90Y9kot6vRZdDUQaG9SqiboTtakzFg0pFavOr3B3ZK3HBgWDXYY9546/TrdbtJRmWCxrdDivG9aN+uKlwJ5uDBRGvIC2LJiwCMoBNZVaFKhWZ/7JIKo8axvyEeelCw/oCJaqaNltx3RW5zfesZo8pgd+buUxMvkMgXeyrVm5Qc9AL7fNdu1s8npty/rzz/stgMfLNKXq6LNejIPWM+sGoq9bjZToM7W6hbBnAvBoVIiZVVU3Lh3w6SjYGPPyyrXbYcPueAJo97S6TbrZuVUqtEtImrKe2JKlKs35yxyFl1WpU2/B62bcdJv63WfNjcGMGAh82enjxr1diAw94fgzzVpG7Ysi4gUV2kRUZd9x6oJZWrDYSBmxBsGPS9lTalazfbJSVtyRUREXKk6/eG6KNjKiWixX7tDIXCzIqw2bwgRERGbFkTFDHleQrmYEtXadbpNp1uUadDukHO+qUxNsbEvU2fRFamQZg0bZsWUFT9HMfWWjSkkiK/IBYH8JU8qpD/fa8Ypl72oXEP4XN9vv8+LKzPkhcBtSqrTb49PyUoZ965NF73j30hbk5WSlvC2f6XdTXb5mF73FgX2s87qcW+RQZS0asW4lHW7fFSfB8Mqe025emvWzCdH5Eqzkltp7w4+++eeA/T96nqzcr1+LOv7nUTXNhYd+epRb7346xbnl+3trC/++ab2OsMnBw0uHdeRvU8yk5ZHIYU1pVS1NZPq9ATyZ8HMWoiPz6sIoWeF3BRSVlHAuVVqkQp78WvhZMtGLLkqL+dGf0WFBilrmuwy56ItS8X9eb0+x3zRlkWb5pWq0ePecFpaEFEIRywQPh625Kq0o2p0ytg077IBhQTbpGUNdlgyrM8D1s2IBiVBVkrKujwmHQ3ZQsfFlSqEv1WFAMEeE95z2XcCXKpEWtyaaSmb5pwNwtNPWA9208JNcFqVVg2263KbrKRx77nkWbPOhEVDVo0u2z2q2Z6Qxjxo3WxImM4G3cpdkiaUqglBd1klqmUlA/GzXlMAu825EAIP2y24LCdr0DOy0mp0Gveeu/2/REQCr+OEFeMO+kkVmlTrMOu0RVeVqg65O336PWzWWXXVtT699/8jHikVi8Rdmjnixp33uDDzplMLz2qL7xctiXxPo/L9HgA/6Jp9+jd/TXqu3L0PHyw2Ktls1sjIqLJElYbcDivZKcOZN7S5WZU2pepUarZivBgOWKXNtNOmHFenV05GfXCCRMUkVDrra8a9p16fJrvNOmvKcSvGHPRTNsxbN6PdzVLWVes075LLXtLhVjt9BIJuY9OmeQ222+9zVo1ZMKjJHtNOKlUT4g8KTJW4MvUGNNimUptpJ0w7HYTZr6jSJqHced/SaJeGECxZmLpcNe+SCs1WTGhzo0Y7LLpizbRaPap1mHZcBJWaitdzvwfVGwgQwyZrJo1626rpQoSCOtcmj4XgxYIGZNirokrCVKKwZs1KyUkH11pBTxMRkbJu0aBme+32cVkZU45rsD3QaJNKrKrUqsc9anTJy1s1rhD8GFWjM1i8o0pUarZHmZqAQJiybMSa6aBziarRbaePFcMcW/2Mdp0WDJr0frBWp9Xqkpez4FI4LGXMxU+pjNf7nZf/T/13lfnrX/j8j9QJxPVm5Xr9GNZ361G++yRKwTb61sUnHR38FUOZ81amMqZG58zNzZqcmLScXrQ936IiWPomHRcVV6PbhPfCjjkhrqx4Yrpm+9wwH6yEtYa9Vmw06m2zYd6GOUuGRcRc8Yq8vFYHtTgQKLbDSlRrsd+KsQBPW7dgsMjEKKTh1tuyKCombV2VVtU6w3SnQpNdVoyLKwtf56LX/C9qdCrXoNNtRcx+TEJGUoPtRZjakOdtmFcWRsXtDilVY8JRu3xc0pp2h0x6z6oJHQ6JKTXhqBmnzTmr3c2WXFahUXt4gJ73LetmbVmWtmXKcTFx9fr0e0iLfTYsGPeO875t3ZxNi5YDLCuhSrsbnPK7rviOMvUabQ8E4Q1RMYNesWVJlzvExFXrcNGTKjVLqDTtpFGHrRgLVt6CPmPZmFUTAYGelVAmFgb3MXGV2pz2e5rtE5Mw64zW6F697jKY+baYErFI3PjCoEjlhht23aq1q86r7z/p9MQLDuy6xfjioLWtRUMLR0Vb574nCO4HXbOZbNrhU8+pjOx19tgluVzO8vKKyZEZo1cnxbJllnMTmuIDpqNPms2dUaPLnLN63Q9mLMkopBeXqHbFy0FK3aZWrxknTTpu1tkgcl4y4g0LBgMAsfAgT6jUaZsTfsuI1wNAb8Sw1wOFuMDaSduUl1Nvm6QVK0GP1OqgEW8Y8YZ1Mzrdbt5FWcmA4u8z4g0RUe1uMOo1F3xLm5vt9Wnd7rFixHnf9qZ/5ib/1XcRlaeUa7AZJo/bfVC5RtEAcdu0oEyNSq22LAVmUYmEqIwkwRq8bta8CwqMpPPWTCikN9cERsqWjLQrnrVs1B6ftGlJXraodanVayvYngsHni1Jy+IqHPRT6vUjqtOtRrzunG8Vp1TzLkmoFJUIB5yEhDJbVqyZNuRFSUsGPGq/zwdqdEq3u9zgrzrjd13xkqgSOWnlGvS534ZZo96wy+OBbZOyzUMB9PhQ+H4vBXTBpOXMsO6yPrXVVQ7ccf9/Fg/oz7quNyvX68euvluP8t2NCgUXxp07P+qFE1/ywjtfc3/D31Ueb5Vf3dSS2GMk9Z6l/Ij66DaxXElxtdDvQbPOGnPYToXTcMpKcZoy5HnLrtqyqM8DFgyp1mWbD8jaUhJWKhExE45YNGSvnwjAqE3NdgeOypB10yo1q9XjipdCauy+MBG5XByjFyYVLRIqJa0EYFYB8Z2xZdSbanXrdqe0jeByWHTRk2LKnPLbxQnENajXtFOhiSmRl9Fgpwbb5eWc9fWgY+hTrk6Xu2yYNeh5i4aCXfR2LQ4Wkf4w5DnLRi25KqGieJJMqDTgAxrtVhqgd/X61Oo27DWnfVmlVt3utOiyGWd1ulWf+w16TsaWnT4aHlCTJh216Kp9PqtGp3kXtdhvzNvO+oY1k1ZNKlNftKLX6VehWb0+pepMOqZCoxJVpp0qvtdDXjDpqCa7lQS9T1ZKU8mAM5lNr47+uu6yQ6JVm/Yd2qmqqkpf2QGXO05arX/GVlfMifRFiYqo2z6w2513fex7Tqo/6JpNZ5OymbzehgOmZkcdfvk99aWdakq2ack3iEXKzecuW0kPWo/OGMm9Jq7CtNPe88vq9MvLy0qFlUpcjY4i82TDHHLWzFpyxQ4f1ul2F3xbtS67fSy8h+9YMCRts5iplAzXf60uZepFxFxLf45JFBt5WDQUmrxzcnLFUMk5F1RpUaFR2oZRhxFRoVGT3Rrs0OGQLrer1a0xCGFP+4o3/e/6PaRae1Fom7ah3U2WXS1OOer1h0noloaQbTTsFR0OhdjBC4GRsqZEpXrblGuwZsq6OZd8y7Ws7umQCZayocX+osA4J2vCUcNeC9dIpWsRFAWH4JbtHlOpRU5OTEyJGtt9yKTjxh1Rb5thr7rk6QDmywTC9LJrkMNRhzXYrtsdGmwTEzPqbU12IWu7D1kwZMlllZpArV4t9jnj98w6L2XVjf6aiIgVY970z8SVhwDVGyRKbpMpW1JbVmIhdcm7r5/w8//1j86yfK2uNyvX68eq0un09+hRvl+trKxoTB5wOvWizfiskkyl8lyDnvpbjCWPm0mfksvl5OX0ecAlT5t0TKdbjAWaZYMBWemQKzNs0jGVIRwvJ2PNtCu+Y8nlwKRIyNi0ZjpYOAs5INW6jHpLh1u02K9ahyXDlo2ac6F486tQmPYkLSsE8RUcBaumVGiSkzXvIgrpyGnretyr1302zEpaCU6XfjPOGPZqcD+UG/a6DXNBNFwpoTzYkhukrLrsxWKA35LhQCW9XU4mxA2kwxRpm4f8r6540bxLGu2SkzbhfeUa7PEZdfpsWXTYP9doux0+KiclaUVeLpy0U+r0a7RDhZaws++zasSk9zXaacBj3vF/OeMryjSIioqI2uNT+txnw1wQPo8HTUOZOn0GPKbJToWgw4tBx7IhZd2SEUR0ucOk960YU6fXqgnTzkgoNxsQ8hWarefmJGOLIomMwfJvSleN2N12h3PD73vhzRHj62dsVo/44Ofv8vnPf1Zra+v3iLz/c67ZRKxULB41vzZmcy2lNN9i+75brCeXDWevSG3NiEVLpbNJK7lxqdiLItlSaevF0MoGO5Srt+CyYa+IiNnpI4GOPBDQ7ls6HFKt06g3rRiVtGLRfnX61OkJDrBRRAIw70thzTNgK6ybrjVGhYdfjbycEtXmnLdqwqYFLfbrdmfI0ylXpt6aGaNeN+uMXg8E/cYDWt1o2RUb5sSUFiMPtvugRZed8VUxpWp1q9KqxT7EQojis9ocUqpajQ4LhtSJWzFqwvuiEhrtDOGgG7b7oDJ1xQTrap2WjYXQwQIkMWXdulnN9sjYNOuccg3mXbRuWrtDNi0acVhW0pQTBjz2XaudXNA/NYpJBOfgDlEx2TCdOe0rNszrc6+48uJKad6giJgW+9XqFVOiVI1oIF9HxSWUabbXmHc02SMqZt2UpDVblopNSZ0eeXmlal31sh0+pMUB1YkGDVXt0mULbmi927vTX3P61JcMDg7as2fPD/+G/ceo683K9fqxqmQyKb2VU1VW/wP/zOjomMb8Lr1tw8bjhx0bf9b26KPGNk6KZcuN+o5V03b4UOCVdHvd/6ZCvSZ7LBp01tcCPrtdrd4wVv2A2/09lzxtxhl1+oLt+JSspE1LyIe9fgHG1aLOhgUXPWWXjynXQMBgL7isQpOGQB69lvY656wONwfM/5px7xVJl012O+cbWh0MIYtRSSWqdVg3o1KrbdoDPC2ty50iog76GT3us27KpjmxsAaZcdoJv2mnx93u7xnyrAnvmXBURNSKMZsWVWnT5gYpK5rsMeYdV7yIqC536naXqJiNEEpXpk5XQKbX6BILAXBNdokpUalVQrn3/FsJFXb6iPO+acFgiCSo0WqfmDI3+ms2zJp1RmOYhCwYtGXFVS+ZdMJen9TigBZ7RQIFtN3NTvgNy8ac9TVJyw75G4H2OeOCJ73n1/S6T40OPe5WqcVlL8jLFbQ7mRP6bmj2q7/xr3zj69/w9O/+O7m1CnUVzXbs2qW9/hELR8f927Ev/UC8/h92zcZjCQNdux15+zv6Mh9WX95hdmXc+MwQ6ZiabLvKaINBr6jL9UnFCw/WKu0itkQlDHtNVCzQW5MhzTdn06JxbznoZ13whKwM8vb4pH4POerXzDobyMz9KjRptN2Cy4ELFA8Py4It9v+x1hb4QDGlxYlgjU4rRrXYFyB7heY4rqw4bSw4U27R7kYn/ba9fkK3O13wzaLuhmsm6mwg464gasAjMrbCKjOjXIMLnjTllH73i0oYd8Q537BsVIPtytUZ81ZopMYQCUnnVZKWLblq04I9PmPGaTkpe3zacnAlXXOTNRhQrkG3u/T7gNngMqrTa9pJKUumHDPsNf0eKGqEYkpEQ8ZVYTq6oM/91s2ZcdqSqyECo1KLAyq0Oe/rwa3XLiouH6a115x8RJSpLQqrC2vSQlPUZLd9Puey513xqhb7pK1pccB2H1YaLVcbazGfOq+ztV0sFjdQebfhlTcce//Y9Wblel2vH2aVlpZKlEWtbSx+39/PZrOmRuckEqWaa7s9dOiTfn35n7i8+oLsRsJ6ZDkE570VaJf1SlWp1GzGGXl5HW4L4sxuAx5xwVNKVOpyl2Wj5l2y00fs9RlVWqyHbJAKLS573pi3VeuwHCYyUTGnfdm4dzXZFQisCya9p80N5p0v4sMzNo27qER1kaR7Ld2ViFN+17IR+3xWRERWSkREmTq1ekLD0qbruzJGanTY7fEgsrsQbJptFl3RbH+gv+bMOKlElZ0+pla3hApZWW/6p+ZcMOARJaoR1WSPo35Vt7s02WUrpB/HlYZgtybtbrRlyaIrqrSadkqdfnV6g+VzzJpp+31WadAbLLps2ajtHtPioKtecdbvqTcgpiRg1y8iKmnJmmnd7tDqBjEJhRC/BZsWpawqVRPyhiaLXJYydXrcY8pJ+33WsNcCIfcGFVrMOG3drGGvms6e1Rsvsbi4aHEs7cP7/5Yb+x5QEi8TjxUmKNcEtT8Ir/9HXbO7Om/27fRvqchuU5WrtzQ9qyrbpaGmz+rymsuZV2zll+3zOSuZMcPekLah2R497jHkOeWaZUOY5Uaw2qath2ycmDVT9vm0fT4vJi4vZ8mIaSdc9bIZp1TrUKLKlJNGvanD7aJB7Llh3pTjEirt8OEgOF8zFVYc14I0t/ugiJg551zxspQ1zfYY8Jhm+2yaFwlTsgoNomIqAi36WoJwqVplasSVmnTchjmrJtTp1+4mebkgVL9k1GEn/Q5YM2W7x/R72LyLRhyWk1FwDR2UlzPmLYVQwsJDvN9D6vSJK3HJ06DJHh1uUaLGab/jRv+VSPj666Z1ucuIN004osV+fR7SYMCKcStGzfqGLrdpV1rUSxWCOTf0e0jKujFvySkx4FFl6kx636i3rRgz4g3VWjU7YNNcACJMBEH9VkgWL4AV07aUSphzXquDytVpsc8FTyhVJWlVh5vVaCeStp6fs5ab0Vh9SC6Xs7Axbm/3HU6+M+gnPvujXQVdb1au149VJRIJN96125GvHrWn8/Y/MFbPZrOy6Zzp9Fl7u3fradqtv+WAqvyNavO7LSemPLfyP6vRqUydIS+GJNgLqrWZdtK496yatM0HDHkhCNqijvjXRSR3v4dsmFWlVWGk3CEqHmyCw9K2zDkvobIYpjbhPaPeRETSqqQVtXrV6g0PgCbrZlz2ksteNOe8bT4gYytoLE4Y9YZMQMnl5a2bC6e40qJlMSahWqeYEqvG7fTRwNYYk7KmXKmNkCy8YV6LA8a8HaykzcXk4YJ1dKQYPVCtU1S0KFotU6vZXnPOF11E18bXJV6WkdRop5R1G+aL+SnLhsWU2DCnKtyUz/iyXveq1u6If2vQM8XT5Lh3LBjU6mDRCrvgsjWTImK2eVi9baHRe1eFppDz0ymuREzcCV+StCoqZsuClDVlatToFlOKnGrt0jZVanbFS4VU7LJ2XWWH/OZvfEnp1C533vDR76uT+sPw+n/UNVtf1aqloc3IxOvmUqe15g6qqmw3njvqavxtc9nLqnO9wdpda8zbchJ2+ZgOt5h2HHFTjqrUatO8Q35eypp+D4XmuV2n22VtiaqQl9ftdpVarJpwzjdCyGQEkSDWrleh0YSj8rJ2+pgmu+VlZWwqVaPL7TbMGvGm2/3dQE+9Kq68+PlJWjHtlElHbVrUYEdwEi3Kh/+2LFkzpdEOVdpExWVsSQSr8Jzztvuwcg3S1pWo0uV2NTpDA3vOdo/Z5yesmFCiSlyJUW+r0WnAI6q1WzUZYIxrGu2QUGHTghpdxcDShPLwZ6fCerTThpkQJbBi0nsyklrs1+HWoqMsoVKv+wx7zZDnrRhDNEDbonrdp88DVoza6aPe9i+c9CXX1m57fMK6ORERq6aMO6rRdl3usGLMkmFx5Wad02h70bk2Ed7XfT4gIqpKe8AdLIXU9Fopq1byo+LRqLLSCqtLmy7MnbMSHZaIbJmPXfqRr4KuNyvX68eu7rrrDkdf/aJ3B5912/bvFSxGo1FX1o9YKx2xt/unxGMJ/R07vXfxfXeX3m8hPSwTNBJd7kDEUb+qw81BqFfYKyetuOIFNbr0e0CLfUpUmXXelgVnfV2LfUpVS6gKI9ucqJgOhxzxq3IBv9/nAWVqiq6CjLSjfkVUTJ8H7POZMOrdVKVNk92qtRr0vHO+IW1DmxvV6XOb/8Yxv+6qVwIlM6KQEXRcVlK5RktGbAXya1yptA1DnjfhqIyt4GyatWJSXIkytRZdVaI6hLzlLLkirkyVNvUGrJqQtKJEZQhYu6hKuya7Ajq8XauDSlQGIe02k94vrsricsWHZ8qqBUMue9Gk9006ZsuSNpd0uEW9Afv9RKCH7jDthIueMu9S0blTpsY2j5jyvjq9KNi7y4MdPK6kCKLrcKshL4aV1oqcrcCniStXb5ePGvSsKceD/XslcEFW1FXuNT0xY+jYcdvbMl6J/Z69PXdorf3ehuSPwuv/YddsPFoim8/qbhowkTthaj1lPT8qFo3q6Nitc32njYkS6cyWYa8Z8JgJ7xVTpsvUm3JcpTYrxgJxdlCpWuXBMt/mRosuB7YOeXkxpaKigTdSiBvISNowp1KTce9osd+yEc326nCLtA0pG9K2FHKiUhrsCOTkM1ocMOI1k46LiISE5F6t9mtxwJxzZp21ZtplL6rULG09RARMhL+zplS1Qc8VnS1lIZOoIN7elJFSqlS9baadMOWkNjcF0OKkau12+aRVU2acUq5ei/2hOVksogo63Cpl1YY5CeWqtIWmtjTop3bISQeXUFJOxqDnlarWaJc6PYEcPaZMnU3zmu014agx7xabhlv8fBFMFxFRHzKdjvi3WuxTodmCQRlJNbq0O6TKqFln5WVVarVm2qwzZpzWYr85F4x5S9KaAz6vWrs10yGEstyKSUtGbJizYEgqv6LFdisbEwbHT4qV5Qy03Gg2M2j26oZf+5df9lM///EfmTPoerNyvX7sqqenx+e+8BFf+fWnPHnqsoGGmwvMimAb3eo+rSHbpqWmG+zquMVLkRecWPmG9GY+7ImrXPCEvLxme8SVmXRMnf6A+96SlzXgEe0OBex8lQrNanS76hWj3gwP+HY1eiSUy9iyatqiQX0ecMBPSVou8jCyMsrUioiHG1KbWee+i9tSSPDp85C0pKteUq1Tp9sC6CqnyR5rprTYr0qbvJwVowop0VGVWox7RzqcXi96Stq6Bttt90FRMROOhhv3knaHdLjFmhnTTmqxX6PtlgzLSdsK4YvXHgqrJjTabtV4oOTmJVQosCqSYhK63Omk33LZi2p0K9do3WwQxkZsmBUR0e5mXe606JIZZ1z1skTAmhNx1u8FrsyGmEQQ2D5oxZispLx8mNB0FJ0ey4ZVh6arYDe/IK5ck90u+rZ2hyy5qsMtmu1Rokq5I5qCbXnNdID7RdTbbiB/m5L8Dn253a6eHTY4/EX3HfqIXR3fe1P/w/D6f9Q1W969pjKZ0bd+wL6mn9BU3iceKTQTx5ff0FTR7vTK02ojXfryDxrzji1LSuwXEQ0NXJ2qMFm56tWQ5t1hzkXJQEC+5GnN9mm2R71tGsL/5y3LcjJaHXDFKxIq7Ap5NhGUa7BsOIQc7payFujC6WJq8lG/as2MBRd0udOcc3rcrcluRKyZUKlVrT5x5S56UsaWTreJK9doZ0jkuhqcN6+KiLvV31Stw3FfNOx19SEhPGXdmgkrJpSqVqvbvItK1SpVLS+tzQ2mnTDpmFY3aLDDjFPyRebtoBI1Jh1Vq0edfmPectkLNszpcZc1U4iExqpK2ro2NxbFxcmQzFyrR5W2oEHZa8TrYX18u0qtBG9RTs6od1zyjA43u8HPKtdoznnj3jHrbGjsH7Tksgue0OqGAFk8b9OC435TjXbVurS7ESy4DEa9GcJMJwu6mUhURb5RZbTB4taEbPmqHd17Ndd2qyytcXnmZQ/f+FmV6zV/rKTwH3Zdb1au149l3XLLLVpaWrx1+G3HDj8vncwVbaOf6f1bvvGbzxdPsfmtUlXpbqc3vx0cPrPGvGvTvDYHxR0w56Je9xW1HlFx7W6zy+OyktLWCQLAhHI7fMiaCYsGlakx6X2lqkVEAk68VL+HNNsjLx8ouaW2LCpXL21dkz22AnmzyZ4wBVm35KpV40pV2bSk2z0BNPf/OHUuecaiKzrdJisV6LDZotsoL+92f8ekk6a8b5/P6nCLCOYN6nO/g37WFS8a8oLtPmiHjxj0tEHPOuAndbjVmmkjDptyUsSXdbtLg+0Bmz7rshd1uUNUIe+nQKBdDRF4h5zwG8WT4pbl4MIZtWnRfp8PuS+ztvuQUrUuekKJDWf8nlq9yNnjky57WdKSaaeshOTcVZOS1qSsGfCoGh2qddq06IJvF6F8k47JSgXN0XBA/E/b5/M2zUsFC26lZjlpcy4EGGDG6PpJsVhcIlats/ygm2o+4tTCs157/yl1lS3fM2H5o/JV/vBr9u/7vS8+4+Sro7YqVpVEKuTzecnMhq30ukhu3byLBvKFFOAanVZNiCmVslFkegx4zJxzgWS6YtBTxQa86rtO5/MuGPBYWGOyYtSCwaL7ZMYZ1TqCNuQsBMhawqLL4X1bL4qws9KIhGlar0nH1OjU6kZZqRBgGZWzKC+nRpcydS55WjZME6q0yMqY9L51U3b7hCnHVGm1ZUHali2LUlaUqJaxhbyYhC3L1oOj6BpvZsuShIri1GLIc3Z5XJVWi65qMGDOBZOOmnHWLo+bdsKg56SsucFfsWFeznRxcpoNYaElKoOm5v2AguwVkygiAlYCv6XXAxYNhqlphxVjNsyG5umAHT6izUEb5jXartVBY952ybMaDChR7ZJnijbxnT5SpNze6u/JBWH+inFblsy7ZNwRO30sOIVmTUdOykVStpc8JJPatK11r/7W/fL5vFMLz8pUztnX8zEtNd1/oqTwH1Zdb1au149t9fT06Onp8alP/8FsoEQi4Su//pTfe+e0pcES8XS1VVPWTKrQpEwNcjrcZs2Uam22+YCcdDFkrdV+MYnAVMgGimqlFaMa7dBsn0HP2LCAnAjStlz1kqiYy16UtqFKa8jBabFpwZJhSaviElrsV65RLDhYYko0KPBfrj30G+0w4APmXZKV1uV2eTknfUkEjXZacEWpCouuWDejzwOiErYsqtSixT4FRPtFMQk1upSq0u8hk46ZckKrAzrcbt2ceYPaHTLqjaJbZ920tE1LrlhwOQQBpkJq9EDRCVQIEiyVC+yPEpWGPGfRVXUBENfnQRUaAnvjrQDP22fTQhC81lgPIXunfEVciXrb9LhHpRYzzoiK2whZM5vmVOtwzjdNO6ndjfo8KGW1iDqfdcqqKWkbAem+x7ppQ76j3jYRUVe8bNGgmLjb/X1rGxMubr6ttDqmZrFOS81fcaDhg+ZnLjs7+naxWfnPzVf5o67ZU2f/sVfP/KYJs6bTZ82mLtvMrEgot2JCd0j97feQEW+45GkRBdZG1NtqdUnbCL8e1ecBbW5SplajXaq1K9fovG96wz9To0O5xgAsrNLnfj3uNe+iJcNiJooQvUKw4KItS8rVq7dNTKmMjQDby0hZVa1b0oI9PqXFPstGZW3JSWm237LhYEa+0wVPyEg67asueQbElavSKm1LyrpRb1s2rM/92txYtPSmravQ5IInnPa74ZCQKBJoNy1adDWEOaYd80WTjulymy0r0jaMO2LMYevmjXjdpgUb5pRrNOO0XvdrslskABoLzqIpqyZs8wErxiSUK1OjLHB9VoyJOqfZHtXaDHle0poqeRvmzTqrVI162wN4byy4AVfMetuiy7YsOuc/abZPuUY97gmvobCCWzfroid0u9OWJbPOBZr0ikN+3k4fdN63bFmyHpk0nz9nOnlSa+SAs5Mz5rND1hOT4nUb7jv0keJ1/CdNCv9h1PVm5Xr92FcikfgDH6xrp9h//n/+S2eOnTWbnULMDh8KorhG53xTjY5AA703pK7GFfJyKpSqFZMIa6NqG+YCjOySVVNK1YoqkbJm2YhN8yo163GvUlXKNVpwyRUv6XFPyKxpcNlLAWiV0WK/vLykJetmRRQC/aq1iyuTk1WtXU4moPQLN7gaXeoNKFHrsheNeVezPXrdp9UBrQ7KyTrjqzrdbsGQuNKgDbmBgPrOo8udzvm6JVfFlarX77IXrJqwbtoOH1al1WUvGfG6qRDwd83WXfh50jaPiBAEkReKpNF+DzruNxCTDMm4qybV6yvqHiIiavXZ5yesmhITt2pCg11Wjeh0mzr9omLysrZ50D4/4aqXXPS0cUetmLTgkk636XGPrYBH73SbCs3O+Zqz/pNSNeLKHPfFotukUrPzvhnOzhviyg1EHpbPMazXaOo7zqw8r3Gqz762+/SU32xo9Hn37kmLReN/7HyV73fN5vN5Lc0t3vKO2bVhTdn9etynRLV0fsNlLzvld5RrDKC0Jcf8B+tmlWkEy8aVqAqwuKx6262bDgyOUouGTQUtRquDKgPDZ8jzYsqc9Q1NdikPYL2zviYqbt5FheTuedU61ekLn42ohPIgnN0uIqLHHca9o06vKm0qQ1rytJOWjMhJFRO0r7nDllxVrcMOH1ETSLJTjklZd8WLet2v210ytgJ5uE5CWQHcZ48W+4p6j0JA4FYgSl/R7mZ7fNKiIUNe8LZ/LS+nzQ3qAoG6Vq8Bj7no20Vdz5zz4R4QD1lLY8a8W2xaCiiCehvmJFVIWgtT2E0bZjTbrVRt+Oy8IGtLo51GvaVJp5QVGZtimiy6bMTryjXY5mH9Hiyya7YsyKPNAVsWTTgqK2nGacuGRUQtuiIq7pAvaHODFRMmvGfTgtJIpa7KGyTSlVZzo2ZyiyZWsirrYz6y8+e+Z535x00K/2HWX4hm5Zd/+Zf9H//H/2FyctK+ffv80i/9knvvvfdH/bKu149pXUu0bWpqUpqrMVB9h8WFZ+30cf0eCZyCemPeUalFiRoJ5SJiIoTR+oIti4FtcC29I6Yi2CunnQqAs0JmSOEhebsmuxRSfgt5PAf8tHHvBIjTrqK1tlR1UQhbqUledcBwFx7ShTPqeDGnaFPB9hpTIidj2vEQavZRM/aZd9E2D9se1lOF9NUkItrdqN6AKcelrAU8+Kq8rJysKi1iSuWxZNiKcVNOmHFGk11mnbZuxrBXrJrS6RZblvR5QIkqFz0ZxJDn1OpWpk6NLrt8XJV2g54z7VQAd21YNy0ib8y0pBUZG0VHRrlGVVpUabNhzpRjdviQO/x9c84bdVhChUqtIiJ2+IhFVySthodLtUot5l1Spy+skgoZUPV2aLLblOPq9RXJwiWq5GRsWigIEa0rUWkuf0l35Db9+YespIYtJ84aSr+kZKZcOpK0nFxwcuR146vn/gBe/49b13D8Pfn7TDbmZDeabfOolpKdIqKmNy6p0WXFmLgy/R4UU2LScWPedsULKrUb9qodPiwiotk+GRsqNElaNeQl887b7jHbfciqCVOOq9DoJl8w7USgt64b9aaq0DT0us+0ky56Qr+HQ2OwJScjJ+uyF6yZsM9nDXpWyoZImHo1K7hL6vRZNiwZmqasrEWXzTpj1bhe92q0U6XGMOWKaLJHVsZxv6lCo2hoU9I2AgOpw6T3bZjT636jDmt3Y5hcrlk1oVStHR5Tq0uTnbb7kPf9B2PeMuAxy67atKjRHle9ZM4FOVmdbtPvYauB0ZMJGrZ6293hF5z2ZUNe0On2QJdeURpSlDfNS9vS55ArQbOVlTTiDTlZayaK6+Fr3JgRb+hypx53u5b6fs1yXciuOqPBgEptstJaHZC0psc9muw244zLXgimAa540UpkTFW02Z1VX3Dvtp80OzlnfWvdeumom7Y94MLKK05ffNe21oPFycoftcr8s6wfebPyla98xS/8wi/45V/+ZXfffbd/9+/+nQ996EPOnj37Iw9Oul4/XvX7E23FMs6ePWtxpVRdbpsBj4qIKVMnKho+5GeDhmQzrHposdeQ74SR8Z1FLkWhmYkEoNkVM86adtqIw4HR0Vx0aNTqDs6KGQM+FPgsFzXZLSOpSocNcy560oBHRAPwKW1dTq7YGFRpC1+nkFS8YcGw1y0att9PSNkUCb6WdTMWDEmE8Lc1U2Esh2E84wABAABJREFUni1qMq7dwOv0BmHuuEXH5GXU6hJXasmwFvuKqPsZZ5zxdRkpe33SpkVRcZsWZSTt8KGwHHpRqVpd7lCqNlhaXzftpLjysAJ6UNJKaJ6OueKlwPA4EbQwC8GhcptJR62a0mi7mIRme804XbQ9V2iWl9Vsr3O+AXb6iDY3WjAUrKnlcnKmnbARoHlT3lerX4NtEsqDVXtJg51K1Vk2rEqrYa9qju1UH+nXlb3D/NZZqbI5rbtLHL/wnoXIRZNVr7rtQ/v+AF7/j1vXcPyJWJmyzTa9FY+pijVZTl6RzWdMRN43kz9ju8c02i2uTNKqPvfb7XEnfcmEo8EWXmrJVRWa5aTMOh9otUMSKpSps2YyTBILjJ64Mt3uCrlOa1rdoN0hOWklqqSsG/SsmBIJpUpU27RgygkLLulyV3Be3VLUu4x5W4v9roUelgVoXZW2oJ05G1Ycd7nRX7VuJmgvVtTqUaFBr/ud9fXwe2MqtchKWTFm0eXg0MkGevSUDfPGvWvdrAqNQcvUXUx93rKoXp9V4477D8o1qtNv1agx76jQJG0j5BDNqdGl0U6lqsMks8K6mQAV/JYlV5SFlVi5Bld9w5KrbvP3ZEKGUr8HLRhy2u+ad7HoHoyIBU1YYaLS455AjM6GqIMCJ2i3Tznrq8a9q0SltHV3+x+c9XULBtXpU6ZWyrqLnrBl0Vp0Ujq/oT7Soz92n+mJWRuba2a2Lutq6FCaKP8D68z/3FXmn1X9yJuVf/Ev/oUvfOEL/vpf/+vgl37plzz33HN+5Vd+xS/+4i/+iF/d9fpxqe+XaLu8PufkyJyJ5aP684+Fh3ZcVEIEnW5zyu+EJN8CPp6sJnuNeNOUYwY9Y6ePStsIjhcBa/6aZcNa7JW2aZePq9YRGoAx004W3SiFUMSEGadEJSy5okqbVgeLwWK1epSplZe3YNCSq8FSfNmEo9odsmrSBU/YtKhcnZN+O+DBp62bNu20EjViEmr1SFpWp9+wN9QbsGUlBL0titouggqNYYdeK2k1pNJO6fOgLncYc1i5xkAsLdzAZp0JOpj9ISyvW40uG2bV6TXuiFRAs0dERJXY7XF7fMqmBXMu6HCLJrslVDrjq8a9o9tdRryuzjalqpWqk7RmI2QDxcSLVNCctHVT4XSakJURV6pcfTEzJmU16FkWgzW1Up3ukKWyN6xL+pSosGrKlsVA6V2WtCYi4oXM/2hv9NPKI3XiuQrL6/N27BownHvdz3/i837qp3/yT31jv4bj76t7yNunX1CV6tZStl19aZe10gXzW8NWNkY12Kbb3cUGsl6/On3I2+YDIV/pjGknRERNOa7VAV1uDRqMMzbMueoVve5ToTkg7kuUa5Sxpctt3vGvbfeYDreYcsyqSa0OWnJFxqZLngtuqbRa3bb7kAg2zKrQJC+jw80uejKsQO+VtqlElYiYTYtGvanTbTbN63GfKq3K1YdsnxPBit1i01yRU1Sm3pKrNgJYLm1DmXrkzLlo0vvmXbRlWbtD+j0YHu4bIa7iUrAlJ1RpMeGIKl3Fazuhwg1+xrA3dLk9WO5LgzA9GiaRGYUgxkNGg/B8w3xRnxIJfr5hr8rJ2OmjOtysUptlIxKqzDlrxilN9toMjfpuj9sK09NCLMV564HjlJNSpc1JX1Khxa3+lhodWuxxxtfMu2DBkFmnLRnW6x5xCanYsr3xn7KZWTW/uWw9vWQ9O2dwYl5lWa3upl3FdeY9u1Pev/KdP9Yq84ddP9JmJZVKOXr0qH/4D//h9/z6o48+6vDhw9/37ySTSclksvjzlZWVP9PXeL3+y68fmMLctEvyYMLI+BW5fC7oNsqtmVKmTp1eO33YSV82601RMd3ukpNRp9+QFx33G8Yd1eqAcnVmnDHluDkXlKmRkbZm2qjD4kolrVk3o9F2/R4O4s4xg5435aR5l6yblbap1UG7PG7KcRc8qUSlMvWqddjlEwY9a9OiS54yF07Hqyb0useO0EDNOiMeTrqFDJxVtXosuiwmocVeg5531tc02a3JLstGLBhUb5vLXrRqQpfbTToeqLvv2woPlBb79bpPrR5xpRYMBe1Ot0Y75WU12I68Ck063eo2f8eIN2SljHhTlXbtDslKSlqVtm7FqIiIXT4eHAzvGvOWBZcd8PkA1Crg2rNS5l1Uq4fQANXosmZaQlkQJieKKbzXTqaLroSHZKUGA9bNGfSsjKQKjRpsDw+kcjFlorZbMaJKmwnv2+vTrnrFam7S1cjLNqMLcptZRy+/KNa+4MGHHv+hnECv4fhL4xXSqUwBaBYts5gcN7V2SSRTQoTe/L1qdQUqa15ceciSokytnT5qxGG1+mya02y3PT6tSnMRm1+ISnjLmLc12y8nhbwytVZt2LIkY0urg5KWJK1acsUNfk4Eldq0uylMrDIiIjYtWQmI+6RlKeuqtNnl4877ljkXNNoppsSioUCPbtLnQed8TUKZiLhIEPL2uNe0k5JWVGrT4ZBlw4E+XGPaadfCPGMSwYG2pM6AcvXB6fSoQnbURBBrz0IA69XYste6OTf5r5SqsmXZsv9vmFKWFNPO182ErLBt8vJS1mWkLBuxZkpOxi4fLwZnXnOsbVm0y+NqdMpKSVuTsSkrbdHl8PksUx++bp7ATYpaNxuAdGWWjISGe15UQoebVWsvpq6XqQ2xFlc02Cljw4jXyedVRhoLeIKtDWX5BpWJZrHyPeY3Rxy7+IaVzXmJiqi5jUnfPvl/K+tc/VOtMv+09SNtVubm5mSzWa2trd/z662traampr7v3/nFX/xF//gf/+M/j5d3vX5M6g9LYc6WLctECw+sgj2yTU7WlkXTTmux113+vtf8ohN+05Tj6vQpVavBdlOOOeN3XfKUhHIJlUVnRcaGFcMEMFuTvaa8r9+DxRtrJCye9vqMegPGgvNlzZQhz8nY1GSPAQ9rtFudXjFlhjxn1mm7fNQuH3fK70hastdnNBgI0fZJHW6x3YeMect53wp8lXU1uq2bDRCtLed9U6dbXEuGHvSsBYM2zKvRJYdhr8hKaXOjrKRWN2i0S4V6UTH1+rW5UdqGDXMWDepyhwI+PaLJLhPe1+1udfpMOiZpWY+CPi0TaB/XQh8LuPB1LfYZ9KxR7+hymzxmnCm6ppZc0RRWHwWb6pKImJy0uHqTwd5aWC2d0OlWzfZptKPIA8nLWTdjznnLRrzhn6nQpMudutyqWpdS1UEz0yHrLYV04CYH/LRT+d8ykz1tK7NhrvqIn/3Cp35oN/VrOP7k2oZESbxAG03OWN6aVpPvUploVZquCoGOcaWqxJRKhDDBRHCoVWpRpk4E3e5SqcWWRS32yskiIidtuw+FCcwxXe4KMQnlFgyZciI02JNGHQ4k2cK6qMOtxdP/rHPFfz8mEdKGs+Zc1GhnyITq1mq/GWdd9UoIySxMNvb4lAYD4sqCu2ZMXIVCrAQRsaKrbdOCUW+54iUVmlGY4jTartFuI16TlbHfZ8RVOGHRihHd7tblDlkpo94IzTVl6qwGXVdhvRtRqlq7m004qlyzq16RUBVQde9aNqJWT9BXLbvgCXMuOuDzutwWYIuF9VtUQre7AlAuEwjBhdyrDrfa5fFiNtGGGVsWA6W6sjjhiystinMLadE1RVLulsUg/C39HpjcHX7BkssuRZ6ymL9iLTMrk0tpKO9RXlaporRGSbxMe9VOgwtvG549J9a8Yr1qxB2fvde9933mRyrN+JGvgfAHHiD5fP4HJub+o3/0j/yDf/APij9fWVnR3d39Z/r6rtd/ufWHJdpOL484OXTYttpbpWY3xfKlQd3RqE6PJcNmnA2wNdKSZpwx6f1Ao1wUU1oc17Y6IGXTmnENWrTYo8H2MGK+4LLnVet0yF+37Ko5F+TlNNgRkmG7w/piWp+HQnLs89I21RuQsmbWOXPOFd1GXe7Q4ebAcyhXrUPKqjyqtGu0U05aj7vNOGPLsgot5lww6k15edU6VGkz46xxR5SqlQjThBUjIRagcFJuslvKugqNDvhpG2asmghj/yzy2h0y64w55/W6T15WRFyHWx3zRYOe1mRvmABElagOwuJFRLS5UVS86IDKSouJByFkr4ueDPqERhUaDXtNIdKgK9ixl827IBJC/FZNaLFPs33e9+vS1tzor4VGsUAQXTdr3nl5Wff4h8rUGPG6y160ad42DxNWSjEl0jatmJCXt2JUtS51kV6R1kH/zT/6goGBgR/aNXwNx/+d//iyLStGNq/KbVWryneIxEotpyalcklbFoqE2WsryU2LcjLy8paNhOt4Xbubw+l/0rh3A4I9XuSANNnlipfFgk1+zbSMlFlnLbrspC9psVdenVI14T0ooO0nHNVif5HqXFjHcdrvmnPBLf6mlGXTTopKuNHPGfO2DbM63GLBJZVaxMTV6DLjtGZ7FZqpDAqOsowtE46a8J4625zzdTGlanQG0WrKGV+xYkyPO3W6Q9JKAN1d1eNeUVGbVhUSx6ulrYuIm3JCi/0q1CtRY9O8Lnd4zf+qQoO8vE2ztnnMhhnzLhkJURdzzpl2Urk6496xYlSXO3S5y6YFy0aMeEuzvdI2DHrGsFf1ul+rG6yb0eNuCwYtG7HoiiHPB2Belw43WzVpzjlJq2ISphwXV2YzZHBlZQ17zZIrkla0O6RWlwr1VvPjhQNFfsFSblw2lyukQ37X9dZTe1Bma9ly7Xl/67/7aT/10z/1Q7ue/6T1I21WmpqaxGKxPzBFmZmZ+QPTlmtVWlr6I1EiX6//MusPS7Q9O/K2+HqTB9p/0n9a+KcmMu/pcrucrE25YsjeWV834o3ASqiVUCkvLyej2X4LLoUcjg15aQf9jAbbgztgQE5aqxtc9C3Lxiy5rFqXJcOyUkWbaaUmPe52wpdUa7XP55z026adNupw0VJbb0C/B0WDGLggHJzT577QfHQWMebl6iwYNOmETfPWTFlQp06fRjtNOa5aux0+rFaXFRNGHbYVUm6jSgy4qwhtq9Jm0DNS1kw7YcAjYZIyb8WYmBLk1ekNup1RebnAmqizzcOGPG/EW8rVS1mx5IproYslKgJBNBsYsVGrxpVrsGXZFf/RVS8r1+ign1Gr26DnXPaCa6nTlZpd8KRlI7JSWt0gbTM8zBKWjTnj93Q4JCNp3Hs2zNkwa5tHbfOgap12+pjjfsN5/0mzvbJSgfh6TkypaceVB5R9TFxH2T4VvdE/k9NnZWW5c6PvKFvYJprIG984ak++TzaXUxZp0JzfG8TgjZJWVWoK8QcFJ1NUiWmnZSTDSmFaVFS5BkkrgTabDXCxwhwhJiEvB6p1hNVEqeqg41g2pt/DKjSG6/OCLStGvRma1Izq8H4tuGzJVTElJhxRq1vKmqQVZ1yQtKLZPv0etmbChjll6lRqCc6683b4sJQ1MQkxCavqTTlmynF5uaKVf8pxTfao0qrBgO0+WMz1iSkJyeRfd9mL6vTLSQXXTRpRg56xZsoBPyVP0Q6/aVZUVJsblWsw7ZRNi1rdoFKLdTOuesmyUW0O6nGPdMhIWjPlRf+DTfPK1Rv0tDGH1dsmbUOrG4Itf1wECwbFldrr03b4iDf9U0su2+6x4qRn04JVkxZdMu2Uam1FsvOm+UDYvVerA9bNet+/FxGxZkbSWpG5M5Z6T2/ubpvJKbVVjSpKq0WjcfO5IbPJM26/42/+0K/nP0n9SJuVkpISN998sxdeeMEnP/nJ4q+/8MILHn/88R/hK7tePy71gxJtM9m0obHz+ssfFdus0Win+dgZa7lx1fnChCNp1ayzNszZ7fEQXjZhwWVpa3JyytRJqAjI8UaNttvpIya9r8U+9bYVbyrNDsjJG/OO7eqUqrVuyqYF5eqVa1AIzcur1GrOOaUqVWnV7gZENNhu0jFXfMeqSe1uLNo1tyyp1a1Kqw2zSlSadMwFT6rUbJePS4U9/4T3rBjT6mBY1VCqVrsW1dqd8VVDXrDXp+3xE6YdV6ZGTIlud0lacynQTwuo9RErRiVUSCgPzVzOed+Sl5MPwsM6/Trc6pyvmbVhybBNi/b6THCdlKvUEh48aXMumQyMlCnH5OVVaNbhVrPOydh0ky9YNuycb+pyW+DajNowr0JjkRgcEbXPT+h1nzFvG/KCCUflpHW5Q51Det0nI2XLohI1bvAz5pwz4YjdPuWSpywYQt6iIV1+SoUmGRuSqaSGksofOoNiZGTEa0+/766ez4uXdTo39ZZ3Vr4mli8z4BFl+YJbZ9hrtiza41MqNQdx56xyTUa9HNZdWSvGgqOlXqUWFRqlrIdcnzWb5i0bC0F3s9ZMWTVu05LdHnfVK+LKikTmBYMqNet1v3INTvhNGSln/Z6SAO9rtsfd/ntxZca9Y9jr5pwXV65cnYN+1pKr6vTocEsgP9ept02FZiPetGRYh1tUaDTmHXPOWzAYkPIHzRksBg1u96h2h4KAdrPIe6GgS5lyLDhjluz0EUnLLno6fK8T9vpMiJuYD41MzvkQxVAQBK8HC/xFZ3xFLEw1Flyx3SMhaXlbuEY31OrR4x7zLup2lzUT5l0y75KEMjf5ayo1q9Vl1UTI7aqwakKtXtt90AXflpHU5TZlGqyZDnq1C1LWNdmpx33hAHBVVtqyEcNe12yPbR5RocGyUZc8bdW4pvh2U/kTNs1qSO8wvxJXWhk3mnrXdOK4gR3tP3Bw8OddP/I10D/4B//Az/7sz7rlllvceeedfvVXf9XIyIi/+Tf/YnRz1+u/7PpBibbpbFI2k1MerbW6tKou0qOv4jbvb/y2S5nnxELAX0JFkU/SZKclw5ZcNeO0Qc8Yd0Tyu5gi7W4KEe0RpWpAmfrgMohqsceYd7U7FF5hVIkqpWqKK6CIwmucdkq/h1Tr0my3SceUqrXbJ4x60wXfNuWEdrdKWrZqXIdbRcVExYx620m/FaZBZca8JaFCh1tVaZNQbjYkIhdEve8pCdkmCVW63anRbjNOhIlOv1K1phzTZLcVI076DR1uC+uGbNB0tDrpS9ZCRlCXO8PJc9aMU877hrQt1bqsmZK2ZdpJuzxeFFrmZKRtWDEqbUunW+3wMaf8thIV0tZtWTLpqBJVBjxqLlBVKzW5wV8JssScVRM2zKvXb9wR66a1OYS8EYf1eUCjHSo1Kws29YKQNCmuTJc7vOvfiCo14YiYEpvm9XnQTh+Tk7YWHTOdO25mfko6nZZO//AIn9c0Vx+97aetrKwYf+OIitla8UjUYP5pmVwqNM61Fl1xyVN63R9Spt9xxtckrajWoVSVipCk3GCHLUua7BENgLzqEElw1atWTTnhS0pU63a7RjvV6BQRlbKuwy3GvavP/RpsL049qrXp97AqbeadDwnGOzUYEBVXpy9Y+PP2+6zBEKJZ0LWkNdlt1jmT3g9p441KVFkwGLQtV62Z1uteO33crFN2etyaXzPhiErNrumJUiFJ+5qguuASWhNXLiNtyIumnVKmzoY5TXYVWTuDnimuKpOWgpOmsNYsCOqb7PYJecy76Kyvq9dfbP4hbUPSihZ79XvIKb+NiG532+MzBj3ntC8b965e94V7QZ0tK0WB7KoxbW4044y4Mhc9BQop6mNiIXCzADdsVKZWTIlOd3jTP1Nvm30+LxF0TGXqZGXMOWs1P2lb7AHpigUTqbetpKeVZuLqK9oc7L1dY8/SX5hNxo+8Wfnc5z5nfn7eP/kn/8Tk5KT9+/d7+umn9fb2/qhf2vX6Manvl2ibiJWKxaNWt+atbOXlYxnJyIq1fGFv3upgcf8966whz4mIaLFfxpYKzbYsOedb4uIabLduSoUW17QNhbVQTl5OqRolKm1KhelDgU0RDf9KoSJh191gzFsqNGpx0KoxORnl6sw6rUa3XvdbcMlIWA+VqrNgSETEluUQ9PaaRjvt8UnlGiwYMu2k076iVpc+D8pIWjOtzQ02Qp5LXKl1U9rdbN2URju12C+uPDhtOm1Z0uKAIc/bqTWscCo12mXWOdNO6XSbXvcrC1C9Gh2iYrYsueI7llxx0E/LyZhwxIjXrJsM4W8rZp2zYdZOH7Vhxuv+V8322u0LobnLOO9bLnlaj3vElKnVY8uCFaOBWNqh2R6rJi25qtUBM8646OkQHJlSobEYMletw6ZFSUsiorYsFUWk0fBYWDOpEGL5aIFuG63XnNjl/fQXXTp3wv/0C/9URVWZm+/d56677vhTrYV+v+aqsqpCNrrlQPtDYmv1duc/YWNz3VpqQSaXURGvczzz2477DYuu2rSgkETdbNHlovNq1hl1+kNDcUGZemXqlISQyEKw4x7rptzhv1WhybyLVozLywYJeuHr9XtI2poqrZJWA1DxLfv9pCodElZC0vPlsB5l0nHN9mi007h3nfU1rQ6G1VSjKm1FJkiBZVSiRmeIR2i03Qc12xPWlm8a946ohJQ1rQ6Yc16HWwKQbVmJKvX6rZsx6ZgJ70koC9dRLpBzk6acMu2USi36PajeNitGjXhTXibA5trDZzce8ANzktbkZXS5TyFdvbQIXiwkpe+VUK7HPc76ukY7VGt3m79j1YQh33HQz2rVLGNLpRYrRrXYJ2XNgkEVGh3yBWmbkpbMu+hkSChvtic4BdtkpCStmPSedoe0O2TDjDp9IoF/VIhg6JPKLlqJjbu76q/KyZjdHDKVOaOtrd5K7VE33fWjYap8v/qRNyvwt//23/a3//bf/lG/jOv1Y1o/KNG2tCLq6Mg3tMVvlqjJOr3wLc3ZA3b7lI1AwKzVZZsPGPKcS55SplZlUNhv8wHjjlg1KSIqG3Jaq7SIK7fospIwKs9JK9do1nkrRgsE1DCWj4oj6rLvWDKs2V6LLuv3oHkX5GSKDIxS1UpUWXRZhWYpq656VYOdFgw66+sqNAVi7q0O+hn1+m2YF1Oiwy3GvOWip9Tq0+MeF3xbtztFg/WZvE0L4krl5Yo49KgEQYg447S4cmXqg4CzEJKYUOmSJ9Xqts/nZKXC1GY4nDLXNdpt1YQlw6Gp23SDnwuCyWNSVhXw+r0a7VSmLjgcatzh76vUaN2cMrVq9Trvmy56Wq44Iq9Vqkpe3qyzxryjXKOslNN+T7NdOtyiwTbHfBGF8MkSlXIh36kQTJlXpTWIjitVazfthD4PGPNWyLPJSOc3nEt+y2JkSHStzpV31oitOHdkzMtPvuVv/MJPu+WWW/6QK/QH1+/XXF2bCO6su9+Z1CsuLL1od/TjlqKF9WU8X6LbncYcsWnejX5WuQYjDhvyvA63aLRTXKk5Zy0aCuvGamXqLRo075KYUgsG7feTgS2zrkSVWedlZSwbBXt80pKrNs2LiIgr1+Fmg5414nXd7kZOykZIBF4y57w553W7y0lfMuWEGt1KVDvvmxKqwkqwMjQXR20Gsm1cqYQKE94z4YgN85YD6bnNQRPeU6bGqklDntdge+DN9BrxpqtelZWyy8fVBvHujDNyUtJyMjaK+pURh21aVK1NlzttmJOxZcuKRZdFxMSUKlOjVq+4MokAX6zRZdVUUbcTVxKAe/VhclkuKiYmYbsPmnLciDc12yMnV8wwykqr1OKkL+l0O/JhzZo14X0rJqSs2+ExFZrMBj1VWiHYcpfHVWuz4LJ62ywYsmoyHJKqbORnXU2/aefG/SrjTTLprGxsS7pxRGX/hjvvuuNPe/v9odVfiGblel2vP+v6fom2pX0rKmNz4stzRi8OS2RqtTmkTK0tC2ISohKiYgY8ZtFlk97X5/7gGmoo6li2LEgoN+w1O31UhUarJqyZVqc3rIai1kxJWfeO/0s8LI7O+5YN88W1SaUWV73sgm+DcvVa3Ujgh5SpVaUtMEx26PdQcTR+1tfU6NbuRu1ukZOxZFhOVlx5GLMXVlHTTuh2t7SNQM4sN+DRMOl4z6YlVZpFxYMDqElERNJKyDUasuCiaaflpEREXfKMeYMO+inV2pSolpWybMyI1016v/h1tiyackpEvihGhJhESOCZR8H5UaVNsz2qNBehbjElSlSp1SMrqd/DbvHzNkMYXLO9tvmAC75tyAvKNcg4ZdmYuHITFqWsF0fwc85LWSv+m9f0MZe9aN2CK16Sl1er15IrLntRj3sM598w5ZgG23RGb3BHxWPSNoysvmfonWP+1S/+qv/tX7b8iSYsv19zdW0iWJIvd0PrB31n+T9ay86I5stlIknz2UErJqRtFpOUo+J63BOakkKzXcDt77VszLi3TDmtRIUSlWLKdCg0V7U6TTtl0jEzTtm0JCYeUoMLU7kS1RrtCuLqnHWzmuw27HULBpWpV6vXuqkiVbUwddhUCLRMa7HfuinX7NP1+kNqcs5Bf9UZv2PJsA1zYdVypwYDZpwx55x5F4J9fpt186JiLnpSk932+rR5F13whCa71Nuu2W5bFl31qjY32eezgdqbMm8wMFJS5l1QolKd3iD+LTzoS9SESIFUIN9etGnBtFMqNEpbs2oqgAlqwnvDsmE5aVXawyFFEXh3xUv2+5y8bEDqk7JqxJsWDNnj07YsI2LIc8Htc9U2Hwhaq62QsDwWXInTciEyoxDO8a4Ns4io12clfL8TueO+Mf/3tJbt0lTVY6n8vJb+Fp/7wsf/QlHkrzcr1+svTX2/RNsTJ0743/7R/+Xi2jkH/azCY2oz/CjABwvTj6gOtxj0bJFYuWFeXl7GpnmXdLjNuLfV6LTLx5WqNm/QliUxCWPeKdoyR7whbUulZllJdfq0ucmWJUOeV6pWrwe0uUHSShE0V61dtXYb5qyENNaomF0+Zs6FYm5IwT2zaNO8UnVFSFbhBpvWbI8rXlIWgtbWzWvQLyqiWo9GO405bK+fUKbGVniwx5VYM63BDuOOiKtw2QvWzajTq80h9fpDbEBKhXLTThr0nDJ1dvpoGK0XVls5KVNOyNjU7pBtHlKi0rIRY46YdzGQQ9oCWTguFXD+hZvzonHvarZXtzvD91eIG4wqEVdml0+ENVBhbbFhwZKrGuyw3QeNedtpX7bNI+ptk1AhbdOyERd8y7STGmyzZdGAR4oTlStetWnJpgW7fUJ7/kap6KyOyr3isbht1bc7ufCMd9//oie+/aS/83f/+NPj36+5iscSBrp2u3r2qPua/msT1aOWNqedyjwhHikRi5Xanv2QRrtUalahSUKleZfk5KybNuz1EFKZU6Jau1vFlOl1XxCEL2q0wxv+mcP+uVgA6zXaqcvt4kG0OuWYnKwON0uoLF5fhWa0kAA854IhL4qIysnKSqnULKZc0qoWvbYsSqjU4z7CJ3DJcOCDbHrHvyjSdG/0czrdZsNcSENvt9vjhr3qpN/R5iZZSavGwoOd875t2km1erS6Qb0+UXEXPaPfg9rdXJy8rJvW635TjrnqNXPOWTerw6FAjr2ksOYtHFaqtAedV6FxK+QY3S9lXU5GTELSahAwzxpxWKPdSlSKK5eXs2VBXJmUVUf8ilYHbZiRk/G+f2/OhdC8z7roKdNOmnLccvgM1ekx6RjBoUhUtVaT0hYNhqZySkKFOn2uBZaWqVOtXXvJPo35XWYzZ4xuvum2Bwf8t//T//gXqlHherNyvf4S1ncn2ubzeYsri9K5dSmrUmosuapEjQ1z6vTIiYuJKlMXcPmjEioNeVFCmUY7TDupSocON4fx7JhOt4kpNe6dkEcyJyctE2BtVTrMu2TTrLiykGAb0+UODXZosiMkIBfQ/wWHzvMqtShVbdFVlVp1uFmVDkkrBjxi0lF1+pWokJPToMD8yNiSth4gX6XWTDvtK0pUmXHCtOPO+JqIqE3zFgO461qCbkG/UWh6rnjZhjkH/aQtK5YMhzXTzYa9Zty76g2Y8J4xb+l1v3aHiq6pgmPjw/JyFg2p0W23T4SHWlpUQqM9prxvxJvKDGuy26Z5GZviKgJk7IIlV233ISWqwqoqV9Qv5GUkVOh2p2knxZTa6SPmXdDhkAY7LLlsygkRMZ1uVakl5BYdNuusCo163SeCZaO2eURU3KBnlKnVYMAhf92Yt6xuLbh48byG+ia1tbUONnzI1dWjnn/yFT//X/+NP9H+//drrvb23GFw+IvOLL2gu36Pks0m+yrSLiVfcVfsv1Gb6pfKb4jnKqybc9a/NeKwJZdtWVSp1V6fkZFCLiSCLxZThGecMugZFRoCtv2ASi3GvWvKSV3usM2jVoJrZd28UsmAnM/ZtCKhIti8zweh9l6zTulwSLlGI94w64wZp+z2yWIKdsGCXKFGl3XTJryn3rZwHVcb8KhrzUJWWtKyiJhGu5WrD3byRl3ucdETYZXbbdlV2z2mxX5l6lzwLZWaDPigrTDBq9FhxZgKTap0hGakTYdb7PRRWUkxTxnynC2rDvi8VVM2LZp2QtKKtDWLLutwS8i/mrFoSETcpCNWjOhzv2knVGhWqS0QexsDsC/quC9aN6PNDap1iyuzZdj7fl0uxEZUaLRmKqAJhuzzuWITQkTKmgrNZpxRrlmDHaHhypv0virtanS6EnnBnqrH3FT5WecWXjJe8ZK6ROZPe4v9M6nrzcr1+ktbIyMjfvfXnlA/f7Pa0nWJVKmO/M3WTCsE8c1bMKQpJMNuWgiCvTXzBgOYrFWfB404bNDT+j2kxV7zLjnmi5KWpW1qc5MDftqCIWd9TbubdbpZvwc85e9aNipjy4DHQrMyYM20BZfU6Q+rqEdDEu5JZaqtGNPh5nCKXZeXUaFRIQk6r862AJYaVa+/mIOyZcmaKRFR3e5Srd26WVPeV6FZox3FPJhRh62bDVqQeqvGjXjTmil1+kyFE+suH3HEvy0mG2+Ys9PHis1Vsz3S1kLQXNqS4UAN7tbu5rAW2FKj24YZ+ZDTVJhWHTHhqN0+ERqQnKRlWxZtWVKqWlxJ0LrEAnW3U0VgiOTllaqVsiZlTYv9Ni2Yd1Haph736HavIc8646uiSmRsqNTqTn/fuHclLdnug/LylgzpcY9pJ8w6527/vRmnZKWV5xvNLI5LLkdMxmY0tNTqjN/o2Nhb1tbW1Nf/Qd7PH1XfT3O1c9s+b596QnIxpjTapLqsSTq1bjUzqTRSL6HSrLMueVZW2g6PmXQ8WIzv0+qADbOi4qGJjDvtd2xasGVJl9vVGxARUW+bmBL1+o067LIXA3n4TluWjXgtOGBiYuJFumrGa2ac0eUOTXbYNOuCJzTba8AjYXKZU6HJsuGgFclaNhqYKIXcnV4POOuretxj1WQQsMbV6jZvy1Uvh9c54IJvyUqJh2nQpkVNditVq0ZXccU463wA/eXCu1xogCIBPJexoc/94koCsbpGtQ5lGlBoWgc9Jydty6Ia3W73dxBzxL+xaSE0RvUh8fgleTk7fUyn22RtWTHhff/elPdValWhCVmlqtXq0eFWKWsqNetyh5hEINU2uuRpsy7Y6zPmXTLtuO0+JKEi3LsWrBhz2XeUqLLf55RFaszmzytRpUZ3Aa4YGdcS+UlLG9OqyurtarpHdO2Ktw6/fX2ycr2u11+UOnz4bZmpRjuq9xoqOWYmddYun1Sr07ijYkrMuxBgaK0ueMK62SJ4qU6vTFjhFNYV5SG3YwIClbZTqTr1esOuui2M2udc9IR180FMV/AWtITGqODwqQ4I8sJDPiKmWoczfleDner0SSi3YU4+ZLAsG1Wn15Kret2nRqdpJ007IaFCylpRINjpFu0Oydgy47Q9Pl1c0VRp02K/Ce+bddpxvxFQ7jXK1dvlE5rsNO5t446ggMuvt02LA654sWgz3ebhcOpbtmnJmkmrxtXqtWJUj3uMOWzZqBXjAfpVIiNp03zxdceUBDbNtA1zrllAZ50raifmnZe2qdFOFZoUEneTFlyStikqKielRodRb8tK2+kjWgOttBCktxB0FzuKwtyLnrJlSasbDHlOo1063Oy43zLvkmqtEioL9ttcSn1Jj1Rm0/LksqXSDVtNqT/VtfoHNFexnP47yiSq86auXjVy6R3JtXkLuYtK81X+/+z9Z5yl93neeX5Pqpxz7uru6hwQupEaGSRBkACDSUmkRVojWaKC5Rl7tN6Z9XjGa693P5qPLI1nVpYoi5YoSpTETAogARAgEpFD5xyqqivnHE/eF+ffxyRFUZQshrX6foUGHnSfrnrqee7/fV/X79rcTBryklo92t1swYB2N9ni7rB2K4T8FcIA59XqllDhssdt9TbtbrEavkeQtCwqod3Nxh0145wut2lzkxGvWjam1QGwbMy0s+ZcCtC9N6WsS1qw3YMa7bRhTtqaXveDNTNhhZcJNv+YRYO2e4cVY2JKQ/5UWRA/Fz4T+dB4F9YyDXbY7f0WDBjzpn5PGfWapCWn/bmb/ZzKEP5XqsqGeRmblo1ZMlJkk5SpU6vHqFdBiSqbAdO/0yOmnJSVVq1TmRqNdgXLcMKmOWd8PoSQThWjHNoDI2bDrDWzxrxp2mlrpi0aUq4xWKf3yNh03hfMG9Dlds12hxVOhWEvBRFvhz0+YNCzxhy14Kp6W5Wpk7Rs1gVzLqvS5ozP6M3fV4wt6Pe0pchV7fEDKktqVNcl1CQ6LcTXtNbe6Pgrz/nAB//u7Pd/F3W9Wblefy/rmiW0r/FtpqYyuisOOrXyDed92Vb3q9Yhbc2i6ZD18ahRr4kqUaZOlTYpa2p1u+p5Jao022OXR0TDKb/ZPhERY94I4tGoBQOmnNLmhpBRE7HFXWF68JacnKhYMXAwpsSycbMuQUgKnlEdTomFCUEBy71oyJRTbvYxw14K3IftanQqUS0nY820GefMuGCHd6nVY95lLfY76KPyMsaC/blau0Y7lKqR95KYUnf5l1aNWTERBLkPOeuz3vA7qnXI2DDluJgSl31Nyroed4mGE+2iM9bMhCZiUyak7WalbIYxekwivJTWNejTZJdhL7nsSa0OSFvTYHtRX5KXM+OsNjeZcFyNzvAiK3y9Zl0y6DkNttswZ9mYhCqrJpG36R6rJq2alJUGTXYqV69UjUot8sHXVKNB0opVkxrsVKralFO2+zWLBhERiUTEY6VKY1XiqVKXNidsplbNz8//rSYr1+q7aa4SiYR0Ou3KlSt+9z/+nlNfGbK62mxg8w0VmtXZqkKTQc/p8w5l6pRrsGgIQyHPquBS2eODjvnP2t1oM9jYM4E1VK5RiUoREa0OGvKipDW99qnU4pKvesPvKFGlUrNGu7Q6YNSrZpy3YECdHgmVVoxJ2wTVWnW6RdqmMa/L2lRjj4ioMW+KKrFhwZppy0ZUaVeuTkKlpGUpq66llseUyMs54zOyUra4W5PdyjWEz9DvVf+nNjdYNWXauSDabhNXKmNDQoUlQ5rsCSvagmOvIIxftWpKu07Vupz3RXW2BI1ONOQIRTXbp8VZt/hVR31CVkq9Xqd9xnF/WLxnyzXo87ABX7dkVEKFNVNy0mEaWKPBdnMuGPaKqIQJR62alpXUYr+J4J5LWTPpePFgU6JC0pqIvFJ1low64zPmgmauVqfdlW+3o/MGvS29otGomeVR0XhEbWWj4WTu7xxu+F9b15uV6/X3sq5ZQmsrmyxUTitZb1Oq0kV/YcYZO71XyoqctGlnrZlWrdOCwQCP2q3VQf2+HlY6tzkbsk+uBfwVTuhTanWr1SsracQrKrUEAW/KLu8RFbPDI/7CP7ZoUEWgU1ZqExELmpM+KcuBb7LPvCtFbHlMiVrdyjWYdcGCK9rc4JhP6HDYDg+LSgQuy0th9XFHsIx2mXLWLo+EEXmBnrtmWqXm8FJY0uYmk06YdlrGpnL1xrxpzmX1eu32Xs32Bpz6MTmZoFM4ZcY5UTFZyTD1KHxtytQGzH3hxdhgWzh5zouIWlbIMMnKqNRi0nFrJmzzYJiiXDbq1bASWgp27dusmTbspWKo3YiXpayr06NMbcC+J8KJd8YZf+qSR8WV2+JufR6UUCkriXzRxpyyYt5FSYvIS1pSqdl6IOReE/Zm8+uiYmApP2rKaa313d568+jfSWbQt2qurv16z549Hnzo7Y4+93uW14alrWl2b4hMKODik1ZsOq8ikJbXzMlJhdTftbBmqNbqhmJIZ14+WJJLgyZjvYjBXzVpzTQBgXbIx/S6LzQOCeMB2FcZMPDN9skG3H+nw+ZcsGLcRY8FovKYlBVV2lUFW/66WSUqVWo257IuR+TlAkxwQ14hXZq8ES+bc1mtLjs9rNsREBVXb5sZ5ywaMuuiCg3mXdHlDimrlo0gqskuU06JiJlxTpM9Wt1gw6xVUyF3K6dGp7iy4KyKSloO8MUqSUuiwevXFhq7Olt0Omy7BwNAMS1pWZsDkhYsGLTH++zwHklLgUq9ZtJp00677HHrZrU6qFrClJOiEi76i0Cnvd8uD8tKm3TSrPMqtUiokpe2alxTyCJqssv2kvtVVEW01PWIRqMF7d7GuI5tTdZTyxIV0R8bGNy1ut6sXK+/l3XNEjo5PWJ5NqcjfoPyklZvpf7IsFekQnJwiSobFtToLGLlYdWERUPK1GqxX9qqZntd8aScrK0ekLJcXKeUqHbOF+RlvcNvGPWaMW/o86BSzTbMabLLqgkpqwGyVq3eNhExmxZkpa0YU6nFijHbvTOQWrOWjZhx3qYlVzwhplS9PpVanfV5mxakravQaL8PaXHAWz5uxhnrZsWUycvKy0ooCxyG2uKJtc0NIbckGjgmNdZM6XRYmxuLgLAG221xryueNOhZW9wraUWTPRLKTTujwXbz8kWc+imfVm+7FvskLUlJiAWc+4zzBj1jzmXd7lClzaQTJhwNn6XGfh824lXnA2Om3vZgQb1syZAZ59TZJiah062O+s82zWu0Q7ublWtQqsacS0a8WnQzUch3G3dUmQZ5zBvU4RZtbnbcHwR2zKqLHlMXVn3p3Jql7JiV7IR+z8iXr7pnx4ccf+XcD2y0fg3Jf2P32zw3+dVgfC00WoVwu0kzzunzzgBmK2hFFgwWeT3XNBg56SD2jNm0pFRtCJnMhwlCVr2tstIGfEMiMGi2eiBM8FLmXTHtTHAhFbJ3xryuRrdaPRYM2rDosifVhVVTjyPhfo+GAMusAU/Z7f3FiIgSX9TtLlGF9OWNENo446wxbyhRqd0hOz1SnLgUco/SmuwEC/qtmJKw7Kpn1eixZkqdXnl5ZepNeMuSIbu8V7l6VVqD+PhscNktFHPDYsoCF6hHr/tMBMJzLKxNo+LWTMvJqrUl/JxPWTSkxb6g86qxxf2SAUJYHhK04xJ63RdyxNJhgnq1GLy53UN63Fmk/MaVarbHJV814BtBwD6oNbpHPF8mli+x5KpMbMWWtkOqymrl83lj81dEKtd1de304vCfuvXtPz4wuGt1vVm5Xn8v65ol9Mu/9Yxtqffr6d1jaKDeLalfdtQnNNntqudV6xCVkFCh0S7VOop8iTFvatRn2ZhKTYHyWuFF/2+LrgabZ7kJR007Z9oZnW6xblq1DuS95H93p/9JxqY6W0w6FaYqraadtm5GpVZRCePeNOp1ORkdbrHHB5AVVyorpVy9Gt0oJAG3u1m19mCPTqm3PbhBCmPiTrca9Jy48jD63hFcNOtBLFli3axImIqUqlURAgULbJh5UYMWDYW1yHblGtSq0hmyezYt2TRv0DN63S8nK6ZEldZgT+0352LQhhTymIjKyShTH+zZaQf8tHL1IWzwbZrtkQyTpirthrygTL0qHSYdt2k5TMXOiSuRtOQmP2PRqE3zOtyq1f5iAF61dj3uMugZZ3zOjX5GtU6Xfc2CAS1usGTIpiU7vMukEyJitnqbyx53wZfU265ah5J8hXOZL1tPTEonlhxsf0Bv616nk2d+YKP1a0j+ne37PJd7VDqyaTU/oVxTaIDXQ/bOtuLKosCSyYX1R5VFg3IyhnxTXIWMpIwNG+aCaLUEMTOBDrtszEV/ISrhJj9b1GqtmjLiZfOuaLFftzs12i0qKmXVXHBw5WU02lmEtG11v6yUUrUW9IfVU4URr7jRz0lZNewV6+Z0OCwTKLoFgelJa+Z0OqTNDbKScrJh5pNXokqZBnFlZl005JvKNTjjs5rtVatHxoZyDUa8ZtZ5Wz0QAHrlAeIWt27OkBeNeV2jnbZ7Z3HtOOZ1L/p1UXH3+F9EJSQtycuac0W7m9UFq3ZWWlwZ2DCnTG1YRRXUa4WV0IxIiM6o1eWiR5VrVqpaIVCywy7vCWLkAjF71aSkFT3uNuG4hHKLroqWJJXFapSly0xkjuktu0EifsTM8qiFjXGRynV7b9rh4syroq2z7jjynr/ze/S/tq43K9fr712l04Wd/403HvQ7m3+iKr3XzupbdW3pkDyfsivzsM38sqQ1s8475Be1OBDSdrcoVaVcs0ZPG/SsU/7Ebu/T635latXZbtSromLGHRVXrkanVjdoDjt+2OoBp3zaK35Lva0a7JCTDaf0Xs32WDcv54QFA5ZD9k/aht3er1qbGedBjS5V2tXZatxRCVUa7NTqoDUzyGu216QTVoyr0a1co5y0joBf73RLSIidKILbrjmjxr2l2T61thj3JROO63Gnre4vBrpNOO6UT+txt0Z9etxpwDM63eaKJ004rsMh5RosGHDFkyJiWt1g2mmv+Q9FjQX5YJU9odMtqrVpd9hyCGG7FkyXUGnEKyYc1+s+u73XhruLcK6jPmHcW+72r1Rp95x/q0aXLrep0BQmTmtWjEta0aDPlNPe8B+Va7RkWLUOU06YdsZu7y9ySgrW9BI1uox6Q9KKpcigSCSmq3y3fW33iqbL7dmz/Qc6Wr+mv6ovOej108/qdIeF2IT5zIDtttu0JCoiZS3EIzwcOEHzgQhbadCzVk3KSBryoqiENjcp1yBlLcQStBjyrCknbQSB8wE/7bInirybjKR1s+ZctsO79XmXtFWbFuVl9bjbeV9yxme1OhgQ/UtaHZC0JBYaywLwr1KL/QZ8w9f939TqChbgQpREJEASu90Z+CJnEVGiSrnGYhN/7f7PSStRXQQM7vKeQGM+bdRrFgxqsV91sC3nQuOUsiKuLKyw8t/C9blThSYVmpSqxu1S1q2YCNqnqGnnrJhQKRO0MoWvcdq6UlUykgHv3yyuJFiyN8Oqa1ypWnlZtbp1uMUd/oVF/Y77pDrbrBoXlbBqSlbatUTmFROqtIu7qDxR6VcO/qnyRI3Xhj/vxeT/YbzuKY/PDuqs2Kerp0d1fZljc18RbZ31oZ9/+MfOCcT1ZuV6/T2q4eFhr7zymhOvXJDezBHLyJdtGEo+LzO9rKv0JqmqZbG1mKup56yb0mxX0FjMaXWDGl02gs6i130yAR52yp8670tgxQTocodyjciJKZGyZt6VQELNhd12iYQqlz2h1CuyNlUqpJwuGpKXsRL28/v8hC3uccqnFZKeC6fcTfMy4Wy3ZESTPa540oJ+jfqK1M8Nc+LKrJk274oNc3Kymu025aTLHtfmRllJ1ToCS2XOjLOuekFFSIKectoW99jvQyo1iSkJ1tZtJp3U7ym1OoNVNK7Nzca95apv2rRkyhkLBsy7otsRmxbCifUlk06oCMnP445JBADZoOdc9bw62yQtu+hRTXZbMmLWOSWqbPNAcHdsyMuEHf9+i65KW/em37NqwkEfEZNQpjbg1QsOIEhaUqpav6cDQVXQdKyDVZOa7LbDu6Usm3JKpWZRMd3uVBVtUFvZoLP8gLZYt83KCV1dnT/Q0fo1/dXE/FXLc5syksrzDVYCHr5OL2h10LAXLRoMq7JkmLgMSFm3y/tNO61Gt8u+KmXFdg9KKDPltFlfCt+fJo3BdVOjy5zLoTE+YMGgsz6vRmfI5lkoNutTTptySqfbXPSoGl2WjYSfo2SAJ5aKiRedVUlLutxhw6JOtytVZdIJSat2ea+b/GMxCdPOhWlgIhBmq4JeJx5cYekAZ1sNFvcC76c2ABBb3eBNv+uwX9FqvxP+yHlfEhEvOmzWzDjvS0rV2O6hIN4uxGcUsr+q7PAug54z6FmjXjXsJSsm7PSw/xLQuSonpUJTsCCfL1iL1dmwKCdn2pngjCsputMKjc6aZvvDFLUz5IHFVGkVUQhFXTFeFKcPekZGxsrGgpmlUZPxY37hlz/iQx/6KS+9+LKzb/VbT09Ll0bdemS3O46858eyUeF6s3K9/p7Um2++6XN/+LjcVJPtDQ+qKqu3tDbr7PyiVGRVvHNR//LjZqpnbGRyakqazayW6nEkIMPrVAXLYwEJnhURUafXrEsqNCukKh+QlXTG5yzoV6IyJPgW7JFVWvV5UJl6cy5Z8WlzLqjRJSJmi7tDhHzhdFZvu7gyVzxuwnHNYXWxYsK0s8a9qdUNet0vImLK6SBMXTXtTHFdkpFUrqHIUFkOnIiIiGVjSlQ66U+MeEW7Q2acl5O1YdaGRT3usmbKZU+otzWwOSIiYtbMBhgVbW60aNCg50O444yjPm7dvF73BP1OrTkX9brHNg/aMKfNTcHxMarDYetm5GVDavPtSlRZNW3aactGzBkwGVxHZRoc9BH1tktbM+OsDQtaHVCiUr+nDXspOCj2abRDxqZ1M7IyGu0w6bhOt6vRbi7YnPu80zGflJNWqtYuj2i0O7iw5iQtq9CkPDRsCaWykjqjt1heH1fdFnHgpt0/8NF6aWmpaCLv3NW35FZrbI+/U31in6vZN4yH7KpChk2dCo0mAmgPomJqdLvRfxdAgP0SynW4VY1u/Z4OwY5LmuwMuP6WAMmLBnF3pxlntTskriwwaR4silDz8mZdCPqJK1JWlamVl5eyKiIS1pgNwbJfUwwgrLdVXKVRr2pzgxb7dLjNpGMWDFg1qUqrVeNq9SBqIsRIFIS/hUVQAY+ftGTEkiGl6ov6mwhhlVISiLJLNi3a4j5RURcCuyWhwro53e6QslzUbyWthHDSiqALa3DCn0hasmEhxBoMO+4P1dkiplRcuTFvGfeWpGWrJq2bsxa0LEkrKjSp1KxEjQknEHHan+n1QPisWSVqwsFnOQi+24Iba4cJJ8WUWM2O2CiZtFRzxdbeMh/96Ef09PTYvn279Ee+3Vn241zXm5Xr9d98DQ8P+9wfPq5p+Va3HiikLoOmXdZ3Jjz96he8Mv28mkSLaD4uIydtTVy5Sm3iSlRosGxELuz6ycsrpCuXqLDdQy76ig6HlKgy61KwCbZbN2PNlG3eptcDEsrElJh1oQiVm3VRXKmtHlCpSZVWS4atm9Fst63eZtmoIS+o0eWq52xa0ut++30oiBgTqsLL9qrnZGxIWVWiush8ISKh0pRTFgyIijvjs5KWzOu3ZsaU0+ptU6dXlTbbvVO1DvP6jTuqSqu4kiD0m7ZhXlRcVExUXIOdBn3DtNNmQrbKzX5BpSZnfNaGOT3utNP7AhY9UiSLDnnBeV+Qtu6An7bTewLjI65ap2a7DWoMOpkZPY7ocltoTKqsmZK2oVq7Gl0GPCtlVbsbtTjgjM9YM63LHbLSppwKwsRyG2ZVqC8yRaIS6vW66DGNdrnscf2+oQDc67XFPUq0BPdI3njkDQ22m81dFK1dV9dX+0MZrScSCftu2e4vPvu0ndn3OlDzXjOpMU2bO4OYufB9G/OGNjep0RXupxHVOqSsykgGjUOlAd8I1NntKjVIWhMVVaJauQYX/IU5F9XaolKrdocMeFa/r4e1RU6pWhFRRMw6Z8W4FvusBXdPIfRzLhCVJ0OzXnAd5WQsG1VI6u425aRrAZMFMXgsNM8zRrxsm3dIWbXb+0w4ZtIJlz1hh3eJBu1VYR00G35uVtTptWpCSbBlj3tLrW5JiwZctmzUHh8o5mClrEqotOhTKjVrsF1MmWgQA6+aNO6tQH9eDDyjFRsWzbssrsykUyo1qbctNBgrcrI63WbCMUd9Qo0uGUmtYR21adms88g54B+acc5r/oM8gVhdgEium1GmXoM+BSF03Lg3lWuUKV202nZSVfeqD/38h7/tPvxOZ9mPc11vVq7Xf/N1TXz4bY0KxsbGnL78qvn1CdXZPr0Vt2sv2282OeD0+lfkZIr5I5Gg7N+0aNxbFgyEB+CUVdMaXQ06gFkX/EXQfozLy1g2plGfPu8Ko/dVK8YNeEbSSoBonVGlxbAXdbq1uDdPGTbrojpbdDvinC/qcY9hX9DhkG5HpK2JisnJqtRcbB4a7DDslbASmQ4PuEXjjlo365CPycmot8Mb/r8qTYmJ63aXI34tjKzX5QJ7JGOzqAdYMiqCGt2a7XEtJPEafbSwQy/A13Z5j2ptFg0Z8bJWN+pwq0WD1sKKa81USKOuDcLKatu905rp8Gc2yMuIhHyhKaetmQkTr3ZzLoort2lRrR6LhqQlTTmhwy22eXuRoTLppD1+QsqKSs1WwouxYIVl1GtFS3I+OGCa7bHFXap12DBv1OvO+6JWB007IxdPuaPjp6xHZ4z4mpZtlRbbFn5oo/Xbb79VNv9/qckU/px0OqMsUqsi36jNQa0OOuVPrBgPYX7brJtVrUPSqnM+b9xRpWqKYuxq7UrVSFm1ZDjY2Zv0uLMIW+t1v1K1Esqd8icqtEgGOGGNDpuWLBlRoVmbGw17SZ2eIqG52xFzLml3OOTp5MSVWjamXJ1184a8VJy6VGiUl7di3IZ5Q54344KkFdPO6nSbEa844ZMmHNXljiLcccybZl0o5l2tm5W2acWYWZc02eWET8lIFleFhZ+lqIRK496QtqZGTwgSzYfpSmFKk7KqzlYZSQllSnQqVWlvWJcuGTHhaBEm1+Ym0UDeTVl3wifFlOp0iw3z5vWbdtaqCS32gWZ7rZoMpOElK8ZMOikrGVZthcZu1KuGvFRgLMUTDjxS7V3v/sm/dB9e0+9dn6xcr+v1I65r4sPtDQ9+W6OytLTk1ddfMj5/1YGq96vc6LaYGVSiXE2i3Rb3mXXZkBf0eiCgvS8b8KxKTXZ6WFy5ES8Z8ZpzviBlxYIhDbar1IyIi/5Cyrpt7pe1KW3doiGjXjPnom536nGXer1K1Fgx7ozP6XGHOltFRM3rDxjthIykK54ImPka8y7Z0BjIn8uyUkXsfFTcLu9zzuec9QUjXlNnS9BcvMuKCRkbVkIgWtq6lIwS5aadUqNLQqWUTHHqUq5WWRg9N9mlVF1Rs1KYUFSYdsaiIR1u0eseUXEzzhl33KaVgPcvJNImVNrqAeVhPbFgQIM+cy6adAIklEtaVKVNqdogND5kxCuWjYaskzatDqjVY8O8rJRjft+wV7Q6GBrDJXl5MODp4pRhxrniy2fIC1aM63SbtE0xcZWa7fAuTXZLKJPRqdl+Vz3vrM9YMqQzetjI0nnNPdVuPrTTL/6PP23Pnj0/tBdAV1eXLdu6pC6uml0ZtZleUxFplpeXzac0Rbbb5RHfyP8vYf2VtGnBlNPmXXHV83JyetxtQb+spDINqnVKBWLsgkHTzqrWoU6vix61aCREPRQSwKNiRr3qsq+KSRQpy012iYopV2/BkKxMaAQSVk0b9Yo9PigvG9g7M+JKTXtLxobtYXpSHdxeI15ToUmfh7S6QZUWi4aMe0uPu8zrN+YNI14VEQtTnnxRKxYTl5E04jHzrhQt1ynrUlac8RmVWoLOJGvWudAI95p0XFcQV19bG12z+E86adhL1gPuoMUBR/yaRf2WjetxRL+nVWgKrJkrJhyzacmyUatmREQCtK9gP9/qbRr1hVVTgXt0yp9qsd+8fstGlWmQlRQPwaHzLitTY9YFkcSC++6/99sale/U7yXKom48stuRI7df16xcr+v1o6hr4sOqsm8nh46MjBqb7dcQ3e7Gyg9YTExYXO83tHZMWa5eQ2SbbfkHnfN5tXqUazTtlB536PU2aSshBbnDEf/CqFdd9FgIOyvR5fYQWHbGWz5uyagJx4OdcdmyMdu83U1+XqlqOWk1umzzNgO+YdDzDmhXqdWcS+b1W3LVkmFxpVrs1+V2eXlzLqjQElJ2CyfPK74eggi77PCwjA2TTihVo0ZXWFNdCG6fhDo9xRN0mQYp65aMFK2tGSkRUXW2GfGqbd6hwY7wsF4IL54YQQPTbK8W+wJZ84SMTWOOKlWtzY3Kw8O1xX6VmotC0A1zrqUr19miVE1A4M9acLVIGq3RJa5cr/uCOLlKRFTWZtHFtWRYu0PIOe3PdLrNfj9l1kUDnjHnkjpbg8ZnIxBTY2p1Q9D2jGq214oxSYuBPROXkxEtPj6jdnq3yc3jTg4/K9q+15/956+48ciFH9rDv7S01J79Oy0sbNjcmJBZjslFMuKRiExuQzafDs1hQVdxxddDaF+jah0a7FCiWq/7RDCvX497bFqUldJkt4hYWL9Nm3TCqmkbFpSq0+GQ2/0PUtYsG7XgqglHA78nXXTl5DHgKYsG9HnInIvIu+gxS0a1u8mGuTCFOC5j00Ef1WCbKWdMOmnQ83rcqd0hU06qt7U48Vg3Y8Qr+rzLNvdL2wh/h4wB37Bi0pjXrZqSk1Nni/0+IqHUnEtKVIvosOCKGefV2xquTbvRf4cCd2fA07ocCUTp6RC+WWJBv1WTWuyzaFCve0VFNdgZ1m0pOz1syVAhm8eoCSfU22a/n9bvSTf5OVXawIxzloyYc0EsuIViyoLgd0qL/QrhhG+ZdV65erV6HfEv1NnqtD9zZe0r/o9/93s+9s8/4vDhw99Vv7e6vuDNzx119IVP+tDPP+zw4cM/8Hv2b1rXm5Xr9d90XYO/ra4vFP9dNps1NjRpNb1oX+IBkUhEOrcpn8vprTysuWy7M1PP2kzPq9ZmwaBJJ3U4rMOtVoyZc1FWSqfbxJRod8ikk0GI+R5XPR8IuIUU2YJ2pEqDHS75qlrdIc5+U16FhHJrplUE8eKSUeOOanejPOJKzRtQqiaIEwu01Gt/dkFolwmJxpW6HTHpuEHPqtCoyV697jXmDa/6LRnJ0DjcpMMtzvqcuAp5eZWh8bnmgIkFkWREVK0e/b6u3jY9jigwLKrD1GfDFU9aMqxRn9P+TK0eNToVUmAbrRhzxmf0eadOt34bfOy/NAD5MHqvUKJSiWqVms04b85FTfZYMiQno9e9FgwY9LwZF0VERUQklIuJq9aqwy2WjRjzphnnijkuw15x2p/btKBElWuJzAWmzm7nfNaqKX1uVGjCJsTNSagUFRNBi/0Fkm7qqN3196mpqNCmXs/67T/Uh38ikXD3O2/x9NVLSpZWzSwuk6Q8Uh98MAUBZ05Gi32qtZs3aMmwPu921bN63KUQRFhh2imXPKbPQ2HNU6ZWl5QV447K2HTYL4opDa61rCknNNlrq/uL2UCLhoMWZFVE1FzIq8nYtGI8CLKHjHrDfLCzF/D/dbJS7vW/anNDEAJ3OeFTEip1us2cS2JKLRgQUyoqq8vtVoy57Gu2eiCgBLaadV6ZOjf6R1JWnfRpdbaq02PBpSJHaZu3a7RD2oZX/Huz4ee8VncxB6jJHoOeNa9fiWo1upSoNONsCA3tlUezPVaMWXBVjQ6laiy7oNHOgDK40YwzFgy6z7+2asIlj4W0963mXDLpZHAZvVuFZkmL+j0tJ6NEjQ3z9voJWz3gtD9zi3+q1X4ITKK3yyUWLA9kfPYPviadTvvSHz+lYfGQm3e9QzweF4sVaMt7Om/zxpUnffYPvqalpeXHbsJyvVm5Xv9N1zX425ufO2pP520ikYhsNiudTomIqIjWy+fzZjYHlEartFXsksvnTOZOqdQaHmI7nfHnYhKuek5ORkypdjeDlFVJy9oDkv6Kr0so1+OuIpBt3mXz+m1YMOm4Hd4tody8Ac3KpKwZ9KwL/kJZCOtbMSkmoVqHFWOi4prtNe20tHVXPW+396vVI2nZhrWwLy9XrsG0s0rVSVox4jUJlZrt1htEvESkrXvL75o3oFy9vKyrnnOff1O0AVdph8DXWAjhdK/LSulwc6DZThv3liWjet2rTL1Zl5SosWRYp1vtd2M4SR43GATCOz1c1EVcc60sG5O0UlwVbIaE5WnnTDpu3LGQw5O3bNS6GQmlEip0OKzRTlXaLRk25ZR+T9vmbepMm3YmTGeSuh0JpNtL8rK2eVsxn2XEy8a9pdf9KrVaN6PdzRr0yUhKWZGxKSJu1OuWIqPqq1t1NL7L4OJTth+60fbmmxy7+vQP7eF/5Mjtjr5wXmJiXtVGzvilE6rybRLRcuW5epvRGRX5enlp8VwhmWbYCxYNioqr0RWEp5OiYhYMOOtzOhxSqsaSERc9Jm3dijETjmm135JBdbbp94xxxzTaKaZEp9ssGjLtjBnnQiZNj+0eVK3NikmDnrdiTMamqLgydW7wUeUavel3zOtXZ6uszbDiXNbr/jBxGBJXJiOpzQ1Bt3JaIjjwanRJWjbqFatmNNpRnA61u8ku79Vgu3VzIZDzddXalQeHT7W2op3/WqbXbu9XoVFG0it+s5jsHRENP13v0e6WIiI/ImrYN+3xD8SVhSY6EdxPtcVGKKbMCZ+SCj+rBWz+cVvdr9UNxViNwjorqlSNMW9aMSmuzKbl8KwoZJZtWpS2pjRarjN+o1TqjPREvU/8/h9YO9OsJFLlhUuvisYj2rqbdHd3qa2tdWvfQ756euB66vL1ul4/iio8xD/pjStPurXvIbFYTCJRIi9vLTsvn75sNTetp/JmkUhELpe2mBu2wyMuekxERLtDtnuHuDIzLoRVQcEbVEgI3lCh2bIx2z1km7cFMmWdm/xjb/m4aWfU6LJuTlbmWwS5XzbtrHINmu1VodG6WZc94ZKvqtAsL6fPg7a4z0mfctwfGfKiets1hUlBVCwIgI+57GvFBNdKzWJKDHnJoiHxkNQ871IRenbQT2t3szn9TvhDT/pnwY6dEBVRo0etbqNelZO21dsllLnqBUkrkpY02OGgj6rUbNoZpaoc9kuWDIVMpIKlslqnWeeKac1tblCmTsqaVeM2LShTZyrYiaedNeGoGl32+omARt9ixYSX/XsrJkTF7PSInR5Rpl7Skhpd2txoyDcN+aY6veZdEXe3RntMOmYpaAOSVp31eYUAxIgON2nQp0anmIRS1QEDPy2uTKlaJSrNuVTQ0uSrnZv4pv0ld5nNzHrmqefE8+UisSrD+U2PPfpVv/pP/8kP9D7v6enxoZ9/2Gf/4GsmBpesl9Qqz9aKZCKuRp6zGZl3MP6ThjNvWfd68ftbyFyaNudSmGgUVpJNdloza8A3ZKSsm7ZqWo87rZs25i0TjitRYd2sSq02zVs1Yc20CceKzcmcS8VVU4UmTfbIo1yDeCBEV2gO6eVl6m0TV+aSrxWb3RJV0jaKqctN9khbM+uCMW/KyQQwWmFSd8qfKlOjzY2qdVoyas4lNbrV6NQWmoCcrG5HnPU5Fz0qaUWNdmUhyLLLbYiYcd52D1qTM+u8Lrc57FfEQxMx64KYhIRyO73HrHOWjJl3RSQI4COixcDPaeeMeUO5Bi/6/0ioctBHi3+fWt1a3RRwCUTELeiXsmqPD1oza9jLwaU0FHhBC6KWRQPjlqz8Zqm11TWlyZ1eePS4I1X/RF1jn5J4mVR60/jZcRNXj9t3804dHR22Nxxy/JWnrqcuX6/r9cOub32If/X0gO0Nh8QbNqQGV5xc+7x9lQ+rLK9WlWgC6XxSJBJVkW8SV2rBoLhyeTmNdlk3p1ZPeMEWnD152fAiK6wS8jIorG8abbfHB5z3ZfOuBDHdSPi9B4x7Q6+3hWaotMie6HK7VZNmXbTLe5SpN+OcDrcY9opxbwV3waYGO5RrtOiqC76kw2HbvF3KWtjFl+l1X4CzPS8rLSpqh3e50c8FF8hmeGHViyv/Fqpu2ri3XPKYtE1x5Wadd7t/pttdppwEtbqDKykXGrMe9baGLJgBo1631wc024O8WRdNOS0vp0ytCSekrel1nya7DHrOpNNWTehxRLcjYdqT1GxvsHlumnFeU0CfV2kPgs+cUjWg0y3hBVqgfI55wwVfsaBfk73qbHHZE1aMKVWrWpsWB4qfedGwtHWTIeCu1QHd7lCjy7RzanWpjLRayPYbGL5gpWxTQ91u5Ylqqcymsvl+f/6fvujQ4ZvdfvvtP9B7/fDhw+rr6/3ar/5LGyvjziy8JrmS02KvrtgtNi0Z9rJNiyo0y0iFCImUfk+LBptvg+1SVjXaocMhGZvO+Jy0NetmbPcOFWFVuNcHpaw647M2LWuyM3zvd+r3RIirKHfFE8rU2e19lozo9/Xw8zSrRo99Puw5/yqEWZyUtKZJh1pbAicnbdmYnJxme0TFrUhqcUC1dktGLblq1oRkiFso1yQjFYIcl1Vo0O5mOSkZSWtByFuuwTZvt6Dfgn51eqwYt2bGsNdkrFow4IKv6HKHBf163FnEECwalLSsTm9ABqzococZn5SyYsVkoM3GwjRwzmVPmHRCQrUt7rTDI+r0GPGqN/2uLrdKW5GRtGBQ2oak5eKzp9luQ16Qx6wLGu1SrqE4vclIWstPykVT5mbnjWZmxdPVdjbeqrmmq3jPNFV3Gpu/4uyxSyorK1WV1UtfT12+XtfrR1OHDx/W0tLi1Vdec/yVpyw0LllvvCCzVCZbc7vy1RaZXIFYmogWMjuuTUIWDYtLmHBcixugaOctpAKnEDPpuGZ7ZazbtFxkSORkVGu3zduc9XkL+g17OWSgDKrWpdXBImE2Y9OaWY36HPRRR/2+lFVb3G3GOctG1eg256JWB62bcdVzxSTiJnvc5OdFw0msABpfFVOiw2Eb5k04HlgauywaDCj+04Z8014f1OMu494Kgte4WlvksWxYj7sMe8kJn9LqoKSlAHurtGHRuDcsG3Wjn1OiUh7tbnbZE2LKVOsUEzflpH5PS1oqBr3t8K6wRqqTtOoV/z7kIh0z7pgKDcH5UPj67/JeG+b+Uo5NqRoJ5bKSIqJa3eCUT1swaF6/Gl3KNNgwb8O8TrcqV6/TLUWH0YjXkFeu3k6PqNdrzbRpZx33R8rVSVrU5gb5fJ583nT2ovbKndpqtxbvvV2xO40uvOzP//OjOjo6fuDj9dbWVn09e+3b+hPkefTpz1pd3jSce8b8+oimyE5bEndK5KtMZE6HtU+pTYuSFtTYolq7EtVWTVgwaMzrwQZ+q06FpqewBouKiFkw6JCPWTYuK6nX/TbNWzelRKW7/C/mXfa8/5fLHrdhQZ1eLfa74Eu63aFEecgcGlGhSbfbwnql0U6PSFp01QvWTIUpTCG1udsROVlQpcO4oxps06AvaJVeD9O+c7Z7uwhyssWcrLRNEUuyNnU4ZMxbmux1wqeUqBAVscO7gxvqtBUTNszpcodlo5JWZGxo0CeioP9IWlKmtrjWfMvHlWtQpc24Y+ZdtmjIsjG9HnCzj6nSirx2NxVhkEtGpK0H5P4WjfpUaJK2ISujRKVLHrVm2nbvCPTfQo7RhnnJ/KqVkiFl2UYbCzkV5RU2c8vfdr9EIhGdDX0uTy8YGRkVrVu5nrp8va7Xj7J6enr09PT4wAcLbIEzZ874nX//h04f+7ySdJuGzf2604cspcetxIYlLdvlvZrzu13KP+GqF5SoUmuLeQNBu7AqKmHRoFkX7fSINbMiIgG1z6ZFEVHl6uzzE0pUyMmFkMCITrcoD5bcaWfNOi8rrVKLZWNqdJlyMkDo4upss25OTJk5l93gH2l1UNqa1/1HO7xTRXihp6yKKxNXViTBlqq2bNQ+H5KVtGDAhgXDXtJolwM+EoSWJaad12y3cW9pd8gVj4cE5AIf4xriO23DomEjXrFqQpsbNNsjplReTrWOIuq/REUYh3eqs9V+H3bKH4fJyroy9YZ806BntLrBdg+q1ytl1aDnLRkOD/5WZWq0Omjcm2Zd1KkurBbKAqguXkwYTlpWb5ut7tdif3FykpXS617Q4bCt3qZSq3n9Wh1Qb7uEsqK4udYWlzym39P2+mDIOhq0akptok11tkculxWNFl5cG9lF7Y1bRGbrfyhagGui8s31VXu7bvf2myLOHr/o4soLetylLrdTdaxVJp/U6XBQBg0Y8qJhryh33pKrutxhxagxb1g05Ga/oMUBa6aNek2tLZYNG/OmOlv0eciE4y74sque1eddbvYxz/t/Ou+L9voprfbb5u36PWWLu5SpF1OmSrtS1bZ70Js+bs5FN/o5UTGDnjfrkmZ7AtDwWcd8IjBNCmToQp5Vi3mXpKzpcIu4ciWqRMUNej40o7dqddCmRTPOWjSoSpt1s8o1qNFlwgln/LkVYw77J1odsGIsTBxLbFo27YzJcL/W2xZ0XREZ68VQznHHrIRpyozzanUHblFWnW0a7bZivOg2y8uJYN1scTLbaJd0mOoUnFwb1sxaCX/nnKzLvqbVDbIyMpJYCQemFSNepmRec2qPyfQZ27bvNLpyQl/+yLehHCKRiPryDhPDV2xkTrvtw9dTl6/X9fqR1zVq4+23367jP3R47NGv+osvPO7EqVf1p5/Q3tTj5pZDLl68YGbltBsiH1WVbXI896fO+HOV2tXpUa9PIcX1nHXzsjLWzOpSXbS3JgPGuyDCi4srV63TFvc66U+smbDdO0w566rnlKnX7c6wUombdMK8ozYtWjJcnBqkram31ZyLZpzTYn+Y8BQyT6Li5vVLWikmHA97qTgZSFo26YRqHWr0FPNzuh1ROHemgk2yoCAo1xiyiW9y2RN2eZ95V4Kg+MkCGE1GuXq9HhBXEk6EuSLbIhFeHt+6OosgoUyjnXIBG37anxVzg+r0Fum0SasqtZpw1JTTytRbN6tMXcCpFxD6NTqCjZq0TXn5YJ/ecMT/XUwiBNWlNdpp3YxBzxRfGpsWzLui063aHbJmUtKyeVdkbAZHzYGworsQBMdvyMbX7at6t0Q0LpvPiorJ5/OGN47avm2P1rqeH4oW4DtF5d3dXb7wyv9pfTFiZ/aQxkhfwZmVm5Q2EIi/HSHLqURMwkl/4pKvKVGlSptaW8w4b8Srlo3ZMKvdzUrVmXHWTg8jr0anSi0mnQyC7j1K1TrtT827LC9v2Iuhge1WrV1ChaRFKWtKVOlwyGl/Zt2MNjfa4x+46gVnfS7Y05OGfNOiYWVqA4eo3LqXLBstrkNKVFo2HuzrLBtVqkaVVhUaxUPm0apppapDqOBZw16yaclW99njA9Lhfi1YvZtVabcZpoFNditXr1y9lBVE5OXFlBaTpys02+lddvuAqJhkYKpMOCGuXKsDgaZ8sjghqtZhwaB2hwIbKBIOR2vWTFszbdJJrQ5odYMrnnDM7+tyh2Z75KSNet189JJ3Nf2y06MvWY9Pev/uX/bmueecnn/SgYZvh2QmYqWuzL+u/eCKO4687wd2f/5t63qzcr1+YPXjSkf81s/V09PjV//pP/GLv/QxL774oi//yVNiCx12Nt2qvrLFE6/+uYWNITsSD9qWuU80lzDphCknJFSKKVWnxw7vMu+SIc9rDMLMnJy0dWumQmDgPhd8SZ0tutxqymkXDBbcJIb1uNMW98pKKVGpUrM2Nznrs075tGEvqdIWxJBXNNgmrtSo12yY0+GwdAhLTIWk2xqdEioM+aYlw2pt0eFm6+ZCzkuzNdNBGFgREo8jga+yKRLalQJKPyGuQkbSqnHlGmxxt7QNGeta7A8vqzbj3jKvX7VOeTnjjoUHe50pp6WsWzAYtDb1wZ7cotcDjvq4Wj3BJj5qzayU1WKjttN7rJm2akJMD/I2zRebt5yMGl2yktZMWzJqxCs63R7AfGtK1cjJqNWlM3wvloyE0LwFCwZs9UBYbSWCcDOq1pbgtqpD3jlftKDfjHOOVP6i1vhuC9F+sUihUTk9/6RM5ay93e+RTG/80LQA3yoq72zYYXZzyG4fKQicI+0iuYhYvlRcmTY3ystYMmTIN3U7oly9FWNag/h5xjnVOjTaod42FSHyYNNycGwtWi+u4zbt81NSVl3yuIwN9Xa44DEVGuRkNdguJ61MrSqtrnpBgx0SKmz1gDFvSlk164KomErNyhxRpkHGuqx0WNW9AsUcnRYHNdiuw80hIPRranSo1uE1/8GIV3W6NQAQV5GzbsZ6yKUa8ZK4imCYL2QelatTojLkUBWa4m53FFk95RoCMK5bRNyyEYOeM+W0Njfb4Z2mnDIdoh02Ldq0bN5lERFpa1rsUaNTVkqZOi32OeGPTDmlWnuAN44HBEJNaJYm7fMT9gXr8lf9shM+FTQthQNTQ0mX8cwpQ7Hnba877MCWO5WXVfjmsa+Zmx7QU35IRbzeembBmfmnzVa/4X/8hX/1Y+cE4nqzcr1+APXjSkf86z7XAw88oK+vr6hriW/POVDa4fSpt5xdmrKe3ZAXsd2DVk0a9YYN82ISzvicrLS0jQCzStk0r0SVjM2AZT9l3Zydbi/C1NrdbMybtrrfAR8RFZOyYt2cDfPiKtTo1mCHtDVdbgtJrqNQPPXmpQ14xqKrrnjKIR8r0kMnnbRs3E6P6PNuk45ZNGzRoB53KtdgyilpGxb0B45L1oIB8cArqQgY/5RVeVlLRgJifE2DPlc8Eai2e9TZIitpyilj3rRs2JIRHQ6HTKKhoGeYdaOftWo6kECOKVElJ6tckwWXZWVCevQuVdpCmm5Ul9uc92XVIXl204oZ55WpD1OooSKLZsIxKas63VLUWWRsfMsKay0kEr8EQXeQD2nKBcJIQoU5l2xa1GK/tM0gfBxQX9auO3PYhfWvS2ZX9LbsNbj6puGNozKVs+65+WGttT3Ojb72Q9MCfKuo/LlX/lwkVaqzah9rGZuZRbFoibVIYaLQGb/ZUm5YXXaLCTUhFiJtt/dbNGzIC3rcrd52FRo06BNTYqsHjHjFvCuhaciLK7NhIWQG3aTOFjmp4r29acGqqeCC+4oKzUpUm3XJiFf1uk+5el1ut6D/W3QhSyF3Z1aDvqIwt0q7ef32+qAyNeZdkZWybtayk/KEFPOcGl2WDDvhU+psDR6vLk12y0gZ9Iw1s3Z5j2UjmoPbqCqQbK+lOSfCz2RW2jlfEFVIZl4yHKzShcNHr/tsmNXhsE2LFgzqdodyTS77alEUu2w0aHKWxJSEaIFqfR5y0WOSlnQ7olaPCceMeUNcqSZ7zDhjVI9ZFwNPaaf16LSV/JhcNKWsddW2/beauNKkoiQqGo3a1XFYdVm908MvGxh/Qi4bEUtEpFuGffhn3vUDF4H/bet6s3K9/k7rx5WO+P1+ru/UtZSWljp58qTf+Y0/8PVHX7I9+16lqk05ZYeHzLkiJyUnr0qZPu825XhIaS1XrSOwJv5MXk6v+4pR8Jmwoiiky24LGT7zgTGSlrYuadB6GLkP+IZj/kBe1i1+1WWPWzEurtQhvwSWjbjka2ad12K/jGQxj6XT7ZYNy8vZ432O+yOjXrHDeywaUqLKuKPq9Vk3bcEghJyhPnOuGPScGj2a7bNqqshdiUoY8Iw8IVW6wbpZFz1m1ZQmu40He+mUU/Jydnm/nIwV49ZNa7bXXh+0aUGDrQQI3YpxyTAlQpFvkrRi2IuWjWnQ55LHAjDsVmXqQ6NSIHt2uEVeVlylMrU2zMvJWDKiXINaPRIqQ9ZL3be8dJtERFRpk1Bh1YQxbwZ2ziXJ6IKYLepjW60mZ70V+0OZ+P2aEu22b9ttb/d7tNb2yOfz+uePuvXtPzwtwDVn0L/4lf9NSUmp5ciIxoqdopm0+qomU3OrGnO7RaNR5RpEchEb+TknfEqD7fo85Jwv6HWvw/6JVRMWDRaF4IJwuc4W446G+23TpsUgAu8ophFvmHODj3rdb2uyU68H9HvKaZ/W42697jXilUJ0gVvlZEw6Ka5cnS1BVzKvSrtavfp93ZxLutxu0aC8jGqdstLmXbEaqLKlKkVEbIYU4wY7nPVZbW7S7XYlqqWsGvOGtHXbvd2cy/IyEiqLOUWFe6Bd1maRLlulRYPtIbj0gmsroFrdSlRpd7PzvmjeZeUaDHtJl9tc8GWzLihTW0TrX/a4VgdVaBIVl7Gpw60GPGvEK8U/Y9WUeVfcHfmfZaVcyP+FMz5r3JvSNrTF9qrXqzRabaXqokO77pHObuo70CEi7amTfywRKzMwelE2kxOJsqVrq1w+o72T97z3kR/Kvfm3qevNyvX6r65ra5Wpqanvnm7sR0tH/CtTl/+Kz/WtayJYW1szPj4lnqrWqM+SEQ367PY+y0YNeEa5Ble9IGVJZ6BoXvI1k05IWpZQ6R7/qwoNJpww7k2TTsjJiopbNuqqb6rQGB5iLBkJ3ISEXDgtxpUr16hWt0N+yXP+NyvG1ei2xT1hJ9/mkseDqK/Lgqt2ecS8y9ZMq9auSrtWB405GgibXeLKzLmo35Oa7dfqhpAmW7hm3FuGvKBCs7gyJaqsm7XkKmhzs0VXnfAp6+aVqlSjS5VWa2Zc8XWrpiRUusUvK1NryVA4Nce1OSChUpl6FZqUq7cW3CTLxqSsqQoahyVDIam6wORIWZEOqP0Z5y0ZsmFBk92yMqq0BnfIqJiSwLoprNua7LFqQoRv0bLsMuq1cBJuDJEEvTYsSFpSpc2cC3Z7r67cLUZyr8lHs+K5MpXlNY4ceNDersIJNZ/Pe+PKk6Kts+448p4f2n1PwRm0rXentnyN1eER22rfaXJ02kZ6STQaFcsl5PN5uWzGTP6CCs3B2rxgyYhZF+zxAasmJFRodaOkJYuu2jCvXEMQWxegh51uUaE5OOMK9uIKzSYcc5t/ZtJJ531Ztzvs9RNO+RNx5XrcGajLJ7zit4oBmqNes25Ojc5g+x02ETRcu71Xo93mXDbuLd2OqNISdESbiMpKFcXjLfbJSetwiwbbQ3NbJSKmSqsb/XfKNTrq94t6rB3ebdZ5eXnr5gI6vw9Ro97QaKed3q1Mg2igm8y74qzPmXdZnYIrrNDAjTnuj6yb1eedcvKGPCeCYS+bdFKve9TosmLcVS+YdMLN/rE+7w5MmXPOxP5cvnzFanLWXOaSRGRIIhrXlLtBdb5DMjEvVpZ2sOZ9Lp2+ar3+kl/8n39KPp/3id/4vIqFnXZW36empMVyatqxsecL1/zET/1Yrn+u1fVm5Xr9res71yqDw1ek5kt99MjPfltDQEFt/qOiI/5Vqcvf+bkee/SrGpuanHjlgoXZJTPzU1bWF80Mr6qY2qc5Uqcy32TSCbu9T72tEiptmCsCzoa8aLsHNdph3ayouN3+gQu+ZNYFeTlDXlShwX4/JRnw5UlL1s2CtI0gHK3V5yFl6gz4hgbb3e/fmXTcFV+334cc9BEv+nWn/KlRr6qzVVyZZnuMecOAb4iKyUqr0qZSk2Vjlo2q1R1It6dd8aT1MGKfcgYRzfZIWpGXNewly0aCJXVBv69LqFSqyoope31QpSaDnpeTtt9P6vOwMtUiIpaMmnTSkBfNOh8IpMNWjEmo0u12RCwaFBEx7q0gSFyVk1WhyabFAN4qcdFjNi24x/+myW6XPGrCsdDMNYST+Ex4ua7KyajUYs1MEA6XKOgV5s26YNjL6vQqVWXdvBqdYcQfc4OflbURBMx5tSGDJhVZs6fk3erjPbaU3uRC8gnH0p+yNhH12Mt/LHnrhtJEuf75o6Ktsz708w//0F8G15xBnY3bnF846crGC3rb7jQ9MSuTT0paUZKpdj73mCmn1ehUpUWv+xVSrJfElVkwoMkutQGoVqnFhKPqbA2TqNKAip8J2qHLhr2kUrNy9WZMGfQNfR6ybMyYt/S4W0yplDUXPVYQJMvrcodqbeptM+dycF59XVRCjU51tmhxIIjbz4dIjAGDng3fw2rN9sgHx92gZ825rFaPKWds9w7N9gRnV6+IiGsrrEIzc9iCgZAS/opEcK812WXZsE3LFlyxZEhTQPOXyUkEcOC17K8Ni7rdrsfdzvuytDVplRrssBL+e0yJYa/odsSKUWd8BlGrJkQlHPRRnQ6LiqioKBePp7XVN9nS2Gp0eV57tMqeW7ude31I2WZSWWJJY6RVe/ke9RVtWrveaSZ7wctPvSwi5oEdP6s5ttvU6JxcZlNdRa337P/HZrIXnH71rOH7hn9sG5brzcr1+lvVd65VKkpqzI88b3FtzmMv/ZF7bn7Yro5vX/dEIpEfOh3xr0pd/s7PFc+W+8x/+rJbtj+oNn+jxf5F1atpo6tPia7VaE/dai03G8IISyRUFV+AnW5TptG8AasmrJpUp9deP+GcL1gxqs+7HfcHVk3a5X263WHDXBAQxq2YsN1Dmu2x5KpSNZrsklAuJ2dev1o9Vo3b5h3mXTHsZe1uVq0jBJvNmXBClTYt9ulwWLubXQwI/wbbisLSlPWQoVOhzY2qtFsz6zb/NDQjT3vTx1VpU61DiQo97v62k/OAb9jvwy77mjpb1Ntm0PNFe/eGaRmr0jYtG5G0qMtt1ky66gUxpbrd5ib/OIQmZtTZJiLmtD814hVtDlo2qlJzmKCsuexr5l2x03tVaragX7kGjXaJiNrjfTYsesvviYi6wU+bcMyg51TrLDpK6sOk5YzPmHTSdg8a9KwFg6acsWREqWqn/LEmu1GwlU6GNd+uxEPaK3eqq2wRiUTtir/N+OYbWnvqTM2veGb0d+3fd8Ctb9/tjiPv+ZG8BP6LM2jM3Te924vHHze3NqC1Yb9IbsXA/NPWcwsmQgbNrujDErlqdbao0Oqq51VpV6PDkhFlGpSrl7Ehjw0LhnzTigmVmgODZF5UifO+qFStPu/U5RbnfUVMQo1uq+G6ElU63aLDYXlZhRf1uAUDZpwz5bRme/W416zzOh3WoE/KioSqAITrFFdmwDPFoM4CDXnChOOmnNJsb9CyzAW9WIFUnJMWE0fEqimlqsSViohqsa+IzN/qAZVapGw47dOSVrXYL67cXLBL1+mRkQw/8+Mh7HO7lDWLBqWtq1CvSosOh5WqtmrKoOdtmA2Ig5ga3Vrs1+EWHQ7JRZIiFZtKqqOSpcOOHL7P3bvv99ipc/7HD/2CaCyuYWXSg3t+Tkm8rHB/Z7NisZhYLCaf3+e3nnhCZb7Fh979QZFIxN692b90zVdPT/1YYvav1fVm5Xr9tfWdrp7vtlZJpVLay6bdULHP8OZbvnnsa+oqW7TWfvuN/8OmI/5VqcvfWlNLw873n9axeZfbOz7k5BvndEX2aO3eYr5/WGq5TDRfSFI+43MhxXVT4WGXCsFrNUpUqdZh2ahzvqDbHVrsd9nj4soCxrtLmTpj3kCuSAxdNWnBgBZ7rZvXaKeIiDyueta6GVvca8moCi0a7XLFE1JWi/k5hYakz41+VrubpCyr0S1p2Zwr2h0SU6pKm1kXJVQGC7Eg9r0xrKFqlKmz1dsc9BFRcUuGTDiOvFKVutxm2agV4zrdas5FHW6VUG6Lu0VEwp9RgYhNC+LB/NznIad8OjQnW02FrKOGALyqMKvOFjPOWA94+02LcrLmXbGgPzg/9lk0EF6m3ZrtNuNcSJS+KGnZTX5Wgx0qNHvDb4fr9spImnXeggHz+mVtOu9LUlaUqFKm3hZ3u9kvmHTCgGfMu6Jah3q9boz+I43xHpvJBSoLq57lzIRd9XfZjF7x0F0/aaTiaf/6f/8XKioqfuD3+feqa86gpeVZ77nrZ50ffV3/yAtWo0tGcudElhqUpas1RHfYGr3PbOSS1exkePF3mnZap1vDxGBUqWozzhddVYVAwoPa3FRMuy64vPabcNRZX9Bst2VjFl3V7Q5LQeBdo9u007rcgVjICUqKihvwDb3uV6snCE9rrZiwzYM2zFoxod5WVdqUqdfr3kCJvmzJsLyshErbvcOqKeOOSlsLK6K8lBWLBjTbF0TXSzYtBcv/kgpNEiosumrIC0a9Ftx9M9rcpNVBjfpMOmXKSQsGzLlk1kXbPGiLO1XrNOylosW+z0M63CIqpkq7GWfElYuIGvANm5a0uUGbG82FZPTaRJvKpkqzZSclqtbs6brN0cGnlXQuuu32R3zi//pTO5seVFFaXfyeXwsnhGwuI7dWokK3XC5XbFC+9ZofxUHyb1rXm5Xr9VfWX+WemZ2d/UtrlVgsJhqPSKeTDjQ8ZG56wLmR1/5Ss7K6ufB34oj4fm3R3y11+Tvr3PBrYqsN+lpuMzk5Jb9WobOlz8T6BRPrlzXlbpLOb1g1rdkelzzuvC+p0W3TgqteMBaordemGrMuOuoTwTGzLmVNRMQe7yuGjHW5TbnGgMzOmfCWNVNiEkpUWtAf9vPLdnhYlVZTTls3Iydl1YwVk1ocKO74C9C3gsOl8JJf0OGwWReMeFmv+8XCybFSq1mXLLoqY0Onw0Ui6ZJhW92vTI2stLhSeVlRiQBZa7TF3QGM9pMhMPBJiWC5brDdqupiMOA1bkvaqozNIqjuWgBc4fRZmEylbeh0q6gSA54y6nVV2pWr1+GwW/33rnhcyor6AAar0q5apxGvKddsORB9S9XJI2VFqXrz+l31TSUqZWyo0WWbt+txp6RVR31CTFxeVofDSlTY4i5tkQNeyf8Hve7RHrlRQ3SraDRCnlw+Z3ZzUK5kXXvNTlcylwovjlxcLpf7r7rP/y7qW51B08OFuIktzXstrk8r6c86euZl1qrtjj4iksioyFUb2Twhly9k5pzzRVc9p8luS4YCmXlKo50mnbBqyk7vDg3H+w17yUWP2rRop0csGZJQFdKGu8KUbLtpp1VqNe6oUp/V6TYlKrXbqd83NOhTq0fKimrtavSYdFK/r9vq/nDPXFVvW0DeXxWVcMA/1Ginco2hwRpRa4sRr5hx3gVf0eMeeVlpazYtKVUjpkxUIjTet4mIqNFluwe1OCAW2EmLhpzzBWd91tYQfpmVdclXpazp804Vmpz2GfMuKzTrS+JKVGmTkwmTqay0DTlptbpDQOKGAU+bcFSZOvXlbVYjV52bG1DekXSw87A3h79aXCu2trb+tYexdDYpplSJquI05bvVjytm/1r9yJqVq1ev+nf/7t959tlnTU5O6ujo8NGPftS/+lf/SklJyY/qY12vUH+Ve+a1z7zhrf4nvWPbr3zbWiUWi2npbDB2dkRDVbue8kP6R55y9560eKxw438/joi/rgn5m9qiv1vq8rdWJpvWP3pejS6tXY2mx+bVl283uPKWoxOPSSaTyjVqCSyKcvXSNgx50St+U0ZKpSbbvKPoALoGD4srV6lZkz1KVRvwDTV6lKlBXkyJUtXK1BN29UnLzvqCGeeVawxJru/TZLdFV0OOSb01M8gpV+dmH/OkX7PN2y25atGwUjXFdNhrJ7pzvmDOJa0hoXbBVf2+btFVB3xElXZ5WSkrspJKA6shIykrg4h6W8UkxJQEJktetVbbvMNZn7dmyrL9qnWEaUhGyqp2hySUm3KK4K6JKTPtjC63K1MvLxeonBm1ekQllKq1zTtCqNtB9bYir82Nrvi6VjdIWVWry1wAc53yRxYNaXXQm35XTMKkU9bMiInpcLM2N8rJqLfVjPPe9HFNdmuy0yVftSVYaLPSuqr2mc1clkmtWjVhPd9tJTIhkU1YM2NzfUq+ZENP+05L+WGxeFQysy5R9eODLP/OuIl0MidRG/X+X7lV9AsLTj83bn1j1rQz8tGcsniF1fSUah063eqq5417S0ypa/qOKacsG9Hjbl2OKAnOmz3B0TXuqDXTdnrEJV+zoF+3O4x6Xbl6W9yjwTY5OWPeMOm4FvvMSRnygraQ0l2ru2hV7nDYFU+ac1GzfbKSFg3p95SUFbW2KFEpKiEiUsht0mIlJHmPedO0c6q02+qBkAV0RUKV8pAUPuWUGt0WA2snLysvI65SpR6NdklZ9obflbImpiQQleZsca9pZ4Lovct2b1dvu0mnpKwY8Iyt7lNriwnHJK3ocrtNi8o1GPSsnIw1Mzais/a0HhKNRkysXLGlqUNky4hbj/yXtWI6nf5rD2OJWKlsoG3/VY0Kf3cHyR9U/cialQsXLsjlcv7Tf/pP+vr6nDlzxsc+9jFra2t+8zd/80f1sa6X7+2e6W3e5/SZEyb6FyxtW1JbW2tpacnIyKjRgQmT87NmZqeV10atVi5JZ5PiscRf64j4bk3IgVv73HzoJn19fRKJxN/aFv2dqcvf+vdJZTZNzA3ZUbVVe3ubyaE5a9lZx2ce15W7Q2PlPpPL/Uqz1YG62Rlya5aMeNU279DldgnlVk3Jy9rmQVlpM8662S/ocZekJTPOatSnWruhMBqu1qHFPle9YMWYPX7ChgU5GYf8IrKSgaC5YiJYKced9yWrIR7+Tb8rZUmFenkZK0a02KdADCmxYU6VVru8x2VPOOYPwjg8WaR/Flw5KyKE4Ld8WHelZaWtGCui6/P+S7ZKRExMmWa7bfM2F3zFgG+IiNu0aIu7NdhRDGgs12DEazYtSig365xLHtfjiGodQRQ5o1xj4F5M2elhucClyesVFQ0NHjEJETF5EfOuKFWrzjZLhpWoLobn1dmqVLUd3q3XvRZdDRqMetu9w4hXjTuqSpuUVYsGNNlhyIvGk6+ajZ4TK8/K5dYkRM1lL0jnNpRWxPW27NJUs1tlaY3T01+2besuVxdPuvXBHy9k+Xez5ScSCbk8g8c+qz7WqCHXK5vOq41GLJmUR5k6TXYa9IJJx8WVawhp33t8QI+7QuJxUiEbuEy7m826EPQtm27wM172GzbMa7TbmNe0O6xGjxIVSlSacsoFX5ELHJ+tHghNRzTAF3utmdHpNvOuuOxxCwYCb3nFdg9qtV9O1opxZWrElUuoVKHBpmU1ukIzMOuyr4krK5KQF/RbMWHTohLVtrgr4AOWVetUqbXYUJeo1uqAgz6qRLUBT7voMfV6ZWza4WE97rIZAIM1OvW4y4yzLntCp1uD/ZtSVaq0ykga8A1tblYaLTNW/rzbD7zNgzf8I+fHXjdY8sRfWiv+dYcxiEXjopUp6/kR0Wj0u94bPwpr/d+0fmTNykMPPeShhx4q/nrbtm0uXrzo4x//+PVm5Udc38s9UxIv0964xcZU2sjIqNXVVeeOX5Zfq9BcvlddS9b01LT+yZddKT3q+bNf0NXY5+riyb/kiLg2RTl9+rQv/fFTclNNeusekN7MGb541Z987RW/W/pn+g50uuXuG5x+7bJt+Qf/xrbo75a6XFVWb3VzwaXZNyTLJ7Vvq9fQ0CAaj7g896qyVJPdlQ9ZzI4Y2zjpauY5W/L3yEY2xPPlslJa3aDBDlNOhTyaUuvmJC3rcnsxCycqFh6YzcYddbt/bsZ5a6atGNdktxGvmnRSqdpAx3zakBd0uU3GhmWjlo1KqDTpuH5fV6VdmToNdoZsk0aVWlz2hFKv2ukRNTplJCUtiykNXNqF8BAvD5k31Ua96qRPOehnlKhUb1uwZu62oN+qSQ36rBhXb7ucjBGvabRTTKLIUmmy26jXzbmoUpu8rFI1kpbNumTWWYsGdDtSDMM77wsu+oo2N+pyu/lAhC3oE96u0S6rJm1asGhQna1SVsIqqbCeuuBLpp1VpUWPI9LhxFtnq4M+Yt2ctFW7vDecuHsURJXjqrTb7p3WzVgxLiJWtFqvmBSP5N3Y9G59sZtcXX9DrGJJ+XKHdRk3992rpqKhSKtNV85IZ6tE23/4NuXvt67FTVyre+65y2d3fMXIiddUb+5QlmtUlWiUzqTEswUnTJ1t6o2Yc1mlZjf4GYVE8Z1KVEGwCc9L2wh05xKVWsw4r0a3m/2Cix7VbJfNABjM2BBXolqHCk1K1IhJeMNvKzQ+JTI2rRi3ad5GsDEXEoe/KWnFPh+0YjIIVBOiYhb0y0pqdUBcubQNF3xJxqab/JycjDf8jnlXNOiTk5GTFVMSQgJTqkIEQdamvKycjA0LSlRZMKDBDrV6VGrR7yk1urzl92xxry53FCnL19LYU5a1OGjCMbMuuNV/b86FIgphzkUZmxpstVB+1u6mu4xPFeCPtRXNIunEd10rfq/D2LVDYufOGhHp73nNj8Ja/zepHyvNytLSkoaGhu95TTKZlEwmi79eXl7+Hldfr79p/XXumXgsobdjh9MTFw1eajeemFaR6tLZ0le8viJR6/zQo+LJWl/85sfVt1U68o4bfPgf/pTDhw9/2xRlbnbBhdNXbCm7Q1tThWcvPWpucpFcXHfNfo0bu02fOufJ82csrMy46e1Nfytb9Hcdg1dE3fHh3fpmH3HlG4OSmXUN7RW+efm0m0r+USHcK97jhup/4MX0f7KQGdCevxnMOK/NzcUwsbgyeVlZKVlJVVp1OFSkcmZsqNJqwDMiohrtlDRl1OtqdevzkOP+wEl/rN3NSlQ567NGvKxWj4goIsYcNeY1lVrt9j4t9kPRFXOLX5WXc8nXLBrW5Q51eiQtO+fzRr2pzQ063WLDnFpbzTqrQrOLHg0hhXfIy4Wgtg1V2sTDznvTkiVDFly1Ykyn28w4Z92cJUOmnFSq2ri3FBKkN1RpkZU24BuIBDjdiGqdDvlFeXlTTptwzDF/YM20etvs9n5VWtXokrRsxbgppywadtVzIeTuOUuGpKzL2NRol2hY9Zzwxxr0aXfIW35Hn3eLispIioiEpOmkFRPq9NrqAS/6dXns9yF5efMumci9rqyszJbtreryBx29/EWJijotiZ3OzmYkEqWm0ueslg5raKqSab/6I7Ep/22rp6fHL/3zn/Evf/k3RFdr3FD2YauZlFR0yVzuciEzJ3/GsBcDxr68SDa+tgLJI6GymEK+Zjron6KmndVkj0pN6m0tAsyWDIkrV6Y+5Ggtqwhuo2Z7jXjZVm+TDPdcmXrN9ph3RdqmDQu63B4iKpKO+gOrJpSqVqLKhgXjjilVY8wb1sy41a+q1aPfU0VBeokqU86o0qRcizrRkG4+YqeHbZg35bRZF1TrNOr1kHL8oIiYS75qw4Lb/DPP+dcSKkwVQznrVGpRpV1OSqVmXW434BsyNpSpN+9KkQXTZJel6KBIWVJf7Z2GMy9IZ5Pfc0XzvQ5j12zzH/v5j8D3vObH/Z79sWlW+vv7/fZv/7bf+q3f+p7X/fqv/7p/+2//7Q/pU/39q+/lnplaGnZu+DVnB99yZfWMudlFu2vuc2PvTQWwVC5nZWXFG8NfsWHew1v+Z1c2n1fdmhEfbfSlP37KpUuXnHntanGKMj/5ssjMqqubo6YvrmuNHNYd6xEpyRtfPWazZFYkXyWRKrc7eocXjz+uvqr1Lwl3vx81+3cbg09MTHj00a96q/9Jp8+cUFveaCE9bDO3Ll+aB6W5Ovvr32Eme8n5+S/K5yPWzIigUot1s7KS4kpVaJKyEpDtEZsWi06fJSOqtJpzWdqaSi0iYiadsGBQ2pp1c877ilKVyjRYNlFkRJSoVKVZhSY1Ou3zYVlJ62Y02Bl4IS/qdsSEY8YdNe4N+ZAGu2HRPh+0w8PWzShTa9moXvcGvPmgSSed9ufyWDdj3mVN9mq1v5jXck3k2+NOM85YMWHJsFWTmu2xzTsc9QkjXhUVD26nKTElDvqoORd0ul27m22YlbSs3Y1qdJpwzFXPI2/DrBKVRrwqba0YqDjoGROOycu77OvKNQTrdiFwsEqzKh2SlpVrtGhQTlZZyFTJSYegu2rN9pl03KxLIQJgTqM+ZWpVRhrFyzOWyo9b3fK69d4VFaVRP/3e+8G5o4PGhk+Zm51X21Vha0+3e9952B0/4liJv029853v9Ec3/ZmrR9/yxsa05rI94mWllpKT5lLDJtPnlGtws18w4ahZ57S6QV5OVEJWUsaGqIRNCyadUKHJiokisbjbHUrUaLFfv2fMOKvOFrt9QEJZ0MMUVowF51e/au26HNHmBnlZmxYlrVo2Ki+vTI05l1Rq1u0OF3zZuLe0OqDRbstGQnOzYYd3SVl1wietmQ38l/Pysjod1u4mFZpkJF30qDM+YzEIzQusoRcDTDCp133WzRrxipRV3eFQUGeLam2qtMnLSaiwYkKFBlPOiOpXqQUsBpjimllTTlkxriJaJxmfc2v1+5TEysXiUfFoyV+7ovmrDmPfaZv/fq75ca2/82bl3/ybf/PXNhNvvvnmt2kLxsfHPfTQQ37yJ3/SL/zCL3zP//df/st/6dd+7deKv15eXtbd3f1f96GvV7G+0z2TzRb8+Jenjnnl5NfF15rsL/8plTU7HVv7qnNLadNnr6qPdyPv6vpbkol59/T+tN1NRySWowY3nvLQ3o958cyjPvEbf+Rg5wOaqpu8duZpRy89L7WeszP/sJ7cveLK1da2qo7X6y293YXNJ51fedSsUXf1/pJTa8vf1WXE969mvzYG/1YNzDu2/YqJ/gVryxuyudOupt6Qz0TE4jG5kg1Nld2y8xGd1Xeqrarz5ORviOUTEko15ncEDUaBhjof8nXWzUpZsaDfqNdtcbdOt5lz0WWPG/eWco0h0XVMp1s12uWkPwkU1T5l6gIMq0uHw8o1WDenwXZrJjXYYc4ltbplbDrtM075U012OeAfikhYMRL4MHFVOuRl1dumXLOLHtPnXcFuHA2o8Gob5uRkzThvwDPGvKYkOIOSluRlrJpSpU2NTq0O2usnZKWMeFnSskpNam3V5oARr+pws1VT6vRqc9CCfqWq1NsmoUKlVlEJy0asmDDplDmXVWkvMmdGvGrNpL0+oFSdSSdNOqZer10eCSF3WRc9Gk629eZclrRc/LxxFcVU7KiYmNJilhF5ve7RFOtTGW80mz2tu2mn7dub/fP/9ec1NjYWNQPX1pjRaFQul/uxC+z8m1QymbSlfac733GvuZUJV4bPkYuIJGcN9j+j2Y0qtRTpvgO+Ie6SUjVq9ciH2E7yBj1rxjltbgpf08KLedpZMXFNdmu0Q9KSq563YcEW9yhTa8Y5iwYtGVWjy6Dni2uZEpUmnTTqNVFxHW4WUypj06yLSlTrdsQJnzKrQONtsD2scLIGPSsrpc5Wve4NKc4Rh/yyXvcpVWPJVevmHfZLRrxszFuWDIuIWDCoVrdKLaactmJcrR43+fnAQyqskTYty0iKKSkSk/PyanSExn5U0oo1UxYNF4GLCRX2Vr1NT8ltspvrBtfesG3/LscGn/m+VjR/lSbpb3rNj2v9nTcr//Sf/lMf/vCHv+c1vb29xX8eHx93//33u+OOO/z+7//+X/v7l5aW/tiqlX/U9f3aeb/XddcEW9/8oxdlF6pMjc5ZWJ90eupZfWX3uaX9/crKyqTW8pJ1aRtr64aTb7iYflJjYou62DbbE3fanI+YKhkSi5RIp9MyuZSGyHabk+XOrB+ztaxUdfqgstUBzbkOXe6yYUGNShurKcnUhNqqRrvLHjK2fMJM9hkbmZXv6jK6Vn8TNft3ExEvbSsIhVePj5uZGLcYGVRdW66upsHK+piaigb7e45IJBKeWshayFzS4za5XF5DdptyTUpDKuu15N5m+4x6I2S43ioScN0tDqjUUkwcPu+LNgKLJCup2xE7vFupapuWTDruvC/p805latXZasW4ZvtlpYq/76wLWh1Up1fSkqxVZeps96BlI4Z9U51eLfZLWRMVVxLyW1odNOeyau0QTq8Z2709rLrK5WQsG7ZsVJla9bar0aVEhXFvmtePiGodIqLGvWHRFRlJnQ6bddEO77JoSJ0tGu2QV5hgxZSEU+v9xYZr2EsWXS3aq2t0arFPiRp5Obu9T7k6ETHV2uRkNdlr07wZZ0VElKjQYp95/boUEPiFfKGUTQuyksWmptEuvZF7NCe22bBoOnteZ7TeW6+c8Rv/+uMqq8u+zX32/y8P+r+urh1SSnPl3n7wp923Ly2dTdpMrRv65GVb83cZSR21bFS3I2ISIYPqgmUjGu20asKoN827okq7JcPWTLnL/0OjPiNetW7GmhkVGjXqk5c34ZhlI+IqZG0Gl1lM1qZd3itpySWPWTNjwVVZKc12GvGaUjV63KXedhkbWuzX4y4laox4SZkGHW4uJnY32KFUtdP+zIJ+ve6zxwdlrIuKabRbiVELBrS5SV5eTKkG24x63R7v1+FWcy6FoM9SGetK1dgwKyphxlm97rdu2rr5kN5doVS1Cq2GvGjJiDM+b9mwZnvdEfsfxPNlarNNUsubzuQes7Rx3mJpm66DFX7h5z/8fU8+vlOT9Le95set/s6blaamJk1NTd/XtWNjY+6//36HDh3yyU9+8q9UKl+v713fr533+72usrLcqZEXzC0nHGr6oJH1y+pSfTqydxsfnpSrXLEem2G9XE/6DjfEf9ab2d8XyyTcVPUREklnV7/m9QufpiRtLTbiP371XxgdHre6vqR89bB4bYfZ5XnZbFq7QxpjOyxnJ6yaUpFpEslFzCQn1FdnNeV36498w+LmlIaaFtlMrugyomA/TmU2XZp9wx0f/v7U7N9NRFxbW6u2tlZDx8969MVPys0su+HmW+zYucOLz76qJrVdOpP08uify0pZyF4ynH9Nr/s0RLdJ5tYU0npbXPAXZpy1z4cN+YSt7jftTDGpOGNDuXp1epWotMW9Xvd/SVrW7U6HfEyTndI2ZSV1uc2AZ/R7Stq6vEywUL9gzZS4cvOh0WixX04GVGhUrUPGpi63O+GTTvm0am3qbBUVs2E+UEfjRTFjVMKSET3uKjZBTXaHaxcthYyWUrUB3z8SbKZbJJSbdiY4KJqkbVozatk48pKW1ehQZ2twFmVkpQJNtFS9bUrVWjOtVE0IPExK2dBkByJFZ1G320VEXPGUiJh1U5aNaLJbmxuNeMku79dkj6ueN+g5Oz0SXE8FvUpUwpSTVox5V+y3tEX3S2XWnMl/wVJ0WM/SbjV1624q+2mb66s/8lDOH0R9p6skHkuIxxIy2bSSWLnOigPWsgsWsoMORH9KlSZ1uR5nfdEJnxJTokSVuDKlqsLqLW27B9XqllCp0Q47PWLQs4a8KK5C0oI7/U+Slk07Y82sBYMqtWi0U6XmEJ0wJxl4K5WadThcXI1OOG7GOZ0OKw1Aw7R13e405xKyutweKM2TzvqMId9UrslWD9gwEwJFl8WVq9Biw2Lx52rA02H12mXIi3Ky1s2KKZGRNOmkciNiSqWtWzXliseVaRARMe+SBjtEJYx42YY5fd5lxagZZ9RE2gtC/FjUQuqS4cxrrka+qTybEFlbVLu29Ud9e/xY1I9MszI+Pu6+++7T09PjN3/zN83MzBT/W1tb24/qY/3A6/udfny/9f3aeb/f64aHh33z8WNu6n6HudkFp1e/bHj5vF2J94nG8qbWBq1sjsqXbMqm4gWoUmRFJpt1JfW88dQZq5EJpfkaiXyFzEZKUokL8wPS1sPK4JDNubiKfKfKSLPqfLtMNhVSb+et5qe1RffL5tJWVpetWVFVXytSsWZo/rRYU0QiVlrU0PSPnjcxNyRZPqlv9hHDw9873+KvExF3NGx1/y3v9ZUX/9BfHLtg/+wR5y5fsrr5BTPpK3KRpNqyVtn8hsv5J2QkbeaXlaqVtGTWefP6bZh31O+BBtuLNNmkpeAsmFdvq7QNEayYVG+7etuQL5I2E8plJG3zNkthnD7qTTUK689rD+1xb2qyV7V2q6bU6lanV7l6KyYllNvhYad82jlfcoN/FCiaFzTbL20tuC26zLukRKUuR8w4GxJoy5WoNuOCaWfMuWzScYXmYV2jPjHxkMY8L67Mdu+w2/u96NelLIsrk7KqSru0VRS+/gUEflqFBosGlKoKqdQ5uZDJ0+Em9bZJWzfmzUAFXlamtvh1ystZMqROr14PeMNvG/eGfT6kz0Mue9Kaad1ut2HRrPOWjVvUr0SVkkiN0dz/j73/jpPssM4772/l7q7OOU/oyQnAYJABAiQCE5gUSMrU2lRaOclre53kz7v2rt9d+3VY+/WulS1Tli2JIs0gJgQiZ8xgZjA5dc90zrmrqivX/tEXLYIEKZAmKS055z9guquqq+6te+45z/N7jrpY/pKJ0FH76h9QV1/j4N532t6xIWb+8wzl/EHWW7lKquO1GuoaLM0Paw5vNVF+3aDHDYQekAy1u63yNwKOyteMeGZTo9VkwDv9M3U6rVuUMS9nTdaSfndbNiJjXtG6mBp7fEhRVsq0ORcsGXLVk4Y9swkxhO3epUqzuBodDqrSqM0+Vz1h2HNu9EkHfcJT/rFON+lyk7KCCa8Z8ZyIhCVDomrU6lCnS1wdQsEUc0nGXEC0zQqLSptzzRMIWzWmgv0+KqlVUdaiIcuGzThl0aCohBHPS2jQ4YBRLxnzirUgk6tGmyWDwfp2CyrOVT4rVomrijTIVc050vKwG1ofNpF93cTZk/79v/ht/8e/+9E51r6X+nNrVh5//HGDg4MGBwf19va+6d8qlcqf06v6wdV3CzN7u4/5dtKEC4WCz//+49/25165/FW//+ufl/g7CadOnVGeafUTt/11s6tjXr/2jLMvjctUZi1HqkWbssZnTllNT6oNt7tSfES+kNEe2hi/zpbPqdGh1W5NBkTERSUsuOyqJ60Yt25JdbkpEOblZa2q0ihi3boFKdNCoYOqQg1mymdkIgsipRrbdvX5wtFfEymmPfr6p1y+dkEk1axe7wYnZXuTq08M+7ev/7aP/+LDbr/99rd8394Ogn939xF7th/ztdf/o5nBi5Yza9qKBx0I/5SGcJ9SKeN8+CtmS+flpV32lcAZUdZqt90eNuIFdXrMOGXd8uaoudlOVZoC/PiYiIg5F5QVNsfZ004FScAbpNmkdhFxHW6wYCiItE/Z7+Pq9UiblTEfxNdXJNQFLqKN2PqYGmV5NVoldYhLuuYp7fa75mnjXpbQqGBdRMKCQTu9V8qErEVJHaLi5owHE5V6N/mkdcvCoqYcN+OMcUe1O6BWt1Z7xCSNeEajLWacDtD/VyQ0iKtVQUQ0sBZvPPes8xLq5KQsGw3AY3cHTVmthAa1OtRokbVszVTQ2IWCf68P0pR3i0hsTl32+oiuQEexkeybDaB9+yy4LGPe0cr/bbUyIWvV1sRt+lv3WE2et6/vg5vHxp9nKOcPsr6dq6Sjvcvg1DN2eL99te82XHjZcuGqzvCNYpWktfJskLxd1mavdcsO+3l1OoPVyi4lOYsGVVTEJXU44JqnJdRbMmRSkzpdCtbV65ZQF2RKjZp13rJhnW4IVpEFqcBJtxHnUAm0XRFXPaHRFnH1pr0uLGK/j6rXZ95F8y5JmbXL+wJhfEG1JusWhUQkNIioUlEWEQ9YLQlJHUoKwUqpaNSzuhxRo0VCvTnnDHtWhxtUabRixIRjgSsuIRRMLZNarJoMXHbtCjJClZiB+DsN1N5hNTRmJnbSfT0/p6Wq3876u5xefMTRE5/y5S99xd/4m3/9z/ko+fOrP7dm5ZOf/KRPfvKTf15P/0Ot7xVm9mdNYb5xlVEqFxVKObFIQjQS2/xC/fKpQb//+/9V9dQet97w5kZlZWXFpUuXDV/MOD4/7NFH/4pYJO5A7z0me6+qr2526473GB6/oq+wTTQc8/LwI+KlRjsrN2kM9xkrn9j4cq+sWXNRWcluH9TjVmV5RdlAhLdFRNxlXzXmZWsmpc1YMeGY35AypcdtwSh/wXJ5xFpl0rIRpaoV9eEuX3j931tJDEuGqn3xpd/WHt3tcOftdg/sk6xNSqXS8uN1Lp9Z83df+t987Jff5yMf+dC3XEwSiYRIvGJpbVZP0463pDrOrIy6ePWMXYmH1DRErefqta0dsaPmPpApLoqH67xU/jU1lVZb3KMmuKAXZV30Jxr12+enFGXMu2iPj6gEX8F14gEwaki3IxZcCaYLG7qMGm3qdIlKSJk266x6fRLqZS1ZNbbpamk2ICwefAmmpM1rt09UQkkh+AzqpExLmRHCFu9wzZMGvNusC875rKT2Tbptzqp1C6io1yeu1opxl31FjyO63aaisOlU2uG9rnrMa37bsuFNSFyjrcGUpVrGQsBPmXTZl+33cWV5WTnFgJg77GmrJgKezCkZcwEOvQEhResBBK4cCBfLxrykwRbVmoOMoZ2BzuWaipJWe0x7XdbSJoF1l/drs09UjSnHlRXkrJkunRaVEI5ELMUvmYoXvffwJ74n99n/G+utXCUte0pmi6tmL73mUOgvubnuZ4znjxvOP6FYylkoXpM25566v66saGLlqLhasVCVVGVGQVa1Rg22bBBylYMpakiT7dJmRVSZdEJBWrUmqybNOKXFLnt92KgXDXhQra5AE7aAjTVntSZlZY22ed3vabHLDu8257wL/kS1J0XEZMyLq5XUrtkO1VpMek2bfSrKm8GecUlrJoLV0JqYOlNet8Xdbve3g0bkNVd8FSEFGTG1mu2yaMjd/qF1c7Z4hxWjrnpKtUY5KXF1gZ3+JlkrZp0x7lXthb1CxbxSzYrDXe/XUtWvWC4oVnL2NT5geO24x7/yjP/xl3/pR+ZY+27rL4x1+Ue13u704xvHyW9nCvPGKqMpfsgzZz9raPyiYqEoHA4Z6NtrS/MB2ZWQxUthzz//irs79jgXPa+vr1dDQ4OJiQmvvnDC4kRaothqe+m9BrNhNaVOr0697OVTT0omGsSjCbnQquHysPpwr/bszbZH77eQvyaTW9DpBgd9wgWfN+I5nQHKPKFeTLWcVXlrwsL63G3cK+ZdUK/HFveJiFtwyYyzxrwScA+ShkvPqwk1WYhctOKyVHpBfbbD++/7FaVc2MjcjHxk2ZXVFy28PiG21imfz1uPzlg1azUb9lv//LOe+PLzfuFv/iUPPPDAplX5pZdecenKRa9fmjfbXtLV37b5vrxR50ZfVlqp0tnborhapTd8j/nQpMn0ec2h7SKhGolyk7pKtwVXtDsYrEqaxSStGLXVOxWsq9NtwlEjnjXgQSGRTSHtpBPO+Yx5l5XkRVVrtJ3AXRFSo9lOK0bMOW/FqBVj4ur1ucu6RVdcVqVJQdqsc7Z515scL2UFZVFVmkx4RVhscw11zRNmnQpgV3VO+T0JDYqymmzTq01Su3VLLvqCOt32+IiyUnDBCG0yNXb5oGmnXfJlEVWBFfp1IWHhANZVq0PeunM+Y9W4dvsldSrJBWyLCX1uN+pFMUnb3L8ZQjfgIVVBanRc0rIRq8YsG7HTw5vk0Y2/bMhVXwcNei3KW7esXq863bocsWbSiC/JWtJoq3I4b0v4TtXxWoVwRiWRDizoG1UsFd50Q/AXPUvle623coycOnXK/+dv/0snzv1HOysPaYr1SESrLcTOiUbbtBXfpUGHU2tfEI6EZSqzCpWMivIm6n3DatxgzbR5F62Z1uFGo54TEtHpBu0OyFk15mU9btHpJqvGVGsKtEz1llzVZECd7qAhzwcTyx3q9ATW56KCjKQ2/e4GBWlhMROOmXLSTu912ddc86RON4mIB+ufXKChOSdnWa0WK8YDF9Bp9Xrt8B45q5aNKAbr7UZbgsywHhExzXZqsVNYxIIrqjT6oP8krsq0U2adsSv6HmPFV004qr7c6J6On5GIVXl19jMmVy4qlcsi4Y2V1MzVOalUSlPTt58I/yjX9WblB1zfiQb7VuPktzuFyeVyxsbGrF1ZUpvt11Q6opJJWC+ueuXycY951O76e7Ql96gqt2gs7zB5Lm1q+KS+HZ0unR2yPJHTVNhY1yxHRlwrPyVfSust3a1+vVs5m1cdrTPtrCvFR20NvcOB2M0y5UWlclFeRoeDKGuxW6Ntlg0HlsCIZgM63aAkb8rrRr1gxZhed9jlAxr0Bmj5ggZbrRk344xeR+SsuFT+qlmnRLNRB6s+bGfHEbftfdCLzxw1ULVTcTXucupZg4XnNIVXTRReVy5XJENtaiJdQqkOC6+F/f2f/xf23/IZDY11VhbWdUcPuqXpZxytfdb0wpjyarWp4ZP2H96lu7tboZj30tnHdNfdJF2Y1l26TzGzYetdC03LV9aEylEr5QmtdrvqKdc8bZ+fVja3SefcEK8uiEsa8JAJR60Y0WyHaIC4H/SoJUNBavIG96HNAeFAw5G1ImdF1IZddtSLwmJa7XTAx6TNyFnTbIewmJxVM05ptVtcUoUgQDBj2LOBTmaHSa8Z84prnlWSs99HZS2p0aLNXlkrSvIi4uZckLOmIG23DwXUWJZd2yTYLrgirtaAB131uIwZBelvcDQtu+ppEVUadYiISZs34vkAqR7RZJs9PmzI1y24oqyoz22WjZjwitUAcpfUYcWoIU/IS+t1u4iIVKCTWTUe2E3D7vGrwmIu+LwVIxYNmnU2IPkuqtZii3vNO6+qNuSW7ffad2iXwfPDGoo7TBfPeuSVP3C+56iFxXmlYlkkGjbQu0csFhdr/4ubpfLfW9/oGDly5Ih/+q//rn/+T/6t0ZEvaanpFY1HHO7fp6/hp1x9fc61mXOKzVMsZoxkntdeOSQkFIQ+1IqrU6dbg63O+KNgLbiRpr1qwpIhrfaadU5CraROayZ0u9W004qyImKBaLxX0bqEWnHJzViIRltVqTflhIi4Le4z6ahut2o2EAhoW1zzlEWDutxkyOPmXDTgflWaTTpm2HNWjNjjwypCVk1ot1+1ZivGgrygslrtQYOcUadLWdGSa7rcpE6XKSe12S9nA16atWDBjKwVNaEW1fEa7eX9kpEmNeV2U8vXDOdeUZVvtS32kJpwk0x5yYXUo5bXM1588UXvfve7f6Sa47db15uVH2D9WUJO3jxOPnLL0NuewhQKBaOD07Ysf9C20gMU4qpidcrRkqbMPqPFo0aLr4vHq8TjMcnqOv2JQ4Ymz3r68kuyuZzaYrfqSKtYIm6pNGS1NGNX+UO2eAdYqFxSLGbsCX/IXOWKRKXZ5dzXA+pko6KcfAAyS5vR5zbzLtvjI0GQ2WuuekJJQZcb1enUoNdW7xIS2hz7bmgR6tRoNu+Ccz6v035CRfFItZbybo2ZfabG5rz0wosW59dULddpiLQYiNzvbPqrpiqP2OpeAx5QHWqRLs8aLb6gXMxqD91m5PQVTcm4ttxh9W2H9HUNaL6ty4unHjWZXlG11OG5l0ZtP9BtLHPaemTO7j07nbgyKb8clqjE1Me7NIf7zOYvmytcVqtbv7tVcNanpc1qtVvFhu13wSVZbYryuhzWapernnLWHwd8iHXrlmzzgEu+JKbatJOqNOh0k2pNQTJxRFTCokHLrtntgwF+fqOhqNGioqTLjQGq/oJjft1uHxAWDzQwrwcC3u2qNRnxXJCgnFGnU9qM7R7Q7oBFg0LCJrwa5Ap1y5gVlggSZgtSpuSlddohImHNlLxUcGGq1+dO2z2o281CwsqKanUb96qzPq3RVllLGmzR4xZ1umQtO+ePXfVkcFfcHkyo7lWQccmXvOzfKisFbp6CiIgxJRuBit3mXLBmQl7aHh/S6zZrJm1xr0yQB9Nirz0+bN2COl2WjVg05ObdN7v7rts1NDRIpzMmz01JVjUbGx6XX6hyc8eH1ESbZApLhs+/ZjD7rPf/4uEfmwvHHXfc4Z/+i3/oD3/7SwrTjXa13qqhpk0qu2Sm/kUXl55UzobtTN5nLHvReOllu31QRVnWSqBXqXPB52XM2eo+8y4KiwVhk2dMOqkkZ7cPKiuKqZK1GAADT+pxWyDkTshbs2ZaXI2wuEnHJLUFoaFPGfCgBVfMu6DZLn3uVFGW1C6h3pCvByucGnlrXvd7MpZkzGvUr0GfZgPmXFIOMpLb7FUOpp5vOO42soZK0mZBrzvUapcxF6yd2tTpDsCJk5I6dIZukA5NGcu+LBaPaC7uMFU8Zm72kj3JB+1Nvnfz+z9fzFovFq2Xlv3zv/8bvva5p7zj3bf/d+kd/99Y15uVH2C9HSEnfwoze/75F9/2FKaC1vBOiVS3aLxefbJZKBSynJoVD9U6XP0JL2RnHZ/+or5dWwwuvaKS6STTJJZpl68saLRdGUvrk874rPpyn043bQKNkjotVC5aKA2r1aHFblUaJDQoyAQskWFhUfXBSHbBoFVjIuKB2G7JsmED3uucT2uzP4hl36CFbij9IwGDoNm6h+T8N02VHXrDt7tceESVJq3hffL5lAsvjcmY16JaKlSQqsxYt2TAu93h74iFEsoKaiptGir91iLDhj0jtZTRUm6yu+l+4yNXTY69qLOn1e7uu6wZN7Vw3tXZM+aWq/zEz75P+ck96kO1MusZ6/m09mSzbCalIdItHq7RHB7QEt5pqTSsttIZBLttsCY2VCnjhj3ngI8Jq1g1ptE2zbbrd6dqbc75jDrdUqb0u9M2D0iZNu2EtDnNBja1PpOOm3NelQY7vNtFX3LMr4mr3UxtrtJg3aJ2B4x71Wt+e5OWuYGTv09Z2YoxWUs6HbJu0bIRcbXaHVSWV6db2ox6va56SpWGAF43Yc6FzRDEJttFJRSsS2qzbNiQx7XYqcVuaTMWXNFsQEhEWMQu7zfrjEv+RLdbVZQMeVxRzroFRTmt9gkHupQxLwXNbSTQ5+zSoCdwXq267CumnUJILsga6nKzvR6U1CFtzqoJYWGt9ph3Qc6qgrRm262aNOmYVHzUfTf/081VYF9fr4uXH3V+5BW7fUBLpd+22luEwxGVSkU83ywXTRu9MPdnus9+lOrNmpanDAcU1MM/2Sr75E71c4c1ZXd5NvMZlzNfNl++ZJt3qVJv2cimzbjJ9uD7Z0MrVFFUo01FMRCdx5TllQL0fVjEnItKshpt3dAViQqLKSoYDYi4XQ4HTXxhMwaiyYAZp6yasMXdoqqsW1RRDoCBK+JqNejT6ZBqLZYMYSPvqCilTpdhz9juATHVgTupEljui+LqjXtVi52q1EmbCbRitRs0ZG2g280S4XrhSFipvC5SSahNJqyurpgrDtlWucfuxLs3v//TuVXzS5PSpUU3dHzAdO6Y3HDRsc/M/cjZ5/+sut6s/ADrm2mw365S2SWRqopzx4YMNL/3z5zCvPb8IyqVso7EXmmp4M6jWaVSls6uiZZrpbMrqkutlkqTdqYOG1k6q7zeZm/4Q+pDvTKVRTVa1ZRbve4/m3PZPh8N+AExFWUF6wrWA1jYpSAPJB5MSPrNOmvGOa12Bif4NVQ02qZWu7Q5EXGXfNk5fyxlI3K+WpOEejVaxNTJW7NoMAgFa5PQICSiptyhw43mnJcvZ4VVaXPAgssbWPVKt6XAprrDe1BRrOQ2haUVZdvD7zJXvChbWGel3kopozd2WCa3Krpcls2tiye3eujGd5ovDJmqfdZHP/bTItGol//wNdFSjfnQRXsSD1rPzUgV5qWKc5oMyFVWqYQt2vhC3u1hIWEps/rd6aT/FDRqD0qbkzKtWotWew16zLxLKoq2uletbs22KyvqdasZZ4x60aBHtdipyxHVmlzyFSNesOiKhDo9btPlJnlrZpy15JpRL4qKy1oO4Fu7VZRMOGrRkJQpXW62y8PO+rSsZS12qdGiICMipjEQrG5QbM8FuqIz4mrd5le02h1k/+SCTNx6IVGLruhxm4qitNnAjrrxuZfkpM2Iq5PQqM0BWYsISwQptwn1qrUY8VwQcrffMb8mqd0O77bdA4qySvJmnbXPT+l3j2FPS5uz30ft8j4VFXkpKdPWTKnSoKygVqeUaXmrrjhlwlGVSEFjTbvW+q7Nc62hoUGodUlirEFX5bCV/LCl9KxypWRpfVIomfGBO37WiYUv/kg5gt5OvZWm5XOf+4LO0A0evv+XZbNZpWJFOpP25PBvOVb4D+KhOqnKrGqNjsR/XrRQa6JyzEEfV61V3qom2xXlnPSf1OoAYRH1+tQE8Qin/L6Som5HhJCXseCSdUu6HJFQJ2NeTJUGW2QtBWGGCasmnPc5AiVNp0Pu8asWDRr2TIDjXzfmRTNO2enhwC3UrMdtLvi8M/7QAR8P1kCl4B0Ju+Czpr3uFn9d2owNZ1oyCPHcEKxXlEXCMZVwUb6Slw+tqY7Wq4lWW6galk7Paipvt15IiZZjcoWMxZVp2cqq2oZqu3uPaMwnXcs87j13/ZIT1578kbPPf6e63qz8AOvtxHe/Ec196IMDTr909c+cwuQK685cOW11NqdtpkFDqN5U4ay14uzGPjW7JFbJyFtVCZW1hAeEJ/rUFnKmIifkQ2tqQh1WTRjzspXQiKHKU8Hwvlu7AwHXY92MM5triA0C6BUdbrDBxghpc8C8yyoqCjIWg51zQp2Y6oDTcZucFa/6NSU5TbaZccayawqyBGPZOt0WXLZm0rRTVoyZdlpBWtqc8/6bhoB4WqcrCP9akDIVoNhrxNUqykmZ3XQc5MprG6PbMolSk+7qQxKRaonQqlxhUX/vHhNLgy6cvKrvQL1yMeTixYsmxscdHXzM8lyFUkS8Um9f1cPW15flSik5acVKzlTohExlwRb3CYeiwqGIRLlOgy2a7TQe8BX63WPZiFrtRr1oxaiwiGqttrpPKsjX2XApNGu0zW4fdNzvqNVun5/0mt8SFTPoa/b4kC63WDEsa1mjLRptExJx0RekAmHvvEuGPRfckW7X4aDtHgh27yOy1lRUhIRlzKrRLi8lqkqHQ8HkpiFYvUQV5cw6F1xYNjQtYREl84Y9LWtFm71qtVtyTdaqvFSQg7Ihe6SixU43+tnNhrhWhxXDlo0EU7l9Jhy103sNe05CvU43SplWtG7NlCVXRSTEJDcFv7U6ZK0E07tlOavCIkHrvSGYTZn2gn+tEsnrSRzQVrlFqWVEVbxm8zwrlgqW0rMOdr9TNF1RWF8xV7kgGovo3t6qr2+3hoYGK5UfPUfQ2603NC3fvO6Ox+Niiai+0I3ua/5b1pZTakNdCpV1Q6UnJIsdFl1Vq0uTAavGJUOtQuGKcKiisdJjvnRRu0MBBK5dSEifO6waN++SjLmAwLyBAuh3l4R6tTo21jmhebOx1yjRWOpTrUWXm4Ok8RpF65aNSpsWVR2kIqdNOyVtVrVWcUlxNUHT32qX97nsy+Zd0Os2dXqsmTTm5U3h7QEfFRKWsyokrCpWK1qIm3HWNvcRKSopiUaj8pVlTVWdKtZpWFHJFRQTSxZKl0VKcensikxoVbKx2vbe/WqrGqyVm5SKZcVy/kfSPv+d6nqz8gOutxPfHe6Yd/c973HhxPB3nMJcnDjm8aOfVc51qouXJMsdemI3Wy6OWyxek81dUCyVxNVpCm1VHWqS1KY+t02dbRZckq8sueRLpp3TqF9LZY+EWgl1anWq0rCpSclakdQmZVaTASNeVFbQaIuSfBAFX4OwScflrOp3t6KMtJKSnLI6jQHIqyRv3Ct63War+zZDw94I8mu01YoxNdpRVpDW7qCwqHb7A1DUE7rcpEF/AF5bUK0pILpmNu9iYpKqQ42yhUWFUgGslWcMLb2sobpDtBK3XBy3dPUaFVays4ZWXjVc+7SXnz+qNrNNX+iBDRZmZdmF7FeNZ0/qj91qLbxgqTxq2TXFcMaO2LtUFWulS7PilWQQ3DclImbAgy75ioKMBUOabFGjVa1Oc87Z5j5xSUmdxr2iSuMm5K2koM/thjxuyONyVrXYI2/VVverCngi416RtWJjqrVVj1sDDsrPW3bVtLMWXLBsRFKntFlTTgSo+fUgm2jckmvWLQZ3g8tKSlImN1d+cUldbnLVE+Zd1OMWSR3WLbrgcyadUKdLrXadDgdhdFeFJURVBVk8G3bQjYvFsJCwmKQ5561bCHg7MXG1yooSGjXaotVOs84pyQcaiCWFABpXCITec86ZckpFWVRCtQ0GTzZYV8ZUaQ5tVRdp1dd4wG29P2llLuW1pc+4e+edm8TWQimnWCooFcsaazqkyquOHD5k957dIpHIm6zuP6qOoO+mvnndHYlEdPa1mjw3rb2pRyk7pVLKWy8syoTnzZcumayc1OsOKVNqQ+2qorWq4lVCVXFSJROlo5I67PK+IMJiXV5Kr9vNu6hevybbZMxrivZKhlo1hwaMlV61Uh61J/GgSLIilVpTr0tjebt0Zc5akJIeEVOr3YqxAHJ43rpFjba4PfrXrJQmjIZetFYZk60sGwk9Y68PyVdSrnranPMiErJW1GpXEyRKV1SU5URCMcVKVr6YMRh6zIKLtkXvEo3ExOM1FotDypGc6miHi4VnpZMTOnobdbQn1JRSCvmS9NSM1uo+2ztvUFu1sZ7MFJdEYmGxSOJH1j7/7ep6s/IDrrcT3/2xX3i/gYGB7ziFmVkZ9dzJr6pP7XL3rQ+YLZ/18tQpLfndymVWM8smy6cUpcXUmKt0WihdUVvqVBHRoEe5XBAJRWQsy1iybtmI51RpULDuSf9Yj9tUa1anW6Mtet0WuFI2yKtjXrTgilZ7AhppWtazgdjyBnkpC4FAc8ONUgksgKvBOuNm/e7WYtdm+Fe3I6550kV/Im1WWckh/4MOB8w6E4ji2rQ7YMKGzW9Aixot0uYkNFgxJqZWVEJCvYJ15UpJprgma01ZSbo8K5ytNZm7IlteURft0l+1V7qyYGH9uNeHPqcYzjhYfbcb2h9WV9PkWua0zFpOb+KgS/lHvVr6DbX1daKFWp1udKD6L0mW2kVrixYXlqTTaWUVk45vCvCi4hoNyFrR7UgAZmsLIG5dwqKiEiKi1kzKSwfslJiSglnn5azZ5QOu+Grg/ImoqAQx8w1K8qo06XGruBqXfdWkY/LSGvXrccRrftNZfxRQhA8EwtX1wNkzspkn9AbHJC+tEMQCzDmnKKfBFlE1xrzkvP8mLBYwc2bd5JPCIuZdss0D2u1XsC5vTZfDKoqWDBv1nLCoeReD190grl5SR3BMFGXMbRJ8Nz7Txs3Mog0IXA4VYdFNh0nUnyhKBcfwRtOzEW+QQdiSq8a8oKW+y60dP628zlTsFYWOazKlvZ4688eujl9SKpaFIxXjKxeEC0/b2rZX/5bd4vH4t5zf300e1Y9qvdW6u6+v19TwSeVUXKImJpZrsBYelypOWDEsX8jb4d0awj1KlYK62nqxeMzltaetFjcCJ1eNOeOP9LtbUouUGWNekLNswUVZC6rCDdobb7eUnnGu9Bmz0XOi2NK+Ryo8aSkzYS5xSnv2oESoVn2kQyyasJydFq7Epcyad0GNNndH/55SJCMXWhGLReyqvtNI5TnFyqzZ/LjGmlo9Vbslxj+0aZHOWbFi1KyzivKO+Q3tDqiuNFoPzxuuPGs89Kq66iYrscueSf8LDekesWhCTazB2fTnLNeecfjGXR48/F4Lx9e9c989stmsF546qiu6b7NRqVQqRtePG9i+ZzNq5MepWb7erPwQ6u3Gd3+nKcy50ZetzZQcbL9Bf3+ftelrJsrHhDL1mm0zX76iUZ9mO4VErRlXdsGwZ014zYAHdVQOGiu+YCz8sqpwg6byNlWaNOoXDuynWSua7ZAxZ8ap4O623qBHpEzb6r7AqnpZxoJFQ2o0O+wXNOg375IVowrSSvLq9ZtyItC+3KDb4QBZXdlEXZfk1ekRUyOhQY1WUQmtdkublbcmLyUvrV6/GWec9cdCwtYtbd5lF2Q06JWXsm5JOiBUTji6MYWI1pgrX1JVatJoQG2l2VJ5xMX047L5nBa7NIe32FX6kJWFNTNL4yLhqFIpoybT5eHu/92F0H/TsiNiYX7B+kitUC4mVFPQ0dlJiLnsimvF5xSkHQx9zHzlipw1k16xYEhUtVv8VS12G/S4YrAGKSuIqVWlHiy5ig17cAgtdstZlbGgxS55q1aMyEsFIt0ZdTplAyvoG9jzpHZt9shLq9UhJKTDIZ1uFFOl0VYtdjnpUxsR9Vo3M4LqdAqJGfeytFk1Wi26ot0BoWDsPuZlJQV3+DuaDFgzYdBjBj26ISZUJyRsxahONxn0uDXTtrrXbh825kVNQUZRLnATVRRNOaGsYMrxgM47Z820tBllBXFJeRSsI2TB66KqVTDjlAEPiqne/FtqtBr1gllndZQedmHpCVoXdBwJO7jjbk989ik1S7vsqrtPfbzdan7W1YUxJ0qfs+emv/smBs8b9cYK99YH3l4e1Y9qvdW6u6Ghwf7Du5x45axMISOTnnG19KKGaK9yrGS+fE1VvEpdslq5HBcPV1krTpkvDGm0RUxSj1stueaKr2ywekIR1ZU2O73fWgBKvFp50nL6PKGQnIzGqi6Huj7g4Qd/Wqa06DPP/QfzExeMFp7VXrxRGHXhVi1VMZPZsyYqr8pFlyRDzS5WvihZbtVY36y5qdFaZFx/bZ9DzfstVp9RW501fuy4UH272dVFtTotuLyZg1WlUc6Ki74ob00xtqwcyakN12nuqNVUXWVu7YLx9XNihTrrpRW5lkmf+KUP+yuf/Mvg/x7/lOPXvu7mbQ+KxiLyhSw2jrUzi48qJuft6/vT9OUfp2b5erPyQ6rvFM39Bqm2q6vrLacwK5k5T77+GTuT9ztw827jq+d96cX/pJQPuVT+qoiYPT5im/tlLVk3r1qLG/2ceRdd8VVDHjfoMVnLqsuN2t2g0VYVZV0Ob2bUDHnMhGN2eVhM0mt+U0VZOrDzvTEN6XWHer1O+pSyvIWAX1Gry3YPqNVl1ZgrvmbJNdWatNuvrKBOr5hqKdOYFhJSrcUeH3bRF9TrNeFVW71DnS6TJhCyZsq8i5YNW3JtMwwwa8mcC4Gj5YAd3iNvzaTjppyQMactvFt9uNuF8hc02GK3h40XXjOy/LS28gF9lR0KkcftjX9AuBSXz+XFKi2qInWakjHTqUHXps6RaDEYedUNe2/xtfH/ajY76M66j1ssrru2ctaw16XD8/b7KVvDd5stnXHIz9gSutv58hdc8AXzLgZcmi0WXNZq7+YUIWtFn7sk1KmomHHaQZ+w3f0mHQ8CA7dYcEW1Fo22Bomvi+LqVKlXURJTrUqTsqJFQxZdtWZauwN220DHx4NJVEXZdvcb9Khlwxpt3QS5TXhFxkIgwt0rZ81FXzLnnGrNanU45Gd1OaysqE6XHd5tyOOmnFClXrVW8y4a95Jlo+JqZcwb8phGW62ZUqdLWERM0pQTSvK63aogJ2XaZGB9rtWuTs8m2Xc9cJWtGNbnDtu805iXpM3ocYu4eouuGPeqhdBF5VhWqXXGbHlZc3vYre96rwvHxrxr5ye1RfaYGV9QLmY11jT48Pa/6ujFr3v23Ofs3LpfZ+OWzfP5G1e4d9z5gW894X/M6q1utDZiU8rCMeZjZ8xGTojGQkJhUuV5o9Fn3LntPYrFopnJOdOrQ1LlBTvK73Et9BQ4EPuwqnjSei6tUogoK1lyVUKdpBaLlSsm1s8JhyNEi2qiCTfceJPm5mbNmr3/jp/1+ed+y/G139Ua2Wtr7A6zxbOWyyMmo69Zrx23tW2rSKnG0OTTspU1u+pvlqlp0dLcIpyISG5b8/O/8Dckk0l//2/+E5PrVy2l02rLXWKVZJBPtFdMjeXQsEgljoqDO27T3Nro7NwTdvbsd6DrHRpr2qVzyy7NviLUvuAT/+M/flMsyBvf/4+cv0pNk7mRWSuVXmPZE4rJee84/P5NmvKPW7N8vVn5Idc3gpa+Han2J/7yQ0ZHxjanMJV4QdvWGrf23mq1MuG/PPLvmOtyW+lh1zwnb021ZiOeDS78zeKSyop63CplxrzzQS7PQmAt3aOioEqLWh1BFHrcFvdaNmLEs3rdISxm3Kt2eTjAXXeqKBr2vJf9OyEVMXWu+roBD206T1YMK8rpcSuYdtK6JSEtarVrtTdQ1G+M8jc2vWuiqiXUbU51oqrBgkvWTKnXo1Zb4BRJaHdQi92iEjLmzTjtaf9ESFiTbcEU4QZV5QYLhUtylQ3b6mj0eWPFl9UVu2yJ3UU4oipcKxKKSZVmJStdemoOWS1OqYrEJBtbzReuSFXWXZ173dbbqnz8bz7gucdfdXruD7QUe1wrXtMU2+WmxC+LZ9uNhJ5WjK/oL94mUWxwW+SvGSw9bsTz1kxp0GfGaVc9ESDDi8KiVoxotdc1T0qbs9N7VWuWtSQvY824sNu1OyCuRsqMijJBnP2c8+KSgWsmpyQvFhBze9yqTresJUXrwiKiqnU7IqbKSf/ZWZ8Ogt7alRTc5OeMelGjPjlrEuqCfJ5O2z2o3T7YXLt0Oaxai5f8a2mzYpIWDUloCLgZK4rWzTmvTrd63ZrtCrQr56wYsdW77PKwQY+o1hyE3qV1uDFwe+WUFTXY5qw/NO+ynd5np/fqcIMJr7risWDqltZkuy2x28Vr837hff9Ye32fE9ee9IU//JS+6C3ed9tPCoVC9u0rKZVKm9qU3t4ev//kv/BfX/pn3nfol95yhfvjIG78s+qb190d8d2unp1UWa+Wb5jR2hX28KF/bXfXEeVKyR+9+K+8fPZxT4z+mts6PybZGlFYW1PMZ8VCNapDDWZCJxxsfFA8ViW0FFMuhxFSCK0qhdPGys/rqhy0I/yQjHmV5IpMbMJnXvnXxtPvtrPzsHwxq39rv6nyaVPzz1qPDgpVwurq6tyz6w537/2wltou+WLWnxz/NbGdk+KlOuVSWFV12U137nFHwDMpFAr27tmnODXlltoHlJdr5DNF88Uh08Vz6irdut0oFq2Wjc1YyZzTt6vs7/3tX7SyvOrssWdNFENitWH3PLTHHXd+9FuOnW+cwj/32DFnZy6ozrS788C77e//wJsalR+3Zvl6s/LnVN+RVNtxwcd+4f1+4ic/IpfLCYfD/tk/+jcWZ8edOPGK2sV99pR/Sk2l3bhX7fQ+UXGLhkRVBcC2jQvVmgkJdcLiOhwUEtYeUGdDIpLa5awhpCQnod5W9xn0WICv7lVvPBByvixlxqKrSnIionKB+LLDDXrdoVpTcKHMyVpSVtRslzkXLbsmFriLEOgJVq2a2My/WXBZMXjdcXVyVmQsWjaq2xHNdjrlP6vSaIt3aLUXZSX5zVj5IV8356wON4mr3nAFSWurHJC1IhtZckP8Y9bLi7odURfqkAuvCYfDZosXNVYGNIa2EKIqXGc9u6ixrk1rZJuGtqiJtS53PXCzv/LJv+y1973mD3/7S7LjtVZGtusN3yIfW3Nm/UtyVhyp/xnRtRbRcrVopVqrXRIapM1YNaogazZINR7wgBotBj3mhN9VkrfN/dZMu+IRM86KS1pw2agXArhVnXSQnzPjnHXPmXVBiwEFGVUatNhlzbS8lKbAVl4VvIa8lIJMAOCq1eVGxWC1klAfTEaaVWnU6QY9bkdFyrSjfn2TlhuVUA6aT8IWXBKTtNPDVoxIm7XDQ5LarJpQUjDjlBHPCwmr1iSmRrUWDbZIm3XS71q3qE63mBqTTkqZ1eUmEQnrFi0ZNOuCak2uelKbfSISWuxSHVixW+0RFnK58kUiJS21XWLRuMPb7velV/6jXf09myvXbxbQ9vT0uP/WD3t1+fdcSzyiXAi95Qr3er35QvvHv/d7ptNZ29sPGujba1/fB9+Ur/Tz7/xn1gpzJlOPe2p1WFt0j0w8pVBZshw+r6t6q4nKCSOe15e9Z+N9D1UrhrIKpXXD5RdU4nkfaPo/NEX7jaSPWa+a1r3zfuemnvPFF39HfVOtmmSVw/fu8r/98j/y/CMn1S0ecPP2B9Qk6jZ1H5VKxZnRF9QP5PzKP/x7urq63jKTLRaL2X/LgBe/et6WtkPqm7vMr46rX27QXOgyV7hkLnxUvpgVayyIdc3r3r3DK0+dVsiWReIcvGO7e+65y8DAwLd9H/90Cv8RL7/8si/+1yek5+ctrE3KFdZ/bJvl683KD7HeWPfMzMy8PVLtr/6pf/7GO/f4r//2yyozfdoLNwqX41aMWTCkTl8ASIoqyQUNyYFg/19SknfZVwJuRkG9Hs12SJlWo1VIWNaiiCobosWN/z7vc5YMarJNLHCdvJEH84ags8NNxr2s283qdImpVlFWp0WbfVaMKjmuQW+wYthm1XhwQZoJ4tqrNdpq3Kt63SFtRknBuFeUFU05rihv3nlXfE3arA6H9Lp9Mx8kZ1XajHXL+t2FinnnEBb2uqKcsoKIKrnSqtczf6ykLCfl9cIfyEWXLZaGzBeH3Vn5e8rRwgYqPBSlQrGUJ1IxkT9l/9YjLpwYVvhEYfML+umnnvHcmd+yuHxNZ2yHzq5OscxepUJZvrwoGY0plFNKpaJxr2oOb1MqFxBWr9+4lxWta7U3WOUtiaoy6nkRCbU65a3JWhEWMu5VKdN63KLZTlRc8kUZ8wFULSqqSlKHWp0u+5qsFRExgsC2Gm3KSkHSbY9Vk/JSspYVg4SWRtuc8UcqSo75TSNe0GaPVnttda/T/ouUKTu9P2C7rFj0OYsGtdqtWosrvmK3D7jRz5l2UrOdOt0ArnnKCb8bBNsx7bS8lDEvBMndu8171QEf06DPhKMu+ZKSorK8qBrdDivIGvGcNVNCQoGWpaLDDer1WDRkonhSQq3By9f09fWKV0fUxdplFv50mvJW1dnaZ2vDDv/4f/+fxGKxbxsser02LrRdXV1ee/6cuzvudaj/ns2m4BsrFAp5aP8nnS19xu3vvMmply9ZuXRN+uxVmdgW9/b+Q1uKOxyf/Iqx1TM6yjerDXVaLA256E8sh4d8qOFfa4lvBY3xHsuZUVfPjxtoelChtqxjS9KB/jsMj7/upcdPu/HuHc68fMGzg3Pf1ujwxvftt/t877nnLv+x7tNGFs+4vXe32vb9+lv3KFVKwqGI8cVLUrERleZFr8z+gbHnI3a1PrT5XGe+dNy5V//r24K5xWIx73jHO2zduvXP1Dv+ONT1ZuWHUN+87rk2Oii/mPCzd35ys1H5xpC0N/zzLzz/gg9+6IMSiYRbbrnZv5z/dd2LB1TKTDhm2ikF6c3QrA0b74ZLJq5OTJWoKs12Smq36KqkVvV61Om2alzWimpNAUMjI6tizZQVYxYNSuoINA9Duhx2s19Qp8eyEcOeds0TYsHd/rJhMTXqdGuzT69bNdlmxaikDkMeV6tLjWaTXgsEsX0abXfN15UVbHGPlBlRVY77bTkpBWuqg78pJCwibqt7AyZMVUDArVej1YoNS3G1JkOe0Os2PW4VU23KCZOOy1o244ySnAUXtZb3COWjiJl32TXPCBcfEClGREJRlVBFtrBmKvGKcu2CA/3vMJ57dlOB39/f7y994me8/PTr1l7utSv+Hv0te6Vzq2ZXRl0ZPS1V3phI5SNLmvQZ8ICm0IDVyqRpZwPgXkyHA3Z4v4iIBZdNes28y676upQplLU6GNh+Q2adM+WkSqB3yVkJEmPLCrKylo141ozToqoCp879wQQkJCKmJC+hQcmIlGkNtgYTnEuWDetw0Fb3bgYwrhgz4Zgd3mu7B5zyB0G4Yh2BcHqLe7Ta67jfUafXTg9bMqRgXb/DAUQuZI+PmHXWuFcd8HFlJU0GJNRZNmzaMXW61OsD27xTQuPmVG7NZOAMCpnwiryUvX5CWNSSIWsmxdS45ItWotc83PiPNjOydt+4TVWiSia39h2blTdEjLW1tdeblLdRuVxOuRDSWtfzlo3KG1Vb1aS6UO8nfuIjfuZnEgYHB/2f/9/fNHxs1tXMSw41v1eyt8Urma8YKjyuUM5ads1qZNRddX9Df9Utf/pg5bD19awW/Xa0HRHLcC31uL29tzrQf6ejg4868/LRb1mxf7cX/oGBAe/7+D2e/d1nNc/0a67pEY9WyRezm7DAHTu3+/yrX9XffYMP3fArbyu89jvVd9I7/jjV9WblB1zfvO6piddbHHvGcnrBl1/4PQd23Sybyxgav7gZktbV3G91ueQ3/tUfOPr0BeuFVZOLwyYnptSVFuTkLbhkt4e12WfRoCYDktp0OSJjzrpFYW3WTDvud0CVeqsmnPaHdngvStZMqNMtod6KMSsGXfFIAEZqsWJMSd5+H7PTe9XrUaNVnzvF1GwCwgY8qF6fnGXzLhrymLy0Fjsl1IqKb+LTN3D5OzUEF6ArvhYksB5QkkM5eKxVcUk7/ZwabZs8lga9mxqLoqy42k08e72eIIckpF6Pw/5H4152wRfUarfLw5thYxtumjV5Ge0OGPCQk37XtFNyVjVndqgKNciFl83nz4nVprx3109LxKrEEm9W4MdiMfe993ZfH70suzLt0vSqpupuLXVd1loXjM8NW4kM2xa6R2NlO4W4SCimrbJPq31mnVGQNhVoe3rcIhWk00YkNgm9Gy6nOVUatNqnRktApJmXMavKgU2GSdG6SgBz2+o+s86YdNwVXzPgIeGAPRuWN+e8q76urKxao6gaIREDHtDhBmERYRtfkJ1usmjQCb8rqU2VeinTOnVptBXlAJb1qhmn3OwXrRqVs6bDgYCLUwkaJnrcasxLCGm01aSjetwmJLzJcZlzxnYParVHJMg72nD5tEuZNu7lYPr3qjWTetyiSqN5l73u9yyEL7in/xNu6f6QSqViYnHQpdevSdbUmkmdFw6H3/L8/XETMX4/6rshd3+jk6Wvr88v/q2f8Wv/+j85euJThteO21J9RF2407q8+fB5zfUdWkKddiTeoVIpK1cqwqGQtdyCUCWipbpPOBxWE92ApxVKOdFIbPMGcHRkzMc+/tFvufAXCgWpVOptNQI/+z98wuiF/8vy8Enh/LpKiXAspHt7q97eXR577dNSlRk/d+Tvv63w2rdb36h3/HGs683KD7BGR0d9+ne+rHnliNv2vW+DWpjP66qadUPNfkcX/9Bnvv6bdjXca1f9Q2qiTeaWx529/LyF0lXF+rKRU8tOXToqu1ZUEVdWlAosph1ulDFvxLOGPeOQTwgRcDcKRr3gvP8mptoeHwpWJXMmvWbJoK3ulbFg0lHVWmWtbHJNDvi4Gi0u+KKkNnt8WEyVlBllZVmLxrxkp/frdIO4Gq12y0vrcFMwdXlSSCjImpm3z0+KSRryuFlntdoVJLF26XSjJts2LH+yGvSKSpjwimY71OrUap8Rz4uIS5vWatcmnI6QirJV48a8aMKr4AX/woKLBrzXjf7yZgLwrLPWHbBkyKpxuz2sXo+U6c0I+hHPy1aWJSMNtjTv1x7fbuFSxUz9Ex765W+9eG3Z0mch8hXLyzSXd5paHlJdUy2RjFnOnFFZozW0R51edVXtMpUlM7lLspUVnW4y7EllRUVZp/2BVeP63a3f3UqyVowLi0rq0GiLGWcMeECdTguu2Oad+tytrGB1M8H6DvPOG/OSTjea9rpT/otpp3W5SVnRsqvmXLZuwXb3WzYciFd7bfFO6+YlNIuplrNqLXBnleRd88zmuqkc5LG8oYNJBTbjjeyXsga9kjoCMTAbJGQi4ioE7qJOOSmXfEVCvYM+atRL1kxpsVtkU1xbEhJRpV5ZYbM563GzVePO+YySopQp86HzPjjwq+7r/qWNZw2F9DTvcHlm0Up8QfW2lOPXvv4doY0/LiLG70d9N+Tu7Te2+tznvvAmk8Ft77zRrhsWvfbSacfGX7Zam5IuZ+1I3OtdW37Zs2OfMpsaUionbAwIK2Zyg2KRhPrGWuFw+E3wNLwlQC0Wi31bk8N3Cgns7+/3i3/74/74d79qfapgS8ONGpItMvlVzw3/F2dXHnf3oZ94k3vsG+vHDeb2/arrzcoPqEZHR/2f/+bfufRkwU0NdZ4ZflFnX6vu7i7haMhs+qq19SVbcg/YE3vAtvoDstms9bWE26M3OFn8Iyfm/zPLjXblP6IlvM+V8qMy5lGxM0iijUtqttOwp4WE9LtLlSYzzjrrj/S7yw7vs2psU3zYaIsz/tioFzToU1JQtB5YWav1u0ufOzXYYtJxfe60bCPMcMLRYD0wqk6nTjeKq7Fu0YIr6vUKixjwkBUjgc34qkWX7fEhSe063egl/6dW+7XaGdBNSxZdsWZSyoyQkKwVa6YMe1a3WwJQ3VaUjXlZn7uCu+ysFaPO+mOTQTT8BkisQZV63Y6gIm1GrQ5hES12WXBZjdvEJV3xqAM+qivIIoqKu8s/MB0+Zmvvdvv6blMulz0z+p9czb7iF7fc/6bP+9ixYz7/+49LljotJiaFc1HtDXvkClmp0riZ0BntkVuVKnkUCVGuFBQqadWaNNoiKm7IYyJiinLB+uWd8laVFXU4IGtVxpwuh+WDJiATkDe3epesJVUaVGkKtDzLdvmAZSPKCu70d13xqGHPGPJYQBYu6HHEgAdAQVbOsm63KEoLC1u3KC8mKrGZEdXlJqvGxNTY5f3aHZC3Jqlds13SZn3FX5WXktQhKq6soGRDD/RGLbgiplqDXpOOqdJgv48Z8qhqrao1ycsY9Kjt7ldRDhxkJWExY16SC61tZMVEl9SFWlUVGoSjZfnIVvXxOre1f/xbztHZwmUrsTH/89/8BS8+dvQ7Qht/nLQB3496O+TumdAp6VfqJTOVN5kMhp46LtxR8qv/69+1e/duY2Nj/u//36esHWu1sDinko0aWT+uJ3aXcDhqvnBFprgiEU4oRjJvCU/jWwFq39Hk8GeEBL6ZnfW00WCldMOHt1p7fIf9zXd+x/fnxwnm9v2q683KD6COHTvmj37ny157ctRN0b+iM3pQvpA1eW7S1PBpifqIk2NPSuRb7av5kKXVq7a0layurqrkYyLxkNhKk87yLeRpCG0XLVdrd8C001JmAiZFtYJ17Q6YdVpBxmVfExI254I6Pfb6SWsmgxTljVyaBZdtc5+wqIiEovUgDfWCHd6rw0H1ei24bMEVYTEZs7JWdDms1+1K8vrdoxy0CnV6rFtQkpPUJiSswRZn/KGsZSvGnPXpTZx/3pq4WvV6hMWUlYx7xbTXtdlnq/vkpcy7aM20l/wb1VpkzOl2xLxLhjxum3cZd9QlX0bZjf7KJv1y0aAl1+z2IVSC9ybijcalICOqWrUWM05JmxeR0OMWo16wEh5WG241sXZRbLloPPe6YuucLXX7XLt6zcGDByUSCVNTU5uC6fe94z1mV8ecH3vF0NhppWLJ1OpVxWhaW2u79lCb6bkrlkvjCqWshnCv6nJbgNbfyD5psccZf6BBr7QpTQY06A/SsKeFkFCr1R5jXlKlyXYPKAQTjTdw9KUg5m3VuHYHDXtaUU63W9TqcsUjVozqMCBvzWVfUZLX5WY5K1rtkbOiQpAXlZO1GoioKxJBCGW9Pg36NjN9NmBsG0umLoetGLXgsk43KspJBFlDFaVAl3JSu/3yMmac0eUmO73bvPNBQ0anQ0Y9b8lVfe5QrdGKCVOOW3DFlsTN1lsHffy9H1Edr/XKcyf0xG90NfWqFxZf9NL8p2ytuVVNtEmmuGR0/bil6kH9O7s88MADDh8+fF3E+H2sP4vcnaoZFBGzrXL/tzUZfO4/P+ZXfrXboUOH/PLf/YR/+b/8umdePK2tcMhqeNTZ4he02CEXWtEa36Y6Xmtq/pqxwmuKtW+Gp/HmtdPo6OjbMzl8B13JW2lJ8GfGpnzza7leb6+uNyvf53rjJGhcvlF/Q0hPdLeGmlbQWtdjYnHQwtwVs/lB+0ofE4tXq5QriqWChcU568WMkeWTEqVG/e4x4lkdblBRkTav2U4TjlpyVas9ygqS2iV1Cos57JfNOW/ScXlrjvkNGXNa7bFmwoRj+txhuwdMOm7QY3KWxSVVaXLFV8VUu+opE44KCUmZVlGyw3u12m3VmHXLYmq02GXOOSU5bfbJmLdqQlFW2pyMBTE1jvhlNdpkLZtwFGWDHhFTtcFB0WjNhAHv0ecONVpNOqZWl35363DAhNfUaJazok6v8z5v2LPS5rXZrcsRzbbLS6nXo9cdRjxrynE3+CsWXHTJn9jtQ5rtVJQTU23Sa5uC4l636nOnpFZTlRMulR8RKqSVokfs3nFIX8O9Llw9vaknqqqJWbekcK3b+27b+NLraOjX0dDvnr0bommVkP/t05+QzWZs7duvrqrZ8PigWv1qdKpUwnKVVSkzEurt8xPGvazRVr3BaynIyFsTk5RQkJcK3s+VTaeTgLETkbBqXFytrOWAIJy2ZtI1TwdrsDPq9Njv72i1R9aKCz5vyOOizstLG/eKWh1iaoTFNehQrTkILiwY8RzCBjyozV51uhGStwZSpvW7ywVfCNZ3CfV6NpOaV4wZ9bySgl53GvSIFSNu9TdUa9Zil5w1NVq02mObdxrxnEGPKsqqBCj+veEPaKhuciE95NzxQQdu2qOqOqGQz1kNjXrHkYc11rQaGnt8QxcWCxvYvsfOaI9U+2mJROK6iPEHUN+J3D03H3Ltqcq3TF14a13HkSNH3Pv+I1YmX1JJTVhfnHQ6+591RPbZ0/QuyXCdleUJ11LPUD3vZw7/rTdZpb9Ze/TSS68oz7R+S6Py7Z7/O9U3a0ne7grsug7qu6vrzcr3ud44Ce7Y97DLI2dlCn/aYb+xK1+enhCPx8SKUQuZUdlYytj8JWPLlyjGKIW1hnbJV7LGvSpaqRIPMPSrJjTYEuRRnFeQllCnx63O+5xH/YqsVTVabXe/Ks0yZq2Zct7ndbrBbh825DGXfVW9Hju9R7UWKVMu+bIz/lCT7Q74qBrthnxdRVGjLZLa1Wgx7NmARtupKGvOOQXrWuxUoyXIPj6raN1D/pWoavMuqNet393mXXDJV8w5b8k1EVF1euz0PjkrJh2zbFi9PvV6tTsgZ1XBulXjEmrVOGDQ19TqUq1FTEIlCCmLq1OQtsU7ZC2ZdEyL3UY8p9ONCtLWLarRolanpDajXlSnS402VZo1VXZIVxZM51/Q37VdUrvB16ellmKS8X77wh+RS2V8+tl/Z2dsp6n+Kd3d3ZufdzQS2xxD791y2NmTZ6zn0lobelwZO62mFNuA3oVCypWSWafFVFm3bN2ChAZFGXkZ6xaD+IJeIRFzzkmbEQlWK8WAkVMKLMcVZTu8x4pR006ZcdaUk5tJxbu8zwEflwg0H3G1bvLzIuIGPSquNmg27hZCzlrwuCUxNdZMueYpFWUtdiopyFkNmqW4RYMKMjodsmTIKf9Fxvymg6sgY82UkoJ2h1zxFROOabRVUqu0OXW6jXhu89jpd7eYGiUFEQmF8Kqp8im1oQ4LodP2t9+rNr/FhdcH1bfVOH72cyIdy+7c/fNvah5jkYRIOOorZ37drXfu+RaWxvULyPevvt304X/5n//FZkrzW9U36zpg/PK8D9/yN+3qulk2nzG/OmVo5nXXJs5ZyqybW18Ql1RX32Kg44bNx/pm7dE3p0S/nef/bo6Jtxtee10H9d3V9Wbl+1jfeBLEonEDvXsMnz9ue92fdtihUEhrzRbl5Ypka8T68qJyfNXwwllV2olSKOfU6jTlFEJCosLCEhotuWqXh006atX4pjtnxmkLLgsJaTKgrGTIk2KqxCQ3tR3Vmsw6Y8jXDXjAdg+KqVJR0aBPjXbVWgIEeq863cLCBnwgyKCZ1uGgWp1mnNZkQNqsiLgVo1JmxFQHF9Tz4uokdZhzTlKHpDZxtWp1WDYia0WVJld93WG/YNlVS67KWkLEmklrJoK8oBqTjqvTZ9bZwMkSs807xSRFVSsEf2NETCEgurbYY9SLtrs/mDZsMEzCYtYtm3LSgA0K5rRT+t1tzUQQ5JcWXq/z6DN/Yr2wpjnRq6dpl+qqats7DiqUcvobDmhIDTh34rJkMvmWOTKHttzj6JXHHF/4vAe3/DXJ6gblXFGoEpIrZ0w7GYiTd1h0WVnZnHPaHVAJwh03tDhVwsLi6pzzWYuGtNplxmktdkmZlLWs2U4J9TrdKG3eRV8SFrHd/ao1O+gTCjLB+0xYTEXJNu+yaixY1RTNu6jf3ao0WzNpyTUJDZYMSQWBghOOGfBg0EhOBCC/BTXaLBsN7Mjb1Wo37FkVpYCRUqVGq8u+ZNWEZOAyG/eqsuLm1HDVuFEviKnR7qCI2AbNN7SqIdrpWugJS6VzuiP/g3BVyfjckNXCOXP1l93U+qD2+g3X2RvN4/ULxg+/vrEJTKVSb0pp/nb1jboObP5ONBJTW92gtrrB1o49ivs3mtC52QUvvfKs0ytfcnr0ea11PW+pPfpenv+7aVbebnjt9fXid1fXm5XvY31zVPq+/tsNjnzKmcVHHWz+0w67OlanqbrLhdUnHOq6X2/bgOVrFYmadlcmTotVqgOy6RkN+pXklZXFJaXNqijpcINZ58wEEK0VI3rcotvNIuJGPC+uVotdWu1WlAvYHGHnfFaDHju8Z9N9UgkMsDHV+txhxmlLrmq3X0VFtSat9ijIWDYcAMtelFC/mTWzZlLOqnVL5l20YlxBxgVf0GG/krwhX98Ey62ZtOyaHT4gLGzJNRUlNVrU6w2STfMmHHPVE0G677yU6SDUb1kYK8YltcrrUBM4ViLim6F4MTVyluWlVVCSFxIREXfRFy0aNOAhWUsu+JwaLfLSpp005aSeyi32FN+tUM5Zy4+bnbsgGl41NHPKQMcNIrGIumS9cjpibGz8LZuVRKza3sNbTA494UsjC6p0ioXTclYMV15SsG5n5b3a7LNmSo1m8y4b9oyd3q9aYxBOOCVnzbJhaybNuyIsYtGQsoIet6vVJRKkIW/Yy89ImUDYijE7vV+tDgXrIqIIy5hTUlCrXbcjpr2u1+3mXJS1pMtNCEmbdc1TMuYVrKvRasxLGvQH6d0ZZaVgzTNqI2TwsqSOQLOyYZOu1SWpw4oRa6Yk1KnRrt1B1RoVrSuH8lpDO82VL5pyymt+W4M+nW7ayBKqhKxFh61VX3PTjYctZk6bK75uJTkj1rXg7/3VX/TiY6d85cyvX79g/AWq79Xa/O1+540mtL+31vSBbnPLVaZqnzXxbWjD3+vzfzf1dsNrr9fbr+vNyvexvvkk6Gjo947D7/fcia9amL2qv/pmNdEmEyuXLFWGFevn5Vp2Wp7rkYxutZhekqukJDQ449MBu+Im53xWxoKidYuG9FrdnCYc9R8Urdvtg7rdokazq560z0/pdsS6eSmz2mzXYpeYGqvGbfPOgOi6cVHfuJMtfUO+y82ueUpMEqHg+XPqdBn0mGUjtniHdcsu+ZIWuwjwbPMuGHdUUqeyvGuekLUoZTrIgundXAFUVFz0OTkpGQt2e1itDnlpjbYYd9S8y+r1aLdfyqxqTeZcMuW1wKWSkgjC8TZEoavSZgMBaEbGvKzlQCNTbdY5BSlTTlkyqNF2S4bkZSwbtWBI2qxRL+pzp8N+SVW+VnWowVjpqFikVlW25OuvfF7j/e3BBO2E/VUfND121e7dBZVKZRPd/saO+qc/8WHJ2hq/9e//s/OnnpWrRHVFbhSL1tjr/TrCB5QLYd1usKv8fpd9zYzTcla124+NlOENN9aIooxmW+zz0xLqTTth1ikt9li3pCQXrIBOB5Onms2snIx5ISFFZK3IS4uqEgo+w7h6+/yUVeOuedqgr6MsZcqE41rtCprUsHP+2IRjdvugFrtEVSlYt+Sqa54y55y8jelRi5363CGu1qKhQJSdRxJlq0Y12SoW6pAxrypcrynWJ12cNlU6ZtY5UVWykTmxWFykUmVX3b3u3/mw5tZGmfyq+dUpl0J/cl04+xe0vhtr8zfqOt7O78zkL/noJz+4GVXyVtqjt/v8l+ePuunDW7/nv/O6Dur7W9eble9jvdVJsLv7iMZke+AOeVypWDJaPGv3uxq1th3w4lf/WG60SXNxj1AhLldJyVrdhK1NOKZKvR5HFKxvAr8WDZr2unr9ajTb7kFlOfMuqdFqh/fIWlKry2zA2Zhz3ppJbOTyvEGDLSsHzUrRilFZy8oKprzmEX9bxowVw0a9qFaHKa9rtt0N/rK8Vdc847Ivi6hSo0WbA8LiImL63eGSL7vmWbf7FVWaXPOkCmo0CwW8jmWjrnrcVvdukmgv+pIRz9riHj1us2JMtYI6XRps1WLAFV+zbkGfO6VNG/OSVrtVlIOAv4oJr8laCdZmfQY9oqKsRqtt3iUsKm3OokGrQdjjeqCvGPDQho26vKoSLqkK1SuH0/pzh50YG/XC6a+5+9D7DI58yqWFZ0Ur1Z567DmhSkQ4GtLaXW+6cFale2bzAnno0CFf/MKf+I1/9QeqlursqLpPqBRTn6y3urymkN/Idcpb1elGVRoNeiQgtQqmbRnlUFGzAY16dVVu0mK7heC4yJhTlNXnTlQsuabNASOekTZn1XgQIhkWU6NKo5KsVeMIqdEqoV69Ptu8M5hGhaTNBUj7sLy0FSNKci77irQZvW4XUyMvvUms3eI+005adE0yyKGKSuh22G4Pe81varFLlSYToZetV+b1h+5UqKybKZ2RCo2ZrBzXW3VYnW5bK/fa2XynfCGrHF23Fhvy2Sd+S6yhKBKOWcnOiPUumpmZMTAwcP2C8Rewvhddx3fzO3+W9ug7Pdb08ogvHftNo6unrD++14UTww7eusPhm2+yY8eO7/r4ua6D+v7U9Wbl+1xvdRK84Q65e0/ey5e/IlSYEC80Kg32e2jfrZ5ZfNzo7KuK5YKMBdWatNlvyOPq9IipNuYlZWW12uSlxdQGQtOkdvtExKybN+usvT6irCAkbMYZ414Rl7TXR4x6XkZa1pI1U0GeUF5JQcacFSPCYmYCOFyVBu0OSKhVrcm8y5YM6nRQUVZYVI9btdm3CW8LCbnqCed9zp3+gWXDRjyvxW4n/Ec5azLmRSWCv3Wffnebccqr/i/tDkqZkpfS4aAd3huQapPy1kQkdLrRNvcpyRvzipwVTQYsGzbnoibbETLsadNOWDaiww3u9Pc39RIbNuoaq8aCoMVhRXn7/KTX/Z5mOwKce1YIbZEdmmN9yqG02uomPenbnDrzBfft/yl7em/xxcFPCRWq3Bb/SUIMrr5k5OwJxfiaA7ds99JLr2Djjutv/U+/on9Ln3/1q7/t2vK6+sI2pXxGuDphqPCStDndlSPmXVCtWY12iVCtYmXdTOi0qdAJO8Pv0Ze4yVphWHtxv2SlTXfoMOWIdUsmvaakYMpJaXN61QYBgqft9kEZcxDkDqUsGZI2J21Wi50qytaDidoGJThq1IugRqsWu3Q4pNvNlg2b9JpFgxptk1ArHhwzYRG7vM+ia+70d6XNWjWmy81mnFZWstW9kqE2pVhaPrZsMP+obHFVKjStPtyht3SHXGlRMtFornJab+6QcKJovX7E0OoLimtR3ZXb9LRudyn9ouzioN/8N3+awXL9gvEXq74XXcf3Uwvy7R7r3NiLXjj9iNpQhwcP/jVtsS5DZy77L199ya8n/tCOgz3e+5H7vyM07nr9YOp6s/J9rrfDF4iVEm/iC6SuVRufzWgIbTVeedVxv2vY03oc0eGQpPZNB8hqoEOY8IqcVfX6ArBXbpPuuYFgL1o2YsjjgfX3kBot1kwa8aJhz2qxW1lKRUXeqoJ0oCMYM+k1bQ7ocUsAF9u4I263X9G6Wee02KkgA5rtFBYJtA/5zeyZkpwON5hwzGl/YMWoBlvs8n5t9m5aa1eM6XKLmBpXPaHf3RLq9LtbjWYF68KiqjR8A2J/o1FacMWUk1aNCotLmTHvghVjZpySldLtiLg6Y16y2wck1CkrBoC4PYY9EwD3yj7vL4uIWjMpLKpWpybbrReWREtxQiWVpZJVs5Zy0146+ZREpk1TdZeGXWFX04+Ymp3QUNzqrrafkwgnZUYnPfd7I44/e2HzAvrhD38Y/Pa//y+uXH5dLl0WqyQ1129Vk2lRKGVlQwvGKs8LV2LioaSqZNyhnUdUrm5zQ/iDOhP7PL/4m6aix7QXD8qWNxw5Sa2a7XDSp0w7LmtNxoKdPuCqR53wO3Z6v4K0rGUZC1KmAxt6QYcDmw1jXFKNZrPOG/SYrd6p0TYtdmq125pJ+cB5ddKnhEVUaQzWc3UO+BlXfFWXQ2pDbUKhipXy6Kb+JaFec3i7ulib1eiwm1rer1DMm1i5YLTwquqaavJRV8Nf197RamjlNZNVz2+4NWaf1V26TV/yTvn8ktJ6Xm/rgFvu+kmX5l7+rjJYrtcPt74XXcf3UwvyzY+1sLDk4tVBh3re791HPi6dTjt/8opEeod31d9jKP2i0dPPeXLl0pvO4+v1w6nrzcoPoL7xJDj+wiOymaKqmqhbP7r/TXyBUrkotb5ibnXERHnENc9vosl73G7AA9rtV69XWcE27zLsGWNe1OsOZ3xaRVm7/cpKWu11wectuqrBVnPOS2rTbMCgRyy4ZMGgRn2mnHDcb9vqnarUi4gHDI9GQx7XYpe9PoLy5spk3aIVYxpsUZAx6OuabFOv14oxq8YC5sdG9s6Sq477bWFhOSumnLDDQ/rdo9vNAYW0otNhl33JoMd0uFGfuzXZYc3EZnNSrVlJXpPtm8LcVnvU6lSv1x4fNuk1gx6zYkRCg+3u1+aAcS8HF941x73ikj8JiLHVEuqERDbTeqs0qtenxS5tdge22YuWDOl0g3A5Ji8nlVuxas6qBV8/8Ufaktt09De76+C7vfD6I3a2v98Nze8TiURVKhVXZo/pStbIrI7549/9qkKhYGRkzOsvXdTT1S8SG7OeyVlfz1iYP65QVVSqFIWiJTf2HFDXVGt9Pa03cZO9HXe6cvWycpGKqIHkHcZDr1rNDGvI7NZoi5wVQ55w1ROqIkkN5V7joZdEy9VqdRv0mBmnN63QZSVp06actN2DIkGSdY2WwFmVc9lX1eow4CE5Kxr0SqgH017XbIcuh1Vp0OMWGQsK1p32B+acd8Qvg1A4JFpOmHNe1pLmyBaheEF1pM5qZcPGHY9WCUfpaGvVUN8kny8I123z0+/8S4bnD3nx1KMen3xCTWarnpoj0oU5s9lL2jqqHTq8R2Njo1sbvrcMluv1w6vvRdfx/dSCfONj/cEf/JG69HYfuOGXra6uOn/yiupcj572HUKhkLb6j8nOLuhKtkqu1l9vhH/Idb1Z+QFWBZXKxj61UqFQLLpw/Jqu+F3+5OhvOH7lWTOzU1KpNSEx7Q6IiNnugc3AtoQ6FeUASV8OJgmXVWtRrUEpIMgSkrOkWotJx3Q4ZMEl9Xqc8vtqtOp0kz53i6tzyZdMO6msbKeHpM2JS5pzwaIhR/yyDgeUA1nvqnE12sTVWTMVBM4d126/Gs2aDMhZtWJUSFTGUV1utN1DrviqnBVt9muxW51O4eDQC6GiqNstllyVMWOrezdFoRFxVRo3LbZRVYG1elJMUl4qIKfu1aBPXI3n/UtFs656SkKtKk0IqdWpPQjSe+P5FwzKWtJqjxXDdnlAg20qimp16nWnAe826FEjnrfHhzTaIl2alzKnXp9sZtWgZzyw65+ZXR4Tz3S4qf0Db7KrN1V3mxkfct+DD/rDF1/zz//Jv7Wr7j4DzQ/Z19wkVbPk+SufNZw553D//W7Yfp/Gmnbp3LLL80fFOpe964O3SqcyXnv+ReXkqtnlC3Z23exIw0P2utHZ+SecHvmaeLleOVwwW7yoPbLXtthd2h2QKswb9IR5lwK9ybio05uTszdiDka9ICKq313KytKmXfOsaSft97GA+ZKzZlpJPlgl5iy6IqnNsGfV6hSRsOyqBZeERZ31GWuVaZVS3nzoomIlpyN0QH1Ni+ni6xrK/cLhDYnv6vqiVGjazt39VhfXXVl52aH9+9RUJ+3ru01DstXvfO2fqiu0mKuck5USa1136913aG5u3nzfr2ew/L+jvpc13fd7tXfxxLAdrRvslbGxcZV0zWajwsbx1F99s6vjj/srD/4jj56/3gj/MOt6s/IDqDdnTrxXbXJjDfTqZ17y/JlXVOUHlTJRjfnd9mffrarSLGXGlNfNOWevn1ClQYM+RXlpQ656yoLLKkoyFmWC5oKwgrRFl0VUabPPjFMGPWrVpJRpW7zDFveac06jreJqVZQDt83LjvuP4up0OKhGs2Y7tTuASmCJzctLy1hQFTBM8lIy5jXYotE2MUnLhjXbZdEVFeWggVnR4ZA554O/Z11EYtPeis1GpMth53xGn7uUFLTYYsoJve6Ql1GSCkTBsU1h7qTjGvQH7paImKQqjXrdBlaMyFiQs6LbETf5ORUVK0asGLPXT5lxymt+S7UW/e4156xqLQqylg1rst0O77HkqnkX7ffTRrxg0RW3+uumnTGU/5oL104qlyu2VX8rbCoerVIuViwsLChMNspUkt7z0V8Si8bBzMqoULrOzvWf0pMZMNB8eNMCfaDvLkcHH/XsV4/6lV/9OT/xkx/Rua3BY/9hSD6yLJFoUxXqd2/vz+so3uTC5CvGi68ZCN1voPou2VJaTblNMtRla/weL+b/vREv2OPDtrgr+OyuOe/zFlyRNhuwbpZVlFVCJaFKSJWGYMqSUatDypQFl4REJLWo023ZqJRp1zyjXo/uyE0OhT9uvbBs0BNe958UK1l7ah7UUbpDqVLUFt7mvC87k/+sgZq7LWemLJWH1XZW7N6z22Ovfdps6LS9vX958/1sru3Q1bDDwcb7tFUNGJp/Xe+B2s1G5Y26nsFyvd5OfSN2olQqmR6b11Q98C3n8RtpzsVy/noj/EOu683K97m+U+ZEQ3Wrrz3zGZVs2JHkz2opHlQRUaMTIX1BnP2ss6KBiHTcK655Wp1Ofe4AGYvGvWLG6SB8sMZ5n5PUbqv7NOh3xaOmvW6be1VrNeOUnFWNtlq3JGfFVvcJCRnxgpv9kn53WzUhZXYzXC4soka3iIS0WUVpeakA1rZha31DqFmSM+OMNZO2eqeoKiV5dbq12adBv7Q56xbEJBWk5awpyoiq2qC5ImtRWFiTnYY96bIv63VH4EBJKcopKbrkK1ZN2u39we9XmXEmCC+82YB3G/Gskz6lzV5JHTLmVAcN2bpFWQu2e8A5nxERVRU0cnE18lImHbPkqga9mu100ResW7Ls2oanJdRgW/gd0uVJR88+Ze+WW9TUfCtsaiUzb3F9ztOPL1iZLVmsnvfUmU+7cft9Ohr6nR99RSzd5t7+nzc499qbeC3fjP/+2Mc/6qc/+lNOv/wvnTv9BZmZ+zTX9IhHq9TVNlgKDwqLaor3qlRn5dMrwomSmmKb5dK4YnjdzvJ79LpDV+hG4VBMolKnWMmb8brL4a9aDF1UX9qmyRa1sVY10QaXCmPWyqPayvs1VPo0hbYohrLWK4sK4bTl0vDG89pmizu12KkttLFKC4fjuso3WQkNS8QT7ur7GanskpXyuEohpVTMuRj+gtXSNZ2xnerbkrbsavf86B+obL1me0eTkbnzOhr6hUKhDQptNCyTXzGzMipSu66vb8+3vO/XM1iu19upb8ROlEol5WJFPFr1LT/3jWnO1xvhH25db1a+z/WdMicuT55QH+nSUN6np3CnVGlJdaURG+uQFjt1OWzCMbPOS+pwzdO63azFbglJtToV5XU5HCDzv2zCUdu8y6TXXPYVGYuK1oMG504tBuSlzFqRMq0gLa5WSEiHGw17RrUmUXGRIMFl0KOO+GtBNtCUmBodDkpokDJj1AvCYkqyhj1jwWWNtupz52ZWzLplc85rtkONFkltclbNuywsLqZaXDIIxitvCjWveUaTAfW6dTlsxAtmnVWvX1HGiOesGBMWk9Tmsq9pst2qMZd9ZWONYEpF2VbvdMUjKiqyllzzTJB3kwzAZeMyFtXrteTqpguqZIMiXFGxbtGMs9ZMWjSoyTbbvctVT1qpjGu2RXNlt9HsC1bTyzLxJcVyQbGSEw0lLKVmDY6fVlfTLF5o0BwJW6n0Gb+0bHTiU+668d2Gxi/aVv2QcDisqbrb9NiQvXtLIpGN6dM3rzT6+/v9yq/+nP/4//+0q5e+aDrTJ56rla2smK85qS9+j86mbt2NOy2sTUktLAqVqoyUn9Mc3WKg+D7z5UvSlTmhACpXF2vRF/15pXJGfUPS1Oqg2dirUrFG7dHdeqO7RdNR9fkWpfy6JrsIbyzxInEurT9ppnxag169sSNyVsyUzgiFokJh6msa9BRuMFR5wmuTX9Hb1613e6OL008prV+yr7NLODKtEloSa+6w3jrh1js3hJOzs7PfIlpP1IQdHfusI10fcODw7m+B8V3PYLleb7e+ETuxq/OIcDQkX8i+6We+Oc35eiP8w62/EM1KLpdz2223OXXqlJMnT7rxxhv/vF/S91RvlTmRzWdk8qvikWpXRs+pCbXoih6UWl9WrpSVKiURZSERWcsgpsZIoBF4YyJRp1OdLqVgJZOQDPQDC656Ssp0IEitl9Rhh3cHYtqtoqrFA9vqsmFJ7Wo1SJkWEVNRseDyNxBIi9JmTDspqUOjfvV6Fa2LqTVuw4Lb4xaLhtTrVaXJLg9rtUtUtYiEqLgQykrq9VhwWau91i1ZM6nDoWDFtC6iypoJCfXmXVSnK+Ci9Nqu1pSTzvq0rOUN1oZ3qtIgLGrFqEu+aGM6dVfA/fiyjBn93iGp3byLitYD4u+akLCSgpikTjeo0WbBZRkLGm3R5bC4WhuKmqyirEGPbSLpC9bF1YqptlaZFq5E5UvrMpm0Z3O/ozH0mHK5olQpyK7l9cdut6/nDlNjs64Vn7ev7Z1ubftpZxYf9czxL8kXc2qqNyYyb6yMSqU/bVb41pXGkSNHtP/zN4Tc52QzE6qjZbuGetzZ9E412kyPjaqU162nFomUrKbH7Y/+pOpQnWghLhlutVIeFwvH9cRuNFe6pCt80HzluN6tXYrVSbWVDjsrD2uv2u6Z0U9ZD0+rVJgorKoqNopHa+TyK0bLL1oKDbkh8VGNdQ3K5XrZTFY0XKW+ukUiXiOyHnIl84gLkc9oHLhVbmuPn/jETY7c8os6Ojo2v/i/WTjZ39//LS6QxNZVyci8aO2Crq6uN52L15H61+u7rTewE8evfV1Hb5+p85Na63qEQiGVSsWZxUcVkxtpztcb4R9+/YVoVv7BP/gHuru7nTp16s/7pfx31TfuPc+NvezpM58xNHJZuVhRDuXlKmnN5T2qo3VmcxeU5EXViNg42IuykgG/YtmwkrxGW4K1RO0mKn4Df9+yCf2a9rpl11QFeS4N+mz1rsCKW1GQDrikdUryEuo3m4KMBWExK8Ztc78K1i3qc7dL/kS9Pvv8lJw1WUtmnbNs2FbvUKXZgsvK8jLmrBoLmqoeIWGzzhnyhAu+KCclZdKaSZ1usuiKkqwqzao0GPGECUfV6rbNu6xbNOxpVZo0BPbsvFX97rTdQ+p1WzVhzYQ+d0nqMOOUfX5Sqz3O+WMXfGFTpFyQtWhIrQ4t7pWQlLNm3FGzzm3qcNrsDxD9STVaEFKQlg9ydCKqvejfqKhIqDXieYlynYw5eSljy+f0xo6ojezQHtttJjNopnDOdPUJ04VeVwsXpCNTdtT/lFAo5GDze8zNXDFbPCoT29Du5ItZ4VjoTY0Kb73S+EY3QyqVUigU/Jt/9mtqs1X29e63d29JqVQyOzvr6MvHZa4sKhcq8pGUQihjJnRaOZbXFO61Uh6XjSzqaRkwVX7SemNKeqlgdmneSOGaWzs+aqD5ZkOLx61VJkVDTYrFnEIoY6L0mrnEWbuS73Co6y5dzVtVKhX5fN7a2prUSkq6nJKNLGptbbPvnXv9q//rf1VbW/uWX/Zv9f/eygVy6tSpYOIycx2pf73+u+obsRPphbMylaT01JK6ZL2x7AnF5Lx3HH6/9vq+643wn0P9uTcrjzzyiMcff9znPvc5jzzyyJ/3y/nvqjf2nk+8/l+duXBSTXqrbeUPixZqpSuLrlWeMuyEalv1uUODftGA9rngojo9mm2XDlglGxqKJnW65a3JSyvJBtqNjaC3krxqLdrs02i7Ca9qtde882DSCZ0OK0jJSwWrm7ysZSFhU15D2LyLrnpcuxusW7LFPeZdUKXh/2Hvv6Psvu/7zv9x+52ZO70PpgCDXggQAHsVi0g10rJktTgua1ux97fOete7v7NHSc7GTrxxfsd2ssnuOnbsWFbkol5JUaLEToIgQABEb4Ppvfe5d277/TFfTSRbzV45VKz75OEfwMy9+GLwnbnv+3m/iqu+JGtVSFiVNvu9T0qLKZe1u12/F+3yLjOuWTalXJNJF133pAqNm+V5V3zRiJNWzaizw6IRCArybqgJ1itRSVMuWTTqmietBzqZLnfb5mFFeWnzamwVEZOTsddPCgub1aPeTnv8ZNBPNKjJTdJByu12bxURl7EkY16DvYYcc9GnrZmzYlKd7RYNSaoKkn6j+j1vxYxOd+vxNTu8TZvDytQZ8bohr4mEo7ZF7ndz+wMWZlYtZSfURLboTNxiqPCqrwz8rlRlpe7w/eoSG+V6oVDI1vLb9C++on/1pG2p28ytjWrrbvi2YeV7vZMbHBx07Nhxbxy7Ipsu6Bm4YmB2XVtqw8IbiUS0t7creyjpwuJXzE31WsqNyyUWFSMbbqvV0LhCfE177RbXl142melzX/wxW3cfMXBlwtjIuCuDp8Qrwtoqd5s3Zjp2UnVtpXR+Ult71nvuf7/LX8lYXpoSCm0TDoeVlZUpKyvT0FBQKBT0T31ZbXXSw++8W23t9y6S+258qwuk1MFS4ofJt95PX/3CC14/9xUWE/Z0HnZz533yhZwnzv9+aRB+E3hTh5WJiQkf/vCHfeELX1BeXv4DPSaTyWy2cMLi4uLf1eX9jYnFYsobwo6dedbe/Pt0Zjb6e8pCNSLhuMbcHsf8n2b02OenlNn4Yb1qSrXOIDF1zKhTytVbMSFtLlhHbJxflKlTVJCzasmUqKQ6OzTaq9khy0ZscZtQ0Ocz6aIRJ7Q4FIhZ14LKwmlz+oTFtLjJuDPO+jO1jsmYD2L5Q7a4PbChxoPemLhyDZaMKsgF65S0ZjeZcd2Mq+b06/W0MrWKCsackVSj2UHt7nLRX7rqi0IiKm3R5R6H/aK4MguGrZnVYI8d3q7Bbqf9J2Vq7fA2ne40q9e8flW2qNZpJijz2+I2Vz2h072iEtrd4Q0fk7WmWqdyjVZMCQclhiERURF1dqjWISRsyDFzbihXvxmWtxE4N2KLo6LKDXpZjY0fUtU6xJRbMmosf0oy16imsVJtY5Wx4UnjYyMWc5PKk/UaEtts3d6kYanLyGyPLXUbtsjyaK3asjbr0XHPD/6J9vrtOjp2b95X32ul8e3Os0ekkrVqay976tqf+8STf+ixt/xDW7ZsAfV1De657UEnj72hI35UVU0LhZDVtQnkVZZVyoQnjcyddnP3fX72Lf+7UChkoXvBubPnXD8/aHylx/ncV9x14O1uazliNH1JrKXCP/hHj2tqavKvbvx7fa89r3ymVnv9zm+zfV5ceNpY4Q3bd1W58647fmjfd6UOlhI/TP7L/fSTenp6nD51xvkTPYYzL4glSoPwm8WbNqwUi0U///M/71d+5Vfccsst+vv7f6DH/fZv/7bf/M3f/Lu9uP8X9PX1q83u1r5+n4pQo8poy2bWSkyFPd7tis+77imH/YKNIWRZre1CIkGA2bhuD1s0YlaPovxm+FaFBkvGpC2KSlozG6THRkWVKShaNqHT3UHAXN4ln3XVlzQ7KCJm1GmTLli3JKXVumUVWhSDfqCN9qEXFeQsm1CjS1hUQT4Q4iYtmxCVsGjEWrBKSmnxho8pytnidru9U07WiBP6PR/YpbdqcVhIxLQr9ni3Vjers0PanDa3BKFvvdIWRCXElG0m8k66qEydnLRhJyRUBkNZTtaKSeed9P8Eg0jCsknzBhzyszLmLQsr17DpRFoxKWPJNm/R53nNbgqqAr9sXr+IhBpddnqHSq0mnRcR0+yAOf0GvSomqcUhBetmsjecP96oJtmiq/WQcHW/VLZDWbhaIZcxOfWqu+/Y5uobfa5PzqktazOZ7pMLpYWrInrXvyBWdYeRpTIL2e+90vhuzrPOht0aKrf4zPN/4D8/89seuu3dWho6LKfnjK1cs1R3Xk3nfo8cfbdCobB5gpPL5Xz+hf9EasX77vnNzeerrq5273332n9g1sDAoK+en9Qbe0p5y03uuWuPO+/6qc3r+qX/6YP+r9/+qJfP/Hsty4c1le+Qs6Z/9aTJ0DndR2p9+H/66b+TH/SlSP0SP0xisZi9e/fau3ev7AdKg/CbzQ99WPmN3/iN7ztMnDx50rFjxywuLvrIRz7yN3r+j3zkI379139989eLi4s6Ojr+Vtf6NyGb/f436+rqqsuvDWgpPiZaLBMrVkqvrwoJKdpot21z2Ipx13xZzlpwGjIuYylYWwxpsl+tbRrt96x/6oovOOhn5OUsGFaUs2rGkhFTroAVE674omXjBjxvi9tV2SJrTcYiQm54OshHmbUeRPXX2qEjsAXnrOn1jIJ1KyZU2mJevw53ysnIWLBmxqpp2cBuPOQVEUk3fM2AV8RVaHXYPu+zbCwoP2xxp/9Fzpo5fdbMbH7NctIyFjcLBDd0ORnrVky5KGtVnW5hceXqrZk164aYMmXqAydRMWg2uiwnHazNlk26IG9NQd6UCyLiWhySl0VRxqJlEyo0qdJuyHEVGoMBZataOywbs2jYlCvq7TDpcqAZapC2aF6fNkdMuaTJTYa8LJ6vlsg0Wp+PqE01WltcUV+x3+LSiJ6JefFE1G33HjY0NGxssMfFtS8r2z3j3T//gM6u9xocGPqBVhrfy3m2Z8utfumdTf7s2L/w2vyf2lq9Q6w87OGf3+MnUoe98OQpT136w2/TefRMn3Rx6Wn3HHyPlpquv3Z/19XVqaurE637B/qTT/nf//X/+tdORG+55Rb/8t82+fKXnvDsE8+5MfwFRXkNe1L+wbse8Njj7yq9Iy3x3xylQfjN54c+rPzqr/6qD37wg9/zc7Zu3eq3fuu3HD9+/K/Zvm655RY//dM/7WMf+9h3fGwikfivahX7q3qAWDLs5rv2fMciqxdeeMH44Ky29ZogPC2+EaqlICctLyupzjYPGfKaZePBgDKsRpcWNzvgfRaNKFMvLKrBbjc8bdWsdrcpU2fIa8aC0riohAZ7tblFlQ4RMWPOWDYmbdawE8JiYpJqdWt2k4iktDkFWWyEsq2YMu2KRSNSWq2ZM+OaFZOqtGlxxKJhcSkVmpRrNOglCwYl1VgwpEKjVoe0OCIsYtwb2t2uPsg0iSmT0hgkzW4UMQ56WbVOGcsqNKrUiqK4SmkLLvhLWWtB2WLGDm+XVG3JmEmXgobodWFxs64ryGlxSEK1LW4z5BUjTmiwb9P2vKE3OSovq9/zwiIy5mWtGvOGfs+BNTNa3GyL2+WtG/CiUSekNJtxTbkGUYmg2fq8pBphMWXqTOduWJgbkUwnZPJL1taXhBNF8lETY1MOHdyiqqrKUuxJ+w7W+scf+XXbt28Hd9555/ddaXwn59lfpaWmyzsOflhf4in/5Ld+7dvErAcPHvxrdRCH3r3d0tM77K+763t+T1RXNJCNKhQK3/HjnZ2d/odf/f/4R7/8YcvLy/BdhbQlSpQo8YPwQx9WGhoaNDQ0fN/P+/f//t/7rd/6rc1fj46OevTRR33yk590++23/7Av62/Fd9IDLK/OOfmpU0698NFvK7IaHBz0mY89JbdelLEkFioTLkYUhYPulaKivKK8jAXl6tzkp0WVGfCCjGW7vEtB1rxBFM3rt9vj6u12zp8ZdExBZiOMTLV6u4PCvyoxSREJ4SAmfcRJvb6++bHdHrfTO4NTmdngtIURJ5z1Z7a4VZujtnlAXKXLPidjUZlaF31Gvxd0uFNMStq8AS9Jm3PQz5rXqy94gW+w16qpTU1Ko31WTKrUpkydSm1m9agKQtbGnDboJQd8UKO9m1qbco0u+Ywhr0moEFdhwaAl46KSVkxIqpHSolqniLhFQ7LWvOz/5yYf0uEeNbZZMWXODXf5/5pyQa+vq9QmL6Mob82ifs8bcUK1Tk1u0uFOVbYYd9Y1T2qwU5ujImKWTVo1HQicsxYMKCjIBNbzleK0MvXK83Wqcg3CyZyJlRvOr31CoporPeeFq1f0L7wh3DztH/7iuzcHlW/y/d7Jfavz7HuRStYqZEPf8fn+ah1EJBoVT0Qtp+e+53P+oPkSsVjsby2iLVGiRIlv5U3TrPzVU4lUKgW2b9+uvb39zbikb+N7JdHu3XK7Ez1f/bYiq2PHjlu+UaGpbLuR7An7ij8lqlxYOBCmRsWlLBkz6JgqW4RFhIWltBr3lF5fV2PbZkBaQqVyDfq9KGNRXtY2D9jpHW74uqL8pvg2KimpVkoLQePxlEtmXLPdI273a8LCgSj2xqbVd9GIbg/qdG+wctnQjtzkp6XNGnNaUu2mXmTIq8IiUlo1u0mVVpGg6XjD1jxgxlULhuz0Ni0OBScni/IyytSpt8uycfV2GvGaPi9IqApOSbJ6fcOwEzLmVWrWYI8u95rTq9/z4srt9z7NDloxqd+LplxQoVm9HeYNmHDRgiG7vFO72130aVMuaHHYsgmXfEatbcLiFgxZM2Ofn7JuyaJhRQVVtkhpMegVg17W5hY3+wXXfMm6JfV2WjJmzGllGvT6hoxlqcDVtVqcll3PSYTjMrFZ+eKq2dykE6t/qjx5p9sePfi3Fup9a+Lm9+I7DRbfrQ7izOdOGVwckM5/w94tt3/HE5tSvkSJEiXeDN506/KPKt9LD/DN+PMvn+3x3LPPe/dP/oTXnntD2Uq7w1UHPbX4O97wMXf4NYL+m5CwMg0u+ERQCPd+s24oyguLanFYj6+Jq9DsoDVzam1zyh+ZcG4zb2SbB405bdhxtba67klxFcHaKIaCGt1aHJTSasWEbUGs/oopI04YdMyaGXP61OhSb5cVE2LKUbRiQrVOLQ5ZMOSaJ+31k1rdLCIZXHPEiilLxmStqrfLgJfNuGqbB1zxJVXaNdonJCSmwpoZa2blpIOG50pFRSHc8LTrviIupcoWW92n1o7AIn3JgBcVcbP/Tq1tm88TFrHfT1lwu3FndLhbvxesmVajwxVfVG+XvHXzhrFRhnjZ51RokrUqbV6dHbZ7VKVWfZ7V73lRCU0O6PawVVOWjSnIaHXYZV/Q4R7l6mUsG/GaYa+JiLvqS7a6X0hYtBg1nT1vPtRvd+qt5qM9Ylt7/cvf+yc/sAPuO/GtiZt/k8Hi+w3hT59Neen6pz139rMeOPTeb/t4KWitRIkSbxY/MsPK1q0bIVI/Cnw/PcDCwoKhoWGTFwt+59Qf+g+/83ELs8u2Lr1LZ/6gDnc658/MuWGHt0tpsWzMdU8Z8KKIhDGnNDmoRqesNbOuWzJm0Yhp1zbzU9Yt6vaIEa+JqXDNE6KSDni/sLgJ5zbLB78ZaDbsuCu+aMWEco0omnZNr6+bcV21Tt0edNWXdLlPTLlFI9atqNAYFAKmFOXU2CZjUa1uHe7aFNquW7VREhCyaERSjaKcpGo7vMs5fxG08a4LBZ/3zfbknIyYMlEJCdXu989Mu6rH17Q6qs5OSZXKNVrQL21+Iw5eq3KNanQpyFsyot5uzW7SHsTpZyw66h+57glr5oLMlouKisrVWAlWOEVFDfao0GjdinkDXvQvtTrsgA9aNm7MaVvcpkaXLve77LNm3bBk1LIJ0y5ZNmnAS8rU6PaQdctGnLRkeGNALVbrStzilsr3y2bXvbE8rZgL/1BOJb6ZuHmi56tu2/G2H2iw+H5D+COHftbgwkXnl79kpRS0VqJEiR8RfmSGlR8lvpceYGRkxKUz161Oh0QWtijPdTuQeJtXl//c8uqipeK8+8L/RKzwb53zacNek1CNokWjImJ2eZcQRp3cbFKut0tIRI+vCokIichJB46e1SC5Nq/d7VocsaDfkGP2erddHjOv16IRTQ5otE+/F4x4zbpVZ3wUYSsmbXGLvd6jXJ0hxzQ7KKlGSNiyCcumRMVlLYtrVpAVERMRJ2g6LiiY12fMaUvGrJm1bEJeVrlm83rEVZhxVcHbhIQhCLpLiCtXpcOSMQW5QMvSaotbNDmwGVo37qxKbULULBgAALYCSURBVLo9KGNRm6NCmHFd1pqEKtU2nGBRcVvc6rqv6nS3RvtNOGfFhCHHhUWNOSOqTEK1Gl1qbQuqDNok1er1DVd9yev+UKO95vSa06fSFuuWzOl33VOWjJlySb8XJFQKi4opV2WL3R7X7g5jTm2InotbhFaKismQpDoV4SbL2dAPpfzsWxM3v7Uz57sNFj+IKDcUCrl7+3tcyH/K0YdrnXutFLRWokSJN5/SsPId+G56gIWFBZfOXBddbpLKlFmW1lS5zc3N7zC7Mmxg+YYFg+oKOzwQ+k3bPOhk8Q+tBLH3OWva3apWt7CI3R4XElGuQdq8G76u1lZ1dpnTZ9ZVETFxlbJWVGrV5IBaWw07pky97R4VQq0dwdAwJqVVowNS2qyascWtyjUFPTyrzvtz2zyIkDVzYspVBjbndNCIXFRUa7tV05JqjTsrpdmw40actGBQVFKVdmGxzUTcEcctGpC1atp1Nzxtu0c2h62opJyMAS9ZNS2lyTVPmnRJmyOiyoJh4LJd3mGXx2St6POcRnslVCtYN+aMWt1CG8sWIWFJdaCoICyiRpdJF1Rosss7pbSYdMGIEyLianUr1yAsIiRkv/dt2qinXZGXsWzMgBdd9nkpzXZ4h3l96uywYlJeRpMDKrUa9brz/kKLw1bNuOZJtbplCyuuTz1tb9VDamqqTSyvCofDP5R79W+S4Po3EeWWZas8/hOPe9/7E6V8iRIlSrzplIaV78B30wMMDQ0rrpQrDzUYXutzPvcpxVDG56/9hhvzp6xZNKfXqNftKD6iPXy7d4X/byP5My77nBWTGu3XaM9mAmy5GnEVTvkjOas63WvZpJRmtbZqc4uUFs/53yXVBmuVjdLDRvtkzItoEEKlNtOuiUiYcVmXe406FTiCyrR7QEqrHl/V6xsSqlz3pC73b6bibgSlTUuqMqHKuDdUaTPoRWNe12CvJvvt9rhFQy77vHIN9vspEUllas3qMafPinF9njfjulZHlKu3ZNyIVwli5JJq3PANCSn1dqmx1YRzanRocVjBupgKIeENl5UKUSkbKyiKiooKCorWzAgJWzUtplxM+WY4XLl6Vbao0YVioAOqkdKsIB+k+k5qst+qGRFxs3osGTXurAa7dLo/yFy5ICSiWrs5fQa95GY/717/zEWfdN5faHRAmyM63RO0PT/r1OInlYciGneWf1fb79+GHzTB9W8jyi3lS5QoUeJHgdKw8l34q3qAQqFgfGhadWKba8PHvLH2eYlQpZbYEZMr13Tk79lMg81JG3FKb+FZTQ5YMmrIcUVFKS1aHJY2Lye9mccy4AVV2vV7QUy5MnUy5q0YV2+XFjeLKbNm1oIBGYtiKiyblJMWl1KQk7FgzKSYcs0OGvGavJysVRkLEqrU2OacPzejJ0itzev2sBrb5GSkzUlb8IY/NexV2zwsYV2D3ertUqZOVNIln9XhTt3eKqHSmjkhIds8pFqncz4urkKFxkAgW7BqSkhUzpoplxVkdQUv6AnVyjXKWLTT2yWkrJpVpkZcpdP+REqjsKglY2p12yoTNEsng5VblbQFtbZZMm7NjGaHxKVMuyquwm4/oSBnyiW1tsua2xwE2Qilq7JFRMKUq8pUq7XDuNP6vaBWt073iIhpcbMeX3XS78tK2+7RoNwxr9Fe5eqVqZXS5pJPu7T4SWVrW/5OsoK+32DxtxXllihRosSbTWlY+S78VT1AZ+VBU8sDRtN9ji99RkfoLnsrH3Y5/ZQdHtFUPCouZcqVwBWSMO2qc/5MmXpZ68LyVk275FOGvCpnPbAwx824bs2sVkd0e1hCStqiBUMu+ay8deUaN9t9s9ZAWERORk7Gqilp82Iq1NsZvGhu9PeERWWlnfVnrvqSoryUJjDlUhD5v02lVhEJE84ZcVxSvSXDtrjNPu836oSivKu+KK5Ct7eqt8u0y5vJtqum1dutxc2GnRAR0+52s3qMOmnZlLgKUK3TDo8acsywY2p1i6uQVCMiISttwMuWjIor1+KIejssGDLurKu+rNPdVk0bcVKnu1VoUK7JOX8uL6vbQ/LWxVWISEhpUm+X654y5aqirLCYervkpDfdUAU5k87b6n6Tzps3EAxnD4tLBSc3YfV2O+s/O+tPZczrdI+Tft+ky0LCmwNUvV2ai4f1XHnN2bNnNzN6/mvytxHllihRosSbTWlY+R58qx7g1MvfcDF02nI6rS62y61lP2Oo+KqyfINt4QfMhUZlikviKlVosmrGVm8REQ3e0SekzevzrO3eaq/3yFqzasq1oJ24y30O+Vl1tstbt2bWNg8a8orz/tKoU+psV6VDizXz+nS6R8ZCcKqyJKVFXlZBwZjT6uwUlbRo1BVfNKtHSouULco1anGzKZdMOGfWDeuWpTQp16RKl3XL5vTb7hEhRCWC4Sit0T5x5aZc3LT5UrRmVsa8Cs0iIqZcciVobi5Xp952cVXWzAqLuBasoga9ZMCLCvKBqDhr2YQ+z9riVlU6rQYnSbW2BRqU8875c0m12t0hKmHaNX2eM+6Mbg9rcciUS6Lqzekzp1/WqpyMLvdIabZu2ajX9XlWSMSSMbNuKMiJKg+C6uq1u1NSjTK1QSNzREpWt7cqKrjiS/b5qc36gkmXhIXFVarSbn/ovV5fHvex3/+spt9o0tra+l9VE/I3FeWWKFGixI8CpWHl+/CteoCPfezj/uRfPeFQwzsVlvLG1y/rKN5jcX1CqBiRUKkgr95OORlZy4F19rIO9xr1mjZHbfWACk2WjCjIarBHpdZA19GgXIOctKK8vKztHjHmtB5Pi3ncLu9UZYsLPmnYce3uMOp1y0FWSs6aweBF/6hfMuGC654SFnGLX9bhHsvGjDppwjnbPKjJfgNe2vxzt3lAXsZVT0ibd91XDDkma02NbQrym2mutbpVabduGUXl6uVkrJmxbNKcPmXqVevQ6nDQipy0bNyCARMuuOyzmt1k2DEZS8IiqnUZ8IKYMg32oiCmSzqoAigqikltrok63W3U63p8yYIBHe623cOWjMlYEJW0bsmk89atqNG12YsUEpJQJWPRoFfc8LSIhJhyE87ISjvoH4grF1chLCocfPvkrIuI2e1xM64Z8LwVU/o9D+LKgxVXvS2Rw1KhZgvXo37vd/+tMrUyq1nhWNGRu/e59757/s4Hhb+JKLdEiRIlfhQoDSs/ILFYzN133+mjyc+TLspHl60uLyoUSKgSj1Raz2+sZpJqVWiUVG3cOTFlFg2os129PRYMWTQkJCosZsWknd4uKmnJsDrbg/VBdZDrsdHXkzZnwnkXfFK72zTY7YovuOaJjRdCt8tLG3HCohH7vc+Q1/R7TrObNNqnw52bjpoKTaZdddy/UWu7jAURcRFx1TqMOmXVlBaHbPewhGpLRs0ZMOuMMvXq7VStU1FeCBFJMeXB2qpBhUazliRU2uFtdntcXLmcjJRmW92v3wvO+biigqN+2VVfMuQ4QuYN2OFRFYGWJSwmL6NCs2qdohJCws77Cxd9KliHzYirNuWSV/yOiASKqnUo16TNLS76pDl9Brykyb6gyXpBta3ynlehSbOD4irkZQ07bt2SjGUFuc1BpYhlo8rVCYmo1GbIa2LKdLpTpS2bxYlLRlzMfc56dF5+4lbHv3jOnVvfKzMXsrq+5ONPH/PpbV/1S//T+73rXe/6O72ff1BRbokSJUr8KFAaVv4G7Nixw66DnVbeGBDNl1ktzljNzYtGyqwXV2WtbeajfDMeP6ZMja3Cotrdrihn3ZKIqJyMMg0KckEJYdSMHoUgIXbVtGXjohJqbVOuwXaPouiKLwam3WRQVhjeDGf7prB0xZSIuPogMC6pWljEoFeCfpxWh/1icMKzbkaPQS+JqXLRZ4w6qUy9qIQZ1+33AVW26PZWWUtmXLHNg4EbJ4eQqKSwqJy0SRdU2mLaVc0O2uZBcSkRGy+K65alzdvpnaZdNuGCW/2qSq263O+Sz1g0JCIuL2vNXOD4CamzS0qTiLg2h0274qgPo+iizxj2qqikGl12eqesFUNeMeaUNTOikjrc6YK/tNUD6nTLWNTnGd0eVqbWFreKSpp2NdDMpKyYDAoMk6LKzbpu3Ypa2027Km1BCLs95m4fERYx45oZV7W7y2WfNZh/UXq2KK5JdLzTlood4smkTHbNqauf9a//tz+AzYHlB2n8/ttScvuUKFHivwVKw8rfgFgs5tF33+/F+QHdZQ1OfyNlqnBBl7uFIhTl5QrrQYx+QZV2fZ7dTEnd4lYhURPOqbIrcMBUCgnJWpHSreCyaVdUajWvX42tKrUZ8LyCnAoNdnvMarBiqdDgVf+nrBUx5VLaTLmkwV7jzloz44APyUkLqTGjR6+ntbvDDm8XFbdo1JwbutwfWHXfUK3NXj9pi9sVrOv3gpP+g0532+I2uz3uWf/MFV+QtSIiJqFKXaBHueYJc/ol1UmqCQaV8qAnaeP0Jh00Heet2eJWQ45bM6OooNUR494w5JgZ18VVWDElLKLdncrVBfUCIetWxCSlNFs2EQhs68VVGnbCdm8NUnHrXPVl1z3piA875Kc965+75suSai0ZUVRUZ4eQsIi4Co0SqgJNT6+ULeb1yVmzalpORq2toNc3LBnV7Cad7gvaoBNBJsuEGVfV6paX1jt/Snllud1Nd4lF4pv32KOV/6PP9834w3/3MfX19QYGhn6gxu8SJUqU+PtMaVj5G7LhprhsYbrH7i1HDExfNpp7XsXqVunQkjWLVkyJKTfgBfMGNDlg3Q1LxpRrFFUWBLAtiKtSrsGIk4ETJmXRiFnXJdWo1CptwaBXZGWMOWOXdylXL29dTsaKiSDf5HrgWLnbEb9kwlnH/J4F/co1SKo24byUVts9qiivICemDEWzbshJ2+1xVTqCE5m9Eiq1OOyKL7juK7JWJFRrccio18VVaHKTBUNueNqcPvP6RAOtRlhUVIIgJbdKu4i4IhJqpIP4/nVLrnpCSMGUy+b0abTPkmG7PSahxrKRzURcNs5yRp1Sb3ewUhsPznc2UnGv+4on/Y8qtQTuolUFOW/4qCXDVk0p12Cr+62ZU67BjB5DXhFVptVh5eps9RZn/IkJbyhTH2S+lAX/DjmXfNaCISERbY4qBCLkooK8jIiENTOSauwIP+zFwr9Wk9wjb10+lyNUEA0lRMMxhxre4UuXXvav/9n/Y0f1nd+38btEiRIl/r5TGlb+FtS2xz350h+ZHc1KZ9dcXn9BS+GwLW4XV6EgZ8CLJpwTU2FOj3lDrvmKbg9Lm5O1KoSMOQ32GnLcJZ/V6R4VwfCyZtYpf2TYccvGJVXp8TU1uhzyc+JSlo1bMSVj0S6PG3JMg90iYiq1iYir1C6lWcaSOTfs8njQ10NeVsaigoI+z0hp1eFOM64Li1oxGbh2YjrcZcJ5fZ6z3/t0us+SCUm1bviaRSMoSmm23wdEJfX6urRFJ/2BMhvJqQlV2hxRrSsY5FbM6rFqxphTImIKcpJqLBoy67qUNvu9z7JRc3rFHRQS1e/rVk3Z6W3m9MpYsWQkiMvvEVOhza0q1KvUoUK9MWeMOKHH12zzFtW2iiiTVNRgt0b7jDltwjmVWoOW6Sa7Pe6CT4qI2ekdQkKmgxXPmjkVmszp3Yz0z1i0cd6WDdqRwiq1WclPWLesf/YN/+n8L1vLzYtHy9UnOuyov4NiQXYlLDG439s/8Mui0f/ybfqdGr9LlChR4u87pWHlb8DJkyd96k++ojDR4CcO/C+OLb3s8tBJTcUdinIGvSgkImNRtS7d3mrZWGC77XfNExaNKFMrrlyNrQips8OwV13xeRlLWt1s1OvB59bpcJdaWxXkXPFlZ33chAt2ead1K9YtSaqybNSiYf1eCMSx08LCplwUV2HdkoKchEp5GWFRRQULhqyYNOO6nd4moVpUQpkaSXXSZq1bE5XU4pA+z6mz04Sz4oEmZ9GQQx601YMW9AWZKqelLUhIiSu3zYPK1VkzZ9o1I05KWxAL7M+7vUu3h2UsyklbNmHYcdMuu+LzplzS6mZ566ZdCQoVl3W6JxjYli0ZCULd2l30KTs84jb/2KzrVkzocKetHvC6PzDsuGYHFWRVaw/0MBERcZ3usWrGnF61tolLaXHQjOtO+Y/SFpQFQXJxFYENfTpwSI3q9qBKbfIy8tatmv62zqOIhK7sA1rtEw2Xm85fsrA+6FrmdWO5s1LabUvd9teSbr/Z+P3E+V6vHjteGlZKlCjxY0FpWPkBGRwc9Jd/9GU18ze7c9+7LC0ueyH/Da2RQ47kfnnzhS0nK2NBu9tFJLzk/3DeJ4SENTukUttm8NmUy274mis+b8WUrFUDXjDgZSvG7fa43R5TrkGZOjHl6ux00af1e968fhsukzEpzXIydnqbSq1GnDTmdXl5C4bNuq7dXdLmLRlRqVVIxIIBc3o2W5RrbJNQKarMqlkVmlXpkLFgxWSgQakVU27WDbW2G3NaUo2t7t/MVen1DRVaVGi22+NBtH2LlBZ1dmpyk2GvuuATwmLm9KvSbk6/qJhJl1VqddSHNzNPhr3mrI8roiCrXKOt7rNqxppZi0bkpO3wNuf9hXKNGuzbbF3OSht3Tr1dGu2TsyYno97OzUHlm5UIRXmV2gx6Sae7gwC4qDXTamyzzYMWDZnXb11IUpVJF220UI9ZMy8ZnCTlrFk2riBnxbS0eVu9RV1xh+3Rh6ViDYrFx1zLfdVw7lW5DLWx7QrFnEgk8tfuxVAoZHvdUWeOPe09782WBLIlSpT4e09pWPkBGBwc9Hu/+2+9/syglrKsV0+/YH0xZHJ+yL7Q+yyblFQtrtKyG6q0mzdgJHDTZK3Z6e2aHDKvz5pZeeuaHRQRM+KkLvdbNbV5orLdI7rcr8n+4BQkYt2SuJSjPixv3XVf3SwH7HS3nd6l19dd8ElFRTuD8r5vptzOuG5OrzM+Ki8rZ03WmqKCrLSYMjkZi4YkVFo1ZU5Sg13yciKSslaEhPR73opJWz2gx1N2eod1S+YNGvCSrR4UCsSvh/ysBQNBou66qPLNHBVCJp1zwIekzen3vIQqO71Nu7uQV6tbpTY3+ZBLgdNnzbxwoFeZclm5eg12a3FIr2eMOGm7t6rUrN4uEQkVGi0aNu4NBVktbjburC73mnZZXLV0UAbZaL9KrXLSZvWo0GTGdSNOCosrV6/TXeb06fOsvd6rIGdWj7yMHk856GeCE6IxWasSaqwYF5FQZ6e4SiOZc1pCe1RH2+yMPGokfVoxlJMqtogkit9xWGGjbDCbKfxQ2ptLlChR4ked0rDyfTh58qRP/NGXXX0ma2/2/eJLDVbWlvSvnrBaWBAWF1Nm2bhV0wpystY235EvmQjsw7ssGxYWEQ10JM326/awN/yppFo7vc0b/rM3fDSwOWfNuCquKhhWVsRVqNIeOFZIqlFjmy5vsW4paBRO2O/9ur3VuNMqtVm3pNIW5eoNeN6Ma3Z6h7Q5yyYU5NTq1udZ7e4Ql7Jm1rgzJl0IWo1r9XvesjFhUbs9rlqXG54OyhIrDHlVSqtub/WG/2SHt4lKqNIerESyZvUoBJ1Edbab16/WNnSb0ydrBWHjThMYtDd6lKp1e8iMa0Hf0bIWtwqLWTMbZMD0ysqCRntscXvQ9JyW0iyu0rplE84FbdYbpysRSRUalKmxaFjGoiWjctLSFlz1ZWtm5KQd8TNWTJp2RYuDKjRrdlDGouP+nUrtbnjaiilV2gNnUczwZv7KvcrUanbQen7JeOaSYrEgX8ipCXcp5LMiMQqZsHw+/x0Hlm8tGyxRokSJv++UhpXvweDgoE/9yVfULdxiXzImtNiqPNeioZjQVLzdjF8z7oKConXLigpiys3rU6lNp/t8w/+myU2Kimptt2ZOmbpAIzEnpUWXe1z3NWFRjfYFpyFReevm9ClXH4SLZaxYNeKEFRMqNEuq1uKwZeNmXJeT1u4OO71DQVZRUUjYmjltDtvuYSEh485KqNRor7CIJaPS5i0YVK5BnR3BaVGHsLB1qwa9ZMgxcZXu8D9LSOnzjBnXjDlj0ZAZV+30dkVZBXnlGoSEFRXEVakIXEnTrqIoocqgl1XbpiAtIqbBXiG0Orx5IrRq2opJCVWqdRlzRk5ar2fU2aFMvVVzslbM6ROVUFQ045r1IMitIB/UAtxkftOxlJC1oqggIqZcozJ15vTp8TWjzoipMG9A1ppG++33PhFx133FNU+IiEsErq6QkKKcnHWjThtzJhDahmStOurDElJqQu1i4qLFepP58/ozL8hH1qSLC9ZDq3rCT2nMfeA7DiulssESJUr8uFEaVr4Hx44dt9JXrr1sq7PDJ9SsVokJiYUj5sI9CvkNcepWD2hxSN66Qa+Yc8MBH7RkxJIR+7xXgz0yFmQsBINArYzFTYFpUV7Gkry0eLCC6faQlBazeszptWRUREJISEyFuJSMJTOumnbZjOubbcsbwWsFFC0aFlOhzi45a7rcG6xzNnp5CvLGnJHStLnWaXYo0NaUyVk35rQ1s7rcZ9YN1zypKK9KuwZ7zLuhIGvRqD7PGnfWtMvGnFFli3XL4iqEhMVUBMNBXM66YnCdIREVmjXZv5lWWx2ULUbEzLkhLhXkuexQlLfXe5z3CSNOqLJFSmtgxabXs1LaVGsXCwTGq6ZMuahMnUs+q1qHtAUF60ECTLmIWuPOBl+jsDIN6u3U7wVd7hGyUSC513sMesmCwW87oVoxISyiy71a3OyiT8oFS65pV9TolLUmG14zF7pmpHhcvFhpZ+QRoUhRW2Sf8fwFx4Y/Yf/4Vvs779i8J0tlgyVKlPhxpDSsfBey2ayvff55mZ7drq1MyK6ylltC1GJo3PXi1+z0DsOOm3FFp7tlLAiJBA6RSj2b/TJlMuYJXg6/KVCFVdMmnLdk1IAXpM2LKjPtqlWzYsrU2WHNrBVTGuy1aMioU0JCGuy23VuVqdfnGaNOWTK+6TraOBXos8VtgX02Etir81od1u7OwApcZdyZIE13ixnXzLq+mYmyIbpNyEkLC7vmCTf72c2E2mmXLRjRbL8u96vV7bonjTmlTL0KDZrsV1SUk5a1KqXNgBcVFczrExLePOUIi1owBApy6u1WkLdgUM6qFZPWLbviy3JW7PaYNrcGothXDHlF2qwV45odEBEXV6HODnN6XfAJc/qCnJppdbol1Vo2ZsI5K2aUa5QKniOuXFFRh7tEJALR7IAGe6UtmHQx6GMqiCpzKFgV9fiqnDW7PB5okk5KqJIrrhvIv2I8ckp37C1aw4c013UYXjtjS+Nbdazu15t5yV8893vefvtP62zYWyobLFGixI8tpWHlu9DT0+PsiWv2zt6jOrrDUmRNJr9suThuonguiEH7oHVLwVH//yml2Zw+K6YMeMGcHg326POCg9pFJeStW7ckISUkJK7SaBABP+TVYP2zZtGIfi9od5uU5iD3JCZvPbBAD7vN/7DpamlykxWTVs3Iy+j3XBCeVpS1Zs2slBYFOWvmQVS5nFUUVWrWY8wVX9DikAM+YIe3ywXtxBFxYRHDjrvkM8rUCIu75gnNbpK1EoTIHVCuVrUOuzzmgk+acyNoIU4LiwdtxgVTLppw3lZv0eSAkJBJF/R73ha3WTQoLiWpSlExsF+vSFvQ6mZJtQa8qN0ddnu3pEo567KWbfOQWt16fd2cPs0OqtAkL2PcGcNBWm6VdmtmXfC6Xs8pC9qbq3VaM6PBHuVBNstGsB2rZszrs25Fo31mXXfD04YcQ8h2b9PuVmvmXPVFabOSqm33Vism3PB19/jfDDshnq/UGbtbPrxqJtMnH19VKMZVNcZ94OCHPXn+Pzg5/xfmqveVygZLlCjxY0tpWPkuPP30N6zOFpSF69VXdJpe7xPO5i0Whwx4yR7vkbWqWodqHSacc9LnZa0IixrwkrCYGl0WDJtyyQ5vC4SgYxKqRCT0eMqoU9rdaqd3ykm76DNWTLrmS0YcFxbxTaHpohHz+nR7WLkG65ZlLFg0KBL8cy4YDJqNYyo0G3VKNgheKyrq9XWzepz0f8vJqLJFVDlBv1CXe7S7AwV5WUlV4ipBt4eNOGHVjKwVVbYICWu0zz7vtWzCYnAiklCtzRFjzph13YIBceUmXZK2YM20cg0O+IBy9WLK7fM+X/M/I2SL20TF5a1bNm7RqBlXA6t0tTn9Guyx209YM6NcnbiYVbPa3WGbh+RlzLquzzMEQXi1tmpxs2WTtrrfuiVrZoKyxAUXfUpcynYPS1vU5qg+z0qqMeaUsJgytRKqLBkx6YJlE3Iy6uyw1b1CwmZcV2+PrDXTLtvmLbrc77Q/9rr/aMGAbd5icv2KQnhVpJBXFo8rpObtP7JLW1ub+8M/oT/5lI/8y1+TSqVKGpUSJUr8WFIaVr4D2WzWC0+9pjm227zrwqFHVCUaTa4NqQp1KhYLirKWjFkzq0aXuCopLXLW7PUep/2xvKxuD+p0j34vWDamwV4LhiwYNKfXoJd1uMu9PmLCOT2+pEqbPR7X70XDjqvWoUqHcnW2uN0NX9VoP8hYsGZO2ryctC1uNeGCZWOqtKvWFegopqyZd8PTZvTY4W1qbVeUM+O6Qa9oc8SIE6p1yliQsxZoTMoUZEUlhSTV223YCUvGdLjDqFP2ea8y9cJB+uyiEXELyjVqdUSfb3jN/yUqIaZCWFTBuv3er9a2oAQyvClOnnDWomE5a4pYMWHBoCUj9nq3lFZ9npWTlpeRVG3egArNQkLGnDbiZJDQu6DJTbrcI6XVoiGDjqnVLSJh0FOa7LfNg5JqLRg04jWDXtHqqKKCdSuqdGhzq7QFKyYsGjPgBXk51bo226CXTVgxbsGwRnvkpfV7QUFBvV1Bq3bIoiFrZoyHljTWVaupb9S1o01HR7vq6mpsWJTz2VCpcLBEiRI/1pSGle/A8vKyyeEF+2sfN7py2ZX0V1VFNkoJ08Ulcwac85fq7BRTrlrRklFJlSrtkTYvIiEiYcGQAz6kxlaDXtHveYuGLRuXsaTBHgd80Lg3XPAJW9ym28Nm9QgJOeqXbHGHEceDwLZqy8Y1u0lBTp3tlo0b84YKjcG6o9lVXzLsOIqiksadMe5sYL39BS0OSaiWVKtRn6K8MW9sXne5xsBwu5G9smZOQrWEShsunpQQyjUF3c/V1kyb1SupWrODVkxsrqWqdRlxSqVWUeWqgrXYRifSdlEJY07LWHLYL+r3jCu+5JonLJtQY6ta3ba41Q6PKigEGpNxV30piLrvk7FsQZ8y9ba6T1KNhSCy/5y/kNISDD5DVk05409U63TIz2uyDyFb3a9apxueNq9PxuLmGisqqUmHZfUu+IRyTSo0WzRkzpQ2twVamBXVOoPhaKPPaNWURSMSquzwqBlXJRPlOrraPPLYA8rKyv6a86dkUS5RokSJ0rDyXSnKK49VOdL6Ts8O/EeLy3NqQju0OKLObjlr1q1YNiYvI2NRVIUOd+v3gpCILveY0+u6J+30ziAe/y1WTcnJeMY/Ua7OvBtGvK5Km73eI23OpAtSmu3ymEkXVGiSVBO0CZdLqBSRsG5ZiyPmDSjXIC6l1c1WTLjgU655Akw4r6jggA9uBtWFRIPywlU7vdOcfnEpeRlps1JaxaWUqbdmNlg3DVkxJSeriKJcIJAdFFOmWqda2+SkxVWosU1MuRu+LqHSfh9CQb2dCDnlDx3ze5rsC+ze9yrKiShTb7c6O1Vpt98HLBlVpT3IRkkryqu3y4gTZlzT4W4TzmlzWIub1dkBooFI+WLw9WhzVJujm7k1Besu+4x1j2iwUS4YU6bVURPOGvCSlBbzBpzxp2p0GfW6ZWNaHbViUkFeQrVqW4LwubQaXYryQSt0hZCIfi8oylkOjUqqspC8al/17u84qJQsyiVKlCixQWlY+Q6kUikNWyr1Xz3p7sqflYxUaUockczWup57wZJxEXFlaoXFTboQvDinRMS0OWrYqxJSWh026Jg1MxodEBGzaMS0K4Ezp1KldnnHNLljcwhaNKLbg5vJtWVqRSXEpdTZYcI5e7zbnF55GQlVopIi4ohYNmGrB+zwaBAr/ydB0FzRjKvWtYmpCELlOq2Y1O42KyYD3cmI5SDLpVwDCpuFfUvBgJa3btwbqrQb9JI9flKtbsUg0yQnI6FSSCTQsURc8yVRZUHh4h6tbrFuxQEfElduybglowrWA7v2iG0eNKc3OF3ZGH6y1oJ12oBO95hw1pp51Trt9/6gGHFqM2cmbVFB3m6PqbV9sxW53h6VWvV6xll/ptNdUprFpEREVWqVt67L/ca8bl6/Xk9LW1Stw5RLamxVMBQUMOZNOCcqYcplIRGDXpZUr8dXDXtVjW16ik+rqKgwnD9pNrpDOHz/t92DJYtyiRIlSvwXSsPKdyAWi3nrY/f7y2vPeW70D1Tnt6qKNTu59mfSVqW0CNl4F7zhrpmVtSpvXZ9n7fa4GlsVFZWpVm+ntEVXfAFF61aVa1ChKUhH/aJJF2WtGfGahOqgnLAmOEEoICwuJWdNm6Mu+owhx5RrChJS4wpy0hbc8DW9vqFap2WjkmoVFZRpsG5JSEiTAyqCx+atWzOnxjYVGiwZd91X5GR8a/5JnZ1mXJG1Yp/36PWceb3a3WXcWfV2buafhIQU5BFy3VcMO67NkaD8L23BkH7PWTUrbd6I1zXYubmaGnFSUo1lo3LWJFVrsEcoEBrHVUiokrcuodqw47KG7fQO0y7r86xFI8JiImLysurssM2DJpzTZL91yxIqZSzZ5gErJuWta7DXvL4gkXjj9CaEmHKtjohKGPaatDlLRi0Zs8UtRpy0ZFS7OxRkTThn0MsWDcnLWbekRpeYcnkZE+lhi8kxE6njnjhftL3uqFSytmRRLlGiRIm/QmlY+S48/vi7vPrMacefeV5tereZ3Ge0FW/RbZ9lE+b1WTYubVFI1LIJ65bFlJnVo8FeOWva3WneoEWDam2TVCtrFYx4zZoZHe60wyOa3SQibtCr5vUb9LJmBwO9SKWwiCKqddnp7c76uJCwkJBV0+b0WjAkbdZO77TNW6yZNeyEOb1BGFq9UPBfVBLkg2C2jQbn1UD8WtDmiBrbghOUc674rOWgYDGpTkzShLNWzQqhz/OWTWpxSFTSgmEzrhn3hh0e1WCPSz6jzg43+ZByTSadd91Thh0TFdPmVuPesGrGIT/rdX8gLCoqacWEah2botusZbW6jXvDmlmEDHnFqhk1umz1gJCwJWMu+uTmgLIRGrdh6c5aU6lVUrV2twfrqmpVOhUVTTgvHiTYVusSV2Gr+4NTrKTyQJA7bzDQwTxp1ElZa6p12eJWne6RDkTQ61a0ullKi9HCSRFFhdC67gdDBq89LZsplCzKJUqUKPFXKA0r34XOzk73ve1WL339hJnsgJ3eoc2tenxVSoubfCBYAV004AUrxoTFEHLOn0tpDt6512pyIHClLElbFFNu1AmrpjcLC7NWhUS0OKzJQQU5V31ZuSZVtshYEhIJgt1C2tyq1zPWzOj1jFEnLRnR7KC7fURDUJRXkNXuTrN6jHrdNg+iaMolSXXB6mKjz+iGpxVk7PETGuy2Zk5eRlFBq8Mo6vUNafOu+r+kzYsp3xweyjXLWnXZ54QCt0/aohY32+kdXvcH2t1un5/CxqnURo5KrWmXXfOks/4sON3YpdfTCvKmXLLPT1kwGNQV1CjIS1tUpsGkC4ryZvTIW3fA++3yLiEhORnLJk27rN7OIExuxZKxzSbkejvlrUuqAekgh6bOTld80awb8tKaHLBiXNqCtHnLxnS6V6tbLOg3r0+tbmnzdntMg72yluXltDqsSpdJbxjwklaHHSz+tMXUGb3Xvgz+5e99RCaTkUgkShqVEiVKlPgWSsPKd2FwcNDVUyMqyqqVZ7bpKNzhRvHr2ot36HSPdUvCYpocCILfntPv2cCOuhCU7WWMO6feTjW2bWaJLBoyq1eHu9TbbcGAqPIgYO60nIyUZpXaDHnZrf4HE84GJzZ7EHbdk/KybvOPjXjNmNPa3GKrB60YV5SX0hQktE5otM/lIL+l3Z0WDRr1umodioomXTDsuF0e0+le6xY1OygqHiTHDslbN+aUSz6ry/3aHBUSUZA17YppFxz2i272c9YtS5v3hj/V7jYDXpRQpdsjwqIi4lZMylpRrt66FRkLUpo02qdC82Yk/4iTrvqyfd5n1aRZvbJWZSxZ8pIVk8FgtS5j3oQL8rK2uE1Kk6iEqKSYCnlpVNridtMuWwoSa2tstWo6eN4FUWXBWmfEinHlGszrVWen3d6yGS6XsaLfcxodsMtjRhzX7CbbPKQg54YzZlzb/Ps0OySmwpQL2sO321XxgKVcn6e++Iz/7hd+XiqVerNv/RIlSpT4kaM0rHwXjh07rjDZoKGyXXy+y1TknIpCo+78Q0GhXrUydSiq1q7NUTlrVkxZt6TNLSacN+SYFdPm9avQolKLVkdU69DqsG4PCQkb9LJ1S9Yti0io0WWndzrjj533lyo0BvkfJ62akTan0T5XfcmcPlBvt0qt5vRZNCwsqoiohDK1tnvUoJcDYelOk86b8IYFQ2ZcVwgGnKwVUUlFeVlp65atmFCQ1eyQsKjb/aqopCHH5GXEVIiIe9W/tdVbbHW/hGpRZfKyBr2i20MiotYtW7cia1mFJlMum9Nnn59SY1vQnfPNSP4WEWUGvGTFhGYHg1OVOaNOm9e/6Rja5gG1tkuoNOO6s/6zLvdpsl+trUad1OkekSCXpcZWldqMe8OsGwa8YNGoU/6j9eBEpEqHZZOSamz1gJ3eLiIWrNvmNdrvhqctGNTikF5ft8VtZlwz4oQKTfZ6r3INclZNumjVtBsGdbjD1Pi0suhWr536so/82r9w50NH3HXXHaX1T4kSJUp8C6Vh5TuQzWa9ceyKyshOa+uLpguvyxRW7Av9lEnnJdVKqlZUUJAXFhMW1uwmw14LrKtD2hxVZYtRpzTZtylgbXXUqNeDduPtYE6PBesa3WTZiIRqBQXlGmUtGzMUrC7GFFFnh5y0pGqFION1I7QttBlFv1GNOCpjUU5anR16PaPXs0Enz4ax9pu2W1gyZtwbKrVJqJSTtmhExrw18yqDUsKopCmXDXlVk/32ea+IuF5fN+aMYa+KKbNgyLx+GUua7TfqdWXqlalVrklRwQ1Pq9Flj58065oKLdYC3QlFDXYpyOj3slk3RJVJm5O1Ji9j3aIqbVJabXGLIhrtN+yYa54MrMiV5oLG6Eb7FOVBUVFUuSkXrZm1w6Mi4hYNBX/vJWFRtbp1uV9IWFjMiilRZbJWtLvDiin9Qc9R1qoRJ3W4U5f7wLol5Rq1u9sVn3PeX5grDKlb2yYUrRRPVulIP+jkp6479cJHfeAX3+mWW255E+7+EiVKlPjR400fVp588kn/4l/8C+fOnVNRUeG+++7zuc997k29pkwmY2hoyMK1GRUr23TE9hvIvK6+uFNOxrxBUWUqNCkGFt2CvHINohJaHdbrG2LKdbnfiBOSaqQ0y1qzbMyaOaENeaV1SzKWJFTLWZWxrE5SRlitbkd82LJR066accO8G8E7+zllaqW0gpgyVbaIqQh6bEKSasy5YcrlTaHuHf6xmJQJFwwGa5SklLy0nIy8jBlX5a1vnvJU6zDipIiYoqIFgwa8qMs99niPpCpLxjTap94uEy644WuqdSpTJ2sl+EotbjYwr5p0xRcNe02jPUHCbVK5xk0tTUqLGT0WDIuIqtBkwZBKbZodUqZOVMKEs/o8F5yA3C9tXpODxp1zw9P2ea9ub3XZZ0y7jA3NzKSL1kzLW7fNw27yQRFxIWGn/LHT/khEwopJr/l3Gu0LRL4ZKS3WLRnyqjk9Zt2wZtbr/qM2R+3w9kA3k5azbt2yMjHt7jToFZMuqrfdfH6AYsi22iOOdr/ViZ6v+uR/elJTU1PphKVEiRIlvMnDymc/+1kf/vCH/at/9a88+OCDisWi8+fPv5mXBCYmJgz2jNuVfr+jzYdNTswYz15RKBS0OBToJm6IKRdTbtmoqOTmKUu1Tkm1ytSLSkibl1SrxSHDXnPVExYN6fG0qISMZXkZdbZbMycuJarCpAvq7Ajak5NyMtYtqQxSU697ypp5C4bEVZjVp82tQcZJQlE+eMFvs2DIpAv2eo8d3mbE61aDbJVqnXLW9Xs+GIDqJVRZNGTVjDm9ioF7ZtIFldrMuKZGl+0eDU5u1ky5LCJuymVLhsVVWjElplyTAwqytrjDggFXfTn4WJk9fkK7262ZNeG8c/5ci0Pa3W7JqGmX1OnW7ICUNt0esmzSlAu6PWK7hzXab8grrvuKGltV65BUrdPdrvuqlLYg96VaTsZVXzLrhqKcXR7T7k5rpoME36ghr5p1TZtbVdkShNXljTkdDGn3ylkz5FVJ1UHmTb+MBXN6pc0b9KIOdwVupri8bBAkOK7DnWZct1acN+q0XC7ntYvPeMe9H3Dbjrd54nyvV48dLw0rJUqUKOFNHFZyuZxf+7Vf8zu/8zt+8Rd/cfP3d+/e/WZd0iYnT57SljygKbtLY027yblR8ULKiJOaHVRjm3XLFo2gKG1eg70u+5xa3Zu9PwsGLZuQNq9g3YjXg3j5Lru8y4IhUy6ps9O6xcAmu+GO2RDDTjvgXnlpWSsSUhKqEJY2JyapiKQq3d5qVo8J52z3iIhoMLR8Mzxt2Jq5QNPSr9c3dAaFhWERY05rdtCC/s1VTYV6zW6SUKmg4LLPGnNaQo0Fg/Z4t6zVYKiZNafXrBsqNNjuEYTM6UXYfHC6M+ENDfZZMqrJAVvcbs10cPLUqsPden09GAJqDTmmw12qtJtwTqU27e4QEnHFF4w5pdl+5eq0u8OiETc87bBfUFQINC8xS0YMeFHavD0eF5eStqDbA1oclpeVk5Yxb0aPCz6p1WG1dprTI6FSQpV6u4w5pdczIuK2eYvt3mZev5zMRjGhS/LWDTomqValVjEVsDnIVOs05LjrnpJMlOuOv9/x88+6ef9t2uq22V531JljT3vPe7MlZ1CJEiV+7Am/WX/w6dOnjYyMCIfDDh8+rLW11dvf/nYXL178no/LZDIWFxe/7f8fJt/Uqxzd/pBwas3M8qhCJCumwpxeN3w9sOxWmHLJvH41tptyyaJROesu+IRo0LHT6rAWh6yZM6fHVvfb4z22uEWtbaZdNe0KQrJWrZp21Rf0+KqEymBdMissasmYuJRlY7LWNDtoj8eltEppst0j5vS56NMmXTSv35wBF3zSpPObfUKXfVZYTIUmY1434CWrpsWVi6vU6xmTLigoCItZMOy6r1g0GrQVj1o2YdW0GdeExTd1HN0ecsjPabBHrW6tjrjFP7I9cAFd9WUv+pfK1Gp1RNqsNTPm9IlLCQvr9rAaW131RWVqdbrXanBC882cmKRq2zwgpcmEC+IqhFBvpzGnDHnFigmzbpjX53V/aMWkXd6pwV4d7hHffL6IJcPSFox5Q6+nVWnX6T5xZZJqAhFxuaxlDfYIB9k6zW62ZtaCATW61NulTI0m+8U2hcFT1syZctmMa0JCZvSYCmzvRQWLuXGLy7OOXdmwMaeStbKZgkwm80O9v0uUKFHiv0XetJOV3t5e8Bu/8Rv+zb/5N7Zu3er3fu/33H///a5du6auru47Pu63f/u3/eZv/ubf2XVlMhnZdEEsEbFYdcXZwT+3MLuiYEMT0uOrplxQb3fgGEk57T+a0ysiuSmcjamwzQMa7dfrG/o9r8lNdvsJqyYUFXS5R0LNZrfNlEviUkEL8ao1s875C/V2CgmbdtmKKfMG5GU02a/bw152Ul7OAY9pdcSQl93wDVkrwiKWjNvhUWVq1Op21se1uUXWiiqdgT4kG7yY9mh2SEqjHk8p0yASrLYOukPGon4vmNMbdOMcUaHRWR9Xrd0u71KwLmPZkhEFWWFhO7zdvAFrpk27GliB+6QtBP08WQsGVOsUV6HVETd83QEfMufGZr9OVEJBbrN7qN4e484oeEhMRdDHUzTtmlUzbnjatGvK1LvJ+3W424yrMpZkg2qDjAUZizrdLSxmxInAll0M7NCL0ublZVXrFFOu3k45a/KylgzLWtXhLlGJoH07rclNLvuclGYpLZv32KJRfZ4REnbYL+gIH7FamJXLF7zyxtP2dtwmFFIqMCxRokSJgB/6sPIbv/Eb33eYOHnypEKhAP7pP/2n3vve94KPfvSj2tvbffrTn/bLv/zL3/GxH/nIR/z6r//65q8XFxd1dHT8kK6eRCJhemHMpXOf1mS/W+s+ZGxuXsaqBcMWDMrLuuZJCwZFJZWp0+2tQkKy0sadFhIOwuDWN3t7kmpMu6woL6FGhUZRSbs9JqHaNV8y4rSb/YKX/esgpj3ruiesmBaXUq5Bs4OyVjfXPXnrFgzKSavQaI/3yAbBZzEVzvm4QhCp3+c5KybV6dbiZhExWWkZC6p0yFrW7k5tjpo3oN1twuLy1n1z5TWvzxVfMOO6w1qtmDTrmj1+UlRCVk5chYwFxeB0JiyqzREXfVqt7TrcrRgEvm3E56csm5CxpNa2oAYgY9pVMWVqbLNmWk5GUVHOWhC8n7Rq2pIxIWFrZqU0a7DbuDNiyj3sX1kxbcgrm2WNDXaLSen1Dd0elg/6jFZNWTUlIiYiYdXM5r2xbFRBTlJN0MEUsmJcxqKw/1JCWKHJtKsiYkFTdbV6u62ZtWzSpPOWjNjvAxurpuJWNaFOjeVbXcj9pRdPf1lVc9LDP18qMCxRokQJ/g6GlV/91V/1wQ9+8Ht+ztatWy0tLYF9+/Zt/n4ikdDd3W1wcPC7PjaRSPydvtscGxuzMLOmavmI+7f99/L5vNzgZfl8RHfxrUa9bthrKrVbMmG7RzQ5IGNRXiZYbcyptW1DOGnNuDcsGRULIuM3NC675a0LiwWC2rgqHS75gtP+WI0uUXuUq5O1ZsolWas63S1jSb/n5OU0OaBWt2UTbviarR4QFt3szykqbopk93qPOTckVEqqExKxZjboAKJMbWDLrlGU39S9kBVCREKd7drcKq7CtKu+7n8VEjUfRP1PuyKpzopxOZnAWRNSVBBVJiyKkGVjqnWJSgqLyFoVEbdu2bgzZtywbMKycXu9W1SZJcMy5hUVFBXBumWrZky7rEqHCefEpQx4Ub/nVGjW6xlRZRrssmDYmDPS5i0ZDtZ4XZJqDDkmrlxRQV4+0BvNiStXrlG1TmvB2mrJmJWgYqHNLRaNWDKq3k5xFap1uuFrZlxVqU1I2KxrRp0OkosfVZB1wSdty7xFV9lRoVhBV+w2fVPPWG045867fk42my2l2pYoUeLHnh/6sNLQ0KChoeH7ft7Ro0clEglXr151zz33YEMv0t/fr6ur64d9WT8wx44d1xa9SU3TIaNzN7TVbhdLRBTWi7L5Zds8ZNirxp2xw1vd6delzVk2ZsyZTeFtrW0yFszqUa3LumVVOjXaY9xZUeVB1kchCGBj0bCMOS0OanXUmNc1ORjoMx4067pRp93s5xTl9XtWq8MqtYkpN+J1S8a0OCwuZdGQAS+ZckFCrbQ5Gcta3GzSBa2OykmLKhOREBYVEpaxZMW0co1BSeJGm3BEQkg4WC9FhYUV5LU5gryYcqtmTLmsIK9Kq6JikEezUfgYUy6p3pgzEmqUqZMLenRWTcpatW7VjKubotQ6O025IGtNTtpcEGtPyISzKrVKqNHrGwa8pEKTjEUN9tjvfaLKhYRMOG/BoIh4cEKzkZB7w9eD05h9kqqERV33hBaHFRVU6dQc/DsgcE69KBx8XaKSqnWYcV1ClSUj1q2YdNGsXiumVGqVtiilSa1ua2Z1uEvarOvFr6qNNUtnZ+XjixpSbdbKRrz44ssunOjZWEsmw26+a08pMK5EiRI/lrxpmpWqqiq/8iu/4p//83+uo6NDV1eX3/md3wHve9/73pRr+qa49kD7I2o6O108fU3P1JxwRUxmZVUuP2NGjzINKOp0N4py0lZMyVsP4tnGZKUtm9DlPru802l/ZF6f7d4apMtGFBSlLZhwzrpVZ/2ZSluktJl2WUKVlBZrpsWU2eoBi4aNOWWndzjjo0a9rkq7VVN2eodJF1zzZNDAPCdjSaMDqrTr9ZysFXu9x6QLLvm0LvduNjaHRdTZadirOty1Way40UlUkLFoxrizPi4vq9vDtnlIKFiHTLmkxc2iQS7JN9dL31yljHpdREKlFtecEpWwy2Pm3FClXUqzeYOu+Ly8rL3eY9QJr/l3mtykzg4LBk26YNX0pvupwz2mXLRkTJMDiop2eoeUZlvcFpx6ZWWtmddn2AlhUZW26PIWI46b12/dijVzQQbORmt0s4Pm9EmbV6lVhRbjTsnLqNAcnJy0qrNDxoJJF8RVWjAkJ223x+SsmdNrt3fZ5R0ylg15xSWfdlv4vzfrukuZz2lJ7bT/0E5robine77itU+N29v0yEYT8+qc45844dVv/IEP/dLj7rjjjjfle6REiRIl3gze1JyV3/md3xGNRv3Mz/yMtbU1t99+u2effVZtbe2bcj3fFNemkrXaGtpUVFQYGho22DNiaXnM6kpaPrexHoFVsyacs2hE2pyCnCHHlGs07rSWIE4/LKrVUdd9Rb9npbRJmxMRt2RUmXpzbijK2+VdQW/OlDrbN5NaY8oV5bQ4rM8ztnpAne16PGW7R13zZRPOqdYV6EQiOoLwsVpbNdqnyT5nfVzarCpten3DsjHt7hQRl7ViTq9hxwOR62G1tskF5wQLhk06b8GwLW7T4rAytQjZ7XEXfcaMq7Z52JiTZt2wbtWwE+b1GveGFofNuKIob9o1OZ+WDHQd65b1eUaf50SVWTZuwWBQYDgbrJ/KzblhzGnzBoSEXfeERvtscaslYwrWVWgSErFq2oIhiwbFVbrJTytTJ21euUazrmm0T6d7DDkWuKwObGphvin6zVkzr9+Ma3LSdnoHuOzzZvVod4dV0yq1mXDBrOuqtKu3U4ubzbhmwnl5OXW2i6lwyh+47PNa4wcMlH/Fve/4kMbaZp9/6qSyYpPHD/6qWDRuYmHQjbGzpsfmjF0Y97++9i988Jff5fHH31U6ZSlRosSPBW/qsBKLxfzu7/6u3/3d330zL2OTRCIhlgxbXp0D1dXVqqur7d27x/DwsHOvXzI+MmVg4azlwrgxZ+SsWjG9mYEy7LhO97nmy8ELZjiIa9+uw12GvSYkJCIhISWuyoxrloyptU2TfZJqLRqSUC0igWWEFOSCFUYaBbW2e8PHLBtXZ6eUFpW2yMmYdN41T5nXZ5d32OZBcSl5WYuG7PcBSXUyFtzwNetBH1Czg0Y0GnHSumWtjihTb16fJePWLarQoNtDEET+16Jou4f1e9GCIUk1gcC3XL9nTbkkodqCfq1usdVbEHbS/yMrbcwZG1qWSZ3u0e2hza/VohH9XjDjupx04E7qcpMPqbPDdU+KSGh2MFhvHZa1Khq0YqfNKtekxlYpzTrcpcfX7PKYIa8YddI2Dxh3VrUOXe5XpVVEcjM7ZtmolaACIK5Sja0i4jrc5YJPmHRRQV6T/RKqNNir3o5gRdSl0X4rJk25FFicd2pxyMXCZ1SotlQY9/ULH1MernVj7ooH7nlULBp3ZeSkl858RXSlwbayR+2rrXF16hUvfazHwMVSLH+JEiV+PHjT4/Z/lIjFYm6+a4+Tnzpl75bbhUIhEIlEdHV1qampMTg4ZOS1F6yNTpsr9qi1VbV2WWsICYsrUyelWcaCcWdV2aKoIKlWg92mXNrs5klpVaFJvd1GnTTholaHJFQpBuH+IWEFWSEpK6ZkLJhw3qCX5a2rt0ejvUJC8tYVZTU5gKIF/bIy4jbafJvdZNRJw15VrUO1uza7cFK2mAxaoje0Iyuue8qiYXEprQ7rdJ8hL2+uPmb1WDMjKqlCkx0e3dSOrJjYtPs+4P8IMlHOqbVdWNSwV6W0abJfQU4+sC/X2WHYa6q06fagFVOB46jHlEtBau1Dpl22akqzQwa9bE6fOTfU2anNrcrUmnFNUo0ytcEpUFFClbx1a2Z0BdH8o07Z4lYjTihTq0KzqDItDinIy1p1yWdV2WLVtGmXtblFRFxSjXkDdnirLW5ToUk0CP6fc8OSEU32BzUMz4GCddW2gnya8ugWW1Ye8PrQl83E+iQTFSYWBr105iuaM7e5qeltm/djLJ8yH9ouNb9ciuUvUaLEjwWlYeWvcNdddzj1wked6Pmq23b8lxcINnQ2q8khN7+t1ezLO01d65Ev3m2H9xv0SiAgTUJgfY3Jy5h0UQhZaQV51boMeEm727W4efPFbdWUSRfU2yUkJGNZUm0gal0TVW7CWTEVQsImXdThLjf5kBlXFeTV6BJTriCn0V4ZSy76lB0eUa5BXMpO79TvBXnpzebhNbN6fM2yCe3uUKZOr69v9uQc9A+Ua9wcltatqAgEuDOumXVD1qqEas1uklTjks8KCwsJGXdamTojTjrnz2QsyljS6rAlY6ZcVG+3TvcY9qpWR+z0TlmrQYR9n4TaYEC6Lu0WNbpNOCtjyZw+hUCsXKYu+Npn5WSUqRMWFREXlZCzJqFShSZ5a1oddtnn7fEThrwShNNFxKWCPp86ITTb74av63SvPt+Q0mrAC6ZcUq5el/u0uFlIxIrxQBfTZlaPomLgssopyCoGkuOwiHmjkpl6C2MZbZGbdVbuc/HaKZNzg6IrDd82qEA8mlTMc2TrWz19daAUy1+iRIm/97xpCbY/qnR2dvrAL77TdNUJT5z/fZeGjxucvurS8HFPnP9901Un/MNfebe65irVuswbdDb0MWtmrZgSEjHoJWEJC4a1OGK7h7W5LTj9CLvmK/LWtblFuzuUa5DSao93CwmZdVVSXZCMOhiEoK277DMmXdDuTssmrFvS6V5Za4qKqnUq1yihUkRCUrV93itrxRs+btGIiJhtHnTEL8hKO+E/OOn3DXpZUq2b/bxGe8VVKtds2InAwrtkTq+QkJQ2w46bN2jVjApNqrTLyVgxJW3JuDPK1Olwt6QaUy447Y+tW9LlXrs9bru3Ksi74WsmnHezn5O3HrQn3xGsXsatWw50LRX2e79yTca9ISyy6VgqU+s2v6rdnZstz8vGA3vxRlHkuiUFBePOarBXStOmnboYdFRvuHs2SiATUkHR5LKCgqT/ElSYseSqLxn1uoTqINH4sjl91swoBpk0EQl5OaumLRsPTo+GXPOkMz5q1Zwxr1suTsol50SiETsr7xNernPq+vM6y45+26AC67m0cDQkGo0GsfxXZLPZ/8rfKSVKlCjxX4/Sycp34JZbbtHU1OTVY8edOfa0bKYgVh5228N73HnXYxoaGqSnwvaXvUNqrdtCcdCIE4a9trlyqLfHgkFXfcEeP4miaVcNekmlZkU5MSkh4WDAqArafFdd8xXTrkioMavHtKvm9JpxTYUmV33RjB7hwEY8r1dK82acfkF2sxeoTJ1G+414zbIRe7xb2qI+zxrxmjI11q1aNCKhxqppZWoNetGIk/Z5jyHHZMyrtEVUmQ53O+UPg6C3W4NAtOLm0NXveeuW7fdTWtxs0Csu+Yy93uOAD25WBsy6oVqnyz7ruqdkLBpzSoe7AjdOIXApxRVkpQNnUZsjej2j3e1y0tbMyFh01edElJsKTrLq7Ja2KGJKWMSyMXN6TbvmiF9UCIoeN9Jp1404EWTTbNiw0xZEJGQsKipuDqM5q0HlQkqnu83pFxbbvI4KTSJiMhYCIXVMRNKE84qKzvtLUQlb3CImpSGxzZQLpl1TEWo2Mn9Ne8Mh52efkqhJfdu9WSwWza2NautuEIlEvi2Wv5TDUqJEib+vlIaV70JnZ6fOzk7vee9fD+UaHx8XLiR0tOyQH61Tm9lqR/HtXvVvLBiy0zuNeE1Y1AWfdMUXRZVZMSkkLCSsSkfgGDqkTK28jIKIJjcpKhp2wjVPCItat2zFtDI14lKqtNttvxu+at6AJvvV2SWhMmj2XVahyXLQjRNTbpd3Ou8vhEUDzcUVzQ7Z6v4gcG3UuDNe8+8k1drmQQ32yEqLShjyqq3eYswpK6Y2m5wjYhrts2ZuU8sxpzfQ7mwIczMW1Nul2UHxIDvlmyWQGYua3GTSRVd83qoZORlXfCF4/nhwgrIRYjfpQtDRs2rekFGvy8s66B+acc2oE1ZMBc6jm7U6HIicY4YcM+mCsIhLPqMgp6hgTq9Fo8LCtnlI3nqQjbOxqvqmjXnIS8Ki+jyvQpNuD0pp2xy2qnWY1SOlRZV2hIM11YoJ5404oaiozVGVgY5pa/huVeFGHeFbzBcu6Sk8K5/OSazEhIQtZSc378lisWhktkeoYlVHx0bh53J6rhTLX6JEib/3lIaV70MsFvtr71irqqrkw2kTq9eUxTutZdbFQykdxbtM+1MLBm33NoNetm4paOvdqd4ulVqMOytrxZjTGuy109tFJeVlpc2r0qHSoKK8lC6zrmpzNDjJOKrNLRIqzekJouHjYsqCPJScog0BZ6V25/25mKSUZhWaNTvkhqdt9RZ7vVeVLRKqFeVs96g+zxhxQkHemjkTzqrSYc4NcZXWLWt3h53eJidt1Cn9npe1Kicta9URv2TcG2Zc02CXGVft8A55GeuWjThhwAtyMkEB4oSctHFnrVtx2Wc12qvT3crUyEobccKqaTnrxpwyo8esHmnz2hwVEVOpRUqLHl+TsyquwqLRoHOpMhhejpp0zrg3gnqApKRqIRHTLlk2Zt2qeb1qdMtYlJMx7YoVM1rdLGNRl3uFhKU02+otRpyQtWLU6yZdsM2Dm3UC065aMWHNgmYHlGtSVFBnm+pQh9XchIrqCq1l7zC5eNVqcsx8JCwTWnRl/kWt8Ztk8xlza6NCFav2H9mlurpasVh0Y/aU2x4uxfKXKFHi7zelYeVvwcWLFxXCWX1zb3ig4lGr4SzFkKXihDZHDXjRtMvCYg75OREJtbrNuKrd7ersNK/fDU/r9Q2LQW5JQsq0K2b1GPaaooKEcgd8SLtbzRs054arvqzZweBkoGjCWY32yxow5JgZVxUVLRuzaNQWt5nTa92S674qpkyrW1RokFQtrjKIwy+3w9tNu+yGp7W7TYPdUpoNOW7ECS1u1hyc/oSE1dkuqVqtbvX2OOOPrZnbzJVpsFvOuoRKOWlXfMGwE+rt1OGuQCkyZsxpNzwNtnnAbX41SM7dSL6t0m7Qy676soRKne7UYJ91yzLmXfeUVocRsmZWvT22uF1CSiHoEopKKsjq9ghCJp1XZ6dyTbZ5SKvD+j3vgk/ocKdyDQoKJp2zYtq6JVOuqLGRsBxTjg0PWEy5GlutmDThgsX/P3v/HSX5fZ/3nq/KXdU55+4JPXkQBgMMAgEQAAmQBMAkikGiLFrBtmzZ3mvt0V3buutr75Fs37v2SlqtdG1JliyKIkWKlBhAAgQIgMhhMAGTY89M5xyrK4f9Y35skxRFSdeicGXV+xwcnDmdarprTn36+/0878eEqqqYpLx19XqkdBhyt6iYBr2qoYqcFcVqTjLeJRQK6Qnf5Ez5swYGm9xx1w4X3zjileXfMdJ2u759nQYHd20OKq9fekK4e8Gdd733LfqXUKNGjRp/PdSGlb8kY2NjPvc7X3NX38ccmz1mvHDYcOwB+Xxes0Hbvcu6Wad81jb3i0kpWJc2HXT4bgRtwbusGvOtKO0Fj20W6TUZUFa0w8O67NdupxbDGvXrd8i0o0Y9jZBhd7nga+b8gqQ2zQb1ujVYC+3VZNCqMcuuKMlbcskBP6FOk6pqsFxaITiPCQtpNixtzjYPWnJJCL0OyljQYVfQgpxWVQk09Xs0GxBTp9ctLnlSq62iEkHPTsGSi/LSlo3aHphvc5blrGjQrUm/OacktemyXzrwzlRVJLUGqZ52HXapKuuwR1xKTNKwe8w47ozPKysqyytKG/eyVltt9Q4xKVOOiKuXCIoMRz2tSb91U+Ka7A5ss+c9FqR1csHeybqkZgktmg2p06rLfhvmULXoopiUNtvNOSGmzjYPGXRIo34LLlh1zRmf12GXqDp5q7LVjAadUtEWsUhCtVoVrkTlQitSW9L+7//qfzU1NeUPf/sx2YWTwi23WS3OmJw46/LSEeHuBR/9qUdqSaAaNWr8D09tWPlL8vLLr6rMdvjAnT+js/4JX37uU44XP69SrchZk7OkKC+pRYM+EbGgBG/QkpJ1E9rtsm5KkwFTjrjd/ySmTknONS+44Ksa9dnjQ1ZdE9cI6rTIWjLgDgvOGvOyet22e8gRv6ndTr0OiohLaTfoLmFxb/o9o55yi5/2ul/TbCiw506r162irKocnMbMqdepQa+SrB43y1kx47ioOn1ukza5KbRrCkr6CtLiGtVpVVW2YV5YTL1udVpNObYZK26304qrKooiYuq0WnJZUrsBd6goWXQxWGgNWXDOlCObg8i6aW12qNetYH3zFCulU96aAQ8YdJe4BuNedtYXDHqbRr0a9YmIajakUa9O+0UkNgeTPreZcsS7/KqKvGueC4aMfQ74hGWjirJBEeKUDfPGvICQqCQidvuAm/24oow1UyoKdnjUBV+x7Io+Byy6oKJopnJCrJi0sdYjVIkYr7xB97If+en3be5N9fX1/ZnL3rVBpUaNGn8bqA0rfwm+1R20ve0hoVBIR0eHzq4OU/lp2Uxx88qkTovLntJt32a3TknekLtd8Uxgdq1XUbDsskse1++QNWMue8qMY273TyW1WTGqJBsUCcYltVozoc0O414x4bC4ekPepscBUQkttgQnOhtmvKlOq3pdXvUrgbdkXYNuGfMWnddkQNqMWSeNe1FJwaprEuoNuVtEQlyDqpKMeQktspYltSvIqNOsKLvZSFxVMeeMpHYb5oRFAtlau4N+OvCWXPevlOQtumDVmDotmycnTfrNOGHMixr0GnaPZsM2zLjkSZNel9Bo2NulzVh22YgHjXlZm+067NZkQK9bnPNFY17Q5zbdblCSl7GwmTZqNiBrRdqsfLBjVJYREjbuVYsuG3KvmAZh0eDkJqtBjxnHlOQkNDnit6ybdsjPiklJaLbgvKKMkKou+02FXtdY7ZHUqjnaL1KJK1TT5tcmbZg12fCKm++8PqB8i++37F2jRo0afxuoDSt/Cb69O2h2dcxzR7+io7xXtbFJU2mf+sJg0MsTC0ytI1LaLTgrbVqdZnWarZmQ0h5EckuO+E0nfVpCo6R2FSVRdUJC6nXLWAjSJRRlhEU3G4lnnVCWc8BP6LQrcLOMqyhdv2qwIiKq282yVjaHg0FvU1EOUkDHzTqlUa8t3qEoI2dJxoIzvrAZT26zw5oJu7zXlMPyVoWElSWFRaybMeqZzQLFZoNO+H3LRpXlFG0oyQdx3oSyog2zNsypqAgLK8kGex5p807b5X22uM+GWUnt1rUJi9swZ9ox7XaKqdegW0TSNS+Ka7JuSqN+IWFb3G/djHmn9TkortGUI5oNEVyFNeg24TUZ86IS5p0155Q140Gj8nmv+3X1OjTqtWHenBPy1u3zUX0OOu1zzvmio35bTEpKO8HPMG1Gl31e9+u67Lcl9HaxUFwpVJCKtGqPD7hQeVw+siw3OuzX/t2fVul/r2XvGjVq1PjbQG1Y+T4Ui9/5m+y3uoPG5s46PvqcK1fGVCtnxbJt2txq0QVJbYFro9u0Iw75x4F/Y1XGgqKMiKicNVPeEJUUkxIWkdSmEKRqvlWal9AsHTQap3RYNmrO6WBYuB6rTZuxZlKTAWExUXVBOicmrl6b7cHVynlhMQvOmXNCW6DVn/CafocMuce6cVlVPd6uTqtRT7vmebu8zw1+xCmfNemwOi3BDsxlFeeCRd8TLvu6vHXJYEhLaddpnyUXg5OYNfPOqm46VKJiUuo0qyib8Kph95pzUlKb7R4Mdmooy0ubEVev36NyVkx4VZf9Wmw151QQCx/YPDX61kDS4yanfc6qcRkL1k3Z5p3K8sHnLijYsG5ak0HTjslYsMN7zDpl2D1GPW3eGUmtwiKG3avdiDqtEhrs9yPKCsa97A3/yYiH9DskLGrRRTkrwtWo2dCbstFZ3eEbtNb1C6cSrpReUI4v2Bo/aEfHQfVrTTWVfo0aNWoE1IaV78HY2JiXX37V8ZfPKeYqYnVhN9+121133aGxO+qrX/l9sZVe+yofcyL7J5qLI4hpsUW9LnlrwuJGfcM5X9Rln5KcJv1WjYlIWHZRVdVu79NsSEhEXL1rXrTonKzrZYprxhRlzDsja9myKxp063GzNjsVbBjzvKzFwG8Sk7cmKrFpsW027LInLbsiKmHVuFf9il4HRdVp0m+Lt9swb920iLgmgxKabHGfVdc2EzlbPeCwX9dii2ZDCtLB45y0ZkKXm1QVrZvW5UaddikrBkPXtEUXNRmS0q7ZoKi6IO9zWdqMi74moUXOkmH3BVco18sg552Vs6bbjSLi+hx0zhc1GQQzjmvSr16HmFRQvjgtqU1VWVHWCX+gIK3fbUFdQEjBhg0Lph0z47g2WwO78CFFGcuu6HHAuJdtca+YlO0eVKdV1qKSnIINCU1GvNu8M0qy8jY06JGxIC9twus6QiOGmm6Wb5w0ufa8TKpXJBzR17bbSNN7rRSmjE486RMP/nNPnBmtqfRr1KhRQ21Y+VMcPnzY537nayqzHba3PaShrlU6s+zw54549rFftrS4qj98q7rSDtFCk0xxRbcbbPNOayYVZdRpsc0OYWEXPW7OSfW6ZC3bsGDOCSuuGnCnIXdrsSWQk12x6poeN1t03qpxg+4UkTDvjFHP6HerViOWXRSTNOB2HXZacEZCkyaDgbL/ko1gWXbRBaOeltJht/dZNyNrwZSj0qbt82FLLirJb+rl553RqE9ZUZ/bTHhFwYasBWVlk44Y85JW29Rp1WTAoDtFJaV0WHTBmBeVbBHXoMeNTge+kU577fAeEdevNKrKElpMBkK3y54M4slvs2FOSc6qMWsmtRkJdl5CgSm2TkXJqKcsu2yHh6XNBdHu6w3Tq65ZdNGaCe12Bibby5ZdkdRq2jGnfE7alIi4eWcU5SR1WnFZWNSM45aNqtelx83CotdPSkSUFTXoFRGT0KRBj6KsBeflpeWlTXndgjN2Jh4w2LvN6mK3vlhKZ2OvzsZBrfXdwuGIQiWrXKooVQqBSv9JP/ShYu36p0aNGn+rqQ0r38a3Yskda4ccuuE7y+P29N/ut77xL61NFz2490EvTx21WjgrGkpIVBtQUac5MNFe38bY6b1K8s77soseFxYTFt1cOq0oarFVRVFUQpMB9ToNuMsJn3LG5y0bDWK2z0hp12GPnBX1ekTVqSpp1GvO6eDUYkCHPdqMOO/LKsquekpVyQN+0ZpxTQaClucjXvP/FRZTUlAK2pmv99eMyVkR1ygkLG3WUb9t1ZikVm22a9TnRh+XsaBOk7CodLCDssPD8lZtmFenGTZttavGHfbrBtwedCAtGvOiRRd1u8G0N9XrMO+Mioq8VSUF7XYoyZp1XKtt1kzKWXbNc5ZdMeI9QqLWjCvLi6rTapuUHpNeN+QuKR3yViW1G/W0tBmrrqnXZbt36HNb4Ll51Wt+RVTSPh92yRPmnPItj8v1c7AuDXrE1ctbVafVmnHXI+BROUsu+ZprXjDhNZVQQTLUoTd6o2h5WbnAzNiMq+HLGusb9Xdssx6eFmkIi0USNZV+jRo1agTUhpVv41ux5O8eVKBcKQmX6vRURizML2hqbDRefl1XaZ/J0GHd1QOi4kGnzPWumUXnxDWIazDkHh12SOl0zQsu+bpVEy76mn6HVBSCNp9lBWlb3Oe8L7vqORc8pqpqv48ICQV7GBUN+uStBac0h1z0uGWjbvCjEpqsuOKix+WsuMcvSGk377Qu+wInygGNesXVB+3CrRr0qNcpa8WKq5aN2jBr2agBdwqLIqRow4DbJDTKWlJRVqdFtx7TjihYM+RuFzymxVajvqGsYIeHtdpq3Msu+CpCm9c8e31Izpp5Z6waN+24FtvUaVaQFhbToFVFyZIrLviKddOwuZgbEZc2K2NRnetlk1d806yTm51Je31IjwOmvGHBGds9aIv7rRnX6xZb3GvQXU75jAu+6oLHFGwYdJc+t+h3h5wV806bcdyQuzUbkrXssm8oyCjYkLEoa1lR2pC7LYbOKERXJMMtqrm0RLnRcHS7lfK41Y1rZkvzzviinbf0i0ZiNZV+jRo1agTUhpWA744l/6m3l/PKpaqe5DbTV6f0t+10Lv2swfCtpirHXC09Y4eHxdXLVBeC7pqx4Lf8om3uDCK5gw74Cesm1Wlx3pctOKvPbaISCjZc803D7jPsbpMOa9ADOuzWYlhRVkVRWCR4kVyU0GSnRxzzX533ZSVZM05YN+0+/0qPG2QsCrEZp27Qo0G3JaNBv9AO7UaUgtOhYXdbNemw39Bpr143uxjErK95XiI4MSnJCQdGmeutzL2WXZbSoaoS6PqvBic3Kc0GNfuInHVVFRnz0ma022nKG6qq+t1m3aRZJwy5W9mKBee0GdFqmyUvKcpKalFWstMjwaLxePATC8uYt27KkosqKra4T7f90mac92VzTmu3006PSmrfjEB32K3DTjs8YtkVU47a7yMGvc2GWRUFw+62wyMu+Zopb2i1zYZZRZng5Cym2aCUdi226LLfpdBXLUROOTz+JVsiD4pVG9VFGvVE9ikWNsyWjypGs+bn58ysXKup9GvUqFEjoDasBHx7LPl7EYskRKJhheqGSjmkNdWrqaGFdNWA242GnrdQuaDdTmVFiy5adllFKTipWNrcJWmzXUJzcGWz04oxF31V2oyCjLiUBef0u9WKMf1uM+k1RVkl+eCq6XpCpiSnTouVoPm3SZ9etyjJCqtz2RMi4vLSQTvy9VORBj3yVvQ44FTQArzfxxBSsC4irk6rK74Z7HrsMu249SCCTUjGorhGZQVlMTkrktrFJIOW4rmglPElFQVFGeNeCbwzWRUlBetKCpr0B6dEV0C/262btOi8krwWwzbMWXTJiivWgkXlmKTb/VM9brZuyhH/2QVfE5HYHP5CIg75xw74hJiUsqK8Va/5Nbu9f7P0MSapKLuZzmrSr82ItGn7fUydJmNWrZkMhpEh/W43400v+w+bC8iXPWHGSVuCgTNtVlKLAYeczl8yGnlBLramtbRHqtCpFM66VP2G6fLr3r/tX5rIH/HlN/6T/hvqair9GjVq1FAbVjb5Viw5nVn+nm+PRmK2D+z25swbGkPbVSoVQ003ms5cNFJ8r+FK1azTQZtv2ZpxXfbrcZOIuC77gpTLdQfJeqDfT2iUsSAqKWfNgEMK0uactOCctGnD7tZhjwXn9TskKrkpYEvpAgUZUw6DnBWwYjSICp9Rp1mTQWExK8aCPqCyTnsktBr3siN+S7+DSkGc97wvSZuzy6OmHHHNc4E6f1RFyRl/ZIsHRESFxTarAqpBF8+EV6ybVlY04A5Tjpryhi77NekTEVcNHm9Z3poJU45psSVomE5p0m/ZFWNetG5Kiy3iUoFwbsGw+9XrsW7KnNMylvQ6GMj5Wi06L2/dKZ+Wt+yAn9SoTyGQ0iW1SWg2481Nt01FUVVFRFxCo1SQGoqqU69TTL2cZXNObRYh5qzY7iEhNBtSVbHb+xSkFayrKEtW2zQUBnSn9km1xE2sPyO3UaASkYw2a4p1i1caySaNrb7pn37iX9WSQDVq1KihNqxsEovF3HzXboc/d8Se/tv/1FXQ7OqYlcy8U8tPGgzdLny5SajabDb3LOV6W9wnrkGjXq22Ou73g8GiTkOgeY+qk9IWXIlkrRiT0mm7hyQ0arFF3poZx/W7w4RXRIM9jH63Ou8xM44a8YiIqHXTshaVlcx606pxOz1s2H1GPSmuUZ9DNswGrcLnVAILbVxKowFpc0o2tNkhJuGCr8mYl9Sm0z773a/FsHrdXvWrouqc80VhEQVpOcu2uE9IyIY5Sy7LS5t2xKw3bfduAw4Z96qYhEoQxO52k4R6IRGN+iw456KvmXfKbX420P+XtRi2w8NWXHHOl+3wHnNObZ5UXfBlE17Ubo+Kgi57VbFmQoNOA27XZMC0oya9tnma0m6XkIicFdVgiTeun6Cg8VsVAkVZUQkx9QoyCGm1LXhbJvg+bIhK2rAgoV6nfZZdMe4VETF569cbmCsXLBtT2ihIVuu0N/Zo7GhVyJdMrZ23Whr39Oyv6OxuN7y13/79+9+Kfwo1atSo8X85asPKt3HXXXc48tzvev3SEw6N/Lcl23OThz1/7KvWZ8t2t91jtTLl2NRndJT3Suk25iWLzmsxJK7JmJfMO6VJvxv9mKINYVEhYYQ2Y8AjHtJmp6xFkcDT0qTfgrPWTBh2r2pQlLfNg+CSJ4OE0J5AGz9v3CuWjdrmHSpKXva/y9vQpE/OiilvaDZkm4dExMwqWHTJqknZQJA24kY7PCIfSNva7dSoV0IjQkF54E4D3uaUz9jlUQ16TXlD1pI+B9XrCmLS31DFIf/EgEPyNlz0VW122uFhUw5bcU2nfcHy7Lo5Z8w5rSArpTPoGmrQoEejXpd9XVy9M/5IQpNd3qdRvw2zZp0w4fUgNtxnxWVD7rbXD8laUq9TmxGN+kx4zZt+3y7vExZxxudt8YC4BkUbGvUKCasoWTdj0YXNosW89c2SxFgQmY5JKslKadPngLCoOadUlSEYVpNmnXTZU1ptM1J5SCQbEY7kXcw9a8Osruheh7o+pLWpw5mVp42NnnHq1Cl33HHHW/OPoUaNGjX+L0RtWPk2hoaGfPSnHvHZ//JVj50ctaXlJhu5Nc8e/ZKWjT1u6LrJ/oO7vHHxm7449xuK1YxYpUFETCWUt2RUuBqzatyya/LWNOm34oqqioQmVzznnC/rcZMdHhFXH5x2LMhaVJTRoM+aMV32qdPitM+77EnbPaROi3GvuuCrygpWjclaktBs1YSs1aCYb07atEb9Btzhoicsu6rHTaJSFl0Idkoq6vVYNiprSad9mwK1qISSgqINU45ot0vRuiFvU69LnSY7vMeUI8H1V0kl8My2GRERd9wnXfO8tBl1WlSV7fJ+q66a9FqwKBzXaqtd3ueET5nyhnY7JII0z9UgzVNW0GG3Xd6v3Q5UZSzpcYuYevPOWHZZsyGDQSFiRFzOqnrd2myTsyJjwRG/qcmgvFXzTuu0T96aXrcoyFhx1RVPW3FNmxEVZXH14uqtuKbDLmU5JXkLzmuzQ4MeKV0ue0pJQd6qiqJO+7zpk3rc7KCfur6bU74mu561z4+Yd9Z49UXNyUeMNN+ukotYae70hd/7ur6+vtpVUI0aNf7WUxtWvotbb71VsVj0h5/5nM899zVL8ysqGwl9u3bYdkOn7u5uF79+2kj4nUZij5jMv3ldjFZtEQ3HhaNki+ue9C+kzbjsSV1uUJSxatxZfxIsyJac8QVRCZ32GAqK+ma9KaEpGBbqdNknbc6Y5624os9t2u0QlzLjhJKsg/6esoKsJWmzyvJWXNVhj21B109Z3oTDxr0sJCxvRaf9bvSjYlKO+z3nfElBRkKDrCVLLotrNOYFadOGvM2Yl/U7KKZBVFJBWqe9UjpkLUnpULDmpM9ZclGDHv1uM+BOTfrlLBv3khHvttsHrJvWbEhY1HlfEpU075SkNllLph0NygWbhbDF/dqMqKoEGv7rGrseN6som3HMbh+Q0iFnVVJrcGWTltAsLmXYPca86BY/7Yw/ctYfm3ZUj5tMes2SUVPesGbSPh+Ws+KSJ2xxn0b95p2RNiOmwaTXrZmwz0cQdt4XZSxqMWzaUa22G/eqpFY9brQcvixbuR5tr6/0aA/v0hraKl2ZcmzycZU80Zacd936MS+M/UHNYFujRo0aasPKn+Lw4cP++JNPis6O+PCBDzn9xmXR+mbp9UnPvvkFV2duMDl31d7yh7VEh6yEpiSrbWKSGiKdkvF6hdiG5kyfOq3GvGTGm5oNuuZFa8Z12G3EuzXp37TFHvd7dnpEoz4bwSnLoot6HTToDjF15px2zp/IWtps9W02rKJizEs67bbL+2Qt6rRP2ozX/bqoOh12uc+7leTlrbrsyWDX5ZRWW3Xaaybow7m+z8GE16waV5ZTr9e04xZd0GWvQXdtDiuVQEyXNqOibMOcnCX9DrrBj0lq3ezUaTNi0mGXPGHEuzUbFBax6pq0WRFxa6ad8oeqynrcZMR7TDms1XbNBkVEVYKdlpCQhCZxDdrtMOn1oGog7vrJy7y4RhmLcpaV5DddLIf9hjXjQkKu+KYpRwL9f05Mytv8vDYjJoKTrFkn9LhZSc4lXw8cL1Vd9pl10qhnpE3pdUBJ3lXftOC8hCY3+rjBultMF0+p0ypnWb0usWhMLNpoW+V+JzN/aHLlgnsP3aGlpcX2dM1gW6NGjRrUhpXv4LsNtsVi0UyipDu1X1Oy3ZHZL3v26pdUy7RFtwkJSYXaFKTVaRapxuULWcm6Jt2RvebLF+30Xtc866w/seKqG3xcm2067NZsUMGGXgdd85xzvmSbB6S0ial31Tc16BWTlNRup0ctu2zFNV1uMOm1YE/knK3uN+ztUjpMeUO3G62bsWpMu512+yFhIWmz4lJGPCxrwarxYIG2U1K7FaOmHRGRkDFvwQX1gT4/b0NSaxDtjStIy1sTEg7EcCUD7jDjuEZ9tnuXqJikNk36bZiTt6bHTWadcMUztnvIvDNBieCkBl3i6g24y6LzmvRp1C/naTF1wdeO+VYEO28taKmOCwW7NTkrSnKoBlHvAiJWTcgEpZLfasFut9OcU8JBVHvEu/W40Rmf3zwZ2uo+fQ6a8aZLnti0+zYbFpM076xFFxVs6HebZlusuiqhSavtwkIawz2ylRX11S4tkSGTlTfURVMicSrVvHg4pT7RpL2pV6FQhJrBtkaNGjUCasPKt/HdBttIJCIcDSkUc0KhkMHQHS7m37QWOypdnhOvNGkNbzVdftO6kNbwsEIlrVjKawh1O+srRn1dmx1yVtVpscN7ZC1ZN6VBj4i4ipIhd1twwaTDmgxIarPgnBM+KSq5GZut16PZkEXnrRnXYqsmA/b5iLQZeasQ9Nkc0+Nm/Q7ZMB00Lp+Xs2LZaCBkW3Da5xzw08E1VFsQa6665AkNug27V4MeF3zVnBNO+yNFOfEgzRMNEi91WkTUmXdar4O67DPjmJw1dZoVbWx2/aybNu2oFdeERZTkN/0vW71DiyEJ9ZZcUrAmrkHWioINKR0qivLWFWU16hUWN+kNBRtmHDfsXvW6lJVQMe+sjAWJYAE6ok7OsgXnFKTd7MekzcpakLGoYMOC8yLietysQa9uN9gwZ92UJv12eVSz4eBnddZlT0npMOm1wCWT06RPNVxSqGQsFtI67BSKhglVxVIhLc0tqtWqlUJJQ6Reb/OImfExe/aUawbbGjVq1AioDSsB38tgG4lEdPW3mTo9oa2+18Za1tb42yyVzpksvqapPKwp0ikajsuYM1V5Q6raYT0762roJYSC37gzMub1uFnRhrh6y2YtuqDVdlwfLobc5awvWnZF1mKwUNssoUm9bjOOm/SqWW/KWzPkHquu2ecjYpKB3GxaSWaz82anR6WCwWfNlFkndNhlp0dFxGQsGvOC876kJ9Dvt9ll3EuSWtzi31oxatQ3dNun234LztkwI2ardROikvrdLm3GCZ+0ZNQu79eoV86Koox1U0ryLnhM2oxG/XZ5n243WHHVhFflrIhp0GWfrEUDbhcSdsnXZa0o2rDooqiEoo1NH0xco/qg3Thj0ZTDrnjGVg8EC7XzspY3T53WjDvgJyW1m/CqVVft8n5pU874Y5c9pcU2W91n2SWjviEqqSgjJukWP23CqzbM2+uHVRQVZaS02+ODllx0wh+ISYmGolLVXksu6rBHItSoUM6IhOPWK7N6jQiFQqZKR/W17ZaMNVorVZVKpZrBtkaNGjUCasNKwHcbbFdXV42PTxgfnTK7tCg9X5SotEpEGrUkepWiy66sfFNP8YBQNKw/drNsZdV04YSrnpMJzbs7/HNC5bgrnpWzKixq1bhmgxr0WDetYENSm6iEvHXTjijK2Ocjeh20YVZJTlxKp31CIuackNASGGA3gv9f786Ja5KXlrGkrCiuXkRC1qo5J233kBHvUpQBaTMIKck470sG3eWaFxVlbHWfpBaXnLfTo7rduDk8LBuVsSCpQ9aycS9bNmrKESVZWQvy0pta/0Z90maMeVGfg9rstOSStBkRCQPusOKqjEVlRVVVDXrt9cMadLvkSdPeEJUUEtZhl7jG4KQm76rnrBpXpwkRJ33aNc9pt1OLrSLi5pw25Y1Aq39FSNhuHzTtsFFPbXYg5a1acdW6CTs9rN9tirLCotrsUFFUkjfjmCoqSjbMKciISGg1IqVDvXalUMGO2IPeLH/GXOmkhmqvVLRZd3KHhdJFU+lzViNX5OILRpreq1DICUU5evUp4e6FmsG2Ro0aNdSGlU2+3WA7OTnpzLGLqhspXcl9GrrWjM+MWtlYtmFGtjFtR+etXtn4I6OFp+wKPSKpXj68biz6jGWX3Fn/0zrWb7Xkiu3eKWNRo14RMUUbElp0aleUtWpMRcmcU1Zd0+92zYaUZIJW5ryqkCr2+Yg6TUry5gNj65pxdcHwkjYjY0FIREVJWVFZ0bLLGvXZ4WGCJE1co5iU66WIveCcL+l1wA1+RJ0Wo74hpd0W98tZFpHQYouUDpd9XdqcRRdkLOi013YPGvOCq57XZqeYlIJ1DUEzdLNhw95uISh5bLVNORjTut1oxnETXtVuh4zFIEVzwBb3O+kPHfe7qOhzUFKbjEUrrppxXEXFHh+UsajXAVOOyFhSlFFVkdRun4+46PHNa6tlF2UtO+VzGnSJqQ+u5oqWXPCCU3rcZL+PGPI2Sa3WTWvSZ8LLll1Wp9msE5KBMbeqaCh0p6u+KRdekg0vaK72Ohb6tMXqRbemflRn/bD82rIXcr+sGF92c9vD8uWM80svyXVeMtRS56M/9UgtCVSjRo0aasPKJt8y2D7/X1/QMn+rVGFAf9fI5pVQe2O/S2OnnJr7nIX8m9Ibo8qptJXSpPnyOTFxrcl+GcvuavtJ/aWbzK6OyYVWhKtR3fYHL6K3WnDWhnmN+lSUlOQUZc06ISxhxLv1OaiiqCAjKm7djJwVLbboccCop/W42ayTJrwqpQNUlUXE1GmR0OyKZ/U6KGPeNg8GvT/XF1TLCjIWNBvSbNCAO0x63YDbtdiqIG3BuUAWt7K5SJuxoKqiy34NetXrdM6XrRoTFlWnxazTXvWrUto2e3qWjOp3aHPZOKXDnNOa9Gu1TVKbDbMmvBY4WSrqAnX+dblatw67tdvlvMesuAZSOjToMuguzYad9jkVZbf6+0EdQVVFWc6yNZNabRUWddWzijKWXRVCWMx2D26meaYcNu4V8066qkub7Zv7PCVZUFZw0eMWXdDjQCCMS2qJ9gtVqjbiEyYanxIJRw2Xd7my8brTEdqrW+STWS2pejv33GhpeczTiy/IN874kU886r3ve7Q2qNSoUaNGQG1Y+TbuuusOf/jbj1mci3nX1rd/h3K/PtGk1LQgnMmJ5JJiS/1uid2jsbHPWnbOVPlNsxunZUNriqENK6FRi6FxjfrENdpZfdRJnzbvtC77jXpK3pqUTvU6TTti3ZSYhIwFc06LSqjXJSoZLMMuWjUmpV1BWrMhIx4y7biMJd1ulDYjoUlMvRHvdtivS5tRkg+K+jJCGhWsWzNpzURwKlMMrlVmxdRvpnwi4kEhYFpEnVXXVFxPq9Tr1qDbUNBGXFUNWo/DEuoltep1i4RmJVmLLrniG/ocss+HpXTKWjTntCueMeAODbpVVDQbUpaXsaBOs6xVU97Qaa8Bd8hZFRIx5G69DmwuDK+4oiSv241abBES2Ty5KcmpqshaFhISkzLsPl0WhEQVbSjaEFNvyN22e9BlTzniP5nwul63yFsVFnfNC6q46hk5q3b7gAbd8tZEJc0V31SMbbh74OO2Nt3m8uph4/PH9TRtky3OWqlW7Wi+TzwRtb17q3DdMe035P3IT//9mrW2Ro0aNb6Lt3RYuXDhgp//+Z/30ksvKRQKbrjhBr/4i7/o/vvvf0seT29vr+b2pLmGC56b/z8MJQ9KRVtlSsvGskesxa+IN4Zs3XiHreUHbG+6TTQSl49lDGQOuVR8xrnKl5xe/5pHDgxZKlRllpd0hndLVTptqd7nkq+b9LqImC77RcRd8DUzjmqzXRWN+tTrlLe6uVuxYU5YRN6KNRObVxsZC0oyjvtdjfoCb0n828r4EhZddD3Gm5Mxb824gg1lBZ12i2uyYdaKUavGTTkqriH4emsyFnS5wYYZFaWg3blfUnugZEtr0GenR415wRXPuNFP2elRaVOmHVMJShu3e0iH3TrskdIuImaL+13wFVc8o8mQDTOB66Q+aFietOCMeWe0GXHG5+WtutHHRYOywZCQkqI5p7Tapkm/6zUBleB7VRaVMuYlFUUj3i2pDSQ1a7NTmxGXPemSJyS1arXViHebdcIFXw2ujtqd8TmjvqHJoC57jXhYswGrxq2b1mKbo35LJjSvrOhLl/6t8kZMZ/iAjsiAaF3ITOmU15b+QKy1oLnxAXe+Z5877/pA7TSlRo0aNb4Hb+mw8sgjj9i5c6dnnnlGMpn0K7/yKx599FGXL1/W09Pz1/548vm8juZeNx96u5WNeZfHn1QuVURiYdu37ba6EZJfjBlKvltDuFM+vCRfCQvF6BlqN9LwE0LjK65kXxFry7jljhu98MQbVsuT0uE51WpFuz1e9cvqNFs3paJsxZhtHrDDw654xooretwsLC4a+Eei4qoqUjqNelbOihVXrBrTZECfg1ZNWHBWUc6yUc22uNcvWDPuFb/svC8bco+YpCYDWm2X1BqYYEOBzG3FOV8wG4js8tLGvCSuSVFa1pI6LZoMKtoAU45KahMRExLVYZdOe9RpktJuzZQV17Taao8PbkrjrkeOI8oKhtxjwXnXPC9r2YRX1GmRtWLV1cAP02DSYZ12u8knglTVqDWTSgoWXTDjuD63Bacn4c3UUFKnUU+Z9oYt7jfsns04c5N+Ke1CQrZ70LJRkw5rMayqoM8trnnBpNchuJqrM+AOWz0Q/N3jm+WOlz1xvR+oWvTCtT8wXHrAzthDWhu6RaMRq7kpzbGtktE21aEz/sE/+3Hbt2//a3++16hRo8bfFN6yYWVhYcGlS5f8zu/8jhtvvBH8+3//7/3Gb/yG06dP/5nDSj6fl8/nN/+8trb2V/aYvrVkm6gk3b//I+7ZU1Qs58Ui1z0Xv/v1X9RQGNQU61FXlzC8bRCEQiHhcBjsarvbxNQxL5583P/8of/Dm0dOW5m7IlXp1qjPsivqNNnpfeo0mvKGYW9zwE+IiNvuQSd92pQ3dNij1TZRcSuuKSm65gUVJa22bhpVu9wgb80Oj2i3U0nOSZ92zheNecEO73GDH3XKpzUacKt/ECj3V62bEhI27YiwuG0eMu2IcS+Z3xxkru9odNqPqnY7RcREtbvs67IWDPugipI1Y/rdbsNscIXTKoRlo7Z7SEVBQoO0OWV55WB3JiquxRYXPCYmacmloOk4bd20Ee/Saa+0WfNOueY5CY3CYgo2zDmlJK9Bj3EvK8oY8a7gyixjzpesuIKwVlvNOxN4W9ZFxMU1BFI5eh1w2VPKysJColJCItZM2OoB3fbLW7Xkkjd9ctPpMu5l17ygrCAkLFdKawvvdlfzT2tItohH60BLvN/V1WOao1tFKxVvHD5SG1Zq1KhR4/vwlg0r7e3t9uzZ45Of/KRbbrlFIpHwn//zf9bd3e3gwYN/5sf9u3/37/ybf/NvfiCP6VtLtoc/d8Se/ttFIzHRyHXHRbaQViyUrhcPVkoamltFIpE/9Tla63u0trZLh6/5wuH/aCMclUgMiyg7n/uSFeNabTfo0OaC6Bb3q9epoqTDLnt8yGl/ZM4p3W5Qr9uii2a9ac2End6nxbAWw3Z41KTXNOnTboeYpJg6t/mHVo257EkrrmnUi7BZxx33X/U5KKFZ1pIZx62Z0G5HcDoScoOP2+LtouqMe9lV35SxqNmwFlsUZcx404pr+tyiz0HLrqKqQY+C9UBSty5jRUUpuHYJKSvaMGPZqKikkpysxSB+HTfgdoPuVrCmzYhRT8tZERbRbb9mg077rDlnxNRr1i8vbaeHxaQsGTXmRWd8Xr0elDXbYrv3OOePg9TU9Uh4WUFZQUVRSFJIZbNwsWRDXKO0aVTUabbgrJCwG3wMIaO+4YpnpU1bcVVU0kDkoLwFiUjS3vp3qIbKiuWCSrWiXCnJFdc1JVtFEoNC4ZBjL5+rKfVr1KhR4/vwlg0roVDIU0895f3vf7/GxkbhcFh3d7cnnnhCS0vLn/lx/+Jf/As/93M/t/nntbU1g4ODf2WP66677nDkud/1+qUnHBp59+aSbSySEI1FrRRmdcQympqavufHZ0rL4smwm2/dK9owZ+riNfH6y6KVBin9DpR+1JXQN21UZkTLSQlNwXVIVFUVVb0OqKo47bMuelxV2ZJREQnbvMuyy8a95ICfEJUI9jaa5YLF0ZCIkLAb/Zg3/Z5WW007qiSn3W4hYZc9tZkKajZkm3daNoqwHgcc8JNS2qRNa9CjxVazTpp21IJzWgzrc6vtHrRhbjM+HRLdNMXWabXqGsriGuSsKEgHRY4VayZVVYNl1/qgcDFntw9qNmjCq+acFpM06bCwmBAWXbDsijY7ddkrKuWSx2UsaLVNiyFJ7zPrzcBAe5M6zVZcteKKDXO2uE/OilknpM3YsCCuAaHNK6SoehvmTXhNNmhrXjMhqV1UQkqnDnukzShI67BXXJ1CKK0aLmmMdhgeGJEqxayuzCqVCqKRuOauZk1N7dYLMaOL19RlizWlfo0aNWp8H/7Kh5V//a//9Z978nH48GEHDx70j/7RP9LV1eWFF16QTCb99m//tkcffdThw4f19vZ+z49NJBI/UP340NCQj/7UIz77X77qsZOjtrcd1FDXKp1bthaasBQvkjokGo2qVCqb1z9QrVaNZd8Qbiy496F7nHjtkh97x0fUV7udPX7F1Jl1qXCHJr1Gy8/a40Mi6mQtIyQkLCQUKOKrdnpUxqJpR8w7Z7cPaNKnQZcLvipt1oRX5a1pMhBEavNS2pXkxdWLa7LbByQ0m3Naux267N8sQoyKK8naML/5Mc0G1Wky7ZgrnlGv201+TETcYb+hrGy7h3TZJ6UjOM24HLy8d7rqWXv8sBVXZS0GX+v652s2LG1aWESTASERYVEb5s04hrCSnEUXhcU06Amul8bNOSUS6PF2+yEZsxZd1KhPpz2qqnrdYsFZCY0aA/HenNPK8mYct+CiZVfc5BM67Ja3bsVVSy4Ii2xabpsMWjfpsq+bdVJcyk3+b9rtsuKqq55z0dc06rPVOzToctXz6nWpKzUph8tCkYipzFmR+HmTziqHyiIi+u0x4g7xaEomsy4VrdaU+jVq1KjxffgrH1b+8T/+xz72sY993/fZsmWLZ555xmOPPWZ5eXnzlOI3fuM3PPXUU37v937PP//n//yv+qH9hbn11lt1dXV55eVXHXv5ScV8RSwVdvujWz3zldeduvA1xbWI9tSghuaUpqYmiUTCiaXHTZaO2b6zyYFbDjjy3AXNqU5DHTs0plp8cfIxi6sXhEJh805rt1O7HWYcNeiuza9/Pa2zLqVTxpJlV+WsSmgQElaSV6dJnWYthi27srl3kQt8KDEp66YVbVhxxboJYRErrmoyqKKoXqewmIS4+eB6Y82kFlttmHfF03ocMOxeUQkhIft9zAl/YN20qCRCUjqCIsJp66Zc86K4BiPeo9V2JXntdrjsKWOe12yLhAZLLgWJpbhF510/1bnBGX8UtDrXqSrLWlZVtc2DQioG3aVBn2JQY7DNO4VFvOh/c9VzGvRq0KXDbnEpFVUXPCYqpcuNph016km7/ZA2I9KmrRqXt27VNVOO6bLPq37FjDdFRB30MwbdYcOcRj1GvFurbWYcc80LVlyVty5vVVyDcqhoPn/ZN8d+x97Uw7Yl3yUVaZWpLJucO2Ji5XdtbTxotnTGu+6+q3aqUqNGjRrfh7/yYaWjo0NHR8ef+36ZzHXd+7efTHzrz5VK5a/6Yf2lGRoaMjQ05Ic+dP2I/uTJk/74k0/qa9jtWuu4Y0uf0rN6k8a1HsXohtW681bqztt2S6u/9z993MjIyKYRF9ra2vT39cuXm2Xz7VL5Tlc9p9NeOctO+5wBt1s3rawQmF1LZhyzZkKnPVpsIfCDjHvZqKdF1SnasGZKnVZhkaB5eM2k10TErJm2Yky/Q4HD5YSkDkVZTfolAiPunJNyVuz1IZc8IW3GnFPmnERIm+0a9Wm3y7LLFpzRaZ82I7IWzDghbVajXmumnPMnOuwWlVKUUbDhvC/rd4et7g9qABbNOqEkb6dHFaWNelqnveIaZCy54KsIadQja0nOqhlvylgSEZWxaMS7tNtp1Dc06rXFfZtJo2nHZCzY5VENejzlfw4kdtcbp0tyclaNe8Wk1xVlzDsjb12Tfnt8SMG6OaeCiPT10sVWW22YsWJMkz5JbdpDO2yL3+NM8ctmHNdVulF75kb99Qc3B5Lh+O3OZh/33OR/0XMzd99z91v0LK9Ro0aNvxm8ZTsrd955p9bWVp/4xCf8q3/1rySTSb/1W7/lypUrHnnkkbfqYf0pYrGY6elpf/zJJ3WsHfLwve82d/O4l88/5s0LL7uytiKTXxWKbvj4T/yQT/zdH990ZXz7sm4kEjE00u/y8rx8ds2wu005CkryTvh9Y17Q42Yd9lgz6YwvBF6Qki3uddlTetxk2VVttkubUZTV44A5p8w7o16XkJhxL9owZ68Pu+YFi87Z7+PKsq55Qb1ObUYsu6wkZ8Jrll2x03sUbbjmef1us92DEpo3r6MmvKpRvz1+yBmfd8KnNOhRp0WLLaLi+txqp0ed8lkXPIaQgoy0KUPuRdUZnxPTEKSK6gx5mzY7ZcxL6VBVCWLdo2JStni7WcflghOnnd4bDGWrZhx3xG9rs0NcIyIueUJcg4iENiNGvEurbbKWDLnbtOOqqpsm3Kpy0KDcGlhy98iYtccPabPNggtCQtrtFBIx6TXNBjXq8opfdbOfkNRhvTohVI4ph3K2hO82Un2PxfJF51e+qb9pj2i4TrGcJZsQD9e5+a6BmlulRo0aNf4c3rJhpaOjwxNPPOEXfuEXPPDAA4rFon379vnSl77kpptueqse1vfk5ZdfVZntcOiG6wu33c1DPnjoH3nvwb8nW0irVqueufApO3f2fMcLz3cv6w4ODpgYnWGlpL9wq2vlF5Vk3ewTIurMO23WCRNes27GkosqSqAgZ9oxOSv2+ahuN5hzwqQ3LAVNxGsmgv2PWRvmJbU57r9acNaqCdc8r1GPXd4rZzVYsk2oKqkoa9RnxYRTPmvI29zsJzXoUlVRVdZlv9M+a9wrBtxp2D1Wjet1wA6PmPWmWW9aNupVv6zbjba6Pxg6rjnr8xqDK5pBd2i3R73OYNH1TSuuBEu2BavGzDmlrGiX96soOuWz4hoNuVuXvSqKclY1GXDCp5z1eWFRzQa126XbDVpt02xIVCIQxG0EiamkYfcGKaOYDbPiUlI67PAevQ466rdFxEGzQaXAX1NVFhLWqNekI6JShr3dhjlLLlksXZINL9mXfERruVcmt2i1MiVaKSkVywrlDcnOkHeMvJ/cOcViLQlUo0aNGt+Pt1QKd+utt/r617/+Vj6EP5disej4y+dsb3voO/T7EI3ENCavtzTv7Djk2MtPfkcE9Xst67Zvi3rzygmrVkSiYWPlF6SrM/rcFnTc9FlyUUTEVg+Iq7fkkmUXFKyLSUlpt25Sq+0Kso75HUmt6nVadFFeWotBWcsKNoTFNRo06VV7fNAWDwihyYBzvmzNuP0+aslFk44YcEinfYrSykGMtxjEiwfdZd2kUd/QYZeYpA3zTvoDVHW5wYpret2szQ4lWTF1+txi3aScFU0GtNmhSV/Q/9MsrkFUynmPWTV2/furzl4/rEm/GW/qdoMVV+UsWzelXpd5Z0x6Xb1Ou31A3rqohKwFF3zVVvdJad9M+myYFxZVUXTGFwInS1ZUnaR2XW7QZsSasc0hrU6ruEYVJeumrZkw7G5RKWkzUoEJt6wgHkrqiu4y5bBwKampsU021G+iNKWhJyIWi+sdGjY8PGS1OOPN/JlaEqhGjRo1/hxq3UB/Dvl8XjFX0VDX+n3fr6GuVTFf+VMvPN++rHvkxcflmkqqN5yVPreufm1Qa3iLcrnkaPl3hIQ06ddiqx63aDciIq7DbosuSps16E5UgyufbvPO2OLtbvH3REQtGTXhVXWaRNUFBtrnlZWUZTTqk7WgUb9+twmLedWvyFmV1KGqLKlNzopFF4IG4nLQm5MR16DXrU74lDXjslZMOazZFm/3/zTuFY167fSosoKqqnrdZhzRZb+LgfRth/dIalOwrqAkKmnDrJQ2u31QncbN/ZeQCKpabJGzot1OG2YsOOuypw243TbvVLAexKq3IOSqZ132lJQubQryVqXNmXI0sN7mDLs3EPLPW3RRswE5q5oN6ncoEO8dCPqZypr0y1oSd30pfNEFHfYIh8IoS4ZbpGLNGkLt8qUNxXJeY12bBo3ueeB2DQ0Nm36eyYmzYqlwLQlUo0aNGn8OtWHlz+FbVttvLcr+WaRzy9/3haeKavV6vHl466BKacL0qVk3hj+sKdynshyWs+xGf0dRGmFluc2F1rKiOi0K0qLqJDRpt9O0o4bdK6nZmgkZc1oMBcu21zuQywpO+rS4RvVBFHjCK2adtBIkiWKuO1+aDUhpV5Sx4ipCGvSIiotJbTpGslaEhR3094x52YpRi85bM26rd6gqK0hLarPonDVTWgyJazTlsCN+004Pi2u0Yswlj6uquMmPb+6IUBUWFRFXVVWW3xzeGnR70f8upcM275TUIhL4ZqKSUtrt9F7LrrriaQXrMhbNOWXWccPu02mPvDUJTarKUjosuqjTPimd2mxzxnFXPGOr631V9brEJC27HFz7XNTjZrnqqlxoVTLaoL2pT39xv/n0eb3ZG+Qjyxo6675jUKlWqy4vHXHonbtrpyo1atSo8edQG1b+HL7bavvdV0F8/xeew4cP+9zvfE1ltsP2tvdoqL/ubFmO/KGL1RctFEetFZaUZG31DkVpDXqUZIW163co2LSY3YwjL7uiXrd1U8FJSKuCDcuuatAjocl1U2xenVa9DrjiGQvOK1i3bsqUNzTqM+RuE16Rt6bXQRNeEddgwJ2WXZaxoKIYlAVG5a1acF7eiqiEvHU7PeyEz7jkKUUbdoiLSFg26pwvWjUuLCokLGtFmx0WXXTJkypKitJKspoN63WLgnVZi7h+tZLQrChjzmm9bhERs25GxnxwCtMk5HqqLCalUa8N8yJiOu121p9Iat809Y54WI8bhMUVZfQ4YMOssrKirGWjet0ipcNuH3DFs9ZN6rBHVcWyKy56XAgNuq0ZkzarEs0baLlJKtFoS+QOY9mjLuSf0pbqs3vn4HcMKq9fekK4e8Gdd733B/r8rVGjRo3/EagNK38B/iyrLd//hWdsbMznfudrOtYObS7nfosdPbeYmfyEYnqeakW1VNUciMji6uWsaNUn5LourtcB9XotuaTFVo16ZSzKW5O1pKIU7LN0iIjLWhaTBOumxTVIaAyivf22eLut3iGkqkG3kz4jbUazIXNOaTasoqxBt5K8tNmgGDBr0ms67bVm0hG/acjdYhJWXFWwbspR08F/bUbs8UOSWoMY8lfkLKso6HGzXrfIW5Wx4JKvu+RxDXplLWmzQ8GGNZOmHDbvrF3eb9Lr1jbFcn2ui+QKlgMxXURMnRY5K1I6gwHk6uZws8N7QNGGDXOobBYxZsxLm5LUqiij1VZxjca84LyvWHZZUVbJhnuaflYq1uLw2h8Il6JGqg9QCcsVM+KVFt2hfY6F/kBPasDupo8ZWzgvnVt2eemIcPeCj/7UI7UkUI0aNWr8BagNK38Bvp/V9vu98Hx3iujbiUXj7jvwAa+8/Krezp3GxytK1ayEZiUFFSVhUUVZRRvSZiU1mwyiw9+yx0bEjXrKgDs1BqK0qrKKgqqEooxZb+q0x4BDXvb/0WxIr4PK8qgE+yu3u+RxYTFrJkQk7PEhDbqV5UTUCQkb86KSgrRrOu2T1KrdTjEpUSmjnnLNc0Fq521GvEu9LmV57Xaq0+KqZ8w547Ing8XZ/VI6xTV4w29KatHroHXTphw27ZglF8EVT9vmHdqNbJYxhoSkTSnLq9cVDCDXbcAJjVLa3OYfetPv6bBLVVVE1KpxMXXK8lI61GkWcn24y0sLC8tYEBIy5B4VZRvmDMYOOufLzlcfM5S42a7+26ysz7mSftpGbkJLtfu65yb2st49EQ99cLulmZfNBnLBQ+/c7c673lsbVGrUqFHjL0htWPkL8mdZbf+sF57vlyL6FvuG7nT4zHOmVk5piLeZzh/V75CEBhlRBRkFV62bEtcoIq7LDSa8ImfJFvdrMmDU0wo27PZBYVFh9aooyZlzUlXFiHcpyWnQrc12yy4FJy/fOruhy34FaZNek7ciLGrQXZr0WTUeRKonDbnLuml1WqW0Bf0+4WD5t+KCr9rhPXZ6r3IgXItKCIsES8QDclZUkbPkgq+pKCpIK8pYdNGSURUlZUVb3e8eH1aQNu2Yca/qtl9EwqinhcQUpXXYpV6PvJXA5Bsx4XUhUZNeN+uEvDV1mkWlRCX0OWjDfNAHFJK3btFFp/yhbjcS/N1mnbDiip2Rh0w5rLWtxUjPLv2lW9zc/qil/LijM181nn1WsafLcnZKy3Dav/x//S/uvPNOxeJ1uWAikajtqNSoUaPGX5LasPKX4Luttt/vhecvkiLqbh5yxw0PeOzYbwqVWm0UikLVMMKiktZNColo1GfRJUUbtnunq5634IKCDfW6hUVd9RwiRrzLhnkrrlhxBWHbPahBtwmHxTXqc5u4BnNOKkgLBdbbHjfqsM+qMVF16rSY8KqqipCIBp1aDGnQq81OFz0moRklLYYltWnUZ8Lr4posuSCmXkVRJNgPKUhL6RLX6LKnDHu7uCZZS/LWRCX0BumbrEXrptRp0uOAuHqd9hj3miueNe+svDUx9bZ5UEXFuklRSTH1rnnBFd8QFrPqmt3eL65JXINJr1pwRkxSrwOaDYlpdNk3VJTMOGrVVRVl9To1G3RT6EeU41mjpSc1x3sc3Hu3UxeO+ObcmKHkQcONB4QTJQ39WYMDET/699/n1ltvxfXdp9qQUqNGjRr/56gNK/8n+Iu88PxFU0SdTQNuu3u/vm1tfufXPufU/CXTjhr0NiUFMUmzTtgwp9WIeaft9UFRSRc9IS5l2D2uetack7Lmcd2K22GXbR7SYaeygllvqtMoJKTFkJKcFVeV5RVlJLWpKIAWW/W5RZcbhRCVkrdqzIuS2oTFlBSC05KqjAVVFVEJLYbF1Fs3pSgnhLgGFWUJTaJi2u0w7bi8lSDFtGjDrP0+6g7/TFjEmgnP+0WXPaWibJt3SGjWpF/eqpQOWQsmvS4sot/tYlIyLpjyhjEvC4nY4T067RMWkbemQZcb/B3XPGvBeSPeLanDmBcRcsg/cs3zCjb0uVW/W6TNuBZ5XqG6ak/Tg0LxdblCxvvu+Qlnxl91efxJC5lpG3Vj/uHHP+7ue+6uXfPUqFGjxl8RtWHlB8RfJkX09o/c4aMf+4gf+dEf8Su//Ks+88nPm10/JSIW7JfcbMjbTDsqoVmX/SrKbvJx415ywVdEpZRkRcQNu080iPhGxOWCIWPdpBbbXPGsBr3qdSrYEJUw64SKspCIjAUxSSV56yY1GVSWU5RBVUKzZVdQERXXYotIsGBblFawbtVVa8ZlzKvXLSapyYAOe3TYZd5pPW5ys0+Yd9aYF+WtGfeqmyxqt0NEQkqHdnvNOWHOKUntwddNKssZcq+sJVPesGw08MKUpM1ZdtkOj7jBx0UlJDRaN2PeaWmzutxow7zTPiumXsaibd6hUb9GfV7za6562nLokpwVdeVm2xpuc+vwu61Wx1wef9I9ez7o/v0fcffugi+f+P+54yP3+NGP/+hf99OtRo0aNf6Hpjas/AD5y6aIYrGYt919t9J6zHNPv2xhatXW8L1awoNy5QVzlVN2eZ+CDTF1QWHfA6KSOuy25JLTPuesz+uwByERUWNesmZSl30i6sx607iXdNgjbSqI/VYVZVzxNELy1jTbYtU1eWuS2pXllZWkzW5GnK87Sq5fvzTq0WTQFc9ZcNYW9xt0u6QOJTkzjjnqt/Q6aNU19TrFpDTpFxK2xX0WnTfmJS22KMmKStrqHiMeNOk1s04qK1o2Kixh3ZScFQVpqybENGi33ZC7ZS1u7gBFJEQkNBkw55RVY5sJqou+Zqv79Tskpt6iC6JiDvgJV0PP2Ba/W0dyWL6YVaiuqoulFCutyqWKYjkvEo46euVpdf3r7rn3h9+S51qNGjVq/I9MbVj5AfKXSRF9u4/lhrYPGz70sE9+9T8Kl2LqIo26G7a5vPyMpDZN+pUVFKzLW1NVVVXRZNB273LK56wYM+YlTfpFJez2AYPuEBIWFnHJk655Qbud2oyYd9asE4pyety02c7c44B1ExZdCE5NMi56zLKr+twipkHWshZbNOqXtShv1YA77PEhdZptmFOnybB7XPO8M74gZ1mLrWadEBFTVdGoV50Wo5621w8Hy8IRaXP6HLTVA1psNeO4eWc1adbrgF4HELJmwrQjirJCaNSrLBvI6eIK0uadtmFOUosOO4OOnz4thhRtmHFMVVlv+CbRUFRDQ8rBbXdrbxiQTqeduPKiE+PPiTeEZOtWXZo57urKm7Uoco0aNWr8AKkNKz9g/iIporGxMZ/5ra9oWbnZnXsfFYvGGeDk6Gumzy2pq7aazV4UjlVsFOcVrEtqF5Oy4pq4emkzOu1VUTLodkPuccUz+twWlPsNiIgLiWq3U86KacckzNowZ8obCjbs8QEN+mTMu+hxs07qdwhVaTMWXTLpdX1uVadNSVaDHl32KQY9RU0G7PIBa8Zk1QuLKgUCuUb9m83GXfa56puBlXaXooyYpDWTXvWrBt2homzMi0ETc1lMyqyTGvW6yY/b5X1KMiCm3pL7nPY5Y15Slg+Wja/ZsCBjzrrpYHm4WVFWWUFKuwF3iYhaM2ktNK4aLkuk4lrqO7U29AqHw5qammzt320ie8zF4jNiA3MmG55x6KFaFLlGjRo1fpDUhpW/Br5fimhsbMx//A+/7I2nxww1h1y4dsr2gd32Dt3h/oPv88nJX5MpTOqO7JCNjllaOqunfLOykg2zMhZVlEXViUqZdkS9LvU6ddprzAsqiorulrcubda0N4REHfKzOu0Nlne3uehxS4G1tk6rDjulzTns/1C0gZCYeoPepmDdFd/Q7SaD7jTtmAmvmXfOzT6hxZCCVutmrBmzpKBBl0Z9dnmva1405F6TDpt1wm7vd8mT4prFLFs34aRPWzaqrKDDLrt90EmfBr1uMeztSrIqymKb3T19htwjY96Mk6Yd02aHep1CQaVjo15ZyzYsWDWmIZDtxdXrCO1SUbBh1lLljB1NN4qG/9sydX2y0VLukl1vb/YPf/4fGxkZqaV8atSoUeMHTG1Y+Wvku1NEhw8f9oe/9RXnny46EP2E/ugumeKyq2eOuHTtd917yyMeuvsDHnv+04prEeFKwnp1ykVf02qrXKC8LytpNuSyr1syaod3y1rWapsGvU74lNM+q163DbMG3GGX90lpV5ZTr0ujXl1BbHnBnHWTGnSpqKooiklpM6LNdkltQiLe9PvO+Ly0GQmNWmzVaa8Ww0JCQT9RTFyDZZfV65HSoayoYMOMo+p1SZs17lXjXlWUFkLGopCIpC7D7jbvjEWXrBkX16DHTfJWVDUEseissIiwmFbDwdXWOSuumnXSNu9UVpLQKGNB1rIll6ybdJefR9WSy9LVWc3RflOJlyzUHddcrHN57VWpaKtMadmppacsNL7un/39X7Bnz5637LlUo0aNGn+bqA0rbxHfUvG3rd7qQHOjnugNmlMdYFvj7U4uPeH5o1+1b8ctwg1Zs2uj4tUW0XDMaOXrWqpbtdohpk5B2oKz8tb1OqCipF6XhGahoGzwhE9JaBQRs80DGnTJWRGRsGZSRdEOD1twznhQTNjntiCGnJLUGqSARrXYosmAkLAX/e+63WjEe8SlvOw/BBK4OnnrqiqSWuS0bXYbzTsra0FFWaM+53zRovM67NHioGaD1kyYcULGnFlvimtQkA4GtKSYlIJ10SAxdN3lUicUnP4QUpLRbpdLHrdmXKttYuqV5eWsmnZEoz5XPavXAQXrrnpWqC5noe6YD9z3d68ntsafVC5VRGIhxa4xH/vx97jjjjve2idQjRo1avwtojasvEV8S8V/+96HffPqSwrF3ObbQqGQG9re7drkG7780n8VTrd4R8O/1BPdK9kSN7cy5uLCa5Zds+KqFVf1O+RGf0dMnUpwHZLQLK5eXEqfg874goi4eWcR3rTGUtXnNhVli84rygiJmHbUmslN4VtISERCIhCrxSRFRIx7WZsdGoIBadob2u1SsiGlw6pxDXq0GLJq0ozjIuqsGbdsVM6q3d6v017Y3IPZoU9e2pKLbvYJrbZ53r+Vt6qirKKiKBt0H9WrqKgoWTNhw5y4Rrf4Sed8SVXFNc+LiKvTYrcPSGqSDLVLhdpdrjxp1bil8AXbE7fa2r/D3bs/AG7dnlatVp0af8lCY9kD77hfsVisXf/UqFGjxl8TtWHlLeDbVfzRaFTPYIep01M6Gvs3482hUIhSyNrChj3J+/SWDyqVMsrLSb2RA6KRHvFKs+nqMc/5JTFJLQatGg+8JqngxKGEiLKi7R6y4Lxxr6q6Ltmv12nQ2yw657Kn1eu0zYO2ekBI2JpJC84acJc+t1k3oawgL23KER32qiiac0LJTiERiy655pu2eocV15TkNOq3btakV5UVbHW/Bt2ueFZCky3u02Zk88po1ZicVY36LDjrlD+034+Iips3adLrdvuAmHpVJXmrvlUdkDYnY1GH3eadtWZCg17D7pW3Zq8fMhy5y3jlVZdDT7ol/Anbw+8wHz/mVOzTQqm8teqcz7z4v1lcWpTL5yxkr6o2rhrZtstv//JnxerCbr5rt7vuuqO2WFujRo0aP2Bqw8pbwHer+AcHB0xfPWZy6ZL+thGhUEipUrScmROt1GsubZMtprWkOrXUd4O12Kpqsaw7tMdA6TYz3jTqGc2GVVXENaiqqKqa9KoVV93qZ4RFvOKXLTiv235d9gd+lW8adKd2OwN53FZLLulxk0kdll3R6+bNNua5QCJ3s0+YDRZZMxZEJHTY44pnzTgupUObEVnLFl1QkjPiIS22mXJYxoLd3q8gLWtRRVFSe6DfX9dtn4x5J3xK2izBDs28M+p12u4hzYZFxVVVzDrpWuB5adBrwmtKsmLqRETtD39Yc2hAPrSqPtohJKQczUqEY0qhLKmMpj0F+Y2KybEZXdHdQqV58eKa5PxOydIuvXX7pZriDn/uiCPP/a4PfeJd9u/fX+v9qVGjRo0fELVh5S3gu1X8zc3N9t2y0+mjF1ycW9aa7FMNlaznFlXKVSohdfGUlvpuoVBIoZQTilRt5JYQUq9H1HlToddMVQ8bcreqspxVUw5Lm9Npj6qyhqCx+Q2/Zd1E0MGzICymXqe0ae12Bk3JBdOOSum0bNRJn9ZquwVnlZUc8FN67NftRqO6jXralMOaDIhJyqrKWHTZU6LqDLvXoLdZcMZxvydrWVnRiivB1VPIcGCkjapTUZLQbNCdFl3QaY+IuDWTTvsjadMWXTTsHklt5p0x7mUZC0a8W1yjJgPSoQnLoVGlSkG4EpNKNMmWV82Xz8lZk4ss2wjlnK98Vbx/WXPDLsPJ+7V37nT+8ilXFk/YUn3QjZ3vkbVoeXTOjnv26GvY7etv/KGf/3u/ZPcNI9o7WmunLTVq1KjxA6A2rLwFfC8Vf19fn/r6euPjE2bGLysUCtZNSjW2qtjQlOgUCoVk8mtW00tC5ahUpM1KeWJzh2Nr9R1O+LQ3/GcpnarKUto16FGSNeWoa15w0meCq5vTVlxTVXWzvyOuXpuRwEGSU7CmrKCipEGvS55QsGHDvLKiq55WltWgV1hUWV7GknY73eYfqSqrqnjTpzTpV5Rx1H+xbly3m+z3MXnrUtosuuC8r6jTqtcBeavBGUrWhjlRdQbcad00SOmwYsw5X3LFM5ua/W43OOCnleWExXTbpyuxw4XS15yu/onXQ79mf+XD+sIHTTsiGgmbjR41Ez6mfs+c+x69z5Vnq0pr7WYzOavZVe3l3fYmPmhjIU0sKZsLO/HmCbm1spb0AR25efHFRkOpOzZPWz76U49slhjWqFGjRo3/PmrDylvE91LxNzc3a25utnt3yWsXv2aktUN2Lm5m4rRdoYcVSjmr6SXxSqNUvE05UraYuWStOmHdlFknNBoIvClb7PUR9TptmDHuVWd8Xsa8Lvv0ulVETNayMS8IiykrCQnbMC9txqpxzQYltdkwJ23WzT4hqs4FX3XZ162ZENdozmkZC7rdoCDtmuf1uU1VSUTUFm9XVZE2Y68P2+kRSa3WTCrJ2uIBZ3zeJY9vRqPDoqYdMeO4hGYrRl30uKqqlHYRcds8JGPOuJe1GNLnduNeVFa8rtgPhaWrNxtpuFelvGGhdNFlj1sIn7ARu2yofY9C8zHbd5X85D/9F37/P39BZXqrVGxAd8ew06NP2Fr3kIZEs/pqk/XskuJa3LkTF+zuud1A907R9aorK0/aeeigPf23e/3SEz77X76qq6urdsJSo0aNGn8F1IaVt4i/iIr/Z/7O3/WlTz/l8MRx57KP2xK+R6gck4q1gfnSeVdDz4lEIm6t/rTJyuvWq7O2e5dWW5VkVFU06NFht2uet8077fNRVWVRcWsmpE2r02zZZSU5dZplLIpISGqT1GbJJVVlzYaERez0sA0zclZFxFG1y/vFpSy7YtYJaTPa7FCwYc2EFVc1GTDs7coKQiLqdZl3xrLLht0rZ8WEl3XYrcmAK75hzMtabDPvtBVXNeg257SUdnF1hn1AwZo22807qUGvQXdKxRoVwutmKicczf++tviwurqUfGXdWMNjbrrlJsNDJQfvvtWdd92hra3N5Niv6y4c0t89Il/ZUK5UpMLXd4tCoZDGZJuFhYRitaKnZYtQKCQV/W89QdFIzKGRd3vs5KhXXn61NqzUqFGjxl8BtWHlLeQvouLv6elx4dL/6rVTv2m09LLh8D0Wi20WyxeMlV5VjZbc3/pP1Jd6xcI582sT2vLbwIJz1k2p0+KKZ/W51Rb32TCtKdQnXI1oi2yTLLdacU2/2+WtSWiybkqzoU3h2pzTYlJmHBPXgJBOe4MrpZKUDkVpRRs2zEjplNRm2SVZy8a9IiJuh/cEIru8og0JTVpsMel1dVo0G3LBY/LWgzblo4a8TZcbXfG0ooyF0DmtiV6Zwpqz1S9YqY7aMKsoq8+tdnpYXTylLhlXLlX0FG92Kf+0K8VvCNVvuOOGdylta/Rvf+V/0dDQsLkUm8lkLC4sGYglhEIh0VBCJByWqSx/x8+tUqYaIhQKX/+40rJILCwWSeD6ULO97aBjLz/phz5UizjXqFGjxn8vtWHlLeb7qfi5PtD8+1/9X/yLn/vXLhx/PBCkpYSjEe2Rne5p/VmlXNVs+ay5wqgh9yqGSlLVTiV5sXBCSFi2shgkZwYtuSAkrBzKC1UTBt3lnC+Zc1JU0qox0KRfoz6nfMaiC4bdY9W4ECLq5KVFxO3yflc8bdW4lA4ZS5aM2uo+vW7Ranug/a8EPdAJFQU5q0qBoC0irqxg1VXzzqgoazEkqU2LbWa9acYxYVFbEve6pe9hnQ2DDs983oXll80XR414t363qkvGRUNRlUJYNJwUj4ftj37QfOG02cqrBrq3mKsb+1NG4Uqlork9ZfbqGdXqe0XDMX3Nu03OHTEcv75bVK1WbVQXxOKx6wWS1aqx7BHbt+0Wjfy3z9VQ16qYr8jn87VhpUaNGjX+O6kNK/8X4btfOL+dO++807//5X/j5376XxtafdiulvtMzY5pLe+wtrFsuTAuEUtKaFGvW9a6qDrVUFUlVNQb2y+WT2oJDWiItNmo1otVkwrhNdG6ilS61WD1Tpc8qWjDoLuktFtwzqinzDurzXbtdumwG1VFWVPeEJUMFPiN9vtYkDjqddnXZSw54dNKcopyKvIWndNqK0JyVmQsyFkRkxISEhLVapvb/IxzvqQs75rnFa3rc5tOe1TKG47OPObBnT/pw3t+USa/5j8ee1RraUhrS5NqpiqqTjyW3PTWRMJRbeVtlitnnT55WuNdGxKJxHd8nxOJhKHhQVemx5xcesINbe820nSHiZXfdS73hF2Jd5nPXZIPL0vEksLCTi49oVS/YO/ge7/jc6Vzy2Kp8J/6GjVq1KhR4y9PbVj5G8Idd9zhR3/mfV74vXH5ypxcZU0utCQTWtGd2qErtc2FpSdMF97UZEhVVUVJtrog3FASLUflKstKuoRDYXXhevFU2fZd20xcndJTGra+NOX16q9LalW0oSRv2Nv1ukWb7Yqyll0KGpCrZhyTs+KCx7QYdt6XbJi7fuKgLG1Kn0P63abNiGP+ixlvSmgNdPyrYuq12SalQ1yjWac0GXTKZ13xjJROXfbb7X0ikcj1k51SRLSa8sLEH1gpTlsoXlaJ5DU1J6RLs+KlqOZk3+agUq1WLZYuiUViOpNbTayed0Pjnx4OY7GYe991h9Wrb5hZf83i3Kih5EFDrfucmv2K88tP6UyMqNZVbYQ3PL/wn5TqF917yyO6m//bbkq1WnV56YhD79xdO1WpUaNGjb8CasPK3yDe975HXTv9uxqXswZ0yy0UNGy0663fZSx/2ET+qJbKdjs8qi7UqlDdMF89Y2r9nJZov7H8y+LlBolok0g0KhKLi0QiwuGQaDghm5gSLUZ1VfdIajVWeUVVSbtd+hyQNisTnrNUuWTCa8a8pN9tkloQEhE35G7NBl3wVfU67fCwdruEhe3zYed8WVlOXD0ICytIC4m45OumHREWs2xUTNJt/oHmaJ+UFvXRNl3REedzT0mEGqWLJUczn9LdOaBDve2D/aaOjQlZVcnnNcW6Fas565UppUhGQ7zFRGVZQ3tMcT3+PZX511NaZ0Wnt4hF6oxOPKkcqmgbqBOOV8wuvCAXWSJSVt9+m/fd+jN6WoY3P75arXr90hPC3QvuvOs7T1tq1KhRo8b/OWrDyt8gvj1BdC2Utb7UYGvibiezX3Jk7Q8MRG5XjZYsFS8aqb5HTFLMQcvFyxqjg077Y6Vqzu2Rvy9RF1UIs7S0ZCOTcWL9Mybjr9sxeKPc4pRboj+svTzg+fVfVahmFG2oCzcSL5kqvG6pctEuj9rvY874gtM+7wY/YpsHnfY53W7UapuCtDmnxKTU67LFvS75upK8FsN63GTGmya9btllDXo16TfoDmumTDtie+NtGqKditkK5bDW0BbL8bN6Enu8sfiGciKrvi3myvJR3e13qSYT5paPmyvGJMIpjYkOzdFur+e+LNS57q4bP2otfup77pN8+/e4MNvh9n0PSERT8qWMqytvGr65yQd+7J3i8bgv/N7XHR57zPb0n05yffSnHqklgWrUqFHjr4jasPI3jG8liD7/R5/367/0aSvFK4rhdR2xEQ80/T8sVi45vvxFC4ULesM3SYZbrFemTVbekI1Nm4isqIY2dGZukIw0KaYzxsuvm4mesLXxNl2RIWdj33Q69hlbcg9pqPYKqbocflwinlSUUQmXbYvcp6Hcq1K5XobYaqs2O5RkLblouwdBs6HNGHPBurgmO73XaZ91xbOKcspymvTpcoNGPaiCpDYzkaPa6wfEIgmleMny+pxseUG+mBPJxPU07vDDB/6BTGXJ1177lOUlbu/+mF39t5lfHbeytqRayTpXfJ7OBT9y/88JhUKyiTN/5j7Jd6a0nrme0moIO/TQf0tpQV9f3/dNctWoUaNGjb8aasPK30CGhob8k3/6T7z52gWFY9tNLFy2I/5OEXHtdtpb94jLlZdcCX+DUEUhvqanrV9P673uP/BBn3r+l7w09aRGHRrqG2zfsstDPf+z4mSjSjoplEw5mfmSmcJlpeiazsh2Q/W3WK/OWCtPq4936E/d6OLyyxby5yy7qtuNVl2zbFRRRlyDnBVFWVFJrbZp0Bdc/1RsmDXpsBt9XFVFWNisk7IWpaItVEJilRYRMZniimgpKVdcV6xbJpTXH96jaTCsrm7WjdvvFI3EdDT2+92v/L99feKXvT324zqahlTq4q5l31DfkPbIwX9qZ+9Bj538jT93n+TPS2n9Rd+nRo0aNWr89/MDHVZ+6Zd+yVe/+lXHjx8Xj8etrKz8qfcZGxvzsz/7s5555hnJZNKP/uiP+g//4T+Ix+M/yIf2N55YLOY9P/SAp5cuSJXiwhk2KnNCYYb6ttsa2WlxftFE+ozW9ib1TSlH87/n0tIberY129t/u/cd+ofqE83q4imwum3VtWtjqtd2m5k57oJn1Cc7jea/bq18WS68LFPYkAq3///bu/vgpuo9j+Pv5DQPfUpIG2gIpLTy/CSUVqBVBO5okcsCgqtycRi8s7qjcxkVZGdh9Vr0WhXx4Q/RZRxZH2Zn1DuX1V0XdeF6WYTlucIVW1RQbNrS0AstTZPSJE1++wdL9sZWLEo5afN9zeSP/HJyzpeck54PJ7/f+RGJtDEwO5+G6HH8nV48TMOKnfOcJUyAc9RixU6AUxhJw0zWhckEjRoxpQipNmKE0TCRZjRhMILVkI7ZnEuOJR+TyuDr1t3EtDAdnMNo8JM9MJtoJILWns5Q1yiOhf/A8JH/P2R4zJDruOeXv2Xjf/4Dn7Q8x1jrdDSLxvARYxjnWcQgm+ey+5NcapTW5SwjhBDip+vVsBIOh7n99tspLS1l8+bNXV6PRqPMmzePgQMHsnv3bs6ePcvy5ctRSvHSSy/1Zmn9QlnZdA786QuidQEc9nQKsj0YDAaMRiNKKfyR02RmKMy2CA0dnxG0eClZXEps5zDGa78kN3twfF2tra3U1dXT1NCMQRnJz57EX4xfMMFzI19+/QWxcJRrLXfQ3hECpXGmvZpGrYaA1kRL5zecMHxMk6r+v3FAYU5RxbUsJWBoJKwCROgAFGmamZjSaIvWEVYBNKMRR4aLdnWWtFga6Vk5YAjREYjh06qwpBtpMzSAMuBvhSZ/LTlphdSzr9shwyPzJ7K49Df81zcv4xicxdhBpdgzBnK27RQHaj+Q/iRCCNEH9WpYefzxxwF44403un1927Zt1NTUUFdXh9vtBuD555/n7rvvprKyEpvN1pvl9Xn5+fks/fsFHP/2WQ7W/AG7wYPFlE64s4OW86cwZLUzY8o0Bg0axAeff8n9d97FgoUL+HzPt2SZHfH1NDQ0UHP4OCqYgSN9OOY0K51GjVhwC4eO7mKq/S6cpvEYIiYsWMhkILmG4XzW8Ra1sQMMS5/K0FgZjsgogrGz1PE/NLCfXMNwCphFPQdo4QTp2ElTaXwZ/YCgdhqjBqfUfrIj82nBS6cWIt1qI6I6qFEf0GT8MxmRXL6O7sCtTSHWGaOlo5Fayy5yjFYWTvl1wpDhiwo9o5iYOZ5p5Xkc++xPfCf9SYQQok/Ttc/K3r17mTBhQjyoAMyZM4dQKERVVRWzZ8/u8p5QKEQoFIo/9/v9V6XWZFVSUsI//W4lz/72n/ms7m0KM69DMxlxX+PE4xmNzWbjwImPMQ85xw0zFl3oV2E1Emi/cAv51tZWag4fJz00hCGDRsTvTXKm08KA7BzSzg7BGhyKp2AE7SE/3zV+RfP5k6RrNnIYTqFxJoUDR+OIjMIaysMSKWZQ23iO8W98we9p0+rJ1gZxrvMbWmPfEYieJqj5KHLNwWkfwtGmT9jV9gVp0UysFhPKVEd7hpc24+dMtd3CYCbT4K/hZPtHWB0W2tvaSTemY04zMSBzULefSaCjBYfTztK7fgV3If1JhBCij9M1rPh8PvLy8hLaHA4HZrMZn8/X7Xuefvrp+BUbcUFpaSmPPGPi7Vf/g3bf54zILcGWmUZD2zE+re06lHZy2RgO/r6KsUOmUVdXjwpmJAQVpRTftR/AYspgypBb8TeFONH0GSPzihnlmcQ3DdWE2s9j01w4MxdS3/nfTHDNod53ktZII2k2C5ONt9JxvonatG1YLZlYzBbO+BtJi2YwPvsWcrOHEomdJ2ZupzFrDxk2E2PHj2VIvouQUeH8ehZLpz1KLBYjGo2iiBGjk6+/PIGvJsSpyEFq6vZ1ubLS3Q3ZJKQIIUTfdtlhZd26dT8aFg4ePEhJSUmP1nfxBPnXlFLdtgOsXbuWVatWxZ/7/X48Hk+PttWfJQ63/eSSP31cuPHZ6+z7eisBbxaO9MSgcrT5Y0LpPgZ05jEwfSi5JhMNHUc4px1HGQxk5UGgvoUcPBiNBs6Hg0SiHZgzDIStZzBnZKIZ0hjScg1B7XOumz4ah2MABWNvobW1lSN7vmRv/X4UUZwTsnjgb5Zxy9w55OXlYTQaeWLNc4weejMGgwFN09A0LV57wbBCmryHsbbk8U1dDTPGRuIdbOWGbEII0T9ddlhZsWIFS5YsueQyBQUFPVqXy+Vi//79CW0tLS1EIpEuV1wuslgsMt/KD+jpUNqLNz77103vU9PUwviM+XSmBWnvbMF7vorOzDPMmryAPUe30R5pwZkxggGWHG74xTQ0TSMajfLJh5+S2W6jzl9Nh6GFoLmewpEuPJ7JZGVlEY1GOXXOw58jQf7xd78hNzc3XkskEiEQCAAkzHoMEAgEiHTEyLI6utQNYLfbGT9lFJ/u8fJt01E+9+7CmT1EbsgmhBD92GWHFafTidPpvCIbLy0tpbKyksbGRgYPvjAyZdu2bVgsFoqLi6/INlJRT4bSlpSU4FjrYFX9Wr6q+3d80Tw0k5Hh14xhnGc+efZ8Trd4+a6mimzrEIxmA2azOR5WrBlWMtPSiZgamDFmFr+YOCvhCoimabSH/VgyTAlB5WJ9Dkf3YeT7fWq643a7uWaCm7+cs9KYtZOGiEE60AohRD/Wq31WvF4vzc3NeL1eotEoR44cAWDEiBFkZWVRXl7OuHHjWLZsGRs2bKC5uZnVq1dz7733ykigq2D48OH86u6/Zd87PsrH/hpzmjX+kwrAuPzpHK/9F6pOb+GGqTfFw4imaeQNzWX3gS1oeee4tnBBQlCBnz6Zn8lkSuhT80M/E54Of8Uddy9g8W2LpAOtEEL0c8beXPljjz1GUVERFRUVBAIBioqKKCoq4tChQ8CFk97WrVuxWq1cf/313HHHHdx6660899xzvVmW+CtlZdMxDW7hqHc3mjExuw6yech1Omiw/ZFjwY+oqd+H98xX1NTv41jwIxpsfyTX6WCQLbHPUGLfkek/qSZj3hkOnPgYpdQl120ymbr8lCSEEKJ/Majvnw36GL/fj91up7W1Va7G/ESHDh3i3c1biZ12Mjyn66R8M+cVEwy0c3jPlxfmwLEYKSobQ2ZWBju3Vv3g++78u3k97mh9uTX9nHULIYTQ3+WcvyWsCODCT3YXRhIlBpLSsunxPiCRSNeOuz15X2/WJIQQom+SsCJ+su4CSW++T+91CyGE0MflnL9l1mWR4KdOytebk/nJRIFCCJHaerWDrRBCCCHEzyVhRQghhBBJTcKKEEIIIZKahBUhhBBCJDUJK0IIIYRIahJWhBBCCJHUJKwIIYQQIqlJWBFCCCFEUuvzN4W7eANev9+vcyVCCCGE6KmL5+2e3Ei/z4eVtrY2ADwez48sKYQQQohk09bWht1uv+QyfX5uoFgsxqlTp8jOzsZgMPTKNvx+Px6Ph7q6Opl/SCeyD/Qn+0Bf8vnrT/bBlaWUoq2tDbfbjdF46V4pff7KitFoZOjQoVdlWzabTQ5Qnck+0J/sA33J568/2QdXzo9dUblIOtgKIYQQIqlJWBFCCCFEUpOw0gMWi4WKigosFovepaQs2Qf6k32gL/n89Sf7QD99voOtEEIIIfo3ubIihBBCiKQmYUUIIYQQSU3CihBCCCGSmoQVIYQQQiQ1CStCCCGESGoSVn5EZWUlZWVlZGRkMGDAgG6X8Xq9zJ8/n8zMTJxOJw888ADhcPjqFppCCgoKMBgMCY81a9boXVa/9sorr1BYWIjVaqW4uJhdu3bpXVLKWLduXZfj3eVy6V1Wv/Xpp58yf/583G43BoOB999/P+F1pRTr1q3D7XaTnp7OrFmzqK6u1qfYFCJh5UeEw2Fuv/127r///m5fj0ajzJs3j2AwyO7du3nnnXfYsmULDz/88FWuNLU88cQTNDY2xh+PPvqo3iX1W++++y4PPfQQjzzyCIcPH2bGjBnMnTsXr9erd2kpY/z48QnH+9GjR/Uuqd8KBoNMmjSJjRs3dvv6s88+ywsvvMDGjRs5ePAgLpeLm2++OT6pruglSvTI66+/rux2e5f2Dz/8UBmNRtXQ0BBve/vtt5XFYlGtra1XscLUMWzYMPXiiy/qXUbKmDp1qrrvvvsS2saMGaPWrFmjU0WppaKiQk2aNEnvMlISoN57773481gsplwul3rmmWfibR0dHcput6tNmzbpUGHqkCsrP9PevXuZMGECbrc73jZnzhxCoRBVVVU6Vta/rV+/ntzcXCZPnkxlZaX87NZLwuEwVVVVlJeXJ7SXl5ezZ88enapKPcePH8ftdlNYWMiSJUv49ttv9S4pJZ08eRKfz5fwfbBYLMycOVO+D72sz8+6rDefz0deXl5Cm8PhwGw24/P5dKqqf3vwwQeZMmUKDoeDAwcOsHbtWk6ePMlrr72md2n9zpkzZ4hGo12O8by8PDm+r5Jp06bx1ltvMWrUKE6fPs2TTz5JWVkZ1dXV5Obm6l1eSrl4zHf3faitrdWjpJSRkldWuuuw9v3HoUOHerw+g8HQpU0p1W276N7l7JOVK1cyc+ZMrr32Wu655x42bdrE5s2bOXv2rM7/iv7r+8eyHN9Xz9y5c7ntttuYOHEiN910E1u3bgXgzTff1Lmy1CXfh6svJa+srFixgiVLllxymYKCgh6ty+VysX///oS2lpYWIpFIl/QtftjP2SfTp08H4MSJE/I/zSvM6XSiaVqXqyhNTU1yfOskMzOTiRMncvz4cb1LSTkXR2H5fD4GDx4cb5fvQ+9LybDidDpxOp1XZF2lpaVUVlbS2NgYP3i3bduGxWKhuLj4imwjFfycfXL48GGAhD8e4sowm80UFxezfft2Fi1aFG/fvn07Cxcu1LGy1BUKhTh27BgzZszQu5SUU1hYiMvlYvv27RQVFQEX+nXt3LmT9evX61xd/5aSYeVyeL1empub8Xq9RKNRjhw5AsCIESPIysqivLyccePGsWzZMjZs2EBzczOrV6/m3nvvxWaz6Vt8P7R371727dvH7NmzsdvtHDx4kJUrV7JgwQLy8/P1Lq9fWrVqFcuWLaOkpITS0lJeffVVvF4v9913n96lpYTVq1czf/588vPzaWpq4sknn8Tv97N8+XK9S+uXAoEAJ06ciD8/efIkR44cIScnh/z8fB566CGeeuopRo4cyciRI3nqqafIyMhg6dKlOladAnQejZT0li9froAujx07dsSXqa2tVfPmzVPp6ekqJydHrVixQnV0dOhXdD9WVVWlpk2bpux2u7JarWr06NGqoqJCBYNBvUvr115++WU1bNgwZTab1ZQpU9TOnTv1Lill3HnnnWrw4MHKZDIpt9utFi9erKqrq/Uuq9/asWNHt3/zly9frpS6MHy5oqJCuVwuZbFY1I033qiOHj2qb9EpwKCUUnoFJSGEEEKIH5OSo4GEEEII0XdIWBFCCCFEUpOwIoQQQoikJmFFCCGEEElNwooQQgghkpqEFSGEEEIkNQkrQgghhEhqElaEEEIIkdQkrAghhBAiqUlYEUIIIURSk7AihBBCiKT2v8F99NlEdpGMAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Only to generate colors\n", - "import numpy as np\n", - "\n", - "from dasf.utils.types import is_gpu_array\n", - "\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Check the data just to plot\n", - "if is_gpu_array(X):\n", - " X_cpu = X.get()\n", - "else:\n", - " X_cpu = X\n", - "\n", - "colors = cm.rainbow(np.linspace(0.0, 1.0, 1))\n", - "\n", - "plt.figure()\n", - "plt.scatter(\n", - " X_cpu[:, 0],\n", - " X_cpu[:, 1],\n", - " s=50,\n", - " c=colors[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - ")\n", - "plt.title(\"Dataset\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6fc4ccc3-e5e3-426d-a145-87ebdf80782b", - "metadata": {}, - "source": [ - "Once, we have the big picture of how our dataset is distributed, let's run two clustering algorithms to understand how it can be classified.\n", - "\n", - "For this tutorial, we decided to use KMeans and SOM (Kohonen's Self-Organized Map) as an example." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f41ed448-e3a8-47b1-8f2a-c92c6013892e", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.ml.cluster import KMeans\n", - "from dasf.ml.cluster import SOM\n", - "\n", - "kmeans = KMeans(n_clusters=3, max_iter=100)\n", - "som = SOM(x=1, y=3, input_len=2, num_epochs=100)" - ] - }, - { - "cell_type": "markdown", - "id": "9afda5c9-f59a-4974-b603-1e51ac69323c", - "metadata": {}, - "source": [ - "As we know our dataset defines 3 centers with 2 classes, we set KMeans `n_clusters` parameter with the same number of classes of our dataset. On the other hand, SOM is based on an activation map and it does not necessary needs a 1-D map with 3 activation points, but we want to use here to help the classification algorithm also. See that as we also know that our dataset contains two classes, the parameter `input_len` of SOM needs to be set as **2** (same number of classes).\n", - "\n", - "Now, it is time to `fit_predict` both classifiers. Let's analyze KMeans first." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "579b0eab-8096-4f00-a993-626f2c5bafc9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 315 ms, sys: 7.29 ms, total: 322 ms\n", - "Wall time: 326 ms\n" - ] - } - ], - "source": [ - "%time result_kmeans = kmeans.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "c2f9beae-6356-4565-bc81-23fe7586851f", - "metadata": {}, - "source": [ - "KMeans is a fast algorithm compared to SOM. For further reference, let's see the speed of the SOM algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3afbb17c-4ac0-435e-a8a4-b9bc4934e697", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 4min, sys: 210 ms, total: 4min\n", - "Wall time: 4min 1s\n" - ] - } - ], - "source": [ - "%time result_som = som.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "1ed936d8-6acd-4d04-8f3d-f13ac73f83d7", - "metadata": {}, - "source": [ - "Now, let's see the performance of each prediction. The first one is KMeans results." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0cc9cff9-b6f6-49d5-9edb-cc38d21113dd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from itertools import cycle\n", - "\n", - "def plot_results(X, result):\n", - " y_unique = np.unique(result)\n", - " \n", - " colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))\n", - " \n", - " for this_y, color in zip(y_unique, colors):\n", - " if is_gpu_array(X):\n", - " this_X = X[result == this_y].get()\n", - " else:\n", - " this_X = X[result == this_y]\n", - "\n", - " plt.scatter(\n", - " this_X[:, 0],\n", - " this_X[:, 1],\n", - " s=50,\n", - " c=color[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - " label=\"Class %s\" % this_y,\n", - " )\n", - "\n", - "plot_results(X, result_kmeans)" - ] - }, - { - "cell_type": "markdown", - "id": "6ba5eac4-86b8-493c-830c-aeb0c8cb99f4", - "metadata": {}, - "source": [ - "Now, let's see how SOM results look like." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "214a124b-4c23-40ae-8e0b-0ae7e2d4b8b9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_results(X, result_som)" - ] - }, - { - "cell_type": "markdown", - "id": "13e71923-abe0-43ee-90c7-5cbef839fd97", - "metadata": {}, - "source": [ - "As we can see, the results do not seem similar but they are accurated.\n", - "\n", - "The idea behind this tutorial is not to exaplain how both algorithms work, but how can you use DASF framework the same way you use the most famous Machine Learning libraries.\n", - "\n", - "If you are curious, try to run the same code using a machine with GPU. Compare the results and see if the behaviour is the same!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/_sources/tutorials/Tutorial_2.ipynb.txt b/docs/_sources/tutorials/Tutorial_2.ipynb.txt deleted file mode 100644 index 95a4059..0000000 --- a/docs/_sources/tutorials/Tutorial_2.ipynb.txt +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "66892f2a-1c92-47d6-a9c1-8cb26940f251", - "metadata": {}, - "source": [ - "### Tutorial 2 - How to extend DASF Datasets\n", - "\n", - "In this tutorial, we will teach you how you can extend DASF datasets to be loaded dynamically to all architetcure.\n", - "\n", - "For this specific scenario we will use DASF Array Dataset class to show you how you can create a dataset like this using a simple NPY file.\n", - "\n", - "To start, the first step is create and save a simple NPY file to be loaded by the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c38bd978-012f-4599-a249-b20577e3700f", - "metadata": {}, - "outputs": [], - "source": [ - "### Serialize a simple array\n", - "import numpy as np\n", - "\n", - "data = np.random.random((20, 20, 20))\n", - "\n", - "np.save(\"data.npy\", data)" - ] - }, - { - "cell_type": "markdown", - "id": "ca8b5760-bbca-4613-9958-bbaacd96ced6", - "metadata": {}, - "source": [ - "Once we have the file saved, we can create our own array dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7774cf93-a8ff-46d6-b378-0a5eff657b69", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "\n", - "dataset = DatasetArray(name=\"My Saved NPY\", root=\"data.npy\")" - ] - }, - { - "cell_type": "markdown", - "id": "a8f92ca2-ecb6-4241-b2be-726146056259", - "metadata": {}, - "source": [ - "From this moment, our dataset is not loaded yet. To load the data from NPY file, we need to run the function `load`. This object has the same dynamic generator from the previous tutorial. Here we are using a ipykernel with a GPU, then we are expecting the dataset to lad a CuPy Array. Let's see if this is true." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4b055d3f-0d96-4f1e-bcde-20bd825976bf", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.load()" - ] - }, - { - "cell_type": "markdown", - "id": "1bd783da-95e1-42cf-8ab9-da5035641a05", - "metadata": {}, - "source": [ - "Once it is loaded, we can slice the dataset and see what is the type of each slice." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d25dbe78-7abf-4ef2-a564-ed72a0bc79e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cupy._core.core.ndarray" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "d733a245-13f1-45b3-8b69-0d09a496c61d", - "metadata": {}, - "source": [ - "What should I do if I'm using a GPU but I want to load a Numpy array?\n", - "\n", - "All the datasets have a protected load wrapper for each platform. The code discovers which platform you are in and bind the method to its respective protected mathod.\n", - "\n", - "In other words, if you are using `load` in a GPU environment as we are doing here, in fact you are executing the protected method called `_load_gpu`.\n", - "\n", - "Then to load Numpy arrays, all you need to do is call directly `_load_cpu`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3ab10622-978a-4e2e-af2c-6cd9c4f6c626", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._load_cpu()\n", - "\n", - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "b2a38825-1bd7-42f0-8d65-b09553e273b4", - "metadata": {}, - "source": [ - "If you need to handle a Dask array in a multi clustered environment, you can use the protected lazy methods called `_lazy_*`.\n", - "\n", - "For datasets, the respective methods for `load` are `_lazy_load_cpu` and `_lazy_load_gpu`. Both returns a Dask Array but with different metadata.\n", - "\n", - "Let's see how it looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e5ec3104-8087-406c-aa86-71961740fcce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array.core.Array" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._lazy_load_cpu()\n", - "\n", - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "b6bc2c21-ce64-42a3-a72a-b9f895decdb9", - "metadata": {}, - "source": [ - "See how the internal array of this Dask dataset looks." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "92a556a1-643d-449e-8f5b-b5223972dbb0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dataset[:2, :2, :2]._meta)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/_sources/tutorials/Tutorial_3.ipynb.txt b/docs/_sources/tutorials/Tutorial_3.ipynb.txt deleted file mode 100644 index c92d213..0000000 --- a/docs/_sources/tutorials/Tutorial_3.ipynb.txt +++ /dev/null @@ -1,252 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a9de8fc4-806e-404b-becc-e79954a1ac9c", - "metadata": {}, - "source": [ - "### Tutorial 3 - How Create Your Own Trasform\n", - "\n", - "In this tutorial, we will show you how DASF organize the structure APIs to generate code for targeted to each architecture.\n", - "\n", - "We will also show you how you can create your own object to and generate code dynamically to each platform.\n", - "\n", - "For this, let's use the same code we had used in **Tutorial 2**. Check how you can create `data.npy` before continue.\n", - "\n", - "Then, we need to define our dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "eec84166-3f6c-420b-81e6-71f9c126a440", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "\n", - "dataset = DatasetArray(name=\"My Saved NPY\", root=\"data.npy\")" - ] - }, - { - "cell_type": "markdown", - "id": "92f243e0-64e9-4457-a18c-ea5c21740a6a", - "metadata": {}, - "source": [ - "Here, we want to create a transform to multiple the data by the same data.\n", - "\n", - "First, let's inpect how the data looks like. We are using a GPU, so it will require to fetch data from GPU to CPU. If you are using a CPU, you just need to print the data." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ef2d45e3-121c-40f1-b8d7-f84735945ee1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.22139306, 0.18095083],\n", - " [0.78598473, 0.28964964]])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.load()\n", - "\n", - "dataset[:2, :2, 0].get()" - ] - }, - { - "cell_type": "markdown", - "id": "aa7e4593-a22d-49db-976f-00b8b8d19de5", - "metadata": {}, - "source": [ - "Now, let's create our own transform called **Multiply**. To generate the code targeted to the running platform, we need to import and set the respective decorator. So, the code will generate the function `transform` for us dynamically. To clarigy even more, we can include some a `print` call in each function." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c53d25f4-3806-46d1-8f94-ac580ee46821", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import Transform\n", - "\n", - "\n", - "class Multiply(Transform):\n", - " def _lazy_transform_cpu(self, X):\n", - " print(\"Lazy CPU\")\n", - " return X * X\n", - " \n", - " def _lazy_transform_gpu(self, X):\n", - " print(\"Lazy GPU\")\n", - " return X * X\n", - " \n", - " def _transform_cpu(self, X):\n", - " print(\"CPU\")\n", - " return X * X\n", - " \n", - " def _transform_gpu(self, X):\n", - " print(\"GPU\")\n", - " return X * X\n", - "\n", - "multiply = Multiply()" - ] - }, - { - "cell_type": "markdown", - "id": "43efcdec-2639-4775-9abc-a92cf6fa7a8f", - "metadata": {}, - "source": [ - "Now, we can transform our dataset and see what happens." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "875e2a54-5506-4226-bf4e-58353408e4e2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU\n" - ] - } - ], - "source": [ - "result = multiply.transform(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "fbf295d3-9c2b-4b4f-ae7c-60c332b2842d", - "metadata": {}, - "source": [ - "See it triggered the GPU local function. Now, let's see and compare what is the content of `result` variable." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e1f6c244-51a2-42fd-964a-0a96fc4dc169", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.04901489, 0.0327432 ],\n", - " [0.61777199, 0.08389691]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result[:2, :2, 0].get()" - ] - }, - { - "cell_type": "markdown", - "id": "0f400794-0660-4ee5-b8bb-eed6e6aad03f", - "metadata": {}, - "source": [ - "See that the result is exactly the dataset multiplied by itself. The values confirm that. Now, what happens if I would like to run CPU code instead of GPU? If I want that, I need to call directly each protected method directly." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "962d9a55-3de1-43e2-86a9-d0c489dd2e90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.04901489, 0.0327432 ],\n", - " [0.61777199, 0.08389691]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._load_cpu()\n", - "\n", - "result = multiply._transform_cpu(dataset)\n", - "\n", - "result[:2, :2, 0]" - ] - }, - { - "cell_type": "markdown", - "id": "9ab0786f-5268-40b0-8845-01900c350098", - "metadata": {}, - "source": [ - "See now that the code triggered the CPU function obviously.\n", - "\n", - "Actually, if you pay attention, the implementation of each function are equal. Then, this class can be reduced to:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7d311947-30bf-4ab2-bf43-8f1fa6dafc0a", - "metadata": {}, - "outputs": [], - "source": [ - "class Multiply2(Transform):\n", - " def transform(self, X):\n", - " return X * X" - ] - }, - { - "cell_type": "markdown", - "id": "bcae55ea-951d-4807-9f0f-569e031fcb23", - "metadata": {}, - "source": [ - "Without decorator and all the other functions. The reason why we have all the diferentiations is that we know we will have different data manipulation for most cases." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/_sources/tutorials/Tutorial_4.ipynb.txt b/docs/_sources/tutorials/Tutorial_4.ipynb.txt deleted file mode 100644 index 9ef5401..0000000 --- a/docs/_sources/tutorials/Tutorial_4.ipynb.txt +++ /dev/null @@ -1,464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "664bfe29-de10-4c07-b23e-75bdb9a330d3", - "metadata": {}, - "source": [ - "### Tutorial 4 - How Create an Agnostic Pipeline\n", - "\n", - "In this tutorial, we will show you how convert a simple code structure into a advanced and agnostic pipeline based on DAGs.\n", - "\n", - "For this, we still can use the **Tutorial 1** with a simple Machine Learning script. There we use `make_blobs` to generate a dataset and them we cluster it using two algorithms: KMeans and SOM.\n", - "\n", - "First, let's generate and save our data (you can use DASF or Scikit-learn). The objective here is just to generate some labeled data and use the `DatasetLabeled` as an example." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2d3ae542-b03f-49e0-86fc-1cdbf19b5a30", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from dasf.datasets import make_blobs\n", - "\n", - "n_samples = 100000\n", - "n_bins = 3\n", - "\n", - "# Generate 3 blobs with 2 classes where the second blob contains\n", - "# half positive samples and half negative samples. Probability in this\n", - "# blob is therefore 0.5.\n", - "centers = [(-6, -6), (0, 0), (9, 1)]\n", - "X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)\n", - "\n", - "np.save(\"X.npy\", X)\n", - "np.save(\"y.npy\", y)" - ] - }, - { - "cell_type": "markdown", - "id": "d90e6b0b-236d-4cab-951b-e973e780c94f", - "metadata": {}, - "source": [ - "Now, let's import our `DatasetLabeled` and assign each file to the respective type." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1d5d935f-065e-4bda-af74-38b148924463", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "from dasf.datasets import DatasetLabeled\n", - "\n", - "\n", - "class MyMakeBlobs(DatasetLabeled):\n", - " def __init__(self):\n", - " super().__init__(name=\"My Own make_blobs()\", download=False)\n", - " \n", - " # Let's assign the train and val data.\n", - " self._train = DatasetArray(name=\"X\", download=False, root=\"X.npy\", chunks=(5000, 2))\n", - " self._val = DatasetArray(name=\"y\", download=False, root=\"y.npy\", chunks=(5000))\n", - "\n", - "make_blobs = MyMakeBlobs()" - ] - }, - { - "cell_type": "markdown", - "id": "37bcedef-d5cb-40ee-a691-dcb2d1636083", - "metadata": {}, - "source": [ - "To reduce the variability and as an example, we can normalize the data to help the algorithms to fit better." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6db9235f-7580-48dd-a9e9-b946a9570a86", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import Normalize\n", - "\n", - "normalize = Normalize()" - ] - }, - { - "cell_type": "markdown", - "id": "eaaed4e1-b799-4448-99ac-922094b18988", - "metadata": {}, - "source": [ - "After, creating our dataset and the normalization transformation, we can start the executor. For this example, we can use Dask." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "aa39f7b9-ffb1-4c93-9293-ecda4139d8cf", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.pipeline.executors import DaskPipelineExecutor\n", - "\n", - "dask = DaskPipelineExecutor(local=True, use_gpu=False)" - ] - }, - { - "cell_type": "markdown", - "id": "0bd517c3-8de0-46a4-89c4-78314ffe6491", - "metadata": {}, - "source": [ - "Now, it is time to create our pipeline objects. We can copy and paste the same code used previously." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d3fc983-f6c8-4a66-9a22-fc1a6b5ccd7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: CuPy could not be imported\n", - "WARNING: CuPy could not be imported\n", - "WARNING: CuPy could not be imported\n" - ] - } - ], - "source": [ - "from dasf.ml.cluster import KMeans\n", - "from dasf.ml.cluster import SOM\n", - "\n", - "kmeans = KMeans(n_clusters=3, max_iter=100)\n", - "som = SOM(x=1, y=3, input_len=2, num_epochs=100)" - ] - }, - { - "cell_type": "markdown", - "id": "07966b83-22d5-4bb6-8592-1ec54f365417", - "metadata": {}, - "source": [ - "As we want to reuse the data after the pipeline execution, we need to persist the data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "297c99be-394d-4c5f-8dd6-04baab437b5f", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import PersistDaskData\n", - "\n", - "persist_kmeans = PersistDaskData()\n", - "persist_som = PersistDaskData()" - ] - }, - { - "cell_type": "markdown", - "id": "7b26ce69-d483-4ec3-a570-cfa761299983", - "metadata": {}, - "source": [ - "Then, we generate the pipeline and connect all the pieces in one single DAG.\n", - "\n", - "Pay attention that we are passing the our fresh executor `dask` to the pipeline by specifying the parameter `executor=`.\n", - "\n", - "To connect all the objects, we use the function `add()` that returns the pipeline itself. The function inputs can be refered as an argument.\n", - "\n", - "At the end, we can visualize the DAG using `visualize()` method. It will plot a image that represents the graph. Let's use one single line to do everything. It should be simple and easy to understand." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bbee98cf-2425-431f-87af-342fcf0c00c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A KMeans and SOM Pipeline\n", - "\n", - "\n", - "\n", - "180662574\n", - "\n", - "Normalize.transform\n", - "\n", - "\n", - "\n", - "128329762\n", - "\n", - "KMeans.fit_predict\n", - "\n", - "\n", - "\n", - "180662574->128329762\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "128329900\n", - "\n", - "SOM.fit_predict\n", - "\n", - "\n", - "\n", - "180662574->128329900\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "182107497\n", - "\n", - "DatasetArray.load\n", - "\n", - "\n", - "\n", - "182107497->180662574\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "16599060\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "128329762->16599060\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "16599058\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "128329900->16599058\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dasf.pipeline import Pipeline\n", - "\n", - "pipeline = Pipeline(\"A KMeans and SOM Pipeline\", executor=dask)\n", - "\n", - "pipeline.add(normalize, X=make_blobs._train) \\\n", - " .add(kmeans.fit_predict, X=normalize) \\\n", - " .add(som.fit_predict, X=normalize) \\\n", - " .add(persist_kmeans, X=kmeans.fit_predict) \\\n", - " .add(persist_som, X=som.fit_predict) \\\n", - " .visualize()" - ] - }, - { - "cell_type": "markdown", - "id": "14815700-715b-4e17-92e0-1203b107c7c8", - "metadata": {}, - "source": [ - "It is time to run our new pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c2dd0613-ccbf-4543-bd01-ba3dda54fbf7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-11-25 04:36:49+0000] INFO - Beginning pipeline run for 'A KMeans and SOM Pipeline'\n", - "[2022-11-25 04:36:49+0000] INFO - Task 'DatasetArray.load': Starting task run...\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'DatasetArray.load': Finished task run\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Starting task run...\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Finished task run\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'KMeans.fit_predict': Starting task run...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.8/dist-packages/dask/base.py:1367: UserWarning: Running on a single-machine scheduler when a distributed client is active might lead to unexpected results.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-11-25 04:37:00+0000] INFO - Task 'KMeans.fit_predict': Finished task run\n", - "[2022-11-25 04:37:00+0000] INFO - Task 'SOM.fit_predict': Starting task run...\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'SOM.fit_predict': Finished task run\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2022-11-25 04:37:23+0000] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2022-11-25 04:37:23+0000] INFO - Pipeline run successfully\n", - "CPU times: user 23.2 s, sys: 1.71 s, total: 24.9 s\n", - "Wall time: 33.2 s\n" - ] - } - ], - "source": [ - "%time pipeline.run()" - ] - }, - { - "cell_type": "markdown", - "id": "eeb8f9cb-de3e-4e9e-bf9f-3d89f59e99ba", - "metadata": {}, - "source": [ - "Notice that our pipeline returns two methods instead of one. To capture the result of some node, you can easily pass the same function or object to the pipeline function `get_result_from()`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f3412afd-4f97-4219-954a-5d95cf92d629", - "metadata": {}, - "outputs": [], - "source": [ - "result_kmeans = pipeline.get_result_from(persist_kmeans).compute()\n", - "result_som = pipeline.get_result_from(persist_som).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "801faa7c-a1c4-48c9-9c6c-de2c480a74dc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from itertools import cycle\n", - "\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_results(X, result):\n", - " y_unique = np.unique(result)\n", - " \n", - " colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))\n", - " \n", - " for this_y, color in zip(y_unique, colors):\n", - " this_X = X[result == this_y]\n", - " plt.scatter(\n", - " this_X[:, 0],\n", - " this_X[:, 1],\n", - " s=50,\n", - " c=color[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - " label=\"Class %s\" % this_y,\n", - " )\n", - "\n", - "plot_results(make_blobs._train, result_kmeans)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e3fe23cb-d5ae-4255-9437-42cddb89d004", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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- -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - - const [docName, title, anchor, descr, score, _filename] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = docUrlRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = docUrlRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) - ); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - // array of [docname, title, anchor, descr, score, filename] - let results = []; - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // lookup as object - objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - results = results.reverse(); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/_static/sphinx_highlight.js b/docs/_static/sphinx_highlight.js deleted file mode 100644 index aae669d..0000000 --- a/docs/_static/sphinx_highlight.js +++ /dev/null @@ -1,144 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/api.html b/docs/api.html deleted file mode 100644 index 65badb1..0000000 --- a/docs/api.html +++ /dev/null @@ -1,128 +0,0 @@ - - - - - - - DASF API Reference — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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- -
-

DASF API Reference

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/datasets/base/index.html b/docs/autoapi/dasf/datasets/base/index.html deleted file mode 100644 index bbd9843..0000000 --- a/docs/autoapi/dasf/datasets/base/index.html +++ /dev/null @@ -1,1419 +0,0 @@ - - - - - - - dasf.datasets.base — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.datasets.base

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Module Contents

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Classes

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Dataset

Class representing a generic dataset based on a TargeteredTransform

DatasetArray

Class representing an dataset wich is defined as an array of a defined

DatasetZarr

Class representing an dataset wich is defined as a Zarr array of a

DatasetHDF5

Class representing an dataset wich is defined as a HDF5 dataset of a

DatasetXarray

Class representing an dataset wich is defined as a Xarray dataset of a

DatasetLabeled

A class representing a labeled dataset. Each item is a 2-element tuple,

DatasetDataFrame

Class representing an dataset wich is defined as a dataframe.

DatasetParquet

Class representing an dataset wich is defined as a Parquet.

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-
-class dasf.datasets.base.Dataset(name, download=False, root=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.TargeteredTransform

-

Class representing a generic dataset based on a TargeteredTransform -object.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
*argstype

Additional arguments without keys.

-
-
**kwargstype

Additional keyworkded arguments.

-
-
-
-
-__set_dataset_cache_dir()
-

Generate cached directory in $HOME to store dataset(s).

-
- -
-
-download()[source]
-

Skeleton of the download method.

-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

Generic __getitem__() function based on internal data.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetArray(name, download=False, root=None, chunks='auto')[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as an array of a defined -shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-__operator_check__(other)[source]
-
- -
-
-__repr__()[source]
-

Return a class representation based on internal array.

-
- -
-
-__array__(dtype=None)[source]
-
- -
-
-__array_ufunc__(ufunc, method, *inputs, **kwargs)[source]
-
- -
-
-__check_op_input(in_data)
-

Return the proper type of data for operation

-
>>> Result = DatasetArray + Numpy; or
->>> Result = DatasetArray + DatasetArray
-
-
-
-
Parameters
-
-
in_dataAny

Input data to be analyzed.

-
-
-
-
-
Returns
-
-
dataAny

A data representing the internal array or the class itself.

-
-
-
-
- -
-
-__add__(other)[source]
-

Internal function of adding two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A sum with two arrays.

-
-
-
-
- -
-
-__sub__(other)[source]
-

Internal function of subtracting two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A subtraction of two arrays.

-
-
-
-
- -
-
-__mul__(other)[source]
-

Internal function of multiplication two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A multiplication of two arrays.

-
-
-
-
- -
-
-__div__(other)[source]
-

Internal function of division two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A division of two arrays.

-
-
-
-
- -
-
-__copy_attrs_from_data()
-

Extends metadata to new transformed object (after operations).

-
- -
-
-__npy_header()
-

Read an array header from a filelike object.

-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetZarr(name, download=False, root=None, chunks=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a Zarr array of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-__repr__()[source]
-

Return a class representation based on internal array.

-
- -
-
-__check_op_input(in_data)
-

Return the proper type of data for operation

-
>>> Result = DatasetZarr + Numpy; or
->>> Result = DatasetZarr + DatasetZarr
-
-
-
-
Parameters
-
-
in_dataAny

Input data to be analyzed.

-
-
-
-
-
Returns
-
-
dataAny

A data representing the internal array or the class itself.

-
-
-
-
- -
-
-__add__(other)[source]
-

Internal function of adding two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A sum with two arrays.

-
-
-
-
- -
-
-__sub__(other)[source]
-

Internal function of subtracting two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A subtraction of two arrays.

-
-
-
-
- -
-
-__mul__(other)[source]
-

Internal function of multiplication two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A multiplication of two arrays.

-
-
-
-
- -
-
-__div__(other)[source]
-

Internal function of division two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A division of two arrays.

-
-
-
-
- -
-
-__copy_attrs_from_data()
-

Extends metadata to new transformed object (after operations).

-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetHDF5(name, download=False, root=None, chunks='auto', dataset_path=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a HDF5 dataset of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
dataset_pathstr

Relative path of the internal HDF5 dataset (the default is None).

-
-
-
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp=None, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function) (placeholder).

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (str) –

    • -

  • root (str) –

    • -

  • dataset_path (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetXarray(name, download=False, root=None, chunks=None, data_var=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a Xarray dataset of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
data_varAny

Key (or index) of the internal Xarray dataset (the default is None).

-
-
-
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function based on internal Xarray data.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetLabeled(name, download=False, root=None, chunks='auto')[source]
-

Bases: Dataset

-

A class representing a labeled dataset. Each item is a 2-element tuple, -where the first element is a array of data and the second element is the -respective label. The items can be accessed from dataset[x].

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-

Attributes

-
-
__chunkstype

Description of attribute __chunks.

-
-
-
-
-download()[source]
-

Download the dataset.

-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data -(train and labels).

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Tuple

A Future object that will return a tuple: (data, label).

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
-
Returns
-
-
Tuple

A 2-element tuple: (data, label)

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function for data and labeled data together.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetDataFrame(name, download=True, root=None, chunks='auto')[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a dataframe.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. CuDF).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. pandas).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function based on internal dataframe.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.base.DatasetParquet(name, download=True, root=None, chunks='auto')[source]
-

Bases: DatasetDataFrame

-

Class representing an dataset wich is defined as a Parquet.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. CuDF).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. pandas).

-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-
- - -
-
- -
-
-
-
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dasf.datasets.blobs

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Module Contents

-
-

Classes

- - - - - - -

make_blobs

Singleton class used to generate isotropic Gaussian blobs for clustering.

-
-
-class dasf.datasets.blobs.make_blobs
-

Singleton class used to generate isotropic Gaussian blobs for clustering. -It automatically selects the implementation based on hardware and available -libraries and return a container suitable for it (cupy, numpy, cupy+dask or -numpy+dask).

-

The class implements __call__ being a callable object.

-
-
-_lazy_make_blobs_cpu(**kwargs)
-
- -
-
-_lazy_make_blobs_gpu(**kwargs)
-
- -
-
-_make_blobs_cpu(**kwargs)
-
- -
-
-_make_blobs_gpu(**kwargs)
-
- -
-
-__call__(**kwargs)
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/datasets/datasets/index.html b/docs/autoapi/dasf/datasets/datasets/index.html deleted file mode 100644 index 4c3aa6f..0000000 --- a/docs/autoapi/dasf/datasets/datasets/index.html +++ /dev/null @@ -1,245 +0,0 @@ - - - - - - - dasf.datasets.datasets — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.datasets.datasets

-
-

Module Contents

-
-

Classes

- - - - - - - - - - - - -

make_blobs

make_classification

make_regression

-
-
-class dasf.datasets.datasets.make_blobs[source]
-
-
-_lazy_make_blobs_cpu(**kwargs)[source]
-
- -
-
-_lazy_make_blobs_gpu(**kwargs)[source]
-
- -
-
-_make_blobs_cpu(**kwargs)[source]
-
- -
-
-_make_blobs_gpu(**kwargs)[source]
-
- -
-
-__call__(**kwargs)[source]
-
- -
- -
-
-class dasf.datasets.datasets.make_classification[source]
-
-
-_lazy_make_classification_cpu(**kwargs)[source]
-
- -
-
-_lazy_make_classification_gpu(**kwargs)[source]
-
- -
-
-_make_classification_cpu(**kwargs)[source]
-
- -
-
-_make_classification_gpu(**kwargs)[source]
-
- -
-
-__call__(**kwargs)[source]
-
- -
- -
-
-class dasf.datasets.datasets.make_regression[source]
-
-
-_lazy_make_regression_cpu(**kwargs)[source]
-
- -
-
-_lazy_make_regression_gpu(**kwargs)[source]
-
- -
-
-_make_regression_cpu(**kwargs)[source]
-
- -
-
-_make_regression_gpu(**kwargs)[source]
-
- -
-
-__call__(**kwargs)[source]
-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/datasets/download/index.html b/docs/autoapi/dasf/datasets/download/index.html deleted file mode 100644 index 7e77126..0000000 --- a/docs/autoapi/dasf/datasets/download/index.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - dasf.datasets.download — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.datasets.download

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-

Module Contents

-
-

Classes

- - - - - - - - - -

DownloadWget

Dataset downloadable via wget.

DownloadGDrive

Dataset downloadable via Google Drive.

-
-
-class dasf.datasets.download.DownloadWget(url, filename, root, download=True)[source]
-

Bases: dasf.datasets.base.Dataset

-

Dataset downloadable via wget.

-
-

Parameters

-
-
urlstr

The url to fetch the resource.

-
-
filenamestr

Name of the file.

-
-
rootstr

Directory to store the downloaded file.

-
-
downloadbool

If it the dataset must be downloaded (the default is True).

-
-
-
-
-download()[source]
-

Download the dataset.

-
- -
-
-
Parameters:
-
    -

  • url (str) –

    • -

  • filename (str) –

    • -

  • root (str) –

    • -

  • download (bool) –

    • -
-
-
-
- -
-
-class dasf.datasets.download.DownloadGDrive(google_file_id, filename, root, download=True)[source]
-

Bases: dasf.datasets.base.Dataset

-

Dataset downloadable via Google Drive.

-
-

Parameters

-
-
google_file_idstr

Id of the google drive resource.

-
-
filenamestr

Name of the file.

-
-
rootstr

Directory to store the downloaded file.

-
-
downloadbool

If it the dataset must be downloaded (the default is True).

-
-
-
-
-download()[source]
-

Download the dataset.

-
- -
-
-
Parameters:
-
    -

  • google_file_id (str) –

    • -

  • filename (str) –

    • -

  • root (str) –

    • -

  • download (bool) –

    • -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/datasets/index.html b/docs/autoapi/dasf/datasets/index.html deleted file mode 100644 index 6694ece..0000000 --- a/docs/autoapi/dasf/datasets/index.html +++ /dev/null @@ -1,1494 +0,0 @@ - - - - - - - dasf.datasets — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.datasets

-
-

Submodules

-
- -
-
-
-

Package Contents

-
-

Classes

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset

Class representing a generic dataset based on a TargeteredTransform

DatasetArray

Class representing an dataset wich is defined as an array of a defined

DatasetZarr

Class representing an dataset wich is defined as a Zarr array of a

DatasetHDF5

Class representing an dataset wich is defined as a HDF5 dataset of a

DatasetXarray

Class representing an dataset wich is defined as a Xarray dataset of a

DatasetLabeled

A class representing a labeled dataset. Each item is a 2-element tuple,

DatasetDataFrame

Class representing an dataset wich is defined as a dataframe.

DatasetParquet

Class representing an dataset wich is defined as a Parquet.

make_blobs

make_classification

-
-
-class dasf.datasets.Dataset(name, download=False, root=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.TargeteredTransform

-

Class representing a generic dataset based on a TargeteredTransform -object.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
*argstype

Additional arguments without keys.

-
-
**kwargstype

Additional keyworkded arguments.

-
-
-
-
-__set_dataset_cache_dir()
-

Generate cached directory in $HOME to store dataset(s).

-
- -
-
-download()[source]
-

Skeleton of the download method.

-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

Generic __getitem__() function based on internal data.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetArray(name, download=False, root=None, chunks='auto')[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as an array of a defined -shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-__operator_check__(other)[source]
-
- -
-
-__repr__()[source]
-

Return a class representation based on internal array.

-
- -
-
-__array__(dtype=None)[source]
-
- -
-
-__array_ufunc__(ufunc, method, *inputs, **kwargs)[source]
-
- -
-
-__check_op_input(in_data)
-

Return the proper type of data for operation

-
>>> Result = DatasetArray + Numpy; or
->>> Result = DatasetArray + DatasetArray
-
-
-
-
Parameters
-
-
in_dataAny

Input data to be analyzed.

-
-
-
-
-
Returns
-
-
dataAny

A data representing the internal array or the class itself.

-
-
-
-
- -
-
-__add__(other)[source]
-

Internal function of adding two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A sum with two arrays.

-
-
-
-
- -
-
-__sub__(other)[source]
-

Internal function of subtracting two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A subtraction of two arrays.

-
-
-
-
- -
-
-__mul__(other)[source]
-

Internal function of multiplication two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A multiplication of two arrays.

-
-
-
-
- -
-
-__div__(other)[source]
-

Internal function of division two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A division of two arrays.

-
-
-
-
- -
-
-__copy_attrs_from_data()
-

Extends metadata to new transformed object (after operations).

-
- -
-
-__npy_header()
-

Read an array header from a filelike object.

-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetZarr(name, download=False, root=None, chunks=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a Zarr array of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-__repr__()[source]
-

Return a class representation based on internal array.

-
- -
-
-__check_op_input(in_data)
-

Return the proper type of data for operation

-
>>> Result = DatasetZarr + Numpy; or
->>> Result = DatasetZarr + DatasetZarr
-
-
-
-
Parameters
-
-
in_dataAny

Input data to be analyzed.

-
-
-
-
-
Returns
-
-
dataAny

A data representing the internal array or the class itself.

-
-
-
-
- -
-
-__add__(other)[source]
-

Internal function of adding two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A sum with two arrays.

-
-
-
-
- -
-
-__sub__(other)[source]
-

Internal function of subtracting two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A subtraction of two arrays.

-
-
-
-
- -
-
-__mul__(other)[source]
-

Internal function of multiplication two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A multiplication of two arrays.

-
-
-
-
- -
-
-__div__(other)[source]
-

Internal function of division two array datasets.

-
-
Parameters
-
-
otherAny

A data representing an array or a DatasetArray.

-
-
-
-
-
Returns
-
-
DatasetArry

A division of two arrays.

-
-
-
-
- -
-
-__copy_attrs_from_data()
-

Extends metadata to new transformed object (after operations).

-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetHDF5(name, download=False, root=None, chunks='auto', dataset_path=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a HDF5 dataset of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
dataset_pathstr

Relative path of the internal HDF5 dataset (the default is None).

-
-
-
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Any

The data (or a Future load object, for _lazy operations).

-
-
-
-
- -
-
-_load(xp=None, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function) (placeholder).

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (str) –

    • -

  • root (str) –

    • -

  • dataset_path (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetXarray(name, download=False, root=None, chunks=None, data_var=None)[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a Xarray dataset of a -defined shape.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
data_varAny

Key (or index) of the internal Xarray dataset (the default is None).

-
-
-
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function based on internal Xarray data.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetLabeled(name, download=False, root=None, chunks='auto')[source]
-

Bases: Dataset

-

A class representing a labeled dataset. Each item is a 2-element tuple, -where the first element is a array of data and the second element is the -respective label. The items can be accessed from dataset[x].

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-

Attributes

-
-
__chunkstype

Description of attribute __chunks.

-
-
-
-
-download()[source]
-

Download the dataset.

-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data -(train and labels).

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load(xp, **kwargs)[source]
-

Lazy load the dataset using an CPU dask container.

-
-
Parameters
-
-
xptype

Library used to load the file. It must follow numpy library.

-
-
**kwargstype

Additional keyworkded arguments to the load.

-
-
-
-
-
Returns
-
-
Tuple

A Future object that will return a tuple: (data, label).

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load(xp, **kwargs)[source]
-

Load data using CPU container.

-
-
Parameters
-
-
xpModule

A module that load data (implement load function)

-
-
**kwargstype

Additional kwargs to xp.load function.

-
-
-
-
-
Returns
-
-
Tuple

A 2-element tuple: (data, label)

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. cupy).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. numpy).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function for data and labeled data together.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetDataFrame(name, download=True, root=None, chunks='auto')[source]
-

Bases: Dataset

-

Class representing an dataset wich is defined as a dataframe.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-property shape: tuple
-

Returns the shape of an array.

-
-
Returns
-
-
tuple

A tuple with the shape.

-
-
-
-
-
Return type:
-

tuple

-
-
-
- -
-
-_load_meta()[source]
-

Load metadata to inspect.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-inspect_metadata()[source]
-

Return a dictionary with all metadata information from data.

-
-
Returns
-
-
dict

A dictionary with metadata information.

-
-
-
-
-
Return type:
-

dict

-
-
-
- -
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. CuDF).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. pandas).

-
- -
-
-load()[source]
-

Placeholder for load function.

-
- -
-
-__len__()[source]
-

Return internal data length.

-
-
Return type:
-

int

-
-
-
- -
-
-__getitem__(idx)[source]
-

A __getitem__() function based on internal dataframe.

-
-
Parameters
-
-
idxAny

Key of the fetched data. It can be an integer or a tuple.

-
-
-
-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.DatasetParquet(name, download=True, root=None, chunks='auto')[source]
-

Bases: DatasetDataFrame

-

Class representing an dataset wich is defined as a Parquet.

-
-

Parameters

-
-
namestr

Symbolic name of the dataset.

-
-
downloadbool

If the dataset must be downloaded (the default is False).

-
-
rootstr

Root download directory (the default is None).

-
-
chunksAny

Number of blocks of the array (the default is “auto”).

-
-
-
-
-_lazy_load_gpu()[source]
-

Load data with GPU container + DASK. (It does not load immediattly)

-
- -
-
-_lazy_load_cpu()[source]
-

Load data with CPU container + DASK. (It does not load immediattly)

-
- -
-
-_load_gpu()[source]
-

Load data with GPU container (e.g. CuDF).

-
- -
-
-_load_cpu()[source]
-

Load data with CPU container (e.g. pandas).

-
- -
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • download (bool) –

    • -

  • root (str) –

    • -
-
-
-
- -
-
-class dasf.datasets.make_blobs[source]
-
-
-_lazy_make_blobs_cpu(**kwargs)[source]
-
- -
-
-_lazy_make_blobs_gpu(**kwargs)[source]
-
- -
-
-_make_blobs_cpu(**kwargs)[source]
-
- -
-
-_make_blobs_gpu(**kwargs)[source]
-
- -
-
-__call__(**kwargs)[source]
-
- -
- -
-
-class dasf.datasets.make_classification[source]
-
-
-_lazy_make_classification_cpu(**kwargs)[source]
-
- -
-
-_lazy_make_classification_gpu(**kwargs)[source]
-
- -
-
-_make_classification_cpu(**kwargs)[source]
-
- -
-
-_make_classification_gpu(**kwargs)[source]
-
- -
-
-__call__(**kwargs)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
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dasf.debug.debug

-
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Module Contents

-
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Classes

- - - - - - - - - -

Debug

Print information about an operator (shape, datatype, etc.), and return

VisualizeDaskData

Visualize DASK data from an operator.

-
-
-class dasf.debug.debug.Debug[source]
-

Print information about an operator (shape, datatype, etc.), and return -the self object reference.

-
-

Parameters

-
-
namestr

Name of the operator.

-
-
**kwargstype

Additional keyworkded arguments to Operator.

-
-
-
-
-display(X)[source]
-
- -
-
- -
-
-class dasf.debug.debug.VisualizeDaskData(filename=None)[source]
-

Visualize DASK data from an operator.

-
-

Parameters

-
-
filenamestr

A path to save the DASK visualization (the default is None).

-
-
**kwargstype

Additional keyworkded arguments to Operator.

-
-
-
-
-display(X)[source]
-
- -
-
-
Parameters:
-

filename (str) –

-
-
-
- -
-
-
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/debug/index.html b/docs/autoapi/dasf/debug/index.html deleted file mode 100644 index f991415..0000000 --- a/docs/autoapi/dasf/debug/index.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - dasf.debug — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.debug

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Submodules

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Package Contents

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Classes

- - - - - - - - - -

Debug

Print information about an operator (shape, datatype, etc.), and return

VisualizeDaskData

Visualize DASK data from an operator.

-
-
-class dasf.debug.Debug[source]
-

Print information about an operator (shape, datatype, etc.), and return -the self object reference.

-
-

Parameters

-
-
namestr

Name of the operator.

-
-
**kwargstype

Additional keyworkded arguments to Operator.

-
-
-
-
-display(X)[source]
-
- -
-
- -
-
-class dasf.debug.VisualizeDaskData(filename=None)[source]
-

Visualize DASK data from an operator.

-
-

Parameters

-
-
filenamestr

A path to save the DASK visualization (the default is None).

-
-
**kwargstype

Additional keyworkded arguments to Operator.

-
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-
-display(X)[source]
-
- -
-
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filename (str) –

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dasf.feature_extraction.histogram

-
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Module Contents

-
-

Classes

- - - - - - -

Histogram

Operator to extract the histogram of a data.

-
-
-class dasf.feature_extraction.histogram.Histogram(bins=None, range=None, normed=False, weights=None, density=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.TargeteredTransform, dasf.transforms.base.Transform

-

Operator to extract the histogram of a data.

-
-

Parameters

-
-
binsOptional[int]

Number of bins (the default is None).

-
-
rangetuple

2-element tuple with the lower and upper range of the bins. If not -provided, range is simply (X.min(), X.max()) (the default is None).

-
-
normedbool

If the historgram must be normalized (the default is False).

-
-
weightstype

An array of weights, of the same shape as X. Each value in a only -contributes its associated weight towards the bin count -(the default is None).

-
-
densitytype

If False, the result will contain the number of samples in each bin. -If True, the result is the value of the probability density function -at the bin, normalized such that the integral over the range is 1 -(the default is None).

-
-
-
-
-

Attributes

-

bins -range -normed -weights -density

-
-
-__lazy_transform_generic(X)
-
- -
-
-__transform_generic(X, xp)
-
- -
-
-_lazy_transform_cpu(X)[source]
-
- -
-
-_lazy_transform_gpu(X, **kwargs)[source]
-
- -
-
-_transform_cpu(X, **kwargs)[source]
-
- -
-
-_transform_gpu(X, **kwargs)[source]
-
- -
-
-
Parameters:
-
    -

  • bins (int) –

    • -

  • range (tuple) –

    • -

  • normed (bool) –

    • -
-
-
-
- -
-
-
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-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/feature_extraction/index.html b/docs/autoapi/dasf/feature_extraction/index.html deleted file mode 100644 index 2d2fbf9..0000000 --- a/docs/autoapi/dasf/feature_extraction/index.html +++ /dev/null @@ -1,391 +0,0 @@ - - - - - - - dasf.feature_extraction — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.feature_extraction

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Submodules

-
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Package Contents

-
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Classes

- - - - - - - - - - - - - - - - - - - - - -

ConcatenateToArray

Concatenate data from different Arrays into a single array.

SampleDataframe

Return a subset with random samples of the original dataset.

GetSubeCubeArray

Get a subcube with x% of samples from the original one.

SliceDataframe

Get a slice of a cube. An inline slice is a section over the x-axis.

GetSubDataframe

Get the first x% samples from the dataset.

Histogram

Operator to extract the histogram of a data.

-
-
-class dasf.feature_extraction.ConcatenateToArray(flatten=False)[source]
-

Bases: dasf.transforms.base.Transform

-

Concatenate data from different Arrays into a single array.

-
-

Parameters

-
-
flattenbool

If the arrays must be flatten prior concatenating. If False, the -arrays must share the shape of last dimansions in order to be -concatenated (the default is False).

-
-
-
-
-__transform_generic(xp, **kwargs)
-
- -
-
-_transform_cpu(**kwargs)[source]
-
- -
-
-_transform_gpu(**kwargs)[source]
-
- -
-
-
Parameters:
-

flatten (bool) –

-
-
-
- -
-
-class dasf.feature_extraction.SampleDataframe(percent)[source]
-

Return a subset with random samples of the original dataset.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the dataset.

-
-
-
-
-run(X)[source]
-

Returns a subset with random samples from the dataset X.

-
-
Parameters
-
-
XAny

The dataset.

-
-
-
-
-
Returns
-
-
Any

The sampled subset.

-
-
-
-
- -
-
-
Parameters:
-

percent (float) –

-
-
-
- -
-
-class dasf.feature_extraction.GetSubeCubeArray(percent)[source]
-

Get a subcube with x% of samples from the original one.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the cube.

-
-
-
-
-transform(X)[source]
-
- -
-
-
Parameters:
-

percent (float) –

-
-
-
- -
-
-class dasf.feature_extraction.SliceDataframe(iline_index)[source]
-

Bases: dasf.transforms.base.Fit

-

Get a slice of a cube. An inline slice is a section over the x-axis.

-
-

Parameters

-
-
iline_indexint

The index of the inline to get.

-
-
-
-
-fit(X, y)[source]
-
- -
-
-
Parameters:
-

iline_index (int) –

-
-
-
- -
-
-class dasf.feature_extraction.GetSubDataframe(percent)[source]
-

Get the first x% samples from the dataset.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the dataframe.

-
-
-
-
-transform(X)[source]
-
- -
-
-
Parameters:
-

percent (float) –

-
-
-
- -
-
-class dasf.feature_extraction.Histogram(bins=None, range=None, normed=False, weights=None, density=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.TargeteredTransform, dasf.transforms.base.Transform

-

Operator to extract the histogram of a data.

-
-

Parameters

-
-
binsOptional[int]

Number of bins (the default is None).

-
-
rangetuple

2-element tuple with the lower and upper range of the bins. If not -provided, range is simply (X.min(), X.max()) (the default is None).

-
-
normedbool

If the historgram must be normalized (the default is False).

-
-
weightstype

An array of weights, of the same shape as X. Each value in a only -contributes its associated weight towards the bin count -(the default is None).

-
-
densitytype

If False, the result will contain the number of samples in each bin. -If True, the result is the value of the probability density function -at the bin, normalized such that the integral over the range is 1 -(the default is None).

-
-
-
-
-

Attributes

-

bins -range -normed -weights -density

-
-
-__lazy_transform_generic(X)
-
- -
-
-__transform_generic(X, xp)
-
- -
-
-_lazy_transform_cpu(X)[source]
-
- -
-
-_lazy_transform_gpu(X, **kwargs)[source]
-
- -
-
-_transform_cpu(X, **kwargs)[source]
-
- -
-
-_transform_gpu(X, **kwargs)[source]
-
- -
-
-
Parameters:
-
    -

  • bins (int) –

    • -

  • range (tuple) –

    • -

  • normed (bool) –

    • -
-
-
-
- -
-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/feature_extraction/transform/index.html b/docs/autoapi/dasf/feature_extraction/transform/index.html deleted file mode 100644 index bbc9cf4..0000000 --- a/docs/autoapi/dasf/feature_extraction/transform/index.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - dasf.feature_extraction.transform — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.feature_extraction.transform

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Module Contents

-
-

Classes

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ConcatenateToArray

Concatenate data from different Arrays into a single array.

SampleDataframe

Return a subset with random samples of the original dataset.

GetSubeCubeArray

Get a subcube with x% of samples from the original one.

SliceDataframe

Get a slice of a cube. An inline slice is a section over the x-axis.

GetSubDataframe

Get the first x% samples from the dataset.

-
-
-class dasf.feature_extraction.transform.ConcatenateToArray(flatten=False)[source]
-

Bases: dasf.transforms.base.Transform

-

Concatenate data from different Arrays into a single array.

-
-

Parameters

-
-
flattenbool

If the arrays must be flatten prior concatenating. If False, the -arrays must share the shape of last dimansions in order to be -concatenated (the default is False).

-
-
-
-
-__transform_generic(xp, **kwargs)
-
- -
-
-_transform_cpu(**kwargs)[source]
-
- -
-
-_transform_gpu(**kwargs)[source]
-
- -
-
-
Parameters:
-

flatten (bool) –

-
-
-
- -
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-class dasf.feature_extraction.transform.SampleDataframe(percent)[source]
-

Return a subset with random samples of the original dataset.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the dataset.

-
-
-
-
-run(X)[source]
-

Returns a subset with random samples from the dataset X.

-
-
Parameters
-
-
XAny

The dataset.

-
-
-
-
-
Returns
-
-
Any

The sampled subset.

-
-
-
-
- -
-
-
Parameters:
-

percent (float) –

-
-
-
- -
-
-class dasf.feature_extraction.transform.GetSubeCubeArray(percent)[source]
-

Get a subcube with x% of samples from the original one.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the cube.

-
-
-
-
-transform(X)[source]
-
- -
-
-
Parameters:
-

percent (float) –

-
-
-
- -
-
-class dasf.feature_extraction.transform.SliceDataframe(iline_index)[source]
-

Bases: dasf.transforms.base.Fit

-

Get a slice of a cube. An inline slice is a section over the x-axis.

-
-

Parameters

-
-
iline_indexint

The index of the inline to get.

-
-
-
-
-fit(X, y)[source]
-
- -
-
-
Parameters:
-

iline_index (int) –

-
-
-
- -
-
-class dasf.feature_extraction.transform.GetSubDataframe(percent)[source]
-

Get the first x% samples from the dataset.

-
-

Parameters

-
-
percentfloat

Percentage of the samples to get from the dataframe.

-
-
-
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-transform(X)[source]
-
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-
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Parameters:
-

percent (float) –

-
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dasf.ml.cluster.agglomerative

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Module Contents

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Classes

- - - - - - -

AgglomerativeClustering

Agglomerative Clustering

-
-
-class dasf.ml.cluster.agglomerative.AgglomerativeClustering(n_clusters=2, affinity='euclidean', connectivity=None, linkage='single', memory=None, compute_full_tree='auto', distance_threshold=None, compute_distances=False, handle=None, verbose=False, n_neighbors=10, output_type=None, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Agglomerative Clustering

-

Recursively merges the pair of clusters that minimally increases -a given linkage distance.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint or None, default=2

The number of clusters to find. It must be None if -distance_threshold is not None.

-
-
affinitystr or callable, default=’euclidean’

Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, -“manhattan”, “cosine”, or “precomputed”. -If linkage is “ward”, only “euclidean” is accepted. -If “precomputed”, a distance matrix (instead of a similarity matrix) -is needed as input for the fit method.

-
-
memorystr or object with the joblib.Memory interface, default=None

Used to cache the output of the computation of the tree. -By default, no caching is done. If a string is given, it is the -path to the caching directory.

-
-
connectivityarray-like or callable, default=None

Connectivity matrix. Defines for each sample the neighboring -samples following a given structure of the data. -This can be a connectivity matrix itself or a callable that transforms -the data into a connectivity matrix, such as derived from -kneighbors_graph. Default is None, i.e, the -hierarchical clustering algorithm is unstructured.

-
-
compute_full_tree‘auto’ or bool, default=’auto’

Stop early the construction of the tree at n_clusters. This is -useful to decrease computation time if the number of clusters is not -small compared to the number of samples. This option is useful only -when specifying a connectivity matrix. Note also that when varying the -number of clusters and using caching, it may be advantageous to compute -the full tree. It must be True if distance_threshold is not -None. By default compute_full_tree is “auto”, which is equivalent -to True when distance_threshold is not None or that n_clusters -is inferior to the maximum between 100 or 0.02 * n_samples. -Otherwise, “auto” is equivalent to False.

-
-
linkage{‘ward’, ‘complete’, ‘average’, ‘single’}, default=’ward’

Which linkage criterion to use. The linkage criterion determines which -distance to use between sets of observation. The algorithm will merge -the pairs of cluster that minimize this criterion.

-
    -

  • ‘ward’ minimizes the variance of the clusters being merged.

    • -

  • ‘average’ uses the average of the distances of each observation of -the two sets.

    • -

  • ‘complete’ or ‘maximum’ linkage uses the maximum distances between -all observations of the two sets.

    • -

  • ‘single’ uses the minimum of the distances between all observations -of the two sets.

    • -
-
-

New in version 0.20: Added the ‘single’ option

-
-
-
distance_thresholdfloat, default=None

The linkage distance threshold above which, clusters will not be -merged. If not None, n_clusters must be None and -compute_full_tree must be True.

-
-

New in version 0.21.

-
-
-
compute_distancesbool, default=False

Computes distances between clusters even if distance_threshold is not -used. This can be used to make dendrogram visualization, but introduces -a computational and memory overhead.

-
-

New in version 0.24.

-
-
-
n_neighborsint, default = 15

The number of neighbors to compute when connectivity = “knn”

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import AgglomerativeClustering
->>> import numpy as np
->>> X = np.array([[1, 2], [1, 4], [1, 0],
-...               [4, 2], [4, 4], [4, 0]])
->>> clustering = AgglomerativeClustering().fit(X)
->>> clustering
-AgglomerativeClustering()
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html -- https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering

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-_fit_cpu(X, y=None, convert_dtype=True)[source]
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-_fit_gpu(X, y=None, convert_dtype=True)[source]
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dasf.ml.cluster.dbscan

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Module Contents

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Classes

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DBSCAN

Perform DBSCAN clustering from vector array or distance matrix.

-
-
-class dasf.ml.cluster.dbscan.DBSCAN(eps=0.5, leaf_size=40, metric='euclidean', min_samples=5, p=None, output_type=None, calc_core_sample_indices=True, verbose=False, **kwargs)[source]
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Perform DBSCAN clustering from vector array or distance matrix.

-

DBSCAN - Density-Based Spatial Clustering of Applications with Noise. -Finds core samples of high density and expands clusters from them. -Good for data which contains clusters of similar density.

-

Read more in the User Guide.

-
-

Parameters

-
-
epsfloat, default=0.5

The maximum distance between two samples for one to be considered -as in the neighborhood of the other. This is not a maximum bound -on the distances of points within a cluster. This is the most -important DBSCAN parameter to choose appropriately for your data set -and distance function.

-
-
min_samplesint, default=5

The number of samples (or total weight) in a neighborhood for a point -to be considered as a core point. This includes the point itself.

-
-
metricstring, or callable, default=’euclidean’

The metric to use when calculating distance between instances in a -feature array. If metric is a string or callable, it must be one of -the options allowed by sklearn.metrics.pairwise_distances() for -its metric parameter. -If metric is “precomputed”, X is assumed to be a distance matrix and -must be square. X may be a Glossary, in which -case only “nonzero” elements may be considered neighbors for DBSCAN.

-
-

New in version 0.17: metric precomputed to accept precomputed sparse matrix.

-
-
-
metric_paramsdict, default=None

Additional keyword arguments for the metric function.

-
-

New in version 0.19.

-
-
-
algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’

The algorithm to be used by the NearestNeighbors module -to compute pointwise distances and find nearest neighbors. -See NearestNeighbors module documentation for details.

-
-
leaf_sizeint, default=30

Leaf size passed to BallTree or cKDTree. This can affect the speed -of the construction and query, as well as the memory required -to store the tree. The optimal value depends -on the nature of the problem.

-
-
pfloat, default=None

The power of the Minkowski metric to be used to calculate distance -between points. If None, then p=2 (equivalent to the Euclidean -distance).

-
-
n_jobsint, default=None

The number of parallel jobs to run. -None means 1 unless in a joblib.parallel_backend context. --1 means using all processors. See Glossary -for more details.

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
calc_core_sample_indices(optional)boolean, default = True

Indicates whether the indices of the core samples should be calculated. -The the attribute core_sample_indices_ will not be used, setting this -to False will avoid unnecessary kernel launches.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import DBSCAN
->>> import numpy as np
->>> X = np.array([[1, 2], [2, 2], [2, 3],
-...               [8, 7], [8, 8], [25, 80]])
->>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
->>> clustering
-DBSCAN(eps=3, min_samples=2)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering

-
-
-

See Also

-
-
OPTICSA similar clustering at multiple values of eps. Our implementation

is optimized for memory usage.

-
-
-
-
-

References

-

Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based -Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. -In: Proceedings of the 2nd International Conference on Knowledge Discovery -and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996

-

Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). -DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. -ACM Transactions on Database Systems (TODS), 42(3), 19.

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-_lazy_fit_gpu(X, y=None, out_dtype='int32')[source]
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dasf.ml.cluster.hdbscan

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Module Contents

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Classes

- - - - - - -

HDBSCAN

Perform HDBSCAN clustering from vector array or distance matrix.

-
-
-class dasf.ml.cluster.hdbscan.HDBSCAN(alpha=1.0, gen_min_span_tree=False, leaf_size=40, metric='euclidean', min_cluster_size=5, min_samples=None, p=None, algorithm='best', approx_min_span_tree=True, core_dist_n_jobs=4, cluster_selection_method='eom', allow_single_cluster=False, prediction_data=False, match_reference_implementation=False, connectivity='knn', output_type=None, verbose=0, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Perform HDBSCAN clustering from vector array or distance matrix.

-

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications -with Noise. Performs DBSCAN over varying epsilon values and integrates -the result to find a clustering that gives the best stability over epsilon. -This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), -and be more robust to parameter selection.

-
-

Parameters

-
-
min_cluster_sizeint, optional (default=5)

The minimum size of clusters; single linkage splits that contain -fewer points than this will be considered points “falling out” of a -cluster rather than a cluster splitting into two new clusters.

-
-
min_samplesint, optional (default=None)

The number of samples in a neighbourhood for a point to be -considered a core point.

-
-
metricstring, or callable, optional (default=’euclidean’)

The metric to use when calculating distance between instances in a -feature array. If metric is a string or callable, it must be one of -the options allowed by metrics.pairwise.pairwise_distances for its -metric parameter. -If metric is “precomputed”, X is assumed to be a distance matrix and -must be square.

-
-
pint, optional (default=None)

p value to use if using the minkowski metric.

-
-
alphafloat, optional (default=1.0)

A distance scaling parameter as used in robust single linkage. -See [3] for more information.

-
-
cluster_selection_epsilon: float, optional (default=0.0)
-

A distance threshold. Clusters below this value will be merged.

-
-

See [5] for more information.

-
-
algorithmstring, optional (default=’best’)

Exactly which algorithm to use; hdbscan has variants specialised -for different characteristics of the data. By default this is set -to best which chooses the “best” algorithm given the nature of -the data. You can force other options if you believe you know -better. Options are:

-
-
    -

  • best

    • -

  • generic

    • -

  • prims_kdtree

    • -

  • prims_balltree

    • -

  • boruvka_kdtree

    • -

  • boruvka_balltree

    • -
-
-
-
leaf_size: int, optional (default=40)

If using a space tree algorithm (kdtree, or balltree) the number -of points ina leaf node of the tree. This does not alter the -resulting clustering, but may have an effect on the runtime -of the algorithm.

-
-
memoryInstance of joblib.Memory or string (optional)

Used to cache the output of the computation of the tree. -By default, no caching is done. If a string is given, it is the -path to the caching directory.

-
-
approx_min_span_treebool, optional (default=True)

Whether to accept an only approximate minimum spanning tree. -For some algorithms this can provide a significant speedup, but -the resulting clustering may be of marginally lower quality. -If you are willing to sacrifice speed for correctness you may want -to explore this; in general this should be left at the default True.

-
-
gen_min_span_tree: bool, optional (default=False)

Whether to generate the minimum spanning tree with regard -to mutual reachability distance for later analysis.

-
-
core_dist_n_jobsint, optional (default=4)

Number of parallel jobs to run in core distance computations (if -supported by the specific algorithm). For core_dist_n_jobs -below -1, (n_cpus + 1 + core_dist_n_jobs) are used.

-
-
cluster_selection_methodstring, optional (default=’eom’)

The method used to select clusters from the condensed tree. The -standard approach for HDBSCAN* is to use an Excess of Mass algorithm -to find the most persistent clusters. Alternatively you can instead -select the clusters at the leaves of the tree – this provides the -most fine grained and homogeneous clusters. Options are:

-
-
    -

  • eom

    • -

  • leaf

    • -
-
-
-
allow_single_clusterbool, optional (default=False)

By default HDBSCAN* will not produce a single cluster, setting this -to True will override this and allow single cluster results in -the case that you feel this is a valid result for your dataset.

-
-
prediction_databoolean, optional

Whether to generate extra cached data for predicting labels or -membership vectors few new unseen points later. If you wish to -persist the clustering object for later re-use you probably want -to set this to True. -(default False)

-
-
match_reference_implementationbool, optional (default=False)

There exist some interpretational differences between this -HDBSCAN* implementation and the original authors reference -implementation in Java. This can result in very minor differences -in clustering results. Setting this flag to True will, at a some -performance cost, ensure that the clustering results match the -reference implementation.

-
-
connectivity{‘pairwise’, ‘knn’}, default=’knn’
-
The type of connectivity matrix to compute.
    -

  • ‘pairwise’ will compute the entire fully-connected graph of

    • -
-

pairwise distances between each set of points. This is the fastest -to compute and can be very fast for smaller datasets but requires -O(n^2) space.

-
    -

  • ‘knn’ will sparsify the fully-connected connectivity matrix to

    • -
-

save memory and enable much larger inputs. “n_neighbors” will -control the amount of memory used and the graph will be connected -automatically in the event “n_neighbors” was not large enough to -connect it.

-
-
-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import HDBSCAN
->>> import numpy as np
->>> X = np.array([[1, 2], [2, 2], [2, 3],
-...               [8, 7], [8, 8], [25, 80]])
->>> clustering = HDBSCAN(min_cluster_size=30, min_samples=2).fit(X)
->>> clustering
-HDBSCAN(min_cluster_size=30, min_samples=2)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering

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References

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dasf.ml.cluster

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Package Contents

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Classes

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AgglomerativeClustering

Agglomerative Clustering

KMeans

K-Means clustering.

DBSCAN

Perform DBSCAN clustering from vector array or distance matrix.

SOM

Initializes a Self Organizing Maps.

SpectralClustering

Apply clustering to a projection of the normalized Laplacian.

-
-
-class dasf.ml.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', connectivity=None, linkage='single', memory=None, compute_full_tree='auto', distance_threshold=None, compute_distances=False, handle=None, verbose=False, n_neighbors=10, output_type=None, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Agglomerative Clustering

-

Recursively merges the pair of clusters that minimally increases -a given linkage distance.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint or None, default=2

The number of clusters to find. It must be None if -distance_threshold is not None.

-
-
affinitystr or callable, default=’euclidean’

Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, -“manhattan”, “cosine”, or “precomputed”. -If linkage is “ward”, only “euclidean” is accepted. -If “precomputed”, a distance matrix (instead of a similarity matrix) -is needed as input for the fit method.

-
-
memorystr or object with the joblib.Memory interface, default=None

Used to cache the output of the computation of the tree. -By default, no caching is done. If a string is given, it is the -path to the caching directory.

-
-
connectivityarray-like or callable, default=None

Connectivity matrix. Defines for each sample the neighboring -samples following a given structure of the data. -This can be a connectivity matrix itself or a callable that transforms -the data into a connectivity matrix, such as derived from -kneighbors_graph. Default is None, i.e, the -hierarchical clustering algorithm is unstructured.

-
-
compute_full_tree‘auto’ or bool, default=’auto’

Stop early the construction of the tree at n_clusters. This is -useful to decrease computation time if the number of clusters is not -small compared to the number of samples. This option is useful only -when specifying a connectivity matrix. Note also that when varying the -number of clusters and using caching, it may be advantageous to compute -the full tree. It must be True if distance_threshold is not -None. By default compute_full_tree is “auto”, which is equivalent -to True when distance_threshold is not None or that n_clusters -is inferior to the maximum between 100 or 0.02 * n_samples. -Otherwise, “auto” is equivalent to False.

-
-
linkage{‘ward’, ‘complete’, ‘average’, ‘single’}, default=’ward’

Which linkage criterion to use. The linkage criterion determines which -distance to use between sets of observation. The algorithm will merge -the pairs of cluster that minimize this criterion.

-
    -

  • ‘ward’ minimizes the variance of the clusters being merged.

    • -

  • ‘average’ uses the average of the distances of each observation of -the two sets.

    • -

  • ‘complete’ or ‘maximum’ linkage uses the maximum distances between -all observations of the two sets.

    • -

  • ‘single’ uses the minimum of the distances between all observations -of the two sets.

    • -
-
-

New in version 0.20: Added the ‘single’ option

-
-
-
distance_thresholdfloat, default=None

The linkage distance threshold above which, clusters will not be -merged. If not None, n_clusters must be None and -compute_full_tree must be True.

-
-

New in version 0.21.

-
-
-
compute_distancesbool, default=False

Computes distances between clusters even if distance_threshold is not -used. This can be used to make dendrogram visualization, but introduces -a computational and memory overhead.

-
-

New in version 0.24.

-
-
-
n_neighborsint, default = 15

The number of neighbors to compute when connectivity = “knn”

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import AgglomerativeClustering
->>> import numpy as np
->>> X = np.array([[1, 2], [1, 4], [1, 0],
-...               [4, 2], [4, 4], [4, 0]])
->>> clustering = AgglomerativeClustering().fit(X)
->>> clustering
-AgglomerativeClustering()
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html -- https://docs.rapids.ai/api/cuml/stable/api.html#agglomerative-clustering

-
-
-_fit_cpu(X, y=None, convert_dtype=True)
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-
-_fit_gpu(X, y=None, convert_dtype=True)
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-class dasf.ml.cluster.KMeans(n_clusters=8, init=None, n_init=None, max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='full', oversampling_factor=2.0, n_jobs=1, init_max_iter=None, max_samples_per_batch=32768, precompute_distances='auto', output_type=None, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

K-Means clustering.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint, default=8

The number of clusters to form as well as the number of -centroids to generate.

-
-
-

init : {‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’

-
-

Method for initialization:

-

‘k-means++’ : selects initial cluster centers for k-mean -clustering in a smart way to speed up convergence. See section -Notes in k_init for more details.

-

‘random’: choose n_clusters observations (rows) at random from data -for the initial centroids.

-

If an array is passed, it should be of shape (n_clusters, n_features) -and gives the initial centers.

-

If a callable is passed, it should take arguments X, n_clusters and a -random state and return an initialization.

-
-
-
n_initint, default=10

Number of time the k-means algorithm will be run with different -centroid seeds. The final results will be the best output of -n_init consecutive runs in terms of inertia.

-
-
max_iterint, default=300

Maximum number of iterations of the k-means algorithm for a -single run.

-
-
tolfloat, default=1e-4

Relative tolerance with regards to Frobenius norm of the difference -in the cluster centers of two consecutive iterations to declare -convergence.

-
-
precompute_distances{‘auto’, True, False}, default=’auto’

Precompute distances (faster but takes more memory).

-

‘auto’ : do not precompute distances if n_samples * n_clusters > 12 -million. This corresponds to about 100MB overhead per job using -double precision. IMPORTANT: This is used only in Dask ML version.

-

True : always precompute distances.

-

False : never precompute distances.

-
-
verboseint, default=0

Verbosity mode.

-
-
random_stateint, RandomState instance or None, default=None

Determines random number generation for centroid initialization. Use -an int to make the randomness deterministic. -See Glossary.

-
-
copy_xbool, default=True

When pre-computing distances it is more numerically accurate to center -the data first. If copy_x is True (default), then the original data is -not modified. If False, the original data is modified, and put back -before the function returns, but small numerical differences may be -introduced by subtracting and then adding the data mean. Note that if -the original data is not C-contiguous, a copy will be made even if -copy_x is False. If the original data is sparse, but not in CSR format, -a copy will be made even if copy_x is False.

-
-
n_jobsint, default=1

The number of OpenMP threads to use for the computation. Parallelism is -sample-wise on the main cython loop which assigns each sample to its -closest center. IMPORTANT: This is used only in Dask ML version.

-

None or -1 means using all processors.

-
-
init_max_iterint, default=None

Number of iterations for init step.

-
-
algorithm{“auto”, “full”, “elkan”}, default=”full”

K-means algorithm to use. The classical EM-style algorithm is “full”. -The “elkan” variation is more efficient on data with well-defined -clusters, by using the triangle inequality. However it’s more memory -intensive due to the allocation of an extra array of shape -(n_samples, n_clusters).

-

For now “auto” (kept for backward compatibiliy) chooses “elkan” but it -might change in the future for a better heuristic.

-
-

Changed in version 0.18: Added Elkan algorithm

-
-
-
oversampling_factorint, default=2

The amount of points to sample in scalable k-means++ initialization -for potential centroids. Increasing this value can lead to better -initial centroids at the cost of memory. The total number of centroids -sampled in scalable k-means++ is oversampling_factor * n_clusters * 8.

-
-
max_samples_per_batchint, default=32768

The number of data samples to use for batches of the pairwise distance -computation. This computation is done throughout both fit predict. The -default should suit most cases. The total number of elements in the -batched pairwise distance computation is max_samples_per_batch * -n_clusters. It might become necessary to lower this number when -n_clusters becomes prohibitively large.

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
-
-
-

See Also

-
-
MiniBatchKMeansAlternative online implementation that does incremental

updates of the centers positions using mini-batches. -For large scale learning (say n_samples > 10k) MiniBatchKMeans is -probably much faster than the default batch implementation.

-
-
-
-
-

Notes

-

The k-means problem is solved using either Lloyd’s or Elkan’s algorithm.

-

The average complexity is given by O(k n T), where n is the number of -samples and T is the number of iteration.

-

The worst case complexity is given by O(n^(k+2/p)) with -n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, -‘How slow is the k-means method?’ SoCG2006)

-

In practice, the k-means algorithm is very fast (one of the fastest -clustering algorithms available), but it falls in local minima. That’s why -it can be useful to restart it several times.

-

If the algorithm stops before fully converging (because of tol or -max_iter), labels_ and cluster_centers_ will not be consistent, -i.e. the cluster_centers_ will not be the means of the points in each -cluster. Also, the estimator will reassign labels_ after the last -iteration to make labels_ consistent with predict on the training -set.

-
-
-

Examples

-
>>> from dasf.ml.cluster import KMeans
->>> import numpy as np
->>> X = np.array([[1, 2], [1, 4], [1, 0],
-...               [10, 2], [10, 4], [10, 0]])
->>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
->>> kmeans.predict([[0, 0], [12, 3]])
-array([1, 0], dtype=int32)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html -- https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html -- https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering -- https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans

-
-
-_lazy_fit_cpu(X, y=None, sample_weight=None)
-

Compute Dask k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_lazy_fit_gpu(X, y=None, sample_weight=None)
-

Compute Dask CuML k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_fit_cpu(X, y=None, sample_weight=None)
-

Compute Scikit Learn k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_fit_gpu(X, y=None, sample_weight=None)
-

Compute CuML k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)
-

Compute cluster centers and predict cluster index for each sample using -Dask ML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_fit_predict_gpu(X, y=None, sample_weight=None)
-

Compute cluster centers and predict cluster index for each sample using -Dask CuML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_fit_predict_cpu(X, y=None, sample_weight=None)
-

Compute cluster centers and predict cluster index for each sample using -Scikit Learn.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_fit_predict_gpu(X, y=None, sample_weight=None)
-

Compute cluster centers and predict cluster index for each sample using -CuML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict_cpu(X, sample_weight=None)
-

Predict the closest cluster each sample in X belongs to using Dask ML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict_gpu(X, sample_weight=None)
-

Predict the closest cluster each sample in X belongs to using Dask -CuML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_predict_cpu(X, sample_weight=None)
-

Predict the closest cluster each sample in X belongs to using Scikit -Learn.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_predict_gpu(X, sample_weight=None)
-

Predict the closest cluster each sample in X belongs to using CuML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict2_cpu(X, sample_weight=None)
-

A block predict using Scikit Learn variant but for Dask.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict2_gpu(X, sample_weight=None)
-

A block predict using CuML variant but for Dask.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-abstract _predict2_cpu(X, sample_weight=None)
-
- -
-
-abstract _predict2_gpu(X, sample_weight=None)
-
- -
-
-predict2(sample_weight=None)
-
- -
-
- -
-
-class dasf.ml.cluster.DBSCAN(eps=0.5, leaf_size=40, metric='euclidean', min_samples=5, p=None, output_type=None, calc_core_sample_indices=True, verbose=False, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Perform DBSCAN clustering from vector array or distance matrix.

-

DBSCAN - Density-Based Spatial Clustering of Applications with Noise. -Finds core samples of high density and expands clusters from them. -Good for data which contains clusters of similar density.

-

Read more in the User Guide.

-
-

Parameters

-
-
epsfloat, default=0.5

The maximum distance between two samples for one to be considered -as in the neighborhood of the other. This is not a maximum bound -on the distances of points within a cluster. This is the most -important DBSCAN parameter to choose appropriately for your data set -and distance function.

-
-
min_samplesint, default=5

The number of samples (or total weight) in a neighborhood for a point -to be considered as a core point. This includes the point itself.

-
-
metricstring, or callable, default=’euclidean’

The metric to use when calculating distance between instances in a -feature array. If metric is a string or callable, it must be one of -the options allowed by sklearn.metrics.pairwise_distances() for -its metric parameter. -If metric is “precomputed”, X is assumed to be a distance matrix and -must be square. X may be a Glossary, in which -case only “nonzero” elements may be considered neighbors for DBSCAN.

-
-

New in version 0.17: metric precomputed to accept precomputed sparse matrix.

-
-
-
metric_paramsdict, default=None

Additional keyword arguments for the metric function.

-
-

New in version 0.19.

-
-
-
algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’

The algorithm to be used by the NearestNeighbors module -to compute pointwise distances and find nearest neighbors. -See NearestNeighbors module documentation for details.

-
-
leaf_sizeint, default=30

Leaf size passed to BallTree or cKDTree. This can affect the speed -of the construction and query, as well as the memory required -to store the tree. The optimal value depends -on the nature of the problem.

-
-
pfloat, default=None

The power of the Minkowski metric to be used to calculate distance -between points. If None, then p=2 (equivalent to the Euclidean -distance).

-
-
n_jobsint, default=None

The number of parallel jobs to run. -None means 1 unless in a joblib.parallel_backend context. --1 means using all processors. See Glossary -for more details.

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
calc_core_sample_indices(optional)boolean, default = True

Indicates whether the indices of the core samples should be calculated. -The the attribute core_sample_indices_ will not be used, setting this -to False will avoid unnecessary kernel launches.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import DBSCAN
->>> import numpy as np
->>> X = np.array([[1, 2], [2, 2], [2, 3],
-...               [8, 7], [8, 8], [25, 80]])
->>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
->>> clustering
-DBSCAN(eps=3, min_samples=2)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan -- https://docs.rapids.ai/api/cuml/stable/api.html#dbscan-clustering

-
-
-

See Also

-
-
OPTICSA similar clustering at multiple values of eps. Our implementation

is optimized for memory usage.

-
-
-
-
-

References

-

Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based -Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. -In: Proceedings of the 2nd International Conference on Knowledge Discovery -and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996

-

Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). -DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. -ACM Transactions on Database Systems (TODS), 42(3), 19.

-
-
-_lazy_fit_gpu(X, y=None, out_dtype='int32')
-
- -
-
-_fit_cpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_gpu(X, y=None, out_dtype='int32')
-
- -
-
-_lazy_fit_predict_gpu(X, y=None, out_dtype='int32')
-
- -
-
-_fit_predict_cpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_predict_gpu(X, y=None, out_dtype='int32')
-
- -
-
- -
-
-class dasf.ml.cluster.SOM(x, y, input_len, num_epochs=100, sigma=0, sigmaN=1, learning_rate=0.5, learning_rateN=0.01, decay_function='exponential', neighborhood_function='gaussian', std_coeff=0.5, topology='rectangular', activation_distance='euclidean', random_seed=None, n_parallel=0, compact_support=False, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Initializes a Self Organizing Maps.

-

A rule of thumb to set the size of the grid for a dimensionality -reduction task is that it should contain 5*sqrt(N) neurons -where N is the number of samples in the dataset to analyze.

-

E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 -hence a map 8-by-8 should perform well.

-
-

Parameters

-
-
xint

x dimension of the SOM.

-
-
yint

y dimension of the SOM.

-
-
input_lenint

Number of the elements of the vectors in input.

-
-
sigmafloat, default=min(x,y)/2

Spread of the neighborhood function, needs to be adequate -to the dimensions of the map.

-
-
sigmaNfloat, default=0.01

Spread of the neighborhood function at last iteration.

-
-
learning_ratefloat, default=0.5

initial learning rate.

-
-
learning_rateNfloat, default=0.01

final learning rate

-
-
decay_functionstring, default=’exponential’

Function that reduces learning_rate and sigma at each iteration. -Possible values: ‘exponential’, ‘linear’, ‘aymptotic’

-
-
neighborhood_functionstring, default=’gaussian’

Function that weights the neighborhood of a position in the map. -Possible values: ‘gaussian’, ‘mexican_hat’, ‘bubble’, ‘triangle’

-
-
topologystring, default=’rectangular’

Topology of the map. -Possible values: ‘rectangular’, ‘hexagonal’

-
-
activation_distancestring, default=’euclidean’

Distance used to activate the map. -Possible values: ‘euclidean’, ‘cosine’, ‘manhattan’

-
-
random_seedint, default=None

Random seed to use.

-
-
n_paralleluint, default=#max_CUDA_threads or 500*#CPUcores

Number of samples to be processed at a time. Setting a too low -value may drastically lower performance due to under-utilization, -setting a too high value increases memory usage without granting -any significant performance benefit.

-
-
xpnumpy or cupy, default=cupy if can be imported else numpy

Use numpy (CPU) or cupy (GPU) for computations.

-
-
std_coeff: float, default=0.5

Used to calculate gausssian exponent denominator: -d = 2*std_coeff**2*sigma**2

-
-
compact_support: bool, default=False

Cut the neighbor function to 0 beyond neighbor radius sigma

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import SOM
->>> import numpy as np
->>> X = np.array([[1, 1], [2, 1], [1, 0],
-...               [4, 7], [3, 5], [3, 6]])
->>> som = SOM(x=3, y=2, input_len=2,
-...           num_epochs=100).fit(X)
->>> som
-SOM(x=3, y=2, input_len=2, num_epochs=100)
-
-
-
-
-_lazy_fit_cpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_fit_gpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_cpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_gpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_fit_predict_gpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_predict_cpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_predict_gpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_predict_cpu(X, sample_weight=None)
-
- -
-
-_lazy_predict_gpu(X, sample_weight=None)
-
- -
-
-_predict_cpu(X, sample_weight=None)
-
- -
-
-_predict_gpu(X, sample_weight=None)
-
- -
-
-_lazy_quantization_error_cpu(X)
-
- -
-
-_lazy_quantization_error_gpu(X)
-
- -
-
-_quantization_error_cpu(X)
-
- -
-
-_quantization_error_gpu(X)
-
- -
-
-quantization_error(X)
-
- -
-
- -
-
-class dasf.ml.cluster.SpectralClustering(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, n_components=None, persist_embedding=False, kmeans_params=None, verbose=False, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Apply clustering to a projection of the normalized Laplacian.

-

In practice Spectral Clustering is very useful when the structure of -the individual clusters is highly non-convex, or more generally when -a measure of the center and spread of the cluster is not a suitable -description of the complete cluster, such as when clusters are -nested circles on the 2D plane.

-

If the affinity matrix is the adjacency matrix of a graph, this method -can be used to find normalized graph cuts.

-

When calling fit, an affinity matrix is constructed using either -a kernel function such the Gaussian (aka RBF) kernel with Euclidean -distance d(X, X):

-
np.exp(-gamma * d(X,X) ** 2)
-
-
-

or a k-nearest neighbors connectivity matrix.

-

Alternatively, a user-provided affinity matrix can be specified by -setting affinity='precomputed'.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint, default=8

The dimension of the projection subspace.

-
-
eigen_solver{‘arpack’, ‘lobpcg’, ‘amg’}, default=None

The eigenvalue decomposition strategy to use. AMG requires pyamg -to be installed. It can be faster on very large, sparse problems, -but may also lead to instabilities. If None, then 'arpack' is -used.

-
-
n_componentsint, default=n_clusters

Number of eigenvectors to use for the spectral embedding

-
-
random_stateint, RandomState instance, default=None

A pseudo random number generator used for the initialization of the -lobpcg eigenvectors decomposition when eigen_solver='amg' and by -the K-Means initialization. Use an int to make the randomness -deterministic. -See Glossary.

-
-
n_initint, default=10

Number of time the k-means algorithm will be run with different -centroid seeds. The final results will be the best output of n_init -consecutive runs in terms of inertia. Only used if -assign_labels='kmeans'.

-
-
gammafloat, default=1.0

Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. -Ignored for affinity='nearest_neighbors'.

-
-
affinitystr or callable, default=’rbf’
-
How to construct the affinity matrix.
    -

  • ‘nearest_neighbors’: construct the affinity matrix by computing a -graph of nearest neighbors.

    • -

  • ‘rbf’: construct the affinity matrix using a radial basis function -(RBF) kernel.

    • -

  • ‘precomputed’: interpret X as a precomputed affinity matrix, -where larger values indicate greater similarity between instances.

    • -

  • ‘precomputed_nearest_neighbors’: interpret X as a sparse graph -of precomputed distances, and construct a binary affinity matrix -from the n_neighbors nearest neighbors of each instance.

    • -

  • one of the kernels supported by -pairwise_kernels().

    • -
-
-
-

Only kernels that produce similarity scores (non-negative values that -increase with similarity) should be used. This property is not checked -by the clustering algorithm.

-
-
n_neighborsint, default=10

Number of neighbors to use when constructing the affinity matrix using -the nearest neighbors method. Ignored for affinity='rbf'.

-
-
eigen_tolfloat, default=0.0

Stopping criterion for eigendecomposition of the Laplacian matrix -when eigen_solver='arpack'.

-
-
assign_labels{‘kmeans’, ‘discretize’}, default=’kmeans’

The strategy for assigning labels in the embedding space. There are two -ways to assign labels after the Laplacian embedding. k-means is a -popular choice, but it can be sensitive to initialization. -Discretization is another approach which is less sensitive to random -initialization.

-
-
degreefloat, default=3

Degree of the polynomial kernel. Ignored by other kernels.

-
-
coef0float, default=1

Zero coefficient for polynomial and sigmoid kernels. -Ignored by other kernels.

-
-
kernel_paramsdict of str to any, default=None

Parameters (keyword arguments) and values for kernel passed as -callable object. Ignored by other kernels.

-
-
n_jobsint, default=None

The number of parallel jobs to run when affinity=’nearest_neighbors’ -or affinity=’precomputed_nearest_neighbors’. The neighbors search -will be done in parallel. -None means 1 unless in a joblib.parallel_backend context. --1 means using all processors. See Glossary -for more details.

-
-
verbosebool, default=False

Verbosity mode.

-
-

New in version 0.24.

-
-
-
persist_embeddingbool

Whether to persist the intermediate n_samples x n_components array used -for clustering.

-
-
kmeans_paramsdictionary of string to any, optional

Keyword arguments for the KMeans clustering used for the final -clustering.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import SpectralClustering
->>> import numpy as np
->>> X = np.array([[1, 1], [2, 1], [1, 0],
-...               [4, 7], [3, 5], [3, 6]])
->>> clustering = SpectralClustering(n_clusters=2,
-...         assign_labels='discretize',
-...         random_state=0).fit(X)
->>> clustering
-SpectralClustering(assign_labels='discretize', n_clusters=2,
-    random_state=0)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering -- https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html

-
-
-

Notes

-

A distance matrix for which 0 indicates identical elements and high values -indicate very dissimilar elements can be transformed into an affinity / -similarity matrix that is well-suited for the algorithm by -applying the Gaussian (aka RBF, heat) kernel:

-
np.exp(- dist_matrix ** 2 / (2. * delta ** 2))
-
-
-

where delta is a free parameter representing the width of the Gaussian -kernel.

-

An alternative is to take a symmetric version of the k-nearest neighbors -connectivity matrix of the points.

-

If the pyamg package is installed, it is used: this greatly -speeds up computation.

-
-
-

References

- -
-
-_fit_cpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)
-
- -
-
-_fit_predict_cpu(X, y=None, sample_weight=None)
-
- -
-
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/cluster/kmeans/index.html b/docs/autoapi/dasf/ml/cluster/kmeans/index.html deleted file mode 100644 index 8e6b460..0000000 --- a/docs/autoapi/dasf/ml/cluster/kmeans/index.html +++ /dev/null @@ -1,690 +0,0 @@ - - - - - - - dasf.ml.cluster.kmeans — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
-

dasf.ml.cluster.kmeans

-
-

Module Contents

-
-

Classes

- - - - - - -

KMeans

K-Means clustering.

-
-
-class dasf.ml.cluster.kmeans.KMeans(n_clusters=8, init=None, n_init=None, max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='full', oversampling_factor=2.0, n_jobs=1, init_max_iter=None, max_samples_per_batch=32768, precompute_distances='auto', output_type=None, **kwargs)[source]
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

K-Means clustering.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint, default=8

The number of clusters to form as well as the number of -centroids to generate.

-
-
-

init : {‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’

-
-

Method for initialization:

-

‘k-means++’ : selects initial cluster centers for k-mean -clustering in a smart way to speed up convergence. See section -Notes in k_init for more details.

-

‘random’: choose n_clusters observations (rows) at random from data -for the initial centroids.

-

If an array is passed, it should be of shape (n_clusters, n_features) -and gives the initial centers.

-

If a callable is passed, it should take arguments X, n_clusters and a -random state and return an initialization.

-
-
-
n_initint, default=10

Number of time the k-means algorithm will be run with different -centroid seeds. The final results will be the best output of -n_init consecutive runs in terms of inertia.

-
-
max_iterint, default=300

Maximum number of iterations of the k-means algorithm for a -single run.

-
-
tolfloat, default=1e-4

Relative tolerance with regards to Frobenius norm of the difference -in the cluster centers of two consecutive iterations to declare -convergence.

-
-
precompute_distances{‘auto’, True, False}, default=’auto’

Precompute distances (faster but takes more memory).

-

‘auto’ : do not precompute distances if n_samples * n_clusters > 12 -million. This corresponds to about 100MB overhead per job using -double precision. IMPORTANT: This is used only in Dask ML version.

-

True : always precompute distances.

-

False : never precompute distances.

-
-
verboseint, default=0

Verbosity mode.

-
-
random_stateint, RandomState instance or None, default=None

Determines random number generation for centroid initialization. Use -an int to make the randomness deterministic. -See Glossary.

-
-
copy_xbool, default=True

When pre-computing distances it is more numerically accurate to center -the data first. If copy_x is True (default), then the original data is -not modified. If False, the original data is modified, and put back -before the function returns, but small numerical differences may be -introduced by subtracting and then adding the data mean. Note that if -the original data is not C-contiguous, a copy will be made even if -copy_x is False. If the original data is sparse, but not in CSR format, -a copy will be made even if copy_x is False.

-
-
n_jobsint, default=1

The number of OpenMP threads to use for the computation. Parallelism is -sample-wise on the main cython loop which assigns each sample to its -closest center. IMPORTANT: This is used only in Dask ML version.

-

None or -1 means using all processors.

-
-
init_max_iterint, default=None

Number of iterations for init step.

-
-
algorithm{“auto”, “full”, “elkan”}, default=”full”

K-means algorithm to use. The classical EM-style algorithm is “full”. -The “elkan” variation is more efficient on data with well-defined -clusters, by using the triangle inequality. However it’s more memory -intensive due to the allocation of an extra array of shape -(n_samples, n_clusters).

-

For now “auto” (kept for backward compatibiliy) chooses “elkan” but it -might change in the future for a better heuristic.

-
-

Changed in version 0.18: Added Elkan algorithm

-
-
-
oversampling_factorint, default=2

The amount of points to sample in scalable k-means++ initialization -for potential centroids. Increasing this value can lead to better -initial centroids at the cost of memory. The total number of centroids -sampled in scalable k-means++ is oversampling_factor * n_clusters * 8.

-
-
max_samples_per_batchint, default=32768

The number of data samples to use for batches of the pairwise distance -computation. This computation is done throughout both fit predict. The -default should suit most cases. The total number of elements in the -batched pairwise distance computation is max_samples_per_batch * -n_clusters. It might become necessary to lower this number when -n_clusters becomes prohibitively large.

-
-
output_type{‘input’, ‘cudf’, ‘cupy’, ‘numpy’, ‘numba’}, default=None

Variable to control output type of the results and attributes of the -estimator. If None, it’ll inherit the output type set at the module -level, cuml.global_settings.output_type. See Output Data Type -Configuration for more info.

-
-
-
-
-

See Also

-
-
MiniBatchKMeansAlternative online implementation that does incremental

updates of the centers positions using mini-batches. -For large scale learning (say n_samples > 10k) MiniBatchKMeans is -probably much faster than the default batch implementation.

-
-
-
-
-

Notes

-

The k-means problem is solved using either Lloyd’s or Elkan’s algorithm.

-

The average complexity is given by O(k n T), where n is the number of -samples and T is the number of iteration.

-

The worst case complexity is given by O(n^(k+2/p)) with -n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, -‘How slow is the k-means method?’ SoCG2006)

-

In practice, the k-means algorithm is very fast (one of the fastest -clustering algorithms available), but it falls in local minima. That’s why -it can be useful to restart it several times.

-

If the algorithm stops before fully converging (because of tol or -max_iter), labels_ and cluster_centers_ will not be consistent, -i.e. the cluster_centers_ will not be the means of the points in each -cluster. Also, the estimator will reassign labels_ after the last -iteration to make labels_ consistent with predict on the training -set.

-
-
-

Examples

-
>>> from dasf.ml.cluster import KMeans
->>> import numpy as np
->>> X = np.array([[1, 2], [1, 4], [1, 0],
-...               [10, 2], [10, 4], [10, 0]])
->>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
->>> kmeans.predict([[0, 0], [12, 3]])
-array([1, 0], dtype=int32)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html -- https://ml.dask.org/modules/generated/dask_ml.cluster.KMeans.html -- https://docs.rapids.ai/api/cuml/stable/api.html#k-means-clustering -- https://docs.rapids.ai/api/cuml/stable/api.html#cuml.dask.cluster.KMeans

-
-
-_lazy_fit_cpu(X, y=None, sample_weight=None)[source]
-

Compute Dask k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_lazy_fit_gpu(X, y=None, sample_weight=None)[source]
-

Compute Dask CuML k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_fit_cpu(X, y=None, sample_weight=None)[source]
-

Compute Scikit Learn k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_fit_gpu(X, y=None, sample_weight=None)[source]
-

Compute CuML k-means clustering.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data -will be converted to C ordering, which will cause a memory -copy if the given data is not C-contiguous. -If a sparse matrix is passed, a copy will be made if it&apos;s not in -CSR format.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
self

Fitted estimator.

-
-
-
-
- -
-
-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)[source]
-

Compute cluster centers and predict cluster index for each sample using -Dask ML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_fit_predict_gpu(X, y=None, sample_weight=None)[source]
-

Compute cluster centers and predict cluster index for each sample using -Dask CuML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_fit_predict_cpu(X, y=None, sample_weight=None)[source]
-

Compute cluster centers and predict cluster index for each sample using -Scikit Learn.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_fit_predict_gpu(X, y=None, sample_weight=None)[source]
-

Compute cluster centers and predict cluster index for each sample using -CuML.

-

Convenience method; equivalent to calling fit(X) followed by -predict(X).

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

-
-
yIgnored

Not used, present here for API consistency by convention.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict_cpu(X, sample_weight=None)[source]
-

Predict the closest cluster each sample in X belongs to using Dask ML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightIgnored

Not used, present here for API consistency by convention.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict_gpu(X, sample_weight=None)[source]
-

Predict the closest cluster each sample in X belongs to using Dask -CuML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_predict_cpu(X, sample_weight=None)[source]
-

Predict the closest cluster each sample in X belongs to using Scikit -Learn.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_predict_gpu(X, sample_weight=None)[source]
-

Predict the closest cluster each sample in X belongs to using CuML.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict2_cpu(X, sample_weight=None)[source]
-

A block predict using Scikit Learn variant but for Dask.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-_lazy_predict2_gpu(X, sample_weight=None)[source]
-

A block predict using CuML variant but for Dask.

-

In the vector quantization literature, cluster_centers_ is called -the code book and each value returned by predict is the index of -the closest code in the code book.

-
-
Parameters
-
-
X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

-
-
sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations -are assigned equal weight.

-
-
-
-
-
Returns
-
-
labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

-
-
-
-
- -
-
-abstract _predict2_cpu(X, sample_weight=None)[source]
-
- -
-
-abstract _predict2_gpu(X, sample_weight=None)[source]
-
- -
-
-predict2(sample_weight=None)[source]
-
- -
-
- -
-
-
- - -
-
- -
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dasf.ml.cluster.som

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Module Contents

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Classes

- - - - - - -

SOM

Initializes a Self Organizing Maps.

-
-
-class dasf.ml.cluster.som.SOM(x, y, input_len, num_epochs=100, sigma=0, sigmaN=1, learning_rate=0.5, learning_rateN=0.01, decay_function='exponential', neighborhood_function='gaussian', std_coeff=0.5, topology='rectangular', activation_distance='euclidean', random_seed=None, n_parallel=0, compact_support=False, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Initializes a Self Organizing Maps.

-

A rule of thumb to set the size of the grid for a dimensionality -reduction task is that it should contain 5*sqrt(N) neurons -where N is the number of samples in the dataset to analyze.

-

E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 -hence a map 8-by-8 should perform well.

-
-

Parameters

-
-
xint

x dimension of the SOM.

-
-
yint

y dimension of the SOM.

-
-
input_lenint

Number of the elements of the vectors in input.

-
-
sigmafloat, default=min(x,y)/2

Spread of the neighborhood function, needs to be adequate -to the dimensions of the map.

-
-
sigmaNfloat, default=0.01

Spread of the neighborhood function at last iteration.

-
-
learning_ratefloat, default=0.5

initial learning rate.

-
-
learning_rateNfloat, default=0.01

final learning rate

-
-
decay_functionstring, default=’exponential’

Function that reduces learning_rate and sigma at each iteration. -Possible values: ‘exponential’, ‘linear’, ‘aymptotic’

-
-
neighborhood_functionstring, default=’gaussian’

Function that weights the neighborhood of a position in the map. -Possible values: ‘gaussian’, ‘mexican_hat’, ‘bubble’, ‘triangle’

-
-
topologystring, default=’rectangular’

Topology of the map. -Possible values: ‘rectangular’, ‘hexagonal’

-
-
activation_distancestring, default=’euclidean’

Distance used to activate the map. -Possible values: ‘euclidean’, ‘cosine’, ‘manhattan’

-
-
random_seedint, default=None

Random seed to use.

-
-
n_paralleluint, default=#max_CUDA_threads or 500*#CPUcores

Number of samples to be processed at a time. Setting a too low -value may drastically lower performance due to under-utilization, -setting a too high value increases memory usage without granting -any significant performance benefit.

-
-
xpnumpy or cupy, default=cupy if can be imported else numpy

Use numpy (CPU) or cupy (GPU) for computations.

-
-
std_coeff: float, default=0.5

Used to calculate gausssian exponent denominator: -d = 2*std_coeff**2*sigma**2

-
-
compact_support: bool, default=False

Cut the neighbor function to 0 beyond neighbor radius sigma

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import SOM
->>> import numpy as np
->>> X = np.array([[1, 1], [2, 1], [1, 0],
-...               [4, 7], [3, 5], [3, 6]])
->>> som = SOM(x=3, y=2, input_len=2,
-...           num_epochs=100).fit(X)
->>> som
-SOM(x=3, y=2, input_len=2, num_epochs=100)
-
-
-
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-_lazy_fit_cpu(X, y=None, sample_weight=None)
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-_lazy_fit_gpu(X, y=None, sample_weight=None)
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-_fit_cpu(X, y=None, sample_weight=None)
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-_fit_gpu(X, y=None, sample_weight=None)
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-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)
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-_lazy_fit_predict_gpu(X, y=None, sample_weight=None)
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-_fit_predict_cpu(X, y=None, sample_weight=None)
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-_lazy_predict_cpu(X, sample_weight=None)
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-_lazy_predict_gpu(X, sample_weight=None)
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-_predict_cpu(X, sample_weight=None)
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-_predict_gpu(X, sample_weight=None)
-
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-_lazy_quantization_error_cpu(X)
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-_lazy_quantization_error_gpu(X)
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-_quantization_error_cpu(X)
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-_quantization_error_gpu(X)
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-quantization_error(X)
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/cluster/spectral/index.html b/docs/autoapi/dasf/ml/cluster/spectral/index.html deleted file mode 100644 index 418bb21..0000000 --- a/docs/autoapi/dasf/ml/cluster/spectral/index.html +++ /dev/null @@ -1,323 +0,0 @@ - - - - - - - dasf.ml.cluster.spectral — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.cluster.spectral

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Module Contents

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Classes

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SpectralClustering

Apply clustering to a projection of the normalized Laplacian.

-
-
-class dasf.ml.cluster.spectral.SpectralClustering(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, n_components=None, persist_embedding=False, kmeans_params=None, verbose=False, **kwargs)
-

Bases: dasf.ml.cluster.classifier.ClusterClassifier

-

Apply clustering to a projection of the normalized Laplacian.

-

In practice Spectral Clustering is very useful when the structure of -the individual clusters is highly non-convex, or more generally when -a measure of the center and spread of the cluster is not a suitable -description of the complete cluster, such as when clusters are -nested circles on the 2D plane.

-

If the affinity matrix is the adjacency matrix of a graph, this method -can be used to find normalized graph cuts.

-

When calling fit, an affinity matrix is constructed using either -a kernel function such the Gaussian (aka RBF) kernel with Euclidean -distance d(X, X):

-
np.exp(-gamma * d(X,X) ** 2)
-
-
-

or a k-nearest neighbors connectivity matrix.

-

Alternatively, a user-provided affinity matrix can be specified by -setting affinity='precomputed'.

-

Read more in the User Guide.

-
-

Parameters

-
-
n_clustersint, default=8

The dimension of the projection subspace.

-
-
eigen_solver{‘arpack’, ‘lobpcg’, ‘amg’}, default=None

The eigenvalue decomposition strategy to use. AMG requires pyamg -to be installed. It can be faster on very large, sparse problems, -but may also lead to instabilities. If None, then 'arpack' is -used.

-
-
n_componentsint, default=n_clusters

Number of eigenvectors to use for the spectral embedding

-
-
random_stateint, RandomState instance, default=None

A pseudo random number generator used for the initialization of the -lobpcg eigenvectors decomposition when eigen_solver='amg' and by -the K-Means initialization. Use an int to make the randomness -deterministic. -See Glossary.

-
-
n_initint, default=10

Number of time the k-means algorithm will be run with different -centroid seeds. The final results will be the best output of n_init -consecutive runs in terms of inertia. Only used if -assign_labels='kmeans'.

-
-
gammafloat, default=1.0

Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. -Ignored for affinity='nearest_neighbors'.

-
-
affinitystr or callable, default=’rbf’
-
How to construct the affinity matrix.
    -

  • ‘nearest_neighbors’: construct the affinity matrix by computing a -graph of nearest neighbors.

    • -

  • ‘rbf’: construct the affinity matrix using a radial basis function -(RBF) kernel.

    • -

  • ‘precomputed’: interpret X as a precomputed affinity matrix, -where larger values indicate greater similarity between instances.

    • -

  • ‘precomputed_nearest_neighbors’: interpret X as a sparse graph -of precomputed distances, and construct a binary affinity matrix -from the n_neighbors nearest neighbors of each instance.

    • -

  • one of the kernels supported by -pairwise_kernels().

    • -
-
-
-

Only kernels that produce similarity scores (non-negative values that -increase with similarity) should be used. This property is not checked -by the clustering algorithm.

-
-
n_neighborsint, default=10

Number of neighbors to use when constructing the affinity matrix using -the nearest neighbors method. Ignored for affinity='rbf'.

-
-
eigen_tolfloat, default=0.0

Stopping criterion for eigendecomposition of the Laplacian matrix -when eigen_solver='arpack'.

-
-
assign_labels{‘kmeans’, ‘discretize’}, default=’kmeans’

The strategy for assigning labels in the embedding space. There are two -ways to assign labels after the Laplacian embedding. k-means is a -popular choice, but it can be sensitive to initialization. -Discretization is another approach which is less sensitive to random -initialization.

-
-
degreefloat, default=3

Degree of the polynomial kernel. Ignored by other kernels.

-
-
coef0float, default=1

Zero coefficient for polynomial and sigmoid kernels. -Ignored by other kernels.

-
-
kernel_paramsdict of str to any, default=None

Parameters (keyword arguments) and values for kernel passed as -callable object. Ignored by other kernels.

-
-
n_jobsint, default=None

The number of parallel jobs to run when affinity=’nearest_neighbors’ -or affinity=’precomputed_nearest_neighbors’. The neighbors search -will be done in parallel. -None means 1 unless in a joblib.parallel_backend context. --1 means using all processors. See Glossary -for more details.

-
-
verbosebool, default=False

Verbosity mode.

-
-

New in version 0.24.

-
-
-
persist_embeddingbool

Whether to persist the intermediate n_samples x n_components array used -for clustering.

-
-
kmeans_paramsdictionary of string to any, optional

Keyword arguments for the KMeans clustering used for the final -clustering.

-
-
-
-
-

Examples

-
>>> from dasf.ml.cluster import SpectralClustering
->>> import numpy as np
->>> X = np.array([[1, 1], [2, 1], [1, 0],
-...               [4, 7], [3, 5], [3, 6]])
->>> clustering = SpectralClustering(n_clusters=2,
-...         assign_labels='discretize',
-...         random_state=0).fit(X)
->>> clustering
-SpectralClustering(assign_labels='discretize', n_clusters=2,
-    random_state=0)
-
-
-

For further informations see: -- https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering -- https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html

-
-
-

Notes

-

A distance matrix for which 0 indicates identical elements and high values -indicate very dissimilar elements can be transformed into an affinity / -similarity matrix that is well-suited for the algorithm by -applying the Gaussian (aka RBF, heat) kernel:

-
np.exp(- dist_matrix ** 2 / (2. * delta ** 2))
-
-
-

where delta is a free parameter representing the width of the Gaussian -kernel.

-

An alternative is to take a symmetric version of the k-nearest neighbors -connectivity matrix of the points.

-

If the pyamg package is installed, it is used: this greatly -speeds up computation.

-
-
-

References

- -
-
-_fit_cpu(X, y=None, sample_weight=None)
-
- -
-
-_lazy_fit_predict_cpu(X, y=None, sample_weight=None)
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-_fit_predict_cpu(X, y=None, sample_weight=None)
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/core/index.html b/docs/autoapi/dasf/ml/core/index.html deleted file mode 100644 index aed3c86..0000000 --- a/docs/autoapi/dasf/ml/core/index.html +++ /dev/null @@ -1,164 +0,0 @@ - - - - - - - dasf.ml.core — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.core

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Module Contents

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Classes

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MLGeneric

-
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-class dasf.ml.core.MLGeneric(name, checkpoint=False, **kwargs)[source]
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-dump(model)[source]
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-load(model)[source]
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/decomposition/index.html b/docs/autoapi/dasf/ml/decomposition/index.html deleted file mode 100644 index 3668f1d..0000000 --- a/docs/autoapi/dasf/ml/decomposition/index.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - dasf.ml.decomposition — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.decomposition

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Submodules

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Package Contents

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Classes

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PCA

-
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-class dasf.ml.decomposition.PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.FitTransform, dasf.transforms.base.TargeteredTransform

-
-
-_lazy_fit_cpu(X, y=None, sample_weights=None)[source]
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-_lazy_fit_gpu(X, y=None, sample_weights=None)[source]
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-_fit_cpu(X, y=None, sample_weights=None)[source]
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-_fit_gpu(X, y=None, sample_weights=None)[source]
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-_lazy_fit_transform_cpu(X, y=None, sample_weights=None)[source]
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-_lazy_fit_transform_gpu(X, y=None, sample_weights=None)[source]
-
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-_fit_transform_cpu(X, y=None, sample_weights=None)[source]
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-_fit_transform_gpu(X, y=None, sample_weights=None)[source]
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-_lazy_transform_cpu(X, y=None, sample_weights=None)[source]
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-_lazy_transform_gpu(X, y=None, sample_weights=None)[source]
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-_transform_cpu(X, y=None, sample_weights=None)[source]
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-_transform_gpu(X, y=None, sample_weights=None)[source]
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-_get_covariance_cpu()[source]
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-get_covariance()[source]
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-_get_precision_cpu()[source]
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-get_precision()[source]
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dasf.ml.decomposition.pca

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Module Contents

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Classes

- - - - - - -

PCA

-
-
-class dasf.ml.decomposition.pca.PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None, *args, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.FitTransform, dasf.transforms.base.TargeteredTransform

-
-
-_lazy_fit_cpu(X, y=None, sample_weights=None)[source]
-
- -
-
-_lazy_fit_gpu(X, y=None, sample_weights=None)[source]
-
- -
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-_fit_cpu(X, y=None, sample_weights=None)[source]
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- -
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-_fit_gpu(X, y=None, sample_weights=None)[source]
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- -
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-_lazy_fit_transform_cpu(X, y=None, sample_weights=None)[source]
-
- -
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-_lazy_fit_transform_gpu(X, y=None, sample_weights=None)[source]
-
- -
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-_fit_transform_cpu(X, y=None, sample_weights=None)[source]
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- -
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-_fit_transform_gpu(X, y=None, sample_weights=None)[source]
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-_lazy_transform_cpu(X, y=None, sample_weights=None)[source]
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-_lazy_transform_gpu(X, y=None, sample_weights=None)[source]
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-_transform_cpu(X, y=None, sample_weights=None)[source]
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-_transform_gpu(X, y=None, sample_weights=None)[source]
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-_get_covariance_cpu()[source]
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-get_covariance()[source]
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/dl/clusters/dask/index.html b/docs/autoapi/dasf/ml/dl/clusters/dask/index.html deleted file mode 100644 index 110e01e..0000000 --- a/docs/autoapi/dasf/ml/dl/clusters/dask/index.html +++ /dev/null @@ -1,285 +0,0 @@ - - - - - - - dasf.ml.dl.clusters.dask — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.dl.clusters.dask

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Module Contents

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Classes

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DaskClusterEnvironment

Create a Dask Cluster environment for workers

-
-
-class dasf.ml.dl.clusters.dask.DaskClusterEnvironment(metadata=None)[source]
-

Bases: pytorch_lightning.plugins.environments.ClusterEnvironment

-

Create a Dask Cluster environment for workers

-

metadata – dictionary containing all data related to workers.

-
-
-property creates_processes_externally: bool
-

Return True if the cluster is managed (you don’t launch processes -yourself).

-
-
Return type:
-

bool

-
-
-
- -
-
-property main_address: str
-

Return master worker address.

-
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Return type:
-

str

-
-
-
- -
-
-property main_port: int
-

Return master worker port.

-
-
Return type:
-

int

-
-
-
- -
-
-detect()[source]
-

Detects the environment settings corresponding to this cluster and returns True if they match.

-
-
Return type:
-

bool

-
-
-
- -
-
-creates_children()[source]
-

Fork children when generate a cluster.

-
-
Return type:
-

bool

-
-
-
- -
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-world_size()[source]
-

Return worker world size.

-
-
Return type:
-

int

-
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-global_rank()[source]
-

Return worker global rank.

-
-
Return type:
-

int

-
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-local_rank()[source]
-

Return worker local rank.

-
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Return type:
-

int

-
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-node_rank()[source]
-

Return worker node rank (which is similar to global rank).

-
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Return type:
-

int

-
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-set_world_size(size)[source]
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Parameters:
-

size (int) –

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Return type:
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None

-
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-set_global_rank(rank)[source]
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-

rank (int) –

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None

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dasf.ml.dl.clusters

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Submodules

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Package Contents

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Classes

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DaskClusterEnvironment

Create a Dask Cluster environment for workers

-
-
-class dasf.ml.dl.clusters.DaskClusterEnvironment(metadata=None)[source]
-

Bases: pytorch_lightning.plugins.environments.ClusterEnvironment

-

Create a Dask Cluster environment for workers

-

metadata – dictionary containing all data related to workers.

-
-
-property creates_processes_externally: bool
-

Return True if the cluster is managed (you don’t launch processes -yourself).

-
-
Return type:
-

bool

-
-
-
- -
-
-property main_address: str
-

Return master worker address.

-
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Return type:
-

str

-
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- -
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-property main_port: int
-

Return master worker port.

-
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Return type:
-

int

-
-
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-detect()[source]
-

Detects the environment settings corresponding to this cluster and returns True if they match.

-
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Return type:
-

bool

-
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- -
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-creates_children()[source]
-

Fork children when generate a cluster.

-
-
Return type:
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bool

-
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-world_size()[source]
-

Return worker world size.

-
-
Return type:
-

int

-
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-global_rank()[source]
-

Return worker global rank.

-
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Return type:
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int

-
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-local_rank()[source]
-

Return worker local rank.

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Return type:
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int

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-node_rank()[source]
-

Return worker node rank (which is similar to global rank).

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Return type:
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int

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-set_world_size(size)[source]
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Parameters:
-

size (int) –

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Return type:
-

None

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rank (int) –

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None

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/dl/index.html b/docs/autoapi/dasf/ml/dl/index.html deleted file mode 100644 index 57b4ec5..0000000 --- a/docs/autoapi/dasf/ml/dl/index.html +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - dasf.ml.dl — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.dl

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Subpackages

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Submodules

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Package Contents

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Classes

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NeuralNetClassifier

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-class dasf.ml.dl.NeuralNetClassifier(model, max_iter=100, batch_size=32)[source]
-

Bases: dasf.transforms.base.Fit

-
-
-_lazy_fit_generic(X, y, accel, ngpus)[source]
-
- -
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-_lazy_fit_gpu(X, y=None)[source]
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- -
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-_lazy_fit_cpu(X, y=None)[source]
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- -
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-__fit_generic(X, y, accel, ngpus)
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-_fit_gpu(X, y=None)[source]
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-_fit_cpu(X, y=None)[source]
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/dl/models/devconvnet/index.html b/docs/autoapi/dasf/ml/dl/models/devconvnet/index.html deleted file mode 100644 index 6486422..0000000 --- a/docs/autoapi/dasf/ml/dl/models/devconvnet/index.html +++ /dev/null @@ -1,737 +0,0 @@ - - - - - - - dasf.ml.dl.models.devconvnet — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.dl.models.devconvnet

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Module Contents

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Classes

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MyAccuracy

Base class for all metrics present in the Metrics API.

NNModule

Base class for all neural network modules.

TorchPatchDeConvNet

Base class for all neural network modules.

TorchPatchDeConvNetSkip

Base class for all neural network modules.

TorchSectionDeConvNet

Base class for all neural network modules.

TorchSectionDeConvNetSkip

Base class for all neural network modules.

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-class dasf.ml.dl.models.devconvnet.MyAccuracy(dist_sync_on_step=False)[source]
-

Bases: torchmetrics.Metric

-

Base class for all metrics present in the Metrics API.

-

Implements add_state(), forward(), reset() and a few other things to -handle distributed synchronization and per-step metric computation.

-

Override update() and compute() functions to implement your own metric. Use -add_state() to register metric state variables which keep track of state on each -call of update() and are synchronized across processes when compute() is called.

-
-
Note:

Metric state variables can either be Tensor or an empty list which can we used -to store Tensor.

-
-
Note:

Different metrics only override update() and not forward(). A call to update() -is valid, but it won’t return the metric value at the current step. A call to forward() -automatically calls update() and also returns the metric value at the current step.

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Args:

kwargs: additional keyword arguments, see Metric kwargs for more info.

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    -
  • -
    compute_on_cpu: If metric state should be stored on CPU during computations. Only works

    for list states.

    -
    -
    -
    • -

  • dist_sync_on_step: If metric state should synchronize on forward(). Default is False

    • -

  • process_group: The process group on which the synchronization is called. Default is the world.

    • -
  • -
    dist_sync_fn: function that performs the allgather option on the metric state. Default is an

    custom implementation that calls torch.distributed.all_gather internally.

    -
    -
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    • -
  • -
    distributed_available_fn: function that checks if the distributed backend is available.

    Defaults to a check of torch.distributed.is_available() and torch.distributed.is_initialized().

    -
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    • -

  • sync_on_compute: If metric state should synchronize when compute is called. Default is True-

    • -
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Initializes internal Module state, shared by both nn.Module and ScriptModule.

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-set_idx(idx)[source]
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-update(preds, target)[source]
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Override this method to update the state variables of your metric class.

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Parameters:
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    -

  • preds (torch.Tensor) –

    • -

  • target (torch.Tensor) –

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-__str__()[source]
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Return str(self).

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-compute()[source]
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Override this method to compute the final metric value from state variables synchronized across the -distributed backend.

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-class dasf.ml.dl.models.devconvnet.NNModule(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: pytorch_lightning.LightningModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

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Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

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-cross_entropy_loss(input, target, weight=None, ignore_index=255)[source]
-

Use 255 to fill empty values when padding or doing any augmentation operations -like rotation.

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-configure_optimizers()[source]
-

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need -one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only -works in the manual optimization mode.

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Return:

Any of these 6 options.

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    -

  • Single optimizer.

    • -

  • List or Tuple of optimizers.

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  • Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers -(or multiple lr_scheduler_config).

    • -

  • Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" -key whose value is a single LR scheduler or lr_scheduler_config.

    • -

  • None - Fit will run without any optimizer.

    • -
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-

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. -The default configuration is shown below.

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lr_scheduler_config = {
-    # REQUIRED: The scheduler instance
-    "scheduler": lr_scheduler,
-    # The unit of the scheduler's step size, could also be 'step'.
-    # 'epoch' updates the scheduler on epoch end whereas 'step'
-    # updates it after a optimizer update.
-    "interval": "epoch",
-    # How many epochs/steps should pass between calls to
-    # `scheduler.step()`. 1 corresponds to updating the learning
-    # rate after every epoch/step.
-    "frequency": 1,
-    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
-    "monitor": "val_loss",
-    # If set to `True`, will enforce that the value specified 'monitor'
-    # is available when the scheduler is updated, thus stopping
-    # training if not found. If set to `False`, it will only produce a warning
-    "strict": True,
-    # If using the `LearningRateMonitor` callback to monitor the
-    # learning rate progress, this keyword can be used to specify
-    # a custom logged name
-    "name": None,
-}
-
-
-

When there are schedulers in which the .step() method is conditioned on a value, such as the -torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the -lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler -should be conditioned on.

-

Metrics can be made available to monitor by simply logging it using -self.log('metric_to_track', metric_val) in your LightningModule.

-
-
Note:

Some things to know:

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    -

  • Lightning calls .backward() and .step() automatically in case of automatic optimization.

    • -

  • If a learning rate scheduler is specified in configure_optimizers() with key -"interval" (default “epoch”) in the scheduler configuration, Lightning will call -the scheduler’s .step() method automatically in case of automatic optimization.

    • -

  • If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.

    • -

  • If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.

    • -

  • If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them -yourself.

    • -

  • If you need to control how often the optimizer steps, override the optimizer_step() hook.

    • -
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-training_step(batch, batch_idx)[source]
-

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or -logger.

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Args:
-
batch (Tensor | (Tensor, …) | [Tensor, …]):

The output of your DataLoader. A tensor, tuple or list.

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batch_idx (int): Integer displaying index of this batch

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Return:

Any of.

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    -

  • Tensor - The loss tensor

    • -

  • dict - A dictionary. Can include any keys, but must include the key 'loss'

    • -
  • -
    None - Training will skip to the next batch. This is only for automatic optimization.

    This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.

    -
    -
    -
    • -
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In this step you’d normally do the forward pass and calculate the loss for a batch. -You can also do fancier things like multiple forward passes or something model specific.

-

Example:

-
def training_step(self, batch, batch_idx):
-    x, y, z = batch
-    out = self.encoder(x)
-    loss = self.loss(out, x)
-    return loss
-
-
-

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

-
def __init__(self):
-    super().__init__()
-    self.automatic_optimization = False
-
-
-# Multiple optimizers (e.g.: GANs)
-def training_step(self, batch, batch_idx):
-    opt1, opt2 = self.optimizers()
-
-    # do training_step with encoder
-    ...
-    opt1.step()
-    # do training_step with decoder
-    ...
-    opt2.step()
-
-
-
-
Note:

When accumulate_grad_batches > 1, the loss returned here will be automatically -normalized by accumulate_grad_batches internally.

-
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-test_step(test_batch, batch_idx)[source]
-

Operates on a single batch of data from the test set. In this step you’d normally generate examples or -calculate anything of interest such as accuracy.

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-
Args:

batch: The output of your DataLoader. -batch_idx: The index of this batch. -dataloader_id: The index of the dataloader that produced this batch.

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(only if multiple test dataloaders used).

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Return:

Any of.

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    -

  • Any object or value

    • -

  • None - Testing will skip to the next batch

    • -
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-
-
# if you have one test dataloader:
-def test_step(self, batch, batch_idx):
-    ...
-
-
-# if you have multiple test dataloaders:
-def test_step(self, batch, batch_idx, dataloader_idx=0):
-    ...
-
-
-

Examples:

-
# CASE 1: A single test dataset
-def test_step(self, batch, batch_idx):
-    x, y = batch
-
-    # implement your own
-    out = self(x)
-    loss = self.loss(out, y)
-
-    # log 6 example images
-    # or generated text... or whatever
-    sample_imgs = x[:6]
-    grid = torchvision.utils.make_grid(sample_imgs)
-    self.logger.experiment.add_image('example_images', grid, 0)
-
-    # calculate acc
-    labels_hat = torch.argmax(out, dim=1)
-    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-
-    # log the outputs!
-    self.log_dict({'test_loss': loss, 'test_acc': test_acc})
-
-
-

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend -setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

-
# CASE 2: multiple test dataloaders
-def test_step(self, batch, batch_idx, dataloader_idx=0):
-    # dataloader_idx tells you which dataset this is.
-    ...
-
-
-
-
Note:

If you don’t need to test you don’t need to implement this method.

-
-
Note:

When the test_step() is called, the model has been put in eval mode and -PyTorch gradients have been disabled. At the end of the test epoch, the model goes back -to training mode and gradients are enabled.

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-class dasf.ml.dl.models.devconvnet.TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.devconvnet.TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.devconvnet.TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.devconvnet.TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/dl/models/index.html b/docs/autoapi/dasf/ml/dl/models/index.html deleted file mode 100644 index 8cbaa8b..0000000 --- a/docs/autoapi/dasf/ml/dl/models/index.html +++ /dev/null @@ -1,415 +0,0 @@ - - - - - - - dasf.ml.dl.models — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
-

dasf.ml.dl.models

-
-

Submodules

-
- -
-
-
-

Package Contents

-
-

Classes

- - - - - - - - - - - - - - - -

TorchPatchDeConvNet

Base class for all neural network modules.

TorchPatchDeConvNetSkip

Base class for all neural network modules.

TorchSectionDeConvNet

Base class for all neural network modules.

TorchSectionDeConvNetSkip

Base class for all neural network modules.

-
-
-class dasf.ml.dl.models.TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-class dasf.ml.dl.models.TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]
-

Bases: NNModule

-

Base class for all neural network modules.

-

Your models should also subclass this class.

-

Modules can also contain other Modules, allowing to nest them in -a tree structure. You can assign the submodules as regular attributes:

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
-
-

Submodules assigned in this way will be registered, and will have their -parameters converted too when you call to(), etc.

-
-

Note

-

As per the example above, an __init__() call to the parent class -must be made before assignment on the child.

-
-
-
Variables:
-

training (bool) – Boolean represents whether this module is in training or -evaluation mode.

-
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

-
-
-forward(x)[source]
-

Same as torch.nn.Module.forward().

-
-
Args:

*args: Whatever you decide to pass into the forward method. -**kwargs: Keyword arguments are also possible.

-
-
Return:

Your model’s output

-
-
-
- -
-
-init_vgg16_params(vgg16, copy_fc8=True)[source]
-
- -
-
-load()[source]
-

This is just a no-op load method.

-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/dl/pytorch_lightning/index.html b/docs/autoapi/dasf/ml/dl/pytorch_lightning/index.html deleted file mode 100644 index e083133..0000000 --- a/docs/autoapi/dasf/ml/dl/pytorch_lightning/index.html +++ /dev/null @@ -1,430 +0,0 @@ - - - - - - - dasf.ml.dl.pytorch_lightning — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
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- -
-

dasf.ml.dl.pytorch_lightning

-
-

Module Contents

-
-

Classes

- - - - - - - - - -

TorchDataLoader

A DataModule standardizes the training, val, test splits, data preparation and transforms. The main

NeuralNetClassifier

-
-
-

Functions

- - - - - - - - - -

run_dask_clustered(func[, client])

fit(model, X, y, max_iter, accel, strategy, devices, ngpus)

-
-
-class dasf.ml.dl.pytorch_lightning.TorchDataLoader(train, val=None, test=None, batch_size=64)[source]
-

Bases: pytorch_lightning.LightningDataModule

-

A DataModule standardizes the training, val, test splits, data preparation and transforms. The main -advantage is consistent data splits, data preparation and transforms across models.

-

Example:

-
class MyDataModule(LightningDataModule):
-    def __init__(self):
-        super().__init__()
-    def prepare_data(self):
-        # download, split, etc...
-        # only called on 1 GPU/TPU in distributed
-    def setup(self, stage):
-        # make assignments here (val/train/test split)
-        # called on every process in DDP
-    def train_dataloader(self):
-        train_split = Dataset(...)
-        return DataLoader(train_split)
-    def val_dataloader(self):
-        val_split = Dataset(...)
-        return DataLoader(val_split)
-    def test_dataloader(self):
-        test_split = Dataset(...)
-        return DataLoader(test_split)
-    def teardown(self):
-        # clean up after fit or test
-        # called on every process in DDP
-
-
-
-
Attributes:
-
prepare_data_per_node:

If True, each LOCAL_RANK=0 will call prepare data. -Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

-
-
allow_zero_length_dataloader_with_multiple_devices:

If True, dataloader with zero length within local rank is allowed. -Default value is False.

-
-
-
-
-
-
-prepare_data()[source]
-

Use this to download and prepare data. Downloading and saving data with multiple processes (distributed -settings) will result in corrupted data. Lightning ensures this method is called only within a single -process, so you can safely add your downloading logic within.

-
-

Warning

-

DO NOT set state to the model (use setup instead) -since this is NOT called on every device

-
-

Example:

-
def prepare_data(self):
-    # good
-    download_data()
-    tokenize()
-    etc()
-
-    # bad
-    self.split = data_split
-    self.some_state = some_other_state()
-
-
-

In a distributed environment, prepare_data can be called in two ways -(using prepare_data_per_node)

-
    -

  1. Once per node. This is the default and is only called on LOCAL_RANK=0.

    2. -

  2. Once in total. Only called on GLOBAL_RANK=0.

    3. -
-

Example:

-
# DEFAULT
-# called once per node on LOCAL_RANK=0 of that node
-class LitDataModule(LightningDataModule):
-    def __init__(self):
-        super().__init__()
-        self.prepare_data_per_node = True
-
-
-# call on GLOBAL_RANK=0 (great for shared file systems)
-class LitDataModule(LightningDataModule):
-    def __init__(self):
-        super().__init__()
-        self.prepare_data_per_node = False
-
-
-

This is called before requesting the dataloaders:

-
model.prepare_data()
-initialize_distributed()
-model.setup(stage)
-model.train_dataloader()
-model.val_dataloader()
-model.test_dataloader()
-model.predict_dataloader()
-
-
-
- -
-
-setup(stage=None)[source]
-

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when -you need to build models dynamically or adjust something about them. This hook is called on every process -when using DDP.

-
-
Args:

stage: either 'fit', 'validate', 'test', or 'predict'

-
-
-

Example:

-
class LitModel(...):
-    def __init__(self):
-        self.l1 = None
-
-    def prepare_data(self):
-        download_data()
-        tokenize()
-
-        # don't do this
-        self.something = else
-
-    def setup(self, stage):
-        data = load_data(...)
-        self.l1 = nn.Linear(28, data.num_classes)
-
-
-
- -
-
-train_dataloader()[source]
-

An iterable or collection of iterables specifying training samples.

-

For more information about multiple dataloaders, see this section.

-

The dataloader you return will not be reloaded unless you set -:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to -a positive integer.

-

For data processing use the following pattern:

-
-
-
-

However, the above are only necessary for distributed processing.

-
-

Warning

-

do not assign state in prepare_data

-
- -
-
Note:

Lightning tries to add the correct sampler for distributed and arbitrary hardware. -There is no need to set it yourself.

-
-
-
- -
-
-val_dataloader()[source]
-

An iterable or collection of iterables specifying validation samples.

-

For more information about multiple dataloaders, see this section.

-

The dataloader you return will not be reloaded unless you set -:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to -a positive integer.

-

It’s recommended that all data downloads and preparation happen in prepare_data().

- -
-
Note:

Lightning tries to add the correct sampler for distributed and arbitrary hardware -There is no need to set it yourself.

-
-
Note:

If you don’t need a validation dataset and a validation_step(), you don’t need to -implement this method.

-
-
-
- -
-
-test_dataloader()[source]
-

An iterable or collection of iterables specifying test samples.

-

For more information about multiple dataloaders, see this section.

-

For data processing use the following pattern:

-
-
-
-

However, the above are only necessary for distributed processing.

-
-

Warning

-

do not assign state in prepare_data

-
- -
-
Note:

Lightning tries to add the correct sampler for distributed and arbitrary hardware. -There is no need to set it yourself.

-
-
Note:

If you don’t need a test dataset and a test_step(), you don’t need to implement -this method.

-
-
-
- -
- -
-
-dasf.ml.dl.pytorch_lightning.run_dask_clustered(func, client=None, **kwargs)[source]
-
- -
-
-dasf.ml.dl.pytorch_lightning.fit(model, X, y, max_iter, accel, strategy, devices, ngpus, batch_size=32, plugins=None)[source]
-
- -
-
-class dasf.ml.dl.pytorch_lightning.NeuralNetClassifier(model, max_iter=100, batch_size=32)[source]
-

Bases: dasf.transforms.base.Fit

-
-
-_lazy_fit_generic(X, y, accel, ngpus)[source]
-
- -
-
-_lazy_fit_gpu(X, y=None)[source]
-
- -
-
-_lazy_fit_cpu(X, y=None)[source]
-
- -
-
-__fit_generic(X, y, accel, ngpus)
-
- -
-
-_fit_gpu(X, y=None)[source]
-
- -
-
-_fit_cpu(X, y=None)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/index.html b/docs/autoapi/dasf/ml/index.html deleted file mode 100644 index 731dc4e..0000000 --- a/docs/autoapi/dasf/ml/index.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - dasf.ml — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
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-
- -
- -
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-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/mixture/classifier/index.html b/docs/autoapi/dasf/ml/mixture/classifier/index.html deleted file mode 100644 index c22ad98..0000000 --- a/docs/autoapi/dasf/ml/mixture/classifier/index.html +++ /dev/null @@ -1,156 +0,0 @@ - - - - - - - dasf.ml.mixture.classifier — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.mixture.classifier

-
-

Module Contents

-
-

Classes

- - - - - - -

MixtureClassifier

-
-
-class dasf.ml.mixture.classifier.MixtureClassifier[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.FitPredict, dasf.transforms.base.FitTransform, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

-
-
-abstract fit(X, y=None, sample_weight=None)[source]
-
- -
-
-abstract fit_predict(X, y=None, sample_weight=None)[source]
-
- -
-
-abstract fit_transform(X, y=None)[source]
-
- -
-
-abstract get_params()[source]
-
- -
-
-abstract set_params()[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/mixture/gmm/index.html b/docs/autoapi/dasf/ml/mixture/gmm/index.html deleted file mode 100644 index 1824d36..0000000 --- a/docs/autoapi/dasf/ml/mixture/gmm/index.html +++ /dev/null @@ -1,156 +0,0 @@ - - - - - - - dasf.ml.mixture.gmm — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.mixture.gmm

-
-

Module Contents

-
-

Classes

- - - - - - -

GaussianMixture

-
-
-class dasf.ml.mixture.gmm.GaussianMixture(n_components=1, *, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)[source]
-

Bases: dasf.ml.mixture.classifier.MixtureClassifier

-
-
-_fit_cpu(X, y=None)[source]
-
- -
-
-_fit_predict_cpu(X, y=None)[source]
-
- -
-
-_predict_cpu(X, y=None)[source]
-
- -
-
-_set_params_cpu(**params)[source]
-
- -
-
-_get_params_cpu(deep=True)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/model_selection/index.html b/docs/autoapi/dasf/ml/model_selection/index.html deleted file mode 100644 index b742ecb..0000000 --- a/docs/autoapi/dasf/ml/model_selection/index.html +++ /dev/null @@ -1,144 +0,0 @@ - - - - - - - dasf.ml.model_selection — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.model_selection

-
-

Submodules

-
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/model_selection/split/index.html b/docs/autoapi/dasf/ml/model_selection/split/index.html deleted file mode 100644 index 9c6ed2a..0000000 --- a/docs/autoapi/dasf/ml/model_selection/split/index.html +++ /dev/null @@ -1,176 +0,0 @@ - - - - - - - dasf.ml.model_selection.split — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.model_selection.split

-
-

Module Contents

-
-

Classes

- - - - - - -

train_test_split

-
-
-class dasf.ml.model_selection.split.train_test_split(output='train', test_size=None, train_size=None, random_state=None, shuffle=None, blockwise=True, convert_mixed_types=False, **kwargs)
-

Bases: dasf.transforms.TargeteredTransform, dasf.transforms.Transform

-
-
-_lazy_transform_cpu(X)[source]
-
- -
-
-abstract _lazy_transform_gpu(X)[source]
-
- -
-
-_transform_cpu(X)[source]
-
- -
-
-_transform_gpu(X)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/neighbors/index.html b/docs/autoapi/dasf/ml/neighbors/index.html deleted file mode 100644 index b258814..0000000 --- a/docs/autoapi/dasf/ml/neighbors/index.html +++ /dev/null @@ -1,183 +0,0 @@ - - - - - - - dasf.ml.neighbors — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.neighbors

-
-

Submodules

-
- -
-
-
-

Package Contents

-
-

Classes

- - - - - - -

NearestNeighbors

-
-
-class dasf.ml.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, verbose=False, output_type=None, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

-
-
-_fit_cpu(X, y=None, **kwargs)[source]
-
- -
-
-_fit_gpu(X, y=None, **kwargs)[source]
-
- -
-
-_get_params_cpu(deep=True, **kwargs)[source]
-
- -
-
-_set_params_cpu(**params)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/neighbors/neighbors/index.html b/docs/autoapi/dasf/ml/neighbors/neighbors/index.html deleted file mode 100644 index 85495b4..0000000 --- a/docs/autoapi/dasf/ml/neighbors/neighbors/index.html +++ /dev/null @@ -1,176 +0,0 @@ - - - - - - - dasf.ml.neighbors.neighbors — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.ml.neighbors.neighbors

-
-

Module Contents

-
-

Classes

- - - - - - -

NearestNeighbors

-
-
-class dasf.ml.neighbors.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None, handle=None, verbose=False, output_type=None, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

-
-
-_fit_cpu(X, y=None, **kwargs)[source]
-
- -
-
-_fit_gpu(X, y=None, **kwargs)[source]
-
- -
-
-_get_params_cpu(deep=True, **kwargs)[source]
-
- -
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-_set_params_cpu(**params)[source]
-
- -
- -
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- - -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/preprocessing/index.html b/docs/autoapi/dasf/ml/preprocessing/index.html deleted file mode 100644 index aaf3e98..0000000 --- a/docs/autoapi/dasf/ml/preprocessing/index.html +++ /dev/null @@ -1,263 +0,0 @@ - - - - - - - dasf.ml.preprocessing — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- -
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dasf.ml.preprocessing

-
-

Submodules

-
- -
-
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Package Contents

-
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Classes

- - - - - - -

StantardScaler

-
-
-class dasf.ml.preprocessing.StantardScaler(copy=True, with_mean=True, with_std=True, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.FitTransform, dasf.transforms.base.TargeteredTransform

-
-
-_lazy_fit_cpu(X, y=None)[source]
-
- -
-
-_lazy_fit_gpu(X, y=None)[source]
-
- -
-
-_fit_cpu(X, y=None)[source]
-
- -
-
-_fit_gpu(X, y=None)[source]
-
- -
-
-_lazy_fit_transform_cpu(X, y=None)[source]
-
- -
-
-_lazy_fit_transform_gpu(X, y=None)[source]
-
- -
-
-_fit_transform_cpu(X, y=None)[source]
-
- -
-
-_fit_transform_gpu(X, y=None)[source]
-
- -
-
-_lazy_partial_fit_cpu(X, y=None)[source]
-
- -
-
-_lazy_partial_fit_gpu(X, y=None)[source]
-
- -
-
-_fit_partial_cpu(X, y=None)[source]
-
- -
-
-_fit_partial_gpu(X, y=None)[source]
-
- -
-
-_lazy_transform_cpu(X, copy=None)[source]
-
- -
-
-_lazy_transform_gpu(X, copy=None)[source]
-
- -
-
-_transform_cpu(X, copy=None)[source]
-
- -
-
-_transform_gpu(X, copy=None)[source]
-
- -
-
-_lazy_inverse_transform_cpu(X, copy=None)[source]
-
- -
-
-_lazy_inverse_transform_gpu(X, copy=None)[source]
-
- -
-
-_inverse_transform_cpu(X, copy=None)[source]
-
- -
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-_inverse_transform_gpu(X, copy=None)[source]
-
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/preprocessing/standardscaler/index.html b/docs/autoapi/dasf/ml/preprocessing/standardscaler/index.html deleted file mode 100644 index c81c45a..0000000 --- a/docs/autoapi/dasf/ml/preprocessing/standardscaler/index.html +++ /dev/null @@ -1,256 +0,0 @@ - - - - - - - dasf.ml.preprocessing.standardscaler — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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dasf.ml.preprocessing.standardscaler

-
-

Module Contents

-
-

Classes

- - - - - - -

StantardScaler

-
-
-class dasf.ml.preprocessing.standardscaler.StantardScaler(copy=True, with_mean=True, with_std=True, **kwargs)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.FitTransform, dasf.transforms.base.TargeteredTransform

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dasf.ml.svm

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Submodules

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Package Contents

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Classes

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SVC

SVR

LinearSVC

LinearSVR

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-
-class dasf.ml.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, nochange_steps=1000, random_state=None)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

-
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-_get_params_cpu(deep=True)[source]
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-class dasf.ml.svm.SVR(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, nochange_steps=1000)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

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-class dasf.ml.svm.LinearSVC(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

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-class dasf.ml.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

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dasf.ml.svm.svm

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Module Contents

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Classes

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SVC

SVR

LinearSVC

LinearSVR

-
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-class dasf.ml.svm.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, nochange_steps=1000, random_state=None)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

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-_get_params_cpu(deep=True)[source]
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-_set_params_cpu(**params)[source]
-
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-class dasf.ml.svm.svm.SVR(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, nochange_steps=1000)[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

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- -
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-class dasf.ml.svm.svm.LinearSVC(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

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-
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-class dasf.ml.svm.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')[source]
-

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/xgboost/index.html b/docs/autoapi/dasf/ml/xgboost/index.html deleted file mode 100644 index 37296e7..0000000 --- a/docs/autoapi/dasf/ml/xgboost/index.html +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - dasf.ml.xgboost — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.xgboost

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Package Contents

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Classes

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XGBRegressor

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-class dasf.ml.xgboost.XGBRegressor(max_depth=None, max_leaves=None, max_bin=None, grow_policy=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, sampling_method=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs)[source]
-

Bases: dasf.transforms.Fit, dasf.transforms.FitPredict, dasf.transforms.Predict

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/ml/xgboost/xgboost/index.html b/docs/autoapi/dasf/ml/xgboost/xgboost/index.html deleted file mode 100644 index cf49b79..0000000 --- a/docs/autoapi/dasf/ml/xgboost/xgboost/index.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - dasf.ml.xgboost.xgboost — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.ml.xgboost.xgboost

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Module Contents

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Classes

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XGBRegressor

-
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-class dasf.ml.xgboost.xgboost.XGBRegressor(max_depth=None, max_leaves=None, max_bin=None, grow_policy=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, sampling_method=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type=None, gpu_id=None, validate_parameters=None, predictor=None, enable_categorical=False, max_cat_to_onehot=None, eval_metric=None, early_stopping_rounds=None, callbacks=None, **kwargs)[source]
-

Bases: dasf.transforms.Fit, dasf.transforms.FitPredict, dasf.transforms.Predict

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/pipeline/executors/base/index.html b/docs/autoapi/dasf/pipeline/executors/base/index.html deleted file mode 100644 index c523249..0000000 --- a/docs/autoapi/dasf/pipeline/executors/base/index.html +++ /dev/null @@ -1,190 +0,0 @@ - - - - - - - dasf.pipeline.executors.base — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.pipeline.executors.base

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Module Contents

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Classes

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Executor

-
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-class dasf.pipeline.executors.base.Executor[source]
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-property ngpus: int
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Return type:
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int

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bool

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-pre_run(pipeline)[source]
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dasf.pipeline.executors.dask

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Module Contents

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Classes

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DaskPipelineExecutor

A pipeline engine based on dask data flow.

DaskTasksPipelineExecutor

A not centric execution engine based on dask.

DaskPBSPipelineExecutor

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Attributes

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GPU_SUPPORTED

-
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-dasf.pipeline.executors.dask.GPU_SUPPORTED
-
- -
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-class dasf.pipeline.executors.dask.DaskPipelineExecutor(address=None, port=8786, local=False, use_gpu=False, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None)[source]
-

Bases: dasf.pipeline.executors.base.Executor

-

A pipeline engine based on dask data flow.

-

Keyword arguments: -address – address of the Dask scheduler (default None). -port – port of the Dask scheduler (default 8786). -local – kicks off a new local Dask cluster (default False). -use_gpu – in conjunction with local, it kicks off a local CUDA Dask

-
-

cluster (default False).

-
-

profiler – sets a Dask profiler. -gpu_allocator – sets which is the memory allocator for GPU (default cupy). -cluster_kwargs – extra Dask parameters like memory, processes, etc. -client_kwargs – extra Client parameters.

-
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-execute(fn, *args, **kwargs)[source]
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-register_dataset(**kwargs)[source]
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-has_dataset(key)[source]
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-shutdown(gracefully=True)[source]
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-class dasf.pipeline.executors.dask.DaskTasksPipelineExecutor(address=None, port=8786, local=False, use_gpu=True, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None)[source]
-

Bases: DaskPipelineExecutor

-

A not centric execution engine based on dask.

-

Keyword arguments: -address – address of the Dask scheduler (default None). -port – port of the Dask scheduler (default 8786). -local – kicks off a new local Dask cluster (default False). -use_gpu – in conjunction with local, it kicks off a local CUDA Dask

-
-

cluster (default False).

-
-

profiler – sets a Dask profiler. -gpu_allocator – sets which is the memory allocator for GPU (default cupy). -cluster_kwargs – extra Dask parameters like memory, processes, etc. -client_kwargs – extra Client parameters.

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-shutdown(gracefully=True)[source]
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-class dasf.pipeline.executors.dask.DaskPBSPipelineExecutor(**kwargs)[source]
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Bases: dasf.pipeline.executors.base.Executor

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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/pipeline/executors/index.html b/docs/autoapi/dasf/pipeline/executors/index.html deleted file mode 100644 index 804ec79..0000000 --- a/docs/autoapi/dasf/pipeline/executors/index.html +++ /dev/null @@ -1,323 +0,0 @@ - - - - - - - dasf.pipeline.executors — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.pipeline.executors

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Submodules

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Package Contents

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Classes

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Executor

DaskPipelineExecutor

A pipeline engine based on dask data flow.

DaskPBSPipelineExecutor

DaskTasksPipelineExecutor

A not centric execution engine based on dask.

-
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-class dasf.pipeline.executors.Executor[source]
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Return type:
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int

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bool

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-class dasf.pipeline.executors.DaskPipelineExecutor(address=None, port=8786, local=False, use_gpu=False, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None)[source]
-

Bases: dasf.pipeline.executors.base.Executor

-

A pipeline engine based on dask data flow.

-

Keyword arguments: -address – address of the Dask scheduler (default None). -port – port of the Dask scheduler (default 8786). -local – kicks off a new local Dask cluster (default False). -use_gpu – in conjunction with local, it kicks off a local CUDA Dask

-
-

cluster (default False).

-
-

profiler – sets a Dask profiler. -gpu_allocator – sets which is the memory allocator for GPU (default cupy). -cluster_kwargs – extra Dask parameters like memory, processes, etc. -client_kwargs – extra Client parameters.

-
-
-property ngpus
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-property is_connected
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- -
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-execute(fn, *args, **kwargs)[source]
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-register_dataset(**kwargs)[source]
-
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-has_dataset(key)[source]
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-get_dataset(key)[source]
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-shutdown(gracefully=True)[source]
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-class dasf.pipeline.executors.DaskPBSPipelineExecutor(**kwargs)[source]
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Bases: dasf.pipeline.executors.base.Executor

-
- -
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-class dasf.pipeline.executors.DaskTasksPipelineExecutor(address=None, port=8786, local=False, use_gpu=True, profiler=None, gpu_allocator='cupy', cluster_kwargs=None, client_kwargs=None)[source]
-

Bases: DaskPipelineExecutor

-

A not centric execution engine based on dask.

-

Keyword arguments: -address – address of the Dask scheduler (default None). -port – port of the Dask scheduler (default 8786). -local – kicks off a new local Dask cluster (default False). -use_gpu – in conjunction with local, it kicks off a local CUDA Dask

-
-

cluster (default False).

-
-

profiler – sets a Dask profiler. -gpu_allocator – sets which is the memory allocator for GPU (default cupy). -cluster_kwargs – extra Dask parameters like memory, processes, etc. -client_kwargs – extra Client parameters.

-
-
-pre_run(pipeline)[source]
-
- -
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-
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-
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-
- -
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-has_dataset(key)[source]
-
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-get_dataset(key)[source]
-
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-shutdown(gracefully=True)[source]
-
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- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/pipeline/executors/ray/index.html b/docs/autoapi/dasf/pipeline/executors/ray/index.html deleted file mode 100644 index c76fba5..0000000 --- a/docs/autoapi/dasf/pipeline/executors/ray/index.html +++ /dev/null @@ -1,200 +0,0 @@ - - - - - - - dasf.pipeline.executors.ray — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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dasf.pipeline.executors.ray

-
-

Module Contents

-
-

Classes

- - - - - - -

RayPipelineExecutor

A pipeline engine based on ray data flow.

-
-
-

Attributes

- - - - - - -

USE_RAY

-
-
-dasf.pipeline.executors.ray.USE_RAY = True
-
- -
-
-class dasf.pipeline.executors.ray.RayPipelineExecutor(address=None, port=6379, local=False, use_gpu=False, ray_kwargs=None)[source]
-

Bases: dasf.pipeline.executors.base.Executor

-

A pipeline engine based on ray data flow.

-

Keyword arguments: -address – address of the Dask scheduler (default None). -port – port of the Ray head (default 8786). -local – kicks off a new local Ray cluster (default False). -use_gpu – in conjunction with local, it kicks off a local CUDA Ray

-
-

cluster (default False).

-
-
-
-property ngpus
-
- -
-
-property is_connected
-
- -
-
-execute(fn, *args, **kwargs)[source]
-
- -
-
-__del__()[source]
-
- -
- -
-
-
- - -
-
- -
-
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-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/pipeline/executors/wrapper/index.html b/docs/autoapi/dasf/pipeline/executors/wrapper/index.html deleted file mode 100644 index 6b5a796..0000000 --- a/docs/autoapi/dasf/pipeline/executors/wrapper/index.html +++ /dev/null @@ -1,161 +0,0 @@ - - - - - - - dasf.pipeline.executors.wrapper — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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dasf.pipeline.executors.wrapper

-
-

Module Contents

-
-

Classes

- - - - - - -

PrefectPipelineExecutor

-
-
-class dasf.pipeline.executors.wrapper.PrefectPipelineExecutor
-

Bases: prefect.executors.local.LocalExecutor

-
-
-property dtype
-
- -
- -
-
-
- - -
-
- -
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- - -
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dasf.pipeline

-
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Subpackages

-
- -
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Submodules

-
- -
-
-
-

Package Contents

-
-

Classes

- - - - - - - - - -

Pipeline

PipelinePlugin

-
-
-class dasf.pipeline.Pipeline(name, executor=None, verbose=False, callbacks=None)[source]
-
-
Parameters:
-

callbacks (List[PipelinePlugin]) –

-
-
-
-
-register_plugin(plugin)[source]
-
-
Parameters:
-

plugin (Union[PipelinePlugin, distributed.diagnostics.plugin.WorkerPlugin]) –

-
-
-
- -
-
-execute_callbacks(func_name, *args, **kwargs)[source]
-
-
Parameters:
-

func_name (str) –

-
-
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-__add_into_dag(obj, func_name, parameters=None, itself=None)
-
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-__inspect_element(obj)
-
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-add(obj, **kwargs)[source]
-
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-visualize(filename=None)[source]
-
- -
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-__register_dataset(dataset)
-
- -
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-__execute(func, params, name)
-
- -
-
-get_result_from(obj)[source]
-
- -
-
-run()[source]
-
- -
- -
-
-class dasf.pipeline.PipelinePlugin[source]
-
-
-on_pipeline_start(fn_keys)[source]
-
- -
-
-on_pipeline_end()[source]
-
- -
-
-on_task_start(func, params, name)[source]
-
- -
-
-on_task_end(func, params, name, ret)[source]
-
- -
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-on_task_error(func, params, name, exception)[source]
-
- -
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- - -
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- - -
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dasf.pipeline.pipeline

-
-

Module Contents

-
-

Classes

- - - - - - - - - -

PipelinePlugin

Pipeline

-
-
-class dasf.pipeline.pipeline.PipelinePlugin[source]
-
-
-on_pipeline_start(fn_keys)[source]
-
- -
-
-on_pipeline_end()[source]
-
- -
-
-on_task_start(func, params, name)[source]
-
- -
-
-on_task_end(func, params, name, ret)[source]
-
- -
-
-on_task_error(func, params, name, exception)[source]
-
- -
- -
-
-class dasf.pipeline.pipeline.Pipeline(name, executor=None, verbose=False, callbacks=None)[source]
-
-
Parameters:
-

callbacks (List[PipelinePlugin]) –

-
-
-
-
-register_plugin(plugin)[source]
-
-
Parameters:
-

plugin (Union[PipelinePlugin, distributed.diagnostics.plugin.WorkerPlugin]) –

-
-
-
- -
-
-execute_callbacks(func_name, *args, **kwargs)[source]
-
-
Parameters:
-

func_name (str) –

-
-
-
- -
-
-__add_into_dag(obj, func_name, parameters=None, itself=None)
-
- -
-
-__inspect_element(obj)
-
- -
-
-add(obj, **kwargs)[source]
-
- -
-
-visualize(filename=None)[source]
-
- -
-
-__register_dataset(dataset)
-
- -
-
-__execute(func, params, name)
-
- -
-
-get_result_from(obj)[source]
-
- -
-
-run()[source]
-
- -
- -
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-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/pipeline/types/index.html b/docs/autoapi/dasf/pipeline/types/index.html deleted file mode 100644 index 82c8356..0000000 --- a/docs/autoapi/dasf/pipeline/types/index.html +++ /dev/null @@ -1,177 +0,0 @@ - - - - - - - dasf.pipeline.types — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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dasf.pipeline.types

-
-

Module Contents

-
-

Classes

- - - - - - -

TaskExecutorType

Enum where members are also (and must be) ints

-
-
-class dasf.pipeline.types.TaskExecutorType[source]
-

Bases: enum.IntEnum

-

Enum where members are also (and must be) ints

-

Initialize self. See help(type(self)) for accurate signature.

-
-
-single_cpu
-
- -
-
-multi_cpu
-
- -
-
-single_gpu
-
- -
-
-multi_gpu
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/database/index.html b/docs/autoapi/dasf/profile/database/index.html deleted file mode 100644 index 110d6e3..0000000 --- a/docs/autoapi/dasf/profile/database/index.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - dasf.profile.database — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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-
- -
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dasf.profile.database

-
-

Module Contents

-
-

Classes

- - - - - - - - - - - - -

TraceEvent

TraceDatabase

SingleFileTraceDatabase

-
-
-class dasf.profile.database.TraceEvent[source]
-
-
-name: str
-
- -
-
-phase: str
-
- -
-
-timestamp: float
-
- -
-
-process_id: str
-
- -
-
-thread_id: str
-
- -
-
-category: List[str]
-
- -
-
-data: dict
-
- -
-
-thread_timestamp: float
-
- -
-
-color_name: str
-
- -
-
-duration: float
-
- -
-
-thread_duration: float
-
- -
- -
-
-class dasf.profile.database.TraceDatabase[source]
-
-
-abstract add_trace_event(trace)[source]
-
-
Parameters:
-

trace (TraceEvent) –

-
-
-
- -
-
-abstract commit()[source]
-
- -
-
-abstract get_traces()[source]
-
-
Return type:
-

List[TraceEvent]

-
-
-
- -
- -
-
-class dasf.profile.database.SingleFileTraceDatabase(path, encoder=json.dumps, decoder=json.loads)[source]
-

Bases: TraceDatabase

-
-
Parameters:
-
    -

  • path (pathlib.Path) –

    • -

  • encoder (callable) –

    • -

  • decoder (callable) –

    • -
-
-
-
-
-add_trace_event(trace)[source]
-
-
Parameters:
-

trace (TraceEvent) –

-
-
Return type:
-

int

-
-
-
- -
-
-get_traces()[source]
-
-
Return type:
-

List[TraceEvent]

-
-
-
- -
- -
-
-
- - -
-
- -
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-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/event/index.html b/docs/autoapi/dasf/profile/event/index.html deleted file mode 100644 index 82412a1..0000000 --- a/docs/autoapi/dasf/profile/event/index.html +++ /dev/null @@ -1,336 +0,0 @@ - - - - - - - dasf.profile.event — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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- -
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- -
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dasf.profile.event

-
-

Module Contents

-
-

Classes

- - - - - - - - - - - - -

Singleton

TraceDatabase

Profile

-
-
-

Functions

- - - - - - - - - - - - - - - - - - - - - -

get_time_ms()

add_trace_duration_begin(name, process_id, thread_id)

add_trace_duration_end(name, process_id, thread_id[, ...])

add_trace_complete(name, process_id, thread_id, ...[, ...])

get_traces()

to_chrome_event_format(trace_events[, trace_options, ...])

-
-
-class dasf.profile.event.Singleton[source]
-

Bases: type

-
-
-_instances
-
- -
-
-__call__(*args, **kwargs)[source]
-

Call self as a function.

-
- -
- -
-
-class dasf.profile.event.TraceDatabase(database=None)[source]
-
-
Parameters:
-

database (TraceDatabase) –

-
-
-
-
-property database: TraceDatabase
-
-
Return type:
-

TraceDatabase

-
-
-
- -
-
-db_name: str = 'traces.txt'
-
- -
- -
-
-dasf.profile.event.get_time_ms()[source]
-
- -
-
-dasf.profile.event.add_trace_duration_begin(name, process_id, thread_id, category=None, timestamp=None, thread_timestamp=None, data=None)[source]
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • process_id (str) –

    • -

  • thread_id (str) –

    • -

  • category (List[str]) –

    • -

  • timestamp (float) –

    • -

  • thread_timestamp (float) –

    • -

  • data (dict) –

    • -
-
-
-
- -
-
-dasf.profile.event.add_trace_duration_end(name, process_id, thread_id, category=None, timestamp=None, thread_timestamp=None, data=None)[source]
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • process_id (str) –

    • -

  • thread_id (str) –

    • -

  • category (List[str]) –

    • -

  • timestamp (float) –

    • -

  • thread_timestamp (float) –

    • -

  • data (dict) –

    • -
-
-
-
- -
-
-dasf.profile.event.add_trace_complete(name, process_id, thread_id, timestamp, duration, thread_timestamp=None, thread_duration=None, category=None, data=None)[source]
-
-
Parameters:
-
    -

  • name (str) –

    • -

  • process_id (str) –

    • -

  • thread_id (str) –

    • -

  • timestamp (float) –

    • -

  • duration (float) –

    • -

  • thread_timestamp (float) –

    • -

  • thread_duration (float) –

    • -

  • category (List[str]) –

    • -

  • data (dict) –

    • -
-
-
-
- -
-
-dasf.profile.event.get_traces()[source]
-
-
Return type:
-

List[dasf.profile.database.TraceEvent]

-
-
-
- -
-
-dasf.profile.event.to_chrome_event_format(trace_events, trace_options=None, format_kwargs=None)[source]
-
-
Parameters:
-
-
-
Return type:
-

str

-
-
-
- -
-
-class dasf.profile.event.Profile(trace_file='traces.txt', remove_old_trace_file=True, processed_filename='profile.json', process_trace_options=None, process_trace_kwargs=None)[source]
-
-
Parameters:
-
    -

  • trace_file (str) –

    • -

  • remove_old_trace_file (bool) –

    • -

  • processed_filename (Optional[str]) –

    • -

  • process_trace_options (dict) –

    • -

  • process_trace_kwargs (dict) –

    • -
-
-
-
-
-__enter__()[source]
-
- -
-
-__exit__(exc_type, exc_val, exc_tb)[source]
-
- -
- -
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-
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-
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/index.html b/docs/autoapi/dasf/profile/index.html deleted file mode 100644 index 6c40b64..0000000 --- a/docs/autoapi/dasf/profile/index.html +++ /dev/null @@ -1,157 +0,0 @@ - - - - - - - dasf.profile — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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- - -
- -
-
-
- -
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- -
-

dasf.profile.plugins.dasf

-
-

Module Contents

-
-

Classes

- - - - - - -

PipelineTaskTimer

-
-
-class dasf.profile.plugins.dasf.PipelineTaskTimer[source]
-

Bases: dasf.pipeline.PipelinePlugin

-
-
-on_task_start(func, params, name)[source]
-
- -
-
-on_task_end(func, params, name, ret)[source]
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/plugins/dask/index.html b/docs/autoapi/dasf/profile/plugins/dask/index.html deleted file mode 100644 index 9898bd7..0000000 --- a/docs/autoapi/dasf/profile/plugins/dask/index.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - - dasf.profile.plugins.dask — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
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- -
-

dasf.profile.plugins.dask

-
-

Module Contents

-
-

Classes

- - - - - - -

TaskTimePlugin

Interface to extend the Worker

-
-
-class dasf.profile.plugins.dask.TaskTimePlugin[source]
-

Bases: distributed.diagnostics.plugin.WorkerPlugin

-

Interface to extend the Worker

-

A worker plugin enables custom code to run at different stages of the Workers’ -lifecycle.

-

A plugin enables custom code to run at each of step of a Workers’s life. Whenever such -an event happens, the corresponding method on this class will be called. Note that the -user code always runs within the Worker’s main thread.

-

To implement a plugin implement some of the methods of this class and register -the plugin to your client in order to have it attached to every existing and -future workers with Client.register_worker_plugin.

-
-

Examples

-
>>> class ErrorLogger(WorkerPlugin):
-...     def __init__(self, logger):
-...         self.logger = logger
-...
-...     def setup(self, worker):
-...         self.worker = worker
-...
-...     def transition(self, key, start, finish, *args, **kwargs):
-...         if finish == 'error':
-...             ts = self.worker.tasks[key]
-...             exc_info = (type(ts.exception), ts.exception, ts.traceback)
-...             self.logger.error(
-...                 "Error during computation of '%s'.", key,
-...                 exc_info=exc_info
-...             )
-
-
-
>>> import logging
->>> plugin = ErrorLogger(logging)
->>> client.register_worker_plugin(plugin)  
-
-
-
-
-setup(worker)[source]
-

Run when the plugin is attached to a worker. This happens when the plugin is registered -and attached to existing workers, or when a worker is created after the plugin has been -registered.

-
- -
-
-transition(key, start, finish, *args, **kwargs)[source]
-

Throughout the lifecycle of a task (see Worker State), Workers are instructed by the scheduler to compute -certain tasks, resulting in transitions in the state of each task. The -Worker owning the task is then notified of this state transition.

-

Whenever a task changes its state, this method will be called.

-
-

Warning

-

This is an advanced feature and the transition mechanism and details -of task states are subject to change without deprecation cycle.

-
-
-
Parameters
-

key : string -start : string

-
-

Start state of the transition. -One of waiting, ready, executing, long-running, memory, error.

-
-
-
finishstring

Final state of the transition.

-
-
-

kwargs : More options passed when transitioning

-
-
- -
-
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/plugins/index.html b/docs/autoapi/dasf/profile/plugins/index.html deleted file mode 100644 index b534ea8..0000000 --- a/docs/autoapi/dasf/profile/plugins/index.html +++ /dev/null @@ -1,146 +0,0 @@ - - - - - - - dasf.profile.plugins — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/profile/plugins/resource_monitor/index.html b/docs/autoapi/dasf/profile/plugins/resource_monitor/index.html deleted file mode 100644 index 79c4d2b..0000000 --- a/docs/autoapi/dasf/profile/plugins/resource_monitor/index.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - dasf.profile.plugins.resource_monitor — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

dasf.profile.plugins.resource_monitor

-
-

Module Contents

-
-

Classes

- - - - - - - - - -

Format

ResourceMonitor

-
-
-

Functions

- - - - - - -

run_continuously(scheduler[, interval])

Continuously run, while executing pending jobs at each

-
-
-class dasf.profile.plugins.resource_monitor.Format
-
-
-static temperature(value)
-
- -
-
-static byte_value(value)
-
- -
-
-static percent(value)
-
- -
- -
-
-dasf.profile.plugins.resource_monitor.run_continuously(scheduler, interval=1)
-

Continuously run, while executing pending jobs at each -elapsed time interval. -@return cease_continuous_run: threading. Event which can -be set to cease continuous run. Please note that it is -intended behavior that run_continuously() does not run -missed jobs. For example, if you’ve registered a job that -should run every minute and you set a continuous run -interval of one hour then your job won’t be run 60 times -at each interval but only once.

-
- -
-
-class dasf.profile.plugins.resource_monitor.ResourceMonitor(path=None, monitor_interval=0.1, verbose=False)
-

Bases: dasf.pipeline.PipelinePlugin

-
-
Parameters:
-
    -

  • path (str) –

    • -

  • verbose (bool) –

    • -
-
-
-
-
-static get_info(event_list, verbose=False)
-
-
Parameters:
-

verbose (bool) –

-
-
-
- -
-
-on_pipeline_start(fn_keys)
-
- -
-
-on_pipeline_end()
-
- -
- -
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/transforms/base/index.html b/docs/autoapi/dasf/transforms/base/index.html deleted file mode 100644 index b7edb17..0000000 --- a/docs/autoapi/dasf/transforms/base/index.html +++ /dev/null @@ -1,450 +0,0 @@ - - - - - - - dasf.transforms.base — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.transforms.base

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Module Contents

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Classes

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Fit

FitPredict

FitTransform

Predict

GetParams

SetParams

Transform

TargeteredTransform

MappedTransform

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-class dasf.transforms.base.Fit[source]
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-abstract _lazy_fit_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_gpu(X, y=None, **kwargs)[source]
-
- -
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-abstract _fit_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_gpu(X, y=None, **kwargs)[source]
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- -
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-fit(X, y, sample_weight=None, **kwargs)[source]
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- -
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-static fit_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.base.FitPredict[source]
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-abstract _lazy_fit_predict_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_predict_gpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_predict_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_predict_gpu(X, y=None, **kwargs)[source]
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- -
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-fit_predict(X, y=None, **kwargs)[source]
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- -
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-static fit_predict_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.base.FitTransform[source]
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-abstract _lazy_fit_transform_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_transform_gpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_transform_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_transform_gpu(X, y=None, **kwargs)[source]
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- -
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-fit_transform(X, y=None, **kwargs)[source]
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- -
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-static fit_transform_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.base.Predict[source]
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-abstract _lazy_predict_cpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _lazy_predict_gpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _predict_cpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _predict_gpu(X, sample_weight=None, **kwargs)[source]
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-predict(X, sample_weight=None, **kwargs)[source]
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- -
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-static predict_from_model(model, X, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.base.GetParams[source]
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-abstract _lazy_get_params_cpu(deep=True, **kwargs)[source]
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- -
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-abstract _lazy_get_params_gpu(deep=True, **kwargs)[source]
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-abstract _get_params_cpu(deep=True, **kwargs)[source]
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-abstract _get_params_gpu(deep=True, **kwargs)[source]
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-get_params(deep=True, **kwargs)[source]
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-class dasf.transforms.base.SetParams[source]
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-abstract _lazy_set_params_cpu(**params)[source]
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-abstract _lazy_set_params_gpu(**params)[source]
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-abstract _set_params_cpu(**params)[source]
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-abstract _set_params_gpu(**params)[source]
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-set_params(**params)[source]
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- -
- -
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-class dasf.transforms.base.Transform[source]
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-abstract _lazy_transform_cpu(X, **kwargs)[source]
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-abstract _lazy_transform_gpu(X, **kwargs)[source]
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- -
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-abstract _transform_cpu(X, **kwargs)[source]
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-abstract _transform_gpu(X, **kwargs)[source]
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-transform(X, **kwargs)[source]
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-static transform_from_model(model, X, **kwargs)[source]
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- -
- -
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-class dasf.transforms.base.TargeteredTransform(run_local=None, run_gpu=None)[source]
-

Bases: Transform

-
- -
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-class dasf.transforms.base.MappedTransform(function, depth=None, boundary=None, trim=True, output_chunk=None, drop_axis=None, new_axis=None)[source]
-

Bases: Transform

-
-
-__lazy_transform_generic(X, xp, **kwargs)
-
- -
-
-_lazy_transform_cpu(X, **kwargs)[source]
-
- -
-
-_lazy_transform_gpu(X, **kwargs)[source]
-
- -
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-_transform_cpu(X, **kwargs)[source]
-
- -
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-_transform_gpu(X, **kwargs)[source]
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- -
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-transform(X, **kwargs)[source]
-
- -
- -
-
-
- - -
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- -
-
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-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/transforms/index.html b/docs/autoapi/dasf/transforms/index.html deleted file mode 100644 index 93c9c90..0000000 --- a/docs/autoapi/dasf/transforms/index.html +++ /dev/null @@ -1,689 +0,0 @@ - - - - - - - dasf.transforms — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.transforms

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Submodules

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Package Contents

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Classes

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ArraysToDataFrame

ArrayToZarr

ArrayToHDF5

ZarrToArray

Normalize

SliceArray

SliceArrayByPercent

Reshape

Reshape data with a new shape.

PersistDaskData

Allow persisting a dask array to memory and return a copy of the object.

ComputeDaskData

Allow persisting a dask array to memory. It will gather the data blocks

Fit

FitPredict

FitTransform

Predict

GetParams

TargeteredTransform

MappedTransform

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-class dasf.transforms.ArraysToDataFrame[source]
-

Bases: dasf.transforms.base.Transform

-
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-__transform_generic(X, y)
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- -
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-_lazy_transform_cpu(X=None, **kwargs)[source]
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- -
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-_lazy_transform_gpu(X=None, **kwargs)[source]
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- -
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-_transform_gpu(X=None, **kwargs)[source]
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- -
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-_transform_cpu(X=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.ArrayToZarr(chunks=None, save=True, filename=None)[source]
-

Bases: dasf.transforms.base.Transform

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-static _convert_filename(url)[source]
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- -
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-_lazy_transform_generic_all(data)[source]
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- -
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-_transform_generic_all(data, chunks, **kwargs)[source]
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- -
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-_lazy_transform_generic(X, **kwargs)[source]
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-_transform_generic(X, **kwargs)[source]
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-_lazy_transform_gpu(X, **kwargs)[source]
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-_lazy_transform_cpu(X, **kwargs)[source]
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-_transform_gpu(X, **kwargs)[source]
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-_transform_cpu(X, **kwargs)[source]
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- -
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-class dasf.transforms.ArrayToHDF5(dataset_path, chunks=None, save=True, filename=None)[source]
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Bases: dasf.transforms.base.Transform

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-static _convert_filename(url)[source]
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- -
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-_lazy_transform_generic_all(data)[source]
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-_transform_generic_all(data)[source]
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-_lazy_transform_generic(X, **kwargs)[source]
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-_transform_generic(X, **kwargs)[source]
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-_lazy_transform_gpu(X, **kwargs)[source]
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-_lazy_transform_cpu(X, **kwargs)[source]
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-_transform_gpu(X, **kwargs)[source]
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-_transform_cpu(X, **kwargs)[source]
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- -
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-class dasf.transforms.ZarrToArray(chunks=None, save=True, filename=None)[source]
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Bases: dasf.transforms.base.Transform

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-static _convert_filename(url)[source]
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- -
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-transform(X)[source]
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- -
- -
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-class dasf.transforms.Normalize[source]
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Bases: dasf.transforms.base.Transform

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-transform(X)[source]
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- -
- -
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-class dasf.transforms.SliceArray(output_size)[source]
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-transform(X)[source]
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- -
- -
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-class dasf.transforms.SliceArrayByPercent(x=100.0, y=100.0, z=100.0)[source]
-

Bases: dasf.transforms.base.Transform

-
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-transform(X)[source]
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- -
- -
-
-class dasf.transforms.Reshape(shape)[source]
-

Reshape data with a new shape.

-
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Parameters

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shapetuple

The new shape of the data.

-
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-
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-run(X)[source]
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- -
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Parameters:
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shape (tuple) –

-
-
-
- -
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-class dasf.transforms.PersistDaskData[source]
-

Bases: dasf.transforms.base.Transform

-

Allow persisting a dask array to memory and return a copy of the object. -It will gather the data blocks from all workers and resembles locally.

-
-
-__lazy_transform_generic(X)
-
- -
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-_lazy_transform_cpu(X)[source]
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- -
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-_lazy_transform_gpu(X)[source]
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- -
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-_transform_cpu(X)[source]
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- -
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-_transform_gpu(X)[source]
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- -
- -
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-class dasf.transforms.ComputeDaskData[source]
-

Bases: dasf.transforms.base.Transform

-

Allow persisting a dask array to memory. It will gather the data blocks -from all workers and resembles locally.

-
-
-__lazy_transform_generic(X)
-
- -
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-_lazy_transform_cpu(X)[source]
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- -
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-_lazy_transform_gpu(X)[source]
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- -
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-_transform_cpu(X)[source]
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- -
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-_transform_gpu(X)[source]
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- -
- -
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-class dasf.transforms.Fit[source]
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-abstract _lazy_fit_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_gpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_gpu(X, y=None, **kwargs)[source]
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- -
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-fit(X, y, sample_weight=None, **kwargs)[source]
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- -
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-static fit_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.FitPredict[source]
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-abstract _lazy_fit_predict_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_predict_gpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_predict_cpu(X, y=None, **kwargs)[source]
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-abstract _fit_predict_gpu(X, y=None, **kwargs)[source]
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-fit_predict(X, y=None, **kwargs)[source]
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- -
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-static fit_predict_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
- -
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-class dasf.transforms.FitTransform[source]
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-abstract _lazy_fit_transform_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _lazy_fit_transform_gpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_transform_cpu(X, y=None, **kwargs)[source]
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- -
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-abstract _fit_transform_gpu(X, y=None, **kwargs)[source]
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-fit_transform(X, y=None, **kwargs)[source]
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- -
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-static fit_transform_from_model(model, X, y, sample_weight=None, **kwargs)[source]
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- -
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-class dasf.transforms.Predict[source]
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-abstract _lazy_predict_cpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _lazy_predict_gpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _predict_cpu(X, sample_weight=None, **kwargs)[source]
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- -
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-abstract _predict_gpu(X, sample_weight=None, **kwargs)[source]
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-predict(X, sample_weight=None, **kwargs)[source]
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- -
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-static predict_from_model(model, X, sample_weight=None, **kwargs)[source]
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- -
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-class dasf.transforms.GetParams[source]
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-abstract _lazy_get_params_cpu(deep=True, **kwargs)[source]
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-abstract _lazy_get_params_gpu(deep=True, **kwargs)[source]
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-abstract _get_params_cpu(deep=True, **kwargs)[source]
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-abstract _get_params_gpu(deep=True, **kwargs)[source]
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-get_params(deep=True, **kwargs)[source]
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- -
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-class dasf.transforms.TargeteredTransform(run_local=None, run_gpu=None)[source]
-

Bases: Transform

-
- -
-
-class dasf.transforms.MappedTransform(function, depth=None, boundary=None, trim=True, output_chunk=None, drop_axis=None, new_axis=None)[source]
-

Bases: Transform

-
-
-__lazy_transform_generic(X, xp, **kwargs)
-
- -
-
-_lazy_transform_cpu(X, **kwargs)[source]
-
- -
-
-_lazy_transform_gpu(X, **kwargs)[source]
-
- -
-
-_transform_cpu(X, **kwargs)[source]
-
- -
-
-_transform_gpu(X, **kwargs)[source]
-
- -
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-transform(X, **kwargs)[source]
-
- -
- -
-
-
- - -
-
- -
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-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/transforms/memory/index.html b/docs/autoapi/dasf/transforms/memory/index.html deleted file mode 100644 index eaf9351..0000000 --- a/docs/autoapi/dasf/transforms/memory/index.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - dasf.transforms.memory — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.transforms.memory

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Module Contents

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Classes

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PersistDaskData

Allow persisting a dask array to memory and return a copy of the object.

ComputeDaskData

Allow persisting a dask array to memory. It will gather the data blocks

-
-
-class dasf.transforms.memory.PersistDaskData[source]
-

Bases: dasf.transforms.base.Transform

-

Allow persisting a dask array to memory and return a copy of the object. -It will gather the data blocks from all workers and resembles locally.

-
-
-__lazy_transform_generic(X)
-
- -
-
-_lazy_transform_cpu(X)[source]
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- -
-
-_lazy_transform_gpu(X)[source]
-
- -
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-_transform_cpu(X)[source]
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- -
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-_transform_gpu(X)[source]
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- -
- -
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-class dasf.transforms.memory.ComputeDaskData[source]
-

Bases: dasf.transforms.base.Transform

-

Allow persisting a dask array to memory. It will gather the data blocks -from all workers and resembles locally.

-
-
-__lazy_transform_generic(X)
-
- -
-
-_lazy_transform_cpu(X)[source]
-
- -
-
-_lazy_transform_gpu(X)[source]
-
- -
-
-_transform_cpu(X)[source]
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- -
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-_transform_gpu(X)[source]
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- -
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/transforms/operations/index.html b/docs/autoapi/dasf/transforms/operations/index.html deleted file mode 100644 index e04979a..0000000 --- a/docs/autoapi/dasf/transforms/operations/index.html +++ /dev/null @@ -1,199 +0,0 @@ - - - - - - - dasf.transforms.operations — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.transforms.operations

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Module Contents

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Classes

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Reshape

Reshape data with a new shape.

SliceArray

SliceArrayByPercent

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-class dasf.transforms.operations.Reshape(shape)[source]
-

Reshape data with a new shape.

-
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Parameters

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shapetuple

The new shape of the data.

-
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-
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-run(X)[source]
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- -
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Parameters:
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shape (tuple) –

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-class dasf.transforms.operations.SliceArray(output_size)[source]
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-transform(X)[source]
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- -
- -
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-class dasf.transforms.operations.SliceArrayByPercent(x=100.0, y=100.0, z=100.0)[source]
-

Bases: dasf.transforms.base.Transform

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-transform(X)[source]
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- -
- -
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- - -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/transforms/transforms/index.html b/docs/autoapi/dasf/transforms/transforms/index.html deleted file mode 100644 index 9f3ecca..0000000 --- a/docs/autoapi/dasf/transforms/transforms/index.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - dasf.transforms.transforms — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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dasf.transforms.transforms

-
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Module Contents

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Classes

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Normalize

ArrayToZarr

ArrayToHDF5

ZarrToArray

ArraysToDataFrame

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-class dasf.transforms.transforms.Normalize[source]
-

Bases: dasf.transforms.base.Transform

-
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-transform(X)[source]
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- -
- -
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-class dasf.transforms.transforms.ArrayToZarr(chunks=None, save=True, filename=None)[source]
-

Bases: dasf.transforms.base.Transform

-
-
-static _convert_filename(url)[source]
-
- -
-
-_lazy_transform_generic_all(data)[source]
-
- -
-
-_transform_generic_all(data, chunks, **kwargs)[source]
-
- -
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-_lazy_transform_generic(X, **kwargs)[source]
-
- -
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-_transform_generic(X, **kwargs)[source]
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- -
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-_lazy_transform_gpu(X, **kwargs)[source]
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- -
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-_lazy_transform_cpu(X, **kwargs)[source]
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- -
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-_transform_gpu(X, **kwargs)[source]
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- -
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-_transform_cpu(X, **kwargs)[source]
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- -
- -
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-class dasf.transforms.transforms.ArrayToHDF5(dataset_path, chunks=None, save=True, filename=None)[source]
-

Bases: dasf.transforms.base.Transform

-
-
-static _convert_filename(url)[source]
-
- -
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-_lazy_transform_generic_all(data)[source]
-
- -
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-_transform_generic_all(data)[source]
-
- -
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-_lazy_transform_generic(X, **kwargs)[source]
-
- -
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-_transform_generic(X, **kwargs)[source]
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- -
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-_lazy_transform_gpu(X, **kwargs)[source]
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-_lazy_transform_cpu(X, **kwargs)[source]
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- -
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-_transform_gpu(X, **kwargs)[source]
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- -
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-_transform_cpu(X, **kwargs)[source]
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- -
- -
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-class dasf.transforms.transforms.ZarrToArray(chunks=None, save=True, filename=None)[source]
-

Bases: dasf.transforms.base.Transform

-
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-static _convert_filename(url)[source]
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- -
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-transform(X)[source]
-
- -
- -
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-class dasf.transforms.transforms.ArraysToDataFrame[source]
-

Bases: dasf.transforms.base.Transform

-
-
-__transform_generic(X, y)
-
- -
-
-_lazy_transform_cpu(X=None, **kwargs)[source]
-
- -
-
-_lazy_transform_gpu(X=None, **kwargs)[source]
-
- -
-
-_transform_gpu(X=None, **kwargs)[source]
-
- -
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-_transform_cpu(X=None, **kwargs)[source]
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- -
- -
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- - -
-
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/benchmark/index.html b/docs/autoapi/dasf/utils/benchmark/index.html deleted file mode 100644 index 697b03a..0000000 --- a/docs/autoapi/dasf/utils/benchmark/index.html +++ /dev/null @@ -1,215 +0,0 @@ - - - - - - - dasf.utils.benchmark — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.utils.benchmark

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Module Contents

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Classes

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TimeBenchmark

MemoryBenchmark

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Attributes

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USE_MEMRAY

USE_MEM_PROF

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-dasf.utils.benchmark.USE_MEMRAY = True
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-dasf.utils.benchmark.USE_MEM_PROF = True
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- -
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-class dasf.utils.benchmark.TimeBenchmark(backend='cprofile')[source]
-
-
-__enter__()[source]
-
- -
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-__exit__(*args, **kwargs)[source]
-
- -
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-run(function, *args, **kwargs)[source]
-
- -
- -
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-class dasf.utils.benchmark.MemoryBenchmark(backend='memray', debug=False, output_file=None, *args, **kwargs)[source]
-
-
-__enter__()[source]
-
- -
-
-__exit__(*args, **kwargs)[source]
-
- -
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-run(function, *args, **kwargs)[source]
-
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/decorators/index.html b/docs/autoapi/dasf/utils/decorators/index.html deleted file mode 100644 index 7062338..0000000 --- a/docs/autoapi/dasf/utils/decorators/index.html +++ /dev/null @@ -1,220 +0,0 @@ - - - - - - - dasf.utils.decorators — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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- -
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dasf.utils.decorators

-

Implementations of important library decorators.

-
-

Module Contents

-
-

Functions

- - - - - - - - - - - - - - - - - - - - - - - - -

is_forced_local(cls)

Returns if object is forced to run in a CPU.

is_forced_gpu(cls)

Returns if object is forced to run in a GPU.

fetch_from_dask(*args, **kwargs)

Fetches to CPU all parameters in a Dask data type.

fetch_from_gpu(*args, **kwargs)

Fetches to CPU all parameters in a GPU data type.

fetch_args_from_dask(func)

Fetches to CPU all function parameters in a Dask data type.

fetch_args_from_gpu(func)

Fetches to CPU all function parameters in a GPU data type.

task_handler(func)

Returns all mapped functions corresponding to the executor in place.

-
-
-dasf.utils.decorators.is_forced_local(cls)[source]
-

Returns if object is forced to run in a CPU.

-
- -
-
-dasf.utils.decorators.is_forced_gpu(cls)[source]
-

Returns if object is forced to run in a GPU.

-
- -
-
-dasf.utils.decorators.fetch_from_dask(*args, **kwargs)[source]
-

Fetches to CPU all parameters in a Dask data type.

-
-
Return type:
-

tuple

-
-
-
- -
-
-dasf.utils.decorators.fetch_from_gpu(*args, **kwargs)[source]
-

Fetches to CPU all parameters in a GPU data type.

-
-
Return type:
-

tuple

-
-
-
- -
-
-dasf.utils.decorators.fetch_args_from_dask(func)[source]
-

Fetches to CPU all function parameters in a Dask data type.

-
- -
-
-dasf.utils.decorators.fetch_args_from_gpu(func)[source]
-

Fetches to CPU all function parameters in a GPU data type.

-
- -
-
-dasf.utils.decorators.task_handler(func)[source]
-

Returns all mapped functions corresponding to the executor in place.

-
- -
-
-
- - -
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- -
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-
-
- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/funcs/index.html b/docs/autoapi/dasf/utils/funcs/index.html deleted file mode 100644 index ebfb52a..0000000 --- a/docs/autoapi/dasf/utils/funcs/index.html +++ /dev/null @@ -1,517 +0,0 @@ - - - - - - - dasf.utils.funcs — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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-
- -
-

dasf.utils.funcs

-

Generic and regular functions.

-
-

Module Contents

-
-

Classes

- - - - - - -

NotebookProgressBar

A class that represents a thread of control.

-
-
-

Functions

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

human_readable_size(size[, decimal])

converts data size into the proper measurement

get_full_qualname(obj)

Return fully qualified name of objects.

get_worker_info(client)

Returns a list of workers (sorted), and the DNS name for the master host

sync_future_loop(futures)

Synchronize all futures submitted to workers.

download_file(url[, filename, directory])

Download a generic file and save it.

download_file_from_gdrive(file_id[, filename, directory])

Download a file from Google Drive using gdrive file id.

get_machine_memory_avail()

Return free memory available from a single machine.

set_executor_default()

Return executor as a CPU (default) instance.

set_executor_gpu()

Return executor as a GPU instance.

is_executor_single(dtype)

Return if the executor is a single machine instance.

is_executor_cluster(dtype)

Return if the executor is a cluster instance.

is_executor_cpu(dtype)

Return if the executor is a CPU instance.

is_executor_gpu(dtype)

Return if the executor is a GPU instance.

is_gpu_supported()

Return if GPU is supported.

is_dask_local_supported()

Return if Dask is supported locally by the executor.

get_dask_running_client()

Get Dask runner stanza.

is_dask_supported()

Return if Dask is supported by the executor.

is_dask_gpu_supported()

Return if any node supports GPU.

get_gpu_count()

Get single node GPU count.

get_dask_gpu_count([fetch])

Get how many GPUs are available in each worker.

block_chunk_reduce(dask_data, output_chunk)

Reduce the chunk according the new output size.

return_local_and_gpu(executor, local, gpu)

Return executor type based on passed preferences.

get_dask_mem_usage(profiler)

Get Dask memory usage profile.

is_notebook()

Return if the code is being executed in a IPyNotebook.

-
-
-

Attributes

- - - - - - -

GPU_SUPPORTED

-
-
-dasf.utils.funcs.GPU_SUPPORTED
-
- -
-
-dasf.utils.funcs.human_readable_size(size, decimal=3)[source]
-

converts data size into the proper measurement

-
-
Return type:
-

str

-
-
-
- -
-
-dasf.utils.funcs.get_full_qualname(obj)[source]
-

Return fully qualified name of objects.

-
-
Return type:
-

str

-
-
-
- -
-
-dasf.utils.funcs.get_worker_info(client)[source]
-

Returns a list of workers (sorted), and the DNS name for the master host -The master is the 0th worker’s host

-
-
Return type:
-

list

-
-
-
- -
-
-dasf.utils.funcs.sync_future_loop(futures)[source]
-

Synchronize all futures submitted to workers.

-
- -
-
-class dasf.utils.funcs.NotebookProgressBar[source]
-

Bases: threading.Thread

-

A class that represents a thread of control.

-

This class can be safely subclassed in a limited fashion. There are two ways -to specify the activity: by passing a callable object to the constructor, or -by overriding the run() method in a subclass.

-

This constructor should always be called with keyword arguments. Arguments are:

-

group should be None; reserved for future extension when a ThreadGroup -class is implemented.

-

target is the callable object to be invoked by the run() -method. Defaults to None, meaning nothing is called.

-

name is the thread name. By default, a unique name is constructed of -the form “Thread-N” where N is a small decimal number.

-

args is the argument tuple for the target invocation. Defaults to ().

-

kwargs is a dictionary of keyword arguments for the target -invocation. Defaults to {}.

-

If a subclass overrides the constructor, it must make sure to invoke -the base class constructor (Thread.__init__()) before doing anything -else to the thread.

-
-
-MIN_CUR
-
- -
-
-MIN_TOTAL
-
- -
-
-show()[source]
-
- -
-
-set_current(current, total)[source]
-
- -
-
-set_error(error)[source]
-
- -
-
-run()[source]
-

Method representing the thread’s activity.

-

You may override this method in a subclass. The standard run() method -invokes the callable object passed to the object’s constructor as the -target argument, if any, with sequential and keyword arguments taken -from the args and kwargs arguments, respectively.

-
- -
- -
-
-dasf.utils.funcs.download_file(url, filename=None, directory=None)[source]
-

Download a generic file and save it.

-
- -
-
-dasf.utils.funcs.download_file_from_gdrive(file_id, filename=None, directory=None)[source]
-

Download a file from Google Drive using gdrive file id.

-
- -
-
-dasf.utils.funcs.get_machine_memory_avail()[source]
-

Return free memory available from a single machine.

-
- -
-
-dasf.utils.funcs.set_executor_default()[source]
-

Return executor as a CPU (default) instance.

-
- -
-
-dasf.utils.funcs.set_executor_gpu()[source]
-

Return executor as a GPU instance.

-
- -
-
-dasf.utils.funcs.is_executor_single(dtype)[source]
-

Return if the executor is a single machine instance.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_executor_cluster(dtype)[source]
-

Return if the executor is a cluster instance.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_executor_cpu(dtype)[source]
-

Return if the executor is a CPU instance.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_executor_gpu(dtype)[source]
-

Return if the executor is a GPU instance.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_gpu_supported()[source]
-

Return if GPU is supported.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_dask_local_supported()[source]
-

Return if Dask is supported locally by the executor.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.get_dask_running_client()[source]
-

Get Dask runner stanza.

-
- -
-
-dasf.utils.funcs.is_dask_supported()[source]
-

Return if Dask is supported by the executor.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.is_dask_gpu_supported()[source]
-

Return if any node supports GPU.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.funcs.get_gpu_count()[source]
-

Get single node GPU count.

-
-
Return type:
-

int

-
-
-
- -
-
-dasf.utils.funcs.get_dask_gpu_count(fetch=True)[source]
-

Get how many GPUs are available in each worker.

-
-
Return type:
-

int

-
-
-
- -
-
-dasf.utils.funcs.block_chunk_reduce(dask_data, output_chunk)[source]
-

Reduce the chunk according the new output size.

-
- -
-
-dasf.utils.funcs.return_local_and_gpu(executor, local, gpu)[source]
-

Return executor type based on passed preferences.

-
- -
-
-dasf.utils.funcs.get_dask_mem_usage(profiler)[source]
-

Get Dask memory usage profile.

-
- -
-
-dasf.utils.funcs.is_notebook()[source]
-

Return if the code is being executed in a IPyNotebook.

-
-
Return type:
-

bool

-
-
-
- -
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-
- - -
-
- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/index.html b/docs/autoapi/dasf/utils/index.html deleted file mode 100644 index 4088534..0000000 --- a/docs/autoapi/dasf/utils/index.html +++ /dev/null @@ -1,148 +0,0 @@ - - - - - - - dasf.utils — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/labels/index.html b/docs/autoapi/dasf/utils/labels/index.html deleted file mode 100644 index 7f0e3d2..0000000 --- a/docs/autoapi/dasf/utils/labels/index.html +++ /dev/null @@ -1,264 +0,0 @@ - - - - - - - dasf.utils.labels — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
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dasf.utils.labels

-
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Module Contents

-
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Classes

- - - - - - -

DaskLabel

-
-
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Functions

- - - - - - -

get_attributes()

-
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Attributes

- - - - - - - - - - - - - - - -

inside_with

g_hash_attrs

g_func_attrs

g_data_attrs

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-dasf.utils.labels.inside_with
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-dasf.utils.labels.g_hash_attrs
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- -
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-dasf.utils.labels.g_func_attrs
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-dasf.utils.labels.g_data_attrs
-
- -
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-class dasf.utils.labels.DaskLabel(start, stop, label=None, color=None)[source]
-

Bases: object

-
-
-start(start)[source]
-
- -
-
-stop(stop)[source]
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- -
-
-__name(x)
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- -
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-__add_item(key, tag, label=None, color=None, atype='data')
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- -
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-__add_func(key, tag, label, color)
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-__add_data(key, tag, label, color)
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-__generate_hashtable(data, delete_dup=False)
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-__enter(dsk)
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- -
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-__enter__()[source]
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- -
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-__exit(dsk, exc_type, exc_val, exc_tb)
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-__exit__(exc_type, exc_val, exc_tb)[source]
-
- -
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-dasf.utils.labels.get_attributes()[source]
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- - -
- -
-
-
- -
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-
- -
-

dasf.utils.logging

-

Logging helpers for functions.

-
-

Module Contents

-
-

Functions

- - - - - - -

init_logging()

Initialize logger objects to be used by modules.

-
-
-dasf.utils.logging.init_logging()[source]
-

Initialize logger objects to be used by modules.

-
-
Return type:
-

logging.Logger

-
-
-
- -
-
-
- - -
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- -
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- - - - \ No newline at end of file diff --git a/docs/autoapi/dasf/utils/types/index.html b/docs/autoapi/dasf/utils/types/index.html deleted file mode 100644 index 07c9e09..0000000 --- a/docs/autoapi/dasf/utils/types/index.html +++ /dev/null @@ -1,464 +0,0 @@ - - - - - - - dasf.utils.types — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
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-
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- -
-

dasf.utils.types

-

Data types handlers.

-
-

Module Contents

-
-

Functions

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

is_array(data)

Returns if data is a generic array.

is_dataframe(data)

Returns if data is a generic dataframe.

is_cpu_array(data)

Returns if data is a CPU arrau like Numpy.

is_cpu_dataframe(data)

Returns if data is a CPU dataframe like Pandas.

is_cpu_datatype(data)

Returns if data is a CPU data type.

is_gpu_array(data)

Returns if data is a GPU array like Cupy.

is_gpu_dataframe(data)

Returns if data is a GPU dataframe like Cudf.

is_gpu_datatype(data)

Returns if data is a GPU data type.

is_dask_cpu_array(data)

Returns if data is a Dask array with CPU internal array.

is_dask_cpu_dataframe(data)

Returns if data is a Dask dataframe with CPU internal dataframe.

is_dask_gpu_array(data)

Returns if data is a Dask array with GPU internal array.

is_dask_gpu_dataframe(data)

Returns if data is a Dask dataframe with GPU internal dataframe.

is_dask_array(data)

Returns if data is a Dask array.

is_dask_dataframe(data)

Returns if data is a Dask dataframe.

is_dask(data)

Returns if data is a Dask data type.

is_xarray_array(data)

Returns if data is a Xarray.

-
-
-

Attributes

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

ArrayCPU

DataFrameCPU

DataCPU

DaskArray

DaskDataFrameCPU

XDataArray

Array

DaskDataFrame

DataFrame

DataDask

ArrayGPU

-
-
-dasf.utils.types.ArrayCPU
-
- -
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-dasf.utils.types.DataFrameCPU
-
- -
-
-dasf.utils.types.DataCPU
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- -
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-dasf.utils.types.DaskArray
-
- -
-
-dasf.utils.types.DaskDataFrameCPU
-
- -
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-dasf.utils.types.XDataArray
-
- -
-
-dasf.utils.types.Array
-
- -
-
-dasf.utils.types.DaskDataFrame
-
- -
-
-dasf.utils.types.DataFrame
-
- -
-
-dasf.utils.types.DataDask
-
- -
-
-dasf.utils.types.ArrayGPU
-
- -
-
-dasf.utils.types.is_array(data)[source]
-

Returns if data is a generic array.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dataframe(data)[source]
-

Returns if data is a generic dataframe.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_cpu_array(data)[source]
-

Returns if data is a CPU arrau like Numpy.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_cpu_dataframe(data)[source]
-

Returns if data is a CPU dataframe like Pandas.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_cpu_datatype(data)[source]
-

Returns if data is a CPU data type.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_gpu_array(data)[source]
-

Returns if data is a GPU array like Cupy.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_gpu_dataframe(data)[source]
-

Returns if data is a GPU dataframe like Cudf.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_gpu_datatype(data)[source]
-

Returns if data is a GPU data type.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_cpu_array(data)[source]
-

Returns if data is a Dask array with CPU internal array.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_cpu_dataframe(data)[source]
-

Returns if data is a Dask dataframe with CPU internal dataframe.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_gpu_array(data)[source]
-

Returns if data is a Dask array with GPU internal array.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_gpu_dataframe(data)[source]
-

Returns if data is a Dask dataframe with GPU internal dataframe.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_array(data)[source]
-

Returns if data is a Dask array.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask_dataframe(data)[source]
-

Returns if data is a Dask dataframe.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_dask(data)[source]
-

Returns if data is a Dask data type.

-
-
Return type:
-

bool

-
-
-
- -
-
-dasf.utils.types.is_xarray_array(data)[source]
-

Returns if data is a Xarray.

-
-
Return type:
-

bool

-
-
-
- -
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-
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-
- -
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- - - - \ No newline at end of file diff --git a/docs/build_doc.sh b/docs/build_doc.sh deleted file mode 100755 index 0a99acd..0000000 --- a/docs/build_doc.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash - -make clean -sphinx-build source/ . -echo " " > ../docs/.nojekyll \ No newline at end of file diff --git a/docs/genindex.html b/docs/genindex.html deleted file mode 100644 index 3304333..0000000 --- a/docs/genindex.html +++ /dev/null @@ -1,3478 +0,0 @@ - - - - - - Index — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - -
- - -
- -
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    -
    • -
  • Index
    • -
  • -
    • -
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Index

- -
- _ - | A - | B - | C - | D - | E - | F - | G - | H - | I - | K - | L - | M - | N - | O - | P - | Q - | R - | S - | T - | U - | V - | W - | X - | Z - -
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_

- - - -
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A

- - - -
- -

B

- - - -
- -

C

- - - -
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D

- - - -
    -
  • - dasf - -
    • -
  • - dasf.datasets - -
    • -
  • - dasf.datasets.base - -
    • -
  • - dasf.datasets.datasets - -
    • -
  • - dasf.datasets.download - -
    • -
  • - dasf.debug - -
    • -
  • - dasf.debug.debug - -
    • -
  • - dasf.feature_extraction - -
    • -
  • - dasf.feature_extraction.histogram - -
    • -
  • - dasf.feature_extraction.transform - -
    • -
  • - dasf.ml - -
    • -
  • - dasf.ml.cluster - -
    • -
  • - dasf.ml.cluster.agglomerative - -
    • -
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Welcome to DASF Documentation!

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DASF is an Accelerated and Scalable Framework

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DASF is a generic framework specialized in acceleration and scaling common -techniques for Machine Learning. DASF uses most methods and function from the -most common libraries to increase the speed up of most algorithms. Part of this -is to use Dask data to scale computation and RAPIDS AI algorithms to extend the -support to GPUs as well.

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Contents

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Indices and tables

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Installation Guide

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The installation can be done using conda or docker.

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Using Docker

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To install DASF using docker, you must in the go to the build/ directory and -execute the command below directory according to your build type: cpu or -gpu.

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./build_docker.sh <cpu|gpu>
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The dasf image will be created and be ready to use. Once it is ready, you -can start a jupyter instance by executing the command:

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Using Conda

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If you just want to create a base Conda environment for DASF, you need to -create it, using the respective YAML file based on architecture: for CPUs -or GPUs. The environment name is always dasf.

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conda env create -f build/conda/{cpu,gpu}/environment.yml
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Development version

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To install this development version, all you need to do is run pip from the -root project directory (the same where pyproject.toml lives).

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python -m pip install -e .
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Testing

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If you have a working environment with DASF installed, you can execute the all -the test set. Make sure you have all development packages installed such as -pytest, parameterized and mock. To run, you need to execute -pytest from the tests/ directory.

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Overview

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DASF offers a wide range of Machine learning algorithms. Below, a table of -implemented algorithms and the respective infra-structure.

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Implemented Machine Learning Algorithms

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The table below is a list of supported machine learning algorithms by DASF framework.

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ML Algorithm

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DBSCAN

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Principles

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The growth in the use of machine learning techniques has led to the emergence of a significant number of frameworks, libraries and tools in recent times. Depending on the technique used or the purpose of the project, there will possibly be a way to develop something using what already exists. With the further growth of deep learning techniques, more of these facilities become available.

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One of the problems with these deep learning techniques is the use of data in batch format. So a large piece of data is subdivided into smaller pieces and iterated during epoch training. Today, there are no tools that process data distributedly on demand in full machine learning pipelines. There are also no tools that still use the maximum computational power using GPUs, for example.

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Taking advantage of this niche space to be explored, the DASF was created whose recursive acronym is DASF is an Accelerated and Scalable Framework. The project seeks to fill this gap in creating machine learning pipelines using large volumes of data without dividing them into batches.

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So that this was also possible, a series of libraries were gathered that could compose the framework, composing most of the functionalities proposed by it. Such tools will be specified in the next sections.

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DASF as a Simple API

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DASF tries to enable a simple API for the user to use. The idea is to make the user’s life easier when using the framework. We believe that the user should not have to worry about the details of the framework, but rather focus on the problem to be solved. The framework should be transparent to the user.

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In order to simplify the learning-curve some concepts were created to facilitate the use of the framework. The main ones are:

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  • Standard API: We try to follow the same API as scikit-learn, so that the user does not have to learn a new API to use the framework. This is a very popular API and is widely used in the community. So, operations in DASF usually implement the same methods as scikit-learn, such as fit, predict, transform, etc.

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  • Extensibility to new devices: DASF allows simple extensibility to be used in new devices (e.g., GPU) by implementing a simple interface. This allows the user to use the framework in different devices without having to worry about the details of the implementation.

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  • Extensibility to scale: For multi-node scalability we use the DASK construct graphs under the hood. This allows the user to use the framework in a distributed way without having to worry about the details of the implementation.

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    - dasf.datasets -
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    - dasf.feature_extraction.transform -
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    - dasf.ml.cluster.spectral -
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    - dasf.ml.dl -
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    - dasf.ml.dl.clusters.dask -
    - dasf.ml.dl.models -
    - dasf.ml.dl.models.devconvnet -
    - dasf.ml.dl.pytorch_lightning -
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    - dasf.ml.neighbors.neighbors -
    - dasf.ml.preprocessing -
    - dasf.ml.preprocessing.standardscaler -
    - dasf.ml.svm -
    - dasf.ml.svm.svm -
    - dasf.ml.xgboost -
    - dasf.ml.xgboost.xgboost -
    - dasf.pipeline -
    - dasf.pipeline.executors -
    - dasf.pipeline.executors.base -
    - dasf.pipeline.executors.dask -
    - dasf.pipeline.executors.ray -
    - dasf.pipeline.executors.wrapper -
    - dasf.pipeline.pipeline -
    - dasf.pipeline.types -
    - dasf.profile -
    - dasf.profile.database -
    - dasf.profile.event -
    - dasf.profile.plugins -
    - dasf.profile.plugins.dasf -
    - dasf.profile.plugins.dask -
    - dasf.profile.plugins.resource_monitor -
    - dasf.transforms -
    - dasf.transforms.base -
    - dasf.transforms.memory -
    - dasf.transforms.operations -
    - dasf.transforms.transforms -
    - dasf.utils -
    - dasf.utils.benchmark -
    - dasf.utils.decorators -
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"Installation Guide", "Overview", "Principles", "Tutorials", "Tutorial 1 - A Quick Demo", "Tutorial 2 - How to extend DASF Datasets", "Tutorial 3 - How Create Your Own Trasform", "Tutorial 4 - How Create an Agnostic Pipeline"], "terms": {"subpackag": 0, "name": [1, 3, 4, 5, 6, 19, 25, 46, 47, 49, 50, 52, 63, 69, 74, 75, 76], "download": [1, 4, 10, 27, 63, 76], "fals": [1, 4, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 30, 32, 33, 34, 37, 38, 39, 40, 42, 43, 44, 46, 47, 55, 61, 65, 73, 76], "root": [1, 3, 4, 69, 74, 75, 76], "none": [1, 4, 5, 6, 7, 8, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 46, 47, 50, 55, 56, 57, 60, 61, 63, 65], "arg": [1, 4, 7, 8, 20, 21, 25, 26, 27, 39, 40, 41, 42, 43, 44, 46, 47, 50, 53, 61, 62, 63], "kwarg": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 32, 33, 34, 35, 36, 39, 40, 41, 42, 43, 44, 46, 47, 50, 53, 56, 57, 60, 61, 62, 63], 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documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../../')) - -import sphinx_rtd_theme - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'autoapi.extension', - 'sphinx_rtd_theme', - 'sphinx.ext.viewcode', - 'sphinx.ext.autodoc.typehints', - 'sphinx.ext.mathjax', - "nbsphinx", - "IPython.sphinxext.ipython_console_highlighting" - # "sphinx_codeautolink", - # "sphinx_gallery.load_style", - # "sphinx_gallery.gen_gallery" -] - -suppress_warnings = [ - 'nbsphinx.localfile', - 'nbsphinx.gallery', - 'nbsphinx.thumbnail', - 'nbsphinx.notebooktitle', - 'nbsphinx.ipywidgets', -] - -# sphinx_gallery_conf = { -# 'examples_dirs': '../../examples', # path to your example scripts -# 'gallery_dirs': 'auto_examples', # path to where to save gallery generated output -# 'filename_pattern': '**ipynb', -# 'source_suffix': ['.rst', '.ipynb'] -# } - -####### Auto API -autoapi_type = 'python' -autoapi_dirs = ['../../dasf/'] -autoapi_member_order = 'bysource' -autoapi_python_use_implicit_namespaces = True -autoapi_python_class_content = 'both' -autoapi_file_patterns = ['*.py'] -autoapi_generate_api_docs = True -autoapi_add_toctree_entry = False -# source_suffix = '.rst' -autodoc_typehints = 'description' - - -######## NBSPHINX -nbsphinx_execute = 'never' -nbsphinx_allow_errors = True -nbsphinx_codecell_lexer = 'python3' -nbsphinx_execute_arguments = [ - "--InlineBackend.figure_formats={'svg', 'pdf'}", - "--InlineBackend.rc={'figure.dpi': 96}", -] - - - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# -- Project information ----------------------------------------------------- - -project = 'DASF' -copyright = '2022--2023, UNICAMP' -author = 'Julio Faracco ' - -# The full version, including alpha/beta/rc tags -version = "1.0b5" -release = "1.0b5" - -source_suffix = ['.rst'] -master_doc = 'index' - - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [ - '**.ipynb_checkpoints', - "**ipynb_checkpoints" -] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -htmlhelp_basename = 'librepdoc' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] - -source_encoding = 'utf-8' - -htmlhelp_basename = 'librepdoc' diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index db20367..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,32 +0,0 @@ -===================================================== -Welcome to DASF Documentation! -===================================================== - - -DASF is an Accelerated and Scalable Framework ------------------------------------------------ - -DASF is a generic framework specialized in acceleration and scaling common -techniques for Machine Learning. DASF uses most methods and function from the -most common libraries to increase the speed up of most algorithms. Part of this -is to use Dask data to scale computation and RAPIDS AI algorithms to extend the -support to GPUs as well. - -Contents ---------------- - -.. toctree:: - :maxdepth: 2 - - principles - installation - overview - tutorials - api - -Indices and tables -+++++++++++++++++++ - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/installation.rst b/docs/source/installation.rst deleted file mode 100644 index 40219c9..0000000 --- a/docs/source/installation.rst +++ /dev/null @@ -1,62 +0,0 @@ -.. _installation: - -========================== -Installation Guide -========================== - -The installation can be done using `conda` or `docker`. - -Using Docker --------------- - -To install DASF using docker, you must in the go to the `build/` directory and -execute the command below directory according to your build type: `cpu` or -`gpu`. - -.. code-block:: bash - - ./build_docker.sh - - -The `dasf` image will be created and be ready to use. Once it is ready, you -can start a jupyter instance by executing the command: - -.. code-block:: bash - - ./start_jupyter_server.sh - - -Using Conda -------------- - -If you just want to create a base Conda environment for DASF, you need to -create it, using the respective YAML file based on architecture: for CPUs -or GPUs. The environment name is always `dasf`. - - -.. code-block:: bash - - conda env create -f build/conda/{cpu,gpu}/environment.yml - - -Development version --------------------- - -To install this development version, all you need to do is run `pip` from the -root project directory (the same where `pyproject.toml` lives). - -.. code-block:: bash - - python -m pip install -e . - -Testing --------- - -If you have a working environment with DASF installed, you can execute the all -the test set. Make sure you have all development packages installed such as -**pytest**, **parameterized** and **mock**. To run, you need to execute -`pytest` from the `tests/` directory. - -.. code-block:: bash - - pytest tests/ diff --git a/docs/source/overview.rst b/docs/source/overview.rst deleted file mode 100644 index a902390..0000000 --- a/docs/source/overview.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _overview: - -========================== -Overview -========================== - -DASF offers a wide range of Machine learning algorithms. Below, a table of -implemented algorithms and the respective infra-structure. - -Implemented Machine Learning Algorithms ------------------------------------------ - -The table below is a list of supported machine learning algorithms by DASF framework. - -+--------------------------+---------+---------+---------------+---------------+ -| **ML Algorithm** | **CPU** | **GPU** | **Multi-CPU** | **Multi-GPU** | -+==========================+=========+=========+===============+===============+ -| K-Means | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| SOM | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| Agglomerative Clustering | ✓ | ✓ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| DBSCAN | ✓ | ✓ | ✗ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ -| HDBSCAN | ✓ | ✓ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| Gaussian Mixture Models | ✓ | ✗ | ✗ | ✗ | -+--------------------------+---------+---------+---------------+---------------+ -| PCA | ✓ | ✓ | ✓ | ✓ | -+--------------------------+---------+---------+---------------+---------------+ diff --git a/docs/source/principles.rst b/docs/source/principles.rst deleted file mode 100644 index 1eaa755..0000000 --- a/docs/source/principles.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. _principles: - -========================== -Principles -========================== - -The growth in the use of machine learning techniques has led to the emergence of a significant number of frameworks, libraries and tools in recent times. Depending on the technique used or the purpose of the project, there will possibly be a way to develop something using what already exists. With the further growth of deep learning techniques, more of these facilities become available. - -One of the problems with these deep learning techniques is the use of data in batch format. So a large piece of data is subdivided into smaller pieces and iterated during epoch training. Today, there are no tools that process data distributedly on demand in full machine learning pipelines. There are also no tools that still use the maximum computational power using GPUs, for example. - -Taking advantage of this niche space to be explored, the DASF was created whose recursive acronym is DASF is an Accelerated and Scalable Framework. The project seeks to fill this gap in creating machine learning pipelines using large volumes of data without dividing them into batches. - -So that this was also possible, a series of libraries were gathered that could compose the framework, composing most of the functionalities proposed by it. Such tools will be specified in the next sections. - -DASF as a Simple API ----------------------- - -DASF tries to enable a simple API for the user to use. The idea is to make the user's life easier when using the framework. We believe that the user should not have to worry about the details of the framework, but rather focus on the problem to be solved. The framework should be transparent to the user. - -In order to simplify the learning-curve some concepts were created to facilitate the use of the framework. The main ones are: - -* **Standard API**: We try to follow the same API as scikit-learn, so that the user does not have to learn a new API to use the framework. This is a very popular API and is widely used in the community. So, operations in DASF usually implement the same methods as scikit-learn, such as fit, predict, transform, etc. -* **Extensibility to new devices**: DASF allows simple extensibility to be used in new devices (e.g., GPU) by implementing a simple interface. This allows the user to use the framework in different devices without having to worry about the details of the implementation. -* **Extensibility to scale**: For multi-node scalability we use the DASK construct graphs under the hood. This allows the user to use the framework in a distributed way without having to worry about the details of the implementation. \ No newline at end of file diff --git a/docs/source/tutorials b/docs/source/tutorials deleted file mode 120000 index 2c914a3..0000000 --- a/docs/source/tutorials +++ /dev/null @@ -1 +0,0 @@ -../../examples/tutorials/ \ No newline at end of file diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst deleted file mode 100644 index f354361..0000000 --- a/docs/source/tutorials.rst +++ /dev/null @@ -1,15 +0,0 @@ -.. _tutorials: - -========================== -Tutorials -========================== - -Tutorials using DASF. - -.. toctree:: - :maxdepth: 2 - - tutorials/Tutorial_1.ipynb - tutorials/Tutorial_2.ipynb - tutorials/Tutorial_3.ipynb - tutorials/Tutorial_4.ipynb diff --git a/docs/tutorials.html b/docs/tutorials.html deleted file mode 100644 index 713cc4c..0000000 --- a/docs/tutorials.html +++ /dev/null @@ -1,132 +0,0 @@ - - - - - - - Tutorials — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
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- - - - \ No newline at end of file diff --git a/docs/tutorials/Tutorial_1.html b/docs/tutorials/Tutorial_1.html deleted file mode 100644 index 9d8bf06..0000000 --- a/docs/tutorials/Tutorial_1.html +++ /dev/null @@ -1,301 +0,0 @@ - - - - - - - Tutorial 1 - A Quick Demo — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
-
-
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- -
-

Tutorial 1 - A Quick Demo

-

In this first tutorial, we want to present some basics of DASF framework and how you can use it to manage your machine learning algorithms in a multi architecture environemnt like single machines, clusteres and GPUs.

-

If you are familiar with all the scikit-learn API, DASF has the same methodology of function notations. The only difference of DASF is that this framework is directly associated with the host environment. If you are using a clustered environment with Dask for example, you will use the optimized functions for that environment type. If you are running your code in a single GPU host environment, your code will have the -specific optimizations for that type and so on so forth.

-

Let’s try our first example with some basic clustering algorithm. First, create a simple dataset using make_blobs function.

-
-
[1]:
-
-
-
from dasf.datasets import make_blobs
-
-n_samples = 500000
-n_bins = 3
-
-# Generate 3 blobs with 2 classes where the second blob contains
-# half positive samples and half negative samples. Probability in this
-# blob is therefore 0.5.
-centers = [(-6, -6), (0, 0), (9, 1)]
-X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)
-
-
-
-

Notice that we are using the same code available in scikit-learn tutorials and demos.

-

To have a better view of the data distribution, we can plot the generated dataset.

-
-
[2]:
-
-
-
# Only to generate colors
-import numpy as np
-
-from dasf.utils.types import is_gpu_array
-
-from matplotlib import cm
-import matplotlib.pyplot as plt
-
-# Check the data just to plot
-if is_gpu_array(X):
-    X_cpu = X.get()
-else:
-    X_cpu = X
-
-colors = cm.rainbow(np.linspace(0.0, 1.0, 1))
-
-plt.figure()
-plt.scatter(
-    X_cpu[:, 0],
-    X_cpu[:, 1],
-    s=50,
-    c=colors[np.newaxis, :],
-    alpha=0.5,
-    edgecolor="k",
-)
-plt.title("Dataset")
-
-plt.show()
-
-
-
-
-
-
-
-../_images/tutorials_Tutorial_1_3_0.png -
-
-

Once, we have the big picture of how our dataset is distributed, let’s run two clustering algorithms to understand how it can be classified.

-

For this tutorial, we decided to use KMeans and SOM (Kohonen’s Self-Organized Map) as an example.

-
-
[3]:
-
-
-
from dasf.ml.cluster import KMeans
-from dasf.ml.cluster import SOM
-
-kmeans = KMeans(n_clusters=3, max_iter=100)
-som = SOM(x=1, y=3, input_len=2, num_epochs=100)
-
-
-
-

As we know our dataset defines 3 centers with 2 classes, we set KMeans n_clusters parameter with the same number of classes of our dataset. On the other hand, SOM is based on an activation map and it does not necessary needs a 1-D map with 3 activation points, but we want to use here to help the classification algorithm also. See that as we also know that our dataset contains two classes, the parameter input_len of SOM needs to be set as 2 (same number of classes).

-

Now, it is time to fit_predict both classifiers. Let’s analyze KMeans first.

-
-
[4]:
-
-
-
%time result_kmeans = kmeans.fit_predict(X)
-
-
-
-
-
-
-
-
-CPU times: user 315 ms, sys: 7.29 ms, total: 322 ms
-Wall time: 326 ms
-
-
-

KMeans is a fast algorithm compared to SOM. For further reference, let’s see the speed of the SOM algorithm.

-
-
[5]:
-
-
-
%time result_som = som.fit_predict(X)
-
-
-
-
-
-
-
-
-CPU times: user 4min, sys: 210 ms, total: 4min
-Wall time: 4min 1s
-
-
-

Now, let’s see the performance of each prediction. The first one is KMeans results.

-
-
[8]:
-
-
-
from itertools import cycle
-
-def plot_results(X, result):
-    y_unique = np.unique(result)
-
-    colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))
-
-    for this_y, color in zip(y_unique, colors):
-        if is_gpu_array(X):
-            this_X = X[result == this_y].get()
-        else:
-            this_X = X[result == this_y]
-
-        plt.scatter(
-            this_X[:, 0],
-            this_X[:, 1],
-            s=50,
-            c=color[np.newaxis, :],
-            alpha=0.5,
-            edgecolor="k",
-            label="Class %s" % this_y,
-        )
-
-plot_results(X, result_kmeans)
-
-
-
-
-
-
-
-../_images/tutorials_Tutorial_1_11_0.png -
-
-

Now, let’s see how SOM results look like.

-
-
[9]:
-
-
-
plot_results(X, result_som)
-
-
-
-
-
-
-
-../_images/tutorials_Tutorial_1_13_0.png -
-
-

As we can see, the results do not seem similar but they are accurated.

-

The idea behind this tutorial is not to exaplain how both algorithms work, but how can you use DASF framework the same way you use the most famous Machine Learning libraries.

-

If you are curious, try to run the same code using a machine with GPU. Compare the results and see if the behaviour is the same!

-
- - -
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- - - - \ No newline at end of file diff --git a/docs/tutorials/Tutorial_2.html b/docs/tutorials/Tutorial_2.html deleted file mode 100644 index 5d7b9d0..0000000 --- a/docs/tutorials/Tutorial_2.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - Tutorial 2 - How to extend DASF Datasets — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
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-
- -
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- -
-

Tutorial 2 - How to extend DASF Datasets

-

In this tutorial, we will teach you how you can extend DASF datasets to be loaded dynamically to all architetcure.

-

For this specific scenario we will use DASF Array Dataset class to show you how you can create a dataset like this using a simple NPY file.

-

To start, the first step is create and save a simple NPY file to be loaded by the dataset.

-
-
[1]:
-
-
-
### Serialize a simple array
-import numpy as np
-
-data = np.random.random((20, 20, 20))
-
-np.save("data.npy", data)
-
-
-
-

Once we have the file saved, we can create our own array dataset.

-
-
[2]:
-
-
-
from dasf.datasets import DatasetArray
-
-dataset = DatasetArray(name="My Saved NPY", root="data.npy")
-
-
-
-

From this moment, our dataset is not loaded yet. To load the data from NPY file, we need to run the function load. This object has the same dynamic generator from the previous tutorial. Here we are using a ipykernel with a GPU, then we are expecting the dataset to lad a CuPy Array. Let’s see if this is true.

-
-
[3]:
-
-
-
dataset.load()
-
-
-
-

Once it is loaded, we can slice the dataset and see what is the type of each slice.

-
-
[4]:
-
-
-
type(dataset[:2, :2, :2])
-
-
-
-
-
[4]:
-
-
-
-
-cupy._core.core.ndarray
-
-
-

What should I do if I’m using a GPU but I want to load a Numpy array?

-

All the datasets have a protected load wrapper for each platform. The code discovers which platform you are in and bind the method to its respective protected mathod.

-

In other words, if you are using load in a GPU environment as we are doing here, in fact you are executing the protected method called _load_gpu.

-

Then to load Numpy arrays, all you need to do is call directly _load_cpu.

-
-
[5]:
-
-
-
dataset._load_cpu()
-
-type(dataset[:2, :2, :2])
-
-
-
-
-
[5]:
-
-
-
-
-numpy.ndarray
-
-
-

If you need to handle a Dask array in a multi clustered environment, you can use the protected lazy methods called _lazy_*.

-

For datasets, the respective methods for load are _lazy_load_cpu and _lazy_load_gpu. Both returns a Dask Array but with different metadata.

-

Let’s see how it looks like.

-
-
[6]:
-
-
-
dataset._lazy_load_cpu()
-
-type(dataset[:2, :2, :2])
-
-
-
-
-
[6]:
-
-
-
-
-dask.array.core.Array
-
-
-

See how the internal array of this Dask dataset looks.

-
-
[7]:
-
-
-
type(dataset[:2, :2, :2]._meta)
-
-
-
-
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[7]:
-
-
-
-
-numpy.ndarray
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/tutorials/Tutorial_3.html b/docs/tutorials/Tutorial_3.html deleted file mode 100644 index f32e302..0000000 --- a/docs/tutorials/Tutorial_3.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - Tutorial 3 - How Create Your Own Trasform — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

Tutorial 3 - How Create Your Own Trasform

-

In this tutorial, we will show you how DASF organize the structure APIs to generate code for targeted to each architecture.

-

We will also show you how you can create your own object to and generate code dynamically to each platform.

-

For this, let’s use the same code we had used in Tutorial 2. Check how you can create data.npy before continue.

-

Then, we need to define our dataset.

-
-
[1]:
-
-
-
from dasf.datasets import DatasetArray
-
-dataset = DatasetArray(name="My Saved NPY", root="data.npy")
-
-
-
-

Here, we want to create a transform to multiple the data by the same data.

-

First, let’s inpect how the data looks like. We are using a GPU, so it will require to fetch data from GPU to CPU. If you are using a CPU, you just need to print the data.

-
-
[2]:
-
-
-
dataset.load()
-
-dataset[:2, :2, 0].get()
-
-
-
-
-
[2]:
-
-
-
-
-array([[0.22139306, 0.18095083],
-       [0.78598473, 0.28964964]])
-
-
-

Now, let’s create our own transform called Multiply. To generate the code targeted to the running platform, we need to import and set the respective decorator. So, the code will generate the function transform for us dynamically. To clarigy even more, we can include some a print call in each function.

-
-
[3]:
-
-
-
from dasf.transforms import Transform
-
-
-class Multiply(Transform):
-    def _lazy_transform_cpu(self, X):
-        print("Lazy CPU")
-        return X * X
-
-    def _lazy_transform_gpu(self, X):
-        print("Lazy GPU")
-        return X * X
-
-    def _transform_cpu(self, X):
-        print("CPU")
-        return X * X
-
-    def _transform_gpu(self, X):
-        print("GPU")
-        return X * X
-
-multiply = Multiply()
-
-
-
-

Now, we can transform our dataset and see what happens.

-
-
[4]:
-
-
-
result = multiply.transform(dataset)
-
-
-
-
-
-
-
-
-GPU
-
-
-

See it triggered the GPU local function. Now, let’s see and compare what is the content of result variable.

-
-
[5]:
-
-
-
result[:2, :2, 0].get()
-
-
-
-
-
[5]:
-
-
-
-
-array([[0.04901489, 0.0327432 ],
-       [0.61777199, 0.08389691]])
-
-
-

See that the result is exactly the dataset multiplied by itself. The values confirm that. Now, what happens if I would like to run CPU code instead of GPU? If I want that, I need to call directly each protected method directly.

-
-
[6]:
-
-
-
dataset._load_cpu()
-
-result = multiply._transform_cpu(dataset)
-
-result[:2, :2, 0]
-
-
-
-
-
-
-
-
-CPU
-
-
-
-
[6]:
-
-
-
-
-array([[0.04901489, 0.0327432 ],
-       [0.61777199, 0.08389691]])
-
-
-

See now that the code triggered the CPU function obviously.

-

Actually, if you pay attention, the implementation of each function are equal. Then, this class can be reduced to:

-
-
[7]:
-
-
-
class Multiply2(Transform):
-    def transform(self, X):
-        return X * X
-
-
-
-

Without decorator and all the other functions. The reason why we have all the diferentiations is that we know we will have different data manipulation for most cases.

-
- - -
-
- -
-
-
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- - - - \ No newline at end of file diff --git a/docs/tutorials/Tutorial_4.html b/docs/tutorials/Tutorial_4.html deleted file mode 100644 index d652292..0000000 --- a/docs/tutorials/Tutorial_4.html +++ /dev/null @@ -1,378 +0,0 @@ - - - - - - - Tutorial 4 - How Create an Agnostic Pipeline — DASF 1.0b5 documentation - - - - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
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- -
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-
-
- -
-

Tutorial 4 - How Create an Agnostic Pipeline

-

In this tutorial, we will show you how convert a simple code structure into a advanced and agnostic pipeline based on DAGs.

-

For this, we still can use the Tutorial 1 with a simple Machine Learning script. There we use make_blobs to generate a dataset and them we cluster it using two algorithms: KMeans and SOM.

-

First, let’s generate and save our data (you can use DASF or Scikit-learn). The objective here is just to generate some labeled data and use the DatasetLabeled as an example.

-
-
[1]:
-
-
-
import numpy as np
-
-from dasf.datasets import make_blobs
-
-n_samples = 100000
-n_bins = 3
-
-# Generate 3 blobs with 2 classes where the second blob contains
-# half positive samples and half negative samples. Probability in this
-# blob is therefore 0.5.
-centers = [(-6, -6), (0, 0), (9, 1)]
-X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)
-
-np.save("X.npy", X)
-np.save("y.npy", y)
-
-
-
-

Now, let’s import our DatasetLabeled and assign each file to the respective type.

-
-
[2]:
-
-
-
from dasf.datasets import DatasetArray
-from dasf.datasets import DatasetLabeled
-
-
-class MyMakeBlobs(DatasetLabeled):
-    def __init__(self):
-        super().__init__(name="My Own make_blobs()", download=False)
-
-        # Let's assign the train and val data.
-        self._train = DatasetArray(name="X", download=False, root="X.npy", chunks=(5000, 2))
-        self._val = DatasetArray(name="y", download=False, root="y.npy", chunks=(5000))
-
-make_blobs = MyMakeBlobs()
-
-
-
-

To reduce the variability and as an example, we can normalize the data to help the algorithms to fit better.

-
-
[3]:
-
-
-
from dasf.transforms import Normalize
-
-normalize = Normalize()
-
-
-
-

After, creating our dataset and the normalization transformation, we can start the executor. For this example, we can use Dask.

-
-
[4]:
-
-
-
from dasf.pipeline.executors import DaskPipelineExecutor
-
-dask = DaskPipelineExecutor(local=True, use_gpu=False)
-
-
-
-

Now, it is time to create our pipeline objects. We can copy and paste the same code used previously.

-
-
[5]:
-
-
-
from dasf.ml.cluster import KMeans
-from dasf.ml.cluster import SOM
-
-kmeans = KMeans(n_clusters=3, max_iter=100)
-som = SOM(x=1, y=3, input_len=2, num_epochs=100)
-
-
-
-
-
-
-
-
-WARNING: CuPy could not be imported
-WARNING: CuPy could not be imported
-WARNING: CuPy could not be imported
-
-
-

As we want to reuse the data after the pipeline execution, we need to persist the data.

-
-
[6]:
-
-
-
from dasf.transforms import PersistDaskData
-
-persist_kmeans = PersistDaskData()
-persist_som = PersistDaskData()
-
-
-
-

Then, we generate the pipeline and connect all the pieces in one single DAG.

-

Pay attention that we are passing the our fresh executor dask to the pipeline by specifying the parameter executor=.

-

To connect all the objects, we use the function add() that returns the pipeline itself. The function inputs can be refered as an argument.

-

At the end, we can visualize the DAG using visualize() method. It will plot a image that represents the graph. Let’s use one single line to do everything. It should be simple and easy to understand.

-
-
[7]:
-
-
-
from dasf.pipeline import Pipeline
-
-pipeline = Pipeline("A KMeans and SOM Pipeline", executor=dask)
-
-pipeline.add(normalize, X=make_blobs._train) \
-        .add(kmeans.fit_predict, X=normalize) \
-        .add(som.fit_predict, X=normalize) \
-        .add(persist_kmeans, X=kmeans.fit_predict) \
-        .add(persist_som, X=som.fit_predict) \
-        .visualize()
-
-
-
-
-
[7]:
-
-
-
-../_images/tutorials_Tutorial_4_13_0.svg
-
-

It is time to run our new pipeline.

-
-
[8]:
-
-
-
%time pipeline.run()
-
-
-
-
-
-
-
-
-[2022-11-25 04:36:49+0000] INFO - Beginning pipeline run for 'A KMeans and SOM Pipeline'
-[2022-11-25 04:36:49+0000] INFO - Task 'DatasetArray.load': Starting task run...
-[2022-11-25 04:36:50+0000] INFO - Task 'DatasetArray.load': Finished task run
-[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Starting task run...
-[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Finished task run
-[2022-11-25 04:36:50+0000] INFO - Task 'KMeans.fit_predict': Starting task run...
-
-
-
-
-
-
-
-/usr/local/lib/python3.8/dist-packages/dask/base.py:1367: UserWarning: Running on a single-machine scheduler when a distributed client is active might lead to unexpected results.
-  warnings.warn(
-
-
-
-
-
-
-
-[2022-11-25 04:37:00+0000] INFO - Task 'KMeans.fit_predict': Finished task run
-[2022-11-25 04:37:00+0000] INFO - Task 'SOM.fit_predict': Starting task run...
-[2022-11-25 04:37:22+0000] INFO - Task 'SOM.fit_predict': Finished task run
-[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...
-[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Finished task run
-[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...
-[2022-11-25 04:37:23+0000] INFO - Task 'PersistDaskData.transform': Finished task run
-[2022-11-25 04:37:23+0000] INFO - Pipeline run successfully
-CPU times: user 23.2 s, sys: 1.71 s, total: 24.9 s
-Wall time: 33.2 s
-
-
-

Notice that our pipeline returns two methods instead of one. To capture the result of some node, you can easily pass the same function or object to the pipeline function get_result_from().

-
-
[9]:
-
-
-
result_kmeans = pipeline.get_result_from(persist_kmeans).compute()
-result_som = pipeline.get_result_from(persist_som).compute()
-
-
-
-
-
[10]:
-
-
-
import numpy as np
-
-from itertools import cycle
-
-from matplotlib import cm
-import matplotlib.pyplot as plt
-
-def plot_results(X, result):
-    y_unique = np.unique(result)
-
-    colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))
-
-    for this_y, color in zip(y_unique, colors):
-        this_X = X[result == this_y]
-        plt.scatter(
-            this_X[:, 0],
-            this_X[:, 1],
-            s=50,
-            c=color[np.newaxis, :],
-            alpha=0.5,
-            edgecolor="k",
-            label="Class %s" % this_y,
-        )
-
-plot_results(make_blobs._train, result_kmeans)
-
-
-
-
-
-
-
-../_images/tutorials_Tutorial_4_18_0.png -
-
-
-
[11]:
-
-
-
plot_results(make_blobs._train, result_som)
-
-
-
-
-
-
-
-../_images/tutorials_Tutorial_4_19_0.png -
-
-
-
[ ]:
-
-
-

-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/examples/tutorials/Tutorial_1.ipynb b/examples/tutorials/Tutorial_1.ipynb deleted file mode 100644 index de77cbd..0000000 --- a/examples/tutorials/Tutorial_1.ipynb +++ /dev/null @@ -1,289 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "981cafbc-1031-4827-b9a5-77ae10b4fa92", - "metadata": {}, - "source": [ - "### Tutorial 1 - A Quick Demo\n", - "\n", - "In this first tutorial, we want to present some basics of DASF framework and how you can use it to manage your machine learning algorithms in a multi architecture environemnt like single machines, clusteres and GPUs.\n", - "\n", - "If you are familiar with all the [scikit-learn](https://scikit-learn.org/stable/index.html) API, DASF has the same methodology of function notations. The only difference of DASF is that this framework is directly associated with the host environment. If you are using a clustered environment with [Dask](https://www.dask.org/) for example, you will use the optimized functions for that environment type. If you are running your code in a single GPU host environment, your code will have the specific optimizations for that type and so on so forth.\n", - "\n", - "Let's try our first example with some basic clustering algorithm. First, create a simple dataset using `make_blobs` function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9a10b3d6-4d4a-498a-a03e-036679fea3fe", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import make_blobs\n", - "\n", - "n_samples = 500000\n", - "n_bins = 3\n", - "\n", - "# Generate 3 blobs with 2 classes where the second blob contains\n", - "# half positive samples and half negative samples. Probability in this\n", - "# blob is therefore 0.5.\n", - "centers = [(-6, -6), (0, 0), (9, 1)]\n", - "X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "id": "d5d5931b-08a2-402c-acc1-6378b63e1308", - "metadata": {}, - "source": [ - "Notice that we are using the same code available in scikit-learn tutorials and demos.\n", - "\n", - "To have a better view of the data distribution, we can plot the generated dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6d8b9470-6079-4b4c-8ad0-639719057ca0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Only to generate colors\n", - "import numpy as np\n", - "\n", - "from dasf.utils.types import is_gpu_array\n", - "\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Check the data just to plot\n", - "if is_gpu_array(X):\n", - " X_cpu = X.get()\n", - "else:\n", - " X_cpu = X\n", - "\n", - "colors = cm.rainbow(np.linspace(0.0, 1.0, 1))\n", - "\n", - "plt.figure()\n", - "plt.scatter(\n", - " X_cpu[:, 0],\n", - " X_cpu[:, 1],\n", - " s=50,\n", - " c=colors[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - ")\n", - "plt.title(\"Dataset\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6fc4ccc3-e5e3-426d-a145-87ebdf80782b", - "metadata": {}, - "source": [ - "Once, we have the big picture of how our dataset is distributed, let's run two clustering algorithms to understand how it can be classified.\n", - "\n", - "For this tutorial, we decided to use KMeans and SOM (Kohonen's Self-Organized Map) as an example." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f41ed448-e3a8-47b1-8f2a-c92c6013892e", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.ml.cluster import KMeans\n", - "from dasf.ml.cluster import SOM\n", - "\n", - "kmeans = KMeans(n_clusters=3, max_iter=100)\n", - "som = SOM(x=1, y=3, input_len=2, num_epochs=100)" - ] - }, - { - "cell_type": "markdown", - "id": "9afda5c9-f59a-4974-b603-1e51ac69323c", - "metadata": {}, - "source": [ - "As we know our dataset defines 3 centers with 2 classes, we set KMeans `n_clusters` parameter with the same number of classes of our dataset. On the other hand, SOM is based on an activation map and it does not necessary needs a 1-D map with 3 activation points, but we want to use here to help the classification algorithm also. See that as we also know that our dataset contains two classes, the parameter `input_len` of SOM needs to be set as **2** (same number of classes).\n", - "\n", - "Now, it is time to `fit_predict` both classifiers. Let's analyze KMeans first." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "579b0eab-8096-4f00-a993-626f2c5bafc9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 315 ms, sys: 7.29 ms, total: 322 ms\n", - "Wall time: 326 ms\n" - ] - } - ], - "source": [ - "%time result_kmeans = kmeans.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "c2f9beae-6356-4565-bc81-23fe7586851f", - "metadata": {}, - "source": [ - "KMeans is a fast algorithm compared to SOM. For further reference, let's see the speed of the SOM algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3afbb17c-4ac0-435e-a8a4-b9bc4934e697", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 4min, sys: 210 ms, total: 4min\n", - "Wall time: 4min 1s\n" - ] - } - ], - "source": [ - "%time result_som = som.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "1ed936d8-6acd-4d04-8f3d-f13ac73f83d7", - "metadata": {}, - "source": [ - "Now, let's see the performance of each prediction. The first one is KMeans results." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0cc9cff9-b6f6-49d5-9edb-cc38d21113dd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from itertools import cycle\n", - "\n", - "def plot_results(X, result):\n", - " y_unique = np.unique(result)\n", - " \n", - " colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))\n", - " \n", - " for this_y, color in zip(y_unique, colors):\n", - " if is_gpu_array(X):\n", - " this_X = X[result == this_y].get()\n", - " else:\n", - " this_X = X[result == this_y]\n", - "\n", - " plt.scatter(\n", - " this_X[:, 0],\n", - " this_X[:, 1],\n", - " s=50,\n", - " c=color[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - " label=\"Class %s\" % this_y,\n", - " )\n", - "\n", - "plot_results(X, result_kmeans)" - ] - }, - { - "cell_type": "markdown", - "id": "6ba5eac4-86b8-493c-830c-aeb0c8cb99f4", - "metadata": {}, - "source": [ - "Now, let's see how SOM results look like." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "214a124b-4c23-40ae-8e0b-0ae7e2d4b8b9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_results(X, result_som)" - ] - }, - { - "cell_type": "markdown", - "id": "13e71923-abe0-43ee-90c7-5cbef839fd97", - "metadata": {}, - "source": [ - "As we can see, the results do not seem similar but they are accurated.\n", - "\n", - "The idea behind this tutorial is not to exaplain how both algorithms work, but how can you use DASF framework the same way you use the most famous Machine Learning libraries.\n", - "\n", - "If you are curious, try to run the same code using a machine with GPU. Compare the results and see if the behaviour is the same!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tutorials/Tutorial_2.ipynb b/examples/tutorials/Tutorial_2.ipynb deleted file mode 100644 index 2e0a1db..0000000 --- a/examples/tutorials/Tutorial_2.ipynb +++ /dev/null @@ -1,225 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "66892f2a-1c92-47d6-a9c1-8cb26940f251", - "metadata": {}, - "source": [ - "### Tutorial 2 - How to extend DASF Datasets\n", - "\n", - "In this tutorial, we will teach you how you can extend DASF datasets to be loaded dynamically to all architetcure.\n", - "\n", - "For this specific scenario we will use DASF Array Dataset class to show you how you can create a dataset like this using a simple NPY file.\n", - "\n", - "To start, the first step is create and save a simple NPY file to be loaded by the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c38bd978-012f-4599-a249-b20577e3700f", - "metadata": {}, - "outputs": [], - "source": [ - "### Serialize a simple array\n", - "import numpy as np\n", - "\n", - "data = np.random.random((20, 20, 20))\n", - "\n", - "np.save(\"data.npy\", data)" - ] - }, - { - "cell_type": "markdown", - "id": "ca8b5760-bbca-4613-9958-bbaacd96ced6", - "metadata": {}, - "source": [ - "Once we have the file saved, we can create our own array dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7774cf93-a8ff-46d6-b378-0a5eff657b69", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "\n", - "dataset = DatasetArray(name=\"My Saved NPY\", root=\"data.npy\")" - ] - }, - { - "cell_type": "markdown", - "id": "a8f92ca2-ecb6-4241-b2be-726146056259", - "metadata": {}, - "source": [ - "From this moment, our dataset is not loaded yet. To load the data from NPY file, we need to run the function `load`. This object has the same dynamic generator from the previous tutorial. Here we are using a ipykernel with a GPU, then we are expecting the dataset to lad a CuPy Array. Let's see if this is true." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4b055d3f-0d96-4f1e-bcde-20bd825976bf", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.load()" - ] - }, - { - "cell_type": "markdown", - "id": "1bd783da-95e1-42cf-8ab9-da5035641a05", - "metadata": {}, - "source": [ - "Once it is loaded, we can slice the dataset and see what is the type of each slice." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d25dbe78-7abf-4ef2-a564-ed72a0bc79e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cupy._core.core.ndarray" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "d733a245-13f1-45b3-8b69-0d09a496c61d", - "metadata": {}, - "source": [ - "What should I do if I'm using a GPU but I want to load a Numpy array?\n", - "\n", - "All the datasets have a protected load wrapper for each platform. The code discovers which platform you are in and bind the method to its respective protected mathod.\n", - "\n", - "In other words, if you are using `load` in a GPU environment as we are doing here, in fact you are executing the protected method called `_load_gpu`.\n", - "\n", - "Then to load Numpy arrays, all you need to do is call directly `_load_cpu`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3ab10622-978a-4e2e-af2c-6cd9c4f6c626", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._load_cpu()\n", - "\n", - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "b2a38825-1bd7-42f0-8d65-b09553e273b4", - "metadata": {}, - "source": [ - "If you need to handle a Dask array in a multi clustered environment, you can use the protected lazy methods called `_lazy_*`.\n", - "\n", - "For datasets, the respective methods for `load` are `_lazy_load_cpu` and `_lazy_load_gpu`. Both returns a Dask Array but with different metadata.\n", - "\n", - "Let's see how it looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e5ec3104-8087-406c-aa86-71961740fcce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array.core.Array" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._lazy_load_cpu()\n", - "\n", - "type(dataset[:2, :2, :2])" - ] - }, - { - "cell_type": "markdown", - "id": "b6bc2c21-ce64-42a3-a72a-b9f895decdb9", - "metadata": {}, - "source": [ - "See how the internal array of this Dask dataset looks." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "92a556a1-643d-449e-8f5b-b5223972dbb0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dataset[:2, :2, :2]._meta)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "test_requirements": { - "single_gpu": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tutorials/Tutorial_3.ipynb b/examples/tutorials/Tutorial_3.ipynb deleted file mode 100644 index 07860e6..0000000 --- a/examples/tutorials/Tutorial_3.ipynb +++ /dev/null @@ -1,255 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a9de8fc4-806e-404b-becc-e79954a1ac9c", - "metadata": {}, - "source": [ - "### Tutorial 3 - How Create Your Own Trasform\n", - "\n", - "In this tutorial, we will show you how DASF organize the structure APIs to generate code for targeted to each architecture.\n", - "\n", - "We will also show you how you can create your own object to and generate code dynamically to each platform.\n", - "\n", - "For this, let's use the same code we had used in **Tutorial 2**. Check how you can create `data.npy` before continue.\n", - "\n", - "Then, we need to define our dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "eec84166-3f6c-420b-81e6-71f9c126a440", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "\n", - "dataset = DatasetArray(name=\"My Saved NPY\", root=\"data.npy\")" - ] - }, - { - "cell_type": "markdown", - "id": "92f243e0-64e9-4457-a18c-ea5c21740a6a", - "metadata": {}, - "source": [ - "Here, we want to create a transform to multiple the data by the same data.\n", - "\n", - "First, let's inpect how the data looks like. We are using a GPU, so it will require to fetch data from GPU to CPU. If you are using a CPU, you just need to print the data." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ef2d45e3-121c-40f1-b8d7-f84735945ee1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.22139306, 0.18095083],\n", - " [0.78598473, 0.28964964]])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.load()\n", - "\n", - "dataset[:2, :2, 0].get()" - ] - }, - { - "cell_type": "markdown", - "id": "aa7e4593-a22d-49db-976f-00b8b8d19de5", - "metadata": {}, - "source": [ - "Now, let's create our own transform called **Multiply**. To generate the code targeted to the running platform, we need to import and set the respective decorator. So, the code will generate the function `transform` for us dynamically. To clarigy even more, we can include some a `print` call in each function." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c53d25f4-3806-46d1-8f94-ac580ee46821", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import Transform\n", - "\n", - "\n", - "class Multiply(Transform):\n", - " def _lazy_transform_cpu(self, X):\n", - " print(\"Lazy CPU\")\n", - " return X * X\n", - " \n", - " def _lazy_transform_gpu(self, X):\n", - " print(\"Lazy GPU\")\n", - " return X * X\n", - " \n", - " def _transform_cpu(self, X):\n", - " print(\"CPU\")\n", - " return X * X\n", - " \n", - " def _transform_gpu(self, X):\n", - " print(\"GPU\")\n", - " return X * X\n", - "\n", - "multiply = Multiply()" - ] - }, - { - "cell_type": "markdown", - "id": "43efcdec-2639-4775-9abc-a92cf6fa7a8f", - "metadata": {}, - "source": [ - "Now, we can transform our dataset and see what happens." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "875e2a54-5506-4226-bf4e-58353408e4e2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU\n" - ] - } - ], - "source": [ - "result = multiply.transform(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "fbf295d3-9c2b-4b4f-ae7c-60c332b2842d", - "metadata": {}, - "source": [ - "See it triggered the GPU local function. Now, let's see and compare what is the content of `result` variable." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e1f6c244-51a2-42fd-964a-0a96fc4dc169", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.04901489, 0.0327432 ],\n", - " [0.61777199, 0.08389691]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result[:2, :2, 0].get()" - ] - }, - { - "cell_type": "markdown", - "id": "0f400794-0660-4ee5-b8bb-eed6e6aad03f", - "metadata": {}, - "source": [ - "See that the result is exactly the dataset multiplied by itself. The values confirm that. Now, what happens if I would like to run CPU code instead of GPU? If I want that, I need to call directly each protected method directly." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "962d9a55-3de1-43e2-86a9-d0c489dd2e90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.04901489, 0.0327432 ],\n", - " [0.61777199, 0.08389691]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset._load_cpu()\n", - "\n", - "result = multiply._transform_cpu(dataset)\n", - "\n", - "result[:2, :2, 0]" - ] - }, - { - "cell_type": "markdown", - "id": "9ab0786f-5268-40b0-8845-01900c350098", - "metadata": {}, - "source": [ - "See now that the code triggered the CPU function obviously.\n", - "\n", - "Actually, if you pay attention, the implementation of each function are equal. Then, this class can be reduced to:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7d311947-30bf-4ab2-bf43-8f1fa6dafc0a", - "metadata": {}, - "outputs": [], - "source": [ - "class Multiply2(Transform):\n", - " def transform(self, X):\n", - " return X * X" - ] - }, - { - "cell_type": "markdown", - "id": "bcae55ea-951d-4807-9f0f-569e031fcb23", - "metadata": {}, - "source": [ - "Without decorator and all the other functions. The reason why we have all the diferentiations is that we know we will have different data manipulation for most cases." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "test_requirements": { - "single_gpu": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tutorials/Tutorial_4.ipynb b/examples/tutorials/Tutorial_4.ipynb deleted file mode 100644 index 9ef5401..0000000 --- a/examples/tutorials/Tutorial_4.ipynb +++ /dev/null @@ -1,464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "664bfe29-de10-4c07-b23e-75bdb9a330d3", - "metadata": {}, - "source": [ - "### Tutorial 4 - How Create an Agnostic Pipeline\n", - "\n", - "In this tutorial, we will show you how convert a simple code structure into a advanced and agnostic pipeline based on DAGs.\n", - "\n", - "For this, we still can use the **Tutorial 1** with a simple Machine Learning script. There we use `make_blobs` to generate a dataset and them we cluster it using two algorithms: KMeans and SOM.\n", - "\n", - "First, let's generate and save our data (you can use DASF or Scikit-learn). The objective here is just to generate some labeled data and use the `DatasetLabeled` as an example." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2d3ae542-b03f-49e0-86fc-1cdbf19b5a30", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from dasf.datasets import make_blobs\n", - "\n", - "n_samples = 100000\n", - "n_bins = 3\n", - "\n", - "# Generate 3 blobs with 2 classes where the second blob contains\n", - "# half positive samples and half negative samples. Probability in this\n", - "# blob is therefore 0.5.\n", - "centers = [(-6, -6), (0, 0), (9, 1)]\n", - "X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)\n", - "\n", - "np.save(\"X.npy\", X)\n", - "np.save(\"y.npy\", y)" - ] - }, - { - "cell_type": "markdown", - "id": "d90e6b0b-236d-4cab-951b-e973e780c94f", - "metadata": {}, - "source": [ - "Now, let's import our `DatasetLabeled` and assign each file to the respective type." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1d5d935f-065e-4bda-af74-38b148924463", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "from dasf.datasets import DatasetLabeled\n", - "\n", - "\n", - "class MyMakeBlobs(DatasetLabeled):\n", - " def __init__(self):\n", - " super().__init__(name=\"My Own make_blobs()\", download=False)\n", - " \n", - " # Let's assign the train and val data.\n", - " self._train = DatasetArray(name=\"X\", download=False, root=\"X.npy\", chunks=(5000, 2))\n", - " self._val = DatasetArray(name=\"y\", download=False, root=\"y.npy\", chunks=(5000))\n", - "\n", - "make_blobs = MyMakeBlobs()" - ] - }, - { - "cell_type": "markdown", - "id": "37bcedef-d5cb-40ee-a691-dcb2d1636083", - "metadata": {}, - "source": [ - "To reduce the variability and as an example, we can normalize the data to help the algorithms to fit better." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6db9235f-7580-48dd-a9e9-b946a9570a86", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import Normalize\n", - "\n", - "normalize = Normalize()" - ] - }, - { - "cell_type": "markdown", - "id": "eaaed4e1-b799-4448-99ac-922094b18988", - "metadata": {}, - "source": [ - "After, creating our dataset and the normalization transformation, we can start the executor. For this example, we can use Dask." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "aa39f7b9-ffb1-4c93-9293-ecda4139d8cf", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.pipeline.executors import DaskPipelineExecutor\n", - "\n", - "dask = DaskPipelineExecutor(local=True, use_gpu=False)" - ] - }, - { - "cell_type": "markdown", - "id": "0bd517c3-8de0-46a4-89c4-78314ffe6491", - "metadata": {}, - "source": [ - "Now, it is time to create our pipeline objects. We can copy and paste the same code used previously." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d3fc983-f6c8-4a66-9a22-fc1a6b5ccd7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: CuPy could not be imported\n", - "WARNING: CuPy could not be imported\n", - "WARNING: CuPy could not be imported\n" - ] - } - ], - "source": [ - "from dasf.ml.cluster import KMeans\n", - "from dasf.ml.cluster import SOM\n", - "\n", - "kmeans = KMeans(n_clusters=3, max_iter=100)\n", - "som = SOM(x=1, y=3, input_len=2, num_epochs=100)" - ] - }, - { - "cell_type": "markdown", - "id": "07966b83-22d5-4bb6-8592-1ec54f365417", - "metadata": {}, - "source": [ - "As we want to reuse the data after the pipeline execution, we need to persist the data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "297c99be-394d-4c5f-8dd6-04baab437b5f", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import PersistDaskData\n", - "\n", - "persist_kmeans = PersistDaskData()\n", - "persist_som = PersistDaskData()" - ] - }, - { - "cell_type": "markdown", - "id": "7b26ce69-d483-4ec3-a570-cfa761299983", - "metadata": {}, - "source": [ - "Then, we generate the pipeline and connect all the pieces in one single DAG.\n", - "\n", - "Pay attention that we are passing the our fresh executor `dask` to the pipeline by specifying the parameter `executor=`.\n", - "\n", - "To connect all the objects, we use the function `add()` that returns the pipeline itself. The function inputs can be refered as an argument.\n", - "\n", - "At the end, we can visualize the DAG using `visualize()` method. It will plot a image that represents the graph. Let's use one single line to do everything. It should be simple and easy to understand." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bbee98cf-2425-431f-87af-342fcf0c00c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A KMeans and SOM Pipeline\n", - "\n", - "\n", - "\n", - "180662574\n", - "\n", - "Normalize.transform\n", - "\n", - "\n", - "\n", - "128329762\n", - "\n", - "KMeans.fit_predict\n", - "\n", - "\n", - "\n", - "180662574->128329762\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "128329900\n", - "\n", - "SOM.fit_predict\n", - "\n", - "\n", - "\n", - "180662574->128329900\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "182107497\n", - "\n", - "DatasetArray.load\n", - "\n", - "\n", - "\n", - "182107497->180662574\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "16599060\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "128329762->16599060\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "16599058\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "128329900->16599058\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dasf.pipeline import Pipeline\n", - "\n", - "pipeline = Pipeline(\"A KMeans and SOM Pipeline\", executor=dask)\n", - "\n", - "pipeline.add(normalize, X=make_blobs._train) \\\n", - " .add(kmeans.fit_predict, X=normalize) \\\n", - " .add(som.fit_predict, X=normalize) \\\n", - " .add(persist_kmeans, X=kmeans.fit_predict) \\\n", - " .add(persist_som, X=som.fit_predict) \\\n", - " .visualize()" - ] - }, - { - "cell_type": "markdown", - "id": "14815700-715b-4e17-92e0-1203b107c7c8", - "metadata": {}, - "source": [ - "It is time to run our new pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c2dd0613-ccbf-4543-bd01-ba3dda54fbf7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-11-25 04:36:49+0000] INFO - Beginning pipeline run for 'A KMeans and SOM Pipeline'\n", - "[2022-11-25 04:36:49+0000] INFO - Task 'DatasetArray.load': Starting task run...\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'DatasetArray.load': Finished task run\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Starting task run...\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'Normalize.transform': Finished task run\n", - "[2022-11-25 04:36:50+0000] INFO - Task 'KMeans.fit_predict': Starting task run...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.8/dist-packages/dask/base.py:1367: UserWarning: Running on a single-machine scheduler when a distributed client is active might lead to unexpected results.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-11-25 04:37:00+0000] INFO - Task 'KMeans.fit_predict': Finished task run\n", - "[2022-11-25 04:37:00+0000] INFO - Task 'SOM.fit_predict': Starting task run...\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'SOM.fit_predict': Finished task run\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2022-11-25 04:37:22+0000] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2022-11-25 04:37:23+0000] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2022-11-25 04:37:23+0000] INFO - Pipeline run successfully\n", - "CPU times: user 23.2 s, sys: 1.71 s, total: 24.9 s\n", - "Wall time: 33.2 s\n" - ] - } - ], - "source": [ - "%time pipeline.run()" - ] - }, - { - "cell_type": "markdown", - "id": "eeb8f9cb-de3e-4e9e-bf9f-3d89f59e99ba", - "metadata": {}, - "source": [ - "Notice that our pipeline returns two methods instead of one. To capture the result of some node, you can easily pass the same function or object to the pipeline function `get_result_from()`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f3412afd-4f97-4219-954a-5d95cf92d629", - "metadata": {}, - "outputs": [], - "source": [ - "result_kmeans = pipeline.get_result_from(persist_kmeans).compute()\n", - "result_som = pipeline.get_result_from(persist_som).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "801faa7c-a1c4-48c9-9c6c-de2c480a74dc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from itertools import cycle\n", - "\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_results(X, result):\n", - " y_unique = np.unique(result)\n", - " \n", - " colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))\n", - " \n", - " for this_y, color in zip(y_unique, colors):\n", - " this_X = X[result == this_y]\n", - " plt.scatter(\n", - " this_X[:, 0],\n", - " this_X[:, 1],\n", - " s=50,\n", - " c=color[np.newaxis, :],\n", - " alpha=0.5,\n", - " edgecolor=\"k\",\n", - " label=\"Class %s\" % this_y,\n", - " )\n", - "\n", - "plot_results(make_blobs._train, result_kmeans)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e3fe23cb-d5ae-4255-9437-42cddb89d004", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_results(make_blobs._train, result_som)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c68ca230-d319-4c35-8e4a-0fb3d017c0cd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tutorials/Tutorial_5.ipynb b/examples/tutorials/Tutorial_5.ipynb deleted file mode 100644 index 2474101..0000000 --- a/examples/tutorials/Tutorial_5.ipynb +++ /dev/null @@ -1,1190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 5: Using profiler\n", - "\n", - "This tutorial shows how to use the profiler to measure the performance of your code. We used the same pipeline of the previous tutorial.\n", - "\n", - "The profile is a tool that measures the time spent in each function of your code. It is very useful to identify bottlenecks and to optimize your code. By default it is not enabled and the simplest way to enable the default profiler is to use the `register_default_profiler` function. It will be shown later, in the notebook.\n", - "\n", - "The profile consists in two steps:\n", - "1. Register the profiler and execute the desired pipeline. It will generate different files, which contains traces of the execution, in the current directory. Each file will have different information, depending on the plugins attached. Using the `register_default_profiler` function, two plugins will be attached, one to measure the dask task time (and other useful information, such as, per task data shape, type, MB, etc.) and a resource usage plugin (which measure GPU, CPU, memory and IO usage).\n", - "2. Perform analysis of the generated files. Some analysis are already implemented in the `profiler` module, but you can also implement your own analysis. For now we have the followig analysis: per function time, per task time and per worker task balance. It will be shown later, in the notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Enabling profiling" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from dasf.datasets import make_blobs\n", - "\n", - "n_samples = 100000\n", - "n_bins = 3\n", - "\n", - "# Generate 3 blobs with 2 classes where the second blob contains\n", - "# half positive samples and half negative samples. Probability in this\n", - "# blob is therefore 0.5.\n", - "centers = [(-6, -6), (0, 0), (9, 1)]\n", - "X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42)\n", - "\n", - "np.save(\"X.npy\", X)\n", - "np.save(\"y.npy\", y)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.datasets import DatasetArray\n", - "from dasf.datasets import DatasetLabeled\n", - "\n", - "\n", - "class MyMakeBlobs(DatasetLabeled):\n", - " def __init__(self):\n", - " super().__init__(name=\"My Own make_blobs()\", download=False)\n", - " \n", - " # Let's assign the train and val data.\n", - " self._train = DatasetArray(name=\"X\", download=False, root=\"X.npy\", chunks=(5000, 2))\n", - " self._val = DatasetArray(name=\"y\", download=False, root=\"y.npy\", chunks=(5000))\n", - "\n", - "make_blobs = MyMakeBlobs()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import Normalize\n", - "\n", - "normalize = Normalize()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 11:55:48,568 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", - "2023-08-15 11:55:48,568 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", - "2023-08-15 11:55:48,568 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", - "2023-08-15 11:55:48,568 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", - "2023-08-15 11:55:48,673 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", - "2023-08-15 11:55:48,673 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n", - "2023-08-15 11:55:48,752 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize\n", - "2023-08-15 11:55:48,752 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n" - ] - } - ], - "source": [ - "from dasf.pipeline.executors import DaskPipelineExecutor\n", - "\n", - "dask = DaskPipelineExecutor(local=True, use_gpu=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.ml.cluster import KMeans\n", - "from dasf.ml.cluster import SOM\n", - "\n", - "kmeans = KMeans(n_clusters=3, max_iter=100)\n", - "som = SOM(x=1, y=3, input_len=2, num_epochs=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.transforms import PersistDaskData\n", - "\n", - "persist_kmeans = PersistDaskData()\n", - "persist_som = PersistDaskData()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A KMeans and SOM Pipeline\n", - "\n", - "\n", - "\n", - "68414549\n", - "\n", - "Normalize.transform\n", - "\n", - "\n", - "\n", - "499072774\n", - "\n", - "KMeans.fit_predict\n", - "\n", - "\n", - "\n", - "68414549->499072774\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "499072781\n", - "\n", - "SOM.fit_predict\n", - "\n", - "\n", - "\n", - "68414549->499072781\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "261617047\n", - "\n", - "DatasetArray.load\n", - "\n", - "\n", - "\n", - "261617047->68414549\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "499067957\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "499072774->499067957\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "499068127\n", - "\n", - "PersistDaskData.transform\n", - "\n", - "\n", - "\n", - "499072781->499068127\n", - "\n", - "\n", - "X\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dasf.pipeline import Pipeline\n", - "\n", - "pipeline = Pipeline(\"A KMeans and SOM Pipeline\", executor=dask)\n", - "\n", - "pipeline.add(normalize, X=make_blobs._train) \\\n", - " .add(kmeans.fit_predict, X=normalize) \\\n", - " .add(som.fit_predict, X=normalize) \\\n", - " .add(persist_kmeans, X=kmeans.fit_predict) \\\n", - " .add(persist_som, X=som.fit_predict) \\\n", - " .visualize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you created your pipeline we can attach the profiler to it. The profiler will collect the data and store it in a file (one for each plugin). The file will be stored in the same directory as the notebook. The file name will be controled by the `name` parameter followed by `.msgpack` extension. If you do not provide the `name` parameter it will be `default`.\n", - "\n", - "The `enable_nvtx` parameter will enable the NVTX markers. This will allow you to see the pipeline in the NVIDIA Nsight Systems. This is only available on NVIDIA GPUs.\n", - "\n", - "Finally, the `add_time_suffix` parameter will add the current time to the file name (useful for unique file names).In this way you can run the same pipeline multiple times and get different files. The file name will be `name_time.msgpack`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Registered worker plugin: Tutorial_5_Profile-TracePlugin\n", - "Registered resource plugin: Tutorial_5_Profile-ResourceMonitor\n" - ] - } - ], - "source": [ - "from dasf.profile import register_default_profiler\n", - "\n", - "register_default_profiler(\n", - " pipeline,\n", - " name=\"Tutorial_5_Profile\",\n", - " enable_nvtx=False,\n", - " add_time_suffix=False \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2023-08-15 11:55:50-0300] INFO - Beginning pipeline run for 'A KMeans and SOM Pipeline'\n", - "[2023-08-15 11:55:50-0300] INFO - Task 'DatasetArray.load': Starting task run...\n", - "[2023-08-15 11:55:50-0300] INFO - Task 'DatasetArray.load': Finished task run\n", - "[2023-08-15 11:55:50-0300] INFO - Task 'Normalize.transform': Starting task run...\n", - "[2023-08-15 11:55:50-0300] INFO - Task 'Normalize.transform': Finished task run\n", - "[2023-08-15 11:55:50-0300] INFO - Task 'KMeans.fit_predict': Starting task run...\n", - "[2023-08-15 11:55:55-0300] INFO - Task 'KMeans.fit_predict': Finished task run\n", - "[2023-08-15 11:55:55-0300] INFO - Task 'SOM.fit_predict': Starting task run...\n", - "[2023-08-15 11:56:21-0300] INFO - Task 'SOM.fit_predict': Finished task run\n", - "[2023-08-15 11:56:21-0300] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2023-08-15 11:56:21-0300] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2023-08-15 11:56:21-0300] INFO - Task 'PersistDaskData.transform': Starting task run...\n", - "[2023-08-15 11:56:21-0300] INFO - Task 'PersistDaskData.transform': Finished task run\n", - "[2023-08-15 11:56:21-0300] INFO - Pipeline run successfully\n", - "CPU times: user 11.8 s, sys: 739 ms, total: 12.5 s\n", - "Wall time: 31.2 s\n" - ] - } - ], - "source": [ - "%time pipeline.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The resource monitor plugin runs indefinitely, until the program exits. As the database are buffered, in order to reduce the IO pressure, the data are flushed to the database only after 5000 entries (or when program exits). We can shutdown the executor, in order to flush the data to the database." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 11:56:54,586 - distributed.active_memory_manager - WARNING - Tried retiring worker tcp://127.0.0.1:35871, but 14 tasks could not be moved as there are no suitable workers to receive them. The worker will not be retired.\n", - "2023-08-15 11:56:54,587 - distributed.active_memory_manager - WARNING - Tried retiring worker tcp://127.0.0.1:35148, but 14 tasks could not be moved as there are no suitable workers to receive them. The worker will not be retired.\n", - "2023-08-15 11:56:54,587 - distributed.active_memory_manager - WARNING - Tried retiring worker tcp://127.0.0.1:38191, but 16 tasks could not be moved as there are no suitable workers to receive them. The worker will not be retired.\n", - "2023-08-15 11:56:54,588 - distributed.active_memory_manager - WARNING - Tried retiring worker tcp://127.0.0.1:39400, but 14 tasks could not be moved as there are no suitable workers to receive them. The worker will not be retired.\n" - ] - } - ], - "source": [ - "dask.shutdown()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysing the profile data\n", - "\n", - "Once the code ran, and traceevents were collected, we can perform analysis to understand the performance of the code. The first step is the load the databases. The `register_default_profiler` attachs two plugins `TracePlugin` (which trace dask tasks) and `ResourceMonitorPlugin` (which traces the resource usage of the workers). So there will be two files: `Tutorial_5_Profile-ResourceMonitor-c096.msgpack` and `Tutorial_5_Profile-TracePlugin-c096.msgpack`. We can load the events using the `EventProfiler` class (the same used to register events)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(EventProfiler(database=FileDatabase at Tutorial_5_Profile-ResourceMonitor-c096.msgpack),\n", - " EventProfiler(database=FileDatabase at Tutorial_5_Profile-TracePlugin-c096.msgpack))" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dasf.profile.profiler import EventProfiler\n", - "from dasf.profile import MultiEventDatabase\n", - "\n", - "db1 = EventProfiler(\"Tutorial_5_Profile-ResourceMonitor-c096.msgpack\")\n", - "db2 = EventProfiler(\"Tutorial_5_Profile-TracePlugin-c096.msgpack\")\n", - "\n", - "db1, db2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Database are collection of events. Each event is a dataclass and follows the [Chrome Trace Event Format](https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview). The `EventProfiler` class is a wrapper around a list of events. It provides methods to add events and to write the database to a file.\n", - "\n", - "We can iterate over the events in a database using the `get_traces` function. It will return an iterator over the events in the database. The events are returned in the order they were recorded. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InstantEvent(name='Resource Usage', timestamp=3869.01274743, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 40.2, 'memory': 2744729600, 'time': 1692111381.5415905, 'host_net_io.read_bps': 10685583.118998485, 'host_net_io.write_bps': 10673829.571746167, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 134589.30723739465, 'num_fds': 189, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272})\n", - "CompleteEvent(name='Compute', timestamp=3837.884328029, duration=0.050518035888671875, phase='X', process_id='c096', thread_id='worker-c096-2', args={'key': 'getGPUs-42237764-1836-4be8-ae62-948ce02e1d6a', 'name': 'tGPUs-42237764-1836-4be8-ae62', 'state': 'memory', 'size': 280, 'shape': [], 'dtype': 'unknown', 'type': \"\", 'dependencies': [], 'dependents': []})\n" - ] - } - ], - "source": [ - "for event in db1.get_traces():\n", - " print(event)\n", - " break\n", - "\n", - "for event in db2.get_traces():\n", - " print(event)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `MultiEventDatabase` allow use traverse multiple databases at the same time (usually, needed for analyses)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiEventDatabase with 2 databases" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "database = MultiEventDatabase([db1, db2])\n", - "database" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InstantEvent(name='Resource Usage', timestamp=3869.01274743, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 40.2, 'memory': 2744729600, 'time': 1692111381.5415905, 'host_net_io.read_bps': 10685583.118998485, 'host_net_io.write_bps': 10673829.571746167, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 134589.30723739465, 'num_fds': 189, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272})\n" - ] - } - ], - "source": [ - "for event in database:\n", - " print(event)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or generate a list with all events" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[InstantEvent(name='Resource Usage', timestamp=3869.01274743, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 40.2, 'memory': 2744729600, 'time': 1692111381.5415905, 'host_net_io.read_bps': 10685583.118998485, 'host_net_io.write_bps': 10673829.571746167, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 134589.30723739465, 'num_fds': 189, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.018545822, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 0.0, 'memory': 2744729600, 'time': 1692111381.5495014, 'host_net_io.read_bps': 24242915.31342638, 'host_net_io.write_bps': 24415081.283950076, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 189, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.118886256, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 69.9, 'memory': 2744729600, 'time': 1692111381.649612, 'host_net_io.read_bps': 30272623.59462569, 'host_net_io.write_bps': 28577096.880356316, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 10024124.945820678, 'num_fds': 189, 'gpu_utilization': 1, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.219099359, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 10.0, 'memory': 2744995840, 'time': 1692111381.7498834, 'host_net_io.read_bps': 3102557.5490633715, 'host_net_io.write_bps': 3103435.1666125604, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 1, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.319312409, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 0.0, 'memory': 2744995840, 'time': 1692111381.850187, 'host_net_io.read_bps': 5343.775123098109, 'host_net_io.write_bps': 5622.927554901742, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.418357631, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 0.0, 'memory': 2744995840, 'time': 1692111381.9493108, 'host_net_io.read_bps': 2703.691829652832, 'host_net_io.write_bps': 3147.58153302867, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.518484708, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 10.0, 'memory': 2744995840, 'time': 1692111382.0494256, 'host_net_io.read_bps': 19927.11012718611, 'host_net_io.write_bps': 19927.11012718611, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3158966272}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.620366242, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 60.1, 'memory': 2745241600, 'time': 1692111382.1493354, 'host_net_io.read_bps': 2815376.423511278, 'host_net_io.write_bps': 2813654.866414532, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3194617856}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.721167103, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 70.0, 'memory': 2745511936, 'time': 1692111382.2492902, 'host_net_io.read_bps': 3200509.781330604, 'host_net_io.write_bps': 3200509.781330604, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3194617856}),\n", - " InstantEvent(name='Resource Usage', timestamp=3869.821650885, phase='I', scope='g', process_id='c096', thread_id=None, args={'cpu': 79.1, 'memory': 2745782272, 'time': 1692111382.350485, 'host_net_io.read_bps': 3472817.2998334193, 'host_net_io.write_bps': 4041423.6761417557, 'host_disk_io.read_bps': 0.0, 'host_disk_io.write_bps': 0.0, 'num_fds': 193, 'gpu_utilization': 0, 'gpu_memory_used': 3194617856})]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_events = list(database)\n", - "all_events[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[InstantEvent(name='Managed Memory', timestamp=3907.782270047, phase='I', scope='g', process_id='c096', thread_id='worker-c096-1', args={'key': \"('reshape-b1532816c7b1745efbdc8cb9a24f29f9', 0, 0)\", 'state': 'memory', 'size': 300048, 'tasks': 17}),\n", - " CompleteEvent(name='Compute', timestamp=3907.792064168, duration=8.106231689453125e-06, phase='X', process_id='c096', thread_id='worker-c096-1', args={'key': 'original-array-64bef1cc631f078a2e8f3d2c24d62e99', 'name': 'iginal-array', 'state': 'memory', 'size': 8, 'shape': [2], 'dtype': 'float32', 'type': \"\", 'dependencies': [], 'dependents': []}),\n", - " InstantEvent(name='Managed Memory', timestamp=3907.792064168, phase='I', scope='g', process_id='c096', thread_id='worker-c096-1', args={'key': 'original-array-64bef1cc631f078a2e8f3d2c24d62e99', 'state': 'memory', 'size': 300032, 'tasks': 15}),\n", - " CompleteEvent(name='Compute', timestamp=3907.792960693, duration=5.4836273193359375e-06, phase='X', process_id='c096', thread_id='worker-c096-1', args={'key': 'original-array-808dd9f17eb2378b6fb7a3aeddc6cb36', 'name': 'iginal-array', 'state': 'memory', 'size': 8, 'shape': [2], 'dtype': 'float32', 'type': \"\", 'dependencies': [], 'dependents': []}),\n", - " InstantEvent(name='Managed Memory', timestamp=3907.792960693, phase='I', scope='g', process_id='c096', thread_id='worker-c096-1', args={'key': 'original-array-808dd9f17eb2378b6fb7a3aeddc6cb36', 'state': 'memory', 'size': 300040, 'tasks': 16}),\n", - " CompleteEvent(name='Compute', timestamp=3907.803720107, duration=5.698204040527344e-05, phase='X', process_id='c096', thread_id='worker-c096-1', args={'key': \"('array-reshape-22612059257db170fa304ae3f488407c', 0, 0)\", 'name': 'array-reshape', 'state': 'memory', 'size': 8, 'shape': [1, 2], 'dtype': 'float32', 'type': \"\", 'dependencies': ['original-array-64bef1cc631f078a2e8f3d2c24d62e99'], 'dependents': []}),\n", - " InstantEvent(name='Managed Memory', timestamp=3907.803720107, phase='I', scope='g', process_id='c096', thread_id='worker-c096-1', args={'key': \"('array-reshape-22612059257db170fa304ae3f488407c', 0, 0)\", 'state': 'memory', 'size': 300032, 'tasks': 15}),\n", - " CompleteEvent(name='Compute', timestamp=3907.804997844, duration=4.4345855712890625e-05, phase='X', process_id='c096', thread_id='worker-c096-1', args={'key': \"('array-reshape-f70b70b2e4718d09ca3086bb97bec087', 0, 0)\", 'name': 'array-reshape', 'state': 'memory', 'size': 8, 'shape': [1, 2], 'dtype': 'float32', 'type': \"\", 'dependencies': ['original-array-808dd9f17eb2378b6fb7a3aeddc6cb36'], 'dependents': []}),\n", - " InstantEvent(name='Managed Memory', timestamp=3907.804997844, phase='I', scope='g', process_id='c096', thread_id='worker-c096-1', args={'key': \"('array-reshape-f70b70b2e4718d09ca3086bb97bec087', 0, 0)\", 'state': 'memory', 'size': 300040, 'tasks': 16}),\n", - " CompleteEvent(name='Compute', timestamp=3907.811160065, duration=5.245208740234375e-06, phase='X', process_id='c096', thread_id='worker-c096-1', args={'key': \"('reshape-22612059257db170fa304ae3f488407c', 0, 0)\", 'name': 'reshape', 'state': 'memory', 'size': 8, 'shape': [1, 2], 'dtype': 'float32', 'type': \"\", 'dependencies': [\"('array-reshape-22612059257db170fa304ae3f488407c', 0, 0)\"], 'dependents': []})]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_events[-10:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Given a `MultiEventDatabase` we can use the `TraceAnalyser` that perform analyses over the database. It is useful to find bottlenecks in the code and to understand how the code is executed. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.profile import TraceAnalyser\n", - "analyser = TraceAnalyser(database)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Function Bottleneck Report\n", - "\n", - "The function bootleneck report will show us the function that is taking the most time to run. This is useful for optimizing code. Here, a function is composed by a set of tasks that applies this function in different chunks of data. It is the function used by `map_blocks` (where a function is applyed to several chunks). This report is a dataframe with the following columns:\n", - "- `Host`: the host where the function is running\n", - "- `GPU`: the GPU where the function is running\n", - "- `Function`: the function name\n", - "- `Duration`: the time spent in the function\n", - "- `Percentage of total time (%)`: the percentage of total compute time spent in the function\n", - "- `Mean GPU Utilization (%)`: the mean GPU utilization during the function execution\n", - "- `Mean GPU Memory Used (GB)`: the mean GPU memory used during the function execution\n", - "- `Mean Data Size (MB)`: the mean data size used during the function execution (as input)\n", - "- `Mean Data Throughput (MB/s)`: the mean data throughput during the function execution (as input)\n", - "- `Num tasks (chunks)`: the number of tasks (chunks) that the function was applied\n", - "- `Mean Task Time (s)`: the mean time spent in each task (chunk)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Creating annotated task graph: 100%|███████████████████████████████████| 122391/122391 [00:00<00:00, 480393.43it/s]\n", - "[function_bottleneck] Analysing traces: 100%|███████████████████████████| 122391/122391 [00:07<00:00, 17441.83it/s]\n", - "[function_bottleneck] Creating dataframe: 100%|██████████████████████████████████████| 4/4 [00:00<00:00, 41.34it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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HostGPUFunctionDuration (s)Percentage of total time (%)Mean GPU Utilization (%)Mean GPU Memory Used (GB)Mean Data Size (MB)Mean Throughput (MB/s)Num Tasks (chunks)Mean Task time (s)
624c0960unc_fit-62a75b12-6b7a-49b0-b2792.47143934.9675630.00.0000000.1600000.06474012.471439
1753c0961unc_fit-2d612856-11c9-4d72-80622.47132835.4973860.00.0000000.2000000.08092812.471328
10c0962unc_fit-abdc6ba0-f141-4e3c-909e2.46367733.6907730.00.0000000.2400000.09741512.463677
1228c0963unc_fit-d813c7ea-7042-4c69-8a722.46343734.9308210.00.0000000.2000000.08118712.463437
8c0962var-partial1.17295916.0402080.00.0000000.0009080.6198546850.001712
1750c0961var-partial1.10595215.8855500.00.0000000.0009070.6243566700.001651
1225c0963var-partial1.04922114.8776420.00.0000000.0009070.6172856390.001642
622c0960var-partial1.01873714.4137640.00.0000000.0009060.6231625700.001787
1223c0963var0.82502211.6985780.00.0000000.04000070.5620867110.001160
621c0960mean_combine-partial0.5341407.5573621.03.1589660.0007521.1985546740.000792
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" - ], - "text/plain": [ - " Host GPU Function Duration (s) \\\n", - "624 c096 0 unc_fit-62a75b12-6b7a-49b0-b279 2.471439 \n", - "1753 c096 1 unc_fit-2d612856-11c9-4d72-8062 2.471328 \n", - "10 c096 2 unc_fit-abdc6ba0-f141-4e3c-909e 2.463677 \n", - "1228 c096 3 unc_fit-d813c7ea-7042-4c69-8a72 2.463437 \n", - "8 c096 2 var-partial 1.172959 \n", - "1750 c096 1 var-partial 1.105952 \n", - "1225 c096 3 var-partial 1.049221 \n", - "622 c096 0 var-partial 1.018737 \n", - "1223 c096 3 var 0.825022 \n", - "621 c096 0 mean_combine-partial 0.534140 \n", - "\n", - " Percentage of total time (%) Mean GPU Utilization (%) \\\n", - "624 34.967563 0.0 \n", - "1753 35.497386 0.0 \n", - "10 33.690773 0.0 \n", - "1228 34.930821 0.0 \n", - "8 16.040208 0.0 \n", - "1750 15.885550 0.0 \n", - "1225 14.877642 0.0 \n", - "622 14.413764 0.0 \n", - "1223 11.698578 0.0 \n", - "621 7.557362 1.0 \n", - "\n", - " Mean GPU Memory Used (GB) Mean Data Size (MB) Mean Throughput (MB/s) \\\n", - "624 0.000000 0.160000 0.064740 \n", - "1753 0.000000 0.200000 0.080928 \n", - "10 0.000000 0.240000 0.097415 \n", - "1228 0.000000 0.200000 0.081187 \n", - "8 0.000000 0.000908 0.619854 \n", - "1750 0.000000 0.000907 0.624356 \n", - "1225 0.000000 0.000907 0.617285 \n", - "622 0.000000 0.000906 0.623162 \n", - "1223 0.000000 0.040000 70.562086 \n", - "621 3.158966 0.000752 1.198554 \n", - "\n", - " Num Tasks (chunks) Mean Task time (s) \n", - "624 1 2.471439 \n", - "1753 1 2.471328 \n", - "10 1 2.463677 \n", - "1228 1 2.463437 \n", - "8 685 0.001712 \n", - "1750 670 0.001651 \n", - "1225 639 0.001642 \n", - "622 570 0.001787 \n", - "1223 711 0.001160 \n", - "621 674 0.000792 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "func_bottleneck_dataframe = analyser.per_function_bottleneck()\n", - "func_bottleneck_dataframe.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Task Bootleneck Report\n", - "\n", - "The task bottleneck report shows the tasks that are the most time consuming in the application. This is similar to the above report, but is per task instead of per function (i.e., each block). This is similar to DASK task breakdown report." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Creating annotated task graph: 100%|███████████████████████████████████| 122391/122391 [00:00<00:00, 426913.80it/s]\n", - "[task_bottleneck] Analysing traces: 100%|███████████████████████████████| 122391/122391 [00:11<00:00, 10857.61it/s]\n", - "[task_bottleneck] Creating dataframe: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 5.47it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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HostGPUTask KeyDuration (s)Percentage of total time (%)Memory usage (Mb)
10453c0960_func_fit-62a75b12-6b7a-49b0-b279-160a8ff3ee3b2.47143934.9675630.000048
31484c0961_func_fit-2d612856-11c9-4d72-8062-7dfff911cf372.47132835.4973860.000048
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31507c0961_update-96127d18-5249-4874-accc-a95d91831a7f0.4524846.4993390.000092
10497c0960_update-988648be-51da-405b-a484-a25c9db01ee30.4307816.0949800.000092
83c0962_update-8c69b565-dc4d-4abc-80bb-e8085e2960bb0.4264655.8319030.000092
21070c0963_update-af7e6144-6bc1-4f85-9b66-4dd7e5f241d00.4259926.0404460.000092
21006c0963('var-e4678ef53eb782384180081aa58da0be', 13, 0)0.3638255.1589300.000450
9c0962('mean_chunk-0ff2c4ec843f304adcd7d25ca35c47a7'...0.2592433.5451430.000376
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" - ], - "text/plain": [ - " Host GPU Task Key \\\n", - "10453 c096 0 _func_fit-62a75b12-6b7a-49b0-b279-160a8ff3ee3b \n", - "31484 c096 1 _func_fit-2d612856-11c9-4d72-8062-7dfff911cf37 \n", - "59 c096 2 _func_fit-abdc6ba0-f141-4e3c-909e-059f1f2eed4f \n", - "21017 c096 3 _func_fit-d813c7ea-7042-4c69-8a72-7bdafcec69fb \n", - "31507 c096 1 _update-96127d18-5249-4874-accc-a95d91831a7f \n", - "10497 c096 0 _update-988648be-51da-405b-a484-a25c9db01ee3 \n", - "83 c096 2 _update-8c69b565-dc4d-4abc-80bb-e8085e2960bb \n", - "21070 c096 3 _update-af7e6144-6bc1-4f85-9b66-4dd7e5f241d0 \n", - "21006 c096 3 ('var-e4678ef53eb782384180081aa58da0be', 13, 0) \n", - "9 c096 2 ('mean_chunk-0ff2c4ec843f304adcd7d25ca35c47a7'... \n", - "\n", - " Duration (s) Percentage of total time (%) Memory usage (Mb) \n", - "10453 2.471439 34.967563 0.000048 \n", - "31484 2.471328 35.497386 0.000048 \n", - "59 2.463677 33.690773 0.000048 \n", - "21017 2.463437 34.930821 0.000048 \n", - "31507 0.452484 6.499339 0.000092 \n", - "10497 0.430781 6.094980 0.000092 \n", - "83 0.426465 5.831903 0.000092 \n", - "21070 0.425992 6.040446 0.000092 \n", - "21006 0.363825 5.158930 0.000450 \n", - "9 0.259243 3.545143 0.000376 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "task_bottleneck_dataframe = analyser.per_task_bottleneck()\n", - "task_bottleneck_dataframe.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Task balance\n", - "\n", - "This report show the number of tasks in memory (per worker), in each instant of time. This is useful to check imbalance in the load of the workers." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[task_balance] Analysing traces: 100%|████████████████████████████████| 122391/122391 [00:00<00:00, 1256360.33it/s]\n", - "[task_balance] Creating dataframe: 100%|███████████████████████████████████████| 71/71 [00:00<00:00, 359222.66it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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Time Interval (seconds from begin)worker-c096-0worker-c096-1worker-c096-2worker-c096-3
000.00.01.01.0
1110.012.016.011.0
2633.013.015.035.0
378.014.032.017.0
4832.015.031.020.0
5932.030.019.019.0
61014.014.014.015.0
71113.015.014.021.0
81216.019.09.016.0
91318.015.017.020.0
\n", - "
" - ], - "text/plain": [ - " Time Interval (seconds from begin) worker-c096-0 worker-c096-1 \\\n", - "0 0 0.0 0.0 \n", - "1 1 10.0 12.0 \n", - "2 6 33.0 13.0 \n", - "3 7 8.0 14.0 \n", - "4 8 32.0 15.0 \n", - "5 9 32.0 30.0 \n", - "6 10 14.0 14.0 \n", - "7 11 13.0 15.0 \n", - "8 12 16.0 19.0 \n", - "9 13 18.0 15.0 \n", - "\n", - " worker-c096-2 worker-c096-3 \n", - "0 1.0 1.0 \n", - "1 16.0 11.0 \n", - "2 15.0 35.0 \n", - "3 32.0 17.0 \n", - "4 31.0 20.0 \n", - "5 19.0 19.0 \n", - "6 14.0 15.0 \n", - "7 14.0 21.0 \n", - "8 9.0 16.0 \n", - "9 17.0 20.0 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "task_balance_dataframe = analyser.per_worker_task_balance()\n", - "task_balance_dataframe.head(10)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/tutorials/Tutorial_6.ipynb b/examples/tutorials/Tutorial_6.ipynb deleted file mode 100644 index 31f75fc..0000000 --- a/examples/tutorials/Tutorial_6.ipynb +++ /dev/null @@ -1,300 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "664bfe29-de10-4c07-b23e-75bdb9a330d3", - "metadata": {}, - "source": [ - "### Tutorial 6 - How Use the ApplyPatches Operator\n", - "\n", - "In this tutorial, we will show you how use the ApplyPatches operator, that subdivides a chunk in Patches with a given shape and apply a function at each patch. \n", - "It is possible to define overlapping patch sets to create a similar effect of a sliding window. The recombination of the results can be done either using a Weighted Average or Voting methods.\n", - "\n", - "This operator is userful when applying functions with a fixed input size (e.g. Deep Learning models), this way the interface between two neighboring patches can be smoothed due to the overlapping patches and the employement of different weight functions.\n" - ] - }, - { - "cell_type": "markdown", - "id": "e8b7f913", - "metadata": {}, - "source": [ - "![title](imgs/tutorial_6_schematic.jpg)" - ] - }, - { - "cell_type": "markdown", - "id": "c8c9f142", - "metadata": {}, - "source": [ - "#### Basic Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2d3ae542-b03f-49e0-86fc-1cdbf19b5a30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 50, 50)\n", - "Output shape is only (50, 50, 50), because its the only whole patch that can be extarcted without overlap/padding. Only 1 patch extarcted\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "Now the output shape is (99, 99, 99), because we added a overlap/padding that makes it possible to extarct patches that cover the whole data. 8 patches extracted\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "(50, 50, 50)\n", - "The output shape is (99, 99, 99) is the samebut we compute 8 patches from the base set and 8 from an overlapping set (patch extraction starts at (0, -1, -1), instead of (0, 0, 0))\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from dasf.transforms.operations import ApplyPatchesWeightedAvg\n", - "\n", - "def func(X):\n", - " print(X.shape) # show input of \n", - " return X + 1\n", - "\n", - "data = np.zeros((99, 99, 99))\n", - "\n", - "# Here we apply only on the small cube (50, 50, 50) as its the only whole patch that can be extracted.\n", - "# Fails when using Dask arrays\n", - "patches_op = ApplyPatchesWeightedAvg(\n", - " function=func,\n", - " weight_function=None, # Defaults to Arithmetic Mean\n", - " input_size=(50, 50, 50),\n", - " overlap=None, \n", - " offsets=[],\n", - ")\n", - "out_1 = patches_op.transform(data)\n", - "print(f\"Output shape is only {out_1.shape}, because its the only whole patch that can be extarcted without overlap/padding. Only 1 patch extarcted\")\n", - "\n", - "# Adding overlap (from Dask) it will pad the chunk/numpy array with the specified value \n", - "# or get the values from neighboring chunks\n", - "patches_op = ApplyPatchesWeightedAvg(\n", - " function=func,\n", - " weight_function=None, # Defaults to Arithmetic Mean\n", - " input_size=(50, 50, 50),\n", - " overlap={\"padding\": (1, 1, 1), \"boundary\": 0},\n", - " offsets=[],\n", - ")\n", - "out_2 = patches_op.transform(data)\n", - "print(f\"Now the output shape is {out_2.shape}, because we added an overlap/padding that makes it possible to extract patches that cover the whole data. 8 patches extracted\")\n", - "\n", - "# Adding overlap (from Dask) it will pad the chunk/numpy array with the specified value \n", - "# or get the values from neighboring chunks\n", - "patches_op = ApplyPatchesWeightedAvg(\n", - " function=func,\n", - " weight_function=None, # Defaults to Arithmetic Mean\n", - " input_size=(50, 50, 50),\n", - " overlap={\"padding\": (1, 1, 1), \"boundary\": 0},\n", - " offsets=[(0, -1, -1)],\n", - ")\n", - "out_3 = patches_op.transform(data)\n", - "print(f\"The output shape is {out_3.shape} is the same, but we compute 8 patches from the base set and 8 from an overlapping set (patch extraction starts at (0, -1, -1), instead of (0, 0, 0))\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "d82d8cd2", - "metadata": {}, - "source": [ - "#### Using different weight functions\n", - "Weight functions can be passed to the ApplyPatches operator in order to attribute weights based on the position of a given point in the NDArray.\n", - "\n", - "Below we show the Gaussian and Radial weight functions" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "789c4521", - "metadata": {}, - "outputs": [], - "source": [ - "from dasf.utils.funcs import weight_gaussian, weight_radial\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "1e0a444e", - "metadata": {}, - "outputs": [], - "source": [ - "weights = {\n", - " \"Gaussian\": weight_gaussian((100, 100, 100)),\n", - " \"Radial\": weight_radial((100, 100, 100))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "c68ca230-d319-4c35-8e4a-0fb3d017c0cd", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_panel(base_name, data_dict, outer):\n", - " f, axarr = plt.subplots(1,len(data_dict), sharex = True,sharey=True)\n", - " f.set_size_inches(15,5)\n", - " for i, data in enumerate(data_dict.items()):\n", - " ax = axarr[i] if len(data_dict) != 1 else axarr\n", - " panel = data[1][outer,:,:]\n", - " subfig = ax.imshow(panel, cmap=\"bone\", interpolation='nearest')\n", - " ax.title.set_text(f\"{base_name} - {data[0]}\")\n", - " f.colorbar(subfig, ax=ax)\n", - " f.show()\n", - " \n", - "def plot_lines(base_name, data_dict, outer, outer_2):\n", - " f, axarr = plt.subplots(1,len(data_dict), sharex = True,sharey=False)\n", - " f.set_size_inches(10,5)\n", - " for i, data in enumerate(data_dict.items()):\n", - " ax = axarr[i] if len(data_dict) != 1 else axarr\n", - " line = data[1][outer,outer_2,:]\n", - " ax.plot(np.arange(len(line)), line, label=data[0])\n", - " ax.title.set_text(f\"{base_name} - {data[0]}\")\n", - " f.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ccf54994", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_lines(\"Weights 25, 25\", weights, 25, 25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f22943b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/tutorials/imgs/tutorial_6_schematic.jpg b/examples/tutorials/imgs/tutorial_6_schematic.jpg deleted file mode 100644 index d664599..0000000 Binary files a/examples/tutorials/imgs/tutorial_6_schematic.jpg and /dev/null differ diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 1db413a..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,89 +0,0 @@ -[tool.poetry] -name = "dasf" -version = "1.0.beta.5" -description = "DASF is an Accelerated Framework for Machine Learning" -authors = ["Julio Faracco "] -maintainers = ["Julio Faracco "] -license = "MIT" -homepage = "https://github.com/lmcad-unicamp/dasf-core" -repository = "https://github.com/lmcad-unicamp/dasf-core" -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Operating System :: OS Independent", - "Development Status :: 1 - Planning", - "Environment :: GPU :: NVIDIA CUDA", -] -readme="README.md" -exclude = ["tests/*", "docs/*"] - -[tool.poetry.dependencies] -dacite = "*" -dask = "*" -dask_cuda = "*" -dask_jobqueue = "*" -dask_memusage = "*" -dask_ml = "*" -dask-pytorch-ddp = "*" -GPUtil = "*" -gdown = "^4.6" -graphviz = "*" -h5py = "*" -hdbscan = "*" -ipympl = "*" -matplotlib = "*" -memray = "*" -networkx = "*" -ormsgpack = "*" -packaging = "*" -portalocker = "*" -protobuf = "~=3.20.1" -psutil = "*" -pyarrow = "*" -python = "^3.8" -pytorch-lightning = "*" -scikit-learn = "*" -torchvision = "*" -xarray = "*" -xgboost = "*" -zarr = "*" - -#xpysom = { git = "https://github.com/jcfaracco/xpysom/", branch = "dask2" } - -[tool.poetry.group.dev.dependencies] -black = "*" -coverage = "*" -flake8 = "*" -hpccm = "*" -interrogate = "*" -isort = "*" -mock = "*" -parameterized = "*" -pytest = "*" -pytest-cov = "*" -wheel = "*" - -[tool.poetry.group.docs.dependencies] -sphinx = "*" -sphinx-autoapi = "*" -sphinx_rtd_theme = "*" -nbsphinx = "*" -pandoc = "*" - -[tool.isort] -profile = "black" - -[tool.coverage.paths] -source = ["dasf", "*/site-packages"] - -[tool.coverage.run] -branch = true -source = ["dasf"] - -[build-system] -requires = ["poetry-core>=1.1.10"] -build-backend = "poetry.core.masonry.api" diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index a635c5c..0000000 --- a/pytest.ini +++ /dev/null @@ -1,2 +0,0 @@ -[pytest] -pythonpath = . diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py deleted file mode 100644 index 1984e05..0000000 --- a/tests/datasets/test_datasets.py +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env python3 - -import unittest - -from mock import patch, Mock - -try: - from dask.distributed import Client - from dask_cuda import LocalCUDACluster -except ImportError: - pass - -from dasf.utils.funcs import is_gpu_supported -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_array -from dasf.utils.types import is_dask_gpu_array - -from dasf.datasets import make_blobs - - -class TestDatasets(unittest.TestCase): - @patch('dasf.datasets.datasets.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_gpu_supported', Mock(return_value=False)) - def test_make_blobs_cpu(self): - n_samples = 500000 - - centers = [(-6, -6), (0, 0), (9, 1)] - X, y = make_blobs(n_samples=n_samples, centers=centers, - shuffle=False, random_state=42) - - self.assertTrue(is_cpu_array(X)) - self.assertTrue(is_cpu_array(y)) - - @patch('dasf.datasets.datasets.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_supported', Mock(return_value=True)) - @patch('dasf.datasets.datasets.is_dask_gpu_supported', Mock(return_value=False)) - def test_make_blobs_mcpu(self): - n_samples = 500000 - - centers = [(-6, -6), (0, 0), (9, 1)] - X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, - random_state=42, chunks=(5000)) - - self.assertTrue(is_dask_array(X)) - self.assertTrue(is_dask_array(y)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - @patch('dasf.datasets.datasets.is_gpu_supported', Mock(return_value=True)) - @patch('dasf.datasets.datasets.is_dask_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_gpu_supported', Mock(return_value=False)) - def test_make_blobs_gpu(self): - n_samples = 500000 - - centers = [(-6, -6), (0, 0), (9, 1)] - X, y = make_blobs(n_samples=n_samples, centers=centers, - shuffle=False, random_state=42) - - self.assertTrue(is_gpu_array(X)) - self.assertTrue(is_gpu_array(y)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - @patch('dasf.datasets.datasets.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_supported', Mock(return_value=False)) - @patch('dasf.datasets.datasets.is_dask_gpu_supported', Mock(return_value=True)) - def test_make_blobs_mgpu(self): - with LocalCUDACluster() as cluster: - client = Client(cluster) - - n_samples = 500000 - - centers = [(-6, -6), (0, 0), (9, 1)] - X, y = make_blobs(n_samples=n_samples, centers=centers, - shuffle=False, random_state=42) - - self.assertTrue(is_dask_gpu_array(X)) - self.assertTrue(is_dask_gpu_array(y)) - - # Compute everything to gracefully shutdown - client.close() diff --git a/tests/datasets/test_generic.py b/tests/datasets/test_generic.py deleted file mode 100644 index 7f72bfe..0000000 --- a/tests/datasets/test_generic.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python3 - -import os -import unittest - -import numpy as np - -from pytest import fixture -from parameterized import parameterized_class - -from dasf.utils.funcs import is_gpu_supported -from dasf.datasets import DatasetArray -from dasf.datasets import DatasetZarr -from dasf.datasets import DatasetHDF5 -from dasf.datasets import DatasetXarray -from dasf.datasets import DatasetLabeled -from dasf.datasets import DatasetDataFrame -from dasf.datasets import DatasetParquet - - -def parameterize_dataset_type(): - datasets = [ - {"name": "Array", "cls": "DatasetArray", "file": "Array.npy", "extra_args": {}}, - {"name": "Zarr", "cls": "DatasetZarr", "file": "Zarr.zarr", "extra_args": {}}, - {"name": "HDF5", "cls": "DatasetHDF5", "file": "HDF5.h5", "extra_args": {"dataset_path": "dataset"}}, - {"name": "Xarray", "cls": "DatasetXarray", "file": "Xarray.nc", "extra_args": {"chunks": {"x": 10, "y": 10, "z": 10}}}, - {"name": "DataFrame", "cls": "DatasetDataFrame", "file": "DataFrame.csv", "extra_args": {}}, - {"name": "Parquet", "cls": "DatasetParquet", "file": "Parquet.parquet", "extra_args": {}}, - ] - - return datasets - - -@parameterized_class(parameterize_dataset_type()) -class TestTypes(unittest.TestCase): - @fixture(autouse=True) - def data_dir(self, request): - filename = request.module.__file__ - self.test_dir, _ = os.path.splitext(filename) - - def test_dataset_load(self): - raw_path = os.path.join(self.test_dir, "simple", - self.file) - - dataset = eval(self.cls)(name=self.name, root=raw_path, download=False, **self.extra_args) - dataset.load() - - self.assertTrue(hasattr(dataset, '_metadata')) - self.assertTrue("size" in dataset._metadata) - - -class TestDatasetArray(unittest.TestCase): - def test_shape(self): - filename = os.getenv('PYTEST_CURRENT_TEST') - test_dir, _ = os.path.splitext(filename) - raw_path = os.path.join(test_dir, "simple", "Array.npy") - - dataset = DatasetArray(name="Array", root=raw_path, download=False) - - self.assertEqual(dataset.shape, (40, 40, 40)) - - def test_add(self): - filename = os.getenv('PYTEST_CURRENT_TEST') - test_dir, _ = os.path.splitext(filename) - raw_path = os.path.join(test_dir, "simple", "Array.npy") - - dataset1 = DatasetArray(name="Array", root=raw_path, download=False) - dataset2 = DatasetArray(name="Array", root=raw_path, download=False) - - dataset1._load_cpu() - dataset2._load_cpu() - - np1 = np.load(raw_path) - np2 = np.load(raw_path) - - dataset3 = dataset1 + dataset2 - - np3 = np1 + np2 - - self.assertTrue(np.array_equal(dataset3, np3)) - - def test_sub(self): - filename = os.getenv('PYTEST_CURRENT_TEST') - test_dir, _ = os.path.splitext(filename) - raw_path = os.path.join(test_dir, "simple", "Array.npy") - - dataset1 = DatasetArray(name="Array", root=raw_path, download=False) - dataset2 = DatasetArray(name="Array", root=raw_path, download=False) - - dataset1._load_cpu() - dataset2._load_cpu() - - np1 = np.load(raw_path) - np2 = np.load(raw_path) - - dataset3 = dataset1 - dataset2 - - np3 = np1 - np2 - - self.assertTrue(np.array_equal(dataset3, np3)) - - def test_mul(self): - filename = os.getenv('PYTEST_CURRENT_TEST') - test_dir, _ = os.path.splitext(filename) - raw_path = os.path.join(test_dir, "simple", "Array.npy") - - dataset1 = DatasetArray(name="Array", root=raw_path, download=False) - dataset2 = DatasetArray(name="Array", root=raw_path, download=False) - - dataset1._load_cpu() - dataset2._load_cpu() - - np1 = np.load(raw_path) - np2 = np.load(raw_path) - - dataset3 = dataset1 * dataset2 - - np3 = np1 * np2 - - self.assertTrue(np.array_equal(dataset3, np3)) - -# def test_div(self): -# filename = os.getenv('PYTEST_CURRENT_TEST') -# test_dir, _ = os.path.splitext(filename) -# raw_path = os.path.join(test_dir, "simple", "Array.npy") -# -# dataset1 = DatasetArray(name="Array", root=raw_path, download=False) -# dataset2 = DatasetArray(name="Array", root=raw_path, download=False) -# -# dataset1.load() -# dataset2.load() -# -# np1 = np.load(raw_path) -# np2 = np.load(raw_path) -# -# dataset3 = dataset1 / dataset2 -# -# np3 = np1 / np2 -# -# self.assertTrue(np.array_equal(dataset3, np3)) -# -# def test_avg(self): -# filename = os.getenv('PYTEST_CURRENT_TEST') -# test_dir, _ = os.path.splitext(filename) -# raw_path = os.path.join(test_dir, "simple", "Array.npy") -# -# dataset = DatasetArray(name="Array", root=raw_path, download=False) -# -# dataset.load() -# -# self.assertEqual(dataset.avg(), 0.0) diff --git a/tests/datasets/test_generic/simple/Array.npy b/tests/datasets/test_generic/simple/Array.npy deleted file mode 100644 index 907c165..0000000 Binary files a/tests/datasets/test_generic/simple/Array.npy and /dev/null differ diff --git a/tests/datasets/test_generic/simple/DataFrame.csv b/tests/datasets/test_generic/simple/DataFrame.csv deleted file mode 100644 index 82971f7..0000000 --- a/tests/datasets/test_generic/simple/DataFrame.csv +++ /dev/null @@ -1,64001 +0,0 @@ -,Dataset -0,0.7063932609783351 -1,0.12458428299176882 -2,0.962183701697323 -3,0.1937916682439188 -4,0.34000743734540895 -5,0.21240357379409458 -6,0.028672133828077095 -7,0.5220634178520068 -8,0.7990692536225442 -9,0.7413241792006954 -10,0.8981589861597749 -11,0.08269121265851309 -12,0.3414438711041572 -13,0.3499833799218369 -14,0.34810155320529745 -15,0.20007240485196476 -16,0.41262395642952443 -17,0.886906762800548 -18,0.7985582040404947 -19,0.8746134574888684 -20,0.906620356446211 -21,0.24340315952408131 -22,0.7334261691702018 -23,0.6558205872107988 -24,0.5143266490021162 -25,0.6971077806001293 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-63991,0.7403159588094188 -63992,0.6827407289923713 -63993,0.9803866226967954 -63994,0.07675428815720142 -63995,0.2768266058252913 -63996,0.14218627289283903 -63997,0.8084505470151284 -63998,0.7896735043294324 -63999,0.44475530718602774 diff --git a/tests/datasets/test_generic/simple/HDF5.h5 b/tests/datasets/test_generic/simple/HDF5.h5 deleted file mode 100644 index 4a74559..0000000 Binary files a/tests/datasets/test_generic/simple/HDF5.h5 and /dev/null differ diff --git a/tests/datasets/test_generic/simple/Parquet.parquet b/tests/datasets/test_generic/simple/Parquet.parquet deleted file mode 100644 index b7f5d7f..0000000 Binary files a/tests/datasets/test_generic/simple/Parquet.parquet and /dev/null differ diff --git a/tests/datasets/test_generic/simple/Xarray.nc b/tests/datasets/test_generic/simple/Xarray.nc deleted file mode 100644 index 3d5c110..0000000 Binary files a/tests/datasets/test_generic/simple/Xarray.nc and /dev/null differ diff --git a/tests/datasets/test_generic/simple/Zarr.zarr/.zarray b/tests/datasets/test_generic/simple/Zarr.zarr/.zarray deleted file mode 100644 index 93b2b99..0000000 --- a/tests/datasets/test_generic/simple/Zarr.zarr/.zarray +++ /dev/null @@ -1,24 +0,0 @@ -{ - "chunks": [ - 20, - 40, - 40 - ], - "compressor": { - "blocksize": 0, - "clevel": 5, - "cname": "lz4", - "id": "blosc", - "shuffle": 1 - }, - "dtype": " q2) - - def test_som_mcpu(self): - som = SOM(x=3, y=2, input_len=2, num_epochs=300) - - da_X = da.from_array(self.X, meta=np.array((), dtype=np.float32)) - - q1 = som._lazy_quantization_error_cpu(da_X) - - som._lazy_fit_cpu(da_X) - - y = som._lazy_predict_cpu(da_X) - - q2 = som._lazy_quantization_error_cpu(da_X) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertTrue(q1 > q2) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_som_gpu(self): - som = SOM(x=3, y=2, input_len=2, num_epochs=300) - - cp_X = cp.asarray(self.X) - - q1 = som._quantization_error_gpu(cp_X) - - som._fit_gpu(cp_X) - - y = som._predict_gpu(cp_X) - - q2 = som._quantization_error_gpu(cp_X) - - self.assertTrue(is_gpu_array(y)) - self.assertTrue(q1 > q2) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_som_mgpu(self): - som = SOM(x=3, y=2, input_len=2, num_epochs=300) - - da_X = da.from_array(cp.asarray(self.X), meta=cp.array((), dtype=cp.float32)) - - q1 = som._lazy_quantization_error_gpu(da_X) - - som._lazy_fit_gpu(da_X) - - y = som._lazy_predict_gpu(da_X) - - q2 = som._lazy_quantization_error_gpu(da_X) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertTrue(q1 > q2) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_som_mcpu_local(self): - som = SOM(x=3, y=2, input_len=2, num_epochs=300, run_local=True) - - da_X = da.from_array(self.X, meta=np.array((), dtype=np.float32)) - - q1 = som.quantization_error(da_X) - - som.fit(da_X) - - y = som.predict(da_X) - - self.assertTrue(is_cpu_array(y)) - - q2 = som.quantization_error(da_X) - - self.assertTrue(q1 > q2) diff --git a/tests/ml/cluster/test_spectral.py b/tests/ml/cluster/test_spectral.py deleted file mode 100644 index 9bb13a4..0000000 --- a/tests/ml/cluster/test_spectral.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import dask.array as da - -from sklearn.datasets import make_blobs - -from dasf.ml.cluster import SpectralClustering -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_dask_cpu_array - - -class TestSpectralClustering(unittest.TestCase): - def setUp(self): - self.size = 1000 - self.centers = 3 - self.random_state = 42 - - self.X, self.y, self.centroids = make_blobs(n_samples=self.size, - centers=self.centers, - n_features=2, - return_centers=True, - random_state=self.random_state) - - def __match_randomly_labels_created(self, y1, y2): - y2 = (y2 * -1) - 1 - - for i in range(len(y1)): - if y2[i] < 0: - y2[y2 == y2[i]] = y1[i] - - if not np.any(y2[y2 < 0]): - break - - return y1, y2 - - def test_spectral_cpu(self): - sc = SpectralClustering(n_clusters=self.centers, - random_state=self.random_state) - - y = sc._fit_predict_cpu(self.X) - - self.assertTrue(is_cpu_array(y)) - - y1, y2 = self.__match_randomly_labels_created(y, self.y) - - self.assertTrue(np.array_equal(y1, y2, equal_nan=True)) - - def test_spectral_mcpu(self): - sc = SpectralClustering(n_clusters=self.centers, - random_state=self.random_state, - n_components=250) - - da_X = da.from_array(self.X) - - try: - y = sc._lazy_fit_predict_cpu(da_X) - except TypeError as te: - self.skipTest("BUG - SpectralClustering Dask Type Error: " + str(te)) - - self.assertTrue(is_dask_cpu_array(y)) - - y1, y2 = self.__match_randomly_labels_created(y.compute(), self.y) - - # Check if the accurary is higher than 99%. - self.assertTrue(len(np.setdiff1d(y1, y2)) <= int(self.size*0.01)) diff --git a/tests/ml/decomposition/test_pca.py b/tests/ml/decomposition/test_pca.py deleted file mode 100644 index fb4ab30..0000000 --- a/tests/ml/decomposition/test_pca.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import dask.array as da - -try: - import cupy as cp -except ImportError: - pass - -from sklearn import datasets -from parameterized import parameterized - -from dasf.ml.decomposition import PCA -from dasf.utils.funcs import is_gpu_supported - - -PCA_SOLVERS_CPU = [("full"), ("arpack"), ("randomized"), ("auto")] -PCA_SOLVERS_MCPU = [("full"), ("tsqr"), ("auto")] # "ramdomized" has a Dask bug -PCA_SOLVERS_GPU = [("full"), ("jacobi"), ("auto")] - - -class TestPCA(unittest.TestCase): - def setUp(self): - self.data = datasets.load_iris().data - self.n_components = 3 - - @parameterized.expand(PCA_SOLVERS_CPU) - def test_pca_cpu(self, svd_solver): - try: - pca = PCA(n_components=self.n_components, svd_solver=svd_solver) - - # check the shape of fit.transform - X_r = pca._fit_cpu(self.data).transform(self.data) - self.assertEqual(X_r.shape[1], self.n_components) - - # check the equivalence of fit.transform and fit_transform - X_r2 = pca._fit_transform_cpu(self.data) - np.testing.assert_allclose(X_r, X_r2) - X_r = pca._transform_cpu(self.data) - np.testing.assert_allclose(X_r, X_r2) - - # Test get_covariance and get_precision - cov = pca._get_covariance_cpu() - precision = pca._get_precision_cpu() - np.testing.assert_allclose(np.dot(cov, precision), - np.eye(self.data.shape[1]), - atol=1e-12) - except NotImplementedError: - unittest.SkipTest(f"Skipped because {svd_solver} is not supported.") - - @parameterized.expand(PCA_SOLVERS_MCPU) - def test_pca_mcpu(self, svd_solver): - try: - data_X = da.from_array(self.data, - chunks=(int(self.data.shape[0]/4), self.data.shape[1]), - meta=np.array(())) - - pca = PCA(n_components=self.n_components, svd_solver=svd_solver) - - # check the shape of fit.transform - X_r = pca._lazy_fit_cpu(data_X).transform(data_X) - self.assertEqual(X_r.shape[1], self.n_components) - - # check the equivalence of fit.transform and fit_transform - X_r2 = pca._lazy_fit_transform_cpu(data_X) - np.testing.assert_allclose(X_r, X_r2) - X_r = pca._lazy_transform_cpu(data_X) - np.testing.assert_allclose(X_r, X_r2) - except AttributeError: - unittest.SkipTest(f"Skipped due to Dask-ML bug.") - - @parameterized.expand(PCA_SOLVERS_GPU) - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_pca_gpu(self, svd_solver): - raise unittest.SkipTest("Check why PCA is not working for floats") - - @parameterized.expand(PCA_SOLVERS_GPU) - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_pca_mgpu(self, svd_solver): - raise unittest.SkipTest("Check why PCA is not working for floats") diff --git a/tests/pipeline/executors/test_dask.py b/tests/pipeline/executors/test_dask.py deleted file mode 100644 index 95620c1..0000000 --- a/tests/pipeline/executors/test_dask.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python3 - -import os -import tempfile -import unittest -import urllib.parse - -from mock import patch - -from dask.distributed import Client, LocalCluster - -from dasf.utils.funcs import is_gpu_supported -from dasf.pipeline.executors import DaskPipelineExecutor -from dasf.pipeline.executors.dask import setup_dask_protocol - - -class TestDaskProtocol(unittest.TestCase): - def test_setup_dask_protocol_none(self): - self.assertEqual(setup_dask_protocol(), "tcp://") - - def test_setup_dask_protocol_tcp(self): - self.assertEqual(setup_dask_protocol("tcp"), "tcp://") - - def test_setup_dask_protocol_ucx(self): - self.assertEqual(setup_dask_protocol("ucx"), "ucx://") - - def test_setup_dask_protocol_foo(self): - with self.assertRaises(Exception) as context: - setup_dask_protocol("foo") - - self.assertTrue('Protocol foo is not supported.' in str(context.exception)) - - -class TestDaskExecutor(unittest.TestCase): - def setUp(self): - self.scheduler_file = os.path.abspath(f"{tempfile.gettempdir()}/scheduler.json") - - def test_dask_executor_remote(self): - - with LocalCluster() as cluster: - conn = urllib.parse.urlsplit(cluster.scheduler.address) - - dask = DaskPipelineExecutor(address=conn.hostname, port=conn.port) - - # Compute everything to gracefully shutdown - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - def test_dask_executor_local_no_args(self): - dask = DaskPipelineExecutor() - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - def test_dask_executor_local_no_args_no_gracefully(self): - dask = DaskPipelineExecutor() - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=False) - dask.close() - - self.assertFalse(dask.is_connected) - - def test_dask_executor_local(self): - dask = DaskPipelineExecutor(local=True) - - client = Client.current() - - self.assertTrue(dask.is_connected) - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_dask_executor_local_gpu(self): - with patch.dict(os.environ, {'CUDA_VISIBLE_DEVICES': '0'}): - - dask = DaskPipelineExecutor(local=True, use_gpu=True) - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - self.assertGreater(dask.ngpus, 0) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_dask_executor_local_gpu_and_rmm(self): - with patch.dict(os.environ, {'CUDA_VISIBLE_DEVICES': '0'}): - - dask = DaskPipelineExecutor(local=True, use_gpu=True, gpu_allocator="rmm") - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_dask_executor_local_gpu_and_unknown_allocator(self): - with self.assertRaises(ValueError) as context: - - dask = DaskPipelineExecutor(local=True, use_gpu=True, gpu_allocator="foo") - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - client.close() - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - def test_dask_executor_scheduler_file(self): - with LocalCluster() as cluster: - client = Client(cluster) - - client.write_scheduler_file(self.scheduler_file) - - client_kwargs = {} - client_kwargs["scheduler_file"] = self.scheduler_file - - client.close() - - dask = DaskPipelineExecutor(client_kwargs=client_kwargs) - - client = Client.current() - - self.assertEqual(hash(dask.client), hash(client)) - - # Compute everything to gracefully shutdown - dask.shutdown(gracefully=True) - dask.close() - - self.assertFalse(dask.is_connected) - - def tearDown(self): - if os.path.isfile(self.scheduler_file) or os.path.islink(self.scheduler_file): - os.remove(self.scheduler_file) diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py deleted file mode 100644 index fc78d30..0000000 --- a/tests/pipeline/test_pipeline.py +++ /dev/null @@ -1,215 +0,0 @@ -#!/usr/bin/env python3 - -import unittest - -import numpy as np - -from mock import MagicMock - -from dasf.pipeline import Pipeline -from dasf.datasets import DatasetArray -from dasf.transforms.base import Transform -from dasf.transforms.base import TargeteredTransform - - -class Dataset_A(DatasetArray): - def load(self): - self._data = np.arange(10) - return self._data - - -class Transform_A(Transform): - def transform(self, X): - return X + 2 - - -class Transform_B(Transform): - def transform(self, X): - return X - 2 - - -class Transform_C(Transform): - def transform(self, X): - return X * 2 - - -class Transform_D(Transform): - def transform(self, X): - return X / 2 - - -class Transform_E(Transform): - def transform_new(self, X): - return X + 4 - - -class Transform_F(TargeteredTransform): - def transform(self, X): - return X - 4 - - -def transform_g(X): - return X - 4 - - -class TestPipeline(unittest.TestCase): - def test_pipeline_creation(self): - dataset_A = Dataset_A(name="Test Dataset A") - - t_A = Transform_A() - t_B = Transform_B() - - pipeline = Pipeline("Test Pipeline Creation") - - pipeline = pipeline.add(t_A, X=dataset_A) \ - .add(t_B, X=t_A) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - all_output = '\n'.join(plogs.output) - - self.assertIn('Dataset_A', all_output) - self.assertIn('Transform_A', all_output) - self.assertIn('Transform_B', all_output) - - def test_pipeline_creation_in_sequence(self): - dataset_A = Dataset_A(name="Test Dataset A") - - t_A = Transform_A() - t_B = Transform_B() - t_C = Transform_C() - t_D = Transform_D() - - pipeline = Pipeline("Test Pipeline Creation 1") - - pipeline = pipeline.add(t_A, X=dataset_A) \ - .add(t_B, X=t_A) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - all_output = '\n'.join(plogs.output) - - self.assertIn('Dataset_A', all_output) - self.assertIn('Transform_A', all_output) - self.assertIn('Transform_B', all_output) - self.assertNotIn('Transform_C', all_output) - self.assertNotIn('Transform_D', all_output) - - pipeline = Pipeline("Test Pipeline Creation 2") - - pipeline = pipeline.add(t_C, X=dataset_A) \ - .add(t_D, X=t_C) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - all_output = '\n'.join(plogs.output) - - self.assertIn('Dataset_A', all_output) - self.assertNotIn('Transform_A', all_output) - self.assertNotIn('Transform_B', all_output) - self.assertIn('Transform_C', all_output) - self.assertIn('Transform_D', all_output) - - def test_pipeline_non_transformers(self): - dataset_A = Dataset_A(name="Test Dataset A") - - t_E = Transform_E() - - pipeline = Pipeline("Test Pipeline Creation Non Transformers") - - pipeline = pipeline.add(t_E.transform_new, X=dataset_A) \ - .add(transform_g, X=t_E.transform_new) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - all_output = '\n'.join(plogs.output) - - self.assertIn('Dataset_A', all_output) - self.assertIn('transform_new', all_output) - self.assertIn('transform_g', all_output) - - def test_pipeline_targetered_transformers(self): - dataset_A = Dataset_A(name="Test Dataset A") - - t_F = Transform_F() - - pipeline = Pipeline("Test Pipeline Creation Non Transformers") - - pipeline = pipeline.add(t_F, X=dataset_A) \ - .add(transform_g, X=t_F) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - all_output = '\n'.join(plogs.output) - - self.assertIn('Dataset_A', all_output) - self.assertIn('Transform_F', all_output) - self.assertIn('transform_g', all_output) - - def test_pipeline_results(self): - orig_data = np.arange(10) - - dataset_A = Dataset_A(name="Test Dataset A") - - t_A = Transform_A() - t_B = Transform_B() - - pipeline = Pipeline("Test Pipeline Creation") - - pipeline = pipeline.add(t_A, X=dataset_A) \ - .add(t_B, X=t_A) - - with self.assertLogs('DASF', level='INFO') as plogs: - pipeline.run() - - self.assertIn('Pipeline run successfully', plogs.output[-1]) - - t_A_r = pipeline.get_result_from(t_A) - t_B_r = pipeline.get_result_from(t_B) - - self.assertTrue(np.array_equal(t_A_r, orig_data + 2)) - self.assertTrue(np.array_equal(t_B_r, orig_data)) - - def test_dataset_registration(self): - dataset_A = Dataset_A(name="Test Dataset A") - - executor = MagicMock() - executor.is_connected = True - executor.pre_run.return_value = None - executor.post_run.return_value = None - executor.has_dataset = MagicMock(side_effect=[False, True]) - executor.register_dataset.return_value = None - executor.get_dataset.return_value = dataset_A - - t_A = Transform_A() - t_B = Transform_B() - - pipeline = Pipeline("Test Pipeline Creation", executor=executor) - - pipeline = pipeline.add(t_A, X=dataset_A) \ - .add(t_B, X=t_A) - - pipeline.run() - - key = str(hash(dataset_A.load)) - kwargs = {key: dataset_A} - - # XXX: Disable register dataset for now - # executor.register_dataset.assert_called_once_with(**kwargs) - # executor.has_dataset.assert_called_with(key) - raise unittest.SkipTest("Datasets are disabled for now") diff --git a/tests/preprocessing/test_standardscaler.py b/tests/preprocessing/test_standardscaler.py deleted file mode 100644 index 1b6d31e..0000000 --- a/tests/preprocessing/test_standardscaler.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import dask.array as da - -from mock import patch, Mock - -try: - import cupy as cp -except ImportError: - pass - -from dasf.ml.preprocessing import StandardScaler -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.funcs import is_gpu_supported - - -class TestStandardScaler(unittest.TestCase): - def setUp(self): - size = 20 - self.X = np.array([np.arange(size)]) - self.X.shape = (size, 1) - - mean = np.mean(self.X) - std = np.std(self.X) - - self.y = (self.X - mean) / std - - def test_standardscaler_cpu(self): - ss = StandardScaler() - - y = ss._fit_transform_cpu(self.X) - - self.assertTrue(is_cpu_array(y)) - self.assertTrue(np.array_equal(self.y, y, equal_nan=True)) - - def test_standardscaler_mcpu(self): - ss = StandardScaler() - - y = ss._lazy_fit_transform_cpu(da.from_array(self.X)) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.compute(), equal_nan=True)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_standardscaler_gpu(self): - ss = StandardScaler() - - y = ss._fit_transform_gpu(cp.asarray(self.X)) - - self.assertTrue(is_gpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.get(), equal_nan=True)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_standardscaler_mgpu(self): - ss = StandardScaler() - - y = ss._lazy_fit_transform_gpu(da.from_array(cp.asarray(self.X))) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.compute().get(), equal_nan=True)) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_standardscaler_mcpu_local(self): - ss = StandardScaler(run_local=True) - - y = ss._fit_transform_cpu(self.X) - - self.assertTrue(is_cpu_array(y)) - self.assertTrue(np.array_equal(self.y, y, equal_nan=True)) diff --git a/tests/transforms/test_base.py b/tests/transforms/test_base.py deleted file mode 100644 index e1358f9..0000000 --- a/tests/transforms/test_base.py +++ /dev/null @@ -1,317 +0,0 @@ -#!/usr/bin/env python3 - -import os -import shutil -import tempfile -import unittest -import functools -import numpy as np -import pandas as pd -import dask.array as da -import dask.dataframe as dd - -try: - import cupy as cp - import cudf - import dask_cudf as dcudf -except ImportError: - pass - -from dasf.transforms.base import MappedTransform -from dasf.transforms.base import ReductionTransform -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.types import is_cpu_dataframe -from dasf.utils.types import is_gpu_dataframe -from dasf.utils.types import is_dask_cpu_dataframe -from dasf.utils.types import is_dask_gpu_dataframe -from dasf.utils.funcs import is_gpu_supported - - -class TestMappedTransform(unittest.TestCase): - def __internal_max_function(self, block, nxp=np, block_info=None): - # Using `nxp` parameter to avoid conflicts - return nxp.asarray([[nxp.max(block)]]) - - def __internal_max_min_function(self, block, nxp=np, block_info=None): - # Using `nxp` parameter to avoid conflicts - return nxp.asarray([[nxp.min(block), nxp.max(block)]]) - - def test_rechunk_max_min_cpu(self): - X = np.random.random((40, 40, 40)) - - mapped = MappedTransform(function=self.__internal_max_min_function, - output_chunk=(1, 2)) - - X_t = mapped._transform_cpu(X, nxp=np) - - # Numpy does not use blocks. - self.assertEqual(X_t.shape, (1, 2)) - - self.assertTrue(is_cpu_array(X_t)) - - def test_rechunk_max_min_mcpu(self): - X = da.random.random((40, 40, 40), chunks=(10, 40, 40)) - - mapped = MappedTransform(function=self.__internal_max_min_function, - output_chunk=(1, 2)) - - X_t = mapped._lazy_transform_cpu(X, nxp=np) - - self.assertEqual(X_t.shape, (1, 2)) - # We split data in 4 chunks of axis 0. If the new chunk size is - # (1, 2), it means that the final size is (1 * 4, 2). - self.assertEqual(X_t.compute().shape, (1, 2)) - - self.assertTrue(is_dask_cpu_array(X_t)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_rechunk_max_min_gpu(self): - X = cp.random.random((40, 40, 40)) - - mapped = MappedTransform(function=self.__internal_max_min_function, - output_chunk=(1, 2)) - - X_t = mapped._transform_gpu(X, nxp=cp) - - # Numpy does not use blocks. - self.assertEqual(X_t.shape, (1, 2)) - - self.assertTrue(is_gpu_array(X_t)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_rechunk_max_min_mgpu(self): - # We need to convert to Dask to use Cupy random. - X_cp = cp.random.random((40, 40, 40)) - - X = da.from_array(X_cp, chunks=(10, 40, 40), meta=cp.array(())) - - mapped = MappedTransform(function=self.__internal_max_min_function, - output_chunk=(1, 2)) - - X_t = mapped._lazy_transform_gpu(X, nxp=cp) - - self.assertEqual(X_t.shape, (1, 2)) - # We split data in 4 chunks of axis 0. If the new chunk size is - # (1, 2), it means that the final size is (1 * 4, 2). - self.assertEqual(X_t.compute().get().shape, (1, 2)) - - -class TestReductionTransformArray(unittest.TestCase): - def __internal_chunk_array(self, block, axis=None, keepdims=False, xp=np): - # Using `xp` parameter to avoid conflicts - return xp.array([xp.min(block), xp.max(block)]) - - def __internal_aggregate_array(self, block, axis=None, keepdims=False, xp=np): - # Using `xp` parameter to avoid conflicts - block = xp.array(block) - - return xp.array([xp.min(block), xp.max(block)]) - - def test_reduction_min_max_cpu(self): - X = np.arange(40 * 40 * 40) - X.shape = (40, 40, 40) - - reduction = ReductionTransform(func_aggregate=self.__internal_aggregate_array, - func_chunk=self.__internal_chunk_array, - output_size=[0, 0]) - - X_t = reduction._transform_cpu(X) - - # Numpy does not use blocks. - self.assertEqual(len(X_t), 2) - self.assertTrue(is_cpu_array(X_t)) - - self.assertEqual(X_t[0], 0) - self.assertEqual(X_t[1], 40 * 40 * 40 - 1) - - def test_reduction_min_max_mcpu(self): - X = np.arange(40 * 40 * 40) - X.shape = (40, 40, 40) - - X = da.from_array(X, chunks=(10, 10, 10)) - - reduction = ReductionTransform(func_aggregate=self.__internal_aggregate_array, - func_chunk=self.__internal_chunk_array, - output_size=[0, 0]) - - X_t = reduction._lazy_transform_cpu(X, concatenate=False) - - self.assertTrue(is_dask_cpu_array(X_t)) - self.assertEqual(len(X_t.compute()), 2) - - self.assertEqual(X_t.compute()[0], 0) - self.assertEqual(X_t.compute()[1], 40 * 40 * 40 - 1) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reduction_min_max_gpu(self): - X = cp.arange(40 * 40 * 40) - X.shape = (40, 40, 40) - - reduction = ReductionTransform(func_aggregate=self.__internal_aggregate_array, - func_chunk=self.__internal_chunk_array, - output_size=[0, 0]) - - X_t = reduction._transform_gpu(X) - - # Numpy does not use blocks. - self.assertEqual(len(X_t), 2) - self.assertTrue(is_gpu_array(X_t)) - - self.assertEqual(X_t[0].get(), 0) - self.assertEqual(X_t[1].get(), 40 * 40 * 40 - 1) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reduction_min_max_mgpu(self): - X = cp.arange(40 * 40 * 40) - X.shape = (40, 40, 40) - - X = da.from_array(X, chunks=(10, 10, 10), meta=cp.array(())) - - reduction = ReductionTransform(func_aggregate=self.__internal_aggregate_array, - func_chunk=self.__internal_chunk_array, - output_size=[0, 0]) - - X_t = reduction._lazy_transform_gpu(X, concatenate=False) - - self.assertTrue(is_dask_gpu_array(X_t)) - self.assertEqual(len(X_t.compute()), 2) - - self.assertEqual(X_t.compute()[0].get(), 0) - self.assertEqual(X_t.compute()[1].get(), 40 * 40 * 40 - 1) - - -class TestReductionTransformDataFrame(unittest.TestCase): - def _internal_chunk_max(self, row): - if row['A'] > row['B'] and row['A'] > row['C']: - return row['A'] - elif row['B'] > row['A'] and row['B'] > row['C']: - return row['B'] - else: - return row['C'] - - def _internal_chunk_min(self, row): - if row['A'] < row['B'] and row['A'] < row['C']: - return row['A'] - elif row['B'] < row['A'] and row['B'] < row['C']: - return row['B'] - else: - return row['C'] - - def _internal_aggregate_min_max(self, pds, xd): - return xd.DataFrame({ - 'min': [pds['min'].min()], - 'max': [pds['max'].max()] - }) - - def _internal_chunk_partition_cpu(self, block, axis=None, keepdims=False, xp=None): - pds_max = block.apply(self._internal_chunk_min, axis=1) - pds_min = block.apply(self._internal_chunk_max, axis=1) - - pds = pd.DataFrame({'min': pds_min, 'max': pds_max}) - return self._internal_aggregate_min_max(pds, xd=pd) - - def _internal_chunk_partition_gpu(self, block, axis=None, keepdims=False, xp=None): - pds_max = block.apply(self._internal_chunk_min, axis=1) - pds_min = block.apply(self._internal_chunk_max, axis=1) - - pds = cudf.DataFrame({'min': pds_min, 'max': pds_max}) - return self._internal_aggregate_min_max(pds, xd=cudf) - - def _internal_aggregate_series_cpu(self, block, axis=None, keepdims=False, xp=None): - return self._internal_aggregate_min_max(block, xd=pd) - - def _internal_aggregate_series_gpu(self, block, axis=None, keepdims=False, xp=None): - return self._internal_aggregate_min_max(block, xd=cudf) - - def test_reduction_min_max_cpu(self): - df = pd.DataFrame({ - 'A': range(0, 1000), - 'B': range(0, 1000), - 'C': range(0, 1000) - }) - - reduction = ReductionTransform(func_aggregate=self._internal_aggregate_series_cpu, - func_chunk=self._internal_chunk_partition_cpu, - output_size={'min': 'int64', - 'max': 'int64'}) - - X_t = reduction._transform_cpu(X=df) - - self.assertEqual(len(X_t.iloc[0]), 2) - - self.assertEqual(X_t['min'].iloc[0], 0) - self.assertEqual(X_t['max'].iloc[0], 1000 - 1) - - def test_reduction_min_max_mcpu(self): - df = pd.DataFrame({ - 'A': range(0, 1000), - 'B': range(0, 1000), - 'C': range(0, 1000) - }) - - ddf = dd.from_pandas(df, npartitions=8) - - reduction = ReductionTransform(func_aggregate=self._internal_aggregate_series_cpu, - func_chunk=self._internal_chunk_partition_cpu, - output_size={'min': 'int64', - 'max': 'int64'}) - - X_t = reduction._lazy_transform_cpu(X=ddf, axis=[0]) - - self.assertTrue(is_dask_cpu_dataframe(X_t)) - self.assertEqual(len(X_t.compute().iloc[0]), 2) - - self.assertEqual(X_t.compute().iloc[0]['min'], 0) - self.assertEqual(X_t.compute().iloc[0]['max'], 1000 - 1) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reduction_min_max_gpu(self): - df = cudf.DataFrame({ - 'A': range(0, 1000), - 'B': range(0, 1000), - 'C': range(0, 1000) - }) - - reduction = ReductionTransform(func_aggregate=self._internal_aggregate_series_gpu, - func_chunk=self._internal_chunk_partition_gpu, - output_size={'min': 'int64', - 'max': 'int64'}) - - X_t = reduction._transform_gpu(X=df) - - self.assertEqual(len(X_t.iloc[0]), 2) - - self.assertEqual(X_t['min'].iloc[0], 0) - self.assertEqual(X_t['max'].iloc[0], 1000 - 1) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reduction_min_max_mgpu(self): - df = cudf.DataFrame({ - 'A': range(0, 1000), - 'B': range(0, 1000), - 'C': range(0, 1000) - }) - - ddf = dcudf.from_cudf(df, npartitions=8) - - reduction = ReductionTransform(func_aggregate=self._internal_aggregate_series_gpu, - func_chunk=self._internal_chunk_partition_gpu, - output_size={'min': 'int64', - 'max': 'int64'}) - - X_t = reduction._lazy_transform_gpu(X=ddf, axis=[0]) - - self.assertTrue(is_dask_gpu_dataframe(X_t)) - self.assertEqual(len(X_t.compute().iloc[0]), 2) - - self.assertEqual(X_t.compute().iloc[0]['min'], 0) - self.assertEqual(X_t.compute().iloc[0]['max'], 1000 - 1) diff --git a/tests/transforms/test_memory.py b/tests/transforms/test_memory.py deleted file mode 100644 index d534a17..0000000 --- a/tests/transforms/test_memory.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import dask.array as da - -try: - import cupy as cp -except ImportError: - pass - -from dasf.transforms import PersistDaskData -from dasf.transforms import ComputeDaskData -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.funcs import is_gpu_supported - - -class TestMemory(unittest.TestCase): - def test_persist_dask_data_cpu(self): - X = np.random.random((40, 40, 40)) - - persist = PersistDaskData() - - result = persist._transform_cpu(X=X) - - self.assertTrue(is_cpu_array(result)) - - def test_persist_dask_data_mcpu(self): - X = da.ones((40, 40, 40), chunks=(10, 10, 10), meta=np.array(())) - - persist = PersistDaskData() - - result = persist._lazy_transform_cpu(X=X) - - self.assertTrue(is_dask_cpu_array(result)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_persist_dask_data_gpu(self): - X = cp.random.random((40, 40, 40)) - - persist = PersistDaskData() - - result = persist._transform_gpu(X=X) - - self.assertTrue(is_gpu_array(result)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_persist_dask_data_mgpu(self): - X = da.ones((40, 40, 40), chunks=(10, 10, 10), meta=cp.array(())) - - persist = PersistDaskData() - - result = persist._lazy_transform_gpu(X=X) - - self.assertTrue(is_dask_gpu_array(result)) - - def test_compute_dask_data_cpu(self): - X = np.random.random((40, 40, 40)) - - persist = ComputeDaskData() - - result = persist._transform_cpu(X=X) - - self.assertTrue(is_cpu_array(result)) - - def test_compute_dask_data_mcpu(self): - X = da.ones((40, 40, 40), chunks=(10, 10, 10), meta=np.array(())) - - persist = ComputeDaskData() - - result = persist._lazy_transform_cpu(X=X) - - self.assertTrue(is_cpu_array(result)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_compute_dask_data_gpu(self): - X = cp.random.random((40, 40, 40)) - - persist = ComputeDaskData() - - result = persist._transform_gpu(X=X) - - self.assertTrue(is_gpu_array(result)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_compute_dask_data_mgpu(self): - X = da.ones((40, 40, 40), chunks=(10, 10, 10), meta=cp.array(())) - - persist = ComputeDaskData() - - result = persist._lazy_transform_gpu(X=X) - - self.assertTrue(is_gpu_array(result)) diff --git a/tests/transforms/test_operations.py b/tests/transforms/test_operations.py deleted file mode 100644 index cc1ce65..0000000 --- a/tests/transforms/test_operations.py +++ /dev/null @@ -1,497 +0,0 @@ -#!/usr/bin/env python3 - -import unittest - -import numpy as np -import dask.array as da -import pandas as pd -import dask.dataframe as ddf - -try: - import cudf - import cupy as cp - import dask_cudf as cuddf -except ImportError: - pass - -from mock import MagicMock - -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.funcs import is_gpu_supported -from dasf.transforms.operations import Reshape -from dasf.transforms.operations import SliceArray -from dasf.transforms.operations import SliceArrayByPercent - - -class TestReshape(unittest.TestCase): - def test_reshape_array_cpu(self): - data = np.random.random((10, 10, 10)) - - reshape = Reshape(shape=(1000)) - - y = reshape.fit(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - def test_reshape_dask_array_cpu(self): - data = da.random.random((10, 10, 10), chunks=(5, 5, 5)) - - reshape = Reshape(shape=(1000)) - - y = reshape.fit(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_array_gpu(self): - data = cp.random.random((10, 10, 10)) - - reshape = Reshape(shape=(1000)) - - y = reshape.fit(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_dask_array_gpu(self): - data = cp.random.random((10, 10, 10)) - data = da.from_array(data, chunks=(5, 5, 5)) - - reshape = Reshape(shape=(1000)) - - y = reshape.fit(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - def test_reshape_array_cpu_from_array(self): - data = np.random.random((10, 10, 10)) - copy = np.random.random((1000,)) - - reshape = Reshape() - - y = reshape.fit(data, y=copy) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - def test_reshape_dask_array_cpu_from_array(self): - data = da.random.random((10, 10, 10), chunks=(5, 5, 5)) - copy = da.random.random((1000,), chunks=(5,)) - - reshape = Reshape() - - y = reshape.fit(data, y=copy) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_array_gpu_from_array(self): - data = cp.random.random((10, 10, 10)) - copy = cp.random.random((1000,)) - - reshape = Reshape() - - y = reshape.fit(data, y=copy) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_dask_array_gpu_from_array(self): - data = cp.random.random((10, 10, 10)) - data = da.from_array(data, chunks=(5, 5, 5)) - copy = cp.random.random((1000,)) - - reshape = Reshape() - - y = reshape.fit(data, y=copy) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (1000, )) - - def test_reshape_array_list(self): - data = np.random.random((2, 5)) - copy = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] - - reshape = Reshape() - - with self.assertRaises(Exception) as context: - y = reshape.fit(data, y=copy) - - self.assertTrue('Missing shape input' in str(context.exception)) - - def test_reshape_unknown_datatype(self): - data = MagicMock(shape=(2, 5)) - - reshape = Reshape(shape=(10)) - - with self.assertRaises(Exception) as context: - y = reshape.fit(data) - - self.assertTrue('X is not a known datatype' in str(context.exception)) - - def test_reshape_dataframe_cpu(self): - data = pd.DataFrame(np.random.random((3, 4)), columns=['A', 'B', 'C', 'D']) - - reshape = Reshape(shape=(12)) - - y = reshape.fit(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (12, )) - - def test_reshape_dask_dataframe_cpu(self): - raise unittest.SkipTest("DataFrame in Dask does not return the proper shape to reshape (BUG?)") - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_dataframe_gpu(self): - data = cudf.DataFrame(cp.random.random((3, 4)), columns=['A', 'B', 'C', 'D']) - - reshape = Reshape(shape=(12)) - - y = reshape.fit(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (12, )) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_reshape_dask_dataframe_gpu(self): - raise unittest.SkipTest("DataFrame in Dask does not return the proper shape to reshape (BUG?)") - - -class TestSliceArray(unittest.TestCase): - def test_slice_array_cpu_1d(self): - data = np.random.random((40,)) - - slice_t = SliceArray(output_size=(10,)) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10,)) - - def test_slice_dask_array_cpu_1d(self): - data = da.random.random((40,), chunks=(5)) - - slice_t = SliceArray(output_size=(10,)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10,)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_1d(self): - data = cp.random.random((40,)) - - slice_t = SliceArray(output_size=(10,)) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10,)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_1d(self): - data = cp.random.random((40,)) - data = da.from_array(data, chunks=(5)) - - slice_t = SliceArray(output_size=(10,)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10,)) - - def test_slice_array_cpu_2d(self): - data = np.random.random((40, 40)) - - slice_t = SliceArray(output_size=(10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - def test_slice_dask_array_cpu_2d(self): - data = da.random.random((40, 40), chunks=(5, 5)) - - slice_t = SliceArray(output_size=(10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_2d(self): - data = cp.random.random((40, 40)) - - slice_t = SliceArray(output_size=(10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_2d(self): - data = cp.random.random((40, 40)) - data = da.from_array(data, chunks=(5, 5)) - - slice_t = SliceArray(output_size=(10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - def test_slice_array_cpu_3d(self): - data = np.random.random((40, 40, 40)) - - slice_t = SliceArray(output_size=(10, 10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - def test_slice_dask_array_cpu_3d(self): - data = da.random.random((40, 40, 40), chunks=(5, 5, 5)) - - slice_t = SliceArray(output_size=(10, 10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_3d(self): - data = cp.random.random((40, 40, 40)) - - slice_t = SliceArray(output_size=(10, 10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_3d(self): - data = cp.random.random((40, 40, 40)) - data = da.from_array(data, chunks=(5, 5, 5)) - - slice_t = SliceArray(output_size=(10, 10, 10)) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - def test_slice_array_unknown_dim(self): - data = np.random.random((2, 2, 2, 2)) - - slice_t = SliceArray(output_size=(1, 1, 1, 1)) - - with self.assertRaises(Exception) as context: - y = slice_t.transform(data) - - self.assertTrue('The dimmension is not known' in str(context.exception)) - - -class TestSliceArrayByPercent(unittest.TestCase): - def test_slice_array_cpu_1d(self): - data = np.random.random((40,)) - - slice_t = SliceArrayByPercent(x=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10,)) - - def test_slice_dask_array_cpu_1d(self): - data = da.random.random((40,), chunks=(5)) - - slice_t = SliceArrayByPercent(x=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10,)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_1d(self): - data = cp.random.random((40,)) - - slice_t = SliceArrayByPercent(x=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10,)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_1d(self): - data = cp.random.random((40,)) - data = da.from_array(data, chunks=(5)) - - slice_t = SliceArrayByPercent(x=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10,)) - - def test_slice_array_cpu_2d(self): - data = np.random.random((40, 40)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - def test_slice_dask_array_cpu_2d(self): - data = da.random.random((40, 40), chunks=(5, 5)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_2d(self): - data = cp.random.random((40, 40)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_2d(self): - data = cp.random.random((40, 40)) - data = da.from_array(data, chunks=(5, 5)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10, 10)) - - def test_slice_array_cpu_3d(self): - data = np.random.random((40, 40, 40)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0, z=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_cpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - def test_slice_dask_array_cpu_3d(self): - data = da.random.random((40, 40, 40), chunks=(5, 5, 5)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0, z=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_array_gpu_3d(self): - data = cp.random.random((40, 40, 40)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0, z=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_gpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_slice_dask_array_gpu_3d(self): - data = cp.random.random((40, 40, 40)) - data = da.from_array(data, chunks=(5, 5, 5)) - - slice_t = SliceArrayByPercent(x=25.0, y=25.0, z=25.0) - - y = slice_t.transform(data) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertEqual(y.shape, (10, 10, 10)) - - def test_slice_array_unknown_dim(self): - data = np.random.random((2, 2, 2, 2)) - - slice_t = SliceArrayByPercent(x=50.0, y=50.0, z=50.0) - - with self.assertRaises(Exception) as context: - y = slice_t.transform(data) - - self.assertTrue('The dimmension is not known' in str(context.exception)) - - def test_slice_array_exceeding_percentage(self): - data = np.random.random((4, 4, 4)) - - slice_t = SliceArrayByPercent(x=150.0, y=150.0, z=50.0) - - with self.assertRaises(Exception) as context: - y = slice_t.transform(data) - - self.assertTrue('Percentages cannot be higher than 100% (1.0)' in str(context.exception)) - - def test_slice_array_zero_percentage(self): - data = np.random.random((4, 4, 4)) - - slice_t = SliceArrayByPercent(x=50.0, y=0.0, z=50.0) - - with self.assertRaises(Exception) as context: - y = slice_t.transform(data) - - self.assertTrue('Percentages cannot be negative or 0' in str(context.exception)) - - def test_slice_array_negative_percentage(self): - data = np.random.random((4, 4, 4)) - - slice_t = SliceArrayByPercent(x=-50.0, y=50.0, z=50.0) - - with self.assertRaises(Exception) as context: - y = slice_t.transform(data) - - self.assertTrue('Percentages cannot be negative or 0' in str(context.exception)) diff --git a/tests/transforms/test_transforms.py b/tests/transforms/test_transforms.py deleted file mode 100644 index f65cc32..0000000 --- a/tests/transforms/test_transforms.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python3 - -import os -import zarr -import shutil -import tempfile -import unittest -import numpy as np -import dask.array as da - -try: - import cupy as cp -except ImportError: - pass - -from dasf.datasets import DatasetArray -from dasf.datasets import DatasetZarr -from dasf.datasets import DatasetHDF5 -from dasf.transforms import Normalize -from dasf.transforms import ArrayToZarr -from dasf.transforms import ArrayToHDF5 -from dasf.transforms import ZarrToArray -from dasf.transforms import ArraysToDataFrame -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.types import is_cpu_dataframe -from dasf.utils.types import is_gpu_dataframe -from dasf.utils.types import is_dask_cpu_dataframe -from dasf.utils.types import is_dask_gpu_dataframe -from dasf.utils.funcs import is_gpu_supported - - -class TestNormalize(unittest.TestCase): - def setUp(self): - size = 20 - self.X = np.array([np.arange(size)]) - self.X.shape = (size, 1) - - mean = np.mean(self.X) - std = np.std(self.X) - - self.y = (self.X - mean) / std - - def test_normalize_cpu(self): - norm = Normalize() - - y = norm.transform(self.X) - - self.assertTrue(is_cpu_array(y)) - self.assertTrue(np.array_equal(self.y, y, equal_nan=True)) - - def test_normalize_mcpu(self): - norm = Normalize() - - y = norm.transform(da.from_array(self.X)) - - self.assertTrue(is_dask_cpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.compute(), equal_nan=True)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_normalize_gpu(self): - norm = Normalize() - - y = norm.transform(cp.asarray(self.X)) - - self.assertTrue(is_gpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.get(), equal_nan=True)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_normalize_mgpu(self): - norm = Normalize() - - y = norm.transform(da.from_array(cp.asarray(self.X))) - - self.assertTrue(is_dask_gpu_array(y)) - self.assertTrue(np.array_equal(self.y, y.compute().get(), equal_nan=True)) - - -class TestArrayToZarr(unittest.TestCase): - def setUp(self): - self.array = os.path.abspath(f"{tempfile.gettempdir()}/array.npy") - self.zarr = os.path.abspath(f"{tempfile.gettempdir()}/array.zarr") - - random = np.random.random(10000) - np.save(self.array, random) - - @staticmethod - def remove(path): - if os.path.isfile(path) or os.path.islink(path): - os.remove(path) # remove the file - elif os.path.isdir(path): - shutil.rmtree(path) # remove dir and all contains - else: - raise ValueError("file {} is not a file or dir.".format(path)) - - def test_array_to_zarr_cpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._load_cpu() - - T = ArrayToZarr(chunks=(100,)) - - T_1 = T._transform_cpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetZarr)) - - def test_array_to_zarr_mcpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._lazy_load_cpu() - - T = ArrayToZarr(chunks=(100,)) - - T_1 = T._lazy_transform_cpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetZarr)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_array_to_zarr_gpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._load_gpu() - - T = ArrayToZarr(chunks=(100,)) - - T_1 = T._transform_gpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetZarr)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_array_to_zarr_mgpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._lazy_load_gpu() - - T = ArrayToZarr(chunks=(100,)) - - T_1 = T._lazy_transform_gpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetZarr)) - - def tearDown(self): - self.remove(self.array) - self.remove(self.zarr) - - -class TestArrayToHDF5(unittest.TestCase): - def setUp(self): - self.array = os.path.abspath(f"{tempfile.gettempdir()}/array.npy") - self.hdf5 = os.path.abspath(f"{tempfile.gettempdir()}/array.hdf5") - - random = np.random.random(10000) - np.save(self.array, random) - - @staticmethod - def remove(path): - if os.path.isfile(path) or os.path.islink(path): - os.remove(path) # remove the file - elif os.path.isdir(path): - shutil.rmtree(path) # remove dir and all contains - else: - raise ValueError("file {} is not a file or dir.".format(path)) - - def test_array_to_hdf5_cpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._load_cpu() - - T = ArrayToHDF5(dataset_path="/dataset") - - T_1 = T._transform_cpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetHDF5)) - - def test_array_to_hdf5_mcpu(self): - dataset = DatasetArray(root=self.array, download=False, name="Test Array") - - dataset = dataset._lazy_load_cpu() - - T = ArrayToHDF5(dataset_path="/dataset") - - T_1 = T._lazy_transform_cpu(dataset) - - self.assertTrue(isinstance(T_1, DatasetHDF5)) - - def tearDown(self): - self.remove(self.array) - self.remove(self.hdf5) - - -class TestZarrToArray(unittest.TestCase): - def setUp(self): - self.array = os.path.abspath(f"{tempfile.gettempdir()}/array.npy") - self.zarr = os.path.abspath(f"{tempfile.gettempdir()}/array.zarr") - - random = z = zarr.array(np.random.random(10000), chunks=100) - zarr.save(self.zarr, random) - - @staticmethod - def remove(path): - if os.path.isfile(path) or os.path.islink(path): - os.remove(path) # remove the file - elif os.path.isdir(path): - shutil.rmtree(path) # remove dir and all contains - else: - raise ValueError("file {} is not a file or dir.".format(path)) - - def test_zarr_to_array_cpu(self): - dataset = DatasetZarr(root=self.zarr, download=False, name="Test Zarr") - - dataset = dataset._load_cpu() - - T = ZarrToArray() - - T_1 = T.transform(dataset) - - print(type(T_1)) - - self.assertTrue(is_cpu_array(T_1)) - - def test_zarr_to_array_mcpu(self): - dataset = DatasetZarr(root=self.zarr, download=False, name="Test Zarr") - - dataset = dataset._lazy_load_cpu() - - T = ZarrToArray() - - print(dataset._data) - - T_1 = T.transform(dataset) - - self.assertTrue(is_dask_cpu_array(T_1)) - - def tearDown(self): - self.remove(self.array) - self.remove(self.zarr) - - -class TestArraysToDataFrame(unittest.TestCase): - def test_arrays_to_dataframe_cpu(self): - array_1 = np.random.random((40, 40, 40)) - array_2 = np.random.random((40, 40, 40)) - array_3 = np.random.random((40, 40, 40)) - - arr_to_df = ArraysToDataFrame() - - y = arr_to_df._transform_cpu(array_1=array_1, - array_2=array_2, - array_3=array_3) - - self.assertTrue(is_cpu_dataframe(y)) - - def test_arrays_to_dataframe_mcpu(self): - chunks = (10, 10, 10) - - darray_1 = da.random.random((40, 40, 40), chunks=chunks) - darray_2 = da.random.random((40, 40, 40), chunks=chunks) - darray_3 = da.random.random((40, 40, 40), chunks=chunks) - - arr_to_df = ArraysToDataFrame() - - y = arr_to_df._lazy_transform_cpu(array_1=darray_1, - array_2=darray_2, - array_3=darray_3) - - self.assertTrue(is_dask_cpu_dataframe(y)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_arrays_to_dataframe_gpu(self): - array_1 = cp.random.random((40, 40, 40)) - array_2 = cp.random.random((40, 40, 40)) - array_3 = cp.random.random((40, 40, 40)) - - arr_to_df = ArraysToDataFrame() - - y = arr_to_df._transform_gpu(array_1=array_1, - array_2=array_2, - array_3=array_3) - - self.assertTrue(is_gpu_dataframe(y)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_arrays_to_dataframe_mgpu(self): - # Need to generate a Cupy first because Dask random does not accept meta - array_1 = cp.random.random((40, 40, 40)) - array_2 = cp.random.random((40, 40, 40)) - array_3 = cp.random.random((40, 40, 40)) - - chunks = (10, 10, 10) - - darray_1 = da.from_array(array_1, chunks=chunks, meta=cp.array(())) - darray_2 = da.from_array(array_2, chunks=chunks, meta=cp.array(())) - darray_3 = da.from_array(array_3, chunks=chunks, meta=cp.array(())) - - arr_to_df = ArraysToDataFrame() - - y = arr_to_df._lazy_transform_gpu(array_1=darray_1, - array_2=darray_2, - array_3=darray_3) - - self.assertTrue(is_dask_gpu_dataframe(y)) diff --git a/tests/utils/test_decorators.py b/tests/utils/test_decorators.py deleted file mode 100644 index 659c23f..0000000 --- a/tests/utils/test_decorators.py +++ /dev/null @@ -1,217 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import dask.array as da - -try: - import cupy as cp -except ImportError: - pass - -from mock import patch, Mock - -from dasf.utils.decorators import fetch_args_from_dask -from dasf.utils.decorators import fetch_args_from_gpu -from dasf.utils.decorators import task_handler -from dasf.utils.funcs import is_gpu_supported -from dasf.transforms.base import Transform - - -class TestFetchData(unittest.TestCase): - def test_fetch_args_from_dask(self): - - @fetch_args_from_dask - def fake_sum(array): - return array + array - - array = da.from_array(np.random.random(1000), chunks=(10,)) - - ret1 = fake_sum(array) - ret2 = fake_sum(array=array) - - self.assertTrue(isinstance(ret1, np.ndarray)) - self.assertTrue(isinstance(ret2, np.ndarray)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_fetch_args_from_gpu(self): - - @fetch_args_from_gpu - def fake_sum(array): - return array + array - - array = cp.random.random(1000) - - ret1 = fake_sum(array) - ret2 = fake_sum(array=array) - - self.assertTrue(isinstance(ret1, np.ndarray)) - self.assertTrue(isinstance(ret2, np.ndarray)) - - def test_fetch_args_from_dask_but_numpy(self): - - @fetch_args_from_dask - def fake_sum(array): - return array + array - - array = np.random.random(1000) - - ret1 = fake_sum(array) - ret2 = fake_sum(array=array) - - self.assertTrue(isinstance(ret1, np.ndarray)) - self.assertTrue(isinstance(ret2, np.ndarray)) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_fetch_args_from_gpu_but_numpy(self): - - @fetch_args_from_gpu - def fake_sum(array): - return array + array - - array = np.random.random(1000) - - ret1 = fake_sum(array) - ret2 = fake_sum(array=array) - - self.assertTrue(isinstance(ret1, np.ndarray)) - self.assertTrue(isinstance(ret2, np.ndarray)) - - -class TestTaskHandler(unittest.TestCase): - def generate_simple_transform(self): - simple_transform = Transform() - - setattr(simple_transform, '_run_local', None) - setattr(simple_transform, '_run_gpu', None) - - simple_transform._lazy_transform_gpu = Mock(return_value=1) - simple_transform._lazy_transform_cpu = Mock(return_value=2) - simple_transform._transform_gpu = Mock(return_value=3) - simple_transform._transform_cpu = Mock(return_value=4) - - return simple_transform - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=True)) - def test_task_handler_lazy_gpu(self): - simple_transform = self.generate_simple_transform() - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 1) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_lazy_cpu(self): - simple_transform = self.generate_simple_transform() - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 2) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_gpu(self): - simple_transform = self.generate_simple_transform() - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 3) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_cpu(self): - simple_transform = self.generate_simple_transform() - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 4) - - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - def test_task_handler_lazy_gpu_force(self): - simple_transform = self.generate_simple_transform() - - simple_transform._run_gpu = True - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 1) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=True)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_lazy_cpu_force(self): - simple_transform = self.generate_simple_transform() - - simple_transform._run_gpu = False - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 2) - - def test_task_handler_is_local_and_gpu(self): - simple_transform = self.generate_simple_transform() - - simple_transform._run_local = True - simple_transform._run_gpu = True - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 3) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - def test_task_handler_is_local_and_cpu(self): - simple_transform = self.generate_simple_transform() - - simple_transform._run_local = True - simple_transform._run_gpu = False - - X = da.random.random((10, 10, 10)) - - self.assertEqual(simple_transform.transform(X), 4) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_only_transform(self): - simple_transform = Mock() - simple_transform.transform = Mock(return_value=5) - simple_transform.transform.__name__ = 'transform' - simple_transform._run_local = None - simple_transform._run_gpu = None - - # Remove the possibility to assign _transform_cpu to Mock - delattr(simple_transform, '_transform_cpu') - - X = da.random.random((10, 10, 10)) - - self.assertEqual(task_handler(simple_transform.transform)(simple_transform, X), 5) - - @patch('dasf.utils.decorators.is_gpu_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_supported', Mock(return_value=False)) - @patch('dasf.utils.decorators.is_dask_gpu_supported', Mock(return_value=False)) - def test_task_handler_not_implemented(self): - simple_transform = Mock() - simple_transform.transform.__name__ = 'transform' - - odd_transform = Mock() - odd_transform._run_local = None - odd_transform._run_gpu = None - - # Remove the possibility to assign _transform_cpu and transform to Mock - delattr(odd_transform, 'transform') - delattr(odd_transform, '_transform_cpu') - - X = da.random.random((10, 10, 10)) - - with self.assertRaises(NotImplementedError) as context: - task_handler(simple_transform.transform)(odd_transform, X) - - self.assertTrue('There is no implementation of' in str(context.exception)) diff --git a/tests/utils/test_types.py b/tests/utils/test_types.py deleted file mode 100644 index a1a8160..0000000 --- a/tests/utils/test_types.py +++ /dev/null @@ -1,276 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -import numpy as np -import pandas as pd -import dask.array as da -import dask.dataframe as ddf -import xarray as xr - -from mock import patch, Mock - -try: - import cupy as cp - import cudf - import dask_cudf as dcudf -except ImportError: - pass - -from dasf.utils.types import is_array -from dasf.utils.types import is_dataframe -from dasf.utils.types import is_cpu_array -from dasf.utils.types import is_cpu_dataframe -from dasf.utils.types import is_cpu_datatype -from dasf.utils.types import is_gpu_array -from dasf.utils.types import is_gpu_dataframe -from dasf.utils.types import is_gpu_datatype -from dasf.utils.types import is_dask_cpu_array -from dasf.utils.types import is_dask_cpu_dataframe -from dasf.utils.types import is_dask_gpu_array -from dasf.utils.types import is_dask_gpu_dataframe -from dasf.utils.types import is_dask_array -from dasf.utils.types import is_dask_dataframe -from dasf.utils.types import is_dask -from dasf.utils.types import is_xarray_array -from dasf.utils.funcs import is_gpu_supported - - -class TestTypes(unittest.TestCase): - def setUp(self): - base = np.random.randint(10, size=100) - - self.data_types = { - "Numpy Array": [base, False], - "List": [base.tolist(), False], - "Dask Numpy Array": [da.from_array(base), False], - "Pandas DataFrame": [pd.DataFrame(base.tolist(), - columns=["test"]), - False], - "Dask DataFrame": [ddf.from_pandas(pd.DataFrame(base.tolist(), - columns=["test"]), - npartitions=20), - False], - "Xarray DataArray": [xr.DataArray(base), False], - } - - if is_gpu_supported(): - cupy_base = cp.asarray(base) - - self.data_types.update({ - "Cupy Array": [cupy_base, False], - "Dask Cupy Array": [da.from_array(cupy_base), False], - "CuDF": [cudf.DataFrame(base.tolist(), - columns=["test"]), - False], - "Dask CuDF": [dcudf.from_cudf(cudf.DataFrame(base.tolist(), - columns=["test"]), - npartitions=20), - False], - }) - - def __set_data(self, keys): - for key in self.data_types: - self.data_types[key][1] = False - if key in keys: - self.data_types[key][1] = True - - def __assert_all_data(self, func): - for key in self.data_types: - self.assertEqual(func(self.data_types[key][0]), - self.data_types[key][1], - "Type '%s' is %s while is expects %s" % - (str(type(self.data_types[key][0])), - func(self.data_types[key][0]), - self.data_types[key][1])) - - def test_is_array(self): - keys = [ - "Numpy Array", - "List", - "Dask Numpy Array", - "Cupy Array", - "Dask Cupy Array", - "Xarray DataArray" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_array) - - def test_is_dataframe(self): - keys = [ - "Pandas DataFrame", - "Dask DataFrame", - "CuDF", - "Dask CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dataframe) - - def test_is_cpu_array(self): - keys = [ - "Numpy Array", - "List" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_cpu_array) - - def test_is_cpu_dataframe(self): - keys = [ - "Pandas DataFrame" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_cpu_dataframe) - - def test_is_cpu_datatype(self): - keys = [ - "Numpy Array", - "List", - "Pandas DataFrame" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_cpu_datatype) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_is_gpu_array(self): - keys = [ - "Cupy Array" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_gpu_array) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_is_gpu_dataframe(self): - keys = [ - "CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_gpu_dataframe) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_is_gpu_datatype(self): - keys = [ - "Cupy Array", - "CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_gpu_datatype) - - def test_is_dask_cpu_array(self): - keys = [ - "Dask Numpy Array" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_cpu_array) - - def test_is_dask_cpu_dataframe(self): - keys = [ - "Dask DataFrame" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_cpu_dataframe) - - @patch('dasf.utils.types.is_gpu_supported', Mock(return_value=False)) - def test_is_dask_cpu_dataframe_no_gpu(self): - keys = [ - "Dask DataFrame" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_cpu_dataframe) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_is_dask_gpu_array(self): - keys = [ - "Dask Cupy Array" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_gpu_array) - - @unittest.skipIf(not is_gpu_supported(), - "not supported CUDA in this platform") - def test_is_dask_gpu_dataframe(self): - keys = [ - "Dask CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_gpu_dataframe) - - def test_is_dask_array(self): - keys = [ - "Dask Numpy Array", - "Dask Cupy Array" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_array) - - def test_is_dask_dataframe(self): - keys = [ - "Dask DataFrame", - "Dask CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_dataframe) - - @patch('dasf.utils.types.is_gpu_supported', Mock(return_value=False)) - def test_is_dask_dataframe_no_gpu(self): - keys = [ - "Dask DataFrame", - "Dask CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask_dataframe) - - def test_is_dask(self): - keys = [ - "Dask Numpy Array", - "Dask DataFrame", - "Dask Cupy Array", - "Dask CuDF" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_dask) - - def test_is_xarray_array(self): - keys = [ - "Xarray DataArray" - ] - - self.__set_data(keys) - - self.__assert_all_data(is_xarray_array) diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py deleted file mode 100644 index a93f8db..0000000 --- a/tests/utils/test_utils.py +++ /dev/null @@ -1,350 +0,0 @@ -#!/usr/bin/env python3 - -import unittest - -import numpy as np -import dask.array as da - -from mock import patch, Mock - -from distributed.utils import TimeoutError as DistributedTimeoutError - -from dasf.utils.funcs import human_readable_size -from dasf.utils.funcs import is_gpu_supported -from dasf.utils.funcs import is_jax_supported -from dasf.utils.funcs import is_dask_local_supported -from dasf.utils.funcs import is_dask_supported -from dasf.utils.funcs import is_dask_gpu_supported -from dasf.utils.funcs import get_gpu_count -from dasf.utils.funcs import get_dask_gpu_count -from dasf.utils.funcs import block_chunk_reduce -from dasf.utils.funcs import trim_chunk_location -from dasf.utils.funcs import get_backend_supported -from dasf.utils.funcs import get_worker_info -from dasf.utils.funcs import sync_future_loop -from dasf.utils.funcs import is_notebook - - -class TestArchitetures(unittest.TestCase): - def test_human_readable_size_bytes(self): - b = 42 - self.assertTrue(human_readable_size(b).endswith(" B")) - - def test_human_readable_size_kbytes(self): - kb = 42 * 1000 - self.assertTrue(human_readable_size(kb).endswith(" KB")) - - def test_human_readable_size_mbytes(self): - mb = 42 * (1000 ** 2) - self.assertTrue(human_readable_size(mb).endswith(" MB")) - - def test_human_readable_size_gbytes(self): - b = 42 * (1000 ** 3) - self.assertTrue(human_readable_size(b).endswith(" GB")) - - def test_human_readable_size_tbytes(self): - b = 42 * (1000 ** 4) - self.assertTrue(human_readable_size(b).endswith(" TB")) - - def test_human_readable_size_pbytes(self): - b = 42 * (1000 ** 5) - self.assertTrue(human_readable_size(b).endswith(" TB")) - - def test_human_readable_size_decimal(self): - b = 42 * (1000 ** 3) - decimal = 5 - number_str = human_readable_size(b, decimal)[:-3] - self.assertTrue(len(number_str.split(".")[1]) == decimal) - - @patch('dasf.utils.funcs.GPU_SUPPORTED', True) - @patch('dasf.utils.funcs.get_gpu_count', Mock(return_value=1)) - def test_is_gpu_supported_true(self): - self.assertTrue(is_gpu_supported()) - - @patch('dasf.utils.funcs.GPU_SUPPORTED', False) - def test_is_gpu_supported_false(self): - self.assertFalse(is_gpu_supported()) - - @patch('dask.config.get', return_value=Mock()) - def test_is_dask_local_supported_true(self, dask_config_get): - self.assertTrue(is_dask_local_supported()) - - @patch('dask.config.get', side_effect=Exception('Test')) - def test_is_dask_local_supported_false(self, dask_config_get): - self.assertFalse(is_dask_local_supported()) - - @patch('dask.config.get', return_value=Mock()) - def test_is_dask_supported_local_true(self, dask_config_get): - self.assertTrue(is_dask_supported()) - - @patch('dask.distributed.Client.current', return_value=Mock()) - @patch('dasf.utils.funcs.is_executor_cluster', return_value=True) - def test_is_dask_supported_remote_true(self, client_current, is_executor_cluster): - self.assertTrue(is_dask_supported()) - is_executor_cluster.assert_called() - - @patch('dask.config.get', side_effect=Exception('Test')) - @patch('dask.distributed.Client.current', side_effect=Exception('Test')) - def test_is_dask_supported_false(self, dask_config_get, client_current): - self.assertFalse(is_dask_supported()) - - @patch('dasf.utils.funcs.is_dask_supported', return_value=False) - def test_is_dask_gpu_supported_false(self, dask_check): - self.assertFalse(is_dask_gpu_supported()) - dask_check.assert_called() - - @patch('GPUtil.getGPUs', Mock(return_value=[])) - def test_is_dask_gpu_supported_zero_gpus(self): - self.assertFalse(is_dask_gpu_supported()) - - @patch('dasf.utils.funcs.is_dask_supported', return_value=True) - @patch('GPUtil.getGPUs', Mock(return_value=[0, 1])) - def test_is_dask_gpu_supported_nonzero_gpus(self, dask_check): - self.assertTrue(is_dask_gpu_supported()) - dask_check.assert_called() - - @patch('dasf.utils.funcs.JAX_SUPPORTED', True) - def test_is_jax_supported_true(self): - self.assertTrue(is_jax_supported()) - - @patch('dasf.utils.funcs.JAX_SUPPORTED', False) - def test_is_jax_supported_false(self): - self.assertFalse(is_jax_supported()) - - @patch('GPUtil.getGPUs', Mock(return_value=[0, 1])) - def test_get_gpu_count_2(self): - self.assertTrue(get_gpu_count() == 2) - - @patch('GPUtil.getGPUs', Mock(return_value=[0, 1])) - def test_get_dask_gpu_count_2(self): - self.assertTrue(get_dask_gpu_count() == 2) - - @patch('GPUtil.getGPUs', Mock(return_value=[0, 1])) - def test_get_dask_gpu_count_2_no_fetch(self): - self.assertTrue(len(get_dask_gpu_count(fetch=False).compute()) == 2) - - @patch('GPUtil.getGPUs', Mock(return_value=[])) - def test_get_gpu_count_0(self): - self.assertTrue(get_gpu_count() == 0) - - @patch('GPUtil.getGPUs', Mock(return_value=[])) - def test_get_dask_gpu_count_0(self): - self.assertTrue(get_dask_gpu_count() == 0) - - @patch('GPUtil.getGPUs', Mock(return_value=[])) - def test_get_dask_gpu_count_0_no_fetch(self): - self.assertTrue(len(get_dask_gpu_count(fetch=False).compute()) == 0) - - -class TestBlockChunkReduce(unittest.TestCase): - def test_different_chunks(self): - output_size = (100, 20) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([0, 1, 2]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([0, 1]))) - - def test_different_chunks_but_same_x(self): - output_size = (5, 20) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([1, 2]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([1]))) - - def test_different_chunks_but_same_y(self): - output_size = (100, 15) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([0, 2]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([0]))) - - def test_different_chunks_but_same_z(self): - output_size = (100, 10) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([0, 1]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([0]))) - - def test_different_chunks_but_greater_than_original(self): - output_size = (1, 1, 1, 100, 20, 20) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([0, 1, 2]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([0, 1, 2, 3, 4, 5]))) - - def test_different_chunks_but_greater_equal_than_original(self): - output_size = (5, 15, 10, 1, 1, 1) - data = da.random.random((20, 30, 40), chunks=(5, 15, 10)) - - drop_axis, new_axis = block_chunk_reduce(data, output_size) - - self.assertTrue(np.array_equal(drop_axis, np.asarray([]))) - self.assertTrue(np.array_equal(new_axis, np.asarray([3, 4, 5]))) - - def test_trim_chunk_location_1d(self): - depth = (5,) - - block_info = [{'array-location': [(40, 60)], 'chunk-location': (2,)}] - - loc = np.asarray(trim_chunk_location(block_info, depth)) - - self.assertTrue(np.array_equal(loc, np.asarray([(20, 30)]))) - - def test_trim_chunk_location_2d(self): - depth = (5, 0) - - block_info = [{'array-location': [(40, 60), (0, 40)], 'chunk-location': (2, 0)}] - - loc = np.asarray(trim_chunk_location(block_info, depth)) - - self.assertTrue(np.array_equal(loc, np.asarray([(20, 30), (0, 40)]))) - - def test_trim_chunk_location_3d(self): - depth = (5, 0, 0) - - block_info = [{'array-location': [(40, 60), (0, 40), (0, 40)], 'chunk-location': (2, 0, 0)}] - - loc = np.asarray(trim_chunk_location(block_info, depth)) - - self.assertTrue(np.array_equal(loc, np.asarray([(20, 30), (0, 40), (0, 40)]))) - - def test_trim_chunk_location_3d_index6(self): - depth = (5, 0, 0) - - block_info = [{}, {}, {}, {}, {}, {'array-location': [(40, 60), (0, 40), (0, 40)], 'chunk-location': (2, 0, 0)}] - - loc = np.asarray(trim_chunk_location(block_info, depth, index=5)) - - self.assertTrue(np.array_equal(loc, np.asarray([(20, 30), (0, 40), (0, 40)]))) - - -class TestBackendSignature(unittest.TestCase): - def func1(self, a, b , c, d, e, f): - return a + b + c + d + e + f - - def func2(self, a, b, backend=None): - return a + b - - def func3(self, *args, **kwargs): - return False - - def test_backend_func1(self): - self.assertFalse(get_backend_supported(self.func1)) - - def test_backend_func2(self): - self.assertTrue(get_backend_supported(self.func2)) - - def test_backend_func3(self): - self.assertFalse(get_backend_supported(self.func3)) - - -class TestWorkerInfo(unittest.TestCase): - def test_get_worker_info_empty(self): - client = Mock() - client.scheduler_info.return_value = {'workers': {}} - - workers = get_worker_info(client) - - self.assertEqual(len(workers), 0) - - def test_get_worker_info_regular(self): - worker_data = {'workers': { - 'tcp://127.0.0.1:11111': { - 'host': '127.0.0.1', - 'nthreads': 4, - }, - 'tcp://127.0.0.1:22222': { - 'host': '127.0.0.1', - 'nthreads': 4, - } - } - } - client = Mock() - client.scheduler_info.return_value = worker_data - - workers = get_worker_info(client) - - self.assertEqual(len(workers), 2) - - for worker in workers: - self.assertEqual(worker['local_rank'], 0) - self.assertEqual(worker['world_size'], 1) - - -class TestSyncFutureLoop(unittest.TestCase): - @patch('dasf.utils.funcs.wait', return_value=None) - def test_sync_future_loop_no_futures(self, wait): - sync_future_loop(None) - - self.assertFalse(wait.called) - - @patch('dasf.utils.funcs.wait', return_value=Mock()) - def test_sync_future_loop_with_futures(self, wait): - futures = [None, None] - - result = Mock() - - ret_1 = Mock() - ret_2 = Mock() - - ret_1.result.return_value = None - ret_2.result.return_value = None - - result.done = [ret_1, ret_2] - result.not_done = [] - - wait.side_effect = [DistributedTimeoutError(), result] - - sync_future_loop(futures) - - self.assertTrue(wait.called) - - @patch('dasf.utils.funcs.wait', return_value=Mock()) - def test_sync_future_loop_with_futures_and_exception(self, wait): - futures = [None, None] - - result = Mock() - - ret_1 = Mock() - ret_2 = Mock() - - ret_1.result.return_value = None - ret_2.result.side_effect = Exception("Test mock") - - result.done = [ret_1, ret_2] - result.not_done = [] - - wait.side_effect = [DistributedTimeoutError(), result] - - with self.assertRaises(Exception) as context: - sync_future_loop(futures) - - self.assertTrue('Test mock' in str(context.exception)) - - -class TestIsNotebook(unittest.TestCase): - @patch('dasf.utils.funcs.get_ipython', return_value=Mock()) - def test_is_notebook_zmq(self, get_ipython): - get_ipython.return_value.__class__.__name__ = "ZMQInteractiveShell" - - self.assertTrue(is_notebook()) - - @patch('dasf.utils.funcs.get_ipython', return_value=Mock()) - def test_is_notebook_terminal(self, get_ipython): - get_ipython.return_value.__class__.__name__ = "TerminalInteractiveShell" - - self.assertFalse(is_notebook()) - - @patch('dasf.utils.funcs.get_ipython', side_effect=NameError()) - def test_is_notebook_exception(self, get_ipython): - self.assertFalse(is_notebook())