From 4e50ba4615bc3045b31b36aec0a6eb1fac427d1a Mon Sep 17 00:00:00 2001 From: Avik Basu <3485425+ab93@users.noreply.github.com> Date: Tue, 21 May 2024 14:10:15 -0700 Subject: [PATCH 01/68] chore!: initial refactor (#381) --------- Signed-off-by: Avik Basu --- docs/ml-flow.md | 5 +- examples/block_pipeline/src/inference.py | 4 +- examples/block_pipeline/src/train.py | 10 +- examples/conv_ae.ipynb | 739 +-- examples/multi_udf/src/udf/inference.py | 6 +- examples/multi_udf/src/udf/threshold.py | 4 +- examples/multi_udf/src/udf/train.py | 10 +- examples/quick-start.ipynb | 662 +- numalogic/blocks/__init__.py | 29 - numalogic/blocks/_base.py | 138 - numalogic/blocks/_nn.py | 93 - numalogic/blocks/_transform.py | 132 - numalogic/blocks/pipeline.py | 174 - numalogic/connectors/druid/__init__.py | 3 - numalogic/connectors/rds/__init__.py | 3 - poetry.lock | 5391 ----------------- pyproject.toml | 91 +- setup.py | 40 + {numalogic => src/core/numalogic}/__init__.py | 0 .../core/numalogic}/_constants.py | 0 .../core/numalogic}/backtest/__init__.py | 0 .../core/numalogic}/backtest/_constants.py | 0 .../core/numalogic}/backtest/_prom.py | 4 +- {numalogic => src/core/numalogic}/base.py | 0 .../core/numalogic}/config/__init__.py | 0 .../core/numalogic}/config/_config.py | 2 +- .../core/numalogic}/config/factory.py | 14 +- .../core/numalogic/models}/__init__.py | 0 .../numalogic}/models/autoencoder/__init__.py | 0 .../numalogic}/models/autoencoder/base.py | 0 .../models/autoencoder/variants/__init__.py | 0 .../models/autoencoder/variants/conv.py | 0 .../models/autoencoder/variants/lstm.py | 0 .../autoencoder/variants/transformer.py | 0 .../models/autoencoder/variants/vanilla.py | 0 .../numalogic/models/forecast}/__init__.py | 0 .../models/forecast/variants/__init__.py | 0 .../models/forecast/variants/naive.py | 0 .../numalogic}/models/threshold/__init__.py | 0 .../models/threshold/_mahalanobis.py | 0 .../numalogic}/models/threshold/_median.py | 0 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.../nlconnectors}/connectors/redis.py | 2 +- .../connectors/utils}/__init__.py | 0 .../connectors/utils/aws}/__init__.py | 0 .../utils/aws/boto3_client_manager.py | 0 .../connectors/utils/aws/config.py | 2 +- .../connectors/utils/aws/db_configurations.py | 0 .../connectors/utils/aws/exceptions.py | 0 .../utils/aws/sts_client_manager.py | 0 .../nlconnectors}/connectors/utils/enum.py | 0 {numalogic => src}/registry/__init__.py | 9 +- src/registry/__setup__.py | 20 + {numalogic => src}/registry/_serialize.py | 0 {numalogic => src}/registry/artifact.py | 8 +- .../registry/dynamodb_registry.py | 12 +- {numalogic => src}/registry/localcache.py | 4 +- .../registry/mlflow_registry.py | 18 +- {numalogic => src}/registry/redis_registry.py | 32 +- Dockerfile => src/udfs/Dockerfile | 2 +- {numalogic => src}/udfs/README.md | 0 {numalogic => src}/udfs/__init__.py | 0 {numalogic => src}/udfs/__main__.py | 2 +- {numalogic => src}/udfs/_base.py | 0 {numalogic => src}/udfs/_config.py | 4 +- {numalogic => src}/udfs/_logger.py | 0 {numalogic => src}/udfs/_metrics.py | 0 {numalogic => src}/udfs/entities.py | 0 {numalogic => src}/udfs/factory.py | 2 +- {numalogic => src}/udfs/inference.py | 6 +- {numalogic => src}/udfs/payloadtx.py | 0 {numalogic => src}/udfs/postprocess.py | 2 +- {numalogic => src}/udfs/preprocess.py | 6 +- {numalogic => src}/udfs/staticthresh.py | 0 {numalogic => src}/udfs/tools.py | 6 +- {numalogic => src}/udfs/trainer/__init__.py | 0 {numalogic => src}/udfs/trainer/_base.py | 2 +- {numalogic => src}/udfs/trainer/_druid.py | 2 +- {numalogic => src}/udfs/trainer/_prom.py | 0 {numalogic => src}/udfs/trainer/_rds.py | 0 tests/blocks/test_blocks.py | 32 - tests/blocks/test_pipeline.py | 212 - tests/connectors/test_config.py | 10 - tests/{ => test_connectors}/__init__.py | 0 .../{connectors => test_connectors}/data.csv | 0 .../rds}/__init__.py | 0 .../rds/db}/__init__.py | 0 .../rds/db/test_factory.py | 6 +- .../rds/db/test_mysql_fetcher.py | 10 +- .../rds/test_base.py | 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.../models/threshold/test_maha.py | 0 .../models/threshold/test_median.py | 0 .../models/threshold/test_static.py | 0 .../models/threshold/test_std.py | 0 .../models/vae}/__init__.py | 0 .../models/vae/test_conv.py | 0 .../synthetic}/__init__.py | 0 .../synthetic/test_anomalies.py | 6 +- .../synthetic/test_sparsity.py | 0 .../synthetic/test_timeseries.py | 0 tests/{ => test_numalogic}/test_backtest.py | 0 .../vae => test_numalogic/tools}/__init__.py | 0 tests/{ => test_numalogic}/tools/test_data.py | 0 .../transforms}/__init__.py | 0 .../transforms/test_postprocess.py | 0 .../transforms/test_transforms.py | 0 .../{synthetic => test_registry}/__init__.py | 0 .../_mlflow_utils.py | 0 .../{registry => test_registry}/test_cache.py | 4 +- .../test_dynamodb_registry.py | 4 +- .../test_mlflow_registry.py | 6 +- .../test_redis_registry.py | 12 +- .../test_serialize.py | 2 +- tests/test_udfs/__init__.py | 18 + .../resources/_config.yaml | 0 .../resources/_config2.yaml | 0 .../resources/_config3.yaml | 0 .../resources/_config4.yaml | 0 .../resources/data/stream.json | 0 .../resources/rds_config.yaml | 0 .../resources/rds_config2.yaml | 0 .../rds_trainer_config_fetcher_conf.yaml | 0 .../rds_trainer_config_fetcher_conf1.yaml | 0 tests/{udfs => test_udfs}/test_factory.py | 0 tests/{udfs => test_udfs}/test_inference.py | 18 +- tests/{udfs => test_udfs}/test_main.py | 0 tests/{udfs => test_udfs}/test_numaflow.py | 0 tests/test_udfs/test_pipeline.py | 59 + tests/{udfs => test_udfs}/test_postprocess.py | 22 +- tests/{udfs => test_udfs}/test_preprocess.py | 16 +- .../{udfs => test_udfs}/test_staticthresh.py | 2 +- tests/{udfs => test_udfs}/test_trainer.py | 14 +- tests/{udfs => test_udfs}/utility.py | 0 tests/tools/__init__.py | 0 tests/transforms/__init__.py | 0 tests/udfs/__init__.py | 0 tests/udfs/test_pipeline.py | 50 - tests/udfs/test_rds_trainer.py | 551 -- 207 files changed, 614 insertions(+), 8405 deletions(-) delete mode 100644 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(97%) rename tests/{udfs => test_udfs}/utility.py (100%) delete mode 100644 tests/tools/__init__.py delete mode 100644 tests/transforms/__init__.py delete mode 100644 tests/udfs/__init__.py delete mode 100644 tests/udfs/test_pipeline.py delete mode 100644 tests/udfs/test_rds_trainer.py diff --git a/docs/ml-flow.md b/docs/ml-flow.md index a7d7a9fc..fac508ce 100644 --- a/docs/ml-flow.md +++ b/docs/ml-flow.md @@ -25,8 +25,9 @@ Here, `tracking_uri` is the uri where mlflow server is running. The `static_keys The `artifact` would be the model or transformer object that needs to be saved. A dictionary of metadata can also be saved along with the artifact. + ```python -from numalogic.registry import MLflowRegistry +from registry import MLflowRegistry from numalogic.models.autoencoder.variants import VanillaAE model = VanillaAE(seq_len=10) @@ -46,7 +47,7 @@ registry.save( Once, the models are save to MLflow, the `load` function of `MLflowRegistry` can be used to load the model. ```python -from numalogic.registry import MLflowRegistry +from registry import MLflowRegistry static_keys = ["model", "autoencoder"] dynamic_keys = ["vanilla", "seq10"] diff --git a/examples/block_pipeline/src/inference.py b/examples/block_pipeline/src/inference.py index 80e3c610..fde48533 100644 --- a/examples/block_pipeline/src/inference.py +++ b/examples/block_pipeline/src/inference.py @@ -12,7 +12,7 @@ from numalogic.models.autoencoder.variants import SparseVanillaAE from numalogic.models.threshold import StdDevThreshold from numalogic.udfs import NumalogicUDF -from numalogic.registry import RedisRegistry +from registry import RedisRegistry from numalogic.tools.exceptions import RedisRegistryError from numalogic.transforms import TanhNorm from pynumaflow.function import Messages, Datum, Message @@ -46,7 +46,7 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: registry=self.registry, ) - # Load the model from the registry + # Load the model from the test_registry try: block_pl.load(skeys=["blockpl"], dkeys=["sparsevanillae"]) except RedisRegistryError as warn: diff --git a/examples/block_pipeline/src/train.py b/examples/block_pipeline/src/train.py index 77b41c87..f10c942c 100644 --- a/examples/block_pipeline/src/train.py +++ b/examples/block_pipeline/src/train.py @@ -12,7 +12,7 @@ from numalogic.models.autoencoder.variants import SparseVanillaAE from numalogic.models.threshold import StdDevThreshold from numalogic.udfs import NumalogicUDF -from numalogic.registry import RedisRegistry +from registry import RedisRegistry from numalogic.transforms import TanhNorm from pynumaflow.function import Datum, Messages, Message from sklearn.preprocessing import StandardScaler @@ -23,7 +23,7 @@ class Train(NumalogicUDF): - """UDF to train the model and save it to the registry.""" + """UDF to train the model and save it to the test_registry.""" ttl_cache = TTLCache(maxsize=16, ttl=60) @@ -35,7 +35,7 @@ def __init__(self, seq_len: int = 12, num_series: int = 1): self._model_key = "sparsevanillae" def exec(self, keys: list[str], datum: Datum) -> Messages: - """The train function here trains the model and saves it to the registry.""" + """The train function here trains the model and saves it to the test_registry.""" # Check if a training message has been received very recently if self._model_key in self.ttl_cache: return Messages(Message.to_drop()) @@ -56,9 +56,9 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: ) block_pl.fit(data) - # Save the model to the registry + # Save the model to the test_registry block_pl.save(skeys=["blockpl"], dkeys=["sparsevanillae"]) - _LOGGER.info("Model saved to registry") + _LOGGER.info("Model saved to test_registry") # Train vertex is the last vertex in the pipeline return Messages(Message.to_drop()) diff --git a/examples/conv_ae.ipynb b/examples/conv_ae.ipynb index c244cd8a..07dfbe62 100644 --- a/examples/conv_ae.ipynb +++ b/examples/conv_ae.ipynb @@ -15,28 +15,6 @@ "metadata": { "collapsed": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", - "The nb_black extension is already loaded. To reload it, use:\n", - " %reload_ext nb_black\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 28;\n var nbb_unformatted_code = \"%load_ext autoreload\\n%load_ext nb_black\\n%autoreload 2\\n\\n\\nfrom matplotlib import pyplot as plt\\nimport random\\nimport numpy as np\\nimport warnings\\nwarnings.filterwarnings('ignore')\\n\\n%matplotlib inline\\n\\nplt.rcParams[\\\"figure.figsize\\\"] = [10, 2]\\nplt.rcParams[\\\"figure.dpi\\\"] = 200\\n\\nrandom.seed(42)\\nnp.random.seed(42)\";\n var nbb_formatted_code = \"%load_ext autoreload\\n%load_ext nb_black\\n%autoreload 2\\n\\n\\nfrom matplotlib import pyplot as plt\\nimport random\\nimport numpy as np\\nimport warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\n\\n%matplotlib inline\\n\\nplt.rcParams[\\\"figure.figsize\\\"] = [10, 2]\\nplt.rcParams[\\\"figure.dpi\\\"] = 200\\n\\nrandom.seed(42)\\nnp.random.seed(42)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "%load_ext autoreload\n", "%load_ext nb_black\n", @@ -57,7 +35,8 @@ "\n", "random.seed(42)\n", "np.random.seed(42)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -70,40 +49,6 @@ "cell_type": "code", "execution_count": 29, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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TIAiLBAdXVEEQ2gM4AeATAAMB1AMQDKALgJcBnBYEYYpqO0Lchq+vL0aPHg0AMBgMyMnJYVsgQgghhBBCCCGEuCVn5jYxGkWUGYwQdRZ5+mX/Raw6ednidSlBlHfXnkVOUZkaxdLcqhMpeOi3CHy5PUbyZ37c656BiM+3Xf8OKowiFu6ItVimqMyAVSdSsDcmQ8uiSZZ8rQh/H7mEk0nZAICCUgO+2RWH/FKD0+vk/dRefCQJLy2LxNkreVjkpsemp9BqzqPvAXxn5/1CO++9B+Cxqn+fRGXwJx5AewCzAPStej8DwFxrKxAEoQ6A9QA6V730E4ClAIoBjAUwG0AogOWCIAwVRfG0410i7mrfvn1o2rQpOnToYPX9srIy7NmzBwBQp04dNGzYUMviEUIIIYQQQgghHutwQhYOxmdhaLv6GNq+vtVlSsor8NfhSyg1GPHfIa0RGuircSmlEUURBqMIHy/BdoBEZiPxpaxCPPL7McSmF2Bqz6b4/K7e8Pfxdr2wGvhyu2VgAAC8qyY9EkURP9gYrWEwilhz6gruH9ZGreJp4kRSNl5aFin7c3oIFB6My8Tba88CAN6d1h3D2jeQvY7YtIJafxuNIm77/hDOp1bOHfTqpM54eqz19jwWLucUY8rCfcgvMcBLAB4e0RbLjqUgt7jcpfXy/mu/sTqKdRGIQrQKHqWLonhG7ocEQeiAygARABwDMEoUxerxiRGCIKwFsAfAAACvCYLwqyiK8VZW9QoqRxgBwCxRFBeYvHdIEIRdAPYCCALwJYBxcstK3MeOHTswf/58jBw5ElOnTkWvXr3QsGFDFBcXIyYmBj/88ANOnDgBAHjkkUfg43P9NCooKMCKFStqrS8uLq7m3ytWrECDBtdvjn369EGfPn3U3SFCCCGEEEIIIcQNHEnIwsyfDkMUga93xmLJo0MwpJ1lAOmFpaew+exVAMDmM1ex7tkRWhfVodzicjz513EcjM9C/9b18M09fbEh6orFciIqG6APxGWiW9MQ9Ggeane9P+xJQGx6ZQP7hqhUTOvTDBO7N1FjFzRTFTvC80tPYW2k5XdU7eyVXGw7l4ZBbcIRGsRnwNCe4rIKzPjuoFOf5T2YYDSKeHl5JFJzSwAAryyLxIHXx8EoAuUVRvj7eEkaYXbsUjZmrYjE+7f2hK+3F3ZFp9cEjgBgwZZoroJHC7fHIr+kcoSRUQR+2ndRkfXuiUlHi3qB6No0RJH1EedITyypX1oFj5z1Iq6X8VmTwBEAQBTFIkEQngVwqGq5FwA8a7qMIAi+AJ6v+vM8gM/MNyKK4iFBEH4G8DiAsYIg9BdF8biSO0L0xWg0Ys+ePTUjjKyZMWMGPvzww1qvZWZm4sEHH7T5mVdffbXW32+//TYFjwghhBBCCCGEEAlmr46qSdckisAryyMxrU8z7I/NxND2DfDSDZ0AoCZwBABRl3Nx7koeujXjq5H135OXcTA+CwBw/FI2hn640+pyKdlFePDXCOSVGODjJeCXBwZiVCfLDChlBiN+O3gRS44m1Xr9nbVnuQoeGSqMuJJTgvA6fqjjL61ZUhAERF/Ntxs4AoBlx1Kw7FgKmoUGYP1zIxEe7KdEkTXz7a44xwvZwPvAo/iMgprAEQBcyS3B3thMfLDhPKLT8jGha2N8c09fJF8rwh+HLqFpWAAeHdnO6rqWHUvBjT2aYFyXxjiVnKPRHjhnY1SqKuv963AS/jqchDemdMFjo9qrsg098ITgDWvcznlUNYfRtKo/L4iieNjaclWvR1f9Od3K3EdjAIRV/ft3URSNNjb5m8m/Z8gtL3Efs2bNwsaNG/Hiiy9iyJAhaNWqFQICAhAQEIA2bdrgrrvuwoYNG7By5UoEBASwLi4hhBBCCCFOMRpF/LAnHjd9vQ+zV51GoQu59wkhRAsJGbVnPUjJLsa3u+IRmZKLH/bEY/3pKzAYLZt90vJKLF5jrTp9lyNfbo9FXtXIBYNRxKsrrKc0e3vtWXyw8YLF60aOggqFpQbc9eNhjFqwC+M/240LV/McfwhAaXkFlh1LlrydK7kl6Dd/G/48lIgKnr4AB75xIXjEO4OV32HRnnhEp+UDALafT8O/Jy9j+rcH8OfhS/hkczQ+2Hje5vpmraiccYT74IHKBbR2zhPt6Ofq4jyeRx61BdC86t+2h39cf78zgBYA2gAwHQM40mw5W46hcu6lYAD8jWcmmgkKCsLkyZMxefJk2Z9t06aNLvLMEkIIIYQQcuTiNXy0qbLR4czlPLSoF8RVqhdCiDzFZRVYF3kFdQJ8MLlHE0kpoNzNS8siMblHU9bFUNS+2Mxaf6fllSI9vwSN6tbuzGo+4qiayFHz5r+nLuP4pWwAlfuxYHM0fn5goMPPLdzpXFDlzTVncTGzCG/d3M2pz+uJCKCg1ACjKCIkgL+UfdYuR9Uj76q9vqr2PDm/Hki0ub6C6g4vnF/nqlPWqUkURY+83vPAE9qAtRp5dIcgCNGCIBQLgpAvCEKsIAi/C4Iw1s5nupr821EY1fT9rmbvSVqPKIoGANXzJZmvgxBCCCGEEELcyhyzyYwXbIm2sSQhhHeiKGLmT4cxa+VpPPX3Ccxfb7vHvrtbbCOI4k5GfrwLm89cdbwg+Epn9sW22Fp/77iQrvo2fzmgzBwzrhJFseY/NUQm56Df/G3o8+5W/LQ3QZVt8KSk3FZiKc/DenTdmcu5GPfpbnR/azO+2+2+o+es0dHARqdpFTzqBqATgAAAdQB0AHAfgJ2CIKwWBMHabH8tTf6d4mD9pmNXW5q9V/13oSiKORLX01AQBH8Hy9YiCEILe/8B4CfBLCGEEEIIIcTjZeSXsi4CIUQhJ5Nzas39wUuDOQvz15+zeI2n0TdKKDUY8drK05KW5WnPPXVwRG5ROf778xG0nb0R9/x0BDlFZapsp8xghFEE3t94Hvkl5apsgydqfY96Yy0loJY+2RKNhMxCFJZV4JPN0bicU8y0PFo6GJfpeCGdUzttXRGAtQB2oHLUTwGAhgBGA3gCQH0A0wGsEQThBlEUTa9sdU3+XeBgO6ZJb+uYvVe9HkfrsLYeOU9T0pOvEkIIIYQQQgghhCgkLk1Kk4e+se5dz5vc4nKk5ZXg6MVr2B2dYXM5nkYeeaqVJ1JwIK4yRduhhCysOJ6CR0a2U3Wbkcm5GNGxgarbYG35sRRu5jwqNVTgs60xOJWUg0k9muDBYW3g5aVN6corjAjw9dZkW9bsjal9/Rn+0U5GJdHeO+ssOyu4G7WDR81tjPbZJgjC1wA2AeiLymDSkwAWmixjmrzVUSjZNMgTaPZe9XqkhKPtrYcQQgghhBCiM0ajiJ/2JWDbuTT0a10PL0/sBH8fdg/YPKH2RH0zVBjxw554nE/Nx/S+zXFDt8asi0RY4qUF1UnLIpLxw954NAsNxIczeqJleJDFMudT8xiUjG+TvtyLnCJHI0z4udrr/DB12jyz0XDvbTivevCIN2oEMd/feB4vTOio/Iqd8PfhJPxYlS7waOI1dGlSF0F+2tQ3DRX8nONaYzma8dtdnpGiT9Xgkb00caIopgmCcDuA8wD8ADyL2sGjEpN/+znYlGmKOfOxcdXrcbQOR+txxDxdnrkmACJkrpMQQgghhBDigj2xGfhwU+XUp8cuZaNFvUDcN7QN20IRooD/7b+IT7fGAAA2RKVi64uj0KlxXQefIu7KWvvZ2E9346u7+6BXizCtiyPL1dwSzKpKwZaQUYiRn+zCkHbhmNanOe4e2LJmIngaQWPJceCIr+/NWkPvh5vOo2W9INw90FGzGiGWFh/hY44z8wDhf/53BBoNPEK50XPnf2IVOLuSU+wxc4VqNeeRVaIoJgDYVvVnB0EQmpm8nW/yb/NUdOaCTf5tPla7ej2O1uFoPXaJophi7z8A0mYyJIQQQgghhCjmlWWRtf5+a83Zmn+LoojNZ1Kx+EgSCkoNWheNEJd8VBUUrfbehvOMSkJ44GWlVf5iZqHFccKj6t76pg4nXMPsVVGITMllUCL3wlHsCIKVMOeiPQmY++8Z3P/rUQYlInqXzvH8jVpl2vTkkUdFZWzq778fTGSyXRaYBo+qmIZmm5v8O8Xk3y0crMO0e4L53EPV6wkWBCFM4noyRFHk9+pDCCGEEEIIkSSr0Hb26k+3RuOJv07gjdVRmPHdARhpPg2iYwkZ7j/nDbHNVuqeg/FZiE3Lt/4mJ+w1/r2z9iyOJGTh0y3ROJyQ5fQ2eBp9ozVRJztfPScQIUQelsEj1nPRFZdVMNmuUSfXVSWoPeeRFLYG8ZkGlbo4WIfp++bdrc4BuM1kucNWCyEIPgDa21gHIYQQQgghxM18uyu+5t8xaQXYE5OBsV0aMSwRIc7zoHYMYoW1kUfV5q0/hz8fHqxhaZRzKjkHd/1otRmHSMRTvwit0njpQUl5BT7adAEnk3PQqVEdvDSxE+siEZ1imbZuyVG2aQOv5JYgu7AMYUG+NSlOtaDltljjYeRRN5N/XzH590WTv0c7WMeoqv9fBpBo9t5+k3/bW88AXE9bd8DB9gghhBBCCCE6VVBqwOtV82uYOudhk7Fb642+6kSKlSUJIazsjk6XtJy9UZb7YjORwXFqJy14cnCVp5FHntTg6shfhy/ht4OJiEzOwfLjKRj64U7WRVIV/fTqYTnyaO6/Z5htu1rf+dvw9OITzEdBuSumwSNBENoBuKHqzwRRFC9XvydW3t3WVP3ZRRCEITbWMQTXRx6tES3virsBVCfJvV+wfad6wOTfqyXtACGEEEIIIUR3Fu2Jx9II82zX1LABAC+ZzRFFiJ4kZBTguSUn8erySKTllbAujstKDRV4dvFJScv+ffiS3ff/t89yXiHiGfJK+JjT72BcJi7nFLMuBjc8aZ669LwSXM3V/zWZV+UV7EYe8WJj1FXsj8tkXQy3pFrwSBCEm6tSwdl6vzGAFQB8q1761spiXwKovst9LQhCoNk6AgF8XfWnoWr5WkRRLAOwsOrPrgBesVKWoQAervpzjyiKEbbKTQghhBBCiF6tjbyC/vO3YeiHO7A3JoN1cZj5emec1detTeRNiF7wNLqABVEUcd8vR7E28gqWH0/BU3+fYF0kl+26kI78UmkN/wmZhXbfX36cRhV6ssVHkpBbXM5s+xVGEQ/9Tk1tnuiX/Rcx5MMdeOBX+v3V8vbaszRvJ4Bvd1mv3xPXqDny6GsAlwRBWCgIwkxBEIYKgtBHEIQJgiC8B+AsgL5Vy+6HleCRKIoxAD6t+nMAgAOCINwlCMIAQRDuQmV6uQFV7y8QRTHWRlkWAIip+vcngiAsEgRhrCAIQwRBmA1gKyrnfyoG8IJLe60Cb29vAIDBYEBFBZuJwAgfjEZjzTFQfVwQQgghhEhRaqjAnNVRyCosQ2puCd5cc8bjG5vNecrIo9zicsSm5aOcYZoTQpR2IikHKdnXRzUcv5SNojI+Rlw4q9RAvcmJMt5YHYXJX+5lNvJnb2wGSsrpePYkFzMLcTghC/PWn+Nq3i13dPxSNk4kZbMuBnMeUo3XnM2RQQppBuDZqv9sWQngEVEUbSXgnQOgEYCHUBlsWmplmZ8BzLW1AVEU8wVBmApgI4COAB6r+s9UHoD/iKJ4yk5ZmQgKCkJpaeXXk5OTg/r16zMuEWGloKCgppEnMDDQwdKEEEIIIdcdS8xGvknqmktZRcgtLkdYkB/DUvElIaMA8RkFaN+wDuuiqObclTzc98tRZBa41/wn5RVGnErOQZOQALQMD7J4XxRF/HUkCWdScjG1V1OM6tSQQSnV5eltc/kllqMqDG7eYmk0ivhyewxWnrjseGHi8a7kluD73XF4b3pPzbf95yH7aRWJe1l5PAWzVp6mOWg09N6G8/j36eGsi0HckJrBo/sBjAYwFEA7AA0AhAAoAJAM4CCA30VRPGRvJaIoGgE8LAjCSlQGfAZWrSsTQASARaIobnJUGFEU4wRB6AvgaQB3AOgAwK+qLBsBfCWKIpd3s7CwMGRnV0aQ09PTUVFRgZCQEPj7+9Nkgx7CaDSioKAAV69erXmtbt26DEtECCGEEL2xNsjI3QceLTmaJGv5ZcdSsOxYCp4b3xEv3dBJpVKxU1JegRf+OemWgaPbfziEyOQc+Pl44dt7+uGGbo1rLbPkaDLerJrU+Z9jyXhuXAe8NLEzi+ISjW04nYrfDyWibf1gvDG1K0IDfR1/iBOlDkZqnEjKxkIbaTh5l5JdhEV7EhDs7yM5NZ8r3Px2J9lfh5OYBI92XkjXfJueTmR41L+8nOZP1BqNVNWWwYNG76sWPBJFcQ+APQqubyMqgzyurKMQwCdV/+lGQEAAQkNDkZubCwDIyspCVlYWBEGg1GUeoqKiolZamcDAQAQHBzMsESGEEEL0xlqfo+raRYVRxKYzqagwipjasyl8vNXMbq2d2auinPrcwh2xeHxUOwT7q52oQTtJWUW495cjuJRVxLooitsYlYrI5BwAQJnBiNmroiyCR2+srn0sLNwZh9GdG6J/63CtikkkyCwoRZCfN4L85J971ppxruQU4+nFlXMfHb14DQG+Xnh3Wg8XS6mdBVuj7b4/b/05jUqiLKNRxF2LDmueQi09vwSLjyQhPNgP9wxqxeRetzbyCladSEHLepYjJAkhyhBF0eM621Mqam39EyGvg5qeuc/TkJtr2rQp/Pz8kJFxfWJjURRhMOg7hzORLzAwEK1atfK4GyEhhBBCXGOv5vDK8kisPlmZ9mhT1FX8cG9/bQrFsdTcYnRo5D4jvb/fE+eWgSMAWGWWskvqyKq5/57FpudHqlEk1ZVZ6WFs1HHDkSiKeHl5JFaduIx6Qb74/r/9MaSd6+nav9pee1rk3w9d0lXwKCPf9rF8MC4TBSXS2wN4eno8lJCleeDo0T+OwdtLqEmjFZdegHkaHwvnU/Pw3JKTmm6TEE9UVFbhVh2ApNBzHUApWjaTFpZVaLcxxjzrTNIxQRDQoEEDhISEoKCgAIWFhSgrK4PRSMMSPYG3tzcCAwNRt25dBAcHU+CIEEIIIYoQRRF5JeU1gSMA2Hz2KtLyStA4JIBhyYjSlhxNZl0E1Tg7p8L51DyFS6KdU1UjrUyl2wk08O5EUk5NEDC7qBzz1p3DRrmBPSuHQVpeiQKl49M9/zsia/ncYss5oVhhlTrT9Frxx6FLmgePPth4XtPtEeKpVp1Iwb1D27AuhqbcaXqp/wxuhb+PeM7IHt5R8Ehn/Pz8EB4ejvBwSq9ACCGEEEKks9b5RASQU2jZoHg1l4JHRD/KKyw71LV5fUPNv5uGesaxrOfuZX8drj398DmFAnvu1JjmKoNRxPJjybhjQEvWRYG3l56PVuclXXPP0Z+E8OaHPQmeFzwyitgVnY7SciMmdG2kyxTUsyd3wf3D2sDX2wuN6gbgi+0xrItEAOjvSCKEEEIIIYTIZm3gsq0UFzTIWZ+iUnKxKzrdajDFnRkcRAhSc22PPvl48wWcT81DqUH/6UekxEkSMwsx7Zv96P3uVny+LUb3cyScu5KH9zecw7KIZKuTw1vbu5Jy/f/Wznp1xWkcSchiPgpJ0HWok8hlbaSk1l5feRp7YjIcL0jcgiemcEvILMSDv0bgib+O44m/TrAujlOm9GyKAF9veHsJeH5CR3x7Tz/WRSKgkUeEEEIIIcTM3pgMpOWV4MYeTVA3wJd1cYhCbDXVWWtwdQe5RfykaNLCn4cS8eaaswCAAa3rYfkTQ2tGmxndfPiFwYVg2fe74/H97ngAwNcz++Lm3s2UKpbmRBG47fuDOJ+ah+l9m2PeLd0teh5/uT0GkSm5AICFO2IxpWcTdGkSwqK4LkvLK8H07w7UzP80vIPlHEnJVkZ6rD11BXcOZD/6hpW7fjyMpqEBWPrYELSuH8ykDB468MhjQ2avLI9kXQQsjUjG0ohkrH92BHo0D9V026m52s7vRZxPZ+sutp9Pw8XMQrRtwOYa7ywvs5uD3HsFdUxQB408IoQQQgghNX7cG4/7fjmKV1ecxrRvD7jUKEt0wMaztd4fvhbtiUff+VtdWodeOq2Kooh/IpJqAkcAcOxSNvbFZgKoDBw98FsEq+JpwtHII6meXXJSN4E2WyOGjl/KRlFZBRYfScKhhCwAwJWcYsSl50MURfx76kqt5T/bykdKGGeuOF/vjK0JHAHAgbgsi2WuFZZZvDZr5Wns9fARCKm5JVi4I47Z9s0bCIm2EjMLNd1eXHqBptuzZ966c5pvc9GeBM236ek8PXgEAAkZ/Jx3UpnfGuTO906ZE9RBwSNCCCFEgqu5Jbio8YMW4UN2YRkSMwt1n9pHqg82Xqj5d0JGITaducqwNERJtuY8spfSS4/yS8rx4aYLHjPXyeKjSXhtZZTF6/f9chTTvj2A5ceT3b6hXMlGovxSg2LrYu31lVFYcTwFoz7ZhQmf78VTf1umscnIL2VQMueJYuWcDpvPpOL4pRyn1/PI78eQV8L36ES10+utPJGi6vrt8fLQFj65DaFqOXYpm3URmIlMydF8m78dTNR8myytOXWZdRGQZaXjgKd54Z9TeGbxCV0FkbzNrpFyL5kH4y07kRDXUdo6QgghxIHlx5Ixe1UUDEYR9wxuhQ9u7cm6SEQju6PT8fTfJ1BYVoFJ3Rvjh//25+bBXyvHL2XrOo2Tp7maW4IlR5PQOCQAdw1sWWtScmsdvTefuYq31561eF3Ph7lScyucSMpGfEZlp4GJ3Roz7ymfkl2Et9ecRWZBKZ4e2wETuzcBAMxZfcbmZyKTcxDJwVwTRB5DhRGnL+eiYR1/tAwPcno9l3OKa6WLstYZQC/plJKvFWF3TAZWHk9R5BwvqzBi8ZEkPDG6veuFU8k/Ecmsi6AaHc7jrggd31rdhp7rN3rx/NJTrItAAOSXGLD+dCqiLudi9ytjdPEM6+/jXevvUR0bMioJMeWht2xCCCFEuldXnK5JibP4SJLmqR4IO3P/PYPCssqev1vOptWkACL6VmqowIItF/DkX8fdavLkMoMRN3+zH1/tiMUbq6Pw0abztd639sxoLXAE6HuiYaVS7r22Mqpq0uHjeGnZKUXW6Yp31p7DjgvpiEzJxdOLTyC3uFw3KdZ0hfFXWmEUcc9PRzDju4MY/9kebD6Tqur2zHv58igluwiTv9qHN/89o1hwGADOXM5VbF1qWHwkiXURVKOHRkx3tvRokseMqPdks1dZjkomnudSVhHOpeaxLoYk/r61wxSBft74aAZ13GWNgkeEEEKITAfiM1kXQXGRyTmY8tU+jP10N7acpTRl1VKya/fIXnGcXYoXopyFO2Lx7a54bDpzFff/chQp2ZYTquvRmlOXa6Wg+mnfRafXdcs3B3QzIkEL/566YnXuFC1tP59W8+/yChF/HEzE/b8eVWVbX2yLwfJjydS4yMCO82k4mngNQOXomJeWqTvRPDeN+HaK8dX2WBSokE5w/Wl1A3POKjMY8e/Jy4hOy2ddFNV4ato6XoYeHbuUjd89LJVaNb3P6ShHSnYxYt34OkKkKy5TNw2qUvx9LMMUdw9qhd2vjJG8jktZ1NFXaRQ8IoQQQgjeWB2Fc6l5uJhZiJeXRaqeZ1+3qB3VLXy7K77W359v42PCeFfFOGwgkNdg8vdhPnu95xaV41KW7XnIMgrUmcMps4CvuWFWn7qMfbHqdGb4akcsXl1xGt/sjFNl/TwTGV/oN0TVDmgUqdzg46WDFoHNHjL3niiKmLM6Cp3mbsIL/5xiXRxVMc4CygxPu/3OunOsi8CE1nFL1h1P/jh0ien2WVJyPkSivnpBvjY7tLRpEIwTb94gaT1f7YhVsliqCPbzxoDW9Wq9NndqV0alcUwHVUVCCCGEqEkURZy9cn0oe0GpAbuj0xmWiBBtJV9zj5FHjshtMPlmVxx2XUjnqrfiwfhMjPhkJ0Yv2I2Hfz9mkbZtzanLePEfdUZq8NToBwAJGer3rPzMTQKrcnjaYCuPHQFShaeJxE+n5OJvN05VZ8rTzrNq3Iz082Ba/gKiKOLuHw9puEVLnnzIPfhbBHWI1IlgP2986CA9XXiwn6R1rTpxWYkiqWr108Ox4slhWPnkUDw+qh1++G9/3MTxHMMUPCKEEEI8nLUHeAP11CLE7ZxMypH9mQd/i8DN3+zn5uH77TVnkV9Smb5q54V07ImtPWfVK8vVS/HlyQ0wvFNydI6e5/tyxqWsIny5PQblFUbWRWHik83RrItQ49tdnjPSr4KT80zr1Jx0G/EsJ5NzEJPGNkDtyR0E9sZk4IWlp1gXg0hwdM4E3NijKetiaKa6rtm/dThmT+mKG3s0YVwi+yh4RAghhHg4Ph7f9YG+K/dWXmHErwcuYuGOWOZpRpS2/Fgy5q93LkVNXHoBVp/koxdfbHrtRhjz1HrlFWqepZ7bAMOTzIJSrDqRgtMpOTWvJSk4epB13wkWR9mX22Px12HPTG3kON2ndjypjsHLfGqsz3eiPS1Hf13NVSeNrhweHDsCAGw+exUGD+0cYYr34yDY34d1EWTrb5Z2Tg6jzg5JCh4RQgghHo6XB3iiPKNRxPsbzqH3u1txxw8HcSWnmHWRuDZ7VRTeXXcOn2+LQb/529wqgPTqitMufX5jFJ8Ty2uJ9wdvT5BdWIbJX+3DS8siMf3bA9h8JhUXM5VN35dfUq7o+tSi9J37XQ+d/4QHB+Mz0eb1Ddh2Lo11UTTDS8OZ1iMN6T7iWXh4xPISBGQVlOKvw5ewz2y0tqfgZaSjHJ0a12FdBF0b/MF2bFL52aVBHWlp9KzR2yh3Ch4RQgghHk5fVRe29BZoO56UjZ/2XURucTkiErOxaE886yJxy2gUseJ4Sq3X+s3fhrQ89r1GCWHpVHIOSg18pC389cBFZOSXAqgcMfDc0lOK9+x+9I9jiq6PEHtEUcQ9Px1hXQzN8dJwxks5iHsqLDOwLgJOJedg8lf7MPffM7j356MeOcpUb6f5qbduwA//7Y9RnRqyLopupeWVYvbqKFVT8jp7XIUF+aJzk7rKFkZlFDwihBBCPJzeKtRaOX7pGusiuOzddWdr/f37Ic97YJQiIjEb7d7YaPW973frJ+Cm5rnsyTnzq3nqNzD92wO49duDKGLUCCaKIj7ZfAH95m/Dwp2154QpMxgVH1EZn1GIrIJSRdepF6tOpDheSCWCh55hVz20g0KJgY+hRzFXtZ2PxlOPc55o+Qv8cShRw61Zd/xSNtLzr9/T5v57hmFp2KjQUX7K/wxuhbAgP7RrWAd/PDQIUe9MZF0k3copKndqvlc1Bfl5452bu8PXW1/hGH2VlhBCCCGKk9vrMi69AMcSr8Goo4q4XD/tTcBt3x+yeF1ve1xu0FuJ+fP3EQq4AYC3F58NXtcKSzXLZa/lPAnmyhg3tJ5LzcPK42wCC5Epufhud7zNNJLf7oqz+roroq+ymwdnVzS7tEIvLYvEPI7S18Wk5SO7sAz5peoFLhMUTnsol0HVedr49euBi6yLAAD4ePMFTbdH/TDYU/N6Yu7M5TzNtkVs01PaOvNrRN0AX5yfdyNm9GvOpkA6x1PWkN8fGoRz827E9L76+y0peEQIIYSYEUUR2YVlKCnnI00PC7Z6Ri45moSJX+zB7T8cwsO/R3BVIZOiwihK6n32/sbzGpRGfaLMcFdEov5HWxF18NredSIpBx3mbML89eo3eLP8DvbEsJ+n4A9GIxc/cHA9VqPx/9klJxVfp1S5xWznXPrlwEWkZBcxLUO1iV/sRd/521gXQ1UGN+6IYw8vvcH3x2WyLgJhID5D2xFnhC29d3gM9PPGmM6NXFwLrzV5danZ8UvuUaXnX4CCR4QQQogJo1HE80tPoe/8bRj5yS6cTMpmXSTVyYn/zF4Vher6967oDJxMzlGlTEorLqvAR5suoOOcjejz7lZsPXuVdZG4k5hZiLsWWY62+u1gIjcNiSzoJT5aZjBiaUSyauvfcSFdtXUr4ef9F3Hhqro9fFn2GH9/Az+jQbRWyqAjR1ZhGd5Ze9bxggyUGirw1+FLWHwkSbVt/HVYvXWT2iqMbEcV/hOR5NGdpbSWqvAcbcQ5LFN0Eu3pKW2dLXoOPJBKeh556sO6AIQQQvgXm5aP9adT0aVJXUzu2dTmclkFpfhkczTySsrxzLgO6N4sVMNSKuPwxSysjbwCAMjIL8VHm7RNZ8GC3NEppvbHZqJfq3rYeSENW86koXfLMMwc1JJpeidzMWn5uOOHQzW9ufNLDXh77Vnc0K2x7HLqJZBQTU55F2yJhq1nq5/3X8TbN3dXplCMHL90DbujM9CvdT1Zn9NLr/CnF59AgcqpWKKv5nM9weuiPQmqrp/mqvAsvx1MxDPjOqBBHX/WRUFuUTkEL2D6Nwc0SbPmSr2AyJNbzGYusWqvrYzC8mMpWPHkMKbl8BSsRxaaO5Wcg7fWnEGZwYg5U7tiZMeGrItEiOJ0lbbORl2To0drUkXuYaXn5wgKHhFCCLErNbcYN3+zHyXllT0jP5rRE3cPalVrmSs5xdh69io+3hyN4qreiwfjsxAxZwL8fPQ1yNW88fHIRdtpvMoMRizYcgE/7buI/w5phfnTenAVNJHKlfq0ACAyOQcP/XYMAPDPsWT4eAu4c0BLZQqngM+3xlg8rKfmlsAoAt76+7lUc/pyjs33fj2QqOvg0emUHNzxwyGbwTFHRFFE0rUilFeI6NCojrKFU0BWQSm2nUtTfTsrjidjztRuqm/HWe7Qs9QWHu4tHBRBczFp+VwEj95ddxarTl7WboMMTqWzV3K13ygHVp9kPwLi2KVsnE7JQa8WYayLwoQn7/sryyMRl16Zwu35padw9I3x8NHZRO7O0HMjLpHvkd+PYfkTQ+Hv4826KA55Yl2re7MQ1dbN0/fJU1nkcv+7AiGEEJd8vTOuJnAEAK+viqr1fmZBKW78ci/eWXeuJnAEVPas2+LmqcG+2x2Hn/ZVTvj71+EkvPjPKbYFcpLRRvSozGDE0qNJ+Cciye5k7e+sq53aZ9aK04qWz1WbbRyHzszXtDbyCpapmBpMaUq2//1vXwJe/OcUdnGevsyat9acdTpwBAAP/haBMZ/uxoTP93CZPiy7qEyT7VSwza7kkK1rmVJYPvTp+HnTZe4bEpRO08ARIxeu5jPd/sebL6D9Gxsx8pOdOHNZu0AWLykCPXkOmLn/ntFsW71a8JWVoTpwBADXCstw7JL7p+smnud0Si52nNff84sn8Pfxwrxp0jsohgX5ylo/q/rz+7f2sHitXcNgBiVRBgWPCCGE2HUkIcvu+z/sjkdeifWUGzmcpWZQ2pfbY2v9/e+pK4xK4hpr6fYFAXjq7+N4fVUUXlsZhWcWn7D6WUGoHHmkR842SM5aeRrf745XtCx68N6G81h98jIe/C1C9blllHbKxWN0d3RGzQi9n/ZdRGZBqeuFUpT9R6PTKTmKbEXt4Iyr1C4f09334OgRy+/dU3un832mKy8uvQDf745HhVFE8rVii/qdpzifmueRcxyeTtEuWNiiXqBm23KGO4/gNRWdxjZYTbT34abzrIvAFI+jXp4e2x4bnx+J/q3DJX/mmbEdZG1D3f22vF52blwXiR9NxX8Gt8ajI9vWvH5r3+ZoGsr39d8eSltHCCHEQlZBKRbuiEW5UcRVBxO77oq23YuHwzpKjcs5xVh1PAUtw4MwrU+zmpRAPFas1Hb4omWAMLOgFNtNemhtPZfGYYO5a1xpkPx48wU8Oaa9coXRmfc3nMefDw9mXQxmzqfmcTUvgL3rVkl5BWb+eFiR7TgzWk9LGfnqXqM8fR4YVoGU86n6Cla7A97PdaV9tzuu1t/bz6ufBpQ3L/4TCQDw8RKw4I5euLVvC8YlIkQ9WqT6BYB+rcJwIilHk20R+6x1luTRfUNbW33d1ToYj7f1Vyd1kf2Ztg34Hr1jOrr0jSldMaFrY5RXiBjeoT7DUrmOgkeEEEIsPPn3CRy1M9ePKQ7rIQ4VlhowdeE+5BRVjoy6nFOMp6t6sUipWNmrvMWm5aNjY34nlLfm0y3RFq9lFlimwcoutHxNEITKwBuPNVIHbDUEu1OvS6kNgLlF5Ui+Vix5vedTPbvHpp5GI6w5dRmFZRWOF5SA91MjItF90+3o54hTnoH3A4/o3rpIfY4cV4PBKOLFfyI9LngkiiIXc8uxRt+AsvQwx46n0EOniEdGtEWHRvpqR3DWfwa3cryQFa3ryw0eaXtVe+GGTte3LAgY3E7fQaNqlLaOEEJILfkl5ZIDR4D9mAGvz2B/H7lUEzgCgAVWgif2VNjpuhSTpr+c8dZ+Qk5/OkVVH7snk7Lx56FEZBeWISO/FDO+P8i2YBpJyS7CzB8Po+ubm9F73laZn+brAexkUjZeWHoSC7ZcQEm5MoESPbF3vu6OzlBsO38evoQDcZmKrU+uyznSA5xqYJo+jdcbqpvz1K9dB21siiqv8LAdJhaeX3oK6fn2sy0oQavOJw3q+GmyHWKfp95DeKRFP5S+rcJc+vzcm7opUxArtD4WX5zQye773l7OFahDozqyRvGoud/mdaWpPZuieZh+U9PZQ8EjQgghtZQZpI3pFkUR76w9i4uZhTaX4bV3fqSd3ObnJKTH+eVAos33fL353Gd7rM0TYq2iZa3O/b99CboeqfPx5gu49buDeHPNWfSdvw1zVkfJmsNp5fEUTPv2AF5YetLqyCyefb4tBocSslCs82BLblE57lp0GP+euoJvd8Xjw42el9PcXmBh05mrim7r1eWRMDI65/+3L4HJdquxvNKZTmpOtHPYwbyP7kq/d3VCnLM28greWHWGdTEUs+uVMayLUEub+kGKru/ZcR1wS+9m3DfUelH0iBsVGvSKqOPvfHIvP2/3ap6/x8HIIlfOjF8eGIjXJ8tPeae2Hs1DHS+kU+51dBJCCHGZ1N7NZy7n4beDiQ7WpUCBVGCvWFLmzLAXMAv0c4/0BFIDf9kmI7j0xiiK+H53fK3XtsrIgZ6YWYiXl0ciMjkH/566gi+2xyhdRFWtOnHZ6c/y1Cv9lwMXUVZxPej9+6FLqm/zvz8fwf5YdiNwWLqSW8KsQf1XO4F7LbBKeeKJE9jz4svtsayLQAjRiLvMdTV/eg/UDfB17sMqPbs1UzjI8/LEzlg4sy/6t66n6HqV5uXk6AqiPO7T1jk4VKy1q6x/doQ6ZVFAw7r+uL2/7fSnt/Rp7vS6/X288cRoaXMPq3kGcn5EKYqCR0Q1RWUGfLL5Al5feZp6SxLihj7bJi/VG0+USP/zyvJIq6/XC9JXmoj0vBLJk8zzXueWy9X9MQ8W/aFB0EIuN/vJrErJZpPK7PE/j3GTIm/Blguabu+e/x3Bn4f5O97Vxup88sTvmrDlbvd7no3v0oh1EYibeHx0OyR+NBX3DmnNuiia4f1SRaEjdbw6qTN6NA+R9ZnMgjKcuWw7+4gedeJ8nuUFt/fCXw8PxuJHB+Pn+wcg0Leyk+3Unk3Rz8UUf1IVllagWKG5Xx3hteO0Eih4RFQzd/UZfLc7HksjknHb9wdRauCjgYUQoh1e759KlGvF8RQF1sLWD3viMfjDHcgvMVi8Zz1tHe+PaPJYS9cnx5pTnjvBdlZhGZZFJLMuhiRZBdKCo3IVllVgY1SqKuuWymgU8b99CdgYpWxqOine/PeMx9XtWDWob1Y49aCe5Bbrd3Srnrnb/Z5nvVuGsS4CcRMz+tru5c+apwakaeCROh4b1Q4f3NpT9uce//M4/yOQ3IggCBjRsQGGtW+A8V0bY/9rY7HrlTH45p6+ms3l+d+fj2DAe9uw9azn1qWVQMEjoppVJ6+nw8ktLvfoRjZC9ETKbVyPla7c4nLkl1Q2Qmk5cSJLJeUV+OvwJSw+kmQxl1VRmQEfbbpgs7zWviKe9k0JRmnTe8nCct6jCqOIBVsuYPhHO/HI78ewLvIKEjIsUywWlloGC50xa+VprI1kf2+3dz6fSs7B+M/3qLbtIo16stny76nLeG8Duzmetp51jxQ/0rG5CEodHeqW3Oy+oxe/HkhEXgkF7rTgbnUrwk6Qm6TOlsPVZ9IygxHlFSo8EGhgcNtw1kVgxtm5pC7nFOPsFcfzG7PgzB7J+Rp4iGPWr+OPtg2CNQscVSssq8Ccf5Wf006PbWLOouAR0cwv+y/i0T+O4fOt0RaNmIQQdkRRRFRKLhIypKeX/GKbtLldeBm6+/P+i+g3fxv6zd+GpUeTrC6z+EgSxn222+Vt8dRb99E/jmHuv2fwxuooPLfkZK33rAUVTFlrNErLK1G0fKy5OvLImpUn2I1IO3IxC9/uisflnGJsP5+GZ81+82r/RCTjnwjr54Fczy05yfyebu8y8/6Gc8hRcV4u1mf7S8usp8/Uit7m+XLVG6vOICYtX/Pt8vJsyuKezsM99Y4fDuLTLdG6bWB01qO/H2NdBI/AwzHuLm7p3Yx1ETTx3PiO+Pg2y1EXSlyjpc55Kpdax7kra31uyUl0mrsJPd/ZgnUcdIaSa/GjQ3Bo9ji0axjMuiia8xKcP1Z5HTXv6PzlpFlFt7ToiOXOvxEFj4hmLlzNx7ZzaVi4Mw4/7UtgXRyigtMpObj56/1o8/oGTPv2AC5c5bNXB6nthX9O4eZv9mPC53vw5+FLkhrUF+6Mk9SYpdYDiBxFZQbMX38OFUYR5RUiXl8VhQqjZeHfWB3lMKAiBS+NfJdzirEvNrPm781nryK3qhHdaBRx4ar9BtCf9l20eO2BXyOULSRjFSr8WCxHgcxZLa1H1bz15/DayijFtrsnJkOxdTnD3sNWRGK2dgXRWE4Ru1Fu1dhf4bV1NPEaZnx3ULPc6dU8tXHZaBRx/y9HWRcDEYnZ+GZXHFafuOx4YTdy5OI1zx71phFe6o1aq577Qim+3gIWzuyL0EBfRderOJk3zo6N6li81rtFKDpYeV3r3vys/HdIK0XWUz16vqTciDdWR8Fo5fmQZ95eApqGBmLh3X1ZF0W2qb2aYkbf5ggLcu58FQSBm06qply5nj82sp1yBfEQj4xoy3T7+rpiuIaCR4SJBVuiWReBqOD1lVGIqpqEMDI5B5O/2ofka0WMS6Wt7efS0Pvdrejy5iZdzAcSk5Zfk1LSKFbOYdH/ve2MS6Ws86mWQZJ0FRtDeKlEpFsZJZRXUo6TSdlo98ZGvLKc7YgFHujtIdGRAoXS0cnFaw8+TTBs9SsuZ/+9xysQcNebglIDFtsYwaoWXhqXHXU6UNrumHREpvAzufWsladZF0FzSqU51SOt5kfwpLQ3pga0qWf3/XXPjMCSR4dIXl/118j79ymnvTvYzxvzpvWQvCYO29JrKPmzPDjcpMFYofXmlxhUmWPP2eLNntwFvz04EC3qBTpctkfzUCe3wk5dfx98flcfnHproqTlH64KEnh7CTVzHXVpUhfhwX5ObJ3PM+WB4fIDIXzuiXYeHsk2eGSOx4CmUih4RIgKYtPyPS5oYqgw4lxq7ZFGogh8utVzAoWiKOKddWeRW1yOknIj3l13FiUcNO7Zcyg+y+nPShptwMEN1OpEpSo+V76yPBLPLD6BuHTtUxuZstX78NbvDmpcEn4Z3Cx4xOp0Y91OY22EoxoNALxh/b17sgVbLmi6PWujZVnRMui+/Bi7NKCkkjs3hDjy5pozmgQi+Dm7teXoq20SGoCh7esrvl49WPLoEPzw337Y/MIo9GgeYvG+IKh3bqq1XiV/lnYNrqdpc9eRuV2bhmBM50bY/tJo1kVRhbfJA3rnxnXtLju8Q328eVM37H11LA68Ng73DK4ceebj7YX5NoOr+uMoEObquemOz0ZNQwPRtanlNZIoj4JHhChs7r9RuOGLvRi9YBf+PJTIujiasVVtO+hCcEJv8ooNSMkurvm7sKwCRy9eY1gix3jvnacEaxNqqvmgEZdegPWnU3Hvz0eZjmyxVr9MztYuqG1t5BNveGqQVQKrRj7W36K1/e797lZEJPJ9/SX6VVKu7dw3PAW6L+cUO15IIe52jdYjHtIPs5KWV4pSDeb084CquFMa1vWXtbw7BTqbhgbgxh5N0TI8yOr7go0z0xMOpQeGtVEtPZ8a358z53eArxcGtgmv+re3w+CKHvl6X2+Kfv/WHjbP3zv6t8AXd/UBALSqH4QmoQG13p/aqykmdG0kc+vqnSlaBzPlnAs/77dMSe8OvrmHXdpGT7p/U/CIAVEUse1cGpZFJHt0KgDeRaXkYuGOWOyLlT6XQ0p2Ef46XJnKxCgCb645q1bxuONJF05rSsor8Nk2zxllJRUPz3HW6lRazIOSmluCwxfZBU+t7behQrsTdTfjeXCkWKvDyXF5xDoIbeu56eNN2o4O0ZqH33YJI1o20EqZg5Goy50a5HnlriMnXGFttI1UvH+b5RWOA5Km552txmFrneO4TsesUNH6ta6d7tDdbhMN6/rjwxk9Eeh3fU6wD2b0VHw73+6KY3q8+JiMPBrQJhxLHh2Cp8a0x6C24ejaNAQzB7XCuXmTsOCO3mhUN8DOmoDXJ3dVu7hce2pMe0nLqdmpeZjZKNE7+rdQbVvm2je0nP+NFXfucOPDugCe6Kd9F/HH6cp0Rr8cuIiNz42El9W8SoSV86l5mPH9AZRXNbT+fP8AjO/a2OHnjrnxxNzWlFcY8cv+i7iaV4K7B1qfuNLdKnS2fLTpAv44dIl1MWQxGkW8s+4c62KoztrDlVZyivgaHu4hp6NkNP+eezt2Sf17Mp1ThKiHRh6xR8Ej9al5mD89tj2+3RWv3gZcYC9o5uMlv4+zXp45MyTMu2raAGn1FJSYtu6W3s3cvqMU77+73OJFzJlg8Vr/1vXw6wMD8eBvETWvta5vfVSaVAu2RKNXi1CM7NjQpfU4y9u79gE8pF19DGknP00lAHRoxE/wwJ6h7erjUIL1jqUf3+Z8gPDxUe1xIC6T6RyR3mbt2Z2buN9oOWs4v/woikYeMfCLyXDBC1fzsTsmnWFplJWeV4JvdsZqngteae9vOF8TOAKAF/45xa4wnCopr0DHOZvw4aYL+PVAIiZ9udfqcpkFpR4x/9NvBxOtvs7zDUWLhlW10groBcu0hdZ6vnyzM5ZBSYhW3Lm3k32eud+ecG/lWZvXN2DC53sQn1HAuihui6d0fZ5Ky3pc8zDHE8O7IzUbv58f3wkdddKwasqVw471aGhHpFzWHO2/1K/n49t6SVxS/rrlcmaEXSMJqQvbNwp2uIw7GN2pIUZ3qgz0BPt5W8z18/rkLrLX+ery04qUzRm+TgSI9cDe5WfJY0MQ/d6NFq/X9ffBzb2bOVx3WJD1OZFCg3yxcKbj1G0e3iyjCXf+jt3zjNWZM5fzWBdBEaWGCtz09X58ujWG2x5OUu2Py6z1d36J8+kFea/AOmvruTTJy45asAv/25egYmmIM77cHqP6Nni4f7K8idsKKrKiRbq+arykztByfg5Plcd4AlaW5zjLW/znW9W/hhP74tIL8P1ufdd55dLymKe0dezxUI9zd0qmrXvrpm7w9hLg4yVg/vQe8PPht7nH3untzHFXXRdwtfr552F1M0nIrbPY2h1rHYbMv1PT1Gd6tO+1sRavmbetPDqyHdfHuRztG9oOhHl5Cfj1gYHY/tIo7J01FqM61R4x9NDwtnhuXAeM7yJ9/p/0fHbz0/p4K3t3GdmxgeRlWVYtrJ23n9/VB0F+jpOCDW4bXquTxYgODWpG/LDMtKInZy6zG52ld+5xldU5dznNlx9LQbqEYdjuzNo1212fe59bclLysqIIvLfhPAwScjzzzGgUkZBRgGuFZayLQmTw1MoU691+fVUUYtLy2RYCwE97KXCttjfXnEWpoYLZ9j3zDAeOJrIb2UiuW3E8hXUR3BalrWNPy7oE63qLNZo8xym0jaHt6uOhEW1xfO4EHJ97A+4d0lqZFTMmNbt/9W81sbvjVPP2vPnvGZc+74iU3TFPAWWxDkGw3u7Acc4LueeSn48X/H0cB7/Cgvyw+qlhuGdwK6dG35hSo9Ov1HWGBfnizZu62V3Gy0tAh0Z1Ub+O5YgsPx8vvDSxM35+YCCWPDpE0jZZ3mJDA30VXd+cqV3hq3BASg2unKOCIGDxo4NxR/8WuG9o61qjjaRMg1JSru+2OCU8+scxRTu4uutAAWsoeEQUc4waMazS4+WkvMKIPw4l4pudsYoGSgpL2TUuuspoFPHIH8cw7rM9GPXJLuyLzZD0OU+6oVjDQ0MA6zK8s/YsSsr1e+y74kcOAje8jf5SE8tjnWUDOtuRR559jSf6c/fAljX/fmK0tEmWTT38ewS+2x2HMoP6jRBGaudQxMRuzjema5kOlXV9zRotGuOVGGHXq0Uo3r6lsuE5LMgPoUHXG2ZfndTZ5fWrQepuv3ajvIDAyzfwub9ymAaP5NQzlKiSaJmq0t+JEUPWgg7dm4Xig1t7OnVP48XJN2/AmM7SRw3Z06dlmCLrUdMdA1o6XkiGLk1CsOrJ4TWp/VixdQ5O7dXU5mfknHGt6wdjwR29MW9aD4QHX09jJzXIfkWljBx6eRxKzS3ByWTPmqdeKRQ84gCPFWVnFJZ5ZuOoI0ZRxD8RSZi1IhLbZaR6Y2nu6jN4a81ZfLo1Brd9f1C56LyOj/VDCVnYeaFyfrKCUgPmmvRK0+t8B+5y7XGE9cij3w4m4t+Tl5mWgZU1pzxzvz3RoXjrE8BqgQYnECLdhzN6YtnjQ7HqqWFO9dKOSSvAJ5uj8e66syqUrjYDRY9cdteAlvjxvgFOf17LkQw8ztunRYOYEttY+8wIdGkSYvW9MZ0bYZITo3F46Rxx98BW6N7M+r5Z06p+EP5+ZLDF63OmdJW8Dtapl02fXayVRID15xslApGnVGpYNS/ZoLbh2P7SaGx5YRTCgqyMQqn6gGk6soZ1/TGyI9vggFqUDNrx/ow/rksj1PF3nKZNrp4tQvHc+I6Kr9dVYUG+eOmGTgAAP2/LJvj+reu5vA1viT/6sI92urwtKbSe91rOSLYiard2CgWPCHFSQkYBIhKvIbOgFL8fTMQ2G4Ghvw5fwmsro7DsWAoe+eMYjl/iP9L9z7Hkmn9fzCzEPrM5oDzRkqNJtf6+lFU5UXnytSKM/2yPzc/x8dhl3TEN5r/hofLKQRHw+qoozbfJw3dfXsHzGUCUxPKXTstll7OdjnD2jWxEHkEQMKhtOPq1cq2x4u8jSY4XchHdQlznavBHy/gBD/UWczrKWmeTn48Xfvhvf0S+PRGN6lqmu7JF7d/e3rFp2vAYGuSLVU8Nw6bnRyIkQFqDc9sGlnPH9GsdhoYS9/9QgnodYqTcMk1HEVhrbG4U4m8jbZ3rPth4AR9sPK/AmmozD0ZO79McLcOD0LlJXWx9cZTNz31xVx/MHNQSt/RuhiWPDnaY0u+W3s0UKa+e8XgtNTVvWnfV1i1lBI6W1daRHRtgywuj0L5hHQCV17bP7ugNPx8vCALw0g2dUM9kBJGz5ARrEjMLXd4ebz67o7fkZSklsnOUD/cS2bSOyqqFk85Jmlh5PAWzVp62uPBY6xX17rpztf5+b8M5rH5quKrlU1pSViEA9+zlI5W1intcej4mfL7X7ucupOYjwMcbg9qGO6zsamlXdDpKNUg5w0PwwE0usUSiwlID3l57FkcuZmFMJ2XSP+gF00Od4anO/irj2QxGEX4c3d+I+6DApOuqn88GtqmHCCc6DWn5C/B4FdFi9I0Wz9CCICA00Bfzp/fA438el/QZoyjCi5Nfxd/HG12bhmD3q2Ox5tRlNA0NwBN/nZC5FgGtw4OQIWGO5lkrTuPA6+OcK6wDUn5u02fGAF9vTOzWGFurOqr2ahGKLk1CkJZn2XFGqXlkftybgOfHd0SwCqNDqpk+nzWqG2BzuQZ1/PHhjF6S1/vChI44lJAl6Xd2V6yzbtjz6Mi2aFEvSLX1S2lbvXPRIfz+0CBVUtyZB8XHd2mExiG1j+/b+rfAhG6NUWEUa6Wec4WcavgPe+Lx0W3SzykpWM+3Nrqz9N9SiRGatrhL2741NPKISLLqRAqmfXsAzy89qdgcOHP/jdJt1Pfl5ZFWy372Sp7Dz55MylGhRNrJLS7HI79HOPXZUh3P++JjZQLGjzZdcPi5jzdfwMyfDuNhJ78ztby6PFKT7cxacdqjK+8s8Zj+RQsrjqdgxfEUJF8rxp+HL7EujsdgeZ6zPNI9qeOMLWo+hBHPpvZzwu39W+hibghXVH+Db9/c3ak0QVqmLuOx0UWLvdfyGjpARnokHq/s4cF+eHB4W9zYw/b8IfZIPcSyCtWr00g5p7zMWoIXzuyL2ZO74OUbOuHPhytT8jUOCaiV0m10p4ZoUEf6yDJHMguU/Q60Op7aNayDLS+MwrLHh+KXB5xP2akUKaf3sPb1Fd0mD1fSLk3qWrz2+Z29MWdqN1W3K3Xf7//lKFKyi1QtC2D7vhYa6KtY4AiArE7KWrTBan0M+lrp6G1LhYL9pz3pEYiCR8ShpKwivLQsEpHJOVhz6go+3xZd6/19sRn4YlsMTiXnyFrvX4eTEJF4TcGSErV8ti2m5t+rT6Rg+/l0p9azNvKKUkXSnI+VG5Kc72F3dAZOJPGTsjCzQJkgsBQD39/OtGGZl5t6er62qbVY9wBi5e216s/DwasrDNO3HWV4P69geJLPW38OK4+ncDM3BAt67QhE+Kd2o/od/Vu4/Z2yc+PKBrwezUOx4+XR+PPhQfjiLunpXTz40gaAv/1vXd+1Hvtyrteqp62zs35nGh5N22itBVE6Na4juWNVSbl62RmkfK/m85cE+Hrj8dHt8ez4jrVGF/103wC8dVM3vH1zNyy6t7/SRVWVo1/CleeY8GA/DGobjnFd5M/1pbXQQF+8IWM+LinkjDzacDpV0W3bY2+EmVLk7PvXO+JULIm25HS+4CkbDguqjjxSbc3sUfCIOPTF9phaf/91+HqO810X0nHvz0fx1Y5Yp3qnmKd08wQ+OrxY5xSVI7eoHADwjgu/2Y97E5QqkuZ8FfjdWE4oz9rA97cjKUv93j3W8PLc/92ueE23l11Yrun2CGHFwDg95svLI7HmlLadI8o0SDsqFcvgHXFvagcmw4P9+IsOKKhugA/+M6RVzd+VIxUaIixQud7OSlL6Cem96T3QzsrcN7JocHhIOc5HdmyAkR0b4Lv/9LPao18qOafU2Su5Tm+HBdNT2c/HC2/f3K0mldMrEzuhboAyKd1cZT6qyBqpjbsBvt54aERbPDi8LQJ8va0us/iRwbLKpxY9XGrVKKK9INi0Ps2w7cVR6NE8VNFtyhnE+fTiE9gXm6Ho9lmSs+8HE5Sf15vVce5rJUuOLWqM8tXD+V2NUiI7h4JHxKEsO2nqXvjnlEvr9sSeuhxmZJAkLiPf5XXo9dfefCYVvx9yPf2VXn97pYxasAsH45WvpDnCy2Xmt4OJmm7vn2PJmm7PFqqgEbUpmfbBWa7Wh+Rax9FI3uRrbDoGKO2+oa3x+Kh2rItBTKgdPBKh37qpPY+MaIsnx7THhmdHIsjPtTlLpi7ch+eWnES2QmnL7VK4nvzfIa2x85UxLq1Di1Hc5RJy6Pz58GD8+fBgdG8W6lK9tnGIP5qFSuv9H5tW4PyGJFD7m31weFscnj0eh2ePxzPjOgLg41ksTMK8RErOWTOsQwNM6dlEsfUphYffwtwxjUfRD25bH41ClB+NIzc48Ory04qXwRresmJokeJdq+M8yM9HcgBJi77sPJ7f1ZQMnvF2TKuJgkcc4PnEAuzX43OLXevZfuGq6wEJveGlIVsuJZ7f9bjv2YVleHrxSUXW9ZOOR14p5Y1VUayL4DF4aVy+/9ejHtlRwBMtYxSwVDpXvB6cT3U8x6JWpi7cj81ntEt7oqZHR7VDgzrsg5FquXNAC9ZFkKXcqNwIu+ZhgVZf790iTLFt8GLuTd3w2o1d0MpGijM5jR15JQasjbyChTtjlSqeTTw+EmtRfZE7ktSVxipBEPD+rT0lLuv0Zlz20Ii2iqynUUgAmpgEy4a0Y19fkPLrKZ1W6rv/9MfNvZspuk655B61LOZAm63Cc6q9awgvDc9X85RPe23t99PieirnsOG9HVauY3NukLScksFpPVJz9935q6XgEXFI7Wu8uzQ4SKXX9C7PLD7hcgOwnhqQSw0VOHrxGhZsjVas52t2EaURS2SQuo6XijkA5Jd43jGwLzYTBz04ZaMnmbfuHErKKzTfrjtX1G1R86rWxok5NbQeeaWWBnX8sen5UbLmhNGDe4e0xosTOmHetB6siyJLhYIpKT+/0/pv+sy4DrLWw3tdtn1Dx2nanNmFXw8kyv+QTCwaix3R4tcukzl7t6uPJWO7NMKtfZs7XE71BkYb+zG0XX2M7dxIlU0qFZRyhZRriBojA5inzjfbb/NRH8+aXYu/uquP2iWyQM/q+idnNJEa1ziWNYTQIF9EvjXR4XJqzHnEedWolsf/PI4/DyUqsi497berKHjEgYsZhayLwNQTf51gXQRZDDIr+O4iLa8UJ5NzXFqHHq6thaUGzF4Vhc5zN+PORYew+EiS4w/JUGYwUhovjfF0U79r0WHuG57UsOXsVdZFIBooKDXg6EVtU34AgIGTa6qU1ENK4e0youbk4lqp/k4b1vXHrX31NULHkfnTe+D5CR2tzonxmAup+i5mqvsMU67QuT13aleb6S0bhwTgORkBJN7OPVMBvl6SRpXwvA+80aLOJnfkkRKTfUtZh5fGLUVzp3bFkTfG469HBiPQz/r8Pa4KDfTlvnPAtD7NuAykqu3eoa0xuG04Any9cGvf5hjbRZ0AImFHi1uPnOuWFqeZ1meyj4TUde6433LNX38eeSp06uV9v11BwSMOLD+ewroIdtmqNKudh5xXhxJc60HP+wXF3kPSjO8OqrZuXny5PQZLjiobMDLVae4m3PXjIWQVlKq2Dd5pPTKBp8PuXGoejl/KZl0MQtzKpig+gpPDPtqp2fmt5ohKnhut+reux7oITATamAhdCY+MdL4n/rjPditXECtcfdYY2bEBVj45FI+MtB8ge35CJ8nrVKLhXi2bnh8lKS2Xs3uQnleial2ex+eE3w4mqt7pS3YHCAWKk19icLiM2iOPzO9j/r7eaBwSoEqveFPT+zRHWJDjeYdYeOfmblhwuzrBLdbnl8XWzX7mRnUD8M/jQ3Fh/mR8cVcfq50d9IjDy5om6vpbzrc3QIM6nJyRR/zWdp0n5frpzXE9XytlFUZsivKsDFiuouARcdoTfx1nXQQmXO1daxSBNacuczv6RM0KjlEEisoMXKfu+mnfRdW3EZGYjX8YzQ3Cg8lf7cOlLO1GXPKUtg5gk7qPNU99cCLa2B+XyboIAICM/FJ8vOmCJttS85xy9uF+w2n1H8JYZ91hZVxX9XpgN6rr/GTdogjkqpjmx9XRfH8+PBj9W4cDAFqGW6ZjbFX1mreXgJAAy4Yua/rN34aDnFxzTA1rXx9tGzhOWQc434g86IMdePzP46p1IJS7VmuNk0r7emcc/laxUxkg//dQ4tu/oVtjh8u8tvK0pse6Vpd3QRBw8PVxGm1NngeGt4Wfj2c00Xno7Vwz3ZqGsC2A2Q/csVEdBGtwzZYTF1ElQM74mVdK8MhLjbR1rHecEU9q4/CMO5MObDidigtX+Zn8WIpt59JYF0G3nl96CvPWn2NdDKvOq3gc5haXo//87ej97lZ8uytOte3owSebo1kXgZmLmYX4ble8ZtvzpJs6IazpdV4/pRxN1CZtn8GoXpq4F26QPgrD1DNL9JWGWE94ntxYjbQf1aQEKV6+oRN6NHfcSBbg6423b+4Gby8BXgLw5k3davVsf3ZcR0llyisx4L0N5yUt6462nkvDvtgMdVYu4/ax6qlhiHx7ItY9M8JhUHn+tO4uFevNf8+49HmlPTi8Ta2/pRz/5m7u3czhMiXlRvzn5yO4klMse/1SsKwuBPn5YKKDAFphqePRWc5gtd+sa2es9rse41FmrBrV507tymS7Ncx2+75hbTTZrJzakiZVK43rb1JGFWmSto7jemu1NaeuKL5OPey3syh4xImnF5/ATQv3Y9eFdNZFkUTpvP6JmYWq9lyU48zlXE2289vBRE22I4coivhiW6yq2ygur4BRBBZsiUZ2YZmq2yL88uSRV57IU3sjeaLt1LFEE2pOr9Q8LNCpz+k9bsjzdUrtdEOvTuqs6vqdZahwvN+CAMyd2k3S+h4c3hYRcyYgYs4EPDyidro+Oc/751L11eHPnKtH0/9UGqUvJyVgv1b14OUloGeLUOx/zf4oknuHtsHKJ4fiu//0wx8PDdIkdZIccn+P2/q1QPdmlQGjugE+ePcW+cGxOhJHAIhiZUpvLchtcwsNrB0UmNSjiazPOwrK/33kkrwCSWTrXtOgjr8q2+OVVo2saqdBdIWcFGtyDevQAF2a1FVt/XJpN7JQxrJuOP5NyqginjskuaJPyzBZyx+Md206EoDvZwelUfCIIwajiFeWR7IuhiRKjzoa8+lujFqwC0dcnE9ICXf/eNjme0ajiNj0fA1Lo53DCVkY+P4ObD+vXcOfEhdsIt9llXoQ8oq3Bk3WOcdZcDXdJ9GPv4+om96HVOI19a3aeL18FpepO5ef2rt9/7A2uGtAS6c+u3BHrGr3Nakj7Ia0q4/Zk7tIWjY82A/1rTTS6r0x5dFR9ud1MuXq76XWV+VsqZpJCHj3bx2OKT2bYlSnhljx5DAnt8SHYH8frHpqGNY9MwI7Xx5Tk5pRriA/aXPK8Jpu+cu7+tQci0F+3nh1orwgeFah/flnP9h4QdWRlab8fbyw4PZemmyLFVaNrLzWG/y8vXBT76asi+GGZMx5pErWOk4POBPf747H1dwSRdfJw3n2jhMdKVx14lJOrb91XpW0i4JHnMnicCRGlJWRODkqjBLKLS7Hu+vYp3IrsDFEvcIo4v5fj7pturEPN11AZoH9SrS7uJhZiElf7EXb2Rswe1VUTSNcQkYB45Kp78vtMRj+0U7WxdCUHipx7m7F8RRsPXuVdTHcWm4xH6N3AXXTWJFKnp4ekDcjPt6JCDVTFrrwc/tLmD+jjr8PPr69F7o6MUfC8uMpiE5Tp2OVnBiplP20R28P/Kadiyd2a4xRHRtK/iyvlw9ey6U2Z/bb38cbPVuEomFd50erTO7BtuHa1Z97bJdGWP74ULx9czese3YE2kic86taRGK2w2WmfLVP8bl6zX/vm3o1xY6XR2NsF/XmtnOG2qMytLrksr6sWDu/6wX5Yt607ggJYJtST02snr/lDDTTYvQbr1WLIR/uQN95W/HAr0dVaQNkUaeSO/JIrti0fNzw+R50mrsJH246D1EUUWaWCkKtOSF5QMEjYtXlnGJ8tzsOb605YzVQdOmaOpPd85wGYueFdOyL5W+CXKVEJudovk1WlYofdscjOi0foggsOZpU0+Dz6Vb3DAxWu5JTjC+3q5uWkEee2hjBm7mczRmgNlEUUWqo0KwSyVNwrsyg3UgzTxtJWc2dH05YMb9XhARIn9g5q7AM72s4D84tvZtZpGyypX6wn+T1znNybpjPtmqT1sqa6sYfV88IXht4bBnZsSHOzZuEo2+Mx6J7+8tKzcTr1UPqSBh3w+r3ePEGafN8acWZYMWANuF4cHhbtG9YR4USASnZxVis8ojqAa3roUW9IFW34Qyln9NZPY+NkxmUU7teOW9ad5x8ayLuHtRK1e0AfD0DaxVMkBMQOq9C2yMP3/lnd/SWtFx2UTl2R2fg1wPqpKJ1N1/uiEVsegHKDEYs2pOAx/88brEMizZVrVDwiFjIKynHlK/24ZPN0fjjkPVcv4v2JGhcKvaWHKVUPO7CfL6dh36LAGB9lJ07mb+e/cg+FjiowxEA6fmeMbIRAD7fFoPnlp5C57mbccPnexCvwahGVUc9yKTlg9MfhxK12xhH1ArQfTijpyrrVYpSh1arcMcNdXK3dUrFB0bzRry2DYKx7aVRkj4rpyFlYJtw/HeI/AYtpedCdYar1x0p8wTwxCiKCPLzQaOQANm9p3lo3LJGakB0Sk95c9sQ61rUC8KvDw50uJxaZ4Ze0jgrna6f1W6z/rrNt69VMOGFCR1lBabHLtiNDadTFdu+Po5y5bE63uQeViXl6qYdZjEC57b+LWQt/+2ueJe3qdfjvMubm/CVxM7V5teFrVbuDRKm6tQtCh4RC7/sv8hV+hst2cv9qZcKrp7wMklhYVkFjEYR3nrLWSLTpjP8jEzQEm/n7qsrTuP4JcepMoh+LdwRi3WRVwAACZmF+HZnnOrb5GlCYC1HlXpiZxYAKCqznmLXVTM16AmrJWu39fem98Cm50dqXxgXWGt0k1qHklu16dJEfuo6fq4+zpO7D9FX2c6B2rmx8xOht6jneI4gnr0/ne8gt1ws66ljO/OTKo3XxzC150PTInWWM1RPW6fRbreoF4QNz43E3KldJS1fVmHE22vPqlYeLX9tnlK3a9XuI/e4UmM6DqKfemFJuRFfbI9BkkJz+/HW7qQkCh4RC8ck5P91V9/ssh11zlEhoObOFxcpeKrQ7I3N4HZCWOIafo6y6970sBRu1Tz1mrfq5GXVtyFxXnlteObPrKliBXpKOjO/jV5cmH8jEj+air2vjrV4r3uzEAT7S09JJ0dKtjr1CGuXTqkNJHIbUhqHBMj7APho/HT5siNzHyZ9uZdZAMnP2wtPjGnv9Od7tQhFj+bOn/9q3crNVzvfShrFVyZ2Qj0ZqRgJv3RTVVD48sbq+VfuVhVPW6fo2uRp2yAYj4xsJ3l5ReeAYbjjLB+7WG1abpDKS+EWcd1c19zUeCfnjlu01/XRV+6OgkfEAk8N+loSRRF/Hbadmu5kUo7i26QpC/jxwK8RrItAVMJjvILn+d3URPO0qKeCowPdU0cva6mli/MjDGtfH1/P7FPrtZdv6FTz7xl9m7u0frVIDUDbiwOoGehQaySced1ckNE8IrchZVyXRmgc4i/rM0aOrj/OauBEQGLSl3uZpOxb9dQwNKgj7zcyJQgCljw6BG9M6YL503ugdX1515P9ce47B6wtWs7l56nYh6CtO6VwGwCr9G284SUDieY4/cHPXlE3fb92cx7JW17t6ovHHOecVANfn9zFqc8pVXweOlOphYJHxAJXvZc19LfKk2Fa46m98AkhnomnAIcU/VqFsS6CZDw13t7wxV7WRXB7rj6b/O/+AejQqC5+vLc/JnZrjOfGdcDjo6+PZHhlUmeM6tQQbRsEu1hSNhRJQ+vEKfXvKXVGGVprbAzwlTaHg9yvwttLwIonhsn6zIVUdinclHpOn9CtMcKdCCCtPXVFmQJINHNQS/RoHuryeuoG+OKxUe1x75DW+OBW90oDJ0W9IGnzK1VLulaoUkn47ORkSq22MN73u1qZygFiXpsalW70pnYPvn2xLYZ1ERTRJFTe6OkjF6/hmcUn8MnmC6rPf+SOMgtKse1cGpKu8ZHBp6MLKX3tyZI4GpHX67kSKHhELHjqyKO5DNJI8dIJ38hLQQhRwbXCMtZFsKq4zPMqqHrrnPDMuA6siyAZb8/kyZw8RLgrV3/vIL/KtG0TuzfBj/cNwEsTO8PP5/pjQbOwQPzx0CDsemWMrPWq3XNVKntzVNh6x/wrrRMgP7Wdv4/0SbldIQAI9vfBmM4NHS7r48R8aC3Dg9C7ZZjk5dXqGODvo92jqq+3F5Y8OkT252LS2M59pITBbcNx14CWrIthScUevFKDr9epVxZPffY258Ydtmth9WvzFrzxnN+b3fcuZ8vbz6cru21Gx5uvtxfem95D8vLPLTmJ9adT8d3ueHyyOdrl7fN2nknlTLmv5BTjxi/34dE/juFqntnc8To7wR3t/tpIbTsK8YiCR8RC8rVi1kXwGEZRhCiKWHPqMj7bGo249AIm5dh+Po3Jdj2dXisXevNPhPajCqXYeu4q6yJozqCz6FF1A7se8JYScHe0sg+hpDa+fu3r7v35qKrrl7rfXk4ETMx9dFsv2Z9RK9hhvt/Vz+Tf3tMPL9/QCc+O64CPZlgfPTJ/mvRGFFNvyEj9oVZ1RsoIm+re8UrUqTo3UafHKu98vL3w8e3yj3fFufATDmkXXuvvkR0b2N+UzG3prB1MF1jfx0IDpY8+U/WZzUMOLnrsraTlr81TW4OW+/3fIa1ldYCp9suBi4qXhdXpLaVzkalnl5yUvY1vdsUpOz8Y4RoFj0gtoijicg4Fj7Sy9VwaFu6Iw/NLT+HrnXG4+ev9yMjX/gI8h8GoKwK8vDySdRE8gtI9qZTy/NJTrIugOZ3FjuyOXuANT2nrAKC8gq/yuBvOfu4aPIz0NG00duV7Gt6+Pm7p3UzWZ7RO7xTs74Nnx3fEyxM7W20InTmoFQa1DbfySccGtAnHzEFSR6Ooc0DKafia2K1Jrb/r+GsT/Of0VPQ4c6d2Q2DVaKIgP2+8MaWr3eXljgY4cSnb6bI5wuv1vNrhhGuabEfruUEeH91O8rKKdtBh9HuzPsws5u7TUR3bFSzP7/uGtmG2bdbHW4gTo8fdyScyO0CtP52K0yk5sj6zmMG0H6x4xtXKPgoecYhlD4HIFD7SjXiK55acxBfbr+eXLS6vwPe74zUvB4uAFQFWnbhMwVriUfQ255ECgxc0w9tXO2/9OdkPIUQ6SnNk21d397X7vq32KvNzyMfbC7OnyJt4V73z0HGjWx8rc7R9cGsP+Hg797jn7SXgwxnSGh/yig1ObcMROe21reoH4eERbQEAgb7eWODkSJpOjes49TnCVo/modj4/Eh8cVdvbHxuJLo2DbG7vNxz9a01Z10onf4dis9iXQTFtZMxp19+iXLXOIsgimJrVpZFGioXWczdp+ja9UPLmNnt/Vs4vBZqxUNihdzUzhuFyJv7CQC2nlUuG5Hefu4lR5Mw4uOdLmescefjnIJHHIpmmDs7r7ic2bZJpZPJ6vVs482BuEzWRWAu5qr+c+UTIlVRmToNjGrRUwWQt7R1APDI78doTj21SPxa7xrQEg8Ob1PrNfP0TnriqMH3x3v7IzzYz6V1mNK6J7wtUsrcNDQQ9w1tDaDy2vXWTd0069ldVmHENztjFV+v+W4/M9ZyHjrTXXzzpm44Omc8Ds8ej8k9mzq1zfdv7Vlr/i+HZVQhYti7heN0fe7I1Ub1tg2CcWvfFmgjIShwa9/mstZdrOJE6rx1/rBm5k+H8eI/pyRPGi7F+dQ8xdblHOlH2MoTKeqVgo/bjIU7Fx1StJOjrfSrWnJmDkA9C/b3weqnhmHVU8NkjbRTAuvrGuvtV/OsI07fUrKLMXtVFKXis4GCRxy68ct9zLbt7WE3VB550i+w5Giyx4964rHBl1j69cGBrIvgFladuMy6CIqo7t3OE97S1gFAen4pjqmY6seTSf21G4f4482p3TC1V2VDeqO6/nh9sv10Tnrl7+OFG7o1ZloGrUYT22p0mzetB7a9OAq7XxmDhzS+Tn26NUbxtIXmgZlW9YMslvE2+zIa1Q1AaJD0uUzMDWwTjg3PjnD68+rj40mhvEJneWjNPDqqHRrV9Zf1mSf/Oo4vtsWgROFAkqORpLw8nq8+eRnvbzyv2PrKDLWPoYJSbTsYyQlevLdBuf3msLpm09c7lOsUYH49Z9E5g0XAivXPHeDrjX6t6qFZaCDTcmj9e7MaoR/PaA5zJVBWg8oR738eusS6GFyi4BGnUnPZpLLiteeLJ/GU/L/V7vnpMBdzJLDCY4OvVv4+op8bc8dGnpPG5u6BUue4kG9jVKpq69bK+mdHYEQH+xNxs8BrHFrN3tq80iL9sNRtCIIALy8B38zsixNv3oC9s8aijxOTCMth3iColZmDWkmqQ9lapLWVwIQzVbLcIuVH8Vv02LbTCNOxcV20ri89HZOSlB5Rbn6YewkCBrapV/O3t5eA2/q3UHSbQOV3yKt+VtITsqD36muDOv7Y9Lzj9HamNp25iq92xOL1ladVLBnw9Nj2tebr+OaefqpuTw41OwGtjbyi2rqt4eWJW7NGdSfO2aURyYoFinkYecTDaGJWZfCSEIVepeIIO62xmGe3wigiT8EUl1pT8r6u5ybNMheueTrebYcoeMSps5fZDOPm4Ybq6bTuXZaUVaTtBs3Ephfgxi/3IiWbbTlY2ROTwboIzMxZfYbpHG9yNHYib7AezRzUCvOm9VBt/XrvqQwADev6czlKl9dziddyqamoTP2AmdRvdVj7+gAqg0jhwX4IqJpQXk1qBonlHk32elE+MKxNzb+D/Lzx3yGtLZZx5kw/m6r8/KEWPbb5uwQBUL5DjPn6BABzpnZDuwbBCAvyxTs3d3OYptBZ7RpKC8BpeYlrGR6I6TLTremJxZwoQuX8VaYmdm+i2Pbq1/HH4kcGo5vMOUH+PaVskMN8vxvVDcCmF0Zh/rTuWPb4UExxMgWj3mhdR5TbYXPRHtfnJU7JLtLdiOylEcnKrIiDOY8+vbO3ptsTRRHRnKSoNx+la81LyyIV2x7r2j+LDrp7YtItXuO1vkZss/aTsT6eecAkeCQIwieCIIgm/42R8JkbBUFYJQhCiiAIpVX/XyUIwo0ythskCMKrgiAcFQThmiAIBYIgnBcE4VNBEFq5sk9KY3Vwctge5nG0DOAlZhZi1IJdmm3PlvT8Uny7K451MZj4+4hrk/LxSE6v9kV7E9QriIJ8nZxwXG9mT+kCPx8vHHh9nN3Krp+PF965uZvs9Rt4HR4jgygCPt783Sy1SpflaUZ2bIB1z8hLYXX/L0dVKs11Up6Jh7Wvj0FttZ/f6IV/Tmm+TTmq61lvTOmKVyZ2wj2DW2H1U8NRx9/HYtk6AZavSV2/kvRy5VS6scZi5JFXZR1j5ytjcOqtibh3aBtFt2fq/ek9JS2nxm9jvs6pPZvi/Vt7YP0zI1Wtj4zp3FDyslqlt/nszt7wr5qD6q4BLdFJ4VFh9YL9sPrpYdj1yhhF1yuHtREZzcMCce/QNppcw3urPBqVV3Kv1B9uuoCruSUubfOTzdGW5eCvSlnLm/+eUWQ9PIw8mqhxatuXl0VapGNk9XtLvXXc+t0BdbIgabzfLOpNWQV8ZdP5cIa0ekw1vdQ1CRuat4YJgtAbwIsylhcEQVgEYBOAWwE0B+BX9f9bAWwSBGGR4KDriCAI7QGcAPAJgIEA6gEIBtAFwMsATguCMEX+HrkXKcNZibq0rFAs2GJZgWVlyVGFejUR5u4fZtl725aPNl1QsSTKkpPaRK9CAirniWgeFoi/Hx6Mm3s3s7rcwrv74oHhbTGgdT2r79tiqNBbtdT6BdnHi79g4gWVejbeM5irvjUWGofIm7NCqodHtMWOl0fj9wcHoWeLUAxqI70B79ilbMSmqdvTVMqZ9MdDgxRJhSt3YnlWQqwEeoL8LF8LCax8zc/HC8+M64gPbu2Jzk2sN0gH+fnITuWpRj3OYkSG8ptQhNJpYixHHmm350OrRu05okXn5jGdG+I/g1u7NJeTFC/d0AnNw6TNi6FVp+4pPZvi4OvjsG/WWHx8ey9VtuHv4422DYIxc5B6aXvl0Pr8fusm95wHzxFnrtU/utjpzVpqPq1+b9bzmViORNf+TqbF6OtqZy7nYtVJfuZ6HdulkaTlTibl4Pmlp1zfIOvMAww2/+oKdVOaynXXAHn3NEXT1nFbU3XMlXq8O09BomnrhyAIXgB+AuADwHJMn3XvAXis6t8nAcwEMKjq/yerXn8MwHw7260DYD2AzlUv/QRgPIBhAOYAKAAQCmC5IAjq1Ep1wn0Pdf3w0vCCs8EN5h8h/NHyGFbK5B6O06CokX7rq+3KTUSrtGEdGuDrmX2tBs0mdG1Us4wcFRqMPOrVItTitUecnDg+zEpDXYCvF5cjj9RS14mRF6bU/sVdmdvF187vGBboi/YN69R0qnltcmeby1qTdE3dVKxSrkc+Co1QeH58RzSRmbrz3p+P4O8jl5S/btpZ3wPDLc/zhnX9awX+ercIlX3MfHBrTyy6t7/k5e/+8TDmrTuHC1eVS0HNQ49tKdQ+33nc70MJWayLoJheLcJw4PVxSPxoKuuiALje+FS/jj9ahlvOSaY0ZilpGbex9m8djsi3J0pefsXxFLdISevM9eSXAxeVL4hGOjRkO3+rXu5j1YwuPrNsOsNXO0ujutLrcUcvXkNWQami29f6576tv7Ydn85esZ6ymGUQxctLqHlmJ8RVWnedfQ6Vo34uAPjZ0cKCIHQAMKvqz2MAhouiuFQUxQhRFJcCGFH1OgC8VjW6yJpXUDnCCABmiaL4mCiKO0VRPCSK4gcAJgIwAAgC8KUT+6W4iMRrTLbrBhmFdI/3ihQh7sheqpav7u4DAGjfSPmHri+2xyC/RPnJ1dXmbKP01TzX0n1IYW3+i+7NnRs11r5hHfQw+ezQdvURFuQHHx2O0n1hQkf8fP8AtKkvrwGuV/MwdQrEOfPqUP/W4TXXAkmfV7k+5Wj1j49qp9i22jQIxpYXRsn6zL7YTMxZfQbLj2k3+bKtuW9+um8AnhzTHo+PaodfHxwke71eXgImyZxn5ZcDFzHtmwO4VqhOChNee3S62thmsT6LuZ742+/zqcrPU2s59w9/+63GNY71Yyirzk8WI0IYlCM0UPqotleWR2JfbKbiZdB6t+Xss5q02u+HR7SrSQHJgl5G0FZbdsy1rCjZRdaf73jf72o/uDjHF+vr+bQ+zWXPZ+eKtTbmwmM94k9OtUxKWUsNFUjLK4HBDeYxtoXXOjZrmt09BEFoieujg54EIOVp6kVUjlICgGdFUayVfFMUxSIAz1b96QPgBSvb9QXwfNWf5wF8Zr6MKIqHcD2YNVYQBOndC1Xy494EJj16WEwsR2rj8PmQEFn0OPLImsdHt8P3/+mHaX0qey69MrH2yINXJnZCPQVSyGw6c9XldajJ3r2Ix1/a2oSwrqRS+uOhwXhidHs8PbY9Ft1XWT3gMW2dI/8Z3BrjuzaGn8yGgxt7NJEdcDK1LyYTMWqmcHOh2mKvymPtvWl9mqNpqLwROKpxsN/3D2uj6OacTZc1ayX7FB6hQb547cYumD2lq80AkxpKDUYs2uv6BOuA5XWY19us6nMeabzfDepod7wQ9tyl/qqFJ/46rvg6tW6G6NdKXuplvQsN8sWaZ4bjvqGtcVOvpppv37xhmseguKk317g211NOEV/z38h1MVPZEfRa/94Bvt5Y/fQwTbZVZjDanMeZdfOqrKwfDhZNvlaEyV/tw+APduC2Hw7ZXZbz09suPZddTVq2fnwHoA6A30VR3O1o4ao5jKZV/XlBFMXD1parer164pbpVuY+GgMgrOrfv4uiaKsJ6TeTf89wVD4tpOcrO1RUCqV7DBL56MGF6J27HMOzJ3fF5J7XH67aNgjGHw8NwrQ+zfDqpM54fHR7fHZnb9c3pOPLLo8/tbWHkwoXau7hwX54fXIXvDqpS82cUHpMW1f9tcjN9+7tJWDNMyPwgJPBiF8OXMTkr/Zh9UntRqAowVbvuzv6t9C4JNY56h3YTOK8JXLIzZ2uBr1dLi+kqjv3FW8+UXguTZZzHgHAgjsUuMc7waKRlUkp7NOiN7XWdYyHrKS+1AIvIzLk3OeLyipc2lZBqcGlzyuBl+CFlte1Lk1CMG9aDzym4OhkKfJLypF8rVY/cM07A1R7WGIq63IX52kttnGOcHLYqcpQYcTpFOtp3LTk7+ONAF91m7yLyyow4/sDNt9nXW+V06nH0ZI/7IlHQkYhACAyOcf5QhFd0iR4JAjCnQBuAnANwKsSP9YWQHWiyj0Olq1+vwWANmbvjbSynDXHABRW/XuEhPKpztYNR02uNLApZe6/UbhWWKZppZKHCiwh7kJuz2O1hz13sTERuimpRR7VqSG+ursvnh7bAb7eXhjdqRGeG98RnRs73oYtWj9E+Ck0DwrAa4cDyzJJOQbk0GPauuoSv3lTN8mfGVY1YXxooK9LI1kqjCJe/CfS6c/b40oDpjOfnDm4laTlVp5IQalBvXoci+ravOndMevGznhkRFtJ88S5m/dv7SH7M0pdKjionktyrbAMsQqONDTfba0vvcPby5vXz5OokraO8YHeyoVRtkpi1bgstVHdVadTcjBmwW6L1z2hUd0qBvut9an24aYLFq+x+r0fHN5G8rLvrD2LZJXnsOSX8wfJRitZNVid3l/c2UfV9X+46TzOXLadvpZ1Zic5z6yO7sF/H0lytTi6kJ7n/CAOd76NqR48EgQhDMBXVX++JopihsSPdjX5t+XdpjbT97uavSdpPaIoGgBU55YwX4dDgiC0sPcfgJqn7B/u7SdpnRsZTLKnxWTmjvx1OAn95m/D4Pe3Y9u5NE22udzFnLZK2hebiZk/HsbvBxOZP0QR4gyDzBxh07+z3VtHCVImWXb2TPP2EvDSDZ2w5UV584GY0rrn4829m8lafmwX2xNt5pXID7yzuK71ahGGR6w0ivh4CVhwey8kfjRVVi9MZpNqu6D6OOvfqh4eGdEWdf190K9VmMVyoYG+uLF7E8zo1xxf3tWn5nV3TN9k71i09VbT0ED8+sBAh+vedOYqOs/djNxideY0s3caTe8j7xyXyt/HG0+N6YC5N3XDazd2cfwBN3PPIGmBQ1NKXd/1lO7n651xiq3Lcu4fxVYtiQ4v9Zpx1yeUgW20T2XGy3cppb6shA82nkdmgfYZVqzRck4UnoRoNN9TSXkFtp1Lw2Irjc6s5hVpUU/6cf7bwURM//YAyhXs6KiX+VRceVx7bslJ5Qriosk9m2LmIHVGzn+06QL+OHTJ7jKsm/NeNku7rxV9HOXW/WOlbZj178gDLUYefYLKwMlBXJ9XSArTM9xRrhPTX9f8ylD9d6EoijkS19NQEAR/B8ta+6y9/yKqF+zTsh4mdW/scIVfbo+VWQTXsY6Mmyosq8Cc1VGabOvddec02Y5UhxKy8PbasziUkMW6KITY1LN5qMVrDwxrI3t+mTOX81Tt1SXlssZy0ly1Klf9W1tvAHlhQkdZ63l0ZLtaczt9dXefmn87M/rg31OXZX9GDvPfu3p/n5/QEa1NehXPmdIVe2aNxR1VqbjkBLWcSc14NbdE9mfkCHMwJ011I6iXl4C5N3VD1LuTsOqp4RbLdW5SFz/c2x+f39kHjUKuz+9TN8AX/x0iv/FcbfZ+tvnTuqNugI/V9yZ0bYz3pve0vV472xzbpRE2PCdtkPrSo+r00rM14qpdw2A8M66DKts0Zet7VRvLaqogCPhUZhoztYIPPD+UK/kTWc71pO2eswrSsQ6aSVFarn2GDJ4o2RHG4jjn+gx33eGEa6yLUOOtm7sxH03OYuttGwSrHjirMIq47fuDePSPY9YX0MlhnlVYhvWnrwAA8krKcT41D0Vl7p+1hp9WQdfNntIVLcOVTeecV1KOH/Y4nteS9ffYvVkInhrTXtKyHDUFu+RJs/19VoPnIk+gavBIEIQRAB4BYADwhCivlmWaY6bAwbKFJv+uY2M9jtbhaD2K+uzOPviPo9QnDE7ePdFSB4Zpg8W8TzyZu9q1iRoJUdOjo9rh2XEd0KCOPwJ8vfD02PZ4fXIXp4LQn25Vdp4EU1JuPTd0a1wrgDSyo3apatRqFPL3sX6Ll9uzNDzYD5ueH4X503tg6WNDMK1P85r3BrYJx9B29WWtT630ZdXMf22/qu+hboAvNjw3Er8+MBA7Xx6NR0e1Q3OTeWHkHLZeTjQ0zF51WvZn5HBUfsmNUXaDMfLTdqnN3m7fO7QNNjw70up7L0/shFv6NKtJzWe5YvtfaNsGwZLKp+QoDFPWBooH+3lj58tj0KGRsmkareFt5MtMJ0YFOUP+qa/QyCOGwYQJXR13eFOL+XGu9VHH11HOlxnfH1Q8Y4X52nj+/tVsZOPs8urWhrSrj1VPDcO7t3RHCKNOEaz8/tAgVde/9exVnL1iO52Xng7zlGvFSMgowKQv9mLyV/tw08L9SM9Tt1MYa0p3Kmd5XQupegZUUlKWxI6vjCMygiBg1o1d4Cthvl43iR3h+fEd8cTo9pjQtTF+fWCgJp3qPIFqd0hBEPwA/IjK+8IXoijKHUISYPLvMgfLmkYYzEPK1etxtA5H63HE0VjIJjAZfVTH3wePj2pvN29kmcrzgFjzu4Nhl0RbydmelWM3La8EjUMCHC9IuODnLeDliZ0thkM7U0eKuKheL0QpxfH19sJfDw/GVztiERLgg9cna5eSSa3KtNzfYe5U2xlbm4QG4N4hrS1e9/IS8OfDgxB1ORe3fndQ8rbS80pqjWpRk2nQpI6/j800fHK+Lm8nfrRd0RkoMxhrgllK0yIdIG8BAymsjdBZ9dQwdK3qcfvXw4ORX2JA73lbzT5nn9RgnFq/C+vegbwdCfWDtUmryOoUsAgeabjtN6Z0wa7odMmBAiXLZn7+OjPq0xU6vORp5lJWEbacvYopPZuyLgoTRlGEl1LBYUXWoox6Qb7ILlIn3SqverUIQ68WYVh/+goiErM13z6rulXDunIT7cizJ8Z+p2S91Sm/2B6L1KosAgmZhfh5/0XMniJ7tgv+KlAeIiTAF92ahuBcqu2AphxS6+G8XN8Ht62P/XGZdpdR8tmC5ekd4OutaVuOqVIGbfhaUXPk0RuonDsoCcC7TnzeNJTv6InQ9M5XbGM9Up4q7a3HLlEUU+z9B8Bi1jid3S8JA+6etsDcURUDCLy7VliGoxevIadISpybbxVO1DzUrFhJbbzt2SIU/7t/AD6/q49mgQ1AvfPcVlorW+4b2sap7fh4e6Fvq3qYLaOS5ig/tCucbazv0zJM8rJeTtae1OwU4mivpR4P/WykO+SVo9/b2tt+3td/QC8vAaFWUv4N72B/9KHUOly5anNJWq5Xy4YYZkEUbh7DpVEqGxLLOY/aNayDlU8Ow6MjLeeNU5t5GlytjztKW2ffq8vVHUnMM1evRKIo4ttdcRizYBdOJuXUeo/lzx3s71mjb0xpHZzmwQPD2kha7uyVXKuvJ18rwm8HLuJQvPxU+3r6tkUA6yKv1Hpt0d4Eh5+R9wZfnHmcOp+ah1UnrM84osf5YpXAurNXNWeyZpDK9Ju7o9OxISpV0vL5TswHrReq1A4EQegCYHbVn8+Kolhob3kb8k3+7SiFnGnuEPP0dNXrkZKGzt56CNGcB9ZhPVJCRgHuXHQYmQWlaBzij+WPD0Or+tpMWusKW5UhZ4a5qznfGid1Npt4OM+fGtPe5RExD41oi5JyI77fE4eScvtBkkINc4VL/X4n92iCtg2CcTGzssry6iTbE4zW9XdujiylU/yYcnQK2dr0Wzd1w7z1lfP+VY6Kbmd3PQ8Ob4NfDyQ6UUI2rO22tQaiD27tiTn/RkEUgWHt62Nw23C765V6XJUZ1AkYWvu9ObiUEDNKXd9ZNz70aRmGPi3DEJNW4LA3uZo8sXGXZ4Vl6s57xOLnltqh589Dl9CvdT1ZHU9MRabkYsEW6ymb6TBnQ83GbaNRxPmr1kc8sPy537qpG3ZFp+OSgxRcdy86jOfGd8RvBxPRKjwIC+7oBR8vL0z5ah/ySyvr9N/c0xddmtTF+tOpGNA63GGd11OP872xGbhzoKOkRezJrXZ8tjXabqpmvQSP3l5zBmM6N7KZqaKa1M5MvMwpL+Xr11sHLS08/ucxbD+fLnl5a5la3IVaXUteROVInwQAQYIg3G1lGdPE+eMEQaiedXtdVbDJNGTdwsH2TK++yWbvpQAYDCBYEIQwURRzJKwnQxRFLibb6TR3E2ZN6oyHR7TV3dBe4jpP+8k99Xa1YEs0MgsqLzlpeaX4akcsPrtT3qTcLAy00bjqzMSzKrapO2x0m96nmXobZ8jafrdRMSjp6+2F5yd0RO+WoXjg1wi7y6o5ObGzh5KPtxfWPDMcG06nolFdf4yz89AQ6OeN2/q1wEobvetsuZJTXGtuLSU5O+LqweFt0CjEHwkZhZjWpxnqOUj/9fCIttgXm4m4dD762Djaa2sPbdburfcMboV+rcNwrbAMg9qEO6xzsR4ZzPp+ycmzsObk/u5nr+Th1eWRaFEvCE+MaQd/H2+ntmsxFwyzkV/asjh/Oa0XG42ior16Lef+4XTHFaan68q89efg7SXg1wcGYlSnhrI//15Vpw3evDGlK576+wTrYjChVuO2KIp48LcIm4F3ls/7Xl4CVj05DP3f2253ufxSA97feB4AcDmnGF9si0WQn3dN4AgAnll8UtWyuovzCqVNU5uc54qNUakO5/hU89lPqjoS5jX7/dAl/H7oEpY/MRQD29juSCY5bR0n9zUpKdcVTVvnBvWWmLR8WYEjABjtRH1AL9RKW1ed/q0dgCU2/rvNZPk3TV6v/rZNa1SOcuGYvn/e7D1J6xEEwQdAexvrUIWUk7PMYMR7G84j6rL1ocLEvbnDRZc4tulM7ayWchuktXJz72Y16Z6eG98RDepYz5XdtoGUgZ61GdUckWHnvRb1AvHs+I6qbVsKtXpSW9vvD27tqcq25ErPV69/hittjSEBvpg5qBXGd23sMHjwye298NXdfdCtau4cKSZ/tQ8xafmOF3SCozPIVqOIIAi4qVczPDe+I1rXD7a6jKkW9YKw8bmROD53AvbNGutESbVldYSOjZ+2S5MQDGvfAD7ejqvHrDt3aDHHlT2selLa2qxWv4fc7aRkF2P58RR8sT0GH2+yPsrAqXIotiZ5tD7uzDfH68ij55ZSo6knqjCKeG3laac+ay+1Dcvnv/Fd7fe2VxPr5161Ouruj8tkOmLTkfo2nufsWXkiBTsvyGtUNRebxq4Tktw0rHJufRVGEQWlBpsjr3gJJijpeQn3QG9nc34raEbf5pKXnb0qyu77Un9GX5XmupVL67R1l3NkzQLDpVNmKWWlUGtuYx7wvGcXAVQnFh3tYNlRVf+/DCDR7L39Jv+2t54BuJ627oCE8rlMzoP3e+s1iWdxafVJPhvSpfj2nn4Y2bEBRnRo4FSPfw46aGjqn4gk1kUgdnw9sy+OzhmPiDkT8NINnWwuN7BNPXRoJC+AZFA1nZf1dR94fRy2vTga7RvKD3YpSbW2MLPdfmBYGwxzMI+LVraeTdNsW2p9v95eAqb1aY7b+zsaHF3bx5suqFIeR1UKJUc8+fl4oX4df7QMD8LMQdJTb0RfVT5w5rgqZWXkkQINVHIasf84lOjy9sxZ3W0N6wxqjhZ1V78cuOj8hzkZenS/k3PjOcv8WYnXavH609Jy4RN5mAQTZG4yNbfE8UJW8JoeyN/HG0+Oae94QRWw/k7Ueu5ee+qK3fc5jYmrrpzhxPKzJ3dFi3qBiq83s6AUt31/ED3e3oJ9sZlWl+HzzLe0LzYTryyPRG5RucNlyysc75WEflmqu03GM5ujDAtSO9P487DjkDbyyJqsglIsO5aMY4ny5iZPvmY/FaYezHKyc4i7UuVIFkXxAVEUBXv/AXjX5CNjTd5LrFqHCGBN1ftdBEEYYm1bVa9XjyhaI1qexbsBVA/buV+w3aXkAZN/r5ayn1rKLipjXQRmXvxHv5OxdmsWgj8fHoy/HhmM7s1CZX/e01IVHoiTP9km0UZ1T52wID80rGu/h5ogCFj8yGBZvbpUHXlkZdVzpnRF87BABPo5l0JISWo1jpg/hLeXGdBTU3F5hWq/udYPZXIv0ztc7Klpi71GFzUnug+REZS6+Zv9uGAj579arJVPiUCanJ/93XXnUFCq7DxfrHuu8pB+xJOYn9+svv0xnbVNx2F+mPM68khp5o+zHrLbXDSqPjeO7Wh0AMyjpGreX9SaB1AJvio18rrzBOquYHm+e3kJeHSk/Tk+TUkNbC45koRTyTl2l+FlDhwpVhxPwZc7YhRZFw8jj9Q6x+1u04ePG3iQv+M2D/O6R15JOSZ/tQ+zVpzG7T8ckrU9NTsGa8Edgl9KY38G2/clgOq77deCINTqHlD199dVfxqqlq9FFMUyAAur/uwK4BXzZQRBGArg4ao/94iiaH+yBoXo+3QiUtRqW3HivsHHrYZ4uoeGt8W86T0cL2iiUUgA5kztJnn5ChUr0uYV/pAAHzwwvI1q25NLrUYhOenbWDRMqTEawxq1ey7z0php7RQKD/bDoyPb4uWJnVXbrpzvt8xgxPsblB1J7ejK0ahuAIZ3qF/z99B29dEkNMDl7cr52SuMIg7GWe+B6izWdUhHc2OphXWbi6MJlNUSkZhd629Wlx0fby88Mdr+qISt567afV8OTw2iXFBhlCaRZmj7+ripV1PVt2Pv3sn6MJfao37KV/tw+/cHcUZGev1/T162+R7rtHVq9YnYfNb+NfFKjnOj1/SO9f38zgHSR85L9dk2x4EW1vst168HEhVZj7t1OpL6M/p5s++oCgB1/R3P93TSLPD53vpzTqeaNxj57SgghRrZMvSO6+CRKIoxAD6t+nMAgAOCINwlCMIAQRDuQmV6uQFV7y8QRTHWxqoWAKi+kn8iCMIiQRDGCoIwRBCE2QC2AvABUAzgBTX2xRo99TpwxV0q3Jj1wrQSfGsf6TlWTVZAdOY/g1theIf6+OG//VkXxULfVmFOfe6tm7uhjoQKhyts5YVWgvml9sUbOjHpeWSLRlnruGt0e2edOpNFaz0nBy/fq7W9XvnkMMyZ2g0Bvnw8uACwmcbDaRJ+75/uG4DXJ3fBazd2wf/uH+BweSnkjgxWus5n7TjX+lDkaVJYrfY9JMAXb0xxNBWrslKy+er96Kj9p6TciFJDhSLbsugEwcn1VmueMnIh0rzHPoPf29tLwNcz+6q+HdYp2uzp1kzaXI7nUvNw7FI2Xl4mPUvIeTujj2/s0UTyetTAqjOQ0iOT9YL1ORDo5416QdJGoitZhWO936zw0tlOKVKPCV9vPvZbSnFPp1zvCPD2mjNYdsz56UOkpDLk2SN/HGNdBO7w03pm2xwAv1T9uy+ApQAiqv5fXbP7GcBcWysQRTEfwFQA1cGlxwDsBHAIwAcA6gDIA3CnKIqnlC2+bR4SO8LQ9vUdL+SmTO+RzvRW1ctNtlW4/Pmc3NX7t/bE348MYf4AZI0/xxP4qRlMlzMCxxU9m8tPTQmoOfLIPM2R7Q21rh9s8z29U/syystVul0Dy9+wqQIjbBzRw20qyM8HT4xujyfHtEewyoFwW7RI3zCyo7bBnGZhys8XoAdyUt0o4avtln3jWPfQd2TGdwdRUu56AMlyziO+91sJiZmFFq9tjHL/OZUSMuzPL6El1mnDWW9/ak95I6+i0/JRKDEAYq+6f0vvZrK2qzSpz90939mCDzaeVzXlth64epjy0Ba2UMFAsdQ0VzzsNws+nARRXJGWV2Jyj5b2Q/px0v4i9bjrOGcj+szbit8PXXJpewaGc5pZE+DLx++gZ9x/g6IoGkVRfBiVwZ81AK4AKKv6/xoAU0RRfEQURbtHpyiKcagMNr0G4BiAHABFAKIBfAGglyiK69XaDxul0nZzjOihYUktXibdM72dGKqrh9G9zcMC8fpkbXvhsmKa+kiPeK6sqjryyHyuCJUuSu/c0h2BTo3wUGvOI7OtmGzm3iGta/4dHuyH6c6MjLSBdaOH1njZX/PRRZ0a1+FqxJFaWF7WXrtR+r3PXqoeZ1i7nr8ySb30hNawaDizuUUNz0NBEPDuLd01q6NdstIgxcllx6azV/Kw6oTrx7zlnEcur5J7Ly+3HMXBIluFlBQ3Stqp0nyAesT6MPfx9sKLEzrJ+owS1yTWWQGk7kN+iQE/7k3AqZQch8tKCaKz/r09WVigtBS8jlJwXcwsxJSv9klaF8vn8e4SRxWqwZm2MJ78efgShny4A2M+3Y13152V/Dv6cZTtRIryChE5ReUur4e3OY94bgfTC2ZHsiiK74iiKFT9t1vC8htFUZwuimJzURT9q/4/XRTFTTK2WSiK4ieiKA4URbGeKIrBoih2EUXxJVEUXQutOsGde3qbalDHn3URmHH1HslLo6QjnnIx/vOhwXjpBnkPU0QaNesXWqW96d+6HjY9PxLNZI720Oo0N93MWzd3w5wpXfH4qHZY8/RwbnpFKUHr6xEvl2nz3X5khDajIzjZfQsPDW+r+jaeHGN/7hdT28+ny5oXwhHzoPidA1qgrZXRZ2rylPTL1tw/rA32zhqLIe3CVd1OSnYRjl68ZvE6r+edqSVHk1xeh3mAUi/1YmeJoojjl7IdL6iBj2/vpen2PPhywqURHeV1mHNUjz+fmof31p/D4iO2rwus25ZDA6WlMKs247uD2HA61W4HuJ/2JjhcDw+XtW/uUT9Vo54t2mP/d/xsazTyJY6+Y1l3eueW7ggJYDMCn4PD3GmiKGLhjtia+9SvBxLxyeZoSZ9llfGANS2yXxBtuU9rkQ75enth7tSukpaNTS/AsohkPPDrUUz4fA+WKvBAphbTClDzsEAMbcf/aI0uTeqqsl5X087p+Sbrjry8BDw3viMi5kzAqxr38FaCM1XVBnXYTIquJIsROCpuq02DYIyRmaLy+aUnkV/ieg8fc/aeTXy9vfDoqHaYPaUrWjJMO5mnwn6bU7ux0bnRZhrQKijJ+EZl7Tjv3LguHhmpfvBIrnnrlZvny3y/uzTRvjdphYe39raoF4T3b+2p6jbe/PeM1ddZn3dSRCkQLPW0kUe2TikW6fqm9GwqKUD+v30JioxCtDYPiJv/3Dbp4fw2Z2++yYz8Ukz79gD+t/8iyuylMmK83w+NkF9veHrxCTy75ITN9z/bFmPzvWo8pKm/qZf2KQP1lPbP0SiK9aelpxZlWXUa2CYc+2aNY1cAnRn36W6M+3Q3Hvn9GDLyS2u9dzTRsmOPNc6mtde7p8d2YF2EWvRzteEXBY8Yu71/C8nLzlp5GrujMxCXXoDXV0VxN4EuAAxoXQ+n3pqIJ8e0x31DW2PZE0NrpW7jlVqNi66u1t17WOpVw7r+km6I3AWYnLhrfnpHb+XLYYNaE8ZaPNCqfF6VG+Tl+C0pN2LlcecnpLTFXto6NcnpaXTvz0cV377WE9FO7tGUjwASo6dR3uYgmT25C9Y9O4LL+Xik5sOXQuPLmlXeEjcqdS4MKew1ULLQvmEdVde/KzrD6ussz7t6QdI7lbg6jw0Px7lUkck5rIuguEZ1HWePeG/DeRyMz3J5W5yd2qqzt788H+e22Gtb/353PMok1I1Z1yfaN6zj1KiMjVFX8cziE04HQ3gIHjnD1WIP1kEHYzU0C2M7IiM0SN4IO0cOxmVKWk6P7VoJmYVIyCzEDhfSqvLSFqr18zF3WbY02P3HRmk7J6rWKHjEmCsV5e93xytXEAUseXQIljw2BKGBvnjtxi6YN60HmnPYeGONWpd0VyvBerjHzrqRswAJR2YOaoXeLfjpbSKn0jBzUEt8e08/jOksbxSNK3q8vQVf74hVvHHQfHVq1+Hs9qq04Z11yo1IqMFoovFOjeuif+t6kpaNTM5B9NV8Vcuj9l4H+nlj4cy+6Ny4Lga0rocxnRuqvEVpdHD7UIT5da1+HX+3SsNoi8VcbgzK4Cvxe9505qrKJWGrLqMUMKzc2KOJ5GUXbJGW1sUW8/oAz41Pb609iwqjiMjkHKc7+Nmq/XC82wCA//58xOV18DbnUXiw/kfea8le3f3MFWmjEHloY3W249/606k4mZyDY4nXMOO7A7hz0SHJqWp5P7/V0qdlGOsiMPnunx3XUfuNmlFy7qN7/uf69Z8QtWkRPHt0JAWPiIpcyXlaVOZ4AkYtDW1fn/lEl87ycrLYX8/si/uHtsYDw9pYX69ZhaRHc3k3at7rkqM7NcSk7tYbESioVPngueLJYTg6Zzxi3pvMujiygtUfzuiFqb2aqlcYGz7bFoOzV/IUXadl2jrezyxlWPzcGu72Hw8Nkrxsen6Jottm0Xv5hm6NseXFUVjx5DB8NEPbeSKqseq0zXqEj8XIBDbF0JzliAzt91zqJMBa5Pdn+bszSb3DcIdbhgdJnhvkfKpr93Pzr5bn8zsuLR/3/nwE0749gHGf7cHmM9LTGLkDV8+DI9bm9mLYqr7+2RGqrt/erumxnmrvMi81KMRDcNiVo3jeurN46u8TOJGUg6MXr+GlZackfU6vI49cxUOwkIWmjEceAcB703vIWr7ciY6RxHO1b8jZqCONNJQwWlvP9NnS70ZcqWd7aD1DFc5W0hvV9ce703rgnVu6W33fvDI4e7K0Oa5sfZ43vz4wEAG+3lYj+XcOaIkBVaMPOjSqgxl9m2tdPEW9fEMnpz7n6+2FRnUDuOgJz6Jx+ebe8nNof7z5gqJlsOy5rOjqLUiZI0ALLBvVWU4OyjrNURNOJgjVqhFmRr/mXI284Pm2qWTRWKWlNHWTxA4Gs1acxr0/H8G1wjKVS8RGOYPgEevDfN406/VepRWX1+4ox3O9uLCsoiZ9W5nBiJeXRcpeB29pGeWISLyGDzedR/e3NuOmr/fhYmahy+tk+X00CwtEp8bqpaXU8U9tlb1OAlLPWx7Obld+l8iUXKSbzIsSkyYtbae3TqMormdY0ed+u4qH4HDfVtIyRFR7e+1Zq6+fcsN0re5Ky3uO2imdndG9GT/ZgPSKfWumh/NxobLA8wOUp3CUw9T8NxreoYGs9V/NU7ZHvjPq20nbYG//QwN98c/jQ3F87gRsfG4kmtfjJ4VhgzrSU1F0bRqCOwe0wINOTKLKGxYP4R/f1hPPjZM3YWJJubKjKi1HHqmrc+O6eHJMe/j5eKFDozro0qSuylu0ziKtFaf3DKUfohKzajdY8bnXymPVEBXg642VTw5js3G4XwOcZByMyJCanhIA9sVmYsnRJBVLw46UuTyUxvp6rsUzyLKIZIvXOL2NWVXoRIYI3i5ncq6vhxOuYdGeBBSWVeDM5Tx8vTPW9e27vAbXdG6iXGonOfR0nFf7ZHM01kZesfqsIXV/9LjfStDrfruaBkqnMTOXaT33jBIWH7Gsv/1xKBHTvz3AoDSEdzxe09610dmfSEfBI8bqBfs53bCoxjk5sqO84EY1vQ/Rc/Ym7ujhWXCDM+yVSZ0xX+bQZqDy+PT2Ericf6JBHWnHa11/H2x6fiQ+ub036jAcSaEUFlXVID8fvDSxs6w0hkoHE7QeiSIIAl67sQti3puM7S+NRsfGjIJHHDQuS6Hk75FZUIrUXPZBdx5o+Xt3knmMqxnI5vGBRQ0W9RYGOy43gOHq/De7LqTjgspzpDnjxQnOjUx2hScc5rNWnrZ4jTrO8euL7TG1/l514rLL62Q9EstTG7ed8c+xZDy35CQWW+kkIHnkEQfnN4tjTq/XteRrxS59noffm0hnOpdfqaECb62xPhrJFqnpbglRQ++WYfjx3v4Y1r4+66LoFl8tuh7qm3v6oXcL+cPo1KhoOHsTZ125d5WzxXc0zFyvlUFTPl4C7h3SGj2byztGnT2WsgpKHS/Eid8eHFjr77sGtGRUEjej8GnDes4jLeb7sIZ1+jYWftybYPGapzyc6qkno2laF1fpZ6+Vpae5YJRwMbMQD/4WYfP9ZgznELh9QAvNt8n6siZ1+7zNz8o7nT9OKY7FdGKkmnMXmTmrz1iuSXLwyKlNKorFIUdBSnY6NNI+vZZer/N5xYaaf1/Jkd9Rr20Dz5wHxxN1bcpm1K4jE7s3weJHh6iy7ik9rc8D704oeMSBDo3qYM0zI5DwwRRZn/Pi6NdT+yb48/6L6m7ASY4qe+5QF5QSALP2+ztbEf5+d7xzH1TBPYNb2X1/eIcGmN6nck6fjo3q4AlO5rqxhWVlle22GbeycvKQwMNDuTVKFisi0XLCbR7d/eMh9Hh7C2avOq1Yyis9BQvVvB7wkEteC1rP5cba1ztsp8AKCfDBdIbzKjbjZJ4zHikZKAbYdMp6Y0oXpz+brdA8X+5+ftvCqvNNNX9GmRMyFD5vWJP6TOihh7lbdDbVqwBfb7x/aw/4emv3G/ASPJI7d6Hp9VhuivnHRrWTtTxRnpaH3UNuMN2DXEF++s9S5AhH4QfiaP4cc2r0pnZ2BJHclDVyzV9/DgWlBscLOsnZm7ijyp47VAarg5Rye7WbH59Sv+PYdGmTi6qtaWgAHhlpv6Lj6+2FL+/ui/gPpmDri6O471Ej9Rd8VuYcRbxjHTtildaT9bPJ3lfHar5Na78tj5fhwwnXUFBqwJKjydgfl6HKNrTe73YN2Vz/9D7y2Vnme+0O9Q17LprNZVatjr8PVj01DP4+3hqX6DoWoxvd/Oe22TDFYr8fG9UeSx4dgk6N5fdQ/3Gf5WhYW4xGEf/bL315T8B65JGanRHs7Vp+iXrPvFozVBixO1paPYeHkeIeWqXwaP8Z3BqRb09kXQzNTevTXNaUFabBo482XZC1LUpZ51lCAuj3dkcUPNIxLatX7R00Cs2Z2tXu+1/P7Fvzbz9vLwxtJz/XZGRyjuzPqM1RHZeDOrDL6gX5OVymqYK9bo8xHDlw8PVx2PPqGPzywABsen6k5EZ/by+Biwceh+w8EYUH+6Fn81Dc0b8FHmXcO0jpb9I88Kl1I+uTY9ozuRZYjEzQOGzWqn6QptsD+Gh4kGvWiihF1sO6weODW3tKXlbNFHs8HwJKHp+sg+LVpM4h6Co/b+uPLB/O6IkOjdjMK8eSO4+wW3YsGb3e3Wr1PVZB0qHt6+OW3s1kf07Os8v282n4ZLP1ecHc+fe2h3XnAKmH254Y6Z1AjEYRi/bEI85OZzke4hfNwwJdXseK4ynoMGeT5OV5SN/Gw3fPyn+H2M+44c60HDXAS5rp0EBf/PnwYPRpGSZp+Vu+OYCdF9IAyLvmAZXTIPBiYJt6rItA3FAvJ6ah0RsKHumYGs9Pturovzww0OrrN/duhh/v7Y8eDubDubl3M/x8/wC8OKETVj89DG04G6Fh7xYeGugLby8Bd/S3zGnv7nMeNarrj5EdGwKw3zDZv3W9WgHGqT2bOr3NwrIKnLmc6/TnpbC1L83CAtG6fjDGdWmMMAlBM7mC/aT3jE7Pl59L2BF7x/n9Q9tg3bMjsOCO3m7XW4R1Oq/GIQH46d4B2m7UCm4vRwqWy9olmdfdrlZmUGdOEK0bG4fI6BTCOtDlDizmcmN0oC+4o5cm2/GzkT7KnXroy8H6ei7n+hJ9Nd/meynZRThzORfGqiEm5RVGzF4VZTOdJ8v9dib4K+c54KVlkTbfq2A9BIcRo072+9Xltn87c4cvZuFDB732eWhnbeJix8DyCiPmrz8n6zM8BElZByxZeu1G51N0erKTSdmsi+ASObe2d9aec+oc8bHRAYiF1yd3RYM6yrfz8M6DL22auNMD5j7n5ywmsqkRmLDVE6J1/WD8fH/txs9b+zbH1zP7YmJ3aZODje/aGM9P6IjuzUJ1UzELCfDB8bkTcGH+jVZztXqb/AbVc9+Y4qHyb8/gtuE235vepxkWPzrEYYAMqHygXvb4UDw7rgNeu7ELPruzt0vlenON5WSr7mDBHdK/lz8PXVJ8+zo57RTHOngEABO6NdZ8m3r5vZVsLLC6LtatrA44+zuJooh1kVfw094EZBaUctKPURoly8r6OLfWsUQTjEcWVhtV1cFEbbbmHilVKfjKA6Xmy2Htqx0xVl9fF3kFYz/djZu+3o+Hf4+AKIq4lFVkN1DCsl7tzK1Ezmfspedu34hRWlAmW+Vn+1J/P6lze326JRr3/HTE4XJ673x4rbAMcekFyC0ul/U5ne+27tUN8MV/HMz3q9i2/N1jfpD/7UvArd8dZF0Ml8g57ZKuFaGoTH69S8MppRzq37oe9r82DiffvIF1UTTG+o7q3gJ82aXP1goFj3RMjWuwvUaYUZ0aYmqvyhElbRsE4+mxzs+L0lHlOZLkshfM8vH2gq+3l9ULQpBJxWewlV7XvFf+fW30ApnUvTG+vLsvOjSSnt+9fh1/vDyxM54c097qdyVniPbJpBzJy+rJhK6NERIgrbL89c44xbfPyzB5R5Q+bSx66HPQs1EL5r+3HlO6yaXHXXR2QvCvdsTi2SUn8f7G87hp4X6UV9Tuqc/zd6FmBxKtj/NXJ3XGDSyCw+YvMPq9vb0ETXLZ+3hZr6/wlApFSTvOp6Hv/G0239fT9by8ovJoPXslFy8tO4WPN19AUZkBLy07VfPerugMHLnoOG2xnvZbSfcMas26CEywH3Gl3PF2KasQ3+ySVrfn5SjvLTGdlbmvd8ZK6nzIIxYdUni6rmlVlLdu7qbNhlT23obzrIvgMrntVd3f3iJ7G7xdDwJ8vVEv2PNGHxHiCgoecWZKT2mjeADtKxq+3l74ZmZfnJ93I7a9OEpWYMHcPYNa1WpEl7KurMIy/LQ3AWsjr2g2csn0O24ZHoQuTa4Hvbo1DamVD1ovE7WbshVMsNa47upX3ipc+/lPbGEVRPHz8cJXd/d1vKBK7P2Gah+rLEcbWsz9w/l5qRRe5kRx5HRKjmLrsvbb8rrf1Zw9M77cHlvz76t5JXbnT+CNkpcD1kHxRiEB+Ok+7dNSmqcCY3mc15HRg9jZxmAfG91WeWp0U8qfhy/h4d+PsS6GXXK+9m3n0pBfUo7bvz+EVScu4/vd8Zi37lxN4Kja1rNpcHRFZPlrO9MhTKlOZJ2b8NXpTiusR5bK/fmKygw4FJ+FlOwii/d+2X9Rte2q5Tcbaesd+fVAolONxbx3uvQEvZqHabId3joSa4n1dc2cFuedO9bViH41CVFuvnZPQsEjznx0m/Tc8VrOeXR9mwIC/bxdzlsa6OeNdc+OwOOj2uGdm7vh1UmdHX7muaoe1s8tOVmr0UxN5t/xLw8MxJ0DWuCuAS3x8wO1G4usfXXWbpTjuzRSsIT6Mb1vc9ZF4MLYLo3ctqe0UuIzCrErOh0l5cqkI+Ktkm5LTJrteSGcwcucKI58uOkCDBXW57eQS48ND86OPCLW6e8IkO/fk5dRajAfacZuzz+c0VPyslvOXnVqG7bum3o7e/JLHKdxevNfx6l79XacP/nXCRSb3NOXRiRbXc7R5ZDlNd6ZLStVXFa9tlmnGdfT/XHZsWQMen8HZv50GGM/3Y37fzmKOaujcO5Knux18dLQ6krPfG8n9oGH3WbRIYWD3a6h1fM6T/vs6Xg474j6dHQ7Vd2Ht0l/biHXUfCIM3ImqtdqzqM7B6iTz791/WDMntIVDwxvK7sC8dUOZYNHUi+mzcIC8cntvfHx7b3QNDSw1ntS11Gd+k+quHRlG5RNybmJyOlZbI2/jzc6e3AvI1MPDGvDZLv2fm+1h27LOdYy8kvx4K8RmPbNAUXms9BL+raJX+xFmcGIS1mFyCyQlj/fHosRVxw/qi07lqLIeqyOPOJ3twEAamXl4fU4BypHIijFEx+GXvjnlMVrLH/tYe0t0/bacig+y+Ey5RVGzFoRiU5zNmHaN/uRkl0Ebxtp6/R2AIxesBvHL9meXNso8YJgHjzk3f64TIfLiBKabVle1pya80jicsnXLEeqeJKPN1+w+rqezu5ZK07XzFtVXiFiT0wG/j6ShFu/O6DruX+ahTrXQ9uZgCcPu83iltKnVZj2G7XBz8b8gkrj6Rj3dPRbEE8zppM287W6GwoecUjqBVyNTmjWKkyBGkz+xTyltQ1yvmKpPZVu7t0MjUP8Ja/3hi/24s9DiTJKIp2tCrK1Y3DO1K61/n5weBvZ25t1o+MRZp7Am9GskbaO0EBfb9zej9Gk73ZEp+VjzckrLq9HL+nbAKDT3E0YvWA3hn+0E5uiUhVdN88PB2+sjkJEouP5Lhyx1qmC56AZAH21jjkgNQ3AvPXnsGhPvCpl4Pk4VxPL/fbx9sLMQS0lLfvn4UsorGpgteVgfBaWHUtBWYURkSm5+Hn/RfjauG/q7fS5VliG9zecs/pedmEZ7v7xsKT15BSVKVks2dQ63PgeeaReGq4/D1+SvW53kZFfiu93W78fsB55pMTRVmow4rcDibI+o8dR1ObKnBhRzkOnFxZHXL9W9RhslS3u6+Yq4q3PizZp61TfBHGgdf1gTbbTqK70dk5WBEHAc+M6sC6G7lDwiENSr61qVLCs3csmaDARNOu0CEoMUZe6C77eXlj3zAi8dEMnjJIQ9RZF4M01Z10snY11y9jvXi1C8eqkzmgVHoRxXRrhyTHtZW9vNCdRfmu/1fxp3TXbvjOpHJRgfp4F+nrjhm6NsfjRwQj0UzdI7OwZtumM6wEU81E8rB7KX5jQUfKypQYjZq047dL2OHs2cUhKqiZHeGh4kEutxjEW30RIoPQRqh9ust7TXC49HedqHp56OvTXnLLfKeCNVVG1/v71QKLNOY+kjtRR238Gt5K87ImkHKuvrziegqMSg+ju0LhsThSdT2uoBadGHkn8THGZMml6lTZUxqhCZ+2JybD5HutGVqVOs8SsQln1E57ObmfrVcec6BDEQ1ZvrY+5L+7qre0GOVHgoBMJ0Y4W1QnW13IC3Du0da1BAZO6K9/G6+0l4JPbpU/DwtKkHk1YF0F3KHjEIamVNFWu81Yu7CM6NFBjS442ywU5FWY5AbBGIQF4bnxHPDOWbcRbzsgjQRDw9NgO2DtrLH55YCAa1ZWfxsDVubLUdEsf7eZk4qXR56PbeuKn+wagL8c93ly9NuQWlSO7qHa6EFZf/9Myz/d8Vx+sOBhxJSdgduGq6yk6re0jJ6ebTax7ViupT8swWctnF7o+ekJP6RkzC0oxf/05fLDxvOIjR3jeb3NvrI6y+761c2JdpPWOBLycPY+NaufyOt7feF7ysqyvawPahCu+zt8OJuLzbTF2l/Fi2LrsXCN67c9EpeTin4gkXM4prvW62h14nNW9WShu7K5OA0txWQVmr4rCK8sjbS7DOjis1HVVgLznRNbntxJeW2n/Om8NDx2AtAiYmuLlmdBUb5l1OWfITeXoTmxl4WXFjR5DuBMWJH1KErXV8ffB6qeHYeaglnj5hk6YM6Wb4tv496nhGNPZM+d39wScXboIIL0hSYsK1ru3dNdkO/4a5de1xWYQRc46nNjuwDZsG+15riuo2fu0wuwH/+aevggN1O7mzqrxg2XlcEY/7YJzpn4+cNHiNVaPab4aB0/Nf24Wz6cvTOik6fY4fAZ3SK3TUg+/96K9CYqXgdUx8LGEyVdLyo34ef9F/Lg3AY/9cVzW+kVRRHZhGRbamPNRb8d+am6x44VM2Gpo4qXRo3X9YMS9P1mz7bFucGxY19+pEeiuYrnXzmzbNHXX7uh0TP/uAF5bGYXJX+6tCSAVlhrwo51rYev6QU5sWTnf/acfVj45DBueG6HoelecSMGSo0l2l+FkYKHLBEFAWp70uSz11BnA3fRpGYaxnSszZQiC+pkpeLmHmXr3FvWzcYzsqH7nZB41DvFH1yYhrItRixbHoN7qqEr57A6+RhZ2aRKCD2f0wrPjO6oyv1nPFqGKr1MtPF57eUfBIw5JPZDVuAibpzHTqpF7ZEc+0pm54uZezWoNt+/SpK7DzzDvYcXgoik1YDbHQc9kZ4miiISMQlXWLRWztHVmP7iWx1+LekFMRtptP5dm8Rrr004rlr1c+d/x9LwSlz5vfc4j7ckZsetOlddmYYGylj+VnC17G5kFpfh2VxyWRSTDaFQi6awy7hooPW0ZABxNvIZrEkdeVRhFPL34BPrO3+ZwZAYrco/j4R/txK9Wgvty8TRyT8vR1Tzcx167sYvm22Q655ETm94bk1EzembWitOoqPp3XokBC7dXBoI3nbHfWSrAh+2oJC8vAf1b10PHRo6fa+SQkqqW9fmt1OF2Mjkbm2V0iuPh/PZk/7t/IP55bAg2Pz8K9w5to+q2+KnFXNenZZjqIyaC/aWnOXYnX9zZh+kIWqKdn+4bgPFd1Z/+w1l0n1HOgNb8ZvFREgWPdEyN853VxPJqRL7lUKLaVi/YD7Mnd4W3l4DwYD+8eZPyQ0G1omaPt4YSJ9HLLFBnMuhDCVkWr2ndw49VnZHV+V3tlUmdcX7ejejaVLseV9bPbc+oLfEw8kiub3fFOf1ZURSxOzrd4nUW+/2OzF6bp5JzFC8Dq57LclIVymWoMOKWr/djwZZozFp5Gh9YSfPF8jB/fbK8xvSScmnznOyLzcDGKPsNj6w7owT5yWsIMorAu+vOweDEpOrEU+5illge5s5uet3pKzidkoP0/NojT9ZGVs799fpK+/Mb2prvS2ssvnvWc+IqtctyO62xvp57Om8vAYPb1UdnCR1BXcVR/4damsvsDEQce2xUOwzTYDoIuXgMYLqDns35HoWj9F2mWaj8KS3cQV1/H7x1s37bfeWg4JGOqdH7To+NjWq6Y0BLWcs/Oqod4j+YguNzJ2A4h5UDcywqC3IaNL/b7XxDsi1bz1qORNGaN6OGAB7O70A/b/jJ2H9XH6qsNTx4ynWNdbDQGfEujAp8beVpblLcdGhUR9by1oIgejWsvfR7n9wA14aoVFzJvT467X/7L3KVf/Wh4W0xc5D0EUhSi/7NTsf3Qtbnt7Nz/hSWSgug2cJrw5sjpQbX9ttTG5eZBo+c3PjzS0/hlm8OWLxeParG0Wp9OOmlzqIUvNzTtcbHL060wOs9rELFk89Db1/Mg+EsdW/GdyBFDdwf5wqX7+Pbeym7Qh34+5HB2PrSKPRqEca6KJqg4JGOqZK2juHE0/1ahWm2LXPWbuZNQwPw6Mi2Tq1PzgOm1DRuUnsoy8F7HeaTzdGISctnXQzFsUpbZ47VyATWhx0f3776WKYp1FpucTmWHUux+p4e5g44evGa4utk9XOruV1r94OEzNoBR5aHuZ+PF54fr/zIKympm1if3k1CA/DJbfIfHAUXn0RYp7Vy1o7zlqMkiWMsr+dKn2OlhspRd472yZuX4BGDiwzr85tVvYn19Zxox5dx9hVbRnVSb0oB3uaB0YqnBsOBylSIhC9K16eGy+g8yANXqxdhQb4Y3qEBmoZ6zihNPu9WRBItKrRaVl7fvaWHdhtz4Na+zbHp+ZGoX0daijVXvHWTtPRG/0QkK75tW9fMEWpOYinzmHrsj2PqlMOE1g9pUhsClO6h5Mk9nky5cxDFlB5HHuWXGpz6nNS5YzwJq99bzcuMlAcdV0avaU3J34iHIOmdA1siJEBe+jpXS63Xu9rHmy+49HlebmN9Ne74xTKOosamd11IlzDyiI/HdRZfvV7Pb1dxcnoD4Oda464mduNzTpT/DG6FuirMS9SzeShm9Guh+Hr14P/t3XeYG9XVx/Hf3e4t3rW9ruvee+/GDQwYTDXV9OLQEkIvoSRASKgpQMKbAAkkpNAJBBKSQOi9dzAQGzCmmmqDcbvvH9Ku5V3tSiNN1Xw/z+PHI2nmzpnVaDSaM/dcfobHS9gPnW4f26M2lldVeX7jScYxCR6Os1HkxJMxjzxoM1ujegbXnbX5dm89vKvqKst8WfeonrX67f4TMs53b5qxPPKVLpnQu2OlFo5vcH1duVq28uugQ3Bd305VWc33zDufu7reMJStk4I/eY7Suc3dr+RWZnHDRqvln32z2XNRuADwvAdj/4T+7L3AeJmkzmYffnnFF56tPxtOPmfZ/qWymS8Kn+908k3mB/194pZ1Dsd+CkOyUEoM/u2nIG/+8GLdJ930fMbPbkhyR4EcY4LveRTUesPx+Ya3hnarUUVpfhcwvdKnU5Vu+94M19uN81hKYR1bqFDOo0In5Ifxag+Sw1HSv7OzEvOpLtxttLYc2sXFaKIhJKejSHV4lnXj//nS+/rim3WurjuKd6q7IuDtzuZEypOydc0eT+nXUXd8fwuVl3h3IpvL3/b1D77Sflc9rr2veDTvgeXD8HtsbpZfNg8s+djdFcf1891MGPaBbB13/XM5LffUMvfLoCFa1kegPoYXn8X1G8K/3U5l88M+Qoe1jJxsS9AXl3P19sqvtfyzTTfHXPng/xwtH5bvsb712d0M45ZAex55sO5PVq3NmAgsLQ7Hz/VAytZF4HsMyNVWw8J98bF/52rtNzX7cRzRtoieruRtXsj387iqKC3WnhPj2Quw0bHzcis1vuekXrG8ySMcZ6PYzL5T+qhfFj/Glny4SmPO/reucviDsy1h6ZkQtDButx8nHAvHN6h9Ramn68jlQHvMdc/qoTc/0WP/+1RHXPt0Xj8m0/0d/X67g6pfH8XP9+NLV+a1fPr3OwIbnpRrGbfjb3i+xXNReL8LyR4TnJ2Qv73S3XJrX65x9+YSLzjdJ7OZPehrjV58V2dz00RYPt8lDi90p+upFpfrKzv/6mG9+dEqSYkxHqNqcNfc7950KtAxjzxad6bPbljGPApC0MdzL8p2AY2ikEwocvnkosZhadtCEsfy8XWVpTpu68FBhxGIKFxvOH+h87FKC0mPHMYrKonxORnJoxDq3alS/zxmZtbzn3vnq/rwyzXurLzZl1qYD3qvrPjStbaC/irv2r4i4zyPL/1UH3zh0vucFJVzmNc+2DRI+gdfrtEL7wVblsgvbl8MbHnSGt7Pd6M165yV8slK+Dc7K1+vXa/VaZJL1lq99/k3LZ4P6g6Z7285MJD1Ngrqx9o5O4/UQdP7Zj3/bv/3iH7/0FLX7rSOyvHdkSz24Sj1RMnmE7n0k+ySimG5A2549/aO5s/33YryxZiVq9dq3s/v16/++4bjZcPxbif8YLthvq2ruDjIsnUetZvh9ThfqNgQcPbooBn9Yp2889MZC/w7jji118RenrQbhW8vt/f+Ax2cFxeasL7fXsb1wMlzNaJHcENTBCkkp+Vtito4RQgWyaOQclr/9ooH3Ol91OLLI8THk4X/97C+8uzOan83vHNNeVbzXfxvd+9MbZlK8H673VjD2vW5JxSi8EXeyO1rYmHpeeS03vWCSx/MuQzbmvUtyz1GaBdo1Y1Pvaux5/xHY87+t/7wyLLNXnvsf+n/VsUBveHf23KQvr/lQG0/qpsGdM7cq/bCu15znEBpa8uCuvbUrqxYp243NOv5P1m1Vufc8Yp+//BSV9YfVBLFyVoffnOl/vL4O66uP+hyfW6PeXThXa9lt97sV+spp4eZ5rvpv1/+QO87uFEmwrmjJhf/e4njZcJ0LjNjYL1v6woykVJR6s3P5kyJ38FdazxZby6OnDPA0fy/uf8tHX/Dc3rwjY+1dv1Gfbp6rdZt2Kg7XliR1fJBJ486VpXp0r3HaUSP9po7pHOgsQTFj2PNMVsN0uKZ2ZXtD8L3PLoJKgrfX/tM6eNqeyN6OLvBpJB0q818s3AQsh0yIxelYRm0LwAdfRo/PQyc3CwZdVG6SdFt8f00FxgvxsORwnMxIp016zbq2sfedqWtqNy5etPTy91tMIBBrtz4ERKmiyZREpYxzU6aP8TR/C+v+FIn3Pi848/p+g0b9fbKr1s8H+yA2/m3sX7DRp16y4tau36j1m+0+tHtL+vUm1/QylXfSlKryYeg7p4tKynS8dsM0eX7TtDMQZkvvlx+31t60sUxm4JMJuQyVsW5d77qyrqjMvbPabe+qBVpesqlk1XZuqDrHLmssaxZJrXtvC0565mUt+udlV/rsGufdrR4XWVEtztPYaoMUFbi38/JIJNH80d0V2XZppv7enV0Z+D3bzL8hls0OTxjjuw0poej+c//52u65Zn3tP/vntCIH92l8T/+jwad/k997y/PZrV8GC7SLBjdXXd+f6auPniyb+uM2++csb3qgg6hTb06VnrSbhSuPwzpVqOF4xtcaeuhU+aGppe038pKirT/VHcTcW6ZPbiz5g3r6knbMX27JcWrV89hHiYgwyb8R23vkDzCZiJwDrOZdz9teWHYDXH5oguiiJkbdya4HWdY3+9MFxTyFdQJ/IDOzsdHSAwunt0F5kb/fOmDtM8H+Xa7se5PVq1tcTfudU++q8OTF11b62Hkdt1yL51520uutRXkj/Mgy92s3+hByUeP/N99b2U1Xza78PSBnfKMJlyy/dh2qArHHY6pF9ezYVPORH7+H+e9q3ca685FLURDkMfUdmXF+uVeYzWoS7XG9a7TubuMcqXdTN9R7Rx+pryUz9fpuhxuaAhD8igIYdpsPxLVNqaX46Ky1efsPNKVdnp28CYJFwXXHzZVNR6PK52rkuIiXXnABD1+2lZ65Zxt9bsDJwYdEiJk0eTe6uGwqkxY5PLdc8RsZz2wCwnJI2ym+Qco7HeHuHVyHZWTN7e16Iniw/s9tled9pzobCD5uPrXy+mTH7mK+o8zp5/3B5Z8nPb5KCVRnHjq7c/02eq1evqdz9K+Hoa6/dkmclZ/m33i9PH/rdRJNz3f6uuF1hMlW7lcqAtKurG7crXdyO6utRUG2V64C8HHW5J04jbOepamHhLSjdWWSWR7XOUrJO+334L+XbLNiG76z/GzdetRMzS0mzvl5DJ9RcX0rZYUfNk6SJ2qw3FjQiGKQs8jyZ1j0LT+hXVjj1PjencIOoQ2GWPUtX2FKstKcqqa0JpC/c2dyZZDuwQdgm9+uqs7yeWoOHhG36BDCAzJI2wmLGWtghbEdi+a7M1gnE74sd3GGF24+xg998Ot9bfvzsixDZeDCql0Jdfy0bwjQtT+jE6TX63NHeT+4/WFrzXrN+jjr75N+1ookkdZzpdtr5nln32tRVc+pieXpU+YSVLQOZRBXZz3tHNDUGXrRjY4Hxg320i/Xps5qRj04PJB9YwNyw/0QV1rHA18ns9e2qdTfO9iDse7jbjx+zCzITodaF0VksO5JOlHO44IOoSCFZHckSv744Aumcc8RTi4efwJ07HMT3G6UTrom3q88pfFU7TD6M1vSNxlbA91qQnn2GV+IHmEzbTsiRJMHNlyredRCE7eJvXt6Ps6nQxK7ba6yjKV+1gnv20h39Hz8OGXa3T89c/p8GufanFXd5Cf78Fdvb+g3lq5kyDf7TE9nV9Yd6KtY1kYkkfZyvaC0c//syTjXdtB9zw6c4fhgax3XUBX3arLS3TGgmEqMlJZlncvZlua6IoH/pdxnqDPW9wsS7L62/V66+PsxjwKS/JIkqOBzz/8co3Ouv1lnXvHK/r863WO1hOeLfZfdXlJ0CFs5miPBpQPM79+OxTqhZlsxLVs3YQ+4emlMKZnrc7acbhG5XBjSLYGdnanF1/UxKljXZy2tbneHo2ZFQVhOjf1U7uycJ2jIXtVZcU6YFofTe7XUZfsPa4pEbhocm+dt3B0wNEFKyxXbpEnt47LLcbAicnxPmrl+tywctW3+mTV5j0U/N7s3H8T5hboOyu/1tUPL8t1pZF03PXP6ZZn39O/Xv6wxWtB7uZn5XAno9P95Z3Wem4FuN1n7+Rt1+62equ1NhaSn7J9D7O9YPTmR5kvrG8I+OLTrMGd9f2tBvm+3nUBjnm0eGZ/PXvmNnrqzHlZzZ/NW/Ti8i+yaivo7+92ZcXaZ0p2A9y///k3uu6Jd/Svlz9oUb7m3U+/1vxLHsi6/GCUksOp9rvqcV3zyDJd9dBSvZHF5zlV0O91OhfvMcaX9Wzl0eDWuTp4Rr+gQ/Bd1EsB58L/nkfx+xtL0oge3t5o5IQxRgfN6Ke/H72FJ+1vP6qbese0F+m84dEobeXGuFdRKNE3e3Dnpmk3j3V3Hz/bvcZ84OY4Z+E7S0Nzp8wfGnQIofLyOfN1zs4jVVJcpOKiRMWkZecv0HkLR4VqDMogkDxCm/wYJDMf6z36URHEVmd7TnXhXa9pzbrsxwNpzRUPtryD2+8fhd1q/e32+bMcBuMO2o1PvZvXCfcjb61s9bUgP9/TB9brthzLFmbrqbfTlzILcrtH9azV1QdN8qz9tnqbFEXoG7/xgtGGjVan3/qiBp3+D213yYNa9snqzebL5qMRiotPAfxoDqpsXaPaylK1z7IXTjbJwuWfZVfGMwxnLT/ZZaQuXTQu43y7/+ZRnXrLizr82qd10b82/3769b1v6t1Psx8DKIR5lKysXL0252XDuMk7jO6uTlXejhFSUmRUFpqe2wkdq8p06BbeJpCC6sXZGt96HvmzmlCqruDu7TDx4jP4q0XjXW8zCqb06xiZcYDcOL8I8H6mrP1g+6Ea1r296qvLcrrJMZ1Hf7Bl6L6vM3HzfLIoojc2xcmRcwZo3rBoJLLdNqRb+6BDiJRoHcnQKrd+wHy91r1Bq/1w8zPLXbkwGIabYbIN4fL73tLF/8o/CXLPqx+1eM7vi+odq8pUlUMGP9eTmtueW+Fqe3446aYX9N/XWr5Xrgh4u53eveHWxzTo93uuh4NotvU3CkPPhEWTs+uRsWbdBq3fsFFPLvtUf378Ha3bYPXq+1/qR7e/7HidYbjbMYjeT+sjNFjEspWrM86T7V8w6M93Igbj+IfY5fe9tdnj655819HyYfh8+y0M73VzFaXF+unCUZ6uw6sbp/LVo66dp+17nZxyqradeyUq2xKm/XxA52pV+njn7VFzBvi2rrDoUlMedAitOmh6X1fbq6koiczF5YXjG1xt7w+HTA5l71mvRKEE5dBu7fXPY2bqqTO21oEu7Ou9O1aqe62334th9pNdva224ZUYfSybeHltIszG9qrTpL6bysQeNiv7sttxRPIoxPz+kXT3Kx+2KHkUhYPn3a+2LMeVryC228kFzqseWpr3+tJtYhDb/eJZ2/q/0og59vrnclou0z4V9Mfb6frdSgIEvd1Ovb1ytf7+/Iqsel609ePs23XBJxOG98juDp9v12/U1PP+q72veGyz5+9f8rGWfpI50ZAqDDmUIC72ThtQ7/s609ltfOZBY19670vXkl1hqa/u980YYdluP4X1wpvX78XQbuEcHyTo8eX8VlVeoj0mxGdQbEkqLS7Sj3ce6cuYpRfvMUY9O8SvnFlFaXjL4rh9k8LJESqXdNr2w1xtryQiSTPJpZ5H8fp6kCRNHxCNnmXNubVn7jLW3YSrX54+Y+ugQ/Bdrr9Zvjc3+uNdXnvoFP1irzG66oCJ+sF20flOCgLJoxA7dIt+vv5AXPzHp3xbl5tue+69vNsIw80wfscQlmsuRUVGy85f4GiZkITum6/WrNdz737ueLlMJ+phvfDmtaht9+yL7tPRf31W213yoN786Ks2520rwdavc5XboXmq+ZhsjS7+t7Oel0GPeSRJGwIoIbf18HCMiXJelr0w9rnycd3bRi/LO194P6t2wvLp9vswE5btbjQrZewAr4Rtmxt5HVdYE4VhONb67YLdvB88OWwlxHeb0FMvn+39jV+7xywxFzfDu7fXruOic3G5vrpcfVwcmylKv0XcOAYdPjt6d/TvOTG/Y1CU3mO3Xbj7aFWVR7PsaEePSw+H0bYjcvvNOLIh+mXfKkqLteu4npo3vGusP7PZIHkUYj3q2umuY2cFHYavdhjdPegQJAWTWGnfzt8v2LBefEB6u/3fI3plxZeOlgl9zyOHAbh1WSpCN/tt5qs163X+P9tOnLT1lmc7/ozXJvftmNfyqUmEbAYsD0PZuuJi/3e6sJQxy7bW+xPLPtXB1zypPz32dovXbnzqXd35YnbJo8APbElx/4pt8LiEmRTev7HXSZTjth7safu5ikJZIrdFpeSW20qKuYTgla7tw1u2zk23f2+GqiN2cfnKAya61lbcjhyDu4azx2xbzt4pv7JrYT1H8dqOY3poz4m9gg4jK82TJjuP7SFJmj+iWxDhBKZTdblOnj/E8TUSki3xwplfgSiUz+2ZOwzXNg7vln5i6WceReOvrYZ1VV2lfxd30x3so/IF4HacUdjqDRutzr3zFUfLZOp5FPzF5WDWH5HdPK1MZTqjcO1u5qD8y6nd9tx7euvjVS5E44/9p/bJaTlrra5+eKkWXv6wfnjbS/pm7QZJ0RrPSJLuP2lO1vOe8beXWjx30k0vZL18WO7SD0schSysf+MVn3/jafuzBoejJGVzcStb16ivi70R0grnbq7OIR6bJ8p+uMOIoEPwRRQTkG4mQKL0WyRKsbqpXVlxXmO8RfbPlmfgly0a504cPjhp2yHqkLz+Vl9dpmPnJW7OOXHbcN6k46Wj5gzUK+fM12IHQ6dEdh9HTqL3rQ1f+X0hsmv7Cl1xwET1r8++vFJrpY3yEcQFidLiIl17yBTf1pd2zCPf1o7J/Zz3vnjkrZWO5s/UKyPo3mdOc1epx6PPv16r/X/3uAaf/k8t/sOT+mrNuqbXVq76Vnv+9tE2WircPT0Kd367sdsdc91z2upn9+ul9zL3xgvDn6Rnh0p1yqEMwlNvf6az//6Knnnnc/3x0bf1+4cT4939t43ybmHUp5N/JRMDz4knxfVii59GNtQGHUJa6z0uU1leEs4xUSKW03bNeQu9LV0X1mPJhbt7X7Ivbn6666iCKAOEzKJyw6ZUyL+aMstn2yP0FsfWwC41+vdxs/WnQ6formNnqV/yGuTALtHrKeeGitJilZdmnyKI0nEM+SN5FAH11cHV3YzChUg3hKGskSSN6lmr3h39GRy2KM2nPyrH/4iE2aZzdxmpCX06aGCXasfLrl2/UTc+9a5ufnp5mz0QMu3WQfc86tupSl0c3bm6aYNuenq5HnzjE63dsFF3v/qR7kgpZfaLu5foiaWfttpKVPbzXEThxm+/Y8ymtJ0f7j5+tuNlzmzWC+eifyXKFh53/XNuhFSQwvJDxu/kfFi22y/FRUbHbT0o6DDSWrcxnlmUuPxmaG5q//xKsUbV7EHej2sWNl6+1zuM7q59pvQO/bF8tg/j2SFc8t0nF4RkWAK/BX2TJrLTuaZcWwyqV301vWklqTjdRcJWsIfHC8mjCMgmmfCnx97R7c+vcH3dUbgQ6YkAj4R+XdBP17sqyBIwTgaldPtcLIgfaoO71ujmI6fr7uNn6+/f2yLr5ZZ8+JV2uOxBnXTTCzrhxud1TBsXkTMnj7JerSeKiozOWzgq6/nn//LBprI459756mav/eCWFyVJ973+kf702DttthP1E513Pv261deeWtZ60iwsNvj8xRKW65kdqsocJ4vf/2JN2udXJ8vXoaWwfL5ziSPXG1m6ta8I9EajdMo8Hufr5iOnq2cHf262cWrd+pAcdHw2rHs879T1+hwyLN9hzcVxvKfTth+m/vVVqikv0eCuzm/+asuZOwx3tT2v/GTX/MaBgVQSsc9OvtF2a1/hShxBqKvM/dxqi4HhLDGbSVhLAsMfxQ7OaRzkmVAAeLsjYPcJ2Q0498PbXtI6l2tGhKVHTia3Prtcr76fuXxRa8K0lX6dT6ZbT5A3yCwY3SPreQvtpKZdWfaH4m1+8YCWfLhprJc7X3hfB/z+CR16zZN66b0vNps3013AYbi7cath2Y9xtn6j1VNvtz3G2Y9ufzljO2HY7nz85v63Wn3tqoeW+hhJbvz+XgnT8X3XcQ15t/HFN+syzxRx+//ucV1y9xs5ndOE5eOdSxyPvrVSP7jlxRY9zjL55d5jQ3dcWzyzv2dtbzeym8b2qvOs/XxtiMi5s9u2Hh6vAaYRP6N71um/J87Ri2dvq38fN1uv/Xi+K+1etPtodY3IBfaeHSpVXV6SVxuX7D3WnWAi6vQFw4IOwZF8Ty+i/JV4wW65l+ec5+A3LqKjV8d2QYfgKSfXIgvtmhzaRvIoArK92PT51+v08orcEiitDe4b1Jf9LIdd4o+7/nnteNlDemDJx47X9ew7n7W4uzvIw2C2PY/+8eL7mWdqS8guNFWXh7OGvz/yey8eWPKx7nntIx109ROb9erI9PF1cmdJWPz63jfbfP3tla33ymkUhq3eeWz2ydLm1q6PdkmkPSZmd0NEIXLjI7f9JQ/m30jIPfjGJ/rF3Ut009PL9e16Z72swpJEySWOfa56XH994h1d+9jbjpYb37uD43V5rVfHSh03z5sBh8N+IWrvSfE8xhUXGR2zVThLCQJeqCgtzvt7vbKsWDs4uIEuDPLZ5At2G6WdxkRre5Gf3SdkX10kbKYN6JTTOMUHTe8by96ZhW6nMT2yKt3ZviK/BHuQHO237OKxQvIoAtqVZX9RPZc7upd/9rW2/cUD6dsL6J7to+YOcLzM+o1Wp9z8guPlmpe/koK9+JRtfdyj/vyM/vPKhzmt49X3v9Tz737e4vkgj/9Odl3Xy9a521xgPlm1Vg+9+YmkxLHg3y9/0Ob8QY95lIv7l3ys+3NIEqcKw7XlY7YapOHd2+dUuqLxPXaia/vw1HHu1bFSR81xfozPVZguNDutf57uO/29Vm72CLtcfkj94JYX9av/tp0wbi4Mn2+/hbUEzmGzvOl9FJZxzFrTo66w70ptS2270qBDCERUyxMhf3tP6p3X8r/ed7yj3/pRNLZXnbYZ3lV/++4M7TUp/OM6tcWN8oJR2/p83q+9J/XS8B7tXYzGX8VFRtcfNlVbD3fWiyjKn+kIfzwDk5pg7N2xMtS94zPpWJV9qUZ2lXgheVRgcvk5fdk9b+qrb9enfS2oMY+61FTkdFdSa+NDtOXpDGWw/Obkgv6x1z2b0zouufuNtM8HebIQ7ktB0bE6+Vn++X+W6Pgbnm9z3qgO5Hng759I+/z5/3wtq+XDsN39O1frH8fM1Js/3d6X9d1w+DRf1pOtk+cP9W1dYbrQHNJr/L44esvceiRc5jR5lNNaoi2sd7eG4FCLAuFkfMQgnLrdULUrje7FQuQu3+Pc3CFd3AnETw63+dajpuuKAyZG+oJqo0Nm9M27jfCclbrvu3MHaPtR3XTK/KF64vStdH4eZd/CwhijK/afoHtOmJ31MlGs7oHMRjXUpn3+8n3Ha98pvbXHhJ768+IpkU6Q7zK2QVUpyc+thrb+HTUuhJUP4B2SRwUmlzusr3/q3VZfyzRmipeCrCcahbJ1Uu6Dpt/Vao+U6H7RIaHxHczmgmuhDXLY1lhAYXba9t4nUvp0qvJ8HaEVol/pTmtDR/nHR3NdfOr9tjHaVR2RhZmDnJU2RrTNHdJZiybn17vDayMbavWPY2Z603iIvsPgrlzKYYWBkzOTwV2rC+pcppC2xYm5Q7L73l00ubcu33eCjpwzQF1qojGOVzaMMRrQuTrrcc5Cek9PVtKFfuUBE7XN8K5aNLmXnjh9K99jCot9p6Y/F6mvLtdPdh2li/YYo14dK32Oyl3tyop1zSGTNWtwZ+08tofO2y39zTsnzx8S297mcVVglw5x7PXPujoYeZjK/cSF014RNz+9XHe99IEr73ugPY8chH/1w8tc3c+D5962OHkP43pXVNg2+zseDiyPcF13c7rvFdZxDnHj1bE2yuMnwLmh3aNR8qhffYxv0oixfA5zo1u5iz3snCRQ4lzGszVRPFb8cq9xOnSLfior2XT5sENlywvHhZ5cqygtVpeaLG6GKrC/w5hetbrigIk6b+Fodamp0P0nzQk6pEBUlkV3LCMnJvXtqD8eMlmX7D2u1STwUXMG+hwVgkbyqMC8++k3OueOV1o8b63Vxhxq0MX1wlWQ3/fbjHBWU/eEG5/XEX96Ou377lRUTnNufma57n39I0fLtLUvF9b5XfYbE9ZSR15z2vvDa4X+QwubZPujY826Dfq/+97Sl2vSl5SNIr9OJ2rTXMwISk2EB8x1g1fH2grKgwEIiXxO4Q5yoQRaENZtyL6Lb0wvJbSqf+cqzR4cvd6ztZWlOnOH4Vpy7nZ68OS5uvP7W+gXe41tMV9pceH/piktjuEl1Gaf49YqWuw5kZt74iBd4hiFL4ZHvsJ39cPLNnv81serNO/n96v/af/QiTc+r/VOTvhcjs2J3h53+Xxh+ee67bn3PF1HLg6Y1lfd2jvv5t38fW9NLklEP4zp5ezuu2Ove87R/CHdbNcZk33Sl55HiIMw3QSx09jsxvIbeuZduuCu7Mbwigo/xp6qr/anNB6yw7E2frz4lMf0PpdIOGL2gKBDCFw+59Lda6PZK+frHMumx9VZOw7X+QtH6dTthuqWI6dH/qaxXh0rNaJHbdrhDUoLrSZ6DGW7fw7o3DKBtPPYBrfDQQg1dIjmdxfyw9E9IspLcn+rLr/3Lb318WpJ0k1PL9fjSz9tei3TnUNBJhp2Gefdl8/fn1+hnX/9sI5pJQERZM+E6vIS3XXsTM0Y2MnxstlcJL3qof+1+lqQJ7PlJc7uJHZ6R/6adfH4oXP4tU/rkGuezGpepyUSC0VMNzu2QpQ7UnV5iX6z34SgwwiEH+/D7w+a6P1KkDUvDrUNESmBtGBU96BDKBhh6y3st9KS8G7/SdsO8WXcxjArj2FPyO/OJWnoRF1lmfae3FtHzB6gusqyoMNxzboNLU/sSmLQ8+ioLPZ/r2+CDoMTthnS4rnCf/fjeU5yyvzNv+fP3mlkQJEgSCSPImJCnw6Ol3lh+ef63UNLdfMzyzd7/id3vto0fdz1z7XZRpC9NcpLij2rp3rMdc+G6oJic3WVZdp1nPNuvxuyeMOue/LdVl8L+qvwB9s5+wH663vf1NSf3qO9r3hUyz/7us15b3m29V5mhZZMuPf1j7OaLyw3h124+2hf1xe3k75cvj8KSdgO9fNHdtM1B08KOgzfTezj/cDgo0I2fkS8jjT+uMjn74tc7TmplyftRvGC1EHT++a1fKGdo2UyqEt10/RWQ7uEeoyF4iKjw2bFO5FQlkcJq+KIdqvbb2of9ajNrkpG2M7BgrDdqG5Bh+CJdNcd4lDSbddxDRrXu67V1ztWlWmnMdlVGgijdN+56T7H0/o7v9EZ0XTwjL76zsx+mta/k85bOErj29j/UbgK/+heIE7bfpij+Z9a9qkWXv6IfpxmHJzGL4TV367XHS+832Y76boj+6lPpyotO3+B6+1myrFE9Yfq+iySR/9L9kJLJ+jt3s3hINgX/et1ffDlGj32v0916T1vtDrfQ298ojP/9lKrrzuo5OiJ3h2DGTg1LD9at/f5Du2QbLZvthzaJegQAhXGGwXmDOmi/9t3fNBh+Kp3J28veh8wrU/oSsEM6VYTdAiBKi4yrvYU2n9qH00fWO9ae17yqh782TuN8KRdL/1wh+F5LR+uT7X3rjtsqo6dN0gnbTtEv9onGt8Tly4aF3QIgYlDT4vmute20z+Omam/fGeKKkrjdzmprVONrdKcczutrhEV6SrYxCF5VFlWohsOn6a7j5+V9vXbvjtDZXlUDQpaut07699SMTgcxvF3dUVpsU5fMFx/PWyqFk3uHbrfW/BHdI9qMTPS4d20Z/ztpYyJhNVrC2cgbrdF9XCYKdn34Btt90gJ+nsgn/EqbnhqeauvnXPHy20u+/Tbn+W8XjeUlRTpp7uO8n29YRnzyO84QrLZm9kly7FwchHG7QXvi9tq24Vv8NYf7Ri9C/1uMsborJ1GqKbcnZ4T84Z3daWdKKupCG8vFCl9+eSiPO/YiNtFio5VZTp23mB9d+5AtSuLxkXnKN9ln69ql45vUVNXWabpA+q1x4S2e1mGadxJt/x8zzFNPc5mDqpv6hE6b1hX/WLvsapK+dzOGtw5kBj9kG6M5rDcmOi10uIiDexSo0d/sOVmz990xDT1imAP4VTp3sN0X8Ppvtvz6YkZFbMHd9bEZFWPIiNdvMeYgCMC/BHPs52Iqigt0pp12XWReO2Dr1p9rengn8W5XAGe72Ul3x+6QWkrYfj512t18NVtj4VTCOW8Xlz+hR5fulJT+3fSl9+s0y/veUNLPlzV5jKZxv7ywz5Tequk2Ojkm17wbZ1huSDjdxjfrg/+/W5u6+Hd9LfnVnjSdiF8rvNhQ1o0JSyfPz/NG9ZVd7/6oSdth/GvObKhVr/Zb4KO+NPTnq0j35JgXtt6eFc996NtJEkDTvtHwNEgiuJ2qIzjd0OU7TGxl85NKQkfNz0zDJw+oHN1m69H0a7jemqLgZ21dsNGNdS109r1G/XNug1qX1EiY4yuOnCSfnn3EtVVlurMPHtehtnkfh3Vt1Ollq1MlI5fMDp+Y/11r22npedtr09Xr1VlWUlkEv5tGd2zTvXV5fpk1beSpH71VepS0/IG39p2pRrStUavf5i47lhfXa5xvQu/VHpRkdFfD5uqJ5Z+qs415RrcNd5VBhAfJI8ixK0LgE7aCbpsXVCietfMhjQDVzb6/cPLsiprF2XPvPOZ9vzNo463Myxvd5XPde3Dsp/7fZ2kKIQXZrwMKYSbK0k6fFZ//faB/3m+nrB+jYX0bfHUXpN6eZY8Cqv5I7tpfO86PfPO5663fcaCYTow5MkjadN3zTNnbq3xP/5PwNHAS3FPfPTvXNVmeWgUntp2pTpjwbDYJpD2ndpHv7h7SdobXIuLjI6aW5hjYnVOuZheVlK0WZmyaQM6adqAaUGE5StjjG48YrqueWSpqspLdMiMfkGHFAhjjDrlUT0lbIqLjC7Ze6x+fMcrKjJGP95lRKvf7T/fa4zOvv0Vrd2wUT/Ybmhori244fDZ/fXb+zf9Tk0dm7u0uEgzIlJGGXALyaMIcev32IvvfaHFf3gyqy61hZhr+Nuz72WcJwwXlyfmMMh9W0mTxrtH2hL1ZOEPb8tcrjGdsPQ08zuMsJSt87tnzNCYjUMSjne5pf2m9vEleRRWXn7PpP7ACRNPx0YIyfHML7MHd9bimf2DDsORjlVlQYcQedE+S8tNGM7Js3XuLiO1z5WPBx0GfLZ4Zn+9/sFXuvHp1ktoF6rq8hLddMR0Xf3wMnWrLdcRswfoj4++raWfrNaiyb3VpaZlaTMUjs415Tpp23CecyJ3MwbW665j04/plGpEj1rdcERhJkoPnt5PDy75RK+8/6Um9OmgvSa1XaITKHQkjyLEzZ9Od7/6UVbzRT2ZkGr1t+t13+sf69jrn8s4bxjumuhbX+V4mQ15ZvvyXT5oL733ZU7LheXChN9hmMIvS9zClH4dQ3lndPuK8I3X4rVKn0o7hLXevpe74eCQJkhD+lZ4zovNPmGbwR60CuTHi+Nt+L6xWzd9QL2OmjNAl9/3VtCh+K6uslSff73OlbbG965zpR0/Hb/N4Fgmj6REidaf7blp3I/vzh0YYDQAkL9utRW6/XsztPrbDaquKAnF9UEgSDG8dBhdYbzgGRUffblGCy59UN/9yzNZzR+WHhlOrd+Y31guUU8e5So877e/cYQlaebnydgV+0/0bV1OTBvQKW09aTeE5G1uoZBKPOTC01KF3jWdl0K6ISVopTEYlLi5sCaC0/GqR22E/gSuCet3WGtOnh/Pu/BPcWm7O1aV6YwIjhPTvbbtsX8AANFSUlyk2spSEkeASB5FShA/njZGMJmw4vNvWjz3p8ffaRrMMRtR+6HayFrpnZVfa8GlD2rQ6f/QD255sSkh9NGXmcvWbYjjVQmF5/32f+wff9fXGj9PyMq9LJuVh+Iio2sPneJJ236XBXTiV/uM83wdYT2qeXlDSFgSw82F9b2IotLicL7HmeSza0Zp/7EeRetXj80w4ea5aBjevX3ebYxsaK/7Tpqj8TEYdB0AACAqwnkVDWkF8dMpSj/UGx3w+yda3J166T1vBBSN/377wFt6ecWXWrfB6q9PvKPHl67U2vUbsxqkPK49j8JyYcLvKMKcVPBKSN7qtIZ0q9G0/p1cbzfM27z9yO6e9bhqNMyFC1pe8PJtGdo9nGXrSjxMFId4N/ekt0h1eTRLXZYW8dMjW3OGdNaYnrVNj3t3rNSIHuE8njXyYlyrMH+HteZne4zJPFOBceMw16djVaTL+B45Z0DQIQAAALiOX3AREsQF7ih2RHnzo1V646NVebUR1SSKtdKfH39ns+fO/NtLGnzGP7Nafv2GaG53vkpD0gXH7894uxDdwXz69sN8WU/YE2ZRvEiWj6Iioz8eOtmz3meda8q1cHyDJ23ny8vPe1gHqJ7qQXI0rmoqGLY0zNz4rvnprqN0+X4TtOu4Bu0wuruuOXhSaG52ac2C0d032ze3Ht5VknTqdrmXNAv793Y6O4/toYY652XMOnt8M4WX3CgrGfLdO6PRDbWZZwIAAIgYkkcREkjZuihmjySt+nZ9XsvnO3aQWxaM6u5o/ntf/6jFc299vDrr5eN6Q3BYcoUR/82clz0n9lL/+irP1xOSPGGrthhU73qbg7qGsxdKo6Hd2uumI6a53u5J2w7RnUdvofKS8CRJU3k1fsuOY3p40q4bvBynJ+oXHZ2qKo9f8qiqLD7bfOHuo9Wjrp0a6trpF3uN1a/2Ga/+nauDDiuj8pJi/fU7U7XdyG5aNLmXLthttCRpv6l9cm4zip/tkuIiHbf1YMfLjetV534wPnHjG22Lge6fA/kpivsqAABAJjG9VIxsRWlw4lSp5+6frl7rePmw9MD54Y7OBoz90e0v57W+od3CXQ7FK2FJksb5R2dtZal+d9Akz9cT9ru2952S+wW21szyICHlNi/G6Pnu3IHq0j6cPXAk7447x84b5Em7yJ3b7/TFMSyJ1a19hSb2ifY4KD9yeE4XVSMbavV/+03QeQtHN5Wxq84j2Rnub213nblDdPeRfL/S+nSq1K4h7SmcvTjtrQAAIC5IHkVIEKejUyJaYib1QuRVD/7P8fI1Iam33dXnC59hHRvEa+FIHcU7eeSXsP+Jq1wuJdi9tiL0CTMpnvv+Bg86uM4f0U0DItA7IXZcThTWtQvHOYqfrjlkkorC3nU0Rfe6ludvu03omfXy0dlS70X1+8Fp2FceMFG9OlZ6Eosfutfm/pvl1O2G6u8h7imcrQgdogAAALJG8ihCgrgAOH1A9JNHf39hhePlh4VosPG43UV+6Bb9fF9nWHrYlRVH+0dzvkp8+NUd9otQbh/ncxlzIQhRHNMiX16MrVdcHL+/YxyF/Tjmtj8cMjlyPaPrq8u167hNvSj2mthL7R3cmBSFpL9fovr9kO1buPS87bXs/AVN40NFVY88zjcWjm9w9PkIKz63AACgEMWneHgBGNqtRo+8tdLXdUb1JDjfsMO03WMjXP88F2csGKbfPbTU13WGpWzdlP4dgw4hUD07tFPfTpVatvJrz9YRps92Om5HV1EajYRkyN8WT3iRK43hn7FJmC8wu/0N40WZx7A6ZEY/zYjojUw/22OMdhjdXcZIc4d0CTqcyOqaR4+WKAj7eYkfwnz89srUmJ/zAwCA6KDnUYScsSC6dbCDFJK8QM5mD+6snh2i0XvADUH8iPagA0BOvBxMPgqMMbp00ThN7NNBQ7uFp/efn9ze/adF5KJrp+qyoEPwXVlJvD/vyENMrrNeffAk/XDH4SqJ6HdjUZHRVsO6asuhXUkQ5KhjVZm2G9kt6DDgsUIp95ZpMzpUJnpXVZeX8LseAABEhme/xowx7Y0xextjfmaMud8Y86Yx5gtjzFpjzEfGmPuMMScbY7K6smWMmW+MucUYs9wY823y/1uMMfMdxFRpjDnJGPOEMeZTY8wqY8yrxpiLjTG9c99afwzv0V7f3ypeJcxyZYy0fsNG/ePF97X8s2+CDicvxhjdcuT0oMMoaGFKMM4cVB90CIEa3bNONx05XXcdO8v1trcaGv47v92+wDhnSGdX2/NK99r4JMgbkTxyV5yuzcdlU2cOjO/3YVze47bsPamXbjpiWuxvrImDQkmutrUZZSVF+texs3TlARN117EzNbKh1r/AAAAA8uBl2brJkv7aymudJc1O/jvJGLOftfZf6WY0ibPJ30g6rNlLDZJ2lbSrMeYKSUfYNgYuMcYMkHSnpCHNXhqa/LfYGLOPtfYfbW9WsI7ferDue/0jvbD8i6BDCbUFlz6kgV2q9eZHq4IOxRUVZdEoPRVVYRnzCN5ZMLq7zls4KugwfDeiBxcnwmpMz7qgQ4BP3P6KGRWTi45R7XGE9OYO6ax7X/846/nP3220h9F4r0DyIb4olD9VWyVFL9p9tLq0r9DWwwu7DCMAACg8Xv8qe1fSHyUdI2mhpGmSZkjaS9KNkjZIqpd0uzGmtV8I52pT4uhZSYuUSEwtSj5W8vUftxaEMaZa0h3alDi6UtJWkqZLOl3SKkm1km5sI47Q+Mt3puqMBcOCDiP0CiVxJMVrfIMgjO/TIegQmviVx6qKWULytO2HFcRgzCgcVeUl+tGOlK2Bc52qy4MOAR4rxNO+A6b1zXrek+c3v9cPhaxgfue0shnzhnXRNsMpvwgAAKLJy55H91pr2yoFd4MxZhdJt0oqk/QjSbulzmCMGSjp5OTDpyTNstY21iB70hhzu6T7JU2UdIox5mpr7Vtp1nWiEr2LJOlka+1FKa89aoy5V9IDkiol/VLSltltYjCqy0u0eGZ//f35FXqeHkixUCA/qUJr62Fdgw6hiXV9aPX0frbnWF/WExZx/AxtP4oLFWF38Ix+2mNiL438UdrO1ygQ5ZQoBFRX2foNHCVFRhftMVpPLP1UA7vU6KDpff0LzCP1WSR5J4bo5qVAFfBJ2jNnbq0OlaUFU5oPAADEj2e/Zq21G7KY52+SXks+TDfAxXHalOA6OiVx1Lj815KOTj4skXRs8waMMaVK9HySpFcl/SxNHI9K+l3y4VxjzIRMsYcCJ6GxwVvtraIQjdTbu2OVL+vZMgLj/yA/XdtTGiUKqsu9vI8nPsJzFG/p9Ga9xYvz+M5ZOL4h33AQAYV43rexjXtjJvbtoF3H9dR5C0fr0C365fUZCYsZA+rVUBe/8fxyUQBvt6T030MlxYbEEQAAiLQw3Aq5Ovn/Zle5kmMd7Zx8+Jq19rF0Cyeffz35cBfT8uxsjqS65PQfrLUbW4njmpTphRmjDgFOQ+OjYMo5ZOmI2QOCDiEw399qoOcXjZadv0BlMbsTPmYfIUnZ3fVcqOJWlrFRFC5Qnbrd0MwzFZixver0vbkDVV9drqn9O+qoObl9x/WordDRWw5yOTrAL61nj46cM9DHOPxRVGR03WFTtc+U1gtx7DGxp48RhVcUvruykW47GFYVAABEXaC3uxpjhkkam3z4WrOX+0lqvL3y/gxN3a/EeEY9JfWVtDTltZnN5mvNU0oksqokbZFhfQA8dMr8IRrRo70efvMT3f78Cn29NmNHxpzddMQ0z9rORffadvrzoVP058ff0Z0vvh90OAXDxDDdvt/UPkGHEJg/LZ4SdAiB2D8C7/kRswdoePf2evG9L3TRv17PvECWwnzt0RijE7cdohO3TYzjcv2T7zhu45/HzFRDh3aM3RYTZcWFlwDvVpu+F852I7tpi4H1Pkfjj14dK/XTXUfpL4+3/MyPbGivncfSk1Aq7J5HPlWjBgAA8Izvt54bYyqNMYOMMcdLuldS46+jS5rNmlrjo3liqbnU14c1ey2rdqy16yU1jpfUvI2MjDE92/onyfXBJ7y+UBK3nglhFuaLYl4wxmjHMT10/m6j9eTp8zxd18S+HT1tPxfTB9br1/uODzqMghKlz9DuE9y5E7m2XbwuMo/uWaszdxiul87eVuN6x3MciUl9o7HdswZ31p4TewUdRmAGda1xvMyw7u1JHMVEdXmJthpWeKVlG+rabVYyd/tR3fT0GfN0+b7jC6JMnVM3HzldFaWFlyTMRaHc4DOx2XdwXWWpaiooTQsAAKLNl+yAMeYgY4w1xlglevcsUWLsocZR6i+W9Odmi6VeVVieYRXvtrJc6uPV1trPs2ynszHGab2fdzP8e9Jhexl5fZodptP4C3Yb5du6Fk0O3wWtuJWtS1XFeCCuGta9fdAhBCJKn6DimH7ez9l5RF7L7za+pw7dol+sxxCKUukfty8WR2nbx/Wq08iGeB6Lh3V3njiLi3G96zS4a7Uu2XtswSYVfrPfBP1sjzG6ZO+xumzReHWqLo/UZ9ctPWorVF5SmO9xLgplF6gsK9E5O49QabFRu9JinbvLyFCNqwoAAJCLoK+wPCfpCGvt42leS/11uSpDO6tTpqtbaSdTG+na+TaLZQLjdUIhTCfyC0b30H9e+Uh3v/qh5+s6KoR110P0ViDifhDD8UakaFUNWbextaH5CtsB0/qqX32VXnzvC114l/NyZpaBBSIlzu+XMUbXHjJF1zyyTJfc80bQ4fjq7J1HapdfPxx0GKF061Ezgg7Bc2UlRdrNpd61CJ89J/bUDU9tuuezrKRIa9dnPqcJ02/OfB0wra/2mtRLRoYqHgAAoCD4dUbzN0mjkv8mS1ok6VYlxjv6szFmhzTLVKRMr83QfmqSp3lB7cZ2MrWRqZ1MemX4N8lhexlVx6gbfHV5ia46cKLO3GG4p+sZ3r29enWs9HQdufDjrsyxveo8X0fYhG28Iz/MGtw56BCQgVtl66Jo5qDOOY/VFOVUhBvjfRwwLfzjHaXaGOU3zAUdqsp03NaDgw7Dd2N71emMBY6rQxeMMTE810JLhXr4O3374dpzYk/NGNhJVx80SU+ell3p6UKrsFBeUkziCAAAFAxfzmqstZ9ba19K/nvSWnudtXahpAMk9Zd0mzHmoGaLrUmZLsuwitQSc9+00k6mNjK10yZr7fK2/kn6wEl72Th0i35uN7mZMNaf9vpO5dO2D+cFDT8qHpy1U34lo6Lm38fNCuV4R146a0dvk69hFr6jWeum9uukrYYW3ngX2cr1IlKUO7L8YPuhKi12tt1PnzFPl+w9VuN712nh+AYdH7FERJzLC8bd4pn9tX8ySVxSZHThbqMDjsg/P9xhmDpWZfOTBIVsSr/CPP+srSzVhbuP0Z8XT9XcoV1UW1mqi/cYk3G5KJ2jAQAAxE2gt8RYa6+VdGMyjl8ZY1JHmfwqZbp5KbrmqlKmm5ena2wnUxuZ2gmdLQbWa7uR3Txr/xd7ZT7Z91vPDt72Cpo2oJOn7efKj55HoxpqPV9HmAzOYcDyqItaqZjFHifIw6qoyOiqAyfqnhNm6+kz5mnfKb0dt3HStkM8iMwfcRweYESPWt161AydMn+o9pua3fvdqbpcO49t0C1HzdDP9xyruspoXZBuV1asPSdG65gE9/x4l5G698Q5evCUudpzUvjGmvTKhD4ddf9Jc2L7/RZXF+2+KUFaVlKkE7aJ7ne0UzuM7q6p/RPJss415TpyzoAW8xRazyMAAIBCEob+1Lcl/6+StF3K88tTpjNdXUj91flus9ca26kyxtRl2c7H1tpQj3ckJRIKl+873pO2dxzTQ3OGhO/O962Hd1X32orMM+Yozj9d4rztceH2APVe6985m5x/YTLGaEDnanWqLtdGh11qRja0zynhFBa5XkRaOL7B5Uj8NbKhVkfOGaBJMeoRef7C0RrTM143LmCTfvVV6l6bqBLdvCzx9+aGb/xJt9RUlKpHndPq2IiyPSb20uX7jtf3txyo2747I5Q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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 29;\n var nbb_unformatted_code = \"from numalogic.synthetic import SyntheticTSGenerator\\nfrom numalogic.synthetic import AnomalyGenerator\\n\\nts_generator = SyntheticTSGenerator(\\n seq_len=12000,\\n num_series=1,\\n freq=\\\"T\\\",\\n primary_period=480,\\n secondary_period=3000,\\n seasonal_ts_prob=1.0,\\n baseline_range=(200.0, 350.0),\\n slope_range=(-0.001, 0.01),\\n amplitude_range=(10, 75),\\n cosine_ratio_range=(0.5, 0.9),\\n noise_range=(10, 15),\\n)\\nts_df = ts_generator.gen_tseries()\\nts_df.plot()\";\n var nbb_formatted_code = \"from numalogic.synthetic import SyntheticTSGenerator\\nfrom numalogic.synthetic import AnomalyGenerator\\n\\nts_generator = SyntheticTSGenerator(\\n seq_len=12000,\\n num_series=1,\\n freq=\\\"T\\\",\\n primary_period=480,\\n secondary_period=3000,\\n seasonal_ts_prob=1.0,\\n baseline_range=(200.0, 350.0),\\n slope_range=(-0.001, 0.01),\\n amplitude_range=(10, 75),\\n cosine_ratio_range=(0.5, 0.9),\\n noise_range=(10, 15),\\n)\\nts_df = ts_generator.gen_tseries()\\nts_df.plot()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.synthetic import SyntheticTSGenerator\n", "from numalogic.synthetic import AnomalyGenerator\n", @@ -123,7 +68,8 @@ ")\n", "ts_df = ts_generator.gen_tseries()\n", "ts_df.plot()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -136,33 +82,12 @@ "cell_type": "code", "execution_count": 30, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((7600, 1), (2000, 1), (2400, 1))" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 30;\n var nbb_unformatted_code = \"train_df, test_df = ts_generator.train_test_split(ts_df, test_size=2400)\\ntrain_df, val_df = ts_generator.train_test_split(train_df, test_size=2000)\\ntrain_df.shape, val_df.shape, test_df.shape\";\n var nbb_formatted_code = \"train_df, test_df = ts_generator.train_test_split(ts_df, test_size=2400)\\ntrain_df, val_df = ts_generator.train_test_split(train_df, test_size=2000)\\ntrain_df.shape, val_df.shape, test_df.shape\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "train_df, test_df = ts_generator.train_test_split(ts_df, test_size=2400)\n", "train_df, val_df = ts_generator.train_test_split(train_df, test_size=2000)\n", "train_df.shape, val_df.shape, test_df.shape" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -176,69 +101,25 @@ "cell_type": "code", "execution_count": 31, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 31;\n var nbb_unformatted_code = \"anomaly_generator = AnomalyGenerator(\\n train_df, anomaly_type=\\\"contextual\\\", anomaly_ratio=0.2\\n)\\noutlier_test_df = anomaly_generator.inject_anomalies(test_df, cols=[\\\"s1\\\"], impact=0.5)\";\n var nbb_formatted_code = \"anomaly_generator = AnomalyGenerator(\\n train_df, anomaly_type=\\\"contextual\\\", anomaly_ratio=0.2\\n)\\noutlier_test_df = anomaly_generator.inject_anomalies(test_df, cols=[\\\"s1\\\"], impact=0.5)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "anomaly_generator = AnomalyGenerator(\n", " train_df, anomaly_type=\"contextual\", anomaly_ratio=0.2\n", ")\n", "outlier_test_df = anomaly_generator.inject_anomalies(test_df, cols=[\"s1\"], impact=0.5)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 32;\n var nbb_unformatted_code = \"outlier_test_df.rename(columns={\\\"s1\\\": \\\"s1_anomaly\\\"}, inplace=True)\\nfig, ax = plt.subplots()\\noutlier_test_df[[\\\"s1_anomaly\\\"]].plot(ax=ax)\\ntest_df.plot(ax=ax)\";\n var nbb_formatted_code = \"outlier_test_df.rename(columns={\\\"s1\\\": \\\"s1_anomaly\\\"}, inplace=True)\\nfig, ax = plt.subplots()\\noutlier_test_df[[\\\"s1_anomaly\\\"]].plot(ax=ax)\\ntest_df.plot(ax=ax)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "outlier_test_df.rename(columns={\"s1\": \"s1_anomaly\"}, inplace=True)\n", "fig, ax = plt.subplots()\n", "outlier_test_df[[\"s1_anomaly\"]].plot(ax=ax)\n", "test_df.plot(ax=ax)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -252,18 +133,6 @@ "cell_type": "code", "execution_count": 33, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 33;\n var nbb_unformatted_code = \"from numalogic.transforms import TanhScaler\\n\\nscaler = TanhScaler()\\nx_train = scaler.fit_transform(train_df.to_numpy())\\nx_val = scaler.transform(val_df.to_numpy())\\nx_test = scaler.transform(outlier_test_df[[\\\"s1_anomaly\\\"]].to_numpy())\\nx_test_good = scaler.transform(test_df.to_numpy())\";\n var nbb_formatted_code = \"from numalogic.transforms import TanhScaler\\n\\nscaler = TanhScaler()\\nx_train = scaler.fit_transform(train_df.to_numpy())\\nx_val = scaler.transform(val_df.to_numpy())\\nx_test = scaler.transform(outlier_test_df[[\\\"s1_anomaly\\\"]].to_numpy())\\nx_test_good = scaler.transform(test_df.to_numpy())\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.transforms import TanhScaler\n", "\n", @@ -272,7 +141,8 @@ "x_val = scaler.transform(val_df.to_numpy())\n", "x_test = scaler.transform(outlier_test_df[[\"s1_anomaly\"]].to_numpy())\n", "x_test_good = scaler.transform(test_df.to_numpy())" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -286,18 +156,6 @@ "cell_type": "code", "execution_count": 34, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 34;\n var nbb_unformatted_code = \"from numalogic.tools.data import StreamingDataset\\nfrom torch.utils.data import DataLoader\\n\\n\\nSEQ_LEN = 12\\nBATCH_SIZE = 64\\nMAX_EPOCHS = 40\\n\\ntrain_dataset = StreamingDataset(x_train, seq_len=SEQ_LEN)\\nval_dataset = StreamingDataset(x_val, seq_len=SEQ_LEN)\\ntest_dataset = StreamingDataset(x_test, seq_len=SEQ_LEN)\";\n var nbb_formatted_code = \"from numalogic.tools.data import StreamingDataset\\nfrom torch.utils.data import DataLoader\\n\\n\\nSEQ_LEN = 12\\nBATCH_SIZE = 64\\nMAX_EPOCHS = 40\\n\\ntrain_dataset = StreamingDataset(x_train, seq_len=SEQ_LEN)\\nval_dataset = StreamingDataset(x_val, seq_len=SEQ_LEN)\\ntest_dataset = StreamingDataset(x_test, seq_len=SEQ_LEN)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.tools.data import StreamingDataset\n", "from torch.utils.data import DataLoader\n", @@ -310,7 +168,8 @@ "train_dataset = StreamingDataset(x_train, seq_len=SEQ_LEN)\n", "val_dataset = StreamingDataset(x_val, seq_len=SEQ_LEN)\n", "test_dataset = StreamingDataset(x_test, seq_len=SEQ_LEN)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -325,68 +184,14 @@ "cell_type": "code", "execution_count": 35, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conv1dAE(\n", - " (encoder): Encoder(\n", - " (encoder): Sequential(\n", - " (0): ConvBlock(\n", - " (conv): Conv1d(1, 16, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): ConvBlock(\n", - " (conv): Conv1d(16, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (3): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Conv1d(32, 8, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (5): ReLU()\n", - " )\n", - " )\n", - " (decoder): Decoder(\n", - " (decoder): Sequential(\n", - " (0): ConvTransposeBlock(\n", - " (convtranspose): ConvTranspose1d(8, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): Upsample(scale_factor=2.0, mode='linear')\n", - " (2): ConvTransposeBlock(\n", - " (convtranspose): ConvTranspose1d(32, 16, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (3): Upsample(scale_factor=2.0, mode='linear')\n", - " (4): ConvTranspose1d(16, 1, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " )\n", - " )\n", - ")\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 35;\n var nbb_unformatted_code = \"from numalogic.models.autoencoder.variants import Conv1dAE\\nfrom numalogic.models.autoencoder import AutoencoderTrainer\\n\\nmodel_1 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(16, 32, 8), enc_kernel_sizes=3)\\nprint(model_1)\";\n var nbb_formatted_code = \"from numalogic.models.autoencoder.variants import Conv1dAE\\nfrom numalogic.models.autoencoder import AutoencoderTrainer\\n\\nmodel_1 = Conv1dAE(\\n seq_len=SEQ_LEN, in_channels=1, enc_channels=(16, 32, 8), enc_kernel_sizes=3\\n)\\nprint(model_1)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.models.autoencoder.variants import Conv1dAE\n", "from numalogic.models.autoencoder import TimeseriesTrainer\n", "\n", "model_1 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(16, 32, 8), enc_kernel_sizes=3)\n", "print(model_1)" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -394,33 +199,11 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "`Trainer.fit` stopped: `max_epochs=40` reached.\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 36;\n var nbb_unformatted_code = \"trainer = AutoencoderTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(model_1, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))\";\n var nbb_formatted_code = \"trainer = AutoencoderTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(model_1, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "trainer = TimeseriesTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\n", "trainer.fit(model_1, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -433,18 +216,6 @@ "cell_type": "code", "execution_count": 37, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 37;\n var nbb_unformatted_code = \"train_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(val_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_formatted_code = \"train_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(val_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_1 = trainer.predict(\\n model_1, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "train_reconerr_1 = trainer.predict(\n", " model_1, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\n", @@ -455,110 +226,31 @@ "test_reconerr_1 = trainer.predict(\n", " model_1, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\n", ").numpy()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 40;\n var nbb_unformatted_code = \"fig, axes = plt.subplots(2, 1, figsize=(12, 8))\\naxes[0].set_title(\\\"Test data reconstruction error\\\")\\naxes[1].set_title(\\\"Scaled anomalous test data\\\")\\n_ = axes[0].plot(test_reconerr_1, color=\\\"g\\\")\\n_ = axes[1].plot(x_test, label=\\\"anomalous\\\")\\n_ = axes[1].plot(x_test_good, label=\\\"original\\\")\\nplt.legend()\";\n var nbb_formatted_code = \"fig, axes = plt.subplots(2, 1, figsize=(12, 8))\\naxes[0].set_title(\\\"Test data reconstruction error\\\")\\naxes[1].set_title(\\\"Scaled anomalous test data\\\")\\n_ = axes[0].plot(test_reconerr_1, color=\\\"g\\\")\\n_ = axes[1].plot(x_test, label=\\\"anomalous\\\")\\n_ = axes[1].plot(x_test_good, label=\\\"original\\\")\\nplt.legend()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n", "axes[0].set_title(\"Test data reconstruction error\")\n", @@ -567,7 +259,8 @@ "_ = axes[1].plot(x_test, label=\"anomalous\")\n", "_ = axes[1].plot(x_test_good, label=\"original\")\n", "plt.legend()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -585,57 +278,11 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv1dAE(\n", - " (encoder): Encoder(\n", - " (encoder): Sequential(\n", - " (0): ConvBlock(\n", - " (conv): Conv1d(1, 16, kernel_size=(5,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv1d(16, 8, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (3): ReLU()\n", - " )\n", - " )\n", - " (decoder): Decoder(\n", - " (decoder): Sequential(\n", - " (0): ConvTransposeBlock(\n", - " (convtranspose): ConvTranspose1d(8, 16, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): Upsample(scale_factor=2.0, mode='linear')\n", - " (2): ConvTranspose1d(16, 1, kernel_size=(5,), stride=(1,), padding=(1,))\n", - " (3): Sigmoid()\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 41;\n var nbb_unformatted_code = \"model_2 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(16, 8), enc_kernel_sizes=(5, 3), dec_activation=\\\"sigmoid\\\")\\nmodel_2\";\n var nbb_formatted_code = \"model_2 = Conv1dAE(\\n seq_len=SEQ_LEN,\\n in_channels=1,\\n enc_channels=(16, 8),\\n enc_kernel_sizes=(5, 3),\\n dec_activation=\\\"sigmoid\\\",\\n)\\nmodel_2\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "model_2 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(16, 8), enc_kernel_sizes=(5, 3), dec_activation=\"sigmoid\")\n", "model_2" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -643,33 +290,11 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "`Trainer.fit` stopped: `max_epochs=40` reached.\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 42;\n var nbb_unformatted_code = \"trainer = AutoencoderTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(model_2, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))\";\n var nbb_formatted_code = \"trainer = AutoencoderTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(model_2, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "trainer = TimeseriesTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\n", "trainer.fit(model_2, train_dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE))" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -677,18 +302,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 43;\n var nbb_unformatted_code = \"train_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(val_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_formatted_code = \"train_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(val_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_2 = trainer.predict(\\n model_2, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "train_reconerr_2 = trainer.predict(\n", " model_2, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\n", @@ -699,7 +312,8 @@ "test_reconerr_2 = trainer.predict(\n", " model_2, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\n", ").numpy()" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -707,33 +321,10 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 46;\n var nbb_unformatted_code = \"fig, axes = plt.subplots(2, 1, figsize=(12, 8))\\naxes[0].set_title(\\\"Test data reconstruction error\\\")\\naxes[1].set_title(\\\"Scaled anomalous test data\\\")\\n_ = axes[0].plot(test_reconerr_2, color=\\\"g\\\")\\n_ = axes[1].plot(x_test)\";\n var nbb_formatted_code = \"fig, axes = plt.subplots(2, 1, figsize=(12, 8))\\naxes[0].set_title(\\\"Test data reconstruction error\\\")\\naxes[1].set_title(\\\"Scaled anomalous test data\\\")\\n_ = axes[0].plot(test_reconerr_2, color=\\\"g\\\")\\n_ = axes[1].plot(x_test)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n", "axes[0].set_title(\"Test data reconstruction error\")\n", "axes[1].set_title(\"Scaled anomalous test data\")\n", "_ = axes[0].plot(test_reconerr_2, color=\"g\")\n", "_ = axes[1].plot(x_test)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -823,57 +368,12 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv1dAE(\n", - " (encoder): Encoder(\n", - " (encoder): Sequential(\n", - " (0): ConvBlock(\n", - " (conv): Conv1d(1, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv1d(32, 4, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (3): ReLU()\n", - " )\n", - " )\n", - " (decoder): Decoder(\n", - " (decoder): Sequential(\n", - " (0): ConvTransposeBlock(\n", - " (convtranspose): ConvTranspose1d(4, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): Upsample(scale_factor=2.0, mode='linear')\n", - " (2): ConvTranspose1d(32, 1, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 47;\n var nbb_unformatted_code = \"# with upsampling\\nmodel_3 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=[32, 4], enc_kernel_sizes=3)\\nmodel_3\";\n var nbb_formatted_code = \"# with upsampling\\nmodel_3 = Conv1dAE(\\n seq_len=SEQ_LEN, in_channels=1, enc_channels=[32, 4], enc_kernel_sizes=3\\n)\\nmodel_3\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# with upsampling\n", "model_3 = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=[32, 4], enc_kernel_sizes=3)\n", "model_3" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -881,34 +381,12 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "`Trainer.fit` stopped: `max_epochs=40` reached.\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 48;\n var nbb_unformatted_code = \"\\ntrainer = AutoencoderTrainer(accelerator=\\\"cpu\\\", max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(model_3, train_dataloaders=DataLoader(StreamingDataset(x_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE))\";\n var nbb_formatted_code = \"trainer = AutoencoderTrainer(\\n accelerator=\\\"cpu\\\", max_epochs=MAX_EPOCHS, enable_progress_bar=True\\n)\\ntrainer.fit(\\n model_3,\\n train_dataloaders=DataLoader(\\n StreamingDataset(x_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE\\n ),\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "\n", "trainer = TimeseriesTrainer(accelerator=\"cpu\", max_epochs=MAX_EPOCHS, enable_progress_bar=True)\n", "trainer.fit(model_3, train_dataloaders=DataLoader(StreamingDataset(x_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE))" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -916,18 +394,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 49;\n var nbb_unformatted_code = \"train_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(val_dataset,\\n batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_formatted_code = \"train_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\nval_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(val_dataset, batch_size=BATCH_SIZE)\\n).numpy()\\ntest_reconerr_3 = trainer.predict(\\n model_3, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\\n).numpy()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "train_reconerr_3 = trainer.predict(\n", " model_3, dataloaders=DataLoader(train_dataset, batch_size=BATCH_SIZE)\n", @@ -939,7 +405,8 @@ "test_reconerr_3 = trainer.predict(\n", " model_3, dataloaders=DataLoader(test_dataset, batch_size=BATCH_SIZE)\n", ").numpy()" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -947,33 +414,10 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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", 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nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n", "axes[0].set_title(\"Test data reconstruction error\")\n", @@ -1046,7 +443,8 @@ "axes[1].set_title(\"Scaled anomalous test data\")\n", "_ = axes[0].plot(test_reconerr_3, color=\"g\")\n", "_ = axes[1].plot(x_test)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -1063,47 +461,14 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 53;\n var nbb_unformatted_code = \"fig, ax = plt.subplots()\\nax.plot(test_reconerr_1, label=\\\"Model 1\\\")\\nax.plot(test_reconerr_2, label=\\\"Model 2\\\")\\nax.plot(test_reconerr_3, label=\\\"Model 3\\\")\\nplt.legend()\";\n var nbb_formatted_code = \"fig, ax = plt.subplots()\\nax.plot(test_reconerr_1, label=\\\"Model 1\\\")\\nax.plot(test_reconerr_2, label=\\\"Model 2\\\")\\nax.plot(test_reconerr_3, label=\\\"Model 3\\\")\\nplt.legend()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(test_reconerr_1, label=\"Model 1\")\n", "ax.plot(test_reconerr_2, label=\"Model 2\")\n", "ax.plot(test_reconerr_3, label=\"Model 3\")\n", "plt.legend()" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/examples/multi_udf/src/udf/inference.py b/examples/multi_udf/src/udf/inference.py index 4ff92886..5c9d05f7 100644 --- a/examples/multi_udf/src/udf/inference.py +++ b/examples/multi_udf/src/udf/inference.py @@ -4,7 +4,7 @@ import numpy.typing as npt from numalogic.models.autoencoder import TimeseriesTrainer from numalogic.udfs import NumalogicUDF -from numalogic.registry import MLflowRegistry, ArtifactData +from registry import MLflowRegistry, ArtifactData from numalogic.tools.data import StreamingDataset from pynumaflow.function import Messages, Message, Datum from torch.utils.data import DataLoader @@ -27,7 +27,7 @@ def __init__(self): self.registry = MLflowRegistry(tracking_uri=TRACKING_URI) def load_model(self) -> ArtifactData: - """Loads the model from the registry.""" + """Loads the model from the test_registry.""" return self.registry.load(skeys=["ae"], dkeys=["model"]) @staticmethod @@ -43,7 +43,7 @@ def _infer(artifact_data: ArtifactData, stream_data: npt.NDArray[float]) -> list def exec(self, keys: list[str], datum: Datum) -> Messages: """ Here inference is done on the data, given, the ML model is present - in the registry. If a model does not exist, the payload is flagged for training. + in the test_registry. If a model does not exist, the payload is flagged for training. It then passes to the threshold vertex. For more information about the arguments, refer: diff --git a/examples/multi_udf/src/udf/threshold.py b/examples/multi_udf/src/udf/threshold.py index 20f71002..60f565cc 100644 --- a/examples/multi_udf/src/udf/threshold.py +++ b/examples/multi_udf/src/udf/threshold.py @@ -1,7 +1,7 @@ import logging from numalogic.udfs import NumalogicUDF -from numalogic.registry import MLflowRegistry +from registry import MLflowRegistry from pynumaflow.function import Messages, Message, Datum from src.utils import Payload @@ -39,7 +39,7 @@ def exec(self, _: list[str], datum: Datum) -> Messages: # Load data and convert bytes to Payload payload = Payload.from_json(datum.value) - # Load the threshold model from registry + # Load the threshold model from test_registry thresh_clf_artifact = self.registry.load( skeys=["thresh_clf"], dkeys=["model"], artifact_type="sklearn" ) diff --git a/examples/multi_udf/src/udf/train.py b/examples/multi_udf/src/udf/train.py index ff137c6c..e1bebaf9 100644 --- a/examples/multi_udf/src/udf/train.py +++ b/examples/multi_udf/src/udf/train.py @@ -8,7 +8,7 @@ from numalogic.models.autoencoder.variants import Conv1dAE from numalogic.models.threshold import StdDevThreshold from numalogic.udfs import NumalogicUDF -from numalogic.registry import MLflowRegistry +from registry import MLflowRegistry from numalogic.tools.data import TimeseriesDataModule from numalogic.transforms import LogTransformer from pynumaflow.function import Datum, Messages, Message @@ -24,7 +24,7 @@ class Trainer(NumalogicUDF): - """UDF to train the model and save it in the registry.""" + """UDF to train the model and save it in the test_registry.""" ttl_cache = cachetools.TTLCache(maxsize=128, ttl=120 * 60) @@ -36,7 +36,7 @@ def __init__(self): def _save_artifact( self, model, skeys: list[str], dkeys: list[str], _: Optional[TimeseriesTrainer] = None ) -> None: - """Saves the model in the registry.""" + """Saves the model in the test_registry.""" self.registry.save(skeys=skeys, dkeys=dkeys, artifact=model) @staticmethod @@ -55,7 +55,7 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: """ The train function here receives data from inference step. This step preprocesses, trains and saves a 1-D - Convolution AE model into the registry. + Convolution AE model into the test_registry. For more information about the arguments, refer: https://github.com/numaproj/numaflow-python/blob/main/pynumaflow/function/_dtypes.py @@ -92,7 +92,7 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: # Train the threshold model thresh_clf = self._fit_threshold(train_reconerr.numpy()) - # Save to registry + # Save to test_registry self._save_artifact(model, ["ae"], ["model"], trainer) self._save_artifact(thresh_clf, ["thresh_clf"], ["model"]) LOGGER.info("%s - Model Saving complete", payload.uuid) diff --git a/examples/quick-start.ipynb b/examples/quick-start.ipynb index 512f8a0d..b6e94952 100644 --- a/examples/quick-start.ipynb +++ b/examples/quick-start.ipynb @@ -22,50 +22,17 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", - "The nb_black extension is already loaded. To reload it, use:\n", - " %reload_ext nb_black\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 67;\n var nbb_unformatted_code = \"%load_ext autoreload\\n%autoreload 2\\n%load_ext nb_black\";\n var nbb_formatted_code = \"%load_ext autoreload\\n%autoreload 2\\n%load_ext nb_black\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%load_ext nb_black" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 68;\n var nbb_unformatted_code = \"import random\\nimport warnings\\n\\nimport pandas as pd\\nimport numpy as np\\nfrom matplotlib import pyplot as plt\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\n%matplotlib inline\\nplt.rcParams[\\\"figure.figsize\\\"] = [9, 2]\\nplt.rcParams[\\\"figure.dpi\\\"] = 200\\nplt.rcParams[\\\"agg.path.chunksize\\\"] = 100000\\npd.set_option(\\\"plotting.backend\\\", \\\"matplotlib\\\")\\nrandom.seed(42)\\nnp.random.seed(42)\";\n var nbb_formatted_code = \"import random\\nimport warnings\\n\\nimport pandas as pd\\nimport numpy as np\\nfrom matplotlib import pyplot as plt\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\n%matplotlib inline\\nplt.rcParams[\\\"figure.figsize\\\"] = [9, 2]\\nplt.rcParams[\\\"figure.dpi\\\"] = 200\\nplt.rcParams[\\\"agg.path.chunksize\\\"] = 100000\\npd.set_option(\\\"plotting.backend\\\", \\\"matplotlib\\\")\\nrandom.seed(42)\\nnp.random.seed(42)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "import random\n", "import warnings\n", @@ -82,7 +49,8 @@ "pd.set_option(\"plotting.backend\", \"matplotlib\")\n", "random.seed(42)\n", "np.random.seed(42)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -106,40 +74,17 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 69;\n var nbb_unformatted_code = \"TOTAL_SIZE = 9000\\nVALIDATION_SIZE = 1000\\nTEST_SIZE = 1000\";\n var nbb_formatted_code = \"TOTAL_SIZE = 9000\\nVALIDATION_SIZE = 1000\\nTEST_SIZE = 1000\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "TOTAL_SIZE = 9000\n", "VALIDATION_SIZE = 1000\n", "TEST_SIZE = 1000" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 70;\n var nbb_unformatted_code = \"from numalogic.synthetic import SyntheticTSGenerator\\n\\nts_generator = SyntheticTSGenerator(\\n seq_len=TOTAL_SIZE,\\n num_series=1,\\n freq=\\\"T\\\",\\n primary_period=600,\\n secondary_period=3000,\\n seasonal_ts_prob=1.0,\\n baseline_range=(200.0, 350.0),\\n slope_range=(-0.001, 0.01),\\n amplitude_range=(10, 75),\\n cosine_ratio_range=(0.5, 0.9),\\n noise_range=(5, 15),\\n)\\n\\n# shape: (9000, 1) with column names [s1]\\nts_df = ts_generator.gen_tseries() \";\n var nbb_formatted_code = \"from numalogic.synthetic import SyntheticTSGenerator\\n\\nts_generator = SyntheticTSGenerator(\\n seq_len=TOTAL_SIZE,\\n num_series=1,\\n freq=\\\"T\\\",\\n primary_period=600,\\n secondary_period=3000,\\n seasonal_ts_prob=1.0,\\n baseline_range=(200.0, 350.0),\\n slope_range=(-0.001, 0.01),\\n amplitude_range=(10, 75),\\n cosine_ratio_range=(0.5, 0.9),\\n noise_range=(5, 15),\\n)\\n\\n# shape: (9000, 1) with column names [s1]\\nts_df = ts_generator.gen_tseries()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.synthetic import SyntheticTSGenerator\n", "\n", @@ -159,49 +104,17 @@ "\n", "# shape: (9000, 1) with column names [s1]\n", "ts_df = ts_generator.gen_tseries() " - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 71;\n var nbb_unformatted_code = \"ts_df.plot(title=\\\"Time series data\\\")\";\n var nbb_formatted_code = \"ts_df.plot(title=\\\"Time series data\\\")\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "ts_df.plot(title=\"Time series data\")" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -214,77 +127,24 @@ "cell_type": "code", "execution_count": 72, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train shape: (7000, 1)\n", - "val shape: (1000, 1)\n", - "test shape: (1000, 1)\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 72;\n var nbb_unformatted_code = \"train_df, test_df = ts_generator.train_test_split(ts_df, test_size=TEST_SIZE)\\ntrain_df, val_df = ts_generator.train_test_split(train_df, test_size=VALIDATION_SIZE)\\nprint(f\\\"train shape: {train_df.shape}\\\\nval shape: {val_df.shape}\\\\ntest shape: {test_df.shape}\\\")\";\n var nbb_formatted_code = \"train_df, test_df = ts_generator.train_test_split(ts_df, test_size=TEST_SIZE)\\ntrain_df, val_df = ts_generator.train_test_split(train_df, test_size=VALIDATION_SIZE)\\nprint(\\n f\\\"train shape: {train_df.shape}\\\\nval shape: {val_df.shape}\\\\ntest shape: {test_df.shape}\\\"\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "train_df, test_df = ts_generator.train_test_split(ts_df, test_size=TEST_SIZE)\n", "train_df, val_df = ts_generator.train_test_split(train_df, test_size=VALIDATION_SIZE)\n", "print(f\"train shape: {train_df.shape}\\nval shape: {val_df.shape}\\ntest shape: {test_df.shape}\")" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 73;\n var nbb_unformatted_code = \"ax = train_df[\\\"s1\\\"].plot(label=\\\"train\\\")\\nval_df[\\\"s1\\\"].plot(ax=ax, label=\\\"val\\\")\\ntest_df[\\\"s1\\\"].plot(ax=ax, label=\\\"test\\\", title=\\\"Time series before injecting anomalies\\\")\\nplt.legend()\";\n var nbb_formatted_code = \"ax = train_df[\\\"s1\\\"].plot(label=\\\"train\\\")\\nval_df[\\\"s1\\\"].plot(ax=ax, label=\\\"val\\\")\\ntest_df[\\\"s1\\\"].plot(ax=ax, label=\\\"test\\\", title=\\\"Time series before injecting anomalies\\\")\\nplt.legend()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "ax = train_df[\"s1\"].plot(label=\"train\")\n", "val_df[\"s1\"].plot(ax=ax, label=\"val\")\n", "test_df[\"s1\"].plot(ax=ax, label=\"test\", title=\"Time series before injecting anomalies\")\n", "plt.legend()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -301,18 +161,6 @@ "cell_type": "code", "execution_count": 74, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 74;\n var nbb_unformatted_code = \"from numalogic.synthetic import AnomalyGenerator\\n\\nanomaly_generator = AnomalyGenerator(train_df, anomaly_type=\\\"contextual\\\", anomaly_ratio=0.2)\\noutliers_test_df = anomaly_generator.inject_anomalies(\\n test_df, cols=[\\\"s1\\\"], impact=1\\n)\\noutliers_test_df.drop(columns = \\\"is_anomaly\\\", inplace=True)\";\n var nbb_formatted_code = \"from numalogic.synthetic import AnomalyGenerator\\n\\nanomaly_generator = AnomalyGenerator(\\n train_df, anomaly_type=\\\"contextual\\\", anomaly_ratio=0.2\\n)\\noutliers_test_df = anomaly_generator.inject_anomalies(test_df, cols=[\\\"s1\\\"], impact=1)\\noutliers_test_df.drop(columns=\\\"is_anomaly\\\", inplace=True)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.synthetic import AnomalyGenerator\n", "\n", @@ -321,7 +169,8 @@ " test_df, cols=[\"s1\"], impact=1\n", ")\n", "outliers_test_df.drop(columns = \"is_anomaly\", inplace=True)" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -329,46 +178,13 @@ "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 75;\n var nbb_unformatted_code = \"ax = train_df[\\\"s1\\\"].plot(label=\\\"train\\\")\\nval_df[\\\"s1\\\"].plot(ax=ax, label=\\\"val\\\")\\noutliers_test_df[\\\"s1\\\"].plot(ax=ax, label=\\\"test\\\", title=\\\"Time series after injecting anomalies\\\")\\nplt.legend()\";\n var nbb_formatted_code = \"ax = train_df[\\\"s1\\\"].plot(label=\\\"train\\\")\\nval_df[\\\"s1\\\"].plot(ax=ax, label=\\\"val\\\")\\noutliers_test_df[\\\"s1\\\"].plot(\\n ax=ax, label=\\\"test\\\", title=\\\"Time series after injecting anomalies\\\"\\n)\\nplt.legend()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "ax = train_df[\"s1\"].plot(label=\"train\")\n", "val_df[\"s1\"].plot(ax=ax, label=\"val\")\n", "outliers_test_df[\"s1\"].plot(ax=ax, label=\"test\", title=\"Time series after injecting anomalies\")\n", "plt.legend()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -385,18 +201,6 @@ "cell_type": "code", "execution_count": 76, "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 76;\n var nbb_unformatted_code = \"from numalogic.transforms import TanhScaler\\n\\nscaler = TanhScaler()\\nX_train = scaler.fit_transform(train_df.to_numpy())\\nX_val = scaler.transform(val_df.to_numpy())\\nX_test = scaler.transform(outliers_test_df.to_numpy())\";\n var nbb_formatted_code = \"from numalogic.transforms import TanhScaler\\n\\nscaler = TanhScaler()\\nX_train = scaler.fit_transform(train_df.to_numpy())\\nX_val = scaler.transform(val_df.to_numpy())\\nX_test = scaler.transform(outliers_test_df.to_numpy())\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.transforms import TanhScaler\n", "\n", @@ -404,7 +208,8 @@ "X_train = scaler.fit_transform(train_df.to_numpy())\n", "X_val = scaler.transform(val_df.to_numpy())\n", "X_test = scaler.transform(outliers_test_df.to_numpy())" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -425,50 +230,6 @@ "cell_type": "code", "execution_count": 77, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv1dAE(\n", - " (encoder): Encoder(\n", - " (encoder): Sequential(\n", - " (0): ConvBlock(\n", - " (conv): Conv1d(1, 8, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv1d(8, 4, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (3): ReLU()\n", - " )\n", - " )\n", - " (decoder): Decoder(\n", - " (decoder): Sequential(\n", - " (0): ConvTransposeBlock(\n", - " (convtranspose): ConvTranspose1d(4, 8, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (bnorm): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (1): Upsample(scale_factor=2.0, mode='linear')\n", - " (2): ConvTranspose1d(8, 1, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 60;\n var nbb_unformatted_code = \"from torch.utils.data import DataLoader\\nfrom numalogic.models.autoencoder import TimeseriesTrainer\\nfrom numalogic.models.autoencoder.variants import Conv1dAE\\nfrom numalogic.tools.data import StreamingDataset\\n\\nSEQ_LEN = 24 # length of the sequence\\nMAX_EPOCHS = 30 # number of epochs to run\\nBATCH_SIZE = 64 # training batch size\\n\\nmodel = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(8, 4))\\nmodel\";\n var nbb_formatted_code = \"from torch.utils.data import DataLoader\\nfrom numalogic.models.autoencoder import TimeseriesTrainer\\nfrom numalogic.models.autoencoder.variants import Conv1dAE\\nfrom numalogic.tools.data import StreamingDataset\\n\\nSEQ_LEN = 24 # length of the sequence\\nMAX_EPOCHS = 30 # number of epochs to run\\nBATCH_SIZE = 64 # training batch size\\n\\nmodel = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(8, 4))\\nmodel\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from torch.utils.data import DataLoader\n", "from numalogic.models.autoencoder import TimeseriesTrainer\n", @@ -481,7 +242,8 @@ "\n", "model = Conv1dAE(seq_len=SEQ_LEN, in_channels=1, enc_channels=(8, 4))\n", "model" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -489,27 +251,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - }, - { - "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 61;\n var nbb_unformatted_code = \"trainer = TimeseriesTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(\\n model,\\n train_dataloaders=DataLoader(StreamingDataset(X_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE),\\n val_dataloaders=DataLoader(StreamingDataset(X_val, seq_len=SEQ_LEN), batch_size=BATCH_SIZE)\\n)\";\n var nbb_formatted_code = \"trainer = TimeseriesTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\\ntrainer.fit(\\n model,\\n train_dataloaders=DataLoader(\\n StreamingDataset(X_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE\\n ),\\n val_dataloaders=DataLoader(\\n StreamingDataset(X_val, seq_len=SEQ_LEN), batch_size=BATCH_SIZE\\n ),\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "trainer = TimeseriesTrainer(max_epochs=MAX_EPOCHS, enable_progress_bar=True)\n", "trainer.fit(\n", @@ -517,7 +258,8 @@ " train_dataloaders=DataLoader(StreamingDataset(X_train, seq_len=SEQ_LEN), batch_size=BATCH_SIZE),\n", " val_dataloaders=DataLoader(StreamingDataset(X_val, seq_len=SEQ_LEN), batch_size=BATCH_SIZE)\n", ")" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -539,53 +281,13 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([7000, 1])\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 79;\n var nbb_unformatted_code = \"# Calculate training set reconstruction error\\ntrain_reconerr = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_train, seq_len=SEQ_LEN)))\\nprint(train_reconerr.shape)\\nplt.plot(train_reconerr)\";\n var nbb_formatted_code = \"# Calculate training set reconstruction error\\ntrain_reconerr = trainer.predict(\\n model, dataloaders=DataLoader(StreamingDataset(X_train, seq_len=SEQ_LEN))\\n)\\nprint(train_reconerr.shape)\\nplt.plot(train_reconerr)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Calculate training set reconstruction error\n", "train_reconerr = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_train, seq_len=SEQ_LEN)))\n", "print(train_reconerr.shape)\n", "plt.plot(train_reconerr)" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -593,53 +295,13 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1000, 1])\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 80;\n var nbb_unformatted_code = \"# Calculate validation set reconstruction error\\nval_reconerr = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_val, seq_len=SEQ_LEN)))\\nprint(val_reconerr.shape)\\nplt.plot(val_reconerr)\";\n var nbb_formatted_code = \"# Calculate validation set reconstruction error\\nval_reconerr = trainer.predict(\\n model, dataloaders=DataLoader(StreamingDataset(X_val, seq_len=SEQ_LEN))\\n)\\nprint(val_reconerr.shape)\\nplt.plot(val_reconerr)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Calculate validation set reconstruction error\n", "val_reconerr = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_val, seq_len=SEQ_LEN)))\n", "print(val_reconerr.shape)\n", "plt.plot(val_reconerr)" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -656,25 +318,14 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 81;\n var nbb_unformatted_code = \"# Calculate the threshold\\nfrom numalogic.models.threshold import StdDevThreshold\\n\\nthreshold_clf = StdDevThreshold(std_factor=3.0)\\n_ = threshold_clf.fit(val_reconerr.numpy())\";\n var nbb_formatted_code = \"# Calculate the threshold\\nfrom numalogic.models.threshold import StdDevThreshold\\n\\nthreshold_clf = StdDevThreshold(std_factor=3.0)\\n_ = threshold_clf.fit(val_reconerr.numpy())\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Calculate the threshold\n", "from numalogic.models.threshold import StdDevThreshold\n", "\n", "threshold_clf = StdDevThreshold(std_factor=3.0)\n", "_ = threshold_clf.fit(val_reconerr.numpy())" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -691,53 +342,13 @@ "cell_type": "code", "execution_count": 82, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1000, 1])\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 82;\n var nbb_unformatted_code = \"# Get the reconstruction error on the test set\\ntest_recon_err = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_test, seq_len=SEQ_LEN)))\\nprint(test_recon_err.shape)\\nplt.plot(test_recon_err)\";\n var nbb_formatted_code = \"# Get the reconstruction error on the test set\\ntest_recon_err = trainer.predict(\\n model, dataloaders=DataLoader(StreamingDataset(X_test, seq_len=SEQ_LEN))\\n)\\nprint(test_recon_err.shape)\\nplt.plot(test_recon_err)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Get the reconstruction error on the test set\n", "test_recon_err = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_test, seq_len=SEQ_LEN)))\n", "print(test_recon_err.shape)\n", "plt.plot(test_recon_err)" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -745,103 +356,28 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 1)\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 83;\n var nbb_unformatted_code = \"# Get the anomaly score using the threshold estimator.\\ntest_anomaly_score = threshold_clf.score_samples(test_recon_err.numpy())\\nprint(test_anomaly_score.shape)\";\n var nbb_formatted_code = \"# Get the anomaly score using the threshold estimator.\\ntest_anomaly_score = threshold_clf.score_samples(test_recon_err.numpy())\\nprint(test_anomaly_score.shape)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Get the anomaly score using the threshold estimator.\n", "test_anomaly_score = threshold_clf.score_samples(test_recon_err.numpy())\n", "print(test_anomaly_score.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 84;\n var nbb_unformatted_code = \"# Convert the score array into a dataframe\\nanomalies_df = pd.DataFrame(data=test_anomaly_score, columns=outliers_test_df.columns, index=outliers_test_df.index)\\nanomalies_df.plot()\";\n var nbb_formatted_code = \"# Convert the score array into a dataframe\\nanomalies_df = pd.DataFrame(\\n data=test_anomaly_score,\\n columns=outliers_test_df.columns,\\n index=outliers_test_df.index,\\n)\\nanomalies_df.plot()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "# Convert the score array into a dataframe\n", "anomalies_df = pd.DataFrame(data=test_anomaly_score, columns=outliers_test_df.columns, index=outliers_test_df.index)\n", "anomalies_df.plot()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 85;\n var nbb_unformatted_code = \"ax1 = plt.subplot()\\nl1, = ax1.plot(outliers_test_df[\\\"s1\\\"])\\nax2 = ax1.twinx()\\nl2, = ax2.plot(anomalies_df[\\\"s1\\\"], color=\\\"orange\\\")\\nplt.legend([l1, l2], [\\\"test data\\\", \\\"anomaly score\\\"])\\nplt.title(\\\"S1 data vs anomaly score\\\")\\nplt.show()\";\n var nbb_formatted_code = \"ax1 = plt.subplot()\\n(l1,) = ax1.plot(outliers_test_df[\\\"s1\\\"])\\nax2 = ax1.twinx()\\n(l2,) = ax2.plot(anomalies_df[\\\"s1\\\"], color=\\\"orange\\\")\\nplt.legend([l1, l2], [\\\"test data\\\", \\\"anomaly score\\\"])\\nplt.title(\\\"S1 data vs anomaly score\\\")\\nplt.show()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "ax1 = plt.subplot()\n", "l1, = ax1.plot(outliers_test_df[\"s1\"])\n", @@ -850,7 +386,8 @@ "plt.legend([l1, l2], [\"test data\", \"anomaly score\"])\n", "plt.title(\"S1 data vs anomaly score\")\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "markdown", @@ -868,25 +405,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 1)\n" - ] - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 86;\n var nbb_unformatted_code = \"from numalogic.transforms import TanhNorm\\n\\n# scale factor makes sure that the scores are normalized between 0 and 10\\npostproc_clf = TanhNorm(scale_factor=10)\\ntest_anomaly_score_norm = postproc_clf.fit_transform(test_anomaly_score)\\nprint(test_anomaly_score_norm.shape)\";\n var nbb_formatted_code = \"from numalogic.transforms import TanhNorm\\n\\n# scale factor makes sure that the scores are normalized between 0 and 10\\npostproc_clf = TanhNorm(scale_factor=10)\\ntest_anomaly_score_norm = postproc_clf.fit_transform(test_anomaly_score)\\nprint(test_anomaly_score_norm.shape)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "from numalogic.transforms import TanhNorm\n", "\n", @@ -894,79 +412,23 @@ "postproc_clf = TanhNorm(scale_factor=10)\n", "test_anomaly_score_norm = postproc_clf.fit_transform(test_anomaly_score)\n", "print(test_anomaly_score_norm.shape)" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 87;\n var nbb_unformatted_code = \"norm_anomalies_df = pd.DataFrame(data=test_anomaly_score_norm, columns=outliers_test_df.columns, index=outliers_test_df.index)\\nnorm_anomalies_df.plot()\";\n var nbb_formatted_code = \"norm_anomalies_df = pd.DataFrame(\\n data=test_anomaly_score_norm,\\n columns=outliers_test_df.columns,\\n index=outliers_test_df.index,\\n)\\nnorm_anomalies_df.plot()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "norm_anomalies_df = pd.DataFrame(data=test_anomaly_score_norm, columns=outliers_test_df.columns, index=outliers_test_df.index)\n", "norm_anomalies_df.plot()" - ] + ], + "outputs": [] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 88;\n var nbb_unformatted_code = \"f, ax1 = plt.subplots(1, 1, figsize=(12, 4))\\n(l1,) = ax1.plot(outliers_test_df[\\\"s1\\\"])\\nax2 = ax1.twinx()\\n(l2,) = ax2.plot(norm_anomalies_df[\\\"s1\\\"], color=\\\"orange\\\")\\nl3 = ax1.plot(test_df[\\\"s1\\\"], color=\\\"g\\\")\\nplt.legend([l1, l2, l3], [\\\"test data with outlier\\\", \\\"anomaly score\\\", \\\"test data\\\"])\\nplt.title(\\\"S1 data vs normalized anomaly score\\\")\\nplt.show()\";\n var nbb_formatted_code = \"f, ax1 = plt.subplots(1, 1, figsize=(12, 4))\\n(l1,) = ax1.plot(outliers_test_df[\\\"s1\\\"])\\nax2 = ax1.twinx()\\n(l2,) = ax2.plot(norm_anomalies_df[\\\"s1\\\"], color=\\\"orange\\\")\\nl3 = ax1.plot(test_df[\\\"s1\\\"], color=\\\"g\\\")\\nplt.legend([l1, l2, l3], [\\\"test data with outlier\\\", \\\"anomaly score\\\", \\\"test data\\\"])\\nplt.title(\\\"S1 data vs normalized anomaly score\\\")\\nplt.show()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "f, ax1 = plt.subplots(1, 1, figsize=(12, 4))\n", "(l1,) = ax1.plot(outliers_test_df[\"s1\"])\n", @@ -976,7 +438,8 @@ "plt.legend([l1, l2, l3], [\"test data with outlier\", \"anomaly score\", \"test data\"])\n", "plt.title(\"S1 data vs normalized anomaly score\")\n", "plt.show()" - ] + ], + "outputs": [] }, { "cell_type": "code", @@ -984,40 +447,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RSnsAQAmq3q+3Iqm0LHjMx/aajVTKUzv3p2FCiBiDxZ2Ole/XO1KnxYaU0g7v/xcflDqlzKWUB6GUmAaU/z+HmNi+LzNV1xv9XZFSLkPDe9MBwGkGi9+guv653us1KVC/8/eorn8tpfyqkeu5HA3l3nMBPKK3oJSyBsrELKcJQoieJrbxbylllY9l1L8n0x2/s0aehVKpAwAuc5RNJyIiIqJ/APbQJiIiIiJSkVJWALhECHEalBOtp0L77+YoKL1ULxNC/AQlS7D46I3UtAmq6187AsxGPgHwqD8bcARbTgTQF0oGZRTgNnm2l+r6QABz/Fm/yTEEQQmaDIQSHIuBkh2uZWAANvkNlLLSoQDGCCE6SikPaIyrK5SyuoBSeniWxrrUz7sVwM0BGJ8/5pgI1KwHcInjerrG46PQ0FO9EErvVF1SSrsQ4kMopaUB9/1UT2MCN1rvt9oWj9vf+7F8Z18bF0JYoHz+J0Ap3RsN99+TaNX1gb7W11yEEGcAeFF1121SykUay4WgIQBYD9/vL6SUUgixEMrvgICyr6j3D/Vn/4uJ39FvoJRjDvOxXKMIITpCqcLRA0qFi3Ao43Zyfu4Cyue6xMcqj5Xvlz/flSoAv5pYfqzjupnvSgSU/0f6Q2nTEA33vs9pqusDfa3Ph/cAvOK4fgOUktqe4wmDEvR1+qCJ22zy77xjTONVd73ehPGcrLr+la/As5RylRBiM5TPB1D2qZ0GTymGxvuqJoRIRcNnmSX1e62rx1EthFgBYBKU8vb9oGSCExEREdFxjgFtIiIiIiINUsp5AOYJIRIBjIMSUBjsuMR6LH4ugCVCiJFSyrKjO1KfBqqu/+1rYSnlHiFEIZTAtCEhRHsAzwG4COaDS57ZmU3iCLBNhzL5oL2PxQM2BillsRBiLoDJUAJbl0N5Lzyps7N/klKWaizzNRoyAW8SQgyDUl74dynlDo3lA22ziWUKVdc9938AGKS6/reUst7EOpepny+EEFJKabD8WhPr9LTVx+Pq4OkRKWWWH8vrZucKIcIBPATgFpjf3wL63TBLCNEHyj7oDB7+T0r5ns7iAwBEOq5XA3hBCKGzqBt11nEHj8cGqq6b+Y0qF0JsQWCyfl2EECOhfIfHwD2AbcTMZ3asfL/8+a7sMjEGs9+VBCgZ7FfDfYKHkaZ+Vz6F8llbAZwnhEiSUhZ4LHM+lB7nALBOSrm+idsMxO/8QMeYAaWagc/viwH1PrVMdyl3y9AQ0B7sY9kNjsx+IyNV14UQ4g2T4+iqut4BDGgTERER/SMwoE1EREREZMBR4vZ7x0WdCXwllPK8znLefQE8DaXHdmuSrLrulUGs4yB8BLSFEIMALAAQ7+d4zAYsfHJkq/0E4PQWGsNnUALagFJ2XCugrc7w0yo3DinlPCHE/6D0vQWUQMMgABBCFEAJIiwCMMtRyjfQjphYRp3Zr5X5rt7P9pvc7j7V9VAon4tWwN+pMWV6fb02dVDOzPugXl6zAoAQIh7An/A/izRg3w2zHBN25qAh4DgXwL0GT2mnuh4F4LZGbNbzN6Oxv1EBC2gLIa4H8D7MB7KdzHxmx8r3qyW+K50A/AWgo4n1qTXpuyKlLBJCzILyux0K5f/zVzwWu151vanZ2YH6nW+jun7Q5MQGPU3dp3xNKjDze93O43ogfk+IiIiI6DjFXjNERERERH6QUtqllKullP+CElBR94+90ZGZ2ZpEqa5XmnxOhdGDjkDyLDScSM4F8B8opVA7QMngDJJSCimlAHCd6umBPAZ5HA3BbDuALwBcDKA3lCzHUOcYHONwvYQAbf9nNPTy7CeEGKB+UAjhLFsMKBmYv+mtSEp5N5RM/+UeDyUBOA/A/wDsF0LMcgSBAskoa9Ms9X5muP8YLGcYpDLRi1Xzac20rJE30RDMrgHwLpTPtjscJcdV+6S6FPNRPT53lEOfBaCL466tAKb4yKrUyh72l+fE+oD/RvlDCNEbymfk/F3YBGVi0jAAKQDCPX5HPlY93cxndqx8v1riu/I5GoLZpVBKpE+E8r2IAhCset/VZdMD8V2ZobquDl47A+2nOG5WQfm/pckC8Duv/gx99Zr2pan7lK9JBWZ+r5vj94SIiIiIjlP8w4+IiIiIqJGklNuFEPdAOSkPKKVAh0HJOGstytFw0jjC5HMifTx+IRoCcYcADJVS5hosH/DMU0dQfbrqrqullJ8bLB/wMUgpa4QQ3wGY6rjrCriXPr1Cdd1n/3Ip5RwAc4QQaVAmB4xxXPo4FhEALgAwTggxSkq5q+mvImDUwRVf+4/ecq2tXL/fHJ/dFMdNG4DTpZRGvwdHPStb5W0o7RQAoADAOSZaJqgDWhuklIN0lzRPve8E6jfKH3eh4dzIXACTfXxXW+IzO+6+X0KIUQBGO26WARjho/R2QN93KeVfQogdUHq79xdCDJNSrnY8fB0aguazpJQlAdxuU37n1Z+hOiDdGOq/DRqzTwVif1L/nvwgpTw/AOskIiIiouMUM7SJiIiIiJrmV4/bqS0yCn3qsp9my7p69rj1dIrq+v98BLMBINAZxQAwHA0n9DcbBbObcQyAexnxy4WjobAQIhjApTrLGZJSZkkpP5dS3iKl7Avl83gUDSf/EwG83LRhB1xj9jP1Z1KLVhZwa6ST0ZDpO9dHMBtovv3SkBDibjT0860FcL6Ucq+Jp6q/610cLRiaqjl+o/yh/j171NfEE7TMZ3Y8fr/U7/tME32km+N9f191/QZAaeQM4FrV/U0uN66lkb/z6u9fByFEU5JUmrpPefYcbwz16+kegPURERER0XGMAW0iIiIioqap9rhd0yKj0LdBdf1EXwsLIbrBR/9suPe93GpiDGNNLONvCdvmGENj/IWGvr/t0ZDxehoa+p1mSClXNHYDUspDUsqnoPRsdzrdkaXeWqxXXR/uCOj7Mlp1fb2fJY9bq9ayX+oSQpwJ4AXVXTdLKZeafPoGNPzGxQAYGYAhbVBdN/MbFQWgXwC262T6MxNCxAIYYLRMMzkev1+t4bvyMRr258uEEBEATkVD4HYPgMXNsF0vJn/nN6Dhb44IACOasEn1PjXK5HPU+9S6JmzbaaXqel8hRPsArJOIiIiIjlMMaBMRERERNc1Aj9sHtBZqQQtV1y919M01co2Jdap77BqWCBZCDIFSht0X9cQAX2P0dwxBAG4ysU6/OYJE6v6qzjLjV6ru85U9btbPqusWAAkBWm8gLEdDYCgZwFlGCzuyINX72p/NNK6jzZ/9MgLA1c07HK9t9gXwJRrOBTwvpZxp9vmOPubqz+quAAxL/Rt1phDC1359KYBATuYw/ZlBaS9g5vcp0I7H75c/35V2UHpPB5SUsgDAbMfNGAAXwb2f9octMBFA93deSlkD9+/L7U3YjnqfmCKEsBotLIQYDPfJHAv1ljXLURViu+quO5u6TiIiIiI6fjGgTURERETkIIS4Wwhxqh/LhwB4UnVXLtyzDVuDLwBUOa53AHC/3oJCiK4wF6DKVF0/z2B9EQBmmFgfABSqrqf5OYZxjsxJPfcBOMHkOBrjU9X1ixwBucmq+wzLjQshkkxuR10W1g6gyOTzmp2jx+zXqrte8NG3/DY0fCZ2AO8109CONvV+eZaPksAvoSGLv9k59rM5UAJ3APATgAcbsar/U12/UAhxrR9jaKtx9+8AshzXIzzW7/n8RAD/Mbs9k8z+nnUH8HiAt23Kcfr9Mvu+B0P5f6S5KlKo35t/AXD2cbYBmBmojQTwd15dhnyKEGJKI4f0BRrK0KfCYN8WQoQCeF1110Ip5c5GbteT+vv+Lz//BtP6PSEiIiKi4xQD2kREREREDYYDmCeEWCuEmC6E0O2HLYToB6V/tvrk6/9JKe06T2kRjkDIi6q7/usI3LsdCwghBgH4A0AklH6rRtQZZFcLIe7xLIHrKF3+B4DBaOgJaiRTtVwnIcRwH8uvR0MQLBbAt44sPvUYwoQQTwJ4zuQYGkVKuQ0NExnioARIIh23V0kpd/tYxQohxJdCiDMdgQMvQojeAD5R3bXAka3XmvwHQLnjeg8AvwshuqgXEEIECSGmA3hFdfebJvs3Hwv+BFDpuN4VwEwhRJx6ASFEjBBiBoBb0Iz7pcc2LQBmAejsuGsTgCsa83slpVwMpVSz04dCiBccwWatbYcJIc4TQsyGEkT3XJ8NwGOqu6Y61uf2XXDsS79DmfDi6zfKH+rfs5eFEBM9FxBCnAJgEYBoHKXPTMPx9v36BQ2tJsYJIV4UQoSrF3AELGdByUhvrvd9IQDnb/RgNATO50opcwK4nYD8zksp5wP4VnXXZ0KIxxwTyDzXFySEmCCEmO056UtKWQrgKdVdDwgh/qvxvWsDJYvdWZa8Ho2bCKPnMzRki4cA+EUI8W8hRKTWwkKIKCHEZUKIP+EeZCciIiKi45zRbHEiIiIion+qwY7La0KIfQA2AygAUAcgHkrZzZ4ez5kNg5OrjsCqZ8nUKI9lNmg89TEppVcQyE9PQ+npfCIAASUz9C4hxBIowbdeUE5WCwA/QHmN4zTXBEBK+bsQYrFjGQElYH6bEGIdgCMAujvWFwwl6PwqgOeNBiiltAshfkBDye6FQojfoJRwtznuK5JSPqNa/lEAHzoeOw3ALiHEcgD7ofQBH+94LYBScjxQpb+1fIaG8vMXeNzviwXAFMelSgixCUqAvxTK+LsCGKJavgrAvU0cb8BJKTOFEFOhvM/BUPor73TsZxlQgvxjoPQad1oJg6oBxxopZbEQ4kU0BGivADBJCPE3lO9CKpT9MhLKfn0r3IPDzWUU3HsQ7wPwnFKZ2qdPpZR/e9x3M5TXcjqU34B7AdwhhFgN5bOugjLRpCuA/gCc5YzXam1ASvmhEOIsNHx37gVwrRBiIZTflM5Qfm9CAKyGEoC83MzgTfgfgBuglPKOB/Cb47dsG5SA62AAfR3L/g4gD8BVAdq2acfb90tKuUMI8Skayu7fA+Byxz6UByAdyj4bCiWT+D4A7zTDOKQQ4n14VwZ4P8CbCuTv/FQofb6HQ9kX/gPgfiHEMgAHoXwn0wAMhfJ/IRz3eXoRwEkAznHcfgTANMf3rhhKVZcJcM+Ov0/j96DRpJQ2IcQlAOYBGATl834OwGNCiJVQ/gaohfIe9QDQBw1l/2cFahxERERE1PoxoE1ERERE1GABlBPEnVX3pTsueqoAPAvgWSllvcFyHeG77LXW403ukyylrBFCnAHl5O8pjrvbA7jMY9G5AK4F8KOJ1V7qWH6w43ZnuL9vgBIQuhjKe2rGg1BOnreDUnr4Ao/H9wN4xnlDSvmRIxP8IcddkVAC22rVAO6SUn4hhGjOgPYXUIL26sz3eriXCdZTproeDmCE46JlL4ArpZSbGjPI5ial/FoIUQElGNQGyjHnBMfF05cApkopqzUeO5Y9CeU3wxmoSwAwyWOZEgDX4ei1KPAMZvnTj3gNALcAluM35UwoZYrvgfJ9DQUw2nHRUgclwKrnMgAfoSFQnQTl90NtFZTfhaf9GL8hKWWeEOI8KNnjzrLQzklNaj9A+X18NVDb9tdx+P2aBqAtlIkRgDJJwnPfPAQlCNycvctnQslWdm7jMJT/3wIpYL/zUspSIcR4KPvi9VCC2pFoeB89VaNhYph6PXYhxAVQJnVMc6wnEUovcU9HANwppZyps41Gk1IWCiFGQymnPhXKfh0B4GSDp1VBZ4IMERERER2fGNAmIiIiInKQUr4H4D1HOfFxUDKae0HJhIqFEhQqg3KyexOUMpnfSSmLW2bE5kkpjwA4VQhxKYBroARr4qFkwm2GkiX6jSNbzcz6coUQo6CcfJ4CoB+UE9B5AHZCCeR+LqWsNFE+3LnOg0KIEwBMh3JivieUEr+6xy1SyoeFEL8CuB1KplkylM/oEIDfAHxgouR3k0kpc4QQC+AeUP9DSpln4ukDoexrE6AE/3uiIahfCWV/2wAl4PZNKyw17kZK+bNjosH1AM6Gkt2aBCUAkQ2lxO8ngczya00cJbSvEUJ8C6UywAgo37ViKNmGPwL4UEqZLYRIb7GBNpGzVLgQ4nUowftToWRPJkEJDJZCmYSyGcpnPldKmW+wvloAVwghPgFwI5QM5CQAhVB+U74A8LGUstZkZrk/r2WFEKIvgDuhZKs6S3nnQAmafSalnAMAgd62v46n75fj/4dJUCYxXAMlQzcGSkWUTCiTsGY6Kh+Mb8Zx5Dky3Z0B1Jk+Jqg1xkAE8HdeSlkF4CYhxMtQvn+nQJlIkwAlozkHyt8p8wB8LaUs01lPPYDpQoh3oOxTp0DJzI6G0r97F5Tg/ntSysJGvXITHK9nmhDi/wBcCeWz6AElwB4EJaCeCWAjlMmHvznKphMRERHRP4SQUvpeioiIiIiIiIiI6DgjhIiCElB29m3ucTQmQhERERERkXlBvhchIiIiIiIiIiI6Lk1BQzB7MYPZREREREStDwPaRERERERERET0jyOUGvLTVXe901JjISIiIiIifQxoExERERERERHRP9HtAAY4ru+H0rebiIiIiIhamZCWHgAREREREREREVFzE0IMhVJiPBRKIHuc6uHHpJR1LTIwIiIiIiIyxIA2ERERERERERH9E/QDcI/G/d9KKT852oMhIiIiIiJzGNAmIiIiIiIiIqJ/mmoAuwDMBPBayw6FiIiIiIiMCCllS4+BiIiIiIiIiIiIiIiIiIjIS1BLD4CIiIiIiIiIiIiIiIiIiEgLA9pERERERERERERERERERNQqMaBNREREREREREREREREREStEgPaRERERERERERERERERETUKjGgTURERERERERERERERERErRID2kRERERERERERERERERE1CoxoE1ERERERERERERERERERK1SSEsP4J9CCBEGoL/jZj4AWwsOh4iIiIiIiIiIiIiIiIgokIIBJDuub5ZS1gRipQxoHz39Aaxu6UEQERERERERERERERERETWzYQDWBGJFLDlOREREREREREREREREREStEjO0j55855VVq1YhNTW1JcdCRERERERERERERERERBQwOTk5GD58uPNmvtGy/mBA++hx9cxOTU1F+/btW3IsRERERERERERERERERETNxeZ7EXNYcpyIiIiIiIiIiIiIiIiIiFolBrSJiIiIiIiIiIiIiIiIiKhVYkCbiIiIiIiIiIiIiIiIiIhaJQa0iYiIiIiIiIiIiIiIiIioVWJAm4iIiIiIiIiIiIiIiIiIWiUGtImIiIiIiIiIiIiIiIiIqFViQJuIiIiIiIiIiIiIiIiIiFolBrSJiIiIiIiIiIiIiIiIiKhVYkCbiIiIiIiIiIiIiIiIiIhapZCWHgD5x263o7y8HKWlpaitrYXNZmvpIRER0VESHByMiIgIxMXFwWq1tvRwiIiIiIiIiIiIiIiaHQPax5CysjJkZWVBStnSQyEiohZQX1+PmpoaFBcXIzY2FqmpqRBCtPSwiIiIiIiIiIiIiI4ZB4sqsSevHIM7xSM23NLSwyETGNA+RmgFs4UQCA4ObsFRERHR0VRfX++6fuTIEYSGhiIpKakFR0RERERERERERER07Fi9rwhXffA3quvsSI214ufpJyExKqylh0U+MKB9DLDb7W7B7KioKCQkJCAiIoKZeURE/yA2mw0lJSXIy8sDAOTn5yMmJgahoaEtPDIiIiIiIiIiIiKi1u+lP3aius4OAMg5Uo1Z6w7hprFdW3hU5EtQSw+AfCsvL3cLZrdv3x6RkZEMZhMR/cMEBwcjMTERiYmJrvvKy8tbcEREREREREREREREx46VmUVut9/4c08LjYT8wYD2MaC0tNR1PSEhgYFsIqJ/uJiYGNf1ioqKFhwJERERERERERER0bErzMLWvscCBrSPAbW1tQCUntkREREtPBoiImppYWFhrslNzv8jiIiIiIiIiIiIiEifzS697gsLYaj0WMBP6Rhgs9kAKKVmmZ1NRERCCAQHKzMH7XZ7C4+GiIiIiIiIiIiIqPUrrKjxuo8B7WMDPyUiIiIiIiIiIiIiIiIiOq7llXoHtIODmEh6LGjWgLYQQpq8LDKxrjOEEN8LIQ4JIWoc/34vhDjDj/FECCHuE0KsEkIUCSHKhRDbhRAvCiE6NunFEhEREREREREREREREVGrlFta7XVfWXV9C4yE/BXS0gPwRSg1tt8BcJPHQ2kAzgdwvhBiBoBbpJTexe8b1tMVwC8Aeno81MtxmSqEuFxKOTdggyciIiIiIiIiIiIiIiKiFperkaFdUlnXAiMhfx2tgPbbAN4yeLzC4LGn0BDMXg/geQAZALoCuB/AIMfj+QAe0VqBECIKwM9oCGa/B+ArAFUAJgB4EEAsgG+FECOllJt8vyQiIiIiIiIiIiIiIiIiOhZoZWhX1dlQXWeD1RLcAiMis45WQDtPSrnF3ycJIbpBCVoDwBoAY6WUVY7bq4UQPwFYDGAogH8LIT6SUmZorOpeKFnYAHC/lPIF1WMrhBALAfwFIALAKwBO9nesRERERERERERERERERNQ65ZV5B7QBoLSqjgHtVq5Ze2gHwF1oCLpPVwWzAQBSykoA0x03QwDc6bkCIYQFwL8cN7cDeMlzGSnlCgAfOG5OEEIMafLIiY5T+/btgxACQgjMnDmzpYdzXHC+n0888UST1vPEE0+41kXefL0/48ePhxAC48ePP7oDIyIiIiIiIiIiIqJmp1VyHABKqlh2vLVrtQFtR+/s8xw3d0gpV2ot57h/p+PmZOEdqRgPIM5x/WMppV1nkzNV1y/wd7xERERERERERERERERErcmBwkocKKxs6WG0ClolxwH20T4WtNqANoDOANIc1xf7WNb5eHsA6R6PjdFYTssaNPTyPsnE+Og4NXPmTFcW5759+1p6OIbS09MhhMC1117b0kMJKGYaN1i0aJHrvVi0aFFLD6fVOZa+r0RERERERERERERH0//m7cLYFxZi7AsL8fqC3S09nBanm6FdWXuUR0L+Olo9tC8WQlwGoCOAegCHASwHMFNKuVDnOb1V13f4WL/68d4A9vq7HillvRAiA8AAj+cQETUrKWVLD4EAThggIiIiIiIiIiKi40ZlbT3eWZzhuv3SvF24elQ6YsMtjVrfD+uz8POmbAxoH4dbxnVFaMjRy5ktrqhFnc2OlBir12N2u8QHS/di4c48jOiciFsndIUl2HtsdTY7CitYcvxYdbQC2n08bndzXK4WQvwA4Fop5RGPZTqorh/ysf6DOs9T366QUpaYWM8AAMlCiDAppfaerUEI0d7HIm3NrouIiIiIqLlU19lQVFGLdnHhLT0UFJTX4Js1B5ESbcUFg9IQFMTqLI1ls0tk5pcjITIUiVFhLT0cIiIiIiIiamF5pTWoqXfvwrvhYAnG9Uj2e13rDhTjzq83AADmb89DVFgIrj+pcyCG6dM3aw7ikdlbUGuzY/rJ3XDP6T3dHl+wIw9Pz90OAFieUYjUWCsuGeYZKlTOQejllh1hyfFWr7kD2pUAfgKwAEp2dDmAZADjANwCIBHAZAA/CiFOk1Kq95ho1fVyH9upUF2P8njMuR5f69Baj+mANtyD6kRERERErc66A8W49sNVKK2ux8S+bfDOlUNarMVHnc2Oc19fiuwjSv+q3XlleHASCyU1ht0uce1Hq7BkdwGsliC8e9XQRp2gCJR6mx0fLN2L3XnluGRoBwzvnNBiYyEiIiIiImpJP27IwqsLdiMpKgzPXtAfXZM9Q1jNxzOYDQD7CyughOn888afe9xuP/nztqMS0LbbJZ7/bSdqbcpreWtRBq4ZlY4k1UTul+ftcnvO/bM2aQa09cqNA0BJFUuOt3bNXQ8gTUp5mZTyfSnlUinlBinlPCnlIwD6AljvWG4cgGkez1XXDfC1J6n3Qs9UE+d6zOyNRuuh45yzV/F1113nuq9z586u/ry++hjPmzcPV155JTp37ozw8HDExMTghBNOwP3334+cnBzDbWdnZ+OBBx7A4MGDERsbi9DQULRt2xb9+/fHZZddhpkzZ6K0tNS1/Pjx4yGEwP79+wEAH3/8sdc4x48f36j3wWaz4c0338SIESMQExOD2NhYDB48GC+++CJqaszN8Vi5ciUeeeQRjB8/Hm3btkVoaChiYmLQp08fTJs2Ddu2bdN8nrMf8n/+8x/XfZ6vy7NXst1ux59//ol7770Xo0ePRlJSEiwWC+Li4jBw4EDce++9OHDgQKPeCwDIz893bffdd9/VXGbq1KmuZaZPn665zCuvvAIhBEJCQtw+S/VrfOKJJ1z37du3D0IITJgwwXXfhAkTvN6LmTNn6o69uroaL7zwAgYPHozo6GhER0dj+PDheOONN1BfX2/+TTCwefNm3HTTTejevTsiIiIQHR2Nvn374q677jLsae1Pb3Ct96ep31ctzu+Vr+9OcXExnnrqKYwcORJJSUkICwtDu3btcN555+H777/367X8+eefuPjii9GhQwdYLBakp6ebHi8Rkb9q6+3411frUVqt/B/w+9ZcrMwsarHx/LIpxxXMBoB3F2e22FiOdX/vLcKS3QUAgOo6O575ZXuLjufNhRl49tcd+G7tIVzx/kpklVS16HiIiIiIiIhaQlFFLe79diMy8yuwam8RnpyjfV68uVTUep8D3nG4rFHr2nSopImjUSqLvfj7Tkx6dQn+M2crqutsPp9TWl2HgvKGuITNLrFsT4HbMttzSj2fpim3tFr3sRJmaLd6zZqhbVTiW0qZK4S4CMB2AKEApgN4TbWIes8K9bEpdU09z7MlzvX4Woev9fjiPd3DXVsAq/1cJx0DKioqcNVVV2H27Nlu91dXV2PTpk3YtGkT3n77bXz55Zc4++yzvZ6/ZMkSnH322V5BztzcXOTm5mLLli346quvkJSUpPn8QCorK8OkSZOwbNkyt/vXr1+P9evX46uvvsJ7771nuI6ZM2e6BRmd6urqsH37dmzfvh3vvfceXnvtNdx6661NHvOTTz7pFgB3OnLkCDZu3IiNGzfi7bffxmeffYbzzz/f7/UnJyejT58+2LZtGxYuXIibb77Zaxl10HThwoWa63EuM3DgQMTExPg9Dn/l5uZi4sSJ2Lhxo9v9q1evxurVq/HHH3/ghx9+QFBQ4+c1Pfvss3jkkUdgt7vP9Nu2bRu2bduGt99+GzNmzMDVV1/d6G20NnPnzsUVV1yBkpISt/tzcnLw008/4aeffsJZZ52Fr776ClFRxrMtH374YTzzzDPNOFoiIndf/L0fB4vc/8TdebgUI7smtsh41u4vbvRzl2cUYFt2KSb2bYsOCREBHNWxaWVmodvtnbllOFhU2WLvzf/mN8yOr7NJvPzHLrx0yQktMhYiIiIiIqLG2HG4FDlHqnFStyTNfsx6ymvqcfsX67BqbxEqa90Dtot35Qd6mIYqa7wDxjtMBn89hYcGN3U4+GVzDt5YqGR6b88pRZfkKFx1YifD5xRWeOeqegbCwy3BqDIRHFcHxj0xoN36Ha0e2pqklJlCiHkAzgLQTQjRTkqZ7XhYPU3EVw2GSNV1z9LizvWYqeNgtB5DUkrDPt8tVcqRzBs2bBg2b96MH3/8EY888ggA4Pfff0e7du3cluvcuaGMhs1mwznnnIOFCxdCCIEpU6bgggsuQOfOnVFXV4dVq1bhpZdewoEDB3DhhRdi+fLlGDJkiOv5NTU1mDJlCkpLSxEdHY1p06ZhwoQJSElJQV1dHfbv348VK1Zg1qxZbmP46KOPUFFRgYkTJyI7OxvnnXcennrqKbdlIiMj4a8rrrjCFcwePnw47rrrLnTv3h25ubmYOXMmvv32W82Arlp9fT3i4+Nx7rnnYty4cejevTsiIyORnZ2NdevW4bXXXkNBQQFuv/129OrVCyeffLLruZMnT8bQoUPx1ltv4e233wagZAB7SktLc9teamoqzj//fIwcORJdunSB1WrFwYMHsXz5crz11lsoLy/H5ZdfjnXr1qF3b/9LmY4bNw7btm3D4sWLvR7LyspCRkaG6/a2bduQn5+P5OSGsi1SSixZsgQATGfOp6WlYfPmzVi9ejWuv/56AMCHH36IYcOGuS3Xvn17zedfcMEF2L59O+644w6cc845SEhIwM6dO/Hf//4X27dvx5w5c/Dee+/5/Dz1vPXWW3jooYcAKEH/f//73xg9ejRsNhvmz5+PF154ARUVFbj22muRlJSEM888s1Hb0dOY72tTzZs3D+eeey5sNhvS09Mxbdo0VyWDrKwsfP311/jss8/wyy+/4JprrvH63qrNnj0bmzZtQv/+/XHXXXehX79+qKqqwoYNGwI2XiJqfnsLKrBgey7Ka+qRGBmKif3aIiXa6vuJLaC8ph6ve5QHA4CSqpY7YPM8sAeU/zN9/d08Z2M2pn+pFHp66Y9dWHTfeLSJaZ3v+9Fis3s3AftzRx6uGZV+9AejYcGO3JYeAhERERERkWnfrDmIf8/aBCmBE9rHYta0UQgxGdR+/c/dWLTz6Aau9WhlaO88XAa7XSIoyL+YVYTFO5xYU29DWIj5QPcDsza53X5l3i7fAe1y74C2Z5A7NtziFdCurrPBanEfm1HQmiXHW78WDWg7bIMS0AaANADOgLY6QKwdsWmgzo727GV9CMAIAJFCiDijrHHVevKllP70z25xdrtEceU/6wsXHxHq94+ukcjISPTr1w9r1qxx3dejRw/DEsCvvPIKFi5cCIvFgh9//BGTJk1ye/zEE0/EVVddhTFjxmDr1q248847XYFNAFi2bBmys5Vd/osvvvDKwB4xYgQuueQSvPDCC6isrHTd7wzSWSwWAEBcXBz69evXuBfuMGfOHMyZMwcAcOaZZ+LHH39ESEjDT8SZZ56JJ598Eo8//rjheiZNmoTLL78cERHuGUGDBg3CWWedhTvuuANjx47Fpk2b8Pjjj7sFtOPi4hAXF4eUlBTXfb5e19SpU/H444+73gunwYMH47zzzsP06dNx4oknIisrC8888ww+/fRT4zdCw/jx4/H222/j8OHD2LFjB3r16uV6zJmR3bdvX1RVVSEzMxOLFy/GRRdd5Fpm48aNKCpSSrqOGzfO1DYtFgv69euHgoKG8imdO3c2/Tk7s7DVAfTBgwdj4sSJ6NOnD3Jzc/HWW281KqCdn5+P++67DwDQrl07rFy5Eh06NPwMjx49Gueeey7GjBmDiooK3HTTTdi7d6/XZ9QUjfm+NoWzEoPNZsPpp5+O2bNnu+3jgwYNwtlnn42xY8fipptuwvfff48FCxbglFNO0Vzfpk2bcMopp+CXX35BWFhDcZCxY8c2y/jp+LEnrxyz1x9Cl6QoXDA4jRPmWtCevHKc98ZSVKiCsu/+lYnf7xyLyLDW8Ce2u18352jOai7WuO9oqarzPrCutdmx4UAJXpm/G1HWEDxyVm90SnSfpKfu21VVZ8NHy/bhxjGdERkW4nWw+k+hNct8QYAD2iWVtdiaXYp+7WIRG+Hf/+mc6U5ERERERMeSD5fuhXTMG9546Ah+2ZyD8wamGT8JSpzGVzstm10iWBXXkFJi3rZc7Mkvx8S+bQPaY7tKYyJ5Ra0NB4srvY61fdHK0M4vq0H7ePOVwTwntmudp/BUVOF9vPvK/N3YnVuOCb1ScO4J7RAbbsFhj3Lih49UIz3J/TWWGkzq53Fr69cazrbpnYlVNxPopbOM1uOeDeO2AbhQtdxKzUEIEQKgq846Wr3iyloMeWp+Sw/jqFr7yKlIjArzvWAzqaurw0svvQQAuP32272C2U7x8fF44YUXcOaZZ2Lp0qXYs2cPunXrBgA4fPiwazmjQFZISEizl6l2ZkSHhYXhvffecwtmOz3yyCP49ttvsWXLFt31qLOntcTGxuLJJ5/E5MmTsXTpUhQWFiIxsfGlTn0FMNu3b4/77rsPd955J3766SdTmV+e1EHoRYsWuQW0nVnb48ePdwW0Fy1a5BbQdi4TFBSEMWPG+LXtxpo+fbpmNnhCQgKuu+46PPfcc9i0aROOHDmC2NhYv9b90UcfuSZYvPTSS27BbKdBgwbhwQcfxCOPPIKsrCz88MMPuPjiixv1WlqDjz76CLm5ubBarfj000+9Jmw43XjjjXj//fexatUqfPTRR7oB7aCgILz//vtuwWwiX4oqanHO60tdM04Lymtw87iuPp5FzeX3rYfdgtkAcKi4Cj9syMIVI4xnF7eEvQUVmvcXNdMBm5QSeWU1iA236AaZtTK0iyvqcNOna3HEcZB5pKoO39w80m2Znbnu/b7eWZyBdxZnIDEyFDOuHoIhnRKaNPY6mx3frT2EmjobLhnWARGhLX/ItLegArd/sQ6Z+RW4fERHPHJWb7e/Z/LKvA/wV2YUoqKmvskTLOx2iU9W7MNzv+1AdZ0dbWLC8P2to5EWF+7XehrzNxgREREREVFL8Owz/eqC3ZjQKwVP/LQVa/YVo6KmHu3iwvH4OX0wNL3hGHT9wRKf666qsyHKcZxWb7Pj3m834ocNStLbB0v24sfbR/sVJDailaENANtzyvwOaGsdzuWWmg9oa1UWM6NAI0O7tt6O2euzMHt9FuIjLJpjyyqp8gpoH2FA+5jW+OapgdNHdT1bdX2v6ravlEZnJDALwD6Px5aqrhutZygaSo4vM1iOCACwatUq5OTkAAAuueQSw2XVweoVK1a4rqemprquf/TRRwEeoXn19fWuoOvpp5/uVbbZKSgoCNdcc41f666oqMC+ffuwdetWbNmyBVu2bHHL1PXs8dxUpaWl2Lt3r9v2nMFH52P+atOmjSuIre6Xrb49fvx4VwBZb5kTTjgBcXFxfm+/Ma644grdx9Rl7xvzfsyfr0yeiYuLw4UXXqi73NSpU72ec6z68ccfASiTG9QVBLQ4v+/q77qn0aNHN1s2OR2/Pl6+z6180rO/7mjB0VCRzizi5XsKNe9vaXq9pJojQ7vOZsfVH67CiGcWYMKLi7Bdpz+X1kzxuZtz3A4wV+0tcjvolVL/ALiwohavzN/dhJEr7vlmIx78fjOemLMNV3+wyufy+WU1ePH3nXh7UYbmazKjoLwGby/KwLdrDqLeZvd6/OV5u7A1uxRVdTZ8sHQv1nj0H8/1mIkOKNnuS3YXeN3vD5tdYvqX6/HEnG2orrM7tlWDWWsbimmVVtfh2V+3Y9pna7F8T4HuZ2Rm5j0REREREVFLq6n3Pq7LzK/Ac7/uwPfrsnCgqBKFFbXYnHUE079cD7vqmHXu5hyf63ceN9rtEnd/0xDMBpTjprcWZeg9FYByXPznjlzMWnvIq5e0J60e2oDSH9xf5dXewfE8jWNRPQeLKr3uC9aowOucZP7d2kOos9l1z784zVy2D+U13mPLKqnyus8ooG30GLUOLZpuIIToAuA0x81MKWWW8zEppRRC/AhgGoBeQogTpZRe2dVCiBPRkKH9o/Q+g7IIwBEAsQCuEUI8r7EMAFyruj67Ma+H/lnUpY5HjhxpsKQ7dVb2SSedhC5duiAzMxN33nknPv/8c5x//vkYN24chg4ditDQ0ICOWU9GRoYr49azR7On4cOH+1xfQUEBXn75ZcyaNQu7d+82PPmsLqndWPv378eLL76IOXPmYP/+/T7H1qVLF7+3MW7cOOzYscOtj3Z2djb27NkDIQTGjRuHmholO0rdR7sx/bMDQZ1F7ikhoWHWYFlZme5yepwZ+oMGDTIsI96mTRukp6dj3759hln9xwLn9/333383nV2m/q57GjBgQEDGRf8sKzJbZ6D0WLBmXxH+3JGHIZ3icUrvNgFZZ6XOLOdlGQWN6kXV3PQOcpsjyDh/W64rkJpzpBrvLM7Aq1MGeS2nFWTfV+idSV5aVYf4SOVvIl8zppsawK2us+GnjQ0nE9bsL8bu3DJ0bxOtubzdLnHpuyuQ6ciA35J9BG9ePtjndux2iS9XH8DqvUUY1zMZL/6+y3WwvT2nDI+d08dt+Tkbs91uf75yP4apsgC0MrQBYO3+IpzRr63P8ejZcLAYv2ickNmTV+66/uzc7fhyldL1acnuAvz6L+1qNLtzy5HUgtWViIiIiIiIzCiu0D7u/GrVAa/7co5Uo6CiBinRVkgp8auJgLbz+Pz3rYfdjj+dvltzCHee0h0pMVbN5788bxded7Ti+njFPvx422jd85ValdEA6E48N6IVNNY7FtXiWW0NAIKEdzWv279Yh9+35gIA5m07jNRY4+pgC3bkIV6jLVa2RkDb6JxCeU096mx2WEz2Sqejr9k+GSHEOY4y3nqPtwHwHQDnnvamxmKvAHB+S14XQrjtuY7brztu1juWdyOlrAXwmuNmbwD3aoxlJIAbHDcXSylX642byCkvL69Rz1P3wrZYLJgzZw569+4NQOl7/NBDD2H06NGIi4vDpEmT8MUXX8Bma1y2j1nFxQ1ZPr6yT9u0MQ4ErF27Fr169cKzzz6LXbt2GQazAaCqyvs/Fn/8+uuv6NOnD9544w2fweymbM8ZjHb20QYa+mf36dMHycnJaN++Pbp06QIppSvwvWnTJhQWKkEos/2zA0GvJDagZNo7NWbfcvYD97UvAEDbtm3dnnMsqqurQ0lJid/PU3/XPcXHxzdhRPRP1crio8eMLVlHcOmMlXhrUQZu+HgNftuiP9nEHxU6s5xLKuuwrREHhs1NL3O4OTK0X5q3y+32jxu8D9AB7d5VxRoHlyWq5bKP+P5/3N7IMmaAkm3taV+h/u/55qwjrmA2APyyKQcF5TXYebjMcBw/bszCw7O34IcN2bjr641uM8c/XLYXtfUNWdpHNN6T3NKGcdbb7CjU6KHta+xm7CvQfv7BYuX+mnqbK5gNKCcAvlrtfZIHAPbkl2ve3xpllVRhxl8ZWLijcX/vExERERHRsatQo2czAOgd4pU5Mpc3Zx1B9hHfGcvOyd2bs45oPl5rs+ODpcpxYUF5jdf5dWcwGwA2HTpiOLFbbzL+/kYcK5ZpZGhrVQvTs1sjoF1nkyhVrTevtNoVzAaA37fmuk2o1pIaa9XO0C72L0PbzOPUspozQ/t1ABYhxCwAK6CUAq8CkARgPIBbADgb5y6FRkBbSrlLCPEigAeglARfJoT4PwAZUPpd/xuAM93jBSmlXo3BFwBcCqAHgOeFEN0AfOUYzwQAD0F5L6oA3NnYF9yS4iNCsfaRU1t6GEdVfMTRyV7Wow4ELlq0yHQfaM+AcZ8+fbB582bMmTMHc+bMweLFi5GRkYGqqir89ttv+O233/Dyyy9j7ty5PoPNjaX+T9FX9qlRgLq2thaXXHIJCgsLYbFYMH36dJx33nno0aMH4uPjXf2CMzMz0bVrV5/r86WwsBCXX345KisrERUVhXvvvRcTJ05E165dERsb68pw//PPP129jBu7Pa0+2ur+2U7jx49366PtXEYIYdgn/VhkJlO5KZ9va6H+rl9yySV49NFHm7zO4GDtfrJERgS8v3O19XaEhnDmqJEZf2W6lay++5sNOKPfGU1er94sZwBYuqcA/dJim7yNQNIbb1FFbcB7G5s9ANTKDs/VOPgvqayFszNQTonvg+WSqjokRPr3d6KUEgXltZol0CpUB8ZHquowf1su2sZacWKXRM0A+NCnlDYbnRIj8PTk/jipe5LXMi/8ttNwPBsOlmB4ZyUDe0++90F/SHDD51VYUat7YuWAnycpflifhW/WHET3lCjcO7Gn22QCNef7tOFAiddjf6hOPqjt0Th50RqVVNbijP/9hTLH5/7sBf1x2fCOLTwqIiIiIiI6WvQytPU4J2tvyzY3ud054Vw9UdnTu39l4pfNOThUXIVRXRPx4bXDYLUEa55rXbqnAGN7JGuuR6+HtlY5biN2uwxAhrZ2YLq4ohax4Ure6yGNcWX6mBwdHxGKHI1zCVoT4n2dryiqqGVlsVasuUuOtwMw3XHRMwvAVCml3p7/MIAUANdDCV5/pbHMBwAe0duAlLJMCHEWgLkAugO4yXFRKwVwhZRyg8FYW62gIIFEftGOKnUAOzQ0FP369Wv0uoKDgzF58mRMnjwZAJCTk4Nff/0Vb731FtauXYu1a9fi5ptvxuzZzVMNX12COjdX+ySkk1Fm+p9//onMzEwAwJtvvokbb7xRczl1RnhTfPvtt67M2e+//x6nnXaa5nKB2F5qaip69OiBXbt2YdGiRbjlllvc+mc7jR8/Hh9++KHrMee/AwYMOG6ychMSEpCTk2NYUtvJuT+p9zHAPUvcbvfuFepUUeFdevZos1qtiIiIQGVlJUpKSpr0XSdqiiCNuHVJZa1uCSpS/LbV/bfKKBDtD71ZzgCwbE8BbhnXVffx7TmlqK6zoV9a7FErZaXXQ7vWZkdFrQ1RYYE7LKjT6AG9eFc+BICTuiUhKEigtt6uObtbq+S4OqiaYyJDO6+s2q+Adp3Njps+WYOFO/N11+d8TVe8vxJbspSTFPef0RPdkqN017u/sBJXfvA3HpjUC7eM64qcI1V4+Y9dqKm3+5y1v2xPgSugvUvjoF89Cz7P4CTI/qIKzRL4Ukq88PtO/LghGwM7xuGBM3qhqs6GO7/eAABYnlEIa2gwwkK0J2AVlNeisrYeyzO8WyHs1pk9r3d/azNvW64rmA0oQX4GtImIiIiIjp7Nh45ge04pxvZIRtvY5j3nUVNvQ3FFHZKiQhHiOD7Xy9DW48wwPqAxQVqL8/g8r8z4uPCQI8N4eUYhZvyViTtO6a55bG/UY1rvHEhZdT1Kq+sQY9VvJ6lWrnMOxDNDu7Ze6X9dXWfDxUPbI1q1/l2HtSc5F1bUIj1JmcSu1fPb1/FzcaX269fK0NaqFKd2oLASPXRajlHLa84zaNcAeBzAbwB2ASiCUha8BMBmAO8CGCWlvEhKWaK3EimlXUp5A4CzAPwIIBtArePfHwGcKaWcKqXUj4go69kDJSD+bwBrHOOoBLATwP8ADJBS/tzI10rHEbMZSoMGNfSC/OOPPwI6htTUVFx//fVYsWIFBg9W+jD+/PPPXuWyA5VN1bVrV1eJ6tWrjSvuGz2+detW1/UpU6boLqfuP67F7Otybi8hIUE3mG1me2Y5s7QXL16M7Oxs7N6929U/22nChAkAlD7aeXl5+OuvvwA0vn92IDPmAsUZ0F2/fj3q6vT/CMjLy3OVgfcMAkdHN/xhYDThYOdO4wy2o/X+OL/vy5YtMywlTtSctAoeaJVnJndx4eYOzvxVYRAY/3tvke4B5ftLMjHp1SU4/63luPPrDU0qj+0PvR7aQODLjtfVe/9Zfs2Hq3D1h6tcAVO9A06t2d3qkttmyrdpZU0bWbwzXzeYDQDPzN2B3o/+hp6P/OoKZgPA2wszdDOY1V6ZvwvVdTbc/sV6fLv2kGaPNE/LMxpKxu3WCGgfKq5yzcw3KvFWXWfXfE9/3XIYby3KQFZJFX7ZlIMzX12Cqz74222Zdxdn4ojO5+QcwwqNgLaeYyWg7fl++urbTkREREREgTNvWy7OeWMp7p+1CWe8+pfPoK9TVa0NczfnYNHOPNPH2YeKKzHplSU48dkFuPDt5a5jT6MAsRZn1u9BjQCq3lgB48nJnl6epxxXamVJGwa0ddqlAdo9pvWUa0xIB7yPv/89axMemr0ZT/68DZe8u9J1LqLOZkdmgfYxoXr8RQbHoHq0srMB5fyBel+ot9ndJi8DQLTVfXK/1iR7aj2aLaAtpVwspXxSSjlJStlTSpkopbRIKeOllAOklLdIKVf4sb65UsrJUso0KWWY49/JUspf/VhHhZTyeSnlMMc4IqWUvaSUd0spfTffpX8Eq7Vh1ldNjf5/KieddJIr6/Sdd95BaWng+2VaLBZXsLS+vt6rj69zrEbjNCMkJMS1nT/++AM5OTmay9ntdnz88ce666mvb/gPQS/oZ7fbMWPGDMPxmP0MnNurqanRzfKtrKzEJ598Yrg9s9R9tN955x0ADf2zndR9tN94440m9882+14cTaeeqrQ3KCkpwaxZs3SX++CDD1wn253PcercubPrutGEgy+++MJwLEfr/Tn33HMBKBnjb77p1SGD6KjQOmjRCwpSA60WJbYABJErNT4Pp9p6Oz5bqf2n5XtLMl3Xf9mUg9+3+q52EQh6GdpAQ+nvytp6vL0oA68t2K3Zt9mM2no7Kg229dPGbOSX1aCw3Py+W6Laz3NMHHD7G9CeofpM9NTbpVdZ77KaeqzM9B3Qra6zY3duOdbuN18xZv2BElcVgN153rPYK2ttuHTGSrw6f7fPvuL7NQ7If1if5Xa7rKZes9yd0aSZnYfLsGpfkeG21fLLalBa3bqCw8UVtV5jKvDYN/VK9BERERERkbFPV+zDsKfn46zXlmDHYXPnzp//bYfrekllHX7ZpH2eWq3eZsd1M1fh1s/X4dqPVuM51TqMx7cfmQXK8dLGQ0fw40blOMnfSd/OrF9/M7RzTQbrnWatO4QKjQC1UUDb6Hgmu0SZKL0l64jPXthaFdYA9wnBVbU2/Lih4Vhze04pXlugdAn+fOV+1Nm0z8UUqTLiC8sDd363tt6OO7/egMz8ctzzzUac/NJir2UGdohzu92Y3uJ09LDpIpGH1NRU1/WMjAzd5axWK+69914ASoBzypQphuWRy8rK8MYbb7jdt2TJEuzZs0f3ObW1ta4ezFFRUW6BU/VYjcZp1rRp0wAoQcGbb77ZrW+w07PPPovNmzfrrqN79+6u63qB7wcffBDr1q0zHIvZz8C5vYqKCnz33Xdej9tsNkydOhXZ2b4zocxQZ1m/9tprXvd5Ludcpin9s82+F0fTdddd58rov+eee3Dw4EGvZTZu3IhnnnkGAJCWluYqp+8UFxeHAQMGAAA++ugjFBV5nxD/66+/XO+hnqP1/txyyy1ISlJ6oD766KP49VfjuVTLli1zZecTBYpWn58SBrR9io3wztAOxAGSZ9muGI9ZvR8v3+eVFV1vs3sFDf/78zbN9dfZ7Hh3cQYe/H4TNh0qMTWmvNJq3Z7JVQYZ5c6D9du/WI//+20HXp63C1d9+Ldmby5fDh+p1qwmoHagqMKvGe/FTczQttklvlx1AC//sROHipX3JyO/HGe88hd6PPwrVu01H5T1NG+bcasWp40mP0OnervE35nKuPboZDav2luE/83fhcd+3Kr5uJPnAXl1nQ1LdhfoLO1up05JOACY/uV6U+tQ0+qR3lJeW7Abg5+ah6FPzcf36w657i/w+H0IVJsCIiIiIqJ/koNFlXhizjbkl9Vga3ap7rGvWkllrVdlJ8/JuFq+XnMQKzMbjus+XLoX5TX1qKytxwdL92Lmsr2abcPe/ct9crPz2KrQ34C2Y5LsIbMB7VobauptfleDen/JXlRoTK5XB5VzjlTh5Xm78P6STJRV1xkezxwqrsKVH/yNs19firHPL8Qzc7dj2NPz0f3huXhnsft51vIa7bEWV9bho2V7UV1nw77CCq+J4G8tysBbi/bgiTn6n7/6/fZn8rsZP23MxskvLcasdYc0JxwMaB/rdpsZ2q1bc/fQJjrmDBo0CFarFdXV1Xj00UcREhKC9PR0V8/ftLQ0hIeHAwDuv/9+LFiwAAsWLMCvv/6KPn364JZbbsHIkSMRFxeHsrIy7Ny5E4sWLcIPP/wAq9WK22+/3bWtBQsW4L///S/GjBmDs846CwMGDEBycjKqqqqwa9cuvPPOO67g79SpUxES4v6VHTVqFBYuXIjVq1fjueeew6RJkxAZqfSbCA8PR1pamunXfc455+Ccc87BnDlzMGfOHIwePRp33XUXunfvjry8PMycORNff/01hg0bplt2fOLEiUhJSUFeXh4efvhh7N+/H+eeey6SkpKwZ88evPfee1iwYAFGjx6NZcuW6Y5l1KhRrut33XUXHn74YaSmprrKS6enpyMkJASXXHIJHnroIdTU1ODaa6/Fhg0bcOqppyImJgZbt27F66+/jrVr1/rcnlnt2rVDt27dsGfPHhw5cgSAfkD7ww8/dC3Tr18/t57r/ujYsSPat2+PQ4cO4cUXX0RaWhp69uzp2hfatGnjVr77aEhOTsYLL7yA2267DdnZ2Rg6dCgeeOABjBo1CjabDfPnz8cLL7yA8vJyCCEwY8YMWCzeAaVbb70Vt9xyC3JzczFmzBg8+uij6NmzJ4qKivDzzz/j7bffxtChQ7FihX4xD3++r00RExODL7/8EpMmTUJNTQ3OPvtsXHjhhbjwwgvRtavSJzcnJwdr167F7NmzsWnTJrz++uuNnshApEXrQIclx33T6g19uLS6yb3HPQ+G/3VqD7cD9MKKWny/Lgvt4qyot0lM6JWiOZM6+0g19uSVoVuK+2/5q/N3442FyqS3WWuzsOLBk5EYFaY7nllrD+HB7zej1mbHVSd2wn8nu7d6qK7T785TWFGLI5V1+HNHnuu+TYeOYE9eObr72TvKGTA2Um+TfvUkO+JnD23PgPYLv+90HYx/seoA/rp/Al6etws7DIK1ZunNUvdkdlKC2gPfb8Iblw/WLZ9m1v4i9wPylZmFhhn7ajtzm/4eqeWV1fi9TzWHgvIavLZgN6RUZu0/++sOTB6YhqAg4XUCReuEUVO3ffc3G7H5UAnOGpCKJ87p6+rVR0RERER0vHh/SaZbdbRlexqqW9XU2/Dg95vx25bDGNghDq9OGYTk6DDNibcRocYhrIqaejz183a3++rtEiszCvHB0r1Y4aiqtXhXPj66bripsftbcry0qh4VNfWmA+FVdTbNcuMdEyIMs7z3FlRgW453pvvh0mrU1NsQJASu+2i161h3zb5iw4D25ysPuI75aurtmKEK8D//2w6cN7AdUmOV86qlBse+/5mzDUt2F+CiIe01H3/+N+OWkkWqYzB/+5c3RVhIEHq2jXG7jwHt1o0BbSIP0dHRuOOOO/D8889j3bp1mDhxotvjCxcudAUxg4ODMWfOHNxyyy345JNPcODAATz00EO6605JSfG6z263Y/Hixa5MbC0XXHABnn32Wa/7p02bhrfffhtFRUV48MEH8eCDD7oeGzduHBYtWuTj1br7/PPPMWnSJCxbtgx///23Vx/swYMH491333X19fYUGRmJTz75BJMnT0Z1dTXeeustvPXWW27LjB8/Hm+88YZXT2W1bt264ZJLLsE333yDP/74w6tH+d69e5Geno727dvj7bffxtSpU1FVVYVnn33W63269NJLceONN3qVvG6scePGubLqPftnOzn7aDs1tn+200MPPYRbb70Ve/fu9cp0/uijj3Dttdc2af2Nceutt6KkpASPPvoo8vLycPfdd3stExYWhhkzZuDMM8/UXMeNN96I3377DT/88AO2bduGyy67zO3xfv36YdasWWjXrp3uOPz5vjbVqaeeit9//x1XXHEFDh8+jG+//Rbffvut7vIxMTG6jxH5y26XmqV6j3bJ8YU78/DK/N2IDbfgiXP6oEty1FHdfmPUavRzzjlSjQHax1mmefbQHtopHmO6J7kdgD80u6GqyVkDUvHQmb011/XRsn14+vz+bvc5g9kAUGuz4/2le/HvM3ppPl9KiRf/2Ilam/JaP125HzeP64L28RGuZYwCmMUVtTioEYjOKqlqREDbd8C5rLq+USXHbXaJLBPrz/fIsFXPLC8or8WsdVmmytYF0saDR/x+Tm5pDS5+x3SHJl37PDK0F2zP01my+Rn1v8vML0dBeS2Gpce7JjE2l+UZhahXnVzLL6tBYUUtkqPDvDK0a+rtqLfZAxZ0/mDpXvy1S+nZ/tnKAxjbPRmn920bkHUTEREREbUWW7O9A69SSgghMH9bHr5fp2ReL88oxMfL9+HeiT2x2PF3str+wgr8sfUwgoRAt5QopCdFutY1a10W7v12o+b2Z6075ApmA8DCnfk4WFSJDgkRmssDQJDjMMTvgHZ1neYxtZ7qOpvXsVFYSBA6JIT7LFuuVZVNSiC7pBrZJVVuE7d/89HizGgCs10Cv285jGtHK20jfU3m/nNHHrqlNO4cUVEzZmgbiQ23ID3RfX/IKq5Cbb0doSGcdNwa8VMh0vDcc8/hvffew5gxY5CQkIDg4GDdZcPDw/Hxxx9jzZo1mDZtGvr27YvY2FiEhIQgLi4OAwcOxA033IDvvvsO27e7zxa7//77MXfuXNx111048cQT0bFjR1itVlitVqSnp+PSSy/FL7/8glmzZrn1CnZKS0vDqlWrcMMNN6Bbt26ay/gjOjoaixYtwuuvv45hw4YhKioK0dHRGDhwIJ599lksW7YM8fHxhuuYOHEi1qxZgyuvvBLt2rWDxWJBcnIyxo0bhxkzZmDBggWuLHIjn332GZ5//nkMHz4csbGxroxbT9dddx2WLFmCyZMnIzk5GRaLBampqTjjjDPw9ddf46uvvjL8/PylDo569s92at++vStrF2h8/2ynadOmYdasWTj99NORkpLilanfUh566CGsX78eN954I7p27Yrw8HBERkaid+/e+Ne//oUdO3bg6quv1n1+UFAQvvvuO7z55psYNmwYIiMjERkZiQEDBuDpp5/G33//7VZSXI8/39emOvnkk5GRkYE33ngDZ5xxBlJTUxEaGgqr1YoOHTrg9NNPx9NPP+3ztRP5q6y6XrOMs7/lqZqivKYet3++DhsPluCvXfl4ePaWo7btpvAs+w3AZ28oX+ptdq9AeWRYMM4fpF8Z5ZdNObqlo9WZ0Xq2ZOkHRI9U1Xll8arLrQHGJceLKms1D5rN9KK22yXeXLgH57y+FI/9uAW7TGT0ltXU+XWCoKSqDj9tzEbXh+Z6lS/Toh63XeMJv24+usFsIPCZzkb6tnOfUPXLphy89MdOVNTUQ0qJBdvNlUn3x4Se3n8PadHKQgCAL1cdwMkvLcYl767AjZ+sDeTQNJVqtHDIcvRn18qqMOoL76+3F7mX7jPb34+IiIiI6FjiObEWUCaLAsDjP7m3TXpj4R7Y7VIzoJ19pBo3fboWUz9Zg/EvLsLl761EdZ0NC3fm6QazAeDXLd7B3HUHil3X62zek9/DQpRziv5naNfhYJHvyddOVbXeGdptYqyICw/1+Vy9qmgHiypdE2cDpVp13qPcRHWyNfvMtfO6bHgHt9tuJcf9fO+bIjbcgk6J7nEKu4RfkxPo6GodURGiVkYIgalTp2Lq1KmmnzNkyBAMGTLEr+1ERERg0qRJmDRpkr9DdOnatSvef//9Rj/fU0hICG6//Xa30uhq6enpPntq9u3bF59++qnu42bWYbFYcN999+G+++7zOeZRo0Zh9uzZuo+PHz++UX1AtVx55ZW48sorfS5n1Bvdk5mxXXDBBbjgggsMl3niiSfwxBNP+FxXIN+PAQMGYMaMGY1+fnBwMG699Vbceuutusv4GqvZ76uv98dsRYOIiAjcdtttuO2220wtrxao953+ebT6ZwMNvY+Pht+3HHbLSl6RWQi7XSIoqHkzKZuqut47EHW4iSWctYJbEaEhhjO9AWC96uBZLedINarrbLBa9Cfk2A1+Pzx7JAOAJbjhc5FSGmZoF5XXaq4ju8T3+7R4Vz5e+F0pH7bZIOiuVlZtvhQbABwoqsRD32/2vaCDOqBdovHd2XiwxPS6jrZebZWM+KaUQx+WnuCVDfH6n3twsKgSU8d0MdWH3B9fTB2BQR3j0fux33wum1tag5p6G75efRBVtTZMGd4RQgBPqvqpzd+ei+05peid2nyVTrQqCWSXVKFfuxjNyheVNTbEWL3bpzTXWAClIsHrf+7GnzvyMDw9Afef0YtZAkRERER0TLDZpVflI0CZKG+1BGv+zb39cKmpSdXLMwrx1aoD2JOvPWHcyNr9xThvoDIRXWt8YRbl723/M7TrfWZWq1XW2bwm2qdEhyE2wvcxh3MirqcDRZVYuse7ZHtTPPfrDrz0x06c2CUR/dNifS6/3uSxduck9yCye4a2731ACGgmfTiN7paIqlob1h0wHk9suAWx4RYkRIa6jWF/YQW6HgMVCf+JGNAmIiIiauVKqrQPpo5mD22tg72y6npTB1wtSat3dFMC2qv2FmkGbiNCg5Eaa1wpRSto7HSwqNJV3ltr8ovNIDVZ68BZ3d/KqH82oGRoBxV5T0zQ61e9J68M9323CblHqhsVHC2rrkeRH32xMvO1e1i1iQnDPaf1xJ878txKqalLjmudEPEsF9+a9E+LRdeUKDz3a+Ozdod3TsDM5fu87v9t62G08bGP+is23IKRXRMhhIDVEuRzX8srq8Y932zEz46S7z9vysHFQ9t7TbjYlVtmKqC9ZHc+vl1zCN1TonDDmM4+++s5HdT4zmSXVKGoslbzxEhFbWD7aKvpTXb7bcthvDJ/NwClp33bWCumjunSbOMgIiIiIgoUvT7E5dX1SIoKQ5AAPI/K1vsIPqotzyj0vZCGNfsaJpnnalSPElCqfPnb3k3J0DYf0K6qtSGvzDtDOzbcREBbZ0LsxoMlmmXem6rOJrFkd4GpYLnZPB7PrGh/So6f2rsN3rt6CE76v4W6wf3I0BAkRYX5DGjHOc5npSdGuI1hXwEztFsrTvEmIiIiauX0MrRLjmIPba14apEf219/oBhnvPIXRj/3J37ckBWQMR2prMPGgyWG5bS1So4fbmTJ8feXZOKSd1fgvz9v83osIjQEbWKsMEpY356jf3CpDnbXaPT9Niq1rRXQLlEdjBllZwPKwaPWwfdXqw9i2NPz8fDszW4l1v8zZxvWHyhpdKZvabV/Jcf1pCdG4pJhHXD36T3c7i+prEONIzPfqGdzY4zrkYyxPcyV126MtPhwDEs3bu9iJCosBGN7JCNEY0esrrPj3cWZbvc1NQv6nBNSXf2u4yPMlMercgWzASWr/7Eft3otZ2bSyb6CClz94Sr8tDEbL83bhctmrDSV0QEA+4u8T7AdKq7SPXlSWWNuEkRFTb3hPqdV1lDvu/3NmoNut5/6Zbv2gkRERERErYxey6zyGmWiaJDwPl7xDPAa2XioxG0iMwA8NbkfzhvYzvB5Ow6XusaQp3FeoLS6HiVVdaZaXbk/z7+AdnWdzSugnhwdhjgTAW294/Bv1x4yvf3G8AxWj2vkcXG0VQk2qxU6JrxX19lQVmM8mTgpKhRCCMPgf1RYCDr6qKAHADHhzoC2e4Bdb0IGtTwGtImIiKhZ/bwpG+e/tQy3fbGuyb2Lj0dSSnyz5iBu/GQN3l+SqdnzV69Xtr+zhpuivMZ7DP5k2T48ewt2HC5DVkkVHpi1GaXVTcsu33m4DONfXIjz3lyGs15bohsg1czQbuR+qBdQsgQLhIYEwRIchDYx+hmwRmWk1UHpCo0DuFV7i3D5eyvx5Jxtrscz8svx4u878b95u7yWV0828BXQLq7Q7qENKBnOn/99AC87tlFbb8eS3U0rY1buZ8lxPZMdPcuTPQ6GgYZZ3WYDnGY8NbkfPr5+OM4ekOpz2Z6ObHt/WYKD0C8tFmGNKC0dF2HBS5ecgKiwEJzUPcnUc64Z2cn0+oODBMao1htuCcYt47q6bpsJaBv1glfLLqlCTb0Nby3ag4dnb8Y2jUyDedty3U6qbDx0BBe/sxxHTFSuOKBZYr9KsxIFoJ+hXVpdh09W7MOcjdlYnlGA0f/3J4Y/vQB3frVeM/NaK2Cul6Gt1T8wEBNBiIiIiIiam9bf74BxQFvvb3EtuaU12HzI/dgiJToMgzrEGT7PLhtagWkF0G126Vdg2qm0qt6r77LVon9MV1Vn85oI2ybG6soYPhZ0T4nCyC6Jfj8vNtyCxEj3Y8fqOjsqa+tNHe8kOJ4bE65fnSvKGoIO8b4D2s6guGfGuFb/d2odWHKciIiImk3OkSpM/3I9pFTKRwUJgdcvG9QiY/ltSw7mbMxBv7RYTB3TGZZg/wNGUkrYJRAk4MpKbKplewpx/3ebACgBmphwCy4Z2sFtGb0M7Yz8CizYnouxPZIb9Xr8UVDmfWBRVGEuKF1dZ8M2VXZyVZ0NS3YV4CwTgUE97/6V4Sq5nllQgR83ZOG60Z29lqvRCOZm5lfguV934KaxXZAQGYrDR6rxv3m7UGe3Y/rJ3b36OQHQnGjgFK7qfd0uLhw5jchcVgeUy3VmJC/PKMTyjEJEhQXj+pM647w3lukuq54EYZTBDihBX62+4GrvLM7AWf1TERSA3exIVR3yNcq7mXX2gFScc0I7TOzbFoByEGoJFqizNXxGczfnYP3BEvyiygZuqi7Jyn6hFUBXiw234NGz++DKD/72exsjuyYiLCQYJ3SIw6q9RW6PnTewHXJKqrFqn3L/yC6JmNi3DTZnlWJIp3icPygN4aHKvvj8RQPw/G878Z3BLP1OiRF+ZZvHWEPwxLl98fQv23Gkqg7TxnVFe9VJgvhI3ydf6k2mOmSVVOP/ft2JD5ftBQD8sD4Lyx44GXGqoPmuXO8JIvsKKzFnUzauPFEJ1Esp8dfuAhRV1OCMvqkIDw1GSWWtW0n+hm0aZGhrBLRtdokL3lqOPXnevft+2JCNq0amY0gn92x7rZN0dgnU2+wIUf2GSyk1S7gv2Z3v6vlHRERERNRa6ZW+rnAFtL0f05p0asTz2CI5OsxUZvWafcUY0z1ZM0MbAPYW+J+dW1pV5zUxvV+7WKzZX6y5fFWtDXkex8Qp0WGIDAvWXD6Q0uLCdUt1+yPaamlUAD423IKEKO/J0EUVtSg2cY7JFdC26m87MiwE7RPCTY0FANKT3IPf+5mh3WoxoE1ERETN5tfNh90y6OZszMZrUwYGLBhs1pasI7jls3UAgF8258ASLLx6kdrsErPXZ6Gyth4XDm6PyDD3P5Mqaupx6+frsHhXPgZ3jMO7Vw1FcrRxYMuMR37Y7Hb75T92mQ5oA8ANH6/BuB7J+Pj64U0eixGtQIzZDG3PAzWg6T1pv1/nXrZ8xl+ZmgFtvezkdxZnYOPBEnx504m4+bO12HiwBACw4UAJnji3LzYcLMEpvVPQt10sAONsePW+0i4uHGt1DlqNmAloO7325x50TYkyXE49Xq2y62q+Sno53T9rE64dZT6jV8+S3QWmt+mpbYwVr182yO03JChIICkqzG0iQXOUZ+6aHAUAXuXR1P7vwv44rU9bxEdYEBoS5Faq3ZeBHeJcGQVDOsV7BbRHd03CpP5t8fnfByAAXDaio+5BfEq0FS9efALCLcH4dOV+zWXO6p/qOhlgRlxEKLomR+HDa4fpPh4o2SVVmL8913W7otaGL1cdxLTxDRnhuzQCyYBSitzpf/N24bU/9wAA3krJwDc3j/TKnFBvUzdDW6Pk+NI9BZrBbKfv1h70CmjrVQworKh1q+6QX16jWV1i8S4GtImIiIio9dtx2P8M7Yx8/b+tzUiODjN1rmlFRiHuOk2/xHlmIwLatTa7V1PwXqnRugHtdQdKvI492sRYEWzUwywAhAB6tIkKSEA7yhrSqGPA2HALosNCvCalF1XUmqrk5gxCx/goOW4mQztOp+T4oeIq1NnszZ44Qv5jQJuIiIiajVZZ6UPFVehgopdNIL3hCGg4PTN3u1dA+6HvN+NrR8/SWWsP4YfbRrsdDP24IdtVAnbdgRJ8unI/7j7NvXdvY3iWMtIqh20U0AaUIMfeggrNzGIzCsprMG9bLrokRWKETsko7YC2uQztXI2esr6yhqtqbfh27UGEBgfhgsHtEaoqwawVyNU68Ku32Q0zQldkFmJPXrkrmA0oB69Xf7gKAPDqgt34/c4x6JYSbdjPKyJUnaGtX3LciHoGsFbwzNPfHsFOT8XqDG0fAW2ztueU4pMV2sFRf/jan42M7ZGkeZIiOTqsUZnx/khxTGBJitY/aB/XI8UVJG4bY9Ut5e50/ejOGN8zGcWVtZjYt63rtQ3t5N1He1S3RERbLW5lvn3p0SZK97Ez+6fCaglGdFiIqQkGRicMACAhgAFtraDz53/vR3AQMKB9HEZ0TsAejQxtAMhx/IZW19nw3pK9rvt355Xjls/W4tJhHTSfV1xZh0PF2id2tDK0l2iUBFer0ZjM4NnnzymvtMYtoK0XKP9rVwHsdomgZj7RRURERETUFHqTr533ax2lN/V4LikqzFQAcvX+IqzMLNRtpZXZxMC6k1EPZ63zKykxYai3+dm820+RoSG4aEgHLNxpfCxjRrQ1pNEZ2kIIJESGuvURLyw3F9B2BtENM7RDg5Eaa4UQ3r2/3cYS0RDQbhMThvTESKQnRqJTUgQD2q0UA9pERETUbCJDvf/U2JZTetQD2vNUmX4AvMpQ2e3SFcwGlF6s6w4UY0inBNd9T/y01e05ry3YHZCAthklJnplZ+aXNyqgvWB7Lu78aoMroDX95G7YnVuOitp6/OuU7hiarrwHBRqleM1maGv1Tt+TV45lewrQq200Eh0Zr3a7RGl1HSLDQnDNR6tcGapL9xTgjcsHu567Pcd7trezB9OWrCP4YtUBbDxY4jPTGVCC2npsdom3FmXg5UsGGvZiVmdop8X5Lmul5WBxlStQpdWv3JOvrGv1PuM5eSAlOgw2u2xUH2u90nFHi16JbF9lwJtqZJdEV7A5MVJ/W9HWhn2hbax+QPvCwe3RMSECN4/rAqvFu6zcqK5JbqXgRnZJdCvvbVZ3nV7e7WKt6NsuBgCQEBVqKqAd5yOgHR/Afm9lGiXBDxVX4Zm5OwAAj53dBxU6k2JyHO/Zsj0FXpM5/t5b5CrZrmXjoRLN+7UmmWiN0Re9DPD88moAsa7bGToB7YLyGmw/XOqqHEFERERE1BrpBRHLq+tht8tGVWwb0TlBd2J3jDUEVkswLMFBCA4SsBlMbJcSmDJjpe7jjSk57kkIpXqbP9pEW5tcyc5Jb9JyeGgwzhqQiviIEdiSfQTdUqJQVWvHbV+s83sbMdYQn8eIWpwZ1klRYW4B7fyyGsPKeE7OILpRD+3IsBCEBAchJMg9C1xvLLERFvz90Kmmxk8tiwFtIiIiajZlGhna23NKXb1vjxatgxl1lpvWH/rrD5S4BbRrbeZLB5tlthyxmYxWdQaxlBLrDhQjNjwU3VL0MzQ//3s/Hp69xe2+11XZ7FuyjmDFg6cgLCRIN0N7ZWYhXv5jF4KCgIfP7IP+7b0DLbkaJcc/Xbkfn67cj+ToMHx2wwi0i7Piho/XYNXeIoRbgt0CUT9vysEzF9S5ZuBuzTritb6y6nqsP1CMC95ebjgD19NKg4A2oJQ2H9U1Ccv3aM/eBjx6aMc2LqBdW29Hblk1UmPDUW4iQ9tXWfMiVbDaM6gXHhqMdrHhhsH8QHj07D7478/bAra+IAGc1C1J87FuKVFYsCMvYNtSS4wMxT2nN0xeCQ0JQmy4xet7GRwk3LL128ZoZ+v3aBOFly45wXCb4aHBePeqIZjxVyZiwkNw6/hujRp7D52A9ql92qgC9KHYb6JfXayvgLYf5cub6rlfd+g+dtiR2THfYyKTk9Hvw6ZD3r8tgHaG9q487QxxJ61JMAVl2idoPNsyGJUyX7OvmAFtIiIiImrV9P7mrqipR3ltvV/H7E5XnthJN6DtbAcXHCSQHBWmWfnOLM8J9DHWEJT6OZk1xmpBXLj546OU6DDEhIcgJDgwlZi6t4nC/sJKr0nskY7j1VHdkjDKcWy9ReP8ihlRYcY9tKPCQjSTDJzHlSnRYVCnjXy//hBWZhpXogMaJlobZWg7J5oLCGjXA3AfCx07mDNPREREzUbrj36t7NrmJHWOlNQB4FKNgLGZmaFNpZW9abV4/3lWUuk7oK0uzzXts3W48O0VOPXlxZi5bK/m8jX1Njzjo89wcWUdFu/KR3lNvWb53P2FFZj22Vqs2leElZlF+PesTZrvd57BwWR+WQ3u+noDZq/PcmVka5XIPqAKuG3RyBLOL6/BN2sO+X1gvDLDd1D33m834vv1WbqPe/bQbizna6wwkS3rKwBZVl2PesckDM9s7nBLMHq21Q50BkpEaDBuOKkzPrl+OE7t3SYg6zyhQ5xuj64Lh7QPyDaceraJxpb/TMSaR07F0n+f7KpU4JQY5T2OqLAQt3LoqbHaAe02OoFuT/3SYvHaZYPw1OT+jd6v9Hpkqz+TBIOMczVf5eTidT6b5ugDZzTBKLesBnU2O+Zv93+Cg14mh2c2uJQSuw4bB7S1Sibqlhz3CH7vNghob1C1SCAiIiIiao2kThCxrKbedKWjXqpj1i7JkZjUr63uMVZKdMP9bXWWMcvzkGBgR+92UL7EhIf4FSz99xm9IIQyQdoSgKB2elKk5vsQoVFF0ezxqadoHz20e+mcc3C2svLcrplgNmCuh7b6HI0Ro6A4tU4MaBMREVGz0TpQ2Z5jHAQINL2yylklDQFBrQxooxLTTmYzrPXs0yhlZZdKsGTGXxk467UluP+7jTjoow8vABw+opTZ3XG4FL9tPey6/5m5OzSfvyevXLdkr9qtn6/D4P/O03xszf5it17N23JK3W47GfWfdj7vsR+3Gi6jDv5rlb0uq67HNhOTJcZ0d8/wbUzZbU/qrNzGlhwHgP2O11jeiFLGWkoc+3VlrXeGdnMHtAc4MvXH9kjGvRP1S/P3S4sxvc6x3bXLjQNKJvKortr93/1x3eh0/HjbaPx4+2hEhYUgKSoM4aHeJcGTNEqcq8uNA/onUtQnW1rKiV0a3qskjeC8lsZmaKcnmiuV7syqaCqbXWLB9lxTv+FmVXpMMjlUXOXz9/OwRkC7QGdMnmM1ytBef8C4OkNrdKSqDv/+bhMmv7kMX6460NLDISIiIqJmplfxu6y6HsUmjsEtwQKfTR2B60d3xhUjOmLmtcMREhyE3qnax4/qYwm9SlmNdUqvFL+fE2O1GJbEVlt073jXBG0hRECyhjsnRmq+D5Fh3se2iY2stBXlo+S43jkHdYZ2Y8S4MrSNS44DwM3juhiu62hWGaPAYECbiIiImo1WyfEDRZWa9zcXvf5Hh4qrXNdLNcazz0QJ3ryyxpexArTHVltvx+Jd+Xhm7g5szS7FN2sOIVsjMOLJmQ3omQFfa7Pjmo9WYcfhUrfsw1255iYW2OzSsOeQ9ziqvO7T6qHtr32FyntVXWfDbp2xb/SRuRgcJHDfxJ5NHosndUDb7EGrFmeGtpne32Y4+2h79tD2N0P71N7+H8CrS2RHG8x6HtopQfcxT3r9s52uGZXucx3PXzjA8PFuKVE4oUOcZl9rNa2e3Z6vc3S3JM3s5JSY5u337WnqSZ3dbp/VPxWhIQ2HgXpZ3J58BrR1Mrg7J+m3PXDq1TYagzvGmRqHGZ//HdigqWfw2szvZ3lNvdf/LXo9tNX/l5RW1xlOAtpXWOnWUuBY8NaiPfh6zUFsOFiCB7/fjG0ak5KIiIiI6Phh1ymd9t3aQzj79aU+n58YGYakqDA8dk4fPH1+f3R0TJLtnap9HOsW0NaYWBwaHIQpwzqYGbr780KCMLqb/xOnY8MtpgLTIUECHRPcJwAbZR6b1TlZO0M7XCNDO6iRFbWMMrRDg4PQOSlS8zFXQLsREw/CLcGuY3Wj8wxRjoD2lSd2QrTjuufLHNM9SXOiOrVuDGgfA4KDlS+pzWbTLZtKRET/HFJK2GzKyfWgoNb9X7leKSmzwdRA0AtoZ5eoAghV3uPcX9jwvDqd8ra5pdU4WFSJL/4+gK3Z/vcd2luoPbZfNuX4vS5nQDsz33udmfkVOOOVJbjmw1WuoPbOw/oZgE2hFbwOREA7M78CNfU27MotQ73edG8fwi3B6NEmOuAlkNVlu9Qlp33xnJHszEI3U3LcjKIKJZjm1UPb8T6Y0attNCb1S/Vru5eP6OiWCe+ZuazW1aDHu1qMNQQnaPRnVzu1dxt0MsgGDgkSuHhoe7w6ZSAm9WuruazZ2elaWc2er7NHm2i8dPEJCPHY37olm3vNgXLNqHSEOQLYYSFB+Nep3d0eT9Q4iPccM2AmoK393nVOMs7QDgsJwuPn9EVanLlMbjOW7Nbved8Ynj20d5r8P8wzS1svoP371lysdPS0z9DIzvb8PHxN3mlt3l2c6Xb7lfm7WmgkRERERHQ0NDWEodXiCQB6tTWRoa0RyE2IDMX9Z/TCmO5Jpif0AsAJ7WORqNOiyajSVYzVYhhwdYqPDPUKKFtDjCdXm5GeGKlZnj1So/pYY0Ub9NBuG2vVPT5sSoa2+pjUKJnAGdBuE2PFH3ePxXMX9MePt52E7U+egcfO7oPHzu6DGVcN9Xv71PJa91lwAgCEhipffiklKit9Z4sREdHxraamxjXByfl/REs6UlWHfQUVrn69amU12pnY245i2XGtst6Ae8lxrR7auaU1riCGXkms9QdKMPnNZXho9mac9dpSLNzhX89WvbGtb0Sw4rBBQNtp6Z4C/OkYY3NNKtDqG5tX2vTSv9+tPYSej/yGc99Y1uh1WC1BsFqC0d1kENUsz7JdZ/Zva+p5fdq5H4y7So43IqB92XDv2eZ/bD2Mksparx7a1tBgRIWFoH28d3n060d3Rh9HGbfgIIE7T+2O0/u2MTVz+Y3LB2HnU2fgmfP7uwX2o0JDoBfn75qsPWvb09geyQgJNj50CQ4SePuKIeiTGuOWNe9Ub5cQQuC8gWl4+8oheHBSb69lzPaT1no/tEqeTR6UhvevGeo62TGoYxwmmdw/AqVDQgSW/vtk/O/SEzD/7nFekxm0gvjqkuROjS053i9NfyLC73eOxfrHTsPIroloF9f40oC+ypp3a+J3Pq+0xvU9stslPly6z9Tz1L+HdTa7ZksGpykzVmLmsr1u7RUAoFNiBAZ79O07FsuOq3m+RiIiIiI6fgQiIU/v+FM3QzvKuOR4YlQoEiJD8ekNI7Du0dPw+dQRpsYxpFMConQmaKfG6rcbiw23mJpIr3UspvXuGU0S15KeFKnZG1urh3Zj1t8u1gqrJUj3GLFtrFX3sVidHtpqIzprV3JTB9CN+l+re2inxoZjyvCO6N8+FuGhwbj+pM64/qTOmq3FqPVjQPsYEBPTcLKzqKiIWdpERP9wpaUNpTojI80Fg5rL2v3FGP/CQox/cRGu/nCVV2ljvQxtrQy05qKXoZ3lo+Q4AOx3lIDW67P81C/b3R77bOV+v8amF9A26p9qCRb46fbRXqWzy2vqUVZdh4x84/d26e58AMDOw80T0M71CGhX1NSjLEAZx00V5pjp3Een71ZjeR4UTh3TxbCfk1Nfj4D2wUYGtC3BAs+c3x/9PQKH7y/di9P+95dX3/pwR4kurQPMiX3b4PtbR+HzqSMw/+5xOKNfKqKtFnxz84m4aWwX3Hlqd/w8/STce7p3X+zU2HDXe6wWFCQQpXPgrJetfPaAVFcQMi7Cgjs9sor19GkXg7n/GoNtT57hc1mt2eQJkebKuyVpzCbXm4E/vmcKltx/MhbdOx7f3jxS9yRCc0qODsP5g9qjQ4JGVrpGZsGJXbxPIPjKMNCb7X9yrxRYgr1P5iRGhqJn22jX+9HY/vPRYSEY0D7OcJlTdMrm9zRZqWDN/mL0evQ3/PfnbTjztSW6mdaeDqtaMBSW+y4T/snK/a7fAaeOCREY5FGOvTGTnloTfypZEBEREdGxJRChC70M7fTESLf2SU6+MrQ9q1KN7paE+8/o6TNLeFh6PCw6E6uNJuQ6s4dvn9BNdxllXOaSVDzLkvsSFRaiGXDX6qENAI+f09f0uq2WIDx8Vh8IIWC1BLvOL6ilxloRq5O93VByXP+912sV556hbRDQZrD6uHX0z6aQ36KioiCEgJQS5eXlOHToEBISEhAREcGTAURE/yA2mw0lJSUoLCx03RcVdXRL13qa8VeGK+NseUYh3l+SiemnNASe9ALah4orsfFgCWLDLUjX6asTKLoB7ZKGQMMRjQxtQCk73js1xlQgAgAWOLKf1x8oxl+7CtAtJQqn9knBN2sOoaq2HhcP6eDKYqyus5nqje3putGdMaB9HHq0icYLv+/0ek37dMqYOy3LKERZdZ3b6w+k1/7cg5WZRbhwSBouGdrBsBfs0Wa1KAeivVNjgPVZAVuvZzbw4I7xWHTfBGSXVCE23IIxzy/UfF5vj8B6UUUt5m3Lxd97i/zafnJUGIQQmhmy+WU1mL891+0+5wFn33Yx2OrRy3ZIp3iEBAdhdLckt/u7JEfhoTMbMprX7PMeo1ZJM6doa4jXxIbYcItmuWtAyah98eITsCevHOlJka6SYYGkNXO/KRnaRrPaw0ODm/23rrG03tth6d4BbV+fgd5xSbTVghGdE7F0j3sZcM+eaZ0SG/f+dE6ORKqP7O7TerfxKnsNAON7JpsuHw4AHyzd69fY1BnaZoLgmfkVXlU2OiZEYGCHOLf7Nhwsgd0uG93v7mjSmox9DAybiIiIiBpJr3+2P/QytEOCg9AlKRI7PCboq4OjWsd5YRpB8FvHd8Ot47th3rZc3PjJGs3tDekUr3k/YJyh7cwevuf0HhjSKR5ZJVV45IctXstplTPX+vvZnzLpTm1jvdcdpHPMdv6gNJRU1mLV3iJU1NZj2Z5Ct8evHtkJ2SXVGJYej/MHpbkdy8VFWFB1xD25JTU2HHE+MrSTosIghPYEiG4pUZqPqSelR4eFaC5jtQT5rO5Gxy4GtI8BQUFBSEtLQ1ZWliuoXV5eDiGEq782EREd39R9s52Sk5NbvOT4wp35brdfmrcLV43shLiIUEgpNUt5A8D87XmYvz0PQQJ4+vz+uGx4R9djheU1+HtvEfqnxWpmE2rJLa3G//26A4UVtZg2vqurXK7dLl1Z1p6yiqsgpVKCWG+c+1wZ2uaDsrd9vg6/bNbugT1rbRaeuaAfNh06gnw/Ar1f3KiUw0qJDkNXR0ar1RKMhMhQFKkyxNftL0F1nXvp9xlXDcFNn6513d6TV+51cBJoq/YVYdW+IpTX2NCvXWCzoZvC6gjkas3YbopIjYzbhEilpFm9zY4gAWi1/e7VNtrrAEzvQNqIczZ6vM4MaE/O0loXDm6Pb9Ycct1/2fCOpg/8OmkEZ5MNZrdHWy2AxwSOtLhwBAcJhIYEobbefb9NjAqD1RJsWK7al2nju+LtRRmu27eM6+r2eHpSJAZ2iMMGR7br6G6Jpk8UaM2k97dMW2uh9X3olhKFYenxWL1PKW2dGmv1KpHvjwm9UrwC2m08ZuT3bBuN+AiLYVluLemJkUg1KFcXFhKEQR21T0SN75mCd//yDnQHirqHdr7JrG5nL22njgkRGOiRoV1WXY8DRZWtdpKEmuf/SQB0WxAQERER0bEvELVljfpTp8RYvQLabiXHNY5v6jRa5Dnp9YHunRqDOJ0+0HrbcXJmJwshMKFXCmx2qRnQNnv8qTeO0JAgXD68I2Yu3+e678LB7R3j8w646537Cg4SmDqmC6aO6YKqWhtGPDMfpY4ElfMGtsOT5/XTHVtsuMWr9VyqQclxZ2a1JTgIiZGhKPBIIIkOC0FsuAURlmBUeFSBVK8zKEggPsL9nBjgnThAx5dj86zLP1B0dLRbUBtQghv19a2jhCcRER1dsbGxSEz07nF6tHkGoQDgncWZeGBSL1TX2VGvFcVTsUvgrUV7XAHt3NJqnPfGMhwurYYQwNc3jcRwnd45TiWVtZgyY6UrE3vZngJ8ddOJGJqegLyyGlR59A92qqi1YdGufPyxNRdfrjqguYyzJLjnH9hG9ILZALAztwwXvr3C9LoA5QBnVNckzcdSY61uf7wvy3APGEWFheDkXimIDnPPjv1YdbDTnP778zacc0K7o7ItM5wBbaPAq9rlIzpiT245VmlkI6tF6JTtApQZ5CnRVhwu9c7Gj48IRWqMtVGZ+moNAW1zB8PO92FEl0TcfVoPfL36IPq0i8GDZ/Yyvc2TuiWhbUzD6zqlV4puKTZAO9jbzlFiOiI02Dug3YgZ6J6uHtkJs9dl4XBpNdrEhGn2Gf/4uuH4cNleBAcJXDc63fS6kzUyBgSOzShdamw4xnRPwpLdyu/HhJ7JSIwKwytTBuHV+btQXWfHHad0M9UDTs+Ensn478/u9xV7nHgIDhI4qXsy5mzM9nr+9aM748Nl2tnRnZMiNU/WOHVMiNAdu2cpb389MKkXiitqdYPibhnaJicxef4edEyIQNsYKxIjQ91aXGzNLj0mAtpaLT30MkOIiIiI6NgXiAxtrczlhse8jxXVx8JWjRLYhgFtncDreQONz2UYBaM9+zvrTeTWCtx3bxPtFbDXG2NUWAiuGtkJ3645iIpaG8JCglzHtVoVtkp1qiiqhYcG4+ubR+L9JXuRGBWK2082LpuudR4iNdaqWxJcfWyWEm31Ot+WFh8OIQQiwkK8Atqegf2zB6TikxUNrf9SY6145KzeoOMXA9rHkOjoaPTo0QPl5eUoLS1FbW2tV7YeEREdv4KDgxEREYG4uDhYrYHNMDVrf2EFcktrMLhjHIQQmuV9Plu5H3ef1gNlOn2pPR0sqoLNLhEcJPDGn3tcATIpgRf/2Ik7Tu6Oersd43oke5W0rbPZcevn69zKitfbJW79fB1+vuMkZJVoZ2c7XffRasPHneW7C01m1jWHLgYBi9RYq1vJ6OUeGZBdkiMREhyEEV0SMH97nuv+FZnNm6GtphWcainOkuNmA9pPndcPZdX1OOHJPwyX8yw57qltrHZAOzIsBB0SIgIW0NabWe5J3ePqjlO64w5VmwCzLMFB+PzGEZixOBNR1hBM93GQG6UR0E5zlIkOtwSjBO6/F4EIaKfGhmPBPeOwK7cMXZKjNGeIx0ZYcNdp3v3AfdEqgVdTf+z+Xf7e1UPx1aoDEELg0mFK4D8tLhzPX3RCQNbfWeN3bEgn78lKY7onef1mxFhD8Ng5fZAaa8XTc7d7PadLcqRh/7pOiUqlj55tor3Ki1stwYgKC/G7bz0APDipF24e1xWHiivxzZqDmpnl6gzt4kr3EzX90mIwPD1RN1Dv1CFBaTPVNy0Wf+1qqIqyJfsIzhqQ6ve4m5uUEr9tOYwDRZWYPChNNwuEiIiIiI5PgeihnWRwzH7x0PaYrWohlp4Y4bMVj1ZLJSe9Xs++AtppcfqTarWOPcMtGhO5NY4rp5/cze2Y6NbxXRGi8/oiw4LRNTkKf9w9Dmv2FWFghzjDVk7J0eaOs3unxuClS8wdC2pNXk+NDdecWOApJSYM2zxyQtrHK8dvkaHByPdY3vN9fezsPujZNhoFZbUY1DEOJ3ZJ1OyxTscPBrSPMUFBQYiJiUFMDEsnEBHR0fXzpmzc+dUG1NslhnaKxztXDdE8UCmvqcfhI9WoNZgB66m4shYhQQKfrtzvdv+qvUW48oO/AQAjuyTi/WuGIlI1y/SbNQexPMM7OJtXVoNn5+7Ayb1STI9By548Z0DbfIZ2oDlLjGvxLHHlGVBxBsNHdU1yC2h7irGGmJqpe6yzhigHVHr9uDwFBQndg1u1CI2S42qpsVZsOOh+X0iQQFhIEDolRvjdM9tTcrSyH5gtV+YrAG9W1+Qo/N9FA0wtG231fh/T4pUTAFrZs1olvRsjMixEt9x0U4RrvIc1GhUrjhVWSzCuHd252dYvhMDdp/XAy/N2ue47s39br+XGdPeuRuH8bUqJ0f7eKhna+gHtjgnK7+At47vgrq83uu53ztxPjAr1O6CdFheOa0alA1BOuMy/exw2HTqCkqpat21klVShpt6GsJBgFFV4/j5H4bFz+iCvrBo/b9Kv7NHREZDv2y7GPaCddcSvMR8tM/7KxLO/7nBdf2XKQK9lKmuP3ckfRERERGQsEAFtownOI7skYmSXRKzILIQQwI1ju3gtc+/pPfDiH8qxR2hIEK48sZPu+rSCz+1irYY9soGGimNaYsK9zxGEW4JxxGOyp9YxfI820Xh1ykB8vfogerSJxm0TuuHr1Qe9lgMa2p+lxYUjbWCa1+NXj+zklsF8XTMc82lVZjTb5q1NtPdy7R3nCcI1zrN4flYhwUG4YoT+Z0vHHwa0iYiIyJQ3/tzj+kN1zf5i/LRBP/M2v7wG/lSnLaqoxQKDgCugZBVfP3M1Zl433BVM+m3LYd3lf996GL1To80PQkNBeQ3yyqr96qHtr+ToMAztFI9fdV5Ll2SjDG3jA6wujmD46G7aJcudJg9KczvIOV45ZwjHWEM0y32p3Texp+n1avXQVtM6mIuyhkAIYTh72ixnhnZIkLmZyOEmZkoHmlHJ8SqN4JZRibnWIjk6DPmqMtJnD2g95fVbSkKkdw8zp5vGdkFlrQ1bso7g7AGpGNLJe6KB0W9aW50+2elJkYgKDdHNtHZmaJ89oB2255Rh0c48DO+c4DqplRAZiv2FxtU81BIiQ/H65YPcMg4So8IwoVcKskqq3JYtr6nHh0v3Ydr4rijxyNB29rxPN/gNiIuwuMoV9mvn3k9+W3YppJRelUua4kBhJaZ/uQ67cstxydD2ePycvj6zXTw5g9kAUFhRi4+W7fNaxmwFFzW7XeKbNQdxoKgSlwztcEyUWyciIiL6JwpEyXGjSehCCHw2dQT+zixEYlQYerb1Pu8zbXw3xEaEIjO/HBcP6WC4vrAQ76pN13q0owoSSss8tVSDoK1nyXFAe2K5Xq/w8wam4TxVgFqvGptWWXG1f53SHSWVdcjIL8dlwzuiR5umnSPTon08b26Cutak5Q4JDRnansxWpaPjF/PviYiIyBTPHj7vLM7QXbagvAZlfmT85pfV4JMV+3wu9/feIjz8w2YAQL3NjnX7i3WXray1YXtOme7jZg1/eoFhdnNjnNAhDo+d3Qf/OqU7Zt86yjAL2+gxowMooCEY3qNNFMb3TNZcZvLAdji1dxufY37hogFYeO943O1Rnrl/WixiNIKVrVGYo+S4EEKzBzIAnNwrBS9cNADTxnV13eerrK9Wtq6a1ufkDII7D9aawvlaurfR31fUrAHK0PaH1oG2M6Ctla2pNUu+tXn07D6u0m8juyRiqEaA9p/m6cn93G7fPK4hW8JqCcYDk3rhs6kjMGV4R91A7NST3LMGrndkEWhNDLFaghBjtSAoSOD0vtq/Y84MZ0twEB46szf+uGscnprc3xWQ9mfyxNSTOmP1w6disE7Wf1pcOIaluz/2xp+7kVta7RXoj3ec5HGOT3Psqt+HfmnuFboKK2pxxft/e7WaaIo3Fu7GxkNHUFVnw8cr9mPVvqZVjwCAP3d4//9Z3oiKIK8u2I0Hvt+MtxZl4Jw3lnpltxARERFR69DUcHZ8hMVnm7DgIIFR3ZI0g9nOx686sRMeP6cv+rTzXen29D4NxxLt48O9sn61jl2slmDdgLTW8azW+5Jg8lhE77xQpI+AdmJUGF67bBB+uWOMYZZ6U1TWeR/Pm50Um6IxadmZoR2h8driwgNTyY2OXQxoExERkU9avWHzyvSzlv0NaC/dU4Ack32Ev1+Xhd+3HsaOw2Wo8FG2dK1HwFsrI/Boe/jM3vj25pG4/qTOuOu0HmgfH6FZjsrJKEO7k0EgBFB6xgLKwderUwZhcMc4r8efuaC/qfLOQ9MT0DkpEuec0A7RjgOLIAE8fk4fzaxKz9LBQni/liABU/2NLhrSXvN+vYD+PTo9kdUZlXoHyP+d3A8XD+3gdgA2/eRurv7bWiLDfPXQ9n5/nAHeTn4GtE/RKKPvPIge3DEefVJ9H6y3RIZ2jcZBrrPnWFUTDoBb0rkntMOCe8Zh1rRR+GzqiGNizM3tlN5tcMHgNISGBGFop3hXMNofN47t4tqnEyJDcfkIpad3G42THerqCJcM7aC5Pl/fMX/6tfdKjdEska/26Nl9oD7fVVFrw2sLdnv10HaWFzTK0FZPeOkQH+H67XVanlGIKz/4G/sKKsy+BEPfrDnkdnvGX5l+Pb/eZKuRilobbBqlCY28tWiP63pZdT0+W3n8VxUhIiIiOhY1NUP7+tGdff7NHWjPXNAf/z6jF24e1wVfTD3RK1B8/iD3ct7OY1m9suMxGgHtylrvc2RmW20NaB+LrhrnhnxlaB8N13tks3tOxDWSEOH9+l0BbY3zFszQJga0iYiIyCd/e0gXltf6VVJ08c583wupPPT9Znz+9wG3+zonRXqVpD1Q5F5G9uwBqQH/g//ELgmmlz1rQCpuHNvFK4irVY4KAEKDgwwzePu2i0VosPafczHWELdZvLHhFnx6wwhcMCgNlmCBgR3i8P41QxERGmKqp7TzgK1zUiR+uWMMnr9wAP64ayyGpicgPtJ7/NNP7o6XLj4BQzrFY/LAdvhl+hjMu2sceqlmUD92dh/dTGmntjFWvHjxCZq9dbu3ifY6oLl6ZCdMHuTdOwpo6KEN6Jcw08o279U2BvPuGodXpwzEJUO9g+tmemh7cgbBO/oZ0D53oHdZa+dBdHCQwHfTRuLNywcbrqMlAtpaE1x8ffbHgk6JkRjSKf6on3BprUJDgvDyJQOx66lJ+G7aKM0gtC9tYqxYdN8EfHHjCMy7ayy6pSi/GVaN/VZdHWFEZ+3f4vbxPgLafvRrd55cMTKgfRwu9piEM2vdIez1CDrHOU7eGE1MUv8+BAUJzewSuwTmb8/1Oa7GqPCzt3iJH1nT/mZp19ncT4watRwhIiIiopbT1IrjWj2xm5vVEoxp47viwUm9NSsoqSe5CwH859y+AIB2GpPXQ0OCNI9dtCqTeU5Y1SOE0OwVrTU5/Gib2Lctuqco557CQoJw/8RerseGexyjnedxPkOrNZnz+E1q5LQfC5XcqHm1/BQOIiIiavXyDbKxtRSU1/gVNNuWU2r4eGRosFs2dmFFLb5c5R7QHtopHnvyy3G4VD/Tu2NCBEZ3S8TvWwNz8v+cE9rhwsFpWJlprixrkk4moNbsXUApIW3RCVgDykFX//axXpnogJKN7pkxGhkWgpcvHYiXLjnBrWRWvMasWLWU6DC3IHzHxAi3g7wjVd6Bid6p0RjeOQEXegR2Zt86Gn/uyEObmDAMTU/A7A3ZXn1n1ZxledM0Zj63i7VieHpnvPjHLgDASd2S8MhZfTRnPgNwy7LWytAOEvr9sDskRKBDQgTySr2/C1q9sNS0ev86sxP9nWHcPy0WZ/Zvi7mblWDOiV0S3GaFR4SG4KwBqfhsZSJWZBZqriM89OjPadXqFc6MZtITFRaCUV29J7F46qsK8AohcNPYLm5Zxe3jw31WgUj0Y2KF1u+QlrtP64nv12Wh3vE9r66zo7rOI0Pb8bubEh0GqyUI1XXe2c2eE176tovF33u9/7/JyHcPltfb7AgOEn71167WOBnm7wQwvf7pWkqr6xBr8vdP6ze9sNy/v0sApdrMR8v2Ib+sBleP7KT5u0RERERETSObENF+dcpAzWBwS+uUqEzsX7QzH/3TYl2B2jSNCa96CQtaAW1//l6/cEh7PPnzNrf7fLU/OxqslmDMmX4S1h0oRof4CLekjH+f0RNTZqxEnU0i3BKM6Sd3c3vusPQExFhDUOqY7No7NcYVtK63aQS0maH9j8eANhEREfnUmIB2nEeQNiRIuE7u+9I1OdJ1gj4xMhQ/3j4aT/y0zTALbVjnBFTW2bD+QInuMqmx4RjXI6XJAe0VD54MS3AQkqLCkJFfbvp5eoETvQMeM+Wjh6bH6wa09XgeNBkFfISAV99sT6mxVmz3mJQQrfOawkOD3XpS+8rSjXccsGgFktrGWnH7yd0xulsSymvqMaprkiOIo/0nrq+S49GOXrxGtErAG006ALRLJTszloUQmDywHX7YkG24Dqf4iFC8fMlAjO2ehTq7xEWDtcuxp8Tov68tcYJgyvAOeHXBLjh/Aq4dlX7Ux0DHvitP7IjPVjZMZrptgvsJkZvGdsGPG7KQ65h4cqlOGXI1rZLjQQLQ+u9Kr82Bp7axVpw9INXwe+2sbCGEQKeESOzMLfNaxjOgfXKvFHy4bK/Xchl5Df8PLdqZhwdmbUZlbT0emNQbl4/oaGrMhzXafvjqyefJn2ou/rQl0fobpKCiFlJKv04C/mfONnzhqO4ye30Wlj9wcqs8YUpERER0LPOzsww6JISjtt6OK0d0wrkneFckay26Jkd59bLWKjmu11LO35Y7nmLDLbh0aAd8veag675x3ZObtM5AsVqCNSckD+mUgJ+nj8GGg8UY0TkR6Unu51PCQ4Px6mWD8MJvOxEeGuzKfAeAWo12RmYz2un4xT2AiIiIfCrwMxOqoKwWKdHuJ6t7pUZjS5ZxJrbTf8/rhxqbHbsOl+H0vm3RPj4CL148ANfNXK0bsB6WnoA9ecbB5dRYK+Iimv4Hf9sYq+skutmMPUC/tK3eAU9vMwHtTgl4F959Tod0Ml8KXc9H1w5DSkwY+raLNVzuyhM74s8dea7bV5gMoAD6vaydnCWo2id4v8/O4NKgju7Be0twkGbGo1uGtsZnYdTL3Gl8zxSkxYW7ssrPMXHArTVhoFQVzPnPuf0QG27B1uxSrNGYnOA0tFO8K2N9ynDj9zjF4H1tiZLjbWKs+Pj64fh0xX50SozAHad0dz324KReePbXHa7bd57aXWsVRPjXKT1QUFaL3XlluGx4RwxoH+f2eFJUGL688UT8vCkHaXHhmiX6PWn9LidEhmn+vxfiY/KK2g0ndTEOaKsqY3RMjPAKaLeJCfOamDS6WyIeOrMXnpm7w+1+58QqKSUe/2mrq1LJIz9sxqiu3ieOtGhVN/H3lJs/Gdr+tCXRCmjX1tuRX1aDFD/K2n+halVSVFGLnzfl4KIh2pOCiIiIiKhx/M3QnnvHGN0J8a1dWpz336LNWRb7obN6Y2duGTYcLMGEnsk4+4RU309qYT3bRqOnqvWcpwk9UzChZ4rX/bX13gFtfyaz0vGJAW0iIiLyqTEZ2p7ZVz3bxJgOaPdrH4sYq8Xtj9q4iFB8fdNIPDN3O2Yu3+e2fFJUGNITIwyz56yWIMRFWBAvQr2y/NTaxIS5svu0jO2R7PZHtD/ZXfp9m3UytDX6pXrSy8Qe2CHO9Lj0TOjlfVChZVyPFFwxoiN+3JCNPqkxmH6y+YCkVmBZS1qcdx8ro/68UWEhXiV+fWVo630OasFBAt/eMhIfLduLGKsFU8c0rr9XeU1DMCc2woL/nNcPUkp0fnCu17KXDe8IS7DA7R7luYykROu/Ny1VlmxM92SM0ZhBfuGQ9vhpYza2ZpeiV9to0xml9M+THB2Gd64aYrhMl+QotwkTviRoZGgnRFr8nsjlqX/7WAzsEIcNB0s0H1cHtIenJ2DetobKIZ2TIvHe1UO9/n9Ryqp3xam92+Dklxa77i+sqEVxRS3q7RL7Cytd99sl8NaiPXjsnL74cOleFFfW4vLhHdG9jfcJpVyNgHa5H0FnACiqNB/QLvejP7fe3yDbD5eZDmhrnVjdrZEVT0RERERN428isl7br2OBVoa2XtuedrFWZGtURfJHbLgFP9w2GlW1tlZRbrw51WlkaBMdu78WREREdNT4e2I/v7zGK/uqg0aGrZb0xAjdwGJoSBCeOLcvBneKxwOzNrl6EN08tguEEJoHE07tYsNdgej/nNsPZw9oh582ZrtlbAFKUHNoegJ+2ZTjdv+QTvGItobg0bP7eK07LCQINRqzRz0l6WZoa79eMxnaWsEYwP+g5ehuiVi2p6Hn8q3ju5p+bnCQwNPn98fT5/f3a5sAEOGjZJQzBqE1WUHvtQPKQWSBR/lba4ivkuPm/jRuFxeOh8/y3g/8MbJLotd9erONn73A//fVKPO9JTK0jSRFheGH20ajpLIOcREWnyXciQJJa6JRcFBg9sE+7WI0A9pWS5Dbb/TlIzpie04pVmQWYlyPZDx4Zm/DzI4OCRGwBAvUqfrKZRaUQysZ5rcth1Fvk/h+fRYAYM7GbPz6r7FevxFaJce1gs6/bTmMD5fuRZtYKx4+szfaqn6bi5qp5HieXkA7pxTjepiruqLVo9xsGxQiIiIiMk/6UefnlF4pPtt+tWZa56BqNP7uBIBHzu6DWz9f57r94sUnNHq7x3swGwCGd07EOoOWgvTPxIA2ERER+ZTvZ0C7rLoef2xz71MdbbXgoiHt8d3aQ4bP7e9RQlbLuSe0w/D0BMzdnINOiREY78jkbherH9BWn3QPDhI4sUsiwkKCvALa0dYQTBnWwRXQFgL46sYTMUIjAOl012k98JyjZHJIkMDdp/fA87/t9FouMVI7yKgXSDVbquqKER3xuep1TD2ps6nnqf3rlB5Yve9v1NbbkRAZimuOUo/jYB8lo5yHwmlx4eiUGOHKPkyODkOvtvoB/yiN9zTMreS4d4A8qBnLVz1/0QDc/90m121/sq0bw7DkeCs8+LUEB/ksP0/UHNSZ0k4C3n209aphGPHsge2U4LHNyLAQvHzpQNPrtQQHoVNipFubjYy8CgRrnAwsra53BbMBoKC8Fi/+vhP/d9EAt+W0So57Bp3X7i/CtM/XugLnAsBrlw1yPV5UYf5vhaaWHAeAHTnmqr7obW/etlxU1towtFM8LhicxhKGRERERAHgq+L4WQNSEWO1ICosGLf7Ud2tNUrUmGRfplOJaGLftrj/jJ5YvDMfJ3ZJxHkm2iP9k11/Ujo+XLbXVXr8kbN6t/CIqDVgQJuIiIh88rfkuJZoawiemtwPnZMisXhXPlbtLdJcrn+a76xkQAlQX+8RuG2n0b/IKVUj2K01m7bOJjGmezI+uGYoluwuwPieyYbBbAC4cUwXRIYGY2duGS4Y3B7VjsxxT3o9tLUyUrUOjPTcNqEbftqYjbLqesRHWHDTOP/LYA/vnIDf7xyLHTmlGNY5Qbc8eqB19tHbdVDHOABAUJDAy5ecgP/M2YZ6m8QjZ/XW7E3tpFXmS12+Nyna+/01k2XfWBcNbo+aejvW7y/GaX3a6PY4j7aG+JW5qCclRvvzEwIIZQY0kYvW74gQwCNn9cGTP29z3ff4Of5XZdALaMf78fuup2uyR0A7vxxhBr+Jal+vOYgHz+yFOFVgXbPkuOpkXL3Njhs/Wet2gnLhjjxIKV2B4EI/emiX+pWhrV2acXuOfsnwXblleHLONpTX1OPu03po/n1woKgSB1YdwJerDiAkWOC8gWmmx0RERERE2rQC2m1jrDhcWo0uSZF49Kw+bgkHxzKtCZEVOgHt4CCBW8d3w63jm3dy+/EiJdqKn24fjdnrs9AtOQoXDWnf0kOiVoABbSIiIvLJs3RzY8T8P3v3HeVGebUB/BnV7b2v11733gvY2IBNMyX0TgiEEEJCIKRAKgkJpBFqgIR0CPmoIfReDdjGBXfj3u21t/eiOt8fWmmlmXeaVtuf3zkcLGk0Gq1315p53ntvkgNJTjtuWjwG588sxQm/+0C43dTSrLhfIyfVpdn+W9SyOl8Q2oYv4J8ysRCnTCw09bp2m4Sr55dHbu84pr7IbpO0ZymJlGaba9EOhIL5z358CjYeasDM4dlxV+COzEs1DJgT7aTx+Zpzy5Oddlw1b0Tk9uwROXjl2wtN7TfNra5ujw60UwRzurw9GGjbbBKuPn4Erj5+hO5291w0Dd+MakN22xnj43q9fI0Z2tOGZbEKkciEr8wfgQ5/AJsONeJL00swzUT3ECXNQFtQFW7V6Pw0AF2dUPZUtyDVwr8xj6/Yj1tPHRe5rddy/N0vKvGjFzahThFYN3v8qG72ROZYKx/Xs2pfHZKd+7D2QB3WH2yABOCHZ04Qhspai+r2VLfA6w+qFiXIsoxbnl6P7Z3/Fn/r/9bhrwaz13/16hcMtImIiIgSIChItF+/ZSFaPQEUZLhjzssHA+UooNMnmbuORMYmFGXgx2eaK3qhoYGBNhERERkyU6Gd7nZotlYCQi3Hw7Sqj+02CTPKsiwfX1h4jva+mlbVY8WC6izRrCbRzFCrRLOybZKkGyTOLc/Gmv31kdtWZlgDoZa1C8bkWXpOf+C02/DfGxfgH5/uQ0ayE19fNBIvrT+CA7VtuGxuGTJTzLVdVxK1cU8yqF70+MWV9b3p9MlFuH7hSLy19RhmDc/G1fP1A3AtGUkOuOw2eAOxIf13TuFqcCKlacMyselwY+T2NQvK4bDbul09UdaDFdpjCtJibu+pbkWGyTEVAPCfzw6iJCsZK/fUwhsICufTtXT48dqmCnz7qfWa+9ld1RJXoP3xzmp8vLM65r7b/7sJ80fnokCxIEdrhrY/KGN/bSvGFabH3L+1oikSZgOhf9dXaXSFCbNSXU5ERERE2kSBdpLTjtxe6gLX235/0TR877mNAELd0L4SVexARInFQJuIiIh0tXsDhiGvy2HD8NwUbK3QnmcZHTAmOe1IcdnRpmjNPbkko9vzfYszk4SB9qRic6s6CzVaNVuRFUf13dcXjcLGQ+vhDQQxZ0Q2TptU1O3jGCjKclJw57mTI7evTsAJoFHLcZF5I8VtwHuT3SbhZ+dMws/Osd7eOJokSRhbmBbzM3n9wpFYMoGrxYmUvnvqOHzjyc/hDQQxKj8V505PzDy7zGQnMpOdaGyPnd+cE+dCnWihCu0uB2pbLVW71LR4cPt/N+lu4w/KeH7tYd1tdle3RBZTWQm0RTz+IN7ZWokvKzpZ6C2q21XZogq039xyVLXdkYb2bh0bEREREZkjajk+mJuEXThrGNLcDmytaMIZk4swPFe8qJWIuo8D9IiIiEhXTYtxdXZOikt35rLLblO1Xs0RVKjNGp5t/QAVRHOxTxiTq1n5fdPi2Ero75wyTridFXZB5beR0ycX4cPbTsb/vrUAz35jflz7oC4pbnWwo1wscUdUaGyTQosKBpNbThmLJGfo4/7pkwpx+9IJfXxERP3T4gkFePd7J+LJr83DG7csSmgbRFHb8XgWPSmNyo8dDxGUoVoklgh7a1p0H99d1YJlO6txyWMrNCuprXh767GY24GgrPs55KMdVfh4Z3XMwru3thxTbXe4vq3bx0ZERERExkSBtm0wJ9oIXc/57mnjMKmE7bGJehIrtImIiEiXmQvUOaku5ArabAOAwybh1tPGqi7g56S6cLg+tmJq9ojuB9oTFZXYI3JT8MgVszTbfX9t4SjsONaMjYcbcfbUYiwen9/tY4hXaVYySgWBPFnnsqvXbSY5YkOqry4oh9thw/ZjTTh/RilG5Pbu/PCedsbkInx8+2I0d/gxKi+Vs7OJdIzITe2R3wHDc1Kw+UhjzH2iBV1WpSc5MSo/FXur1R1JEulQXey/06VZyTHVzp8fqMdL64+gqSO+cR0jclNwoLYrbF65pxYNbd7IZ4a6Vi+CgouiYc9/fhjPf34YI/NS8cq3T8Cxxg7sEXxNlO9DpM3rR4qLl0iIiIiIukOGzoc3IqJu4NkaERER6TIzPzs3zQWHoKL4mRuOx8TiDGQK5nrWt6lbkyYi0L541jC8tP4INh9pxKj8VPz16jm680pzUl34+zVzu/26RrQCf+oZDpsg0HbG3mezSarWtoNNQXoSCtKNtyOiniGao52IGdoAMKMsq8cDbaVTJxbgiZUHIrf1Ro2YccfZk3DTU+vg8QcBhNqcv7etChfPHgbA3GcQANhX04r3t1XhUJ24EttMy/HqZg9G5PISCREREVF3iBYjDvYKbSLqHWw5TkRERLrMtByfW54jbJE9e0S2MMwGxNVSonbhVmWmOPHitxbgsx+fgje/swhjCtKMn9QDbjgxtn31z87u3kxkssZhV38/uhPYRpiIyAxRy/HsBMzQBqA5SqMnzRuZm7B92SRg/uhcnDQutjPKX5btiQTZVc0dpvf38c5qrDtYH/fxKMPzPdUt+Pq/1+Kaf67GFkWVPRERERGJyYKe45yoRkSJwECbiIiIdCkv8LodNkwuyUBxZhImFKXjhhNH4caTRuPMKcUx250yoQBOQdvnsG8vHhNze/qwzIQds8NuQ1FmEtyOvgswrzthJGaUZUGSgLOnFuP0yYV9dixDkahjgLJCm4iop5XlqBdqZSdghjagH2j31O+72SOyE3ZBckJRBlLdDiydUhRz/66qFlzy2AocqmtTfQYpyUzS3J/baY9pX25V9Gt5/AFc88/VePeLSizbWY2bn16PoF7vcyIiIiICIK7Q5vgrIkoE9tMiIiIiXc2KuZjnTi/BHy6ZrtruhDF5OGtqEd7YfAylWcm4bel43f2eO6MEf/90Lzp8QUgScMc5g6uCuSgzCS/ddEJfH8aQ5RAsphDN1SYi6klFGeoANiMpMRXaE4oy4HLY4O1s1x3tlImFeH3TUVP7eejyGfjv54fxya4a3e0kCchPd2NEbir21XS/1fnM4VkAgLOmFuOh93fFhNH7a9tw8WMrMH9UbEX4mMJ0+IKysBV5Q5sXh+q7EWhHdaT537ojOFzf1UlmX00rDte3Y3iuuuKeiIiIiLqwQpuIegoDbSIiItLlDQRibidptG222yQ8euUstHoDcNltcDn0w8Nxhel47eZFWLmnBnNH5mBCUUbCjplINI+dq8KJqLeNzk/DyLyuAHhEboqwajseLocNk4ozsOFQQ8z96W4HvnnSaNOB9ryROThvRinqWr24/ok1WHewQbhddooLdpuE0fmJCbQnFIf+3U9y2vHv6+bhy/9YFTOOpLLJg5c2VMQ8pyjDjZpmtzDQ3nykEb5A/FXU4X3Ksoz/fHZA9XhThy/ufRMRERENFaJPYzwXJ6JEYJkKERER6VJWfukF1ZIkIc3tMAyzw8YUpOHq+eUMsynhZpRlYcHorsq+7546rg+PhoiGKptNwsNXzMSJ4/KxaGweHr1yVkIv6InGddx8yhhMLDb/72q4ijwn1aXbDj0nNfTYtGFZ1g4SwCNXzlTdN39UTuTPI3JT8cKNCzChKF13P+OLMlCQ4RY+Fl1RHY9woL1mfz22VjSpHm9qZ6BNREREZCSoqNBmlk1EicJAm4iIiHR5LATaRP3JE9fNw1+vno1nbjge3zl1bF8fDhENUVNKM/Hv6+bhya8dhyml6gC6O+aPzou5PSI3BdcsKIddo6/jeTNKYm5PKs6ICdjTkrSbuOV0ht1fml6iuQ0AOO0SpnUG7ZIE3HbGeJwzrQRnTyuObHPqxAKMKYgNrwsykvDsDfMxOj9Vc9+TijNwwcxS3dePV1VnoP3M6oPCxxsZaBMREREZUnYcZ55NRInCluNERESkS1mh7WagTQOE027D6ZOL+vowiIh6zOmTCnHKhAK8v70KmclO/PHymXA7xKNBAOAr80fg5ag23jcvGRPzeJpbJ9DurNAemZeKWcOzVK3JZw3PwoWzhmHm8CxMLsnE7qoWuB02lOWE5k4/dNkMnDaxEIGgrBmKZ6Y4cdqkIuxZtkf4+KTiDMwtz8b/1h3Bsp3Vmscaj3CFtqg6G+hqOb5mfx1aOvw4cVy+auFAY5sPbqdNczwLERER0WCnrNC2sUSbiBKEgTYRERHpYoU2ERFR/2SzSfjHtXNxpKEdeWmumDD7quOG4/9WdVUb37xkDGaPyMET183Dh9urMG9kDpZOiV30o1uhndbVjvyi2cNUgfbi8QX48vEjIrfHFKTFPO6w23C+ierqGWVZwvtLs5KRmeIEEOrAsbOyGac/8LHh/syqbvZAlmUcrGsTPt7Y7sP97+7EH9/fBQBYPD4f//rqPLR6/Hj4g914bVMFDte3I83twAOXzcBpkwoTdmxEREREA4WyQpuBNhElCq9IExERkS7VDG07Pz4QERH1J6VZyarK7GsXlCOjM6DOS3Ph0jllAICTxuXjznMn46ypxap53uk6Fdq5qV2B9jlT1RXW0zWCaKtmDhfvZ2JxbIvyMflpmq3V41HT4kF1iwftvoDw8YY2Hx6Lqhz/cEc1dlY245evbsVjy/ZEZni3ePz47ZvbEnZcRERERAOJMtBmz3EiShRWaBMREZEuVctxttEkIiLq98YWpuP975+M7ceaMLkkM9IyXI+ZluNAqDX4TYtH49EPQwHv2II0LBid2/2DBlCYkYSSzCRUNHbE3D+pOCPmts0mITfVFZl93V3+oIzNhxs1H99d1aL6TPTZ3lq8vbVSte3e6lZ4/AHd9u9EREREg5G65XgfHQgRDToMtImIiEiXxx9bqeRmhTYREdGAkJ/uRn56vunt05Kcmo8pA/HvnTYeU0szUdnkwXkzSuBI4OeDGcOzULH5WMx9ExWBNgDkpbkTFmgDwDee/FzzMdFs7epmDxrbfcLtq5s9GJadkrBjE2nu8GH9wQbYJAkjclMi88qJiIiI+oq6QJuJNhElBgNtIiIi0sUZ2kREREOD2QptALDbJCydUtwjxyEKgieVqAPt3DRx1bnDJiE3zYXKJmthtz+ovATb5UhDu+q+z/bWam5f1cOBdmObD+c++ikO1HbN/D5pXD7+cvVsJLGbDhEREfURVmgTUU/hFWkiIiLS5Q0oWo4z0CYiIhqU0pPMB9o96YzJRTG3M5IcKBOEw/lpbuHzS7OTkZsqfiyR1uyv13ysymKYbtVbW4/GhNkAsGxnNT7cXtWjr0tERESkRzlDW5KYaBNRYvCKNBEREeny+FihTURENBToVWj3RkAcNmt4Fo4bmRO5/fVFo2ATlPfkpYuPaXxhOjKTtdunh5VkJsV/kAaqmzuMN+qG/YowO+xwvbqSnIiIiKi3yIpEm3k2ESUKW44TERGRLmWFNgNtIiKiwSlNp0I7O9U4IE4USZLwn+uPw0c7qpGV4sTc8hzhdrkaVeMnjsvH8t01wsdmDs/CV08YiaxkJxaNzcPWiiac8/CnCTv2sETO9hapbBIH5q1ef4++LhEREZEe5QQXGxNtIkoQXpEmIiIiXV6/suU45zISERENRukaFdppbkev//vvtNtw2qRCzTAbAPI0Wo6fNC5fs0I7L82Nc6eX4MRx+ZAkCVNKM3HXeZMTcszRerrleLVGYN7uDfTo6xIRERHpYYU2EfUUBtpERESky+OPvTDKCm0iIqLBKTfNLQyCe3N+thWiluOj81NRlpOCzBRxoC2aE75gTF7Cj62yh1uOs0KbiIiI+iNWaBNRT+EVaSIiItKlrNB22fnxgYiIaDCy2yT8/qKpqvtLsnpu1nR3iFqOLxqbDwCaFdqi54zKS03sgQH4aEc1/rfuMBravAnfNwBUalSAt7FCm4iIiPqQDEWFdh8dBxENPrwiTURERJr8gaBqda3byY8PREREg9XSKcV44ZvzI+28bRJw9fHlfXtQGvIFFdrHj8oFAGQli6vKT5tUpLpPkiQUZyY+tP/ecxtx9h8/RYcvsSFzhy+Axnaf8DGjluNB5Qc7IiIiogRSdByHxAptIkoQXpEmIiIiTR5FdTbACm0iIqLBbvaIHCy77WT8+apZeP2WRTh7WnFfH5JQQbob5bkpkduFGW6cPF67QvvCmaWYN1I8k/vn50yKuf2V+SOQ6ur+3PAjDe34aEeVqW3rWr34+yd78fqmo6r5k9H05nO3agTajW0+XPX3zzDqJ2/g6n+sQouHrcmJiIgo8dSBdt8cBxENPrwiTURERJqU7cYBwM0Z2kRERINeqtuBM6cWY2JxRl8fiiZJkvDIlbNw/KgczCvPwaNXzkKSMxRCTyqJPe6CdDd+8aXJmvtaOqUIVx03HMlOO+aWZ+NbJ49Bhkbbcqs+3lWDNq8fd732Ba76+2d4bu0hVWDt9QfxpYc/xd2vb8NNT63Dg+/t0tyf3nzudo0Z2s+sOYjlu2sBAJ/sqsETK/ZbfyNEREREBoKKzzg2BtpElCB9ckVakqR7JEmSo/472cRzlkqS9D9Jkg5LkuTp/P//JElaauF1UyRJuk2SpNWSJNVJktQiSdI2SZLulSRpeHfeExER0WDkDYgC7e5XKxERERElwpTSTDxzw3w8d+N8zCnvqr4emZeKW08dC5fDhhG5KfjntXORmaIdUEuShF9fMBXb7lqK529cgKLMJM053CJJThscGldskxx2PPrhbvzj031YvrsWt/93E3756hcx7b9f3ViBIw3tkdsPva8TaDdpB9qtHnGF9mPL9sTc/sPbOzT30dc6fAG8tqkCq/fV9fWhEBERkUXqQJuJNhElhqO3X1CSpOkAvmthewnAYwBuUDxUCuACABdIkvRXADfKOj25JEkaDeB1AOMVD03o/O96SZKulGX5DbPHRkRENNh5fIKW46zQJiIiogHg1lPH4TunjI17dmNGkvlAuyQzGW3eAI4JwubK5g7srW6Nue/xFfshyzJ+ed4UAMDy3TWa+/YHgnjo/V1494tKzBqRjRKded/tGvO669vUM7e9/mC/+1wXDMq48E8r8MXRJgDAHedMwtcWjuzjoyIiIiKzlAEN42wiSpRePXORJMkG4G8IBenmhkgBd6MrzF4P4AoA8zr/v77z/hsA3KXzumkAXkNXmP03AKcAWADgpwBaAGQCeF6SpGkmj4uIiGjQ8wbUF0X724VPIiIiIi3xhtkANFuO/+aCqar7mjp8KMhwC7c/Ut+OPVUtqvv//dkB1Ld6AQCtglbh/s5OOR9sr8LDH+zG9mPNeGrVQTyg0468VWM2tqh6fGtFo+Z+esP+mlbsVnxdlu2sjoTZAHDXa1/09mERERFRNyhrDrvzWYyIKFpvX5G+BcBcANsB/MNoY0mSxgC4vfPmWgAnyLL8jCzLa2RZfgbAws77AeCHnVXYIj9AqAobAG6XZfkGWZY/kGV5pSzLvwFwOgA/gBQAD8bxvoiIiAYlj2KGtsMmwc4BSERERDQEZCSrm9qNyE3BpXOGqe6fWJyBlg5xmLzhUINwjIssA7urQ4Fum1e9iLC5c3+/fDU21A0ENZvToV2wnw5fAH7Bcz4/UK+5n57254/24OR7P8Kp9y/Db9/YFrlfr1KdiIiI+j9lD13m2USUKL0WaEuSVIauKupvAvCaeNp30dUW/WZZltujH5RluQ3AzZ03HQBuFbyuE8B3Om9uA3CfchtZlleiK2BfLEnSbBPHRkRENOgpA21WZxMREdFQIZqhffzIXDjsNtyyZEzM/WdNLY6rp+a+zlbkoiC6qSPUJjx6traRNl9AVRml9fw1+/tmRrXXH8Tv39oeuf2Xj/eioS10icgmWDipF+ATERFR/6L8Z5sztIkoUXrzqvSfAKQBeEKW5Y+MNu6cnX1e583tsix/Jtqu8/4dnTfPl9Q9LE4GkNX55ydkWVYviw55POrPFxodHxER0VDgZaBNREREQ1S6W12hffzoHADAt5eMxTnTipGZ7MSFM0txwcxSXDlvuOXX2FMTqtBuaFfPuG4U3GckEJSx7mADqpq7ZnkfrhcH2mv31+Ofn+7DZX9ZiT+8vV31ua+n1Lep6xu2H2sGIF4T0KLRRp2IiIj6H3XL8T46ECIadNRnZz1AkqRLAZwDoA7AbSafNhJAaeeflxlsuwyh+djDAJQD2Bf12CLFdlrWAmgFkIpQK3MiIqIhT3lh081Am4iIiIaIo40dqvuOG5kLILTI75ErZ0GW5chsyAtmluKZNYdUc6H1hCu0KwWv1dQeX5B70Z9XIMVlx1+uno1FY/NxRCPQrm314ledM6pX7atDYUYSvjK/PK7XtEJUjR6+1i2as9ni8Qur5YmIiKj/YYU2EfWUHr8qLUlSFoCHOm/+UJblapNPnRj15+2aW6kfn6h4zNR+ZFn2A9ijsQ8iIqIhiS3HiYiIaKhaODZPdV9JVnLM7egANjfNjdduXoi3bl1kOoDdW9OKFo8fzYIq5HDL8Xi0eQN46L1dAIAjDW2mnvPzl7fG/XpWiCquwzO+Zajbizd34+tAREREvYsV2kTUU3qjQvseAEUAVqBrTrUZZVF/Pmyw7SGN50XfbpVlucHEfqYByJckyS3LssfwKDtJkjTMYJMis/siIiLqL1Qtx+0MtImIiGhoOGlcPtKTHGjuCAWwPzvbeO17ktOOCUUZKM9LxcZDDYbbH6htRYXGjOumOFqOR1t7oB6Adsvx3tbi8WPV3lpUNasvtbR2htyi6u3w15+IiIj6P+XSNObZRJQoPRpoS5K0EMD1APwAbpSVy3P0pUf92ahfV2vUn9M09mOm55dyP6YDbcSG6kRERIOCNxB7UdHtsPfRkRARERH1rqwUF167eSFe23QUI/NSsXSy+XXqxRlJ2GhiO19Axso9tcLHGtt9qionpRSXHYGgrOqqE+bxBzRbjvem5g4fznjgY1QIWqsDQKs3FFqLqrdZoU1ERDRwBBWfXdhynIgSpccCbUmSXAD+itAinAdkWd5scRdJUX/2GmwbHTwnKx4L78doH0b7ISIiGnI8PrYcJyIioqFrRG4qblo8xvLzijKThPenux2QJKApqur4F6+IW303dfjQ4RMH1WG5aS60egLw+MWXPI42dOCIRgV4T+rwBbCzshmj89OQ6nbgqVUHNcNsAGj1BDr/Lwq0WaFNREQ0UCjX4jHPJqJE6ckK7Z8gNIv6IIBfxvH86DMdl8G27qg/K8/Uwvsx2ofRfowoW50rFQFYY3GfREREfcobYKBNREREZFVhhjjQHl0Qaiq3wUQ78qZ2Pxra9dfm56W5EQxqN5fbX9uKY03aQXJ3yLKMbUebUZDhRl5a1+WU+lYvLvrzCuytaUVuqgvPfmM+Hvlwt+6+wkF2ONiOxkCbiIho4GCFNhH1lB4JtCVJmgDgx503b5ZluVVvew3NUX9WthFXSo36s7K1eHg/Rvsw2o8uWZZ153xL/MVNREQDkHKGtpuBNhEREZGhYo0K7RPH5eNwXZupQLux3YeGNv1223lpbmFVc9i6A/WqSqlEkGUZX//353hvWyUkCfjzVbOwdEoxAOCh93dhb03oMlBtqxf/Wr7PMJQOvwdxy3EG2kREREREQ11PVWh/F6GK6L0AUiRJulywzZSoPy+RJCk8jOrVzgA8OiAeZvB60dXRylnWhwEcByBVkqQsWZYbTOynWpZlK/OziYiIBiXlPEYG2kRERETGRBXaeWluXL9oJP69Yr+pfTR1mAm0Xahq1r58sWpfnanXAgCbFAqqzSzI/3BHFd7bVgkg1Fr0jpe34tSJhfD4g3hc8f5e2VBhuL9Wr17Lcc7QJiIiGihYoU1EPaWnAu1wr6lRAJ42sf0dUX8eCaAVwBdR900weH7049sUj30B4KKo7T4T7UCSJAeA0Rr7ICIiGpKUgTZbjhMREREZG5adrLrvJ2dNQEaSE1NKM03to6ndh0YTLcdTXW2ajysD7aKMJM0W5EEZaPcFkOJyoLbFg5++uAVbjzbiolnD8J1TxsYE3S98fiTmudXNHqzYU4tD9epjafUaV1h3tRxXbyuq2iYiIqL+KRh7GQk2XkYiogTpz79O9gEIL+M9yWDbEzv/fwTAfsVjn0b9WW8/c9DVcny5ieMjogGkvtWLm/5vHZbc9xEe+WAX5J7ou0c0gFU2deBQnfoCpLrluL23DomIiIhowCrLScGC0bmR26dOLMQFM0sBACeOzcepEwsN97HuYAO+88wG3W1yU11IcZn/fDa7PFu34044PH7kw914a+sxHKprx4Pv7cIrG7uqrDt8AXy8s1r13BfXH8GTKw+o7g+aOPUKV2gnsuW4xx/Ar179Auc98ike/XA3gmYOhIiIiLpF+a+tBFZoE1Fi9EiFtizL1wK4Vm8bSZLuBPCLzpuLZVn+SLEPWZKklwF8E8AESZKOl2VZVV0tSdLx6KrQfllWp1QfAWgEkAngGkmS7hFsA8Xxvqh37EQ08Dz28R68vvkoAODed3bi+FG5mFOe08dHRdQ//N+qA/j5y1sRCMq44cRR+MlZEyOPefyBmG1d9v68Fo6IiIio//jntXPxwrrDcDvsOG9GSaTC2WaT8LevzMbRxg4EgjIW3fOh5j6U3XKU8tLdSHaZv7QzOi8V27KSIzOulVo6/ChIBz7YXhVz/63PbsCXppXAZpPwya4aNAuC5xfXH1HdZ1arxw9ZliPBdrR4W47/7MUteP7z0DS7jYcbMak4A4snFMR9jERERGRM3XK8jw6EiAad/n5V+kEA4bOkhyVJiunZ1Xn74c6b/s7tY8iy7AXwx86bEwH8QLmNJEnzAXyt8+YyWZbXdPfAiah/+cuyvTG373lrRx8dCVH/IssyHnh3JwKdFSt//2QvKqPaUCortNlynIiIiMicJKcdVx03AhfPHganYlGgJEkoyUpGWU4KfnSm0ZQ1baGW4+YrtEflp6E4Sz3fO6zVE0Cb148DtbGde2QZOOuPn+C1TRV4ZvXBuI9XS4vHD48/GPlMGq0pjgrtd7Yei4TZYZ/urjH13GBQxsc7q7Fidw1kWcZrmypwz1vbsbWi0fJxEBERDTmqEm0m2kSUGD01QzshZFneKUnSvQB+hFBL8OWSJP0ewB6E5l3/EMDMzs3/IMvyLo1d/QHAZQDGAbhHkqQxAJ4B0A5gMYCfIPS1aAdwa8+8GyLqTyqbxXPjiAYbWZbx3NpDeG3TUUwtzcQtp4xFkrPromezx4+alq7ZjEEZWLajGpfOLQMgajnOQJuIiIgokb5x4igsGpuHDYca8NMXt1h6bl6aC8kWAu3R+WkoyVTP9w5r9viw/Viz8LHtx5rx7afWWzo+s9q8fs1Z2S0WA+1Wjx8/eXGz6v4j9e2mnv+D5zfif53V5klOGzp8oc/Df/tkLz74/skoy0mxdDwAUNPiQXWzB2ML0uBgxyMiIhrEWKFNRD2lXwfanX4KoADAdQiF188ItvkHgJ9p7UCW5WZJks4G8AaAsQBu6PwvWhOAq2RZ3pCAYyaifs7O1YHUDwWDMmpaPchMdiZsVvW6g/X44QuhC3qf7KpBVooTN5w4OvJ4Y5u6hWNTVFtHZZtLVmgTERERJZYkSZhckomijKQ4Am03Ui20HB+Zn4qSLO1Au9UTwD6NduQ9qdUTQKtGoN3ssdZy/MMdVTELNsPqWtX3KVU3eyJhNoBImA0AvoCMJz87EDOex4zlu2vw9X+vRZs3gLnl2Xjq68erKvaJiIgGC/UMbSKixOj3n6BlWQ7Ksvw1AGcDeBlABQBv5/9fBnCWLMvXy7KsO1hKluXdCAXiPwSwFkADgDYAOwA8AGCaLMuv9dT7oP6rzetHh089p4sGN+bZ1N90+AK44m+fYd6v38dp93+csAuJD74X27zkN29sj7ndIAi0oy/2seU4ERERUe9IT3Ja2t5hk5CZ7DRdoV2Y4Uaa24GMZO3XafX4se1ok6n9SRLw83MmwZGA0qsWj3aFdrPFCu0dGhXmZrp07a1u0X1cOVvcjHve3oG2ztnga/bXY7nJ1udEREQDkbpCmxdhiSgx+uyqtCzLd8qyLHX+95GJ7d+QZfl8WZZLZVl2d/7/fFmW37Twmq2yLN8jy/JcWZazZVlOlWV5gizL35Nl+UC33hANOLIs4xcvb8HkX7yNE+/5EKv21vb1IVEPkWX1HDZ+mKLeJssy3tpyDE+vPiisPnllYwVW7asDABysa8NfP96r2iYem4/oz/praFdXqhyqb0cgKOOl9Ufw1tZjMY8lqnKciIiIiGK5HDYkO81/1spNc0GSJNMztEflpQEAMpK0K7ob233YcKhBdf+isXkoyeyave2wSfjj5TNx3cKRWDAmz/Qxa2nz+NHqES80b+7wC8/ptOyuEofSB2rb8OP/bcYD7+5Eu1f8Wg3t+tXgqW5rjQ79gSA2Kr6er286amkfREREA0lQ8U82r8ESUaIMhJbjRD3ig+1VeGJlaB1DVbMH976zA+dMK0FNiwdXHjccxTpzxWhgiW4TF2bnABfqZfe8vQN//mgPAOCJFfvx5ncWQYr6UH//Oztjtn969UH89sKp3X5do3aGogrtQ3Vt+O0b2/D3T/epHmOFNhEREVHPyUx2ot1kB7GcVDcAIMVky/FR+akAgCUTCuCy2+ANqM+TfvHKVtV9j391Lk4eXwBZlrF6Xx22H2vGkgkFkVnSZ00pwsc7q00dg5ZWbwAtGq3FA0EZHb6g6Up0rUAbCH3GBoBjjR24+ZQxqGv1YkpJJmyd54dVTfpV3OkWA+091equS1Yr8YmIiAYU5SI0XoIlogThVWkaMmRZRlVTR2Ql9rf+b13M42v21+MXr2zFwx/sxrmPLFe12aWBq7lDfWFE4upASoAdx5rx1pZjmvP+ooXDbADYfqwZK/bEdoWobfWonmOlEkWLSxBoR49ZEFWhHKxrE4bZAANtIiIiop6UYjK0BQB35+eyFLfJCu38UIV2bpobd547GZk6rcejTSrOABA6hzpuVC6uWVAeCbMB4PTJRarnLBprvWq7uln9eThMdE4n4gsETY3ueXbtISy+9yOc+8hyfPXxNZHP3ZVN2scAAJ/ursFxv3kPlz62Egdr2wxfZ2uFulsS11aLHWvswKE6468pERH1b+oK7b45DiIafHhVmoaEQFDG1/+9FvN+8z4W3fMBXvj8MDw6gXV1swdvK9rs0sDVLAgb+WGKuuutLUdx1h8/wY3/+RxnPvSJZttCQBxMbzoce3FLtMiipiXUDtwXCCKoPCMwyS0IoKMvFja2qVuOR8/QNrM/IiIiIkqMWp3PYUpOe+jzo9kQfHRnhTYAXHnccGz8xem4bE6Z7nPy0lzIT3frbpOT6sL5M0oitzOTnbj6+BGmjimaXpjcFDVH2y+oLA87UNsGv8nPzb5AaLtlO6vx+YH6zmMwnrNd2eTB6v11uPedHYbbbjminkfeaNDWfCh6cuV+nPD7D7Dong9xz1vb+/pwiIioG5TXwCSWaBNRgvCqNA0JK/bU4L1tVQBCAdH3n99o+JxdOm3KaGBp7lAH2j6diyA08NW2ePDAuzvxt4/3xlQjJ9L97+5EoPNi2cG6Nry4/ojmtqK2kcGoD/iBoBzZV7R9Na34/nMbMfanb+LU+5dhd1Wz5eMUXdCrau66UFcvaDmuh4E2ERERUc+xEnY6bJ0V2oKW4zctHo28NFfkdrLTjunDslTbpenM0waAKaWZprpb/er8KfjGSaPwpekl+Oe1czC5NNPwOUrRn1GVmjt88AeC+PZT6zDp52/jsr+sFH6t9NqN63l+7WEAQKVOlbjSKxsrDLcRVWjHE2hvPtyIk//wIcb/7E089N4uy8/vzwJBGfdFnVv96aM9ugtsiYiof1NVaPMyEhElCH+d0JDwfmeYbYWDJbyDRosg0G719EzISX0vGJRx2V8/w0Pv78Kv39hmagFLPHZWxl4se2r1Ac1tWwxaklc0tAsD7adWHcAL60IX1/bWtOKRD3ZbPs56QQV2dPWLaIa2HgbaRERERP2DQ6dCe0RuKh7/6jxMLM5AaVYyfnfRVGSnulTbperMhHY5bPj6olGmjiUjyYkfnzkRD18xE7NH5KDAoKpbZNXeOs3HWjx+/HvlAby26Si8gSBW7avDfYIK6T3V8QXa4QXPRjO0lfRGBAWDMr6oSEyF9u/e2ob9tW3w+IN44L2dcb/P/qip3ac6JwlXzBMR0cCj/JeRFdpElCi8Kk1Dgi2OecnhiwM08LV41BcMWr3GM49pYNp0pDGmMuP1TUd7rEo7ms+vfTFLtKgi+uLXAY35ey9tqNC9rWfjoQa8urFC2KEg+kJdY7u16gfO0CYiIiLqOd8/bZzpbWePyAYgDrQzkpyYUpqJN7+zCMt/tATnzSgV7iNdI9BOdtrx3ndPwgljrM/CBgCn3fpnRr0uac0dfjz43s6Y+/698kAkiO7wBfDH93fhD28btwEX8XTux0zL8WitOmOHDtW3CcdfxRNoL99dG3P7uTWHLO+jvxJ9PYI6CwWIiKh/U7Uc5yV2IkoQXpWmIcHjtx5m9cX50xcVTXhlYwXq2V4roZoEgV4bK7QHrYN16nBYdF93iOb26bWxF3UE8Pi7tj9Q15qYA+v09OqDOO/R5bj56fXCx6NbKVqt0HbZzc1oJCIiIiLrLp1bhnGFaQCA8YXpuGT2MAChLjmjomZgux02XDO/HIC45XiS09zlHq0K7SvmDcfw3BQrh65y8vj8mNtfPaE87n2t3FMrPK8754+f4vdvbccf39+F+9/dqXo8M9lpav9N7T54/AHL43hqoj5Xe/wBbDrcELn+sFVQnQ1YD7TbBIuxrX6G788aBF+Pdp2FAkRE1L8pFyXFU2hGRCSiPyyJaABZsbsGB+vasHRKEbJSYlupxTN/yahFcKK9+0UlvvHkWgRloCDdjXe/exIyU8ydfJM+UXWsNxCE1x9ktekgJPr73lfTinGF6Ql7DdFFKK9OoN0s6BIQXTl9UKNCW8QXCOpWvMiyjIff15+rV9XkQXOHD8t312CtxXZ+bpMXR4mIiIjIusKMJLx28yJUt3hQmO6Gw27D7UsnIMlpQ7LTjic/O4ADtW24fF5ZpIW4aCRMhskgV2uGdnFmUvxvotPNS8Ziy5FG1LR4cdbUIlx13HD8a/n+uPb15Gfi8T47Kpuxo7JZ83knjMnFG5uPGe7/aGMHqprMz88Oq27xoDwvFVVNHTj3keU41tSB/HQ3Xv32QuzUOC6rgbbouAZTBXODYETSUJmhLcsy/vHpPjyz5hDG5Kfh7gumIC/Nert+IqL+RPlPFPNsIkoUBto0KDy5cj/ueHkrAOCP7+/CBz84GUnOrirCeE6GWnUCbVmW4QvICQ1D73rtC4RH6FY1e/DShiO4ZkF5wvY/0ByobcU9b++APxDE908f360wUmtxQrs3wEB7EDomaBO4ryaxFdCiudRev7UK7ehAW6vluEhtixdFgguMT67cj4fe343MZAcqGvVbJe6vbcXSBz/BkYZ2068b5oqjfSQRERERmedy2FCalRy5nR81j/qrJ4xUbZ+T6sKo/FTsrQ595i3McGNaaaap10pzi7vviD5vWjV7RDY+uX0Jmjt8KMhIQk2L9cC4O5x2CSeNyzcXaDe0W243DnRVaN/3zs7IeUh1swf3v7sDdpv4c3ObN2C4SDWa6LhEY4V6098/2Yu/fbIXI3JT8fAVM1GYEf/3iyjgF4Xcg9EXR5tw9+vbAAC7q1pQnJWEX3xpch8fVf9T1+rF3uoWjCtKR0YSCz+I+rugItBmhTYRJQqvStOgEA6zAaCisQOvbIydMxtPoP3mlmO485WteG1TRczsj4O1bTj9gY8x4Y43celfVuKtLUdVs0HioWyJ/PTqg93e50D27afW4/VNR/H21kp89V9rEFR+GrKguUO8Ap5ztAenKsEFn/0JD7TV31P1bV7N3wWiBTLRs90PWGiJXt2svhC45Ugj7nh5K2paPNhTbfxePz9QH1eYDXCGNhEREVF/I0kSHrxsBmaPyMb0siw8dPlMOEyGpWlucTiUiAptAEh22VHQGXaabf+dKN88aTQmFmeY2rbVG8DOSu0Z3lpqWjxo9fjx7NrYmdbPrT2Mw/Xan/GtVGmLFuyK7uste6pb8Os3tqGyyYPV++rw3Wc3dGt/oq9F3RAJtJUz3+PtYDCY7aluwekPfIyLH1uJMx74OO7z2L72ya5q3PnKVry+KTHXEKPtq2nF+Y8ux6y73sWjH+5O6L6J4iFDMUO7j46DiAYfXpWmQemDbVUxt0XVlEaqmz14fMV+fPup9ZEV3bIs43vPbcCuqhYEZWD1vjrc+J91+MmLW7p1vAFBWJviGrpzaqubPdh8pDFy+0hDO7Yf024jZ0SrQls0i4wGpoY2b+TvU3RxZ2+CA23RIhlfQNacudcs+B5csbsW972zA8t31+Bgrfnjq25Rv7+fvLjZ9PO7S9TSkoiIiIj61rRhWXjhmwvw8k0n4PhRuaafl9qDFdpKZiuSE+FX503G904fjyILlcMbDlkbxQMAH+2oxg9f2CR87JNdNZrPszIDW9Ry/JhBR6aetHx3TUw72RV7arG7Kv7z9UbhYuHBMyNcz2Cahd5THv1wd6S7w9HGDvz5o4EX2K47WI+r/7Eaj6/Yj5ueWmeqa4QV9769AxsONaCu1Ys/vL0DX1Q0JXT/RFapW44z0iaixOBVaRrwRJW7b209huufWIMH3t2JDl9AeDKU5nZg0dg8U6/x05dCYdF726qE82afWXNQWBVqlqjiMtU9eCcCeP1B/OiFTZj5q3fwtcfXoF4RDja2C9o568wnNtKk0Y5N1AaaBp67XvsCM371Lo77zfv4aEeV8OJOoiu0tVrgabUpFFVoN3v8ePiD3bjq76vQ6jX/vaj8fVHV1IFNhxs1trbusS/PwoQi7Rb/rNAmIiIiGjzSNSq0C9ITH2h3l90m4arjhuO1mxeiLCdZd9uxBaHPs7kW5hFvONRg+Zje316F1zYdtfy8vdUtuO7xNTjuN+/hd29uj1RsrthTg4ff34UtUQu8RecY1S0e4cL43nBUcL717JpDgi21ybKMO1/Zigl3vIn73t2pelx5jWCwstsGX8hT3+rFj/+3Gd94cm1cP1NK/1t3JOb2fz4beN0MX1of+x7+8Pb2hO7/9c2xv4P+NABDfxpclF0ImGcTUaIM3sSMBj1ZDlVD7qwUrwR+b1sV3ttWhQ5/QHWi9/VFI3HFvOF4bu1h3VXTYQ1tPvgDQfz+LfGHTlkG1h1swNIpRdbfCCBsmTQYT2zC3t56DM90nvC+v70KT352ALecMjbyeG2LqPo1/kC7RSvQZoX2gLe/phX/+HQfgNAcufve2YkqwQKRqmYPWjx+pCVooUhdq3glfVWzBxOL1feLAu14/fCFzXh8xQHcvnQ8Fo8vwH9WJe6E3m6TcMbkIiydUozmDh+W3LdMFaAz0CYiIiIaPLQqtPvDZ77HvzoXxZnJWLGnBs0dfpw9rRij89MAAJ/cvgRjf/oGfAFxqBvueGa3SThxXD4+3llt+HrxtByP1z1v78DuqtDrPbZsD44flQNZBr76+BoAwIPv78JrNy/ExOIMVArObwJBGTUtnm7Nro5XjeB4Xlh3BD84YzzcDnOd5j7aWY3HV+zXfDyesXED0WC87PPTlzZHKpA/2VWDz392GpKHcAdCAPj3ygMxt/fXtqHDF0CSs+vrcrC2Dfe/uwNBGbj11LEY1fm7Lh57TYwhI+pJ6hnafXMcRDT49P0ZClEcXt1YgfE/ewuz7noXl//1M91t/7Jsr+q+758+HqPy0yzN8PpwR3XkhFNk4+EG3edvrWjEd5/dgDtf2apqqVUhCLT7qvVUomf5iPxI0ZLtfsWK7FrByWt3AkHNluOs0B7w/vv54Zjbm480al78SGSVtpkK7bpWL6qaQ7ebNRZVxGvb0SZ85+n1aPcG8OL6w8ZPMCkz2RlpBZWe5ER2ivp3pNmLVERERETU/6UlqRd8pgvu6wsuuw3ji9Lx1RNG4pZTxkbC7LA8nerr6KD+DxdPw4WzSi2//g+XTsAbtyzCkgkFlp9rRHlt4ZevfhFzXhwIynhpQ6iqU6sLlKhSOlowKKOioR3rDtZHzksSQTTiqa7Vi/cVo9/0/P5N/QrVodKK2zYIyxaj22m3eQOR7+OhLNmpPodeva8u8mdZlnHjfz7HSxsq8MrGClz/77XCbpQiXr+6+KOhzQt/IIgnV+7HHS9twecH6gTPJOo5QcW15cH4u46I+gYDbRqQ0pMccbegTnXZI6sgrQTaa/frfwDccLBB87EWjx/X/HMNXlx/BI+v2I/vP78x5nFRhXZje++ewDW2+3Dd42sw/o63cP0Ta1Hbol51nShG7ZVFr92d9uDNHeKvJSu0Bz5f0PzvgX0JDLS1QvPw6IG/fbwXx/3mPRz3m/dx/7s7LS3IWDw+39R2TR1+rNpXi0N16t8f8cpS/E7MSnGptuEMbSIiIqLBQ7RYMSPJ/HmyVfdcNM3UdklOG6aXZeluk6JT9Zns6grlCzOScP+lM/DCNxeYeu2w0uxkTCrJQHluqqntu9NlbV9NKzYfiR0jFF6crxVo683R/nBHFeb/7n0s+N0HuPBPK7Do9x/iw+3mA2c9ogX5ALB2v/kZ5NuP6c/crmvzdnuxvSzL8HdeN/IFgli+uwZ7qrWLFNq9gV6/DjOYO/OFmam2D7egH/ezN7H0wY+xV+fvaaBpbPOh3ae+nvXp7q5ukUcbO/DF0a6513urW7Hb5NegQTCyr77Nh38t3487Xt6KJz87gEv/8hkO1rbFcfRE8VHP0O6b4yCiwYdXpWlAKsqMv61WdmpXQGMl0F53MPbkrDAjdjX4psMN2HKkERf+aTnOeOBjvLP1GBrbffj8QB2eXHkANVEh7XvbKmOeKzohrNeoANWy41gz/vrxHnwumPFtRJZl/Ph/m/DB9ip4/UG8t60SX/nn6m6fzPkCQazZX2f5ZKRG0HK8O+GzVsvxNgtzi6l/Cmi0GBRJZKBdr1ExUNnkwd8/2Ytfv7ENvoAMWQYe+WAXdul0d4iW5nbguoUjTR9HImaSRctUVGSLKrRddn50ICIiIhrMerJC+9K5ZXjsy7PxnVPGYtHYPNXj5bkpmFCUjocun4lUg3FBKS7tx1MFYXdOqnqxpp7R+aEg28x1g5xUFz65fTEunTPM0mvoSXXZIcuyTqAtDpZlWcaPXtiEyqauaxAefxCPfBg7V7epw4cVe2rw+YF6UzOr270BbDnSiIoG8fEcaTAXmJlZ7Ov1B4UhoFlHG9tx9h8/xdifvYkbn/wcX3r4U1z191U4/YGP8erGCtX2q/bWYuHvP8D0X76DH/53U690rgOGRtWimbe47mADHl+xH15/ENuPNePRD/f0/IH1kl1V4sUb0eMPRT/j0dcQZVnG5sONMfeF1QvGobX7Avj1G9sitwNBGc+ttTbnnqg71DO0B//vOiLqHf2jjxSRRYXp8QfauXEG2sqg+IzJRTFzcFq9AZzz8KeR2zc8+bnu/qLn5YgC7cZ2HwJB2dSK3Z2VzTj3kU/h6Ww1dN0JI/GTsybAYTJ4enXT0Zi2UACwtaIJtzy9Hk9cN8/UPpQ6fAFc+6/V+GxvHWwScM/F03HxbHMn97Wtogrt+APtZo3nJnKuMfWNgIULDYlsOa614OSp1QcRULQGC8rmg+dTJxZgfGG66eNYtTexrcOUvxOzBRXatiFQRUBEREQ0lKUZBMndtXRKEZZOKcKdr2yNCXUA4MMfnGz6wrd+hXb3Am1JQqTFeZZgkafSpXPKUJKV3K25t0reQBD1bT50+MRdqY41ibuqNbb7YsLssG1HmyDLMiRJQkVDO85/dDmqOudh2yTgK/PL8YsvTRJ+/XdVNuOCP63QHOcFAIfrzXWO2mjy3Kiu1au7aEHPH97eEal4fWtr17WOQFDGQ+/vwpeml8Rs/5s3t0dGnz279hCuPaEcE4sz4nptKwbbuZVoIYCZ0P7et3fE3H5h3WHcd+n0hB1XX9Ja3L7taBOqmz3IT3cLuy1Udf4My7KMa/+1Bst2VkOSgL98eTZOn1wU2c5sMUyiF8MT6VH+Jhhcv+mIqC+xzIoGpKwUZ9xVgjlxBtrK8TVTSjJRmpUc1zEAodXQYaITP1kOrci867UvcOGfluNvH+/VXCX8xuajkTAbAP65fB++88wGU6uKO3wB/OLlLcLHlu2sxrbOk8Bdlc34YHslPH5zq6Tve2cHPusM24Iy8KhiNbieWkGFdrzV1MGgrD1D28Q+ZVnGliONCa3uHchW7KnBL17eghfXH+61Vet6PIJ5UVr29kKgrQyzrbrq+BHIT3cLK0pEVu6t7dbrKbUrfiZELceJiIiIaHBxKUbKnBEVlvQkZah48vh8S1VcWhXcdpskvF6QYaHyfHhOSmQBuplAe1JJKPxUzvnuDl9A1h19plWhrTV/us0bQHVngH3v2zsiYTYQOmd/fMV+bDsaqiZdd7Ae//38MBo6z3vufWeHbpgNmA+015rsKieqPDXD6w/if+u05zYr55fLsqwK2d/cErvgv6cMsjwbfsH5sJm32N3z6P5sZ6V2e/1w4UyFINA+2nnfij21WLazGkDoOuFPX9oSaaUPIPIzaqS4G50uiaziDG0i6ims0KYBSZIkFGS4TZ8wRYu35bhSYWYSppdlCudfm9HU7kdBZyGm1gyqe9/egec/Pwwg1IJpXFE6Thqnnq8bfSIa9vrmo7hi93CU5SSjODNZdZEibPW+Os32yQCwp7oFe6pb8J1nNiAQlDGuMA2v3bxIc3/hff7tk30x9+2raUWrx2/YNg5AZGV0NKOTZy1tvoBqdkuYmTbm33tuI15cfwSSBPzinEm49gTz7aAHm60Vjbjyb6sAAE+sPIBgELjIZNW9GbIs4+2tldhT3YJzp5egLCfF8DlW2uLvr7UWaL+ysQIPv78LeWlu3H3BlJiLU2Za8ll1yyljMbc8BwAwIjc1ZoZWb6lWtDC7YGYpHlvW1e5tXGHiLtARERERUf/wkzMn4M5XvwAQ6mh21fHDe+V1Zw3PwmVzyvDs2kMozUrGD04fb+n5WhXaKS67MBi3EpaPLej63Jth4rrBpOLQyf2ofHPzts3SW8B6rLNNcbs3gGfXHITdJuGSOWW6FZt/XrYHeWlu/G+9OPA9UNuKbUeb8P3nNwIIjVp7//sn4+2tlcLtozW2+9DU4TOcwW460LY4hi0YlPHxrmqs3GNt0a9o5JmVxQ/dYR9kIY8voF5wbuYt2gZxuZVyAUW08PVE0eKUo533fdwZZodVN3uwbGc1TplYCEB7HJpStsWRC0TdwRnaRNRTGGjTgFWYkRRXoB1vy3GloowkzCjLUrXqNiscxDV3+NCkMeM5HGaHvb6pQhhoa4VrX/5HKHzMTXXh2W8cjzEF6lbGWvO4wmpbvHh1Y0VkxezOyha8uP4wLpurfZHj7te/EN6/q6pFMxALBmWs3l+Hlzccwep96lXobXEG2s0d2h/u2zz6FdqH6trwYueJviwD97y9A5fPGx5ZqT/U/Gv5/pjbf3h7R0ID7SdW7I9cSHts2R58fNtiw5Mus6uRQ9v6UN/qNXUiV9HQjh88vxFefxC7qlrwi5e34j/XHwcgtHq8u/Plo9kkYNtdS+F2dH1fjczrm0B7VF7sz+f4onRcPrcMz6w5BJfDhpuXjO31YyIiIiKinnXtCSMxPDcF+2va8KXpJXG3ebZKkiT8/uJp+NX5k2GTJDgtdmHTC7S7K/rcOcvEdYORnZ+jh+ekwGGTVJWquaku4cJtI3rhbLhN8deeWIMVndst21mjuyBBeU6n1OoN4K8fdy1orWzy4E8Wuq0dqW9HRrH21ysQlLG+hwLt2/67CS+sO2y8IULBa/j77VC9evZ3frrb0mvHa7C1HPf542s5nsjqzQO1rfjmf9Zhd1ULLp07DL86d0rCv85tXj/8Qdlw8QagX6F9tDPQ1qvQFl0XeG7toahA29zPiacbM+mJrFI2XWCFNhElyiBeA0eDXWFGfCcY0WFWejdW3RZlJGHx+IK4V5mFW45XNOgHytG2HxN/EK4zODGubfXi3rd3xvfcFo9qBfWfP9qjsXVo+02HG4WP7axsFrYTB0If0q/422d4evUh4eMtBuGzlhaNxQKAcYW2ciVtmzeAVYKwfaj4r2KBxTGDxRBWPbOm6+++ucOPv3+61/A5Wu30tOwzWaX9xIr98Ea1M/90d02kxXpTu0/14bw7clJdMWE2AJTnGVenx2Nuebbu4189oVx13+8umoZlt52MFT9aomoLSURERESDw5IJhbhu4cheC/KiuR12y2E2AM3gPRGBfHSFtpkxPPbOwMxpt2F4rvqz/JiC+DodaV0DAEKB16G6tkiYDQDvbavEvur4Ry1VN3uwszL2PPhPOuf/SkZFBxsPN6DZ5GJ15cL9nZXN+Men+7BZcL2hrtWL/603F2YDofPNsEN16kC7t6ZriXLW/jDaK15eYYW28UUzewID5z9/tAdfHG2CNxDEfz47iDU6bfvj8fTqg5h25zuYc9d7eGLFft1ttebZh1U0hiu01ddWjjV2IBiUhbOv399WFRkfYPaaSIfP/Lg2ou5S/h5jnk1EicJAmwasgvT45r9EV2jHu0ozyWlDRrIDYwvT8eBlM+BWtN8uykhCXpr+hYCm9nCgbb7KXGsel5kPsG9tPYagIIUzWiVeLQigDwhO+MI2Hm7QfGxXZbNmgP7PT/fpnjS2mWgPLqJ3smxUoS2qwv1we1Vcx9HT2rx+3Pv2Dnz/uY3YciR0gt/Y5sPfP9mLX7y8BZt0/l7Mcgh+XvbXtCas/bbyYs2TKw8YPsdyoG3y4s5ngtZ+rZ3zpa1WChgR/a4oz01sq8IwUYeHgnQ3Jpdk4Pal4zF/VK7weSNyUw1/pxERERER9aZUd89VaI+N6ixm1H56hCLAVnY9AoBxhepuad3l8QexXhB2bakQLzA3Y9U+a+26lQ4Lqp2jfbDN/Pl0XdS53rajTTjroU9w12tf4Pw/LVfNFt9f22ophG6KOtcXBdq9NdNZVLXoCwzcQNsfVIemZi67JbJ6M3qhPAD85s3tCdu3LxDE797cDn9QhjcQxG/f3BYpVhHZXaW9IAXoKnA5KrguuPlII2bd/W7M4oswf1DGa5sqAJgfh9bOCm3qRcrfYhKYaBNRYrDlOA1YhRnxBdrZJlZXm3nt8CrT82aUYlxhOn7+8hZsPNyIs6YU4d5LpsPRucL80sdWYrVgRWi4zbiVGdztnYHa8t01+HR3DRaNycOCMXmoMxmw7a1pwbDslJiW2VoV02FHBbN8ZBnwB4KR9xht/cEGzX3trGzRDLRf23RU9zhaPH7sq2nFXa99gaZ2H249dRwWjs3TfQ4A4Yf/MKMK7ZoW9Ura97dX4hdfmgRJkiKzi8YVpmHasCzDY+lJd7++DU+tOggAeGPzUdx66lg88uHuyPt/Yd0RvHXrIgzLjq/yNxiUhSsqT773IzhsEn64dAJqW73YWtGIc6eX4JI5ZZb2L1qFrtWKP5pRy/F0tyNmUcO+GuNAW5Zl7BEE39XNHqS5HQkPtHPT1L+TRuYlPtC+eckYTBV8nz75teMwvijxF9iIiIiIiHqSdoW2dqB9xuTCmHnQM4dnCc9hoxeTZ6e4UJqVrHnufueXJsfcHinotqQ1equ71gnad39REf/oohW7uxto61/feN/CAvHooO6Bd3dG2rgHgjKeWHkAc8pzTL+uUvR1goN9GWgL0l5fIAiXY2DWH4lajpuJsXqy83oiW23XtXpjCh86fEEs21Gt2clsV6X2/GwgdL0tEJRR2Syu4tZbwL+18+fc7PUJBtrUm5QFVYNsugIR9aGB+QmJCPG3HBeFR9ZfOzZMn1icgedvXIBtv1qKBy+fGRP0PnzlTJw+qVC1j/CKYKMZ1tGaPT6s2FODq/6+Cn/+aA+u+scqfLa31vSKzFPv/xhz7n4Pz0WtWK1tjf3gXJIZ+972VIs/gCvbkIXpBdq7Kps1K8KNTtjavAH8+H+b8MH2Kqw9UI9v/ufzSMCvR7fluEGrsxpB2H+orh17qltQ3+rFmQ99jB88vxHnPbocb22Jb5Z6ooTDbCB0ovLbN7fHnKS3ePx4RqOduxmVzR2aK8X9QRm/fmMbHlu2B5/sqsFt/92E9QfNzUWLPj4RvZb4Xn8wUjWtZbaixbaZluN7qluExxNu6XWgVr/qwCpR5fPo/LSEfeC/7YzxeOGbC/D908dj9ojsmAt86W6HqqKEiIiIiGgg0J6hrV27cfOSsXB1nq+77Db88tzJwu1S3V37sNkk/Oq8ychLcyMjyYF7LpqGm5eMwfGjcvDzcybh5PGxXZBELcrHmqjQnlGWhf99a4Hm4z86cwKGZSfH3Cdqp6zXpjzahKJ0zBqeFXOfqGW0nuE5secSogptWZbxt4/3YvZd72KbYB6wluig7p0vKmMee3VjheHr6omuqj1Upw7DA73U9tsuWDXuH8AV2olsOa7Xer2v2rKLriHt0pmRrbxuply4XtXsQUVDe1wLKMLf8/WmW44z0Kbeo6rQZqBNRAnCQJsGrKI4K7RzUrvfNlfrtUUfwgszkvDXr8zB+TNiV2y+sqECjy/fh21HzZ1sAqFw9vb/borclmXgbx/vjaxUNrUPjx+/eu0LNHeewCkDw3GKSk3RyR0AYQvrYFDGRkHLs7CKxg7h6ufwcelp7vDhs71dJ+vNHj/e3951UivLMj4/UIe91S0xJzctHu0P920GYWitoEIbCM0remHd4UjgLcvAz1/eoruvnmT2ZO7ljUfiPvGzOoftvW2Vmo9tP9aEx5fvi7RGB7SD6w2HtIPxhnbjhRxzRigCbRPvY+UecVVCdbMHj364G997bqPhPqwQBdrZqS5cNreryn3OiGzDeYYFGo+fM60Yszu/DmluB+44ZxLcDhtcdht+cvbEmI4NREREREQDRWocFdpTSjPx+i0L8esLpuC1WxZi2rAsnDqxIGabGWVZquedMrEQa392KjbdeQYunVuG758+Hs/cMB/XLRypCuwmFWeonq/Xctxlt+HRK2fh+RvnY3JJhnBh60OXz8CNJ41GaVZsoL01zmrsv31lDt669UTMKMs23ljHTEUgLqqUXrm3Fr9+Y5vhuDMlK52xrFZoR7ccF12jsHKNpTtE15B8grbdA4VPEGibuQYhajmut7jC4zf/NQomMPwWXUP6QmeRxi5Fy3HlCDBZhnBGthnh73nTFdomCkKIEkX5c5fIsQJENLSx5TgNWAVxBNpJThuKM+MLwqMVxbGPjGRnzO0dlc2489UvLO2j2eNXnahZadkV1uLxY9vRZswbmaNqOT6uMB0f7ag23MfGw424fF7sfXuqW3RnVgPAKsFsYjOONaor2Zvau17rlmc2RFZp//LcybhmQTkAoK5VO9COp+U4EKpC36+o9K0StIjyBYJwCtqyJ5rZ1lGH6tqx/lADZg23ftHCTGVzNK0LKx9ur8I3nvwc3kAQDpuER66chaVTioTV8ACw4WADlkxQdzgAQjPC9dhtEqYrLkaFZqvJuqvEV2p8j64/WI9/Lt+n+5rRhmUno80b0K0yB8SBNgD85oKpkdZl80flotUbwLHGDvzwhU34XNBacObwrJj2iWFZybEVIlfMG47zZpTAJkkMs4mIiIhowErRmKGdbDBDe2xhekzF9LeXjMV7UbOdrzpueLeOa+HYPAzLTo6cu58xuRDZKU6kuuzCDlMXzR6Gs6cVR26fMCYPn+yqARA6p3n4ipk4a2ro8dLsZMD8KYmmeSND7bq15pCbYZOAxeML8PKGrmppUbAcfi9Kp04s1F0IHT6XN9ORTjkH+wenj8PLGyqwq0rcWS7czcwXCArHrAUsVqrHS3RaOpArtEXHbmZxgCjsuv/dncIKdiBUbWz2XDaRaxNEhRgbDjVoXmPYrfj+m1OejWfXHIq5hiM6tzfjaGMH/IGgblvyaGw5Tr1JuY6EeTYRJQortGnAiqfl+EWzhqk+9C4yMYdZ/dpxBNpJTuONDBgFY2F6K9LDdhxrgizLqtBWb+V4NFEltrLdeElmEkblx7ZUWrVP3RLNDNE85fBi5t1VLTEtx37zxrZIC7GqZu2W7m0egwptja93RWM70tzq9UDhlcf1rV5c8KflGPezN3HDv9cmvLVTfasXv31jG370wibsrmoxfQIDhDoDWHWorg0vrjti6TmiauGjje343nMbIiut/UEZt/93I440tGtWw6/XWa1s1ForO8WFUfmxs+ravAHh4oOwQ3Vt+EBjkciL649YOhnOSXWZmoWdpzEGQZIkLBidhwWj8yBJEtLcDowpSFNVZYTN1FiokJ6k/l5NcTkYZhMRERHRgKZ13qtVua1lRlkWHv/qXFwxrwz3XDwNF88e1q3jctptePrrx+PaBeW4eckY3HfpDEiShGHZ4lE/mYrF7/dcPA0XzCzFkgkF+L/rj4uE2QAwTONcwKrwa6YKzmv1FGUkRYKJaxaUqxYQN7b7VCGe1pi1C2eV6r5W+FqFVijt8XedZx9RBOnDslN0CxHC1wsqGtqF53i9lSmLglxRlfNAIaqqFrXT3nioAb99cxte3VgBWZaFlep/WbYXf/poj/B1Onzdq9B+evVBnPyHD3HFXz/DQcFIMX8giH9+ug93vrIVO6Ja+LcJiiJqWrw40qBeFNHU4cNRRWHGuMJ0FGfFfl+uMzmuTTluIBCUcbSxAw0WZmjXtXrZepx6hbIzg5nRA0REZrBCmwYsUaBo5Bsnjlbd9/3Tx2PV3jpLs6LiCdMzkrv/42YmuCzKSMJtZ4zHr9/YBgnaoez2Y81o8wZUrZrGmwy0vzjahJV7ajF/dC4O1LYiEJSxQdGGfObwbASCMvZabFdtVvjvTDmHy+MP4q3Nx3Dp3DLd8NKwQlvjuUcbO4Rt5GpbvchLc+NnL22JhPvvfFGJN7ccxQUzu3dRJNqP/rcpUon73rZK/P2auaaf+9qmCvz8nEmwaQxoXrW3Fp/ursGxxg6MyE1BUWYyfvy/TZrzs7U0tsd+r8qyjO89u1EVQjd1+PHdZzbgAo2LGRsONiAYlIXHa3TilpvqQnFGEtwOW8z3+b6aVs1FKb98davmybHoZ2nJhAKMyE3Bv5bvVz2WleJCYbrbcMV1nkErcSXlCTAQ6j5x4axS/O7N7arHtP6uiYiIiIgGMq1Z2WYWeCudPL4AJ48vMN7QpLKcFNypmM9dmp2MHYJ5u1kpsYF2cWYyHrhshnC/WqG4HmVl+BXzhsc8ZpbDJuG1WxaipcMPXyCIsYXpMaFy2Kn3L8OzNxyP40blAgCqmtTn1WdNLcLSyUW6r1fT4oE/EFS1bQ6rbvZgWHYKgkEZhxuUgXaybiFCuOW41oi1QC+1/RZVIA/kQFt07MoK7b3VLbjwzysiQXeHL2D5nNVSKKu4lFHR0I4f/28zAGB/bRt+99Y2/Omq2THb3PfuTvy5M0x/fu0hLP/REmSluNCiURSx4VCD6mdTubDDYZNQnpuK0qzkmGtkmw43Qs+k4gxcNrcMF84qxfG/eT/mZ3lrRZNqQcZDl8/Ad5/doLp/b3UrZt31LgrS3fjzl2dh9ogc3dcl6g7l9x8vSxFRojDQpgHL6uqu40flYHiu+uRvRlkWXrtlIT4/UI/3vqg01cI7nvndiajQNiM71YWLZg/DuTNK4LBJuPyvnwmrov9v1UGsEMwKLs9LgdMumQowf/jCJpw/owQPf7hb1U4GACaVZKiCzUQKn4SKZl69sO4wLp1bhmrByXNYhy+IQFC8GliWZdRoLAaoafEIn3O0oQO1LV68vvlozP0bDjYYBtrBoAxfMAi3Q3xBYeWeWjy1+iCyU5wxbaVrWrx47wvtNm3qY/eiusUjPLl/5INduPednab3pUe5+GLT4UbNVt6r99fBYRf/PDd7/DjW1IGSzkqEQ3VteG7tIZRlp0BWnpkq5KS6YLNJGJmXiu1Rq6r31bTi+M6LK9E+2F4Z02rQyH2XTMdFs4fh/1YdEL9+ihMj801UaKdaC7QL09V/d9csKEeB4H4iIiIiosFKe4Z2/7zUpaywDFNWaOsp1diHnm+ePBoPvrcL/qAMt8OGb5w4KvKYlQrtk8fnIy/NHTMyye2wY0JResz5FgDc/fo2PHj5DFQ2daBC0dL7Z2dPxPWLQseQleLUXLgvy6Hz112V4grtyqZQoL23pgVexUL9spwU3es24Q5wh+rV1xIAoLcyZVGQ21vzu3uCMNBWXFu6950dMVXbt/13E86bUWLpdToECym0BBQXq/75aWzP/jc2H1M9589RleGt3gAeX7Eft546Dm0aI/bWH2zAOdNi38MuxeKV8rxUuBzWxyDef9l0TCgKFVQMy06JWRSz5Yg6DD9tUiH+eMVM/Ph/myOt9aNVNXvwjSc/xxvfWcRrCNRjlNfrJDDRJqLE6J+f8okSLD3JgbvPn6r5+LjCdIwrTEdlU4cq0M5NdakqM+NqOW7hJLU7sjtXd4dnN9+0eAzW7F8tbKO1rya2ctrlsCHN7UBuqhvHNNqCRTtY14Y/frBb8/GCdHePzkkJh+XK9wGEWpsfrm/TbTkOhFpGpQsWGzR7/KqT4jBZDs0LV6pobMfLG9StuaOrkjcdbsDNT6/H0cYOfOvk0bj11HHYdrQJNzy5Fofq2nHFvDL85oKpMQs2qpo68OV/rBK26gKArRX6K3qVqprEgfY/BVXG8VIuZFht0GpetLgi7GhjO0qyktHi8eOsP34iPCkTye1s5V2eGxto7+/8ftlxrBnbjjbh+FG5KMpMElZZ6wmPK8jVCKSzUlwYlZcmfCxaXrq45biW0QWx+3Q7bLhR0H2CiIiIiGgw05qhHU+Fdm/QGh2UZSXQjqPl+OmTi3DqpEJsONjQOd+7a6G/lfD/wlniRdp3nT8Flzy2Mua+zUcacdr9y4TXIaK7nd19/hR8+6n1AEJdp5TdsiqbOjQrtNcfrMe9b+9QLZx22W3IT3Oj0ETLcdHieKD3KrSVbXmB/luh3e4NRBZzX3XcCOGsetGxK7+Wnwpmqotar+vR6qom+noqW44bdeoT+WhHNW49dZxwhjYA/OezAzhzShHmlHdVPSsXYoztPI8vzrT2MxzdRXGYosvDJkWg7XLYkOy045xpJSjNSsYFf1oh3GdNixffe3Yj/n3dPHZ0ox7BCm0i6imcoU2D2vULR+KWU8bimRuOx5gC42BJ1Mb8xpNig6Ikp63PZmibkZ0aG46dOC4fH/7gZNz5pUmGz81LdUGSpEgQ2F356W5kpyRmXyLh0HS/INAGgPve2WkYzGuFo7Ut+u2sDwjmLK3aWydc3RtdfXz369twoLYNXn8QD763C7urWvDgezsjrc6eXn0In+2tw+6qlshJ9qe7azTDbADYq/H+tYi+Jq0ev+kZ7WYoV9mbnQslcqQhdLwf76w2HWYDocUoAFRV0ntrWrFsZzXOfOhj3PrsBpzz8KdYsacGn+6OPbE+c4p2C7wJReko6Pw9oDUDOyfVpZohLz5OaxXaC8fkRS4CSVLoIpDy556IiIiIaLDTCq5FQVt/oFVdnZli/lqBaPyQkawUJyYUZeDyecNVbZHNjnIryUzCKRPFLdnnlufg6a8fr7pf6xS2IOp6yjnTSvCfrx2Hn5w1AW/cskgV2Fc2dWhWaN/9+jZhF7DS7GTYbJJ+hXZ7Z4W2RqDdW1XSovN8ZUVzf/GN/3yOu1/fhrtf34Ybnlwr3EbU6U/5tRR197P69dZqOS56feXrGXV7DAqOJXytpM0rfl2PP4ivPbEWx6JmZu9UtBwf2xlMW1mUcv3CkTHHq+zyoKzQzklxRbY3+j346e4azS56RN1l9eeOiMgsVmjTgLZgdK5uZedX5pcL24xrEbXbmj86F+dOL8ErGysAhFr7uhzW14KYmaFtt0m6waUZOYIAeURuKq5ZUI773t2pGwjmdAZz0S3ElKaUZmDLkSbNx6PlpbmtzTayKHwSur9WHOi+uF5dLa206XBDpJ11tJoW7VblWl7fXCG8v61zzpLHH1BVKr+15WhMC3EAuOJvnwEIBaL/unausBo8mihc11MpCLT1Zo0rjcpPxT+vmYtdVS04flQO/rV8P+5/N7ZVeUN7Vzguy7Iq0L5m/gg8sVLcqlvp2TUHcaS+HUcarL3PnM6geGRubKi8v6YVT6zYH7nAUtPiwZV/WxWzTbrbga8tHIk3t6gXKAChhSJhWj8v2SlODM8x/v1j9feJ3SbhpZtOwCe7qlGSlYyJURUOBeluS3+XREREREQDlVZ1capG5XZf05p/baXluNtht/yZPytZe/GrVpU7AJw3owRHGzvgdtjww6UTNMdjAcAIC9ddCjNiz58Wjs3Dws7uVwUZbhyJmoe9s7LZ8vlNOPRTvk605o7wDG3xOaYo1OwJopfpjxXaNS0efLyzOnL7k101ONrYrqo2Fldox75JZcV0aBtr71nrOpNX8PqqYM1g3+HCgmi1ndeHWjUqtIFQwcXrm4/iawtHAgD2KAPtcIW2xqKU6xeOxCVzylDR0I5n1hzEyLw03LxkTMw2yt8hyqKErKjFMclO49+Da/fX44QxeYbbEVml7JbAPJuIEoWBNg1oty+dgPMfXa75eI7FSmPRStHsVBcevGwGLp9XBrfDjlnDsyweZYiZk9TxhekIynKkPXJZTjKqmjzwaLS+FsnWWN0tSRLGF6Zj7QHtStlwpahehfbc8hzTgXZBuluzJZOeLx8/HN9ePBYn3vOh8IQkrLHdh+YOH2oMqqmjzRmRHfM1WL67FkunFKu2q40j0K7UmNcdbmklWlnepLPAoK7Vi79+shej84yrfPUUZrhjjq1KFGibaDEPhC4O3H3+FJTnpaK887huOWUsvjS9BIvv/SiyXYcviA5fAKv21eH1TRWqr83V80fgjS3HUG3i4sTy3bVYvtv6yuHw97GyQvtAbZvhIoCzphajTCeMDrcbj34dpexUF5IMTiKvXVCu+7gWl8OGUyYWqu7/1XlTcON/Po/c/t2F2qMWiIiIiIgGMs0KbWf/vNSl2XLcYlez0uxk0yFvqsuuu4BWr0J7yYQCnDej1NTrpCeZ+5qnuOy6r1momOl77zs7NbbUFg60zc3Qbhc+3lsV2qJwV1Rl3NdEQW51s8dUoK18P+IQ32qFtvg6kWhsnPJrbNTevF4w073VG4AvEDRsV97Y1nVtSnktLNzpsUywsOXRK2fh7Gmh61Lji9KxeIK4G4KyQlspukOimUC7t1rr09CjXkjCRJuIEoMtx2lAm1GWhae+fhyuXzgSf75qFkZGBX/zR+Wabp8V5hd8mMtKdsJmk7BgdB5mj8iOu02KmZbjBRluPPbl2Th7ajFOn1SIv3x5DnIsthLWaz1cbNDaKNdEhfak4gxTH4wlKVRhHE/L8ZF5aSjKTDI8KW5s92F/jfmq3XS3QxUCLt+tnt8EANUWQnIj7Z1tqb44ql4IoNUuPez1TUeFJ1RWKNvti4J3MxdESrOS8ekPl2DBaPUKXtGCjcv+shLX/HM1nlt7WLXtqLw0LB6fr3pOIoVbjpcrKrS9gaDuQgkAuHBWKXJSXcJVpG6HDXOjZmOluR3Ci0Ra3/vpSQ7cdsZ4/PbCqbjjHONRAFacNqkQt50xHnPLs3HT4tGac+6IiIiIiAY6p90Gl139Oby/ztDWGlVkpUIbsNay2Cgs1/taWTmuVJfDVAVeYUaS7jUVvapqs8Iha26aW/N6SlO7Dy06Y7cComqHHiBsOd6DIePmw434/nMbcf+7Oy1103MKfs5E4bHPb1x9LQrx/Rar0j1+jQptU4G2/r4b2sTfEwdq2yLd9zSPK+p9KI8lfM2gPC8V80Z2XU84fVIhzpqqPe4smlaXh7Ds1K6f2SQTvwd7ad0GDUFWf+6IiMzqn8tWiSxYMDovErBNHZaJP3+0B26HHd9WtOYxQxRAJepk2MyK5YJ0N8rzUvHoVbMi91kN5fUC8Lnl2Xh1o7gtNtAVAObq7CM8F3hrhX6Vdm6qCw67TbNiXE9xZmjlaorbDo1u4gBCraD2KTYYlp2M+aNy8fznh1Xb52e4sXBMHn4fdd/emlZUNLSr2o7HU6GtpTUcaAu+Zp/rVMyH1WucUJk1Oj8tpsJZNEPbTKCdl659cSFD8P298XCjYEtg5vAs2GwSlkwoUIXdiRT+WchLcyHd7UCzyW4Bw7KTMbc8BzabhJwUF2oVFzmOG5UbU3ktSRJyU1042hj7dQ3/Prl96Xjc89aOyP2/PHdyjwXNdpuEmxaPwU2Lrf/+IyIiIiIaaFLcdnjbYoOj/tpyXCvITbV4zcEo1IqWZXA+rne9wUqgbbNJSHM7dEecAaFrHrqP61RVmzVtWCaA0LnRbWeMx89f3qKqAG7u8Gm2GweAQC9VSYvD3Z557cY2Hy5+bEWkA2Bjmxe/PG9K3PsTBtqCML4nZmh7LFRom5nlGwjKsHcmbg0aBQV7qlsMOxBGv76yWt1p73rdJ746D//9/BDcTjsumFlqunDGqEK7IKrDQZLOiICw3lq4QUOP8kfaxkSbiBKEFdo0qAzLTsGvL5iKn39pkuXKZiBU3RgdPC+ZUBB3RbaSw24zPFEtSFefvKWZbN0VprcCe+mUIt2APrezMluvQjsrxamq+BUJ78Nq+zSgqxVTqsZMtLBQhXZsoD0yLxWTSzKE2xekuzGpJEN1Yi6q0lbO0O7Ot0FbZ1uqbYIKbWVYKtLdmcjqCm1RoG3ccjxf5/vCYbeZbjM3a3g2AGDh2PyYk7pEC38/S5IUaY9uxCYBd50/JfJhO19wweXEseoKddGK9fDq6KuPH4GzphYhL82FS2YPw1lT1S3uiYiIiIjIuhRB97D+2nIcQCQ0i2b1msOJ48zPvDUKtLXmkAPWK8fNdKUrNAis9QLvb548Wnj/D5dOiCykP31SIU4c29UJ7Ip5w/HZj0/BE9fNi3lOs8ePAzqr5/u25XjPVGj/c/m+mHF2T6w8YPq5okpyj6i9uCBQNjND22qI36FVoR1Q3698PdGPW/TXvKFdfI1mT3UL2rz6Fdrh/QSDsup7KLqbRLLLjqvnl+PSOWXCawlaslKcuotQijK7fr6cdkn4+yZab82Kp6FHhuLnro+Og4gGn/77KZ+oDyQ57Xjiunl49IPdyEx24odnTkjo/jOSnZFqXRFReGa5QlsnQC5IT8J/rj8Oz605hA93VKlaT4cXAejN0M5KcWFMvnGgHX4vLocNaW6HpVna4QrtVIP33uLxY2dlc8x95bmpmFAsDrQLM5Jgt0lYMDoXb245Frl/xZ5aXDKnLGbbmubYk5ippZnYpFFxbKTVE4Asy8KW42bsqVLP3jbLbpNULbdFAXm1xvzvaPnp+osTslKchivygVCFNhD63p43Mieu+dhmRHcaGJmXis1HxH9/ZTnJuOPsSXDYJcwoy45ZDBNamBH7PbZorLpVuuhEMVyhnZ7kxJ+umh3PWyAiIiIiIh12wQLZ/lqhDYTOgRrbuzdSasHoPPzh4ml4ffNRSACKMpNx+dwy3PbfjdhZGXvuaLTA3OUItW0XjWSyGmibWeBs1FJcK/AuzHDjktnD8OeP9qge+8r8Ebh+0UjUtXqFz89Nc6sWecsysO1os2rbMFHo2hNE3cV7aob2jmPa79eI6OshrNAWHLt6hnb326xrtUv3mDgm0SxfXyAY6cJW3yr++dxdpa7QTnXZY67xhb8mop8nvVn2ZkmShGHZydiu8XdZHBVoS5KEZKdd91qcaKECUSKY6YxARBQPBtpECrOGZ+Mf187tkX0nGcyeFq1GNlv1GhY9M0dk1vBszBqejVV7a3HZXz+LeSwcnuutms5KNlehHV3Nm5XiNB1ou+y2yNfBKNCWZeC1TUdj7ivPS8XEIu0KbQBYMCYvJtAWBZ21rbEB75RuBNrtXj8O17ebCntFlFXcypMmPWluR8wqXQCoa/Vid1UzHv1wD7z+IG4+ZYy5luM6FdoAkJXswiG0624zKi8Vx4/KjdxePL5AFWg77ZKpk/h55TlYvb9O8/HoCzB6FdpnTinG6ZPFM6uUi0wK0t0YV6j+/hctfDb6eSciIiIiosRL7qcztAFgckkGVuzp/oLeS+aUqRZlf2laCe57d2fMfaLRUEqitu1AaEG+FWYqtEVd6aJpXYuYUZYlbEeeneKMXDfQu44h+jrojVHrrQptUcvnnpqhrVXVbIYo+BRVkouCXPUMbfX+rX69Oyy0HFeG36Jz9+8+uxGBYBBfP3GU5gztPdWtaPfGXtPJTnWh1dt1DST8+qKvjZVKbD16gXaR4mcgySDQZp5NPUU20RmBiCgeDLSJelG7QQhZIFitnMgZ2tEml2aq7guvlh5bkIb0JPH8q0yzgXZUEJid4sLhev2gM2xCcTocnR/00+JYWT8qPxWZKU6UZCahQjHTOHzyPFZx/Ecb2iHLMiRJgj8QhMNuU82Znir4epnV5gsYzhy34t9fOw7ffXYDDurM/ApLcztQKLhocOlfPkNdZ1C+/mA9kkxc9DEMtDXa2X3vtHEozHCjvs2naqm1eEIB7n59W8z2pVnJaO7wG7Zj/91FU3Ggtg21rV784PmNqsejZ/SM0gm09ToODM+JnU934rh84cpSGz+dExERERH1C3pttPvabWeMxwV/WhG5fcOJoxK273OmqwNtrXnA0VJdDtV2aW6H5QDOzGJ80TWPaFoV3DPKsoXXRkaZ6B4HhN6jTYoN8L6o0F6w3lutmEWv01MV2lpVzWaIvhziCm3jGdoilluOa7wX0TF5/MHI9R5AHKy9t60SALB2fz0WabT031vVolrkobzWFQ70RX+HiQu0UzQfK86MnbGd7NJ/zd7qREBDj2qGNi+ZEVGCcIY2US9q9epX6IpWK6ebWOUc5nLYkGyyKjTN7cDSqKrUkswkTB+WBSA0D3nhGPWH+HS3Aw67DSNyjecR5ykqtM2aEhUcW70QUZKZhONHhqp/JwrajodPnksUH/JbvQE0e/x49MPdmPbLd1D+o9dxqC42gJ9ckmE4A12LLIdC40TJTnGqqq61pCc5kJHsgFvR3qouKiyuaOzA3mrt+WFhRoG2Vku6kXmpuGzucNx40mjVgotReakYkRt7QpaT6kJJVuzfkfB40t1YPKEAF88eZrjtSJ1Ae7TOAo2LZw+LvO9Ul11zbpuZ4yUiIiIiosQStQ82e07cF2YOz8bPz5mECUXpOH9GCb6lcX4Rj5F5qZFZ0mELBOf1SqIW7VbbjQNmW47rn8dqve70stB1gmnDYhea33LKWFPHZrNJqkBcuQA+Wl/O0Pb30AxtUTtus0THKdqfKJg209ba6txwzQptjf1EH6veYvRmjx9vbD6m+VidYtF9tuL6RqTluOBrk4iW40CoQltLYWbsNZskh/7vQrYcp56iml3PKdpElCAMtIl6UZtBhXZ3Z2jnproszSX5/cXTcMuSMbh2QTmeu3F+pDIaCFWiKoVXo5r5IK6s0DYruhLaanX6s9+YH2kvN6E4XfOYlB/yAeCtLcfwh7d3CP+O8tJcmFySaTpEFlm5N3FzorNTXKpWUlrSkxyQJKlbxx6WpzNbllOePgABAABJREFUHdBeuKCsco4mSRIWjy+Iua8wI8lwEYTLYUO6he+P0QVpSHKKv2/1Og6U5aTg7VsX4d/XzcMnP1yC0RoVAMoLKVcdN9z0sRERERERUeLY+3kp2HULR+KtW0/Eg5fPNJxxbdUfLp4e+XNOqgtnTCo0fI5o1JfVduOAucX4RoG21vWMaZ2L77918pjI9YiTxuXjxLHGgX2YlffUWxXaosJkXw+9tlYIbIYo+BRVScdboa0VRGvRap8uCpIBwBP93rvx66Fd8Z6VC0g8Oi3HXQlsOS6Sl+aCWxFgG41fELW8J0oE5XdWP/9nmYgGkP7bh4loENJb/Xj53DLhzF0rM7TNthsPy0x24nunjxc+Jgq0jzR0VS1fMW84nl59UHPfsYG2hQrtkugKbfMr6x+8bAbKooLTkXnq4DFcAe922JGX5kJNS9fq2gcVrdminTKhEHabhBG5qdhjopJZJN7520qSFDoRLzZdoR362hemJ+FArXGLcj15ggUX0bKSxd9/eoE2AHz5+BF4atXByEnsudNL8NjHe/WPxeLijTS3A9cuGInHlu2JuT8/3W1YfZCb5hb+PESbNTwL3zllLJ5dcwhjC9Nw8xJzlQJERERERESJcuqkQrx+y0J8UdGEhWPzhHOnlVIFndEyk61fLjTVctzgnFJLeLH70ilF+OT2xahp8WBScYalc8LQjG9zo9B6q0JbOWcWAHzdqKTW4+lGy3HRtSxluAuIg2kzFedG4/mUrLQcB0IBeCZC5/2JrBRVFm+EX19UvZ64Cm3x9RVREYPoGmO03lq4QUOPeoY2E20iSgwG2kS96EdnTsDv3tweub10chHOnFoEt8OG0ycVCZ9jqULboCW0FaUGLZRvXjIGu6uase1oM1o86lbq0YG2lVXn44q6gmjRSnEtk0piW4xPLlG3HI+ex1WcmRwTaOu1Gzu1c1W7Xtvq3pKZ7ITdZr7iOvz9YzSrzAxRB4FooqrqdLfDsNp6TEEaXrn5BLy7tRLTy7Jw4rh8rDtYj42HGjSfo/xe/9V5k/Hzl7dGbt92hnqhxg9OH4dNhxuwYk9XtfwUwfdJPCRJwndPG4fvnjYuIfsjIiIiIiKKx+SSTEwuyTTesFPiWo7rP2dsQZqpc/zpwzKxMWpB+ISi2O5rhRlJhpXeIhkWQvreasUseh1/sGcC7e7N0BZVaCduhnar4JqSHo/FluPR7120iCBeqkA7IK7QtkmJ6xyhVaFdlKG+32j8AmdoU09RfmsxzyaiRGHLcaJedOHM0khQnJXixK2njcV5M0qxdEoxbBofbtMsVGjnWqzQNnLmlNiQ/aJZXbOKS7KS8fyNC7Dll2dgbnm26rnR85ZFFdpaH+ajWySZnVltt0koV8z1nlCUjjkjuo7r1IkFMSfYZiucAUTmiY/K7/tAO3zCZL5CO/T9Y7ZFOaC9mMGoxbfookdZToqplZgTijJw8yljI5XQl8/Tb9mtbH9+/sxSLOpsN3fcyBxcIXi+w27DI1fOisxdS3c78K3FYwyPjYiIiIiI+ideJO8+UYW2VvctPXoV2qPyUvGbC6ea2s81C8pjbv/8nEmWj0XETEv0sL4MtH2iPuQJ0CGoGjb7PkWbiQLyeGdoi6q99WiF81pzwqPD90RW3+ekxn5PeTVajjsT1G4cCF13ERW+iK4RGQXaPTSunUi1WEJvdj0RkRWs0CbqRQUZSXjz1kXYcawZo/JSTVVUW6nQttpy3MiPzpyAD7ZXweMPwmGTcNGsUuF2og8mWVHhZrbguPLT3DjWFFsVrawANluhPSInRdW+SZIk/Ourc/H06oNw2Gy4UjHTuMSgAj3s1ImFkblDiarQHp6TgoN18bX/Dlc7Tyk1t+I+vCDCygr2/HR3THv5MKNgWlSJb9RuXMvo/DT8/Stz8MK6w3hzyzHV48qfnYwkJ5782nGR1dZax5qT6sIL31yAnZXNKMpISmhXAyIiIiIiooFGdN6daWFsWJjWjOqfnzMJ1y0caXo/F8wsRZs3gFX76nDqxAIsGGN+TraeEo1F4RlJDiyZUICXNlRE7uu9luPq+0ShcCJozby224wLCYQtxwVtwoUtx6Oeq1UdbTXEtzxDO2r7RC5WyNRoOa48jkS1GwdC1zqGZSdj+7HmmPtFXfyMZmizQpt6iqpCu28Og4gGIVZoE/WyjCQn5pbnmA7SrKwizk1LbKA9IjcV733vJNx1/hS8dNMJmieSokA7uuJc2YYJCLX/Vq7gPm96ScztcYWxrcW0jC5Qz8sGQl+7G04cjesWjlTNDjLbsvtrUSfeowRzueNx+bwyzTZRi8fnx1S3K4W/lsOyU3DhTPECg2gZnd8/c0fmmD6+eOeaiVqLD8+NL9AGQq3e//zl2fieoI231ve6JEmGwbvTbsPkkkyG2UREREREA9xlc8tibse7oHYoS0lYy3HxgnStoFuLJEn48vEj8PAVM3HeDONzXrMunVumqlhNdtrxr6/Ow4Ti2FFUvRX0BUQztHuobFZUBW02uBe3HBcF5OrtVu+rwx0vbcErGys0W4JbJWp3DujM0I7aPlFf3xSXHW5FUB1+f8r36UpghTYgnqMtqtBOcuq/bm91IqChR/mtpdWVlIjIKgbaRP2cXtsupUS3HAdCLaOvPn6EbkXwNQtGxNyePix2W1GgnZ+ehOsXjorcTnXZ8c2TR8dsM21YJuaVGwexYzQCbT1aLbsdNgl//8ocfPPk0Xjq68dh/ujcyGOFGW6kmGyDrmfOiBzcvETc6vpfX52Hl25aoPnc6ND4J2dPNHytcIX/jLIs3HXeZDjtxh8i4523nSW4UGGltbsWUbv0fIbRRERERERD3lXzRkTOOZx2Cb88d3IfH9HAkyZoOW41hAZClc5W7u9tk0sy8dFtJ+MnZ03AhTNLcdGsYXjhmwswe0Q27IpF0b1VoS1sOd5DM7RFGX3AZGW0sEJbFGhrBMpPfnYAtzy9Hi+vrxA+bpVWy3EzM7QTFeKmuh2qyuuuluOxr5HIluOAeI62qGhDWdihJFpQQZQIiZxVT0QUrX98qiQiTdZajvdNyHfKxEJMLsnA1oomuOw2fHvJ2JjHRZW7qW47bl4yBnnpLuyvacUlc8pUFbOSJOHnX5qEcx7+VPf1R+dbD7S1Wo5PKc3EqZMKceqkQtVjkiRhZF4qtlY0WX69MIdNwtTSTMwcnoUfvrA55rHF40Pzo81UaIe3u++S6fj+8xs1t49eEHH1/HJML8vC3a9vw9YjjWgVtAgDgPLcVJw0Lh/LdlZH7ptnosJb1JZO9HdvVaHgxCzR3QiIiIiIiGjgyUxx4q1bT8TKPTUYlZ9mussXdUkRtRyPq0Jb/Jx4wvGeUpiRhBtOHK26366oHgwkOFR+ecMR/O7N7ahv8yIvzY2Tx+fjZ2dPEoY+PdVyXMRseG62QttvsL/bX9hk7sAMaM3K1grUo7dP1GKFVJcdbkVQfbSxA0vu+wh7q1tj7k9ky3FAXDRSnKm+xmU0Q5uhI/UU5XcWZ2gTUaIw0Cbq59IEq5nz0tyoafGo7u+rkM9pt+HFb52AdQfrUZKZrGozLZqh7bLbYLNJuOq4EarHok0pzcSVxw3HU6sOam6TyApto+B2VH5atwLt4TkpkTlGD142A7c+uyHy2MnjCwCEVtFmJjvR2O5TPT9bERBfNHsYUlx2fLK7Bmv21WFXVUvM48oFEdOGZeG5b8xHMCjj6/9ei/e3V8U87rRLOHd6CeaU5+CTXdUIyoAkAd8XtP1WykpW/z1PNTnrW0+hoGI8ngssREREREQ0+GQmO7F0SnFfH8aAlSZoOS7qvmVEs+W4hTFqfUUdaCcu6Gvz+nHbfzdFqncP17fjP58dRF6aW1gh6xdUGS/fXYPaVi9On1RoWHUr4tGYOW02PBfl1KK2395eCuP7a4U2AFWYDcBUpzwr3IJW4qJrXEaBNluOU09RLoJhx3EiShQG2kT9XKqg/deI3BRxoN0DLcfNcjlsOH5UrvCxVEGbbqeFFaq/+NIkSAA+P1CPnZXNqlkso/NTrRwqgNDKbBGjFucj86y/VrTodt7nzyxFY7sPb205htkjsmPmv+Wnu4WBdpZgJe6ZU4tx5tRi/P2Tvbj79W0xj4kWRACh+TWiKvXzZ5SiICMJBRlJePFbJ2D5nhocNzIXs0dkG743l8OGK+YNx9OrQ4sPThqXjzEF3a+QKEy33jqLiIiIiIiIjKUIrjkktkK7/1967MlA+2Bdm3C286bDjcKg2Kd47Yff34X73t0JIDTe7aWbToBksdqxpcMvvN/sPGlR8G6l5XiiHa5vx6V/WYlfnz8FY6O6MmjP0O461sRVaIsDbRGXI7HXL5ZOLsLv3tweuV2Wkyy8RpJsMDKvh8a1E6lGHLBAm4gSpf9/qiQa4uydLao3H2kEEFphef6MEnx+oF61rbJld38hOtmaW24ckIa5HXb8+oKpAEKh9kV/XhHzuNaJsx6tGUZzDQLt0ix1uDoqLxV7a9SrcEUKFOHsNQvKcc2CctV2RRlJ2K2otgaAHJ1FC6mCVnF6q+EnFKvD5q+f2DXXfHpZFqaXZWk+X+TX50/BSePy4PEHcdbUxFRJZKU4MbYgLVJ9npnsxKzh5r9/iIiIiIiISEwZ5gLxBdqihexA/2o5rsXRg4G2VhW0xx8QtvJWhsLhMBsANh5uxNoD9YbXLZRaPOJA22y4GxTN0BaMMDMbkCfC6n11+OmLW/DcjfMj92m1Iu+Iuj9R7eRT3HbzgXaCK7TL81Lx5eOH4z+fHYTLYcMPTh8v3M6oEED0/UeUCOoKbSbaRJQYiR3iQUQ94tcXTMGk4gyU5STjnounoUgwG8flsGmeQPYH3zipKygtykjC2VNL4trPzLIsTB/W1cb6rvMmd/vYoonmQEcTzSUaZaFCXNQ+W+SUiQXC+/VmUotCer0Z7CeOzY85cT99UmG3Z87ZbBKWTinGeTNKNRcNWCVJEu65eBqmlmZiXGEaHrhsOiu0iYiIiIiIEkAUQsYTaGtVDacJKsD7G1sPBtpaIa/XHxS+TnTILAqSNx9utHwMzRoV2qL25iKi4xS1/e7NQBsAVu+vi6nK1mo57ok6Vl+C2qKnuh2mr3kk6tpItLvPn4pPbl+MFT9agvNmlAq3MbpuwpbjREQ00PT/T5VEhGnDsvDGdxZFbq/YXaPaJjfVZbntVG/64RkTMCY/DVXNHlwyZ5jplaxKNpuEZ78xH5/uqkFBhhvThmXFfUxLJhTgg6gZ0ieNyzd8zryROchKcaKhLdQOvDQrGWML0/HetthZ1JKkbrEDqCu0tVx13Ag8u+YQth9rjrk/W9ByPEwWvKDWHDMAKMtJwf2XzcC/lu9DeW4qfpngxQGJNHN4Nl69eWFfHwYREREREdGgIjrHTGRVtTIs7o+UFdqJakutty+PPyiu0I4KZVu86iA6VTDz3IhWhbbZcFd0nOJAu/cD0sP1bRiVnwZAu+W4J6ZCO1Etx+1wmQyq473+ZaQsJ0X3caMZ2qzQpp7CCm0i6ims0CYagEStpXPT+m5+thk2m4RL5pThpsVjTIe6WpKcdpw6qbBbYTYAXDx7WOTPkgTcdoa4TZPytX934TSUZCahLCcZv7toKgrT1VXXxRozugtMVmi7HDY8cNkMOKNaU6W47IYnLEpaM7TDzp1eghe/dQIeuGyGbntyIiIiIiIiGnwWjM5FXtT4skVj84RtyAeznpyhrVehLXqZ6BbljZ0L6aMlx1HxrjVD22+y/bYo+BTO0O6DocwHatsif+7uDO3SrGQ8cNl0nD21GL88dzJSdLogprodcJsMqnuiQtuMZJf+6zLQpp6i/NXCPJuIEoUV2kQDkGhFbjwtwYa6s6YW469Xz8bnB+px2qRCTCnNNH4SgKVTirB0SlHkdlO7+uSwKDMJFY0dqvvzBeG3lonFGXjgshn4wfMbEZSBH581UbeF+PzRuTG37TYJbgdbcxMREREREZGYw27DU18/Do98sBupbju+d5rxQu/BpicDbe0Z2uIK7eiQualDHWibrQqO1t0Z2qKcur8E2vtrWyN/NlehrX2MTruEC2YOwwUzQ8UP972zQ3PbVJfDdOV1XwXabDlOfUUGK7SJqGcw0CYagFIEK3KTGFzG5fTJRTh9cpHxhjpEIXVo1naD6n6r1ennTCvBmVOK4QsEDU9GhmWn4Ip5w/H06oOwScCd5/bfFuJERERERETUP4wrTMcfr5jZ14fRZ+yKsCWQgMrVVzdW4KMd1WgTtA0HQuGrKOPxRldot6sD7XhCyGatQNtki3DR16PDF4QsyzGj7/qi5XhMhbZGoB5Toa1zjMqAWq9TQarbfKBttpI70Qxbjvf++gMaIpS/poZY0w8i6kEMtIkGoFRRoK3TCol6Vp6g3Xtmirhi3mzL8Wh2mwS7zdzf728vnIqvnlCOZKf19uREREREREREQ02iK7Q/3VWDm59er7uNxx8QVlv7o0LZJkGgbbZNeDTNluMmK6qDOnPAoxfe90WF9r4a4wptsy3H1YG2dhCdYmGGdvQoud6UbHCdMBELN4iElN9arNAmogThDG2iAShF0HKcFdp9R1ShPVwQJic77UjXaRmeKOMK0xlmExERERERUa8alZcacztbY6F3f+NQBI5mK5e1/OTFzYbbeEzM0BaNN4unCrpVo0LbZzK415q13O6NbTveNzO0zQTaXffrBtp2ZaCt/bpuhw0Ou81U5anZSu5EM7pOyBna1FOU31us0CaiRGGgTTQAiebvJDn549xX0pOcOGVCQeT2tGGZmCqYx12Q4Y5px0VEREREREQ0WNzxpUkxt399wdQ+OhJrlPNdrQR9DW1eXPf4Gkz5xdu45en1aPcGcLCuzfB5Hn9QWCHri6rAFrUcjyc01pyhbXJfWhXryjnafdFy/HB9e+Rr4tF4Px5/13Hqz9COva7m0KnQDlf1mwmr+2qGttPg2LQq74m6S1WgDV4LJaLEYMtxokGiKMPabGZKrIevnIl/rzwAfyCIq+eXY291i2qbAkElNxEREREREdFgcPK4fNx/6XR8vLMa80fn4swpRX19SKYog0u9Kl6lZ9ccwgfbqwAAr2yswInj8k09z+sPCoNiv8EMbbMhdLRmjZbjZgNorYC/QxFoa82w7kn+oIyKhnaMyE01V6FtYYa2Tp4dqep32W0x+xfpq0A7PUn/sj9bjlNPYYU2EfUUBtpEA9SpEwvx3rZKAKF5PFceN7yPj2hoS3E5cONJo2NuK+WlMdAmIiIiIiKiwUmSJFw4axgunDWsrw/FEmVwaaVy9bdvbo+5/YPnN5p+rkcRCAOxFdhNHaIKbeshZItHvR/A/DxurZxaXaHd+4E2AOyvbesMtNVfTyA2eNebj+52WKnQDj3mctgBiBcMaO23t2QkOXHmlCK8ueWY8PE++uuiIUD5Y8ZmlUSUKD0WaEuSlAHgLABzAcwBUAogH0AygAYAXwB4A8A/ZFmuNbG/pQBuADCvcz/VAFYD+Kssy2+ZPKYUADcBuATAGAAuAIcAvA7gj7IsHzT/Don61i++NAlBWUZVcwe+dfIY5DIs7VdSXOpZRUarY4mIiIiIiIiod3WnQrs7lIEwEBsKCyu0TYbQ0bRbjieuQjsQlNHTBb8TizPws7Mn4rdvbsOWI02R+/fXtOKkcfmaFeIev7kZ2spKar2qUkfng2bC6r6q0AaAP14xEy+uOwJ/UIbTLuG2/26KPCazQpt6iuJ7i+MXiShRejJdmQfgaY3H8gGc1PnfbZIkfVmW5bdFG0qh33iPIRRmRysFcAGACyRJ+iuAG2Wdf4klSRqNUHA9XvHQhM7/rpck6UpZlt/Qf1tE/UNZTgr+ee3cvj4M0pDqVv96Fd1HRERERERERH1HmTf21mxh0ctEB65NwhnacVRoa7QcNxvcawfaXUFxb1RnTynJwAlj8jAqLy0m0N5X0woAOi3Hu4J3vQUBypbjA32Gdvi1L51bBgB4bVNFzGN61epE3aH81rIx0CaiBOnpf1EPAfg3gO8AuBDAfAAnALgMwPMAAgDyALwiSdI0jX3cja4wez2AKxAKy6/ovI3Ox+/SOghJktIAvIauMPtvAE4BsADATwG0AMgE8LzOcRARmSas0GagTURERERERNSv2PuoQlvEeIa29WNr1qzQNttyXPya7d6uoLg35meH51YPy06Oub+6xRM6Bo1AO7pCO6A3Q1sRPNt1SrTDFdrK5wj320ctx5WUoSJnaFNPkaGo0O6j4yCiwacn05UPZVnWG+r7nCRJ5wN4EaHW378AcFH0BpIkjQFwe+fNtQBOlGW5vfP2GkmSXgGwDKGW5j+UJOlfsizvEbzWDxCqwgaA22VZ/kPUYyslSfoQwMcAUgA8CGCJubdIRCQmajuVkezsgyMhIiIiIiIiIi0ORXDZl5Wr3kAQy3fX4IkV+7HuYIPq8bhajmtUaPtMvk/NQDuq8tmnESYnUrhiOisl9trKJzurcfU/VqG+TTwrPLZCW6fluMN8oG2tQrt/xHnKQLu3OhHQ0KP8NaXT7ICIyJIe+3Uiy7J6EIx6m5cAbO+8eaJgk++iK3S/OSrMDj+/DcDNnTcdAG5V7kCSJCdCFeIAsA3AfYLjWAngH503F0uSNNvo2ImI9EiShAlF6ZHbDpuEc6eX9OEREREREREREZFSf6pcrW724Cv/XI13vqgUPh5Py/HWblZom5mh3RtV7eEK7Yyk2EC7qcOPT3bVaD4vpkJb5zitVGiH24ibCbTNzNnuDcr3wwpt6inK7yyJNdpElCD94V/U1s7/J0Xf2Tk7+7zOm9tlWf5M9OTO+3d03jy/83nRTgaQ1fnnJ2RZ1vq09njUny80PGoiIgM/PXsislKccNol3L50PAoykoyfRERERERERES9xmFXV2jLfRj26YWuVmdVB4MyWr3imiOz7cu1Dic60NZq951I4Ur6TIvd76KPU+/rpwyeTVVom2g53pcztKOpZ8X3zXHQ4Kf8/ckR2kSUKH060FWSpIkAZnTe3K54eCSA0s4/LzPY1TKE5mMPA1AOYF/UY4sU22lZi1C4ngpgocHrEREZWjQ2H+vvOA2BoAxHPzmBISIiIiIiIqIuygptIBTi9pNO0THMVlWH+XRSS73HoplqOd4rM7RD11WsjnPr8AUgyzIkSdJdLKAMns3M0Fa2KTez376iajnOCm3qIcrvLXX9IRFRfHr9X1RJklIkSRorSdL3AHwIwN750EOKTSdG/VkZditFPz5R8Zip/ciy7AcQnr+t3AcRUVwkSWKYTURERERERNRPKWdoA/HNqu4NZudeR7bXqcI2XaGtFWh7u75G8bRCt8oZZ4V2UO46Pr3W6Mr24XadEM5KhbaZtuS9QdVanzO0qYco10rorA0hIrKkVyq0JUm6FsC/dDa5F8D/Ke4ri/rzYYOXOKTxvOjbrbIsN5jYzzQA+ZIkuWVZ9hhsHyFJ0jCDTYrM7ouIiIiIiIiIiIh6nqgSt7+GfZYrtHVagZvdl9as5Q5/71Zo222dFdpJ1gJtAPD4A3A5bPoztBXBs7IVfcxjncdiZj52f6nQVn6fs0Kbeorye0vUBYOIKB592nIcwAYAN8qyvErwWHrUn1sM9tMa9ec0jf0Y7UO0H9OBNmJDdSIiIiIiIiIiIurnBlagbbVCWyfQNvketTZr9/Z2y/H4KrQBoMMXRJpb1n3PyuBZL4SLVGibCLRdjv4R5rFCm3qL8jurf/wEENFg0FtLxF4CMLXzv3kArgDwIkLzs/9PkqRzBM9Jivqz12D/0cFzssZ+jPZhtB8iIiIiIiIiIiIaREQtx/tr2Ge15bg3EYG2xnYdMTO0e6HleGegnZZkvT7L4w9oBvNhqgptvRnadgstx+12w216g7pCu48OhAY9ZfE/Z2gTUaL0SoV2Z6vvhqi71gB4RpKkqwE8AeBlSZK+Jsvy41HbdET92WXwEu6oP7crHgvvx2gfRvsxomx1rlSE0PsmIiIiIiIiIiKifsA2gAJtyy3HdYJms1XVmi3HfX3Tctxuk5Ce5EBzh9/0cz3+oOExupUV2jqBtpUKbadO6/LepMzetRYqEHWXrPidwTybiBKlT1uOy7L8ZGd19qUAHpEk6WVZlus7H26O2lTZRlwpNerPytbi4f0Y7cNoP7pkWdad882VSERERERERERERP3LgKrQTmDL8X8t348Ulx2XzC5DeV6q5nZawWd7VKCtVwmeKNHBcEaS01qg7Qsa/p06Fa3B9Sq0nZ3huqlA28Q2vUF5bVproQJRdyl/1DhDm4gSpT/8i/py5/9TAZwZdX90QDzMYB/R1dHKWdbh/aRKkpRlcj/VsixbmZ9NREREREREREREA4yoEtdsO+7eFh1Qb61oxPVPrMXNT6/H4fo24fZev37Q/OiHe3DV31ehsc2nuY1WENzu69q31dne8YhumZ1hcY62xx8w/DtVtgbXrdC2W5ihbaIteW+wc4Y29ZKgskK7j46DiAaf/vAvanXUn0dE/fmLqD9PMNhH9OPbFI+Z2o8kSQ4AozX2QURERERERERERIPMQKrQ9gdDIXIgKOOaf67Be9sq8erGCnzvuY3C7c20Aj/S0I4H3tup+bjWl6LD27stx8NV0QCQmWyt6ajHb1yhbWmGts3CDO1+UqGtnqHdP7/HaeBTfmvZ+sePABENAv3h10lp1J+j23zvA1DR+eeTDPZxYuf/jwDYr3js06g/6+1nDrpaji83eD0iIiIiIiIiIiIa4JRBH2Au0FbOie0N4Zbja/bXoaalq7nk6n118PgDmtsbeXzFfs353FrBZ0O7N+p1ej7Qdihajlvh8QcjiwG0KGddKyuaYx6zMEO7v1RoK9s+99M1GzQIqGdos0abiBKjP/yLeknUnzeH/yCHfvOF25FPkCTpeNGTO+8PV16/LKs/TX4EoLHzz9dI2r9Br43684vGh01EREREREREREQDmSi4NDNfuC+quMOhc/T86rAWwUxpK0Hze9sqhfdrvc/Kpq5A3ai1uVkLRufi1W8vRKagpXj0wgPR43o8voBhW3RlOC1a6BAWrtB2D6AZ2sr301+7ENDAp/zOYpxNRInSY/+iSpJ0rSRJSQbbfBfAWZ039yO2mhoAHgQQ/jT2sCRJyYrnJwN4uPOmv3P7GLIsewH8sfPmRAA/EBzHfABf67y5TJblNXrHTURERERERERERANfvBXaZqufEyk8BzrZaVc91iwItL0WAu0nPzsgvF8r3G9s96GjM1hP1NeiIN2NqcMykeZWtxR3RlU6W5+hbdxyXBlO6wXaViq0lZXffUX0doIMtakHKLs6KLsDEBHFqyeXiN0J4IgkSX+VJOkrkiSdIEnSdEmSFkqS9E1Jkj4FcH/ntl4AX5dlOeaTlyzLOwHc23lzDoDlkiRdJknSHEmSLkOoNficzsf/IMvyLo1j+QOA8DCYeyRJ+oskSYslSTpekqQfA3gHgANAO4Bbu/3OiYiIiIiIiIiIqN+TJEkV9hlV8wLWwuJ4TSrOiLkdDo5FYWuLR1ChbaFyet2BBmEbdb3Q81hjBwAYtvM2y945bFcUFDu6U6HtD0YWA2hx2q1UaHcep4l24m67evFBX7CJFm5wjjb1AOW3FfNsIkoU9XK3xMoB8PXO/7QcBnCdLMvvaTz+UwAFAK4DMBPAM4Jt/gHgZ1ovIMtysyRJZwN4A8BYADd0/hetCcBVsixv0DlWIiIiIiIiIiIiGkQcNltMQK01Nzqa1szpRFJWKodfU9RKvKnDp7rPSuV0uy+AFo8f6Yr51Hpfi8qmDpTnpSas5Xi4mtkhCF9jZ2hbu6Tt8QcQMAjdlSG66BjCLFVoO/pHmidqrW/m+5zIKuXaEVZoE1Gi9GSgfQqAUwEsRqjVdyGAXAAdACoBbADwGoDnZFlu09qJLMtBAF+TJOkFhELouQDyANQAWAPgL7Isv2l0MLIs75YkaSaAmxCa2z0GgAvAIYSC7odkWRb31iEiIiIiIiIiIqJByW6TgKix1EbVvEDvtBxPdcdW94aPS1RBLmo5bmWGNgBUNXtUgbbeLo41dXS+TmK+FuHQWlktDXRVRQNAZorVGdrGFdrKamtRRXPXsVhpOd4/Z2gDQIIK64liKDs9MM4mokTpsUBbluU9APYA+EuC9vcGQsFzd/bRCuCezv+IiIiIiIiIiIhoiFOGfeZmaPd8GpiqqNAOV0KLWnwnJNBu8mB0flrMfXpVvFVNnrheR0s4tHaKWo7HVGjH0XLcIHRXBs9aFdo2qSvsNmo5Lkn6ld69iS3Hqbcov6skVmgTUYL0jyViRERERERERERERH0gnkC7N2Zop7oULcc7g2yvX1Sh3b2W4wBQ1dyhuk/vaxGu0E5U+/Vw+OsUtRyPqtDOsDxDO2BYoe12mKvQjj4OUfAesw9J6jdhnujtmPk+J7JKuQimn6zpIKJBgIE2ERERERERERERDVnxBNpGFb+JoKzQDr+mqEK7JQEV2tXNHtV9ehXa4UDbm7CW450V2qKW41EV2pkGgbayFbjHH0zYDO3o7xW3QYV2f5pRLZyhzUCbeoDy276/LOogooGvJ2doExEREREREREREfVrWoH2sp3VuPOVrZAA/Oq8KVg4Ni+yTW+0HE9TzNAOv6botZs9iZmhraTfcjw8QzvBFdqiluM2/Zbj4wrTcOmcMkwqycBL64/gubWHI495fMYtx5WBtigAVh6H0QztfpRnCyvO+1PgToMHK7SJqKewQpuIiIiIiIiIiIiGLGV46Q8GEQjKuO35jdhX04q9Na24/b8bYypae6PleIqyQrvz9UWtxEUtx60eYzigjmam5XjCAm27uZbjogrt8txUXL9oFBaMzoPbEbsQwOMPGFbdK6vC7TbxZXO73Xyg3Z+IAnrO0KYeoarQ7pvDIKLBZ+D8q0tERERERERERESUYMoK7aAsY1dVc0zFckVjBw7Vt0Vu92nLccFrN4lajgtmbesRVWjrZdWVTR7IspywQDscKiv/PkKPdd2X5FRf0rZFpWbKedgefxA+g0Bb2WJcq5u4lQrt/kRYod3zazJoCFJWaLPlOBElysD5V5eIiIiIiIiIiIgowaLnMwOhwNjrV6d90TlNb7QcT1cE2r6gdsvxRMzQttpy3OsPoqHNJ6wYj0c4yLYJArDokFsUkMXMtnZan6Gt3KdmhXZ0oG0wQ7s/EbV9ZoU29QTl2hHG2USUKAPnX10iIiIiIiIiIiKiBFO2Yw7KsjCkjW7h3Rstx5UV2rIcagEuCqr31rRg46GGmMcsB9qCluNGc5aPNXUkfIa2qKBT2RJcaW55duTPqpbjvoDlinrtCu2uBwZShbao6j1oULVOFA8ZyhnajLSJKDEGzr+6RERERERERERERAmmDPv8QRkdvoBqu3Zv13290nLcZVfdN+anb+Du17ep7j9U147zHl2Oi/68Ah5/6Dithu5NHX7V+zaaPX2sqSNhX4twaC3Kv5RV9N87bVzkz1kpTlwypyxyW9Ry3Oh9KGlVaDsG6AxtUahotFiBKB7KHzUG2kSUKA7jTYiIiIiIiIiIiIgGJ2WgHQjKaPGoW3i3R4W9vdFyXFmhDcS2PRfZdLgRH26vwtIpxXEdY3WzB2U5KZHbRqFnVVNHwqrVw38PkqBJsfLv6OYlYzAsOxmH69tx0exhMV8rdaAdgN9qoK2RwcW0NrerFxz0V6IKbashP5EpypbjzLOJKEEYaBMREREREREREdGQJQq0WwWBdkc/CLTN+Nsn+0KBtt96YFnV3BETaBuFnjUtXvgtfi0cNkkYMDvDKbKo5biiYlqSJFw4a5hw/26nouV4PBXaGj3HHTZWaBPpUX5fMdAmokQZOP/qEhERERERERERESWYQ9ByvNWrbjkeHWh7/T0faKfFGWiHA/p4QvfKJk/MbaMc2B+QLVc/56W5hfeH51OL8i9ly3E9qgptX9Dy10I5Vz1y/wCdoS0o0EYvrMmgIUj520DUcYGIKB4D519dIiIiIiIiIiIiogSzKdK+oEaFdnTLcashbjySBTO0zXB1VhfH0wq8qqkj5nbQ4H36g0HL4X5eukt4fzi0lgRhskNjprWI26Gs0A5YrtBWLnIQ3S9q491fSZKkCrXZcpx6grJC28KPLhGRLv46ISIiIiIiIiIioiFLWKEtCrS9XcFtT7ccdzlskWDaqnCQHc8xVjXHVmgHDNpS+yxWaEsSkJ2iEWjrJF/dqtD2By0vQFAucghThti/vmDKgAm2lcfJluPUE5TfVqzQJqJE4QxtIiIiIiIiIiIiGrKU84VDM7T7tuW422GzFOJGq2kJhdK+gPXAMvzcMKMK7UAwaGmGdpLDjmHZycLHwu9XlA9rVUyLuJ3qQLsnKrQB4KrjRmDx+AJ0+AJYct8yS6/R20Lf511fBwbalGiy4HtqgKz3IKIBgIE2ERERERERERERDVnK4DjQD1qOux02SyFutOrmcKBtPXT3KIJ6ZYW2y26LaWXuC8jwWgjOk112DMtOET7mDLccFzwmakOuRdVy3Bew/PelVXUtWmRQkiUO6Psb0cINokQSfUtZ+dklItLDluNEREREREREREQ0ZImCvhavOtCOrtD29XiFth2SJEVCXiuaO/zo8AVMVZGnJ8XWOymfE1TsQtnO22+5QtuGUo0A2J6gYbvCluMWw33NQHsADwRmy3HqaaIKbebZRJQoA/dfYCIiIiIiIiIiIqJuUlZCB2QZbcIZ2lGBdg/P0A6HsvEGqNXNHlPHmJHkjLmtfI4y9HQpwuJAULb0tUhyarccd9rCLce7l4CJWo4nqkLbyrzsZKfdeKNepDz0wVig/fzaQ5h4x1uYdufbeHvrsb4+nCFH9D3V3Z9nIqIwBtpEREREREREREQ0ZCkrg7VmaEe3HPfFkQa67OYvxYaD43jnaFe3eEyFuMoKbVXL8aB+oO0LyJZmdYcCbXHLcUf469PN/EvZchyIXYxghl0jhNNrA/+Hi6fF3L73kumWXrOnKcP4wdZyvMMXwC9f/QLtvgCaOvz45StbDWfAU2LJEFRo98FxENHgxBnaRERERERERERENGQpc2Z/QEaLoEK7wxc1O9piy3GHTYLbETt/Wk+4QttpIQSPVtXkMdVyPCM5tkJb+RzlDG1Vy/FAEH5lX3IdyS47CtLdcNolVRBupfpZj/IYAaBV0EJej11jIYHeMZ4/sxQH69qwck8tThyXjzMmF1p6zZ6majk+yMLedQfrY35uKxo70NjuQ3aqqw+PamgRdbFnhTYRJQoDbSIiIiIiIiIiIhqylG29A7KMNqMZ2hZbjjvsEtxOG5o95rYPVxnrVQTrqW4x23I89vKwquW4QYW2P2itQjvZaYfNJqEkKxkHattiHgvPC+92y3FBoN0mqLjXo1mhrVMx77Tb8P3Tx1t6nd4kKWfFD7IZ2qKKc84J712irzfzbCJKFLYcJyIiIiIiIiIioiHLpmrFHESLQctxr4UQFwCcNpulluPhOdDxVmiHZmgbH6NyhrayglyZESrbefsDVmdoh96PaI52eGFBd/Mvt2B2dYvFCm2thQTK9vQDiTKkH2wtx0Vvx2xHBEoM0foBBtpElCgD919gIiIiIiIiIiIiom5ShpeBINAqaDkePYfZH1eFtjpo1RIOv+Oeod1srkJbOUPb549NpIxmaPuDQUuBdlFmEgCgNEsQaHe+1wtnDYu5X7StHnGFtnagfftSdVW1cpFDWLwV8/2BsuX4YCteFlUHm2m7T4kj+jtgy3EiShQG2kRERERERERERDRkKYM+XyAYU40d1uHvTsvxvqjQNhNoG1VoKwJtu7rluN9ktbokATeeNBoAUJSRpHo8HBYfPyoHp04sABBqUf6r8yab2n/0fpS5c6tX3HJ82rBMXDanTPNYzN4/ECiLywddhbbg/TDQ7l2i7yjm2USUKJyhTUREREREREREREOWshVzc4dPuF10hbaVudEA4LRJkZDajETM0DYT5mUkx14eVj5HGXoq34MvEITfIBgty0lGqsuB6xeNwrDsFABAYaY60A6H95Ik4W9fmYM91S3ITHYhP91t+D6iSZIEt8MesyhBORP9inll+P7p45GV7IRDsGhAs0I7zor5/kDVcnyQlWiLvt89DLR7lSz4crNCm4gShYE2ERERERERERERDVl2RUjZ2C4OtDt8vVih7TBfoZ2b6sK8kTl4c8uxyH01JmdoG1doi48rrMNn/HV4/KvzMDo/LeY+UYV2dKW8JEkYU5BuuG8tbqctNtBWzERPctqRl6YdlGvP0B644ZwyWBRVNPc3u6ta8ItXtqClw4/vnjYOJ48v0NxWFF5zhnbvErUcH7g/MUTU37DlOBEREREREREREQ1ZysrVpnbxvOX2bgXakqX24eFZ1WYqgn913hR8o7OVd1h1s8dUmJehDLT9Bi3HHbFzwNs1WnnHPEfwvofnpKjuS2QlpzJ4b1HM0DaqfNc6Foeyb/cAoqw6HwgV2j/+3yYs312LjYcbcfNT61WV9tE6BGMC2HK8d4lbjjPSJqLEGLj/AhMRERERERERERF1k7LqtqlHWo7bIiG1GZEKbRMBqtMuqdpyewNBUzOSlS3HlUG9ch/KcFoUIqqPT/0exhSkYUJResztIkEb8ni5FcF7myJ4txt8XbUWEgzkCm3lwo2eKNBubPfh/1YdwPvbKiF3MzCXZRlr9tdHbjd7/Hh76zHN7Rlo9z1hhfbA/ZEhon6GLceJiIiIiIiIiIhoyDIbaHdEhWM9XaEdmaFtokLbabehKCMJ2SlO1LeJj12LquW4skJbGWgrQvl2E4G2KMiXJAmPfXk2Hnp/F2RZxvdPH2/2kE1RVmi3eq1VaCvDX7PP68+UFdqJbjnuCwRx7iOf4kBtGwDgtjPG46bFY+Len2g2e3WzR3P7DlHLcQbavUq0hoEztIkoUVihTUREREREREREREOWMqRs1AiFvf6uque4Zmg7zAc77kjLcTMV2jbYbRLOmVZi6ZgAINUVW8nsD8oxQaeyLbUyKDYTaDs1QvnyvFQ8cNkMPHj5TJQJWpB3h9sZe5zKoM2o0lrr8QFdoa34VjJTwW/FG5uPRsJsAPjD2zu6tT9RGN3i0f5+E1Zoc4Z2rxJV5Q/gHxki6mcYaBMREREREREREdGQpa7QNp7Ta73luCScJa3FFWk5bpwGhau4L5hVaumYol8nWnQIqGwhrAyKzczQtlKZnijKluNKWiF7mFZwPaArtKWenaH9+YF61X2r99Xh9U1HNVvTy7KMyqYO4eMeQaDd5tH72WSFdl8TztDGwP2ZIaL+hS3HiYiIiIiIiIiIaMiyUnXb7gsg1e3ohZbj4QptMy3HQ9vMLMtCeW4K9kdVyRo/VxxoJzlDgXBQ8Tbdiu1FoaOSlSA/UZSV5EpGM7Q1A+0+eC+Jogy0tVqO76tpxae7qjG9LAvThmV16zUv/ctKAMDkkgy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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 89;\n var nbb_unformatted_code = \"_, axes = plt.subplots(3, 1, figsize=(12, 8))\\nl1, = axes[0].plot(outliers_test_df[\\\"s1\\\"], label=\\\"test data with outlier\\\")\\naxes[0].legend()\\naxes[0].set_title(\\\"S1 data vs normalized anomaly score\\\")\\nl2, = axes[1].plot(norm_anomalies_df[\\\"s1\\\"], color=\\\"orange\\\", label=\\\"norm anomaly score\\\")\\naxes[1].legend()\\nl3 = axes[2].plot(test_df[\\\"s1\\\"], color=\\\"g\\\", label=\\\"test data without outlier\\\")\\naxes[2].legend()\";\n var nbb_formatted_code = \"_, axes = plt.subplots(3, 1, figsize=(12, 8))\\n(l1,) = axes[0].plot(outliers_test_df[\\\"s1\\\"], label=\\\"test data with outlier\\\")\\naxes[0].legend()\\naxes[0].set_title(\\\"S1 data vs normalized anomaly score\\\")\\n(l2,) = axes[1].plot(\\n norm_anomalies_df[\\\"s1\\\"], color=\\\"orange\\\", label=\\\"norm anomaly score\\\"\\n)\\naxes[1].legend()\\nl3 = axes[2].plot(test_df[\\\"s1\\\"], color=\\\"g\\\", label=\\\"test data without outlier\\\")\\naxes[2].legend()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "_, axes = plt.subplots(3, 1, figsize=(12, 8))\n", "l1, = axes[0].plot(outliers_test_df[\"s1\"], label=\"test data with outlier\")\n", @@ -1027,7 +456,8 @@ "axes[1].legend()\n", "l3 = axes[2].plot(test_df[\"s1\"], color=\"g\", label=\"test data without outlier\")\n", "axes[2].legend()" - ] + ], + "outputs": [] } ], "metadata": { diff --git a/numalogic/blocks/__init__.py b/numalogic/blocks/__init__.py deleted file mode 100644 index 748319c3..00000000 --- a/numalogic/blocks/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Module for numalogic blocks which are units of computation that can be -chained together to form a pipeline if needed. A block can be stateful or stateless. -""" - -from numalogic.blocks._base import Block -from numalogic.blocks._nn import NNBlock -from numalogic.blocks._transform import PreprocessBlock, PostprocessBlock, ThresholdBlock -from numalogic.blocks.pipeline import BlockPipeline - -__all__ = [ - "Block", - "NNBlock", - "PreprocessBlock", - "PostprocessBlock", - "ThresholdBlock", - "BlockPipeline", -] diff --git a/numalogic/blocks/_base.py b/numalogic/blocks/_base.py deleted file mode 100644 index 072431ac..00000000 --- a/numalogic/blocks/_base.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from abc import ABCMeta, abstractmethod -from typing import Generic, Union - -import numpy.typing as npt - -from numalogic.tools.types import artifact_t, state_dict_t - - -class Block(Generic[artifact_t], metaclass=ABCMeta): - """ - Base class for all blocks. - - A block is a unit of computation that can be - chained together to form a pipeline. A block can be stateful or stateless. - - A stateful block is one that has a state that can be updated by calling the - block with new data. A stateless block is one that does not have a state and - can be called with new data without any side effects. - - A block can be used as a callable. The call method is an alias for the run method. - - Args: - ---- - artifact: The artifact that the block operates on. - name: The name of the block - stateful: Whether the block is stateful or not. (default: True) - """ - - __slots__ = ("_name", "_stateful", "_artifact") - - def __init__(self, artifact: artifact_t, name: str, stateful: bool = True): - self._artifact = artifact - self._name = name - self._stateful = stateful - - @property - def name(self) -> str: - """The name of the block.""" - return self._name - - @property - def stateful(self) -> bool: - """Whether the block is stateful or not.""" - return self._stateful - - @property - def artifact(self) -> artifact_t: - """The artifact that the block operates on.""" - return self._artifact - - @property - def artifact_state(self) -> Union[artifact_t, state_dict_t]: - """ - The state of the artifact that needs to be serialized for saving. - - This needs to be overridden if something other than the artifact itself - needs to be serialized, e.g. statedict, or a torchscript module. - """ - return self._artifact - - @artifact_state.setter - def artifact_state(self, state: Union[artifact_t, state_dict_t]) -> None: - """ - The state of the artifact that needs to be deserialized for loading. - - This needs to be overridden if something other than the artifact itself - needs to be deserialized, e.g. statedict, or a torchscript module. - """ - self._artifact = state - - def __call__(self, *args, **kwargs) -> npt.NDArray[float]: - """Alias for the run method.""" - return self.run(*args, **kwargs) - - @abstractmethod - def fit(self, data: npt.NDArray[float], *args, **kwargs): - """ - Train the block on the input data. - - Implement this method to train the block, using the block's artifact. - - Args: - ---- - data: The input data to train the block on. - *args: Additional arguments for the block. - **kwargs: Additional keyword arguments for fitting the block. - """ - pass - - @abstractmethod - def run(self, stream: npt.NDArray[float], *args, **kwargs) -> npt.NDArray[float]: - """ - Run inference on the block on the streaming input data. - - Implement this method to run inference on the block, - using the block's artifact. - - Args: - ---- - stream: The streaming input data. - *args: Additional arguments for the block. - **kwargs: Additional keyword arguments for the block. - """ - pass - - -class StatelessBlock(Block, metaclass=ABCMeta): - """ - Base class for all stateless blocks. - - A stateless block is one that does not have a state and - can be called with new data without any side effects. - """ - - def __init__(self, artifact: artifact_t, name: str): - super().__init__(artifact, name, stateful=False) - - def fit(self, data: npt.NDArray[float], *args, **kwargs) -> npt.NDArray[float]: - """ - A no-op for stateless blocks. - - Args: - ---- - data: The input data to train the block on. - *args: Additional arguments for the block. - **kwargs: Additional keyword arguments for fitting the block. - """ - return self.run(data, *args, **kwargs) diff --git a/numalogic/blocks/_nn.py b/numalogic/blocks/_nn.py deleted file mode 100644 index c28a7c10..00000000 --- a/numalogic/blocks/_nn.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -from torch.utils.data import DataLoader -import numpy.typing as npt - -from numalogic.blocks import Block -from numalogic.models.autoencoder import TimeseriesTrainer -from numalogic.tools.data import StreamingDataset -from numalogic.tools.types import nn_model_t, state_dict_t - - -class NNBlock(Block): - """ - A block that uses a neural network model to operate on the artifact. - - Serialization is done by saving state dict of the model. - - Args: - ---- - model: The neural network model. - seq_len: The sequence length of the input data. - name: The name of the block. Defaults to "nn". - """ - - __slots__ = ("seq_len",) - - def __init__(self, model: nn_model_t, seq_len: int, name: str = "nn"): - super().__init__(artifact=model, name=name) - self.seq_len = seq_len - - @property - def artifact_state(self) -> state_dict_t: - """The state dict of the model.""" - return self._artifact.state_dict() - - @artifact_state.setter - def artifact_state(self, artifact_state: state_dict_t) -> None: - """Set the state dict of the model.""" - self._artifact.load_state_dict(artifact_state) - - def fit( - self, input_: npt.NDArray[float], batch_size: int = 64, **trainer_kwargs - ) -> npt.NDArray[float]: - """ - Train the model on the input data. - - Args: - ---- - input_: The input data. - batch_size: The batch size to use for training. - trainer_kwargs: Keyword arguments to pass to the lightning trainer. - - Returns - ------- - The error of the model on the input data. - """ - trainer = TimeseriesTrainer(**trainer_kwargs) - ds = StreamingDataset(input_, self.seq_len) - trainer.fit(self._artifact, train_dataloaders=DataLoader(ds, batch_size=batch_size)) - reconerr = trainer.predict( - self._artifact, dataloaders=DataLoader(ds, batch_size=batch_size) - ) - return reconerr.numpy() - - def run(self, input_: npt.NDArray[float], **_) -> npt.NDArray[float]: - """ - Perform forward pass on the streaming input data. - - Args: - ---- - input_: The streaming input data. - - Returns - ------- - The error of the model on the input data. - """ - input_ = torch.from_numpy(input_).float() - # Add a batch dimension - input_ = torch.unsqueeze(input_, dim=0).contiguous() - self._artifact.eval() - with torch.no_grad(): - reconerr = self._artifact.predict_step(input_, batch_idx=0) - return torch.squeeze(reconerr, dim=0).numpy() diff --git a/numalogic/blocks/_transform.py b/numalogic/blocks/_transform.py deleted file mode 100644 index 6f62f8f1..00000000 --- a/numalogic/blocks/_transform.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy.typing as npt - -from numalogic.blocks._base import Block, StatelessBlock -from numalogic.tools.types import transform_t, thresh_t - - -class PreprocessBlock(Block): - """ - A stateful block that is used to preprocess the input data, before it is fed to an ML model. - - Serialization is done by saving the preprocessor object. - - Args: - ---- - preprocessor: The preprocessor object. - name: The name of the block. Defaults to "preprocess". - stateful: Whether the block is stateful or not. Defaults to True. - """ - - def __init__(self, preprocessor: transform_t, name: str = "preprocess", stateful: bool = True): - super().__init__(artifact=preprocessor, name=name, stateful=stateful) - - def fit(self, input_: npt.NDArray[float], **__) -> npt.NDArray[float]: - """ - Fit the preprocessor on the input data. - - Args: - ---- - input_: The input data to train on. - - Returns - ------- - The transformed/scaled input data. - """ - return self._artifact.fit_transform(input_) - - def run(self, input_: npt.NDArray[float], **__) -> npt.NDArray[float]: - """ - Transform the streaming input data. - - Args: - ---- - input_: The streaming input data. - - Returns - ------- - The transformed/scaled input data. - """ - return self._artifact.transform(input_) - - -class ThresholdBlock(Block): - """ - A stateful block that is used to threshold the output of an ML model. - - Serialization is done by saving the threshold object. - - Args: - ---- - thresh_model: The threshold model object. - name: The name of the block. Defaults to "threshold". - """ - - def __init__(self, thresh_model: thresh_t, name: str = "threshold"): - super().__init__(artifact=thresh_model, name=name) - - def fit(self, input_: npt.NDArray[float], **__) -> npt.NDArray[float]: - """ - Fit the threshold model on the training data. - - Args: - ---- - input_: The input data to train on. - - Returns - ------- - The anomaly scores of the training data. - """ - self._artifact.fit(input_) - return self._artifact.score_samples(input_) - - def run(self, input_: npt.NDArray[float], **__) -> npt.NDArray[float]: - """ - Transform the streaming input data. - - Args: - ---- - input_: The streaming input data. - - Returns - ------- - The anomaly score of the streaming data. - """ - return self._artifact.score_samples(input_) - - -class PostprocessBlock(StatelessBlock): - """ - A stateless block that is used to postprocess the output of an ML model. - - Args: - ---- - postprocessor: The postprocessor object. - """ - - def __init__(self, postprocessor: transform_t, name: str = "postprocess"): - super().__init__(artifact=postprocessor, name=name) - - def run(self, input_: npt.NDArray[float], **__) -> npt.NDArray[float]: - """ - Transform the streaming input data. - - Args: - ---- - input_: The streaming input data. - - Returns - ------- - The postprocessed streaming data. - """ - return self._artifact.transform(input_) diff --git a/numalogic/blocks/pipeline.py b/numalogic/blocks/pipeline.py deleted file mode 100644 index 4aa3056b..00000000 --- a/numalogic/blocks/pipeline.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from collections.abc import Sequence -from collections.abc import Iterator - -import numpy.typing as npt - -from numalogic.blocks._transform import Block -from numalogic.registry import ArtifactManager -from numalogic.tools.types import artifact_t -from typing import Optional - - -class BlockPipeline(Sequence[Block]): - """ - A pipeline of blocks. - - A pipeline is a sequence of blocks that can be chained together to form a - pipeline. A pipeline can be used as a callable. The call method is an alias - for the run method. - - Args: - ---- - blocks: A list/tuple of blocks that form the pipeline. - registry: The registry to use for storing artifacts. - """ - - __slots__ = ("_blocks", "_registry") - - def __init__(self, *blocks: Block, registry: Optional[ArtifactManager] = None): - self._blocks = blocks - self._registry = registry - - def __call__(self, *args, **kwargs): - return self.run(*args, **kwargs) - - def __getitem__(self, idx: int) -> Block: - """Get the block at the given index.""" - return self._blocks[idx] - - def __len__(self) -> int: - """Get the number of blocks in the pipeline.""" - return len(self._blocks) - - def __iter__(self) -> Iterator[Block]: - """Get an iterator over the blocks in the pipeline.""" - return iter(self._blocks) - - def named_blocks(self) -> Iterator[tuple[str, Block]]: - """Get an iterator over the blocks in the pipeline along with their names.""" - names = [block.name for block in self._blocks] - return zip(names, self._blocks) - - def _get_block_params(self, **fit_params) -> dict[str, dict]: - """ - Get the parameters for each block from the fit_params. - - Inspired by sklearn Pipeline - (https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html). - - Args: - ---- - fit_params : keyword dict of string -> object - - Returns - ------- - A nested dict of blockname -> parameter -> value - - Raises - ------ - ValueError: If the keyword arguments are not of the form - blockname__parameter, e.g. `block_pipeline.fit(data, nn__max_epochs=50)` - """ - block_params = {name: {} for name, block in self.named_blocks()} - err_msg = ( - "Invalid kwarg: {pname} found. Keyword args of " - "BlockPipeline must be of the form blockname__parameter, " - "e.g. `block_pipeline.fit(data, nn__max_epochs=50)`" - ) - for pname, pval in fit_params.items(): - if "__" not in pname: - raise ValueError(err_msg.format(pname=pname)) - blockname, param = pname.split("__", 1) - block_params[blockname][param] = pval - return block_params - - def fit(self, input_: npt.NDArray[float], **fit_params) -> npt.NDArray[float]: - """ - Fit the pipeline on the input data. - - Args: - ---- - input_: The input data to fit the pipeline on. - fit_params : dict of string -> object - Parameters passed to the ``fit`` method of each block, where - each parameter name is prefixed such that parameter ``p`` for step - ``s`` has key ``s__p``. - - Returns - ------- - Final fit block output. - """ - fit_params = self._get_block_params(**fit_params) - for block in self._blocks: - input_ = block.fit(input_, **fit_params.get(block.name, {})) - return input_ - - def run(self, data: npt.NDArray[float]) -> npt.NDArray[float]: - """ - Perform inference on streaming data. - - Args: - ---- - data: Streaming input data - - """ - for block in self._blocks: - data = block.run(data) - return data - - def save(self, skeys: Sequence[str], dkeys: Sequence[str]) -> None: - """ - Save the state of the pipeline. - - Args: - ---- - skeys: Sequence of source keys. - dkeys: Sequence of destination keys. - - Raises - ------ - ValueError: If no registry is provided. - """ - if not self._registry: - raise ValueError("No registry provided.") - - artifacts: dict[str, artifact_t] = {} - for block in self._blocks: - if not block.stateful: - continue - artifacts[block.name] = block.artifact_state - self._registry.save(skeys, dkeys, artifacts) - - def load(self, skeys: Sequence[str], dkeys: Sequence[str]) -> None: - """ - Load the state of the pipeline. - - Args: - ---- - skeys: Sequence of source keys. - dkeys: Sequence of destination keys. - - Raises - ------ - ValueError: If no registry is provided. - """ - if not self._registry: - raise ValueError("No registry provided.") - - artifact_data = self._registry.load(skeys, dkeys) - artifacts = artifact_data.artifact - for block in self._blocks: - if not block.stateful: - continue - block.artifact_state = artifacts[block.name] diff --git a/numalogic/connectors/druid/__init__.py b/numalogic/connectors/druid/__init__.py deleted file mode 100644 index d72b3e32..00000000 --- a/numalogic/connectors/druid/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from numalogic.connectors.druid._druid import DruidFetcher, make_filter_pairs, build_params - -__all__ = ["DruidFetcher", "make_filter_pairs", "build_params"] diff --git a/numalogic/connectors/rds/__init__.py b/numalogic/connectors/rds/__init__.py deleted file mode 100644 index 865b364d..00000000 --- a/numalogic/connectors/rds/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from numalogic.connectors.rds._rds import RDSFetcher - -__all__ = ["RDSFetcher"] diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index c7af659f..00000000 --- a/poetry.lock +++ /dev/null @@ -1,5391 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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"dda939314986db31121d74b38f9ebb0b73da1a66badb6177e692cf52065987b6" diff --git a/pyproject.toml b/pyproject.toml index f9aa5fb0..b087195a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,87 +1,6 @@ -[tool.poetry] -name = "numalogic" -version = "0.10.2" -description = "Collection of operational Machine Learning models and tools." -authors = ["Numalogic Developers"] -packages = [{ include = "numalogic" }] -readme = "README.md" -license = "Apache-2.0" -maintainers = [ - "Avik Basu ", - "Vigith Maurice ", -] -classifiers = [ - "Topic :: Software Development :: Libraries", - "License :: OSI Approved :: Apache Software License", - "Intended Audience :: Developers", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11" -] -repository = "https://github.com/numaproj/numalogic" -documentation = "https://numalogic.numaproj.io/" -homepage = "https://numalogic.numaproj.io/" - -[tool.poetry.dependencies] -python = ">=3.9, <3.12" -numpy = "^1.26" -pandas = { version = "^2.0", extras = ["performance"] } -scikit-learn = "^1.3" -omegaconf = "^2.3.0" -cachetools = "^5.3.0" -orjson = "^3.9" -pynumaflow = "~0.6" -prometheus_client = "^0.18.0" -structlog = "^24.1.0" - -# extras -mlflow-skinny = { version = "^2.0", optional = true } -redis = { extras = ["hiredis"], version = "^5.0", optional = true } -boto3 = { version = "^1.24.64", optional = true } -pydruid = { version = "^0.6", optional = true } -PyMySQL = { version = "^1.1.0", optional = true } - - -[tool.poetry.extras] -mlflow = ["mlflow-skinny"] -redis = ["redis"] -dynamodb = ["boto3"] -druid = ["pydruid"] -rds = ["PyMySQL", "boto3"] - -[tool.poetry.group.torch] -optional = true - -[tool.poetry.group.torch.dependencies] -pytorch-lightning = "^2.0" - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.dev.dependencies] -matplotlib = "^3.4.2" -black = "^23.0" -pytest = "^7.1" -pytest-cov = "^4.0" -torchinfo = "^1.7.2" -ruff = "~0.0.275" -pre-commit = "^3.3.1" -fakeredis = "^2.11.2" -freezegun = "^1.2.2" -pympler = "^1.0.1" -moto = "~4.1" -deepdiff = "^6.5.0" -pytest-mock = "^3.12.0" - -[tool.poetry.group.jupyter] -optional = true - -[tool.poetry.group.jupyter.dependencies] -jupyter = "^1.0.0" -ipykernel = "^6.15.1" -ipympl = "^0.9.1" -ipython-autotime = "^0.3.1" +[build-system] +requires = ["setuptools >= 61.0", "wheel"] +build-backend = "setuptools.build_meta" [tool.black] line-length = 100 @@ -123,7 +42,3 @@ max-statements = 30 [tool.ruff.pydocstyle] convention = "numpy" - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" diff --git a/setup.py b/setup.py new file mode 100644 index 00000000..66fba0ec --- /dev/null +++ b/setup.py @@ -0,0 +1,40 @@ +import os +from contextlib import contextmanager +from importlib.util import spec_from_file_location, module_from_spec +from types import ModuleType + +from setuptools import setup + + +def _load_py_module(name: str, location: str) -> ModuleType: + spec = spec_from_file_location(name, location) + assert spec, f"Failed to load module {name} from {location}" + py = module_from_spec(spec) + assert spec.loader, f"ModuleSpec.loader is None for {name} from {location}" + spec.loader.exec_module(py) + return py + + +@contextmanager +def cd(newdir): + prevdir = os.getcwd() + os.chdir(os.path.expanduser(newdir)) + try: + yield + finally: + os.chdir(prevdir) + + +pkg_name = os.getenv("PKG_NAME", "numalogic") +if pkg_name == "numalogic": + with cd("src/core"): + setup() + +elif pkg_name == "connectors": + setup_module = _load_py_module( + name="connectors_setup", location="src/test_connectors/__setup__.py" + ) +elif pkg_name == "registry": + setup_module = _load_py_module(name="registry_setup", location="src/test_registry/__setup__.py") +else: + raise ValueError(f"Invalid package name {pkg_name}") diff --git a/numalogic/__init__.py b/src/core/numalogic/__init__.py similarity index 100% rename from numalogic/__init__.py rename to src/core/numalogic/__init__.py diff --git a/numalogic/_constants.py b/src/core/numalogic/_constants.py similarity index 100% rename from numalogic/_constants.py rename to src/core/numalogic/_constants.py diff --git a/numalogic/backtest/__init__.py b/src/core/numalogic/backtest/__init__.py similarity index 100% rename from numalogic/backtest/__init__.py rename to src/core/numalogic/backtest/__init__.py diff --git a/numalogic/backtest/_constants.py b/src/core/numalogic/backtest/_constants.py similarity index 100% rename from numalogic/backtest/_constants.py rename to src/core/numalogic/backtest/_constants.py diff --git a/numalogic/backtest/_prom.py b/src/core/numalogic/backtest/_prom.py similarity index 99% rename from numalogic/backtest/_prom.py rename to src/core/numalogic/backtest/_prom.py index d5d2fbff..0b8e2d09 100644 --- a/numalogic/backtest/_prom.py +++ b/src/core/numalogic/backtest/_prom.py @@ -31,8 +31,8 @@ PostprocessFactory, ThresholdFactory, ) -from numalogic.connectors import ConnectorType -from numalogic.connectors.prometheus import PrometheusFetcher +from connectors import ConnectorType +from connectors import PrometheusFetcher from numalogic.tools.data import StreamingDataset, inverse_window from numalogic.tools.types import artifact_t from numalogic.udfs import UDFFactory, StreamConf, MLPipelineConf diff --git a/numalogic/base.py b/src/core/numalogic/base.py similarity index 100% rename from numalogic/base.py rename to src/core/numalogic/base.py diff --git a/numalogic/config/__init__.py b/src/core/numalogic/config/__init__.py similarity index 100% rename from numalogic/config/__init__.py rename to src/core/numalogic/config/__init__.py diff --git a/numalogic/config/_config.py b/src/core/numalogic/config/_config.py similarity index 99% rename from numalogic/config/_config.py rename to src/core/numalogic/config/_config.py index a37469e7..e391c825 100644 --- a/numalogic/config/_config.py +++ b/src/core/numalogic/config/_config.py @@ -56,7 +56,7 @@ class RegistryInfo: Args: ---- - name: name of the registry + name: name of the test_registry conf: kwargs for instantiating the model class """ diff --git a/numalogic/config/factory.py b/src/core/numalogic/config/factory.py similarity index 96% rename from numalogic/config/factory.py rename to src/core/numalogic/config/factory.py index db5ff8ec..14eb9a38 100644 --- a/numalogic/config/factory.py +++ b/src/core/numalogic/config/factory.py @@ -148,21 +148,21 @@ class ModelFactory(_ObjectFactory): class RegistryFactory(_ObjectFactory): - """Factory class to create registry instances.""" + """Factory class to create test_registry instances.""" _CLS_SET: ClassVar[frozenset] = frozenset( {"RedisRegistry", "MLflowRegistry", "DynamoDBRegistry"} ) def get_instance(self, object_info: Union[ModelInfo, RegistryInfo]): - import numalogic.registry as reg + import registry as reg try: _cls = getattr(reg, object_info.name) except AttributeError as err: if object_info.name in self._CLS_SET: raise ImportError( - "Please install the required dependencies for the registry you want to use." + "Please install the required dependencies for the test_registry you want to use." ) from err raise UnknownConfigArgsError( f"Invalid model info instance provided: {object_info}" @@ -171,14 +171,14 @@ def get_instance(self, object_info: Union[ModelInfo, RegistryInfo]): @classmethod def get_cls(cls, name: str): - import numalogic.registry as reg + import registry as reg try: return getattr(reg, name) except AttributeError as err: if name in cls._CLS_SET: raise ImportError( - "Please install the required dependencies for the registry you want to use." + "Please install the required dependencies for the test_registry you want to use." ) from err raise UnknownConfigArgsError( f"Invalid name provided for RegistryFactory: {name}" @@ -186,13 +186,13 @@ def get_cls(cls, name: str): class ConnectorFactory(_ObjectFactory): - """Factory class for data connectors.""" + """Factory class for data test_connectors.""" _CLS_SET: ClassVar[frozenset] = frozenset({"PrometheusFetcher", "DruidFetcher", "RDSFetcher"}) @classmethod def get_cls(cls, name: str): - import numalogic.connectors as conn + import connectors as conn try: return getattr(conn, name) diff --git a/numalogic/connectors/rds/db/__init__.py b/src/core/numalogic/models/__init__.py similarity index 100% rename from numalogic/connectors/rds/db/__init__.py rename to src/core/numalogic/models/__init__.py diff --git a/numalogic/models/autoencoder/__init__.py b/src/core/numalogic/models/autoencoder/__init__.py similarity index 100% rename from numalogic/models/autoencoder/__init__.py rename to src/core/numalogic/models/autoencoder/__init__.py diff --git a/numalogic/models/autoencoder/base.py b/src/core/numalogic/models/autoencoder/base.py similarity index 100% rename from numalogic/models/autoencoder/base.py rename to src/core/numalogic/models/autoencoder/base.py diff --git a/numalogic/models/autoencoder/variants/__init__.py b/src/core/numalogic/models/autoencoder/variants/__init__.py similarity index 100% rename from numalogic/models/autoencoder/variants/__init__.py rename to src/core/numalogic/models/autoencoder/variants/__init__.py diff --git a/numalogic/models/autoencoder/variants/conv.py b/src/core/numalogic/models/autoencoder/variants/conv.py similarity index 100% rename from numalogic/models/autoencoder/variants/conv.py rename to src/core/numalogic/models/autoencoder/variants/conv.py diff --git a/numalogic/models/autoencoder/variants/lstm.py b/src/core/numalogic/models/autoencoder/variants/lstm.py similarity index 100% rename from numalogic/models/autoencoder/variants/lstm.py rename to src/core/numalogic/models/autoencoder/variants/lstm.py diff --git a/numalogic/models/autoencoder/variants/transformer.py b/src/core/numalogic/models/autoencoder/variants/transformer.py similarity index 100% rename from numalogic/models/autoencoder/variants/transformer.py rename to src/core/numalogic/models/autoencoder/variants/transformer.py diff --git a/numalogic/models/autoencoder/variants/vanilla.py b/src/core/numalogic/models/autoencoder/variants/vanilla.py similarity index 100% rename from numalogic/models/autoencoder/variants/vanilla.py rename to src/core/numalogic/models/autoencoder/variants/vanilla.py diff --git a/numalogic/connectors/utils/__init__.py b/src/core/numalogic/models/forecast/__init__.py similarity index 100% rename from numalogic/connectors/utils/__init__.py rename to src/core/numalogic/models/forecast/__init__.py diff --git a/numalogic/models/forecast/variants/__init__.py b/src/core/numalogic/models/forecast/variants/__init__.py similarity index 100% rename from numalogic/models/forecast/variants/__init__.py rename to src/core/numalogic/models/forecast/variants/__init__.py diff --git a/numalogic/models/forecast/variants/naive.py b/src/core/numalogic/models/forecast/variants/naive.py similarity index 100% rename from numalogic/models/forecast/variants/naive.py rename to src/core/numalogic/models/forecast/variants/naive.py diff --git a/numalogic/models/threshold/__init__.py b/src/core/numalogic/models/threshold/__init__.py similarity index 100% rename from numalogic/models/threshold/__init__.py rename to src/core/numalogic/models/threshold/__init__.py diff --git a/numalogic/models/threshold/_mahalanobis.py b/src/core/numalogic/models/threshold/_mahalanobis.py similarity index 100% rename from numalogic/models/threshold/_mahalanobis.py rename to src/core/numalogic/models/threshold/_mahalanobis.py diff --git a/numalogic/models/threshold/_median.py b/src/core/numalogic/models/threshold/_median.py similarity index 100% rename from numalogic/models/threshold/_median.py rename to src/core/numalogic/models/threshold/_median.py diff --git a/numalogic/models/threshold/_static.py b/src/core/numalogic/models/threshold/_static.py similarity index 100% rename from numalogic/models/threshold/_static.py rename to src/core/numalogic/models/threshold/_static.py diff --git a/numalogic/models/threshold/_std.py b/src/core/numalogic/models/threshold/_std.py similarity index 100% rename from numalogic/models/threshold/_std.py rename to src/core/numalogic/models/threshold/_std.py diff --git a/numalogic/models/vae/__init__.py b/src/core/numalogic/models/vae/__init__.py similarity index 100% rename from numalogic/models/vae/__init__.py rename to src/core/numalogic/models/vae/__init__.py diff --git a/numalogic/models/vae/base.py b/src/core/numalogic/models/vae/base.py similarity index 100% rename from numalogic/models/vae/base.py rename to src/core/numalogic/models/vae/base.py diff --git a/numalogic/models/vae/layer.py b/src/core/numalogic/models/vae/layer.py similarity index 100% rename from numalogic/models/vae/layer.py rename to src/core/numalogic/models/vae/layer.py diff --git a/numalogic/models/vae/variants/__init__.py b/src/core/numalogic/models/vae/variants/__init__.py similarity index 100% rename from numalogic/models/vae/variants/__init__.py rename to src/core/numalogic/models/vae/variants/__init__.py diff --git a/numalogic/models/vae/variants/conv.py b/src/core/numalogic/models/vae/variants/conv.py similarity index 100% rename from numalogic/models/vae/variants/conv.py rename to src/core/numalogic/models/vae/variants/conv.py diff --git a/numalogic/monitoring/__init__.py b/src/core/numalogic/monitoring/__init__.py similarity index 100% rename from numalogic/monitoring/__init__.py rename to src/core/numalogic/monitoring/__init__.py diff --git a/numalogic/monitoring/metrics.py b/src/core/numalogic/monitoring/metrics.py similarity index 100% rename from numalogic/monitoring/metrics.py rename to src/core/numalogic/monitoring/metrics.py diff --git a/numalogic/synthetic/__init__.py b/src/core/numalogic/synthetic/__init__.py similarity index 78% rename from numalogic/synthetic/__init__.py rename to src/core/numalogic/synthetic/__init__.py index c510e22b..43504fd5 100644 --- a/numalogic/synthetic/__init__.py +++ b/src/core/numalogic/synthetic/__init__.py @@ -10,9 +10,4 @@ # limitations under the License. -from numalogic.synthetic.timeseries import SyntheticTSGenerator -from numalogic.synthetic.anomalies import AnomalyGenerator -from numalogic.synthetic.sparsity import SparsityGenerator - - __all__ = ["SyntheticTSGenerator", "AnomalyGenerator", "SparsityGenerator"] diff --git a/numalogic/synthetic/anomalies.py b/src/core/numalogic/synthetic/anomalies.py similarity index 100% rename from numalogic/synthetic/anomalies.py rename to src/core/numalogic/synthetic/anomalies.py diff --git a/numalogic/synthetic/sparsity.py b/src/core/numalogic/synthetic/sparsity.py similarity index 100% rename from numalogic/synthetic/sparsity.py rename to src/core/numalogic/synthetic/sparsity.py diff --git a/numalogic/synthetic/timeseries.py b/src/core/numalogic/synthetic/timeseries.py similarity index 100% rename from numalogic/synthetic/timeseries.py rename to src/core/numalogic/synthetic/timeseries.py diff --git a/numalogic/connectors/utils/aws/__init__.py b/src/core/numalogic/tools/__init__.py similarity index 100% rename from numalogic/connectors/utils/aws/__init__.py rename to src/core/numalogic/tools/__init__.py diff --git a/numalogic/tools/aggregators.py b/src/core/numalogic/tools/aggregators.py similarity index 100% rename from numalogic/tools/aggregators.py rename to src/core/numalogic/tools/aggregators.py diff --git a/numalogic/tools/callbacks.py b/src/core/numalogic/tools/callbacks.py similarity index 100% rename from numalogic/tools/callbacks.py rename to src/core/numalogic/tools/callbacks.py diff --git a/numalogic/tools/data.py b/src/core/numalogic/tools/data.py similarity index 100% rename from numalogic/tools/data.py rename to src/core/numalogic/tools/data.py diff --git a/numalogic/tools/exceptions.py b/src/core/numalogic/tools/exceptions.py similarity index 94% rename from numalogic/tools/exceptions.py rename to src/core/numalogic/tools/exceptions.py index abb359ec..528e0b1f 100644 --- a/numalogic/tools/exceptions.py +++ b/src/core/numalogic/tools/exceptions.py @@ -59,7 +59,7 @@ class ConfigError(RuntimeError): class ModelVersionError(Exception): - """Raised when a model version is not found in the registry.""" + """Raised when a model version is not found in the test_registry.""" pass @@ -77,7 +77,7 @@ class DynamoDBRegistryError(Exception): class ModelKeyNotFound(RedisRegistryError): - """Raised when a model key is not found in the registry.""" + """Raised when a model key is not found in the test_registry.""" pass diff --git a/numalogic/tools/loss.py b/src/core/numalogic/tools/loss.py similarity index 100% rename from numalogic/tools/loss.py rename to src/core/numalogic/tools/loss.py diff --git a/numalogic/tools/trainer.py b/src/core/numalogic/tools/trainer.py similarity index 100% rename from numalogic/tools/trainer.py rename to src/core/numalogic/tools/trainer.py diff --git a/numalogic/tools/types.py b/src/core/numalogic/tools/types.py similarity index 100% rename from numalogic/tools/types.py rename to src/core/numalogic/tools/types.py diff --git a/numalogic/transforms/__init__.py b/src/core/numalogic/transforms/__init__.py similarity index 100% rename from numalogic/transforms/__init__.py rename to src/core/numalogic/transforms/__init__.py diff --git a/numalogic/transforms/_movavg.py b/src/core/numalogic/transforms/_movavg.py similarity index 100% rename from numalogic/transforms/_movavg.py rename to src/core/numalogic/transforms/_movavg.py diff --git a/numalogic/transforms/_postprocess.py b/src/core/numalogic/transforms/_postprocess.py similarity index 100% rename from numalogic/transforms/_postprocess.py rename to src/core/numalogic/transforms/_postprocess.py diff --git a/numalogic/transforms/_scaler.py b/src/core/numalogic/transforms/_scaler.py similarity index 100% rename from numalogic/transforms/_scaler.py rename to src/core/numalogic/transforms/_scaler.py diff --git a/numalogic/transforms/_stateless.py b/src/core/numalogic/transforms/_stateless.py similarity index 100% rename from numalogic/transforms/_stateless.py rename to src/core/numalogic/transforms/_stateless.py diff --git a/src/core/pyproject.toml b/src/core/pyproject.toml new file mode 100644 index 00000000..f5c130cb --- /dev/null +++ b/src/core/pyproject.toml @@ -0,0 +1,68 @@ +[build-system] +requires = ["setuptools >= 61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "numalogic" +version = "0.10.1a0" +description = "Collection of operational Machine Learning models and tools." +authors = [ + {name = "Numalogic Developers"} +] +readme = "README.md" +maintainers = [ + { name = "Avik Basu", email = "avikbasu93@gmail.com"}, + { name = "Vigith Maurice", email = "vigith@gmail.com"} +] +classifiers = [ + "Topic :: Software Development :: Libraries", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11" +] +requires-python = ">= 3.9" +dependencies = [ + "numpy>=1.22", + "pandas[performance]>=2.0", + "scikit-learn>=1.3", + "torch>=2.0", + "pytorch-lightning>=2.0", +] + +[project.optional-dependencies] +dev = [ + "matplotlib>=3.4", + "black>23.0", + "pytest>=7.0", + "pytest-cov>=4.0", + "torchinfo>=1.7", + "ruff>=0.0.275", + "pre-commit>=3.3", + "pytest-mock>=3.12", +] +jupyter = [ + "jupyter>=1.0", + "ipykernel>=6.15", + "ipympl>=0.9", + "ipython-autotime>=0.3", + "black[jupyter]" +] + + +[project.urls] +Repository = "https://github.com/numaproj/numalogic" +Documentation = "https://numalogic.numaproj.io/" +Homepage = "https://numalogic.numaproj.io/" + + +#[tool.setuptools] +## packages = ["numalogic"] +#package-dir = {"numalogic" = "../numalogic"} + + +#[tool.setuptools.packages.find] +#include = ["numalogic*", "numalogic.*"] +#exclude = ["*egg-info*", "build*", "dist*", "docs*", "tests*"] \ No newline at end of file diff --git a/numalogic/connectors/__init__.py b/src/nlconnectors/connectors/__init__.py similarity index 77% rename from numalogic/connectors/__init__.py rename to src/nlconnectors/connectors/__init__.py index 8c8e2d19..ce072de1 100644 --- a/numalogic/connectors/__init__.py +++ b/src/nlconnectors/connectors/__init__.py @@ -1,6 +1,6 @@ from importlib.util import find_spec -from numalogic.connectors._config import ( +from connectors import ( RedisConf, PrometheusConf, ConnectorConf, @@ -10,7 +10,7 @@ RDSConf, RDSFetcherConf, ) -from numalogic.connectors.prometheus import PrometheusFetcher +from connectors import PrometheusFetcher __all__ = [ "RedisConf", @@ -31,6 +31,6 @@ __all__.append("RDSFetcher") if find_spec("pydruid"): - from numalogic.connectors.druid import DruidFetcher # noqa: F401 + from connectors import DruidFetcher # noqa: F401 __all__.append("DruidFetcher") diff --git a/src/nlconnectors/connectors/__setup__.py b/src/nlconnectors/connectors/__setup__.py new file mode 100644 index 00000000..5572c9c3 --- /dev/null +++ b/src/nlconnectors/connectors/__setup__.py @@ -0,0 +1,20 @@ +from setuptools import find_packages + + +def setup_args(): + return dict( + name="numalogic-test_connectors", + version="0.10.a1", + packages=find_packages( + where="src", + include=["test_connectors", "test_connectors.*"], + exclude=["tests"], + ), + package_dir={"": "src"}, + install_requires=[ + "numalogic", + "pydruid>=0.6", + "boto3>=1.20", + "PyMySQL>=1.0", + ], + ) diff --git a/numalogic/connectors/_base.py b/src/nlconnectors/connectors/_base.py similarity index 100% rename from numalogic/connectors/_base.py rename to src/nlconnectors/connectors/_base.py diff --git a/numalogic/connectors/_config.py b/src/nlconnectors/connectors/_config.py similarity index 100% rename from numalogic/connectors/_config.py rename to src/nlconnectors/connectors/_config.py diff --git a/src/nlconnectors/connectors/druid/__init__.py b/src/nlconnectors/connectors/druid/__init__.py new file mode 100644 index 00000000..fe81e106 --- /dev/null +++ b/src/nlconnectors/connectors/druid/__init__.py @@ -0,0 +1 @@ +__all__ = ["DruidFetcher", "make_filter_pairs", "build_params"] diff --git a/numalogic/connectors/druid/_druid.py b/src/nlconnectors/connectors/druid/_druid.py similarity index 100% rename from numalogic/connectors/druid/_druid.py rename to src/nlconnectors/connectors/druid/_druid.py diff --git a/numalogic/connectors/druid/aggregators.py b/src/nlconnectors/connectors/druid/aggregators.py similarity index 100% rename from numalogic/connectors/druid/aggregators.py rename to src/nlconnectors/connectors/druid/aggregators.py diff --git a/numalogic/connectors/druid/postaggregator.py b/src/nlconnectors/connectors/druid/postaggregator.py similarity index 100% rename from numalogic/connectors/druid/postaggregator.py rename to src/nlconnectors/connectors/druid/postaggregator.py diff --git a/numalogic/connectors/exceptions.py b/src/nlconnectors/connectors/exceptions.py similarity index 100% rename from numalogic/connectors/exceptions.py rename to src/nlconnectors/connectors/exceptions.py diff --git a/numalogic/connectors/prometheus.py b/src/nlconnectors/connectors/prometheus.py similarity index 99% rename from numalogic/connectors/prometheus.py rename to src/nlconnectors/connectors/prometheus.py index 531cf32a..f6c092e5 100644 --- a/numalogic/connectors/prometheus.py +++ b/src/nlconnectors/connectors/prometheus.py @@ -21,7 +21,7 @@ import requests from orjson import JSONDecodeError -from numalogic.connectors._base import DataFetcher +from connectors._base import DataFetcher from numalogic.tools.exceptions import PrometheusFetcherError, PrometheusInvalidResponseError LOGGER = logging.getLogger(__name__) diff --git a/src/nlconnectors/connectors/pyproject.toml b/src/nlconnectors/connectors/pyproject.toml new file mode 100644 index 00000000..f504988d --- /dev/null +++ b/src/nlconnectors/connectors/pyproject.toml @@ -0,0 +1,79 @@ +[build-system] +requires = ["setuptools >= 61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "numalogic-connectors" +version = "0.10.0" +description = "Collection of operational Machine Learning models and tools." +authors = [ + {name = "Numalogic Developers"} +] +readme = "README.md" +maintainers = [ + { name = "Avik Basu", email = "avikbasu93@gmail.com"}, + { name = "Vigith Maurice", email = "vigith@gmail.com"} +] +classifiers = [ + "Topic :: Software Development :: Libraries", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11" +] +requires-python = ">= 3.9" +dependencies = [ + "numpy>=1.22", + "pandas[performance]>=2.0", + "scikit-learn>=1.3", + "torch>=2.0", + "pytorch-lightning>=2.0", +] + +[project.urls] +Repository = "https://github.com/numaproj/numalogic" +Documentation = "https://numalogic.numaproj.io/" +Homepage = "https://numalogic.numaproj.io/" + + +[tool.black] +line-length = 100 +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | \.idea + | _build + | buck-out + | build + | dist + | examples/.*/venv + | tests/.*/setup.py +)/ +''' + +[tool.ruff] +line-length = 100 +src = ["numalogic", "tests"] +select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] +ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +target-version = "py39" +show-fixes = true +show-source = true + +[tool.ruff.per-file-ignores] +"tests/*" = ["D101", "D103"] + +[tool.ruff.pylint] +max-args = 15 +max-statements = 30 + +[tool.ruff.pydocstyle] +convention = "numpy" diff --git a/src/nlconnectors/connectors/rds/__init__.py b/src/nlconnectors/connectors/rds/__init__.py new file mode 100644 index 00000000..498d4c59 --- /dev/null +++ b/src/nlconnectors/connectors/rds/__init__.py @@ -0,0 +1,3 @@ +from connectors import RDSFetcher + +__all__ = ["RDSFetcher"] diff --git a/numalogic/connectors/rds/_base.py b/src/nlconnectors/connectors/rds/_base.py similarity index 100% rename from numalogic/connectors/rds/_base.py rename to src/nlconnectors/connectors/rds/_base.py diff --git a/numalogic/connectors/rds/_rds.py b/src/nlconnectors/connectors/rds/_rds.py similarity index 100% rename from numalogic/connectors/rds/_rds.py rename to src/nlconnectors/connectors/rds/_rds.py diff --git a/numalogic/models/__init__.py b/src/nlconnectors/connectors/rds/db/__init__.py similarity index 100% rename from numalogic/models/__init__.py rename to src/nlconnectors/connectors/rds/db/__init__.py diff --git a/numalogic/connectors/rds/db/factory.py b/src/nlconnectors/connectors/rds/db/factory.py similarity index 78% rename from numalogic/connectors/rds/db/factory.py rename to src/nlconnectors/connectors/rds/db/factory.py index b8cd8c9d..d41359f0 100644 --- a/numalogic/connectors/rds/db/factory.py +++ b/src/nlconnectors/connectors/rds/db/factory.py @@ -1,7 +1,7 @@ import logging -from numalogic.connectors.utils.aws.config import DatabaseTypes -from numalogic.connectors.utils.aws.exceptions import UnRecognizedDatabaseTypeException +from connectors import DatabaseTypes +from connectors import UnRecognizedDatabaseTypeException _LOGGER = logging.getLogger(__name__) @@ -27,7 +27,7 @@ def get_db_handler(cls, database_type: DatabaseTypes): """ if database_type == DatabaseTypes.MYSQL: - from numalogic.connectors.rds.db.mysql_fetcher import MysqlFetcher + from connectors import MysqlFetcher return MysqlFetcher diff --git a/numalogic/connectors/rds/db/mysql_fetcher.py b/src/nlconnectors/connectors/rds/db/mysql_fetcher.py similarity index 100% rename from numalogic/connectors/rds/db/mysql_fetcher.py rename to src/nlconnectors/connectors/rds/db/mysql_fetcher.py diff --git a/numalogic/connectors/redis.py b/src/nlconnectors/connectors/redis.py similarity index 98% rename from numalogic/connectors/redis.py rename to src/nlconnectors/connectors/redis.py index 51d6f5af..60c10e87 100644 --- a/numalogic/connectors/redis.py +++ b/src/nlconnectors/connectors/redis.py @@ -2,7 +2,7 @@ import os from typing import Optional -from numalogic.connectors._config import RedisConf +from connectors import RedisConf from numalogic.tools.exceptions import EnvVarNotFoundError, ConfigNotFoundError from numalogic.tools.types import redis_client_t from redis.backoff import ExponentialBackoff diff --git a/numalogic/models/forecast/__init__.py b/src/nlconnectors/connectors/utils/__init__.py similarity index 100% rename from numalogic/models/forecast/__init__.py rename to src/nlconnectors/connectors/utils/__init__.py diff --git a/numalogic/tools/__init__.py b/src/nlconnectors/connectors/utils/aws/__init__.py similarity index 100% rename from numalogic/tools/__init__.py rename to src/nlconnectors/connectors/utils/aws/__init__.py diff --git a/numalogic/connectors/utils/aws/boto3_client_manager.py b/src/nlconnectors/connectors/utils/aws/boto3_client_manager.py similarity index 100% rename from numalogic/connectors/utils/aws/boto3_client_manager.py rename to src/nlconnectors/connectors/utils/aws/boto3_client_manager.py diff --git a/numalogic/connectors/utils/aws/config.py b/src/nlconnectors/connectors/utils/aws/config.py similarity index 98% rename from numalogic/connectors/utils/aws/config.py rename to src/nlconnectors/connectors/utils/aws/config.py index 9bfa4313..2475be5e 100644 --- a/numalogic/connectors/utils/aws/config.py +++ b/src/nlconnectors/connectors/utils/aws/config.py @@ -1,7 +1,7 @@ from dataclasses import dataclass, field from typing import Optional -from numalogic.connectors.utils.enum import BaseEnum +from connectors import BaseEnum class DatabaseServiceProvider(str, BaseEnum): diff --git a/numalogic/connectors/utils/aws/db_configurations.py b/src/nlconnectors/connectors/utils/aws/db_configurations.py similarity index 100% rename from numalogic/connectors/utils/aws/db_configurations.py rename to src/nlconnectors/connectors/utils/aws/db_configurations.py diff --git a/numalogic/connectors/utils/aws/exceptions.py b/src/nlconnectors/connectors/utils/aws/exceptions.py similarity index 100% rename from numalogic/connectors/utils/aws/exceptions.py rename to src/nlconnectors/connectors/utils/aws/exceptions.py diff --git a/numalogic/connectors/utils/aws/sts_client_manager.py b/src/nlconnectors/connectors/utils/aws/sts_client_manager.py similarity index 100% rename from numalogic/connectors/utils/aws/sts_client_manager.py rename to src/nlconnectors/connectors/utils/aws/sts_client_manager.py diff --git a/numalogic/connectors/utils/enum.py b/src/nlconnectors/connectors/utils/enum.py similarity index 100% rename from numalogic/connectors/utils/enum.py rename to src/nlconnectors/connectors/utils/enum.py diff --git a/numalogic/registry/__init__.py b/src/registry/__init__.py similarity index 70% rename from numalogic/registry/__init__.py rename to src/registry/__init__.py index 6f227fd5..f7d349ba 100644 --- a/numalogic/registry/__init__.py +++ b/src/registry/__init__.py @@ -10,29 +10,28 @@ # limitations under the License. -from numalogic.registry.artifact import ArtifactManager, ArtifactData, ArtifactCache -from numalogic.registry.localcache import LocalLRUCache +from registry import LocalLRUCache __all__ = ["ArtifactManager", "ArtifactData", "ArtifactCache", "LocalLRUCache"] try: - from numalogic.registry.mlflow_registry import MLflowRegistry # noqa: F401 + from registry import MLflowRegistry # noqa: F401 except ImportError: pass else: __all__.append("MLflowRegistry") try: - from numalogic.registry.redis_registry import RedisRegistry # noqa: F401 + from registry import RedisRegistry # noqa: F401 except ImportError: pass else: __all__.append("RedisRegistry") try: - from numalogic.registry.dynamodb_registry import DynamoDBRegistry # noqa: F401 + from registry import DynamoDBRegistry # noqa: F401 except ImportError: pass else: diff --git a/src/registry/__setup__.py b/src/registry/__setup__.py new file mode 100644 index 00000000..4e1e493c --- /dev/null +++ b/src/registry/__setup__.py @@ -0,0 +1,20 @@ +from setuptools import find_packages + + +def setup_args(): + return dict( + name="numalogic-test_registry", + version="0.10.a2", + packages=find_packages( + where="src", + include=["registry", "registry.*"], + exclude=["tests"], + ), + package_dir={"": "src"}, + install_requires=[ + "numalogic", + "redis[hiredis]>=4.0", + "mlflow-skinny>2.0", + "boto3>=1.20", + ], + ) diff --git a/numalogic/registry/_serialize.py b/src/registry/_serialize.py similarity index 100% rename from numalogic/registry/_serialize.py rename to src/registry/_serialize.py diff --git a/numalogic/registry/artifact.py b/src/registry/artifact.py similarity index 96% rename from numalogic/registry/artifact.py rename to src/registry/artifact.py index 6dedfa38..9fe5d2a1 100644 --- a/numalogic/registry/artifact.py +++ b/src/registry/artifact.py @@ -48,7 +48,7 @@ class ArtifactManager(Generic[KEYS, A_D]): uri: server/connection uri """ - _STORETYPE = "registry" + _STORETYPE = "test_registry" __slots__ = ("uri",) @@ -58,7 +58,7 @@ def __init__(self, uri: str): def load( self, skeys: KEYS, dkeys: KEYS, latest: bool = True, version: Optional[str] = None ) -> ArtifactData: - """Loads the desired artifact from mlflow registry and returns it. + """Loads the desired artifact from mlflow test_registry and returns it. Args: ---- @@ -76,7 +76,7 @@ def save( artifact: Union[artifact_t, state_dict_t], **metadata: META_VT ) -> Any: - r"""Saves the artifact into mlflow registry and updates version. + r"""Saves the artifact into mlflow test_registry and updates version. Args: ---- @@ -88,7 +88,7 @@ def save( raise NotImplementedError("Please implement this method!") def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: - """Deletes the artifact with a specified version from mlflow registry. + """Deletes the artifact with a specified version from mlflow test_registry. Args: ---- diff --git a/numalogic/registry/dynamodb_registry.py b/src/registry/dynamodb_registry.py similarity index 97% rename from numalogic/registry/dynamodb_registry.py rename to src/registry/dynamodb_registry.py index 3ee1d49f..20a996f2 100644 --- a/numalogic/registry/dynamodb_registry.py +++ b/src/registry/dynamodb_registry.py @@ -17,10 +17,10 @@ import boto3 from botocore.exceptions import ClientError -from numalogic.registry import ArtifactManager, ArtifactCache, ArtifactData +from registry import ArtifactManager, ArtifactCache, ArtifactData from numalogic.tools.exceptions import DynamoDBRegistryError from numalogic.tools.types import KEYS, artifact_t, META_VT -from numalogic.registry._serialize import loads, dumps +from registry import loads, dumps _LOGGER = logging.getLogger(__name__) @@ -85,13 +85,13 @@ class DynamoDBRegistry(ArtifactManager): Examples -------- >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.registry import DynamoDBRegistry + >>> from numalogic.test_registry import DynamoDBRegistry >>> ... - >>> registry = DynamoDBRegistry(table="mytable", role="arn:aws:iam::1234567890:role/role-name") + >>> test_registry = DynamoDBRegistry(table="mytable", role="arn:aws:iam::1234567890:role/role-name") >>> skeys, dkeys = ("mymetric", "ae"), ("vanilla", "seq10") >>> model = VanillaAE(seq_len=10) - >>> registry.save(skeys, dkeys, artifact=model, **{'lr': 0.01}) - >>> loaded_artifact = registry.load(skeys, dkeys). + >>> test_registry.save(skeys, dkeys, artifact=model, **{'lr': 0.01}) + >>> loaded_artifact = test_registry.load(skeys, dkeys). """ __slots__ = ("table_name", "role", "connector", "models_to_retain", "cache_registry") diff --git a/numalogic/registry/localcache.py b/src/registry/localcache.py similarity index 95% rename from numalogic/registry/localcache.py rename to src/registry/localcache.py index a69187bd..14af55fc 100644 --- a/numalogic/registry/localcache.py +++ b/src/registry/localcache.py @@ -14,12 +14,12 @@ from typing import Optional from cachetools import TTLCache -from numalogic.registry.artifact import ArtifactCache, ArtifactData +from registry import ArtifactCache, ArtifactData from numalogic.tools.types import Singleton class LocalLRUCache(ArtifactCache, metaclass=Singleton): - r"""A local in-memory LRU cache registry with per artifact Time-to-live support. + r"""A local in-memory LRU cache test_registry with per artifact Time-to-live support. Args: ---- diff --git a/numalogic/registry/mlflow_registry.py b/src/registry/mlflow_registry.py similarity index 95% rename from numalogic/registry/mlflow_registry.py rename to src/registry/mlflow_registry.py index b9909881..bd9360a5 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/src/registry/mlflow_registry.py @@ -24,8 +24,8 @@ from sklearn.base import BaseEstimator from torch import nn -from numalogic.registry import ArtifactManager, ArtifactData -from numalogic.registry.artifact import ArtifactCache +from registry import ArtifactManager, ArtifactData +from registry import ArtifactCache from numalogic.tools.exceptions import ModelVersionError from numalogic.tools.types import artifact_t, KEYS, META_VT @@ -59,14 +59,14 @@ class MLflowRegistry(ArtifactManager): Examples -------- >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.registry import MLflowRegistry + >>> from numalogic.test_registry import MLflowRegistry >>> from sklearn.preprocessing import StandardScaler >>> >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] >>> scaler = StandardScaler.fit(data) - >>> registry = MLflowRegistry(tracking_uri="http://0.0.0.0:8080") - >>> registry.save(skeys=["model"], dkeys=["AE"], artifact=VanillaAE(10)) - >>> artifact_data = registry.load(skeys=["model"], dkeys=["AE"], artifact_type="pytorch") + >>> test_registry = MLflowRegistry(tracking_uri="http://0.0.0.0:8080") + >>> test_registry.save(skeys=["model"], dkeys=["AE"], artifact=VanillaAE(10)) + >>> artifact_data = test_registry.load(skeys=["model"], dkeys=["AE"], artifact_type="pytorch") """ __slots__ = ("client", "models_to_retain", "model_stage", "cache_registry") @@ -141,7 +141,7 @@ def load( version: Optional[str] = None, artifact_type: str = "pytorch", ) -> Optional[ArtifactData]: - """Load the artifact from the registry. The artifact is loaded from the cache if available. + """Load the artifact from the test_registry. The artifact is loaded from the cache if available. Args: ---- @@ -207,7 +207,7 @@ def save( run_id: Optional[str] = None, **metadata: META_VT, ) -> Optional[ModelVersion]: - """Saves the artifact into mlflow registry and updates version. + """Saves the artifact into mlflow test_registry and updates version. Args: ---- @@ -254,7 +254,7 @@ def is_artifact_stale(artifact_data: ArtifactData, freq_hr: int) -> bool: return date_updated < stale_date def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: - """Deletes the artifact with a specified version from mlflow registry. + """Deletes the artifact with a specified version from mlflow test_registry. Args: ---- diff --git a/numalogic/registry/redis_registry.py b/src/registry/redis_registry.py similarity index 93% rename from numalogic/registry/redis_registry.py rename to src/registry/redis_registry.py index 234233ac..d221fe1e 100644 --- a/numalogic/registry/redis_registry.py +++ b/src/registry/redis_registry.py @@ -18,8 +18,8 @@ from redis.exceptions import RedisError -from numalogic.registry.artifact import ArtifactManager, ArtifactData, ArtifactCache, _apply_jitter -from numalogic.registry._serialize import loads, dumps +from registry import ArtifactManager, ArtifactData, ArtifactCache, _apply_jitter +from registry import loads, dumps from numalogic.tools.exceptions import ModelKeyNotFound, RedisRegistryError from numalogic.tools.types import artifact_t, redis_client_t, KEYS, META_T, META_VT, KeyedArtifact @@ -36,22 +36,22 @@ class RedisRegistry(ArtifactManager): jitter_sec: Jitter (in secs) added to model timestamp information to solve Thundering Herd problem (default = 30 mins) jitter_steps_sec: Step interval value (in sec) for jitter_sec value (default = 120 secs) - cache_registry: Cache registry to use (default = None). - transactional: Flag to indicate if the registry should be transactional or + cache_registry: Cache test_registry to use (default = None). + transactional: Flag to indicate if the test_registry should be transactional or not (default = False). Examples -------- >>> import redis >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.registry.redis_registry import RedisRegistry + >>> from numalogic.test_registry.redis_registry import RedisRegistry >>> ... >>> r = redis.Redis(host='127.0.0.1', port=6379) - >>> registry = RedisRegistry(client=r) + >>> test_registry = RedisRegistry(client=r) >>> skeys, dkeys = ("mymetric", "ae"), ("vanilla", "seq10") >>> model = VanillaAE(seq_len=10) - >>> registry.save(skeys, dkeys, artifact=model, **{'lr': 0.01}) - >>> loaded_artifact = registry.load(skeys, dkeys) + >>> test_registry.save(skeys, dkeys, artifact=model, **{'lr': 0.01}) + >>> loaded_artifact = test_registry.load(skeys, dkeys) """ __slots__ = ( @@ -159,7 +159,7 @@ def __get_artifact_data( def __load_latest_artifact(self, key: str) -> tuple[ArtifactData, bool]: """ - Load the latest artifact from the registry. + Load the latest artifact from the test_registry. Args: key: full model key. @@ -170,7 +170,7 @@ def __load_latest_artifact(self, key: str) -> tuple[ArtifactData, bool]: Raises ------ - ModelKeyNotFound: If the model key is not found in the registry. + ModelKeyNotFound: If the model key is not found in the test_registry. """ latest_key = self.__construct_latest_key(key) cached_artifact = self._load_from_cache(latest_key) @@ -229,10 +229,10 @@ def load( latest: bool = True, version: Optional[str] = None, ) -> Optional[ArtifactData]: - """Loads the artifact from redis registry. Either latest or version (one of the arguments) + """Loads the artifact from redis test_registry. Either latest or version (one of the arguments) is needed to load the respective artifact. - If cache registry is provided, it will first check the cache registry for the artifact. + If cache test_registry is provided, it will first check the cache test_registry for the artifact. If latest is passed, latest key is saved otherwise version call saves the respective version artifact in cache. @@ -286,7 +286,7 @@ def save( _pipe: Optional[redis.client.Pipeline] = None, **metadata: META_VT, ) -> Optional[str]: - """Saves the artifact into redis registry and updates version. + """Saves the artifact into redis test_registry and updates version. Args: ---- @@ -328,7 +328,7 @@ def save( return str(version) def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: - """Deletes the model version from registry. + """Deletes the model version from test_registry. Args: ---- @@ -338,7 +338,7 @@ def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: Raises ------ - ModelKeyNotFound: If the model version is not found in registry. + ModelKeyNotFound: If the model version is not found in test_registry. RedisRegistryError: If there is any RedisError while deleting the artifact. """ key = self.construct_key(skeys, dkeys) @@ -403,7 +403,7 @@ def save_multiple( **metadata: META_VT, ): """ - Saves multiple artifacts into redis registry. The last save stores all the + Saves multiple artifacts into redis test_registry. The last save stores all the artifact versions in the metadata. Args: diff --git a/Dockerfile b/src/udfs/Dockerfile similarity index 99% rename from Dockerfile rename to src/udfs/Dockerfile index 6c559028..ac0f352a 100644 --- a/Dockerfile +++ b/src/udfs/Dockerfile @@ -46,7 +46,7 @@ ENV VIRTUAL_ENV=/app/.venv COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} ENV PATH="$VIRTUAL_ENV/bin:$PATH" -COPY . /app +COPY ../.. /app WORKDIR /app ENTRYPOINT ["/usr/bin/dumb-init", "--"] diff --git a/numalogic/udfs/README.md b/src/udfs/README.md similarity index 100% rename from numalogic/udfs/README.md rename to src/udfs/README.md diff --git a/numalogic/udfs/__init__.py b/src/udfs/__init__.py similarity index 100% rename from numalogic/udfs/__init__.py rename to src/udfs/__init__.py diff --git a/numalogic/udfs/__main__.py b/src/udfs/__main__.py similarity index 96% rename from numalogic/udfs/__main__.py rename to src/udfs/__main__.py index 51e6ad78..f03fb20a 100644 --- a/numalogic/udfs/__main__.py +++ b/src/udfs/__main__.py @@ -4,7 +4,7 @@ from typing import Final from numalogic._constants import DEFAULT_BASE_CONF_PATH, DEFAULT_APP_CONF_PATH, DEFAULT_METRICS_PORT -from numalogic.connectors.redis import get_redis_client_from_conf +from connectors import get_redis_client_from_conf from numalogic.monitoring import start_metrics_server from numalogic.udfs import load_pipeline_conf, UDFFactory, ServerFactory, set_logger diff --git a/numalogic/udfs/_base.py b/src/udfs/_base.py similarity index 100% rename from numalogic/udfs/_base.py rename to src/udfs/_base.py diff --git a/numalogic/udfs/_config.py b/src/udfs/_config.py similarity index 98% rename from numalogic/udfs/_config.py rename to src/udfs/_config.py index d4a4d3f1..989e84ce 100644 --- a/numalogic/udfs/_config.py +++ b/src/udfs/_config.py @@ -6,7 +6,7 @@ from numalogic.config import NumalogicConf, RegistryInfo -from numalogic.connectors import ( +from connectors import ( ConnectorType, RedisConf, PrometheusConf, @@ -66,7 +66,7 @@ class PipelineConf: stream_confs (Dict[str, StreamConf]): Dictionary of stream_confs associated with pipeline. redis_conf (Optional[RedisConf]): The configuration for Redis. - registry_conf (Optional[RegistryInfo]): The configuration for the registry. + registry_conf (Optional[RegistryInfo]): The configuration for the test_registry. prometheus_conf (Optional[PrometheusConf]): The configuration for Prometheus. druid_conf (Optional[DruidConf]): The configuration for Druid. rds_conf (Optional[RDSConf]): The configuration for RDS. diff --git a/numalogic/udfs/_logger.py b/src/udfs/_logger.py similarity index 100% rename from numalogic/udfs/_logger.py rename to src/udfs/_logger.py diff --git a/numalogic/udfs/_metrics.py b/src/udfs/_metrics.py similarity index 100% rename from numalogic/udfs/_metrics.py rename to src/udfs/_metrics.py diff --git a/numalogic/udfs/entities.py b/src/udfs/entities.py similarity index 100% rename from numalogic/udfs/entities.py rename to src/udfs/entities.py diff --git a/numalogic/udfs/factory.py b/src/udfs/factory.py similarity index 98% rename from numalogic/udfs/factory.py rename to src/udfs/factory.py index 93c0f785..4d7c4a3b 100644 --- a/numalogic/udfs/factory.py +++ b/src/udfs/factory.py @@ -20,7 +20,7 @@ class UDFFactory: """Factory class to fetch the right UDF.""" from numalogic.udfs import NumalogicUDF - from numalogic.udfs.payloadtx import PayloadTransformer + from udfs.payloadtx import PayloadTransformer from numalogic.udfs.inference import InferenceUDF from numalogic.udfs.postprocess import PostprocessUDF from numalogic.udfs.preprocess import PreprocessUDF diff --git a/numalogic/udfs/inference.py b/src/udfs/inference.py similarity index 97% rename from numalogic/udfs/inference.py rename to src/udfs/inference.py index 0f19d7b8..ba1ff807 100644 --- a/numalogic/udfs/inference.py +++ b/src/udfs/inference.py @@ -10,7 +10,7 @@ from pynumaflow.mapper import Messages, Datum, Message from numalogic.config import RegistryFactory -from numalogic.registry import LocalLRUCache, ArtifactData +from registry import LocalLRUCache, ArtifactData from numalogic.tools.types import artifact_t, redis_client_t from numalogic.udfs._base import NumalogicUDF from numalogic.udfs._config import PipelineConf @@ -203,8 +203,8 @@ def is_model_stale(self, artifact_data: ArtifactData, payload: StreamPayload) -> config_id=payload.config_id, pipeline_id=payload.pipeline_id ) if artifact_data.extras.get( - "source", "registry" - ) == "registry" and self.model_registry.is_artifact_stale( + "source", "test_registry" + ) == "test_registry" and self.model_registry.is_artifact_stale( artifact_data, _conf.numalogic_conf.trainer.retrain_freq_hr ): return True diff --git a/numalogic/udfs/payloadtx.py b/src/udfs/payloadtx.py similarity index 100% rename from numalogic/udfs/payloadtx.py rename to src/udfs/payloadtx.py diff --git a/numalogic/udfs/postprocess.py b/src/udfs/postprocess.py similarity index 99% rename from numalogic/udfs/postprocess.py rename to src/udfs/postprocess.py index 540c503a..ed86f5b9 100644 --- a/numalogic/udfs/postprocess.py +++ b/src/udfs/postprocess.py @@ -16,7 +16,7 @@ AggregatorConf, ) from numalogic.models.threshold import SigmoidThreshold -from numalogic.registry import LocalLRUCache +from registry import LocalLRUCache from numalogic.tools.aggregators import aggregate_window, aggregate_features from numalogic.tools.types import redis_client_t, artifact_t from numalogic.udfs import NumalogicUDF diff --git a/numalogic/udfs/preprocess.py b/src/udfs/preprocess.py similarity index 98% rename from numalogic/udfs/preprocess.py rename to src/udfs/preprocess.py index d91de0ba..64f55ea5 100644 --- a/numalogic/udfs/preprocess.py +++ b/src/udfs/preprocess.py @@ -22,7 +22,7 @@ UDF_TIME, _increment_counter, ) -from numalogic.registry import LocalLRUCache +from registry import LocalLRUCache from numalogic.tools.types import redis_client_t, artifact_t from numalogic.udfs import NumalogicUDF from numalogic.udfs._config import PipelineConf @@ -148,7 +148,7 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: # Make StreamPayload object payload = make_stream_payload(data_payload, raw_df, timestamps, keys) - # Check if model will be present in registry + # Check if model will be present in test_registry if any(_cfg.stateful for _cfg in _conf.numalogic_conf.preprocess): preproc_artifact, payload = _load_artifact( skeys=[ @@ -173,7 +173,7 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: msgs.append(get_static_thresh_message(keys, payload)) logger.exception("Artifact model not loaded!") return msgs - # Model will not be in registry + # Model will not be in test_registry else: # Load configuration for the config_id _increment_counter(SOURCE_COUNTER, labels=("config", *_metric_label_values)) diff --git a/numalogic/udfs/staticthresh.py b/src/udfs/staticthresh.py similarity index 100% rename from numalogic/udfs/staticthresh.py rename to src/udfs/staticthresh.py diff --git a/numalogic/udfs/tools.py b/src/udfs/tools.py similarity index 98% rename from numalogic/udfs/tools.py rename to src/udfs/tools.py index 83075f59..3734c910 100644 --- a/numalogic/udfs/tools.py +++ b/src/udfs/tools.py @@ -8,7 +8,7 @@ from pandas import DataFrame from pynumaflow.mapper import Message -from numalogic.registry import ArtifactManager, ArtifactData +from registry import ArtifactManager, ArtifactData from numalogic.tools.exceptions import RedisRegistryError from numalogic.tools.types import KEYS, redis_client_t from numalogic.udfs._config import StreamConf @@ -151,7 +151,7 @@ def _load_artifact( skeys: KEYS dkeys: KEYS payload: payload dictionary - model_registry: registry where model is stored. + model_registry: test_registry where model is stored. Returns ------- @@ -183,7 +183,7 @@ def _load_artifact( else: logger.debug( "No version info passed on! Loading latest artifact version, " - "if one present in the registry" + "if one present in the test_registry" ) load_latest = True try: diff --git a/numalogic/udfs/trainer/__init__.py b/src/udfs/trainer/__init__.py similarity index 100% rename from numalogic/udfs/trainer/__init__.py rename to src/udfs/trainer/__init__.py diff --git a/numalogic/udfs/trainer/_base.py b/src/udfs/trainer/_base.py similarity index 99% rename from numalogic/udfs/trainer/_base.py rename to src/udfs/trainer/_base.py index cfeb25a0..bba37aec 100644 --- a/numalogic/udfs/trainer/_base.py +++ b/src/udfs/trainer/_base.py @@ -319,7 +319,7 @@ def artifacts_to_save( _______ skeys: list keys dict_artifacts: artifact_tuple which has dkeys and artifact as fields - model_registry: registry that supports multiple_save + model_registry: test_registry that supports multiple_save payload: payload. Returns diff --git a/numalogic/udfs/trainer/_druid.py b/src/udfs/trainer/_druid.py similarity index 99% rename from numalogic/udfs/trainer/_druid.py rename to src/udfs/trainer/_druid.py index 7a348bae..815372e4 100644 --- a/numalogic/udfs/trainer/_druid.py +++ b/src/udfs/trainer/_druid.py @@ -4,7 +4,7 @@ import pandas as pd from numalogic.config.factory import ConnectorFactory -from numalogic.connectors import DruidFetcherConf +from connectors import DruidFetcherConf from numalogic.tools.exceptions import ConfigNotFoundError, DruidFetcherError from numalogic.tools.types import redis_client_t from numalogic.udfs._config import PipelineConf diff --git a/numalogic/udfs/trainer/_prom.py b/src/udfs/trainer/_prom.py similarity index 100% rename from numalogic/udfs/trainer/_prom.py rename to src/udfs/trainer/_prom.py diff --git a/numalogic/udfs/trainer/_rds.py b/src/udfs/trainer/_rds.py similarity index 100% rename from numalogic/udfs/trainer/_rds.py rename to src/udfs/trainer/_rds.py diff --git a/tests/blocks/test_blocks.py b/tests/blocks/test_blocks.py deleted file mode 100644 index e7027cfc..00000000 --- a/tests/blocks/test_blocks.py +++ /dev/null @@ -1,32 +0,0 @@ -import unittest - -import numpy as np - -from numalogic.blocks import Block -from sklearn.ensemble import IsolationForest - - -class DummyBlock(Block): - def fit(self, input_: np.ndarray, **__) -> np.ndarray: - return self._artifact.fit_predict(input_).reshape(-1, 1) - - def run(self, input_: np.ndarray, **__) -> np.ndarray: - return self._artifact.predict(input_).reshape(-1, 1) - - -class TestBlock(unittest.TestCase): - def test_random_block(self): - block = DummyBlock(IsolationForest(), name="isolation_forest") - self.assertEqual(block.name, "isolation_forest") - - block.fit(np.arange(100).reshape(-1, 2)) - out = block(np.arange(10).reshape(-1, 2)) - self.assertEqual(out.shape, (5, 1)) - - self.assertIsInstance(block.artifact, IsolationForest) - self.assertIsInstance(block.artifact_state, IsolationForest) - self.assertTrue(block.stateful) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/blocks/test_pipeline.py b/tests/blocks/test_pipeline.py deleted file mode 100644 index 9bfe2684..00000000 --- a/tests/blocks/test_pipeline.py +++ /dev/null @@ -1,212 +0,0 @@ -import os -import unittest - -import pandas as pd -import torch -from fakeredis import FakeRedis, FakeServer -from pympler import asizeof -from sklearn.preprocessing import StandardScaler - -from numalogic._constants import TESTS_DIR -from numalogic.blocks import ( - BlockPipeline, - PreprocessBlock, - NNBlock, - PostprocessBlock, - ThresholdBlock, -) -from numalogic.models.autoencoder.variants import ( - VanillaAE, - LSTMAE, - Conv1dAE, - TransformerAE, - SparseVanillaAE, - SparseConv1dAE, -) -from numalogic.models.threshold import StdDevThreshold -from numalogic.registry import RedisRegistry -from numalogic.transforms import TanhScaler, TanhNorm, LogTransformer - -ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") -DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") -server = FakeServer() -SEQ_LEN = 10 - - -class TestBlockPipeline(unittest.TestCase): - x_train = None - x_stream = None - - @classmethod - def setUpClass(cls) -> None: - df = pd.read_csv(DATA_FILE, nrows=1000) - df = df[["success", "failure"]] - cls.x_train = df[:990].to_numpy() - cls.x_stream = df[-10:].to_numpy() - assert cls.x_train.shape == (990, 2) - assert cls.x_stream.shape == (10, 2) - - def setUp(self) -> None: - self.reg = RedisRegistry(client=FakeRedis(server=server)) - - def test_pipeline_01(self): - block_pl = BlockPipeline( - PreprocessBlock(TanhScaler()), - NNBlock(VanillaAE(SEQ_LEN, n_features=2), SEQ_LEN), - ThresholdBlock(StdDevThreshold()), - PostprocessBlock(TanhNorm()), - registry=self.reg, - ) - block_pl.fit(self.x_train, nn__max_epochs=1) - out = block_pl(self.x_stream) - - self.assertTupleEqual(self.x_stream.shape, out.shape) - self.assertEqual(4, len(block_pl)) - self.assertIsInstance(block_pl[1], NNBlock) - - def test_pipeline_02(self): - block_pl = BlockPipeline( - PreprocessBlock(StandardScaler()), - NNBlock(LSTMAE(SEQ_LEN, no_features=2, embedding_dim=4), SEQ_LEN), - PostprocessBlock(TanhNorm()), - registry=self.reg, - ) - block_pl.fit( - self.x_train, - nn__max_epochs=1, - nn__accelerator="cpu", - ) - out = block_pl.run(self.x_stream) - self.assertTupleEqual(self.x_stream.shape, out.shape) - - def test_pipeline_03(self): - block_pl = BlockPipeline( - PreprocessBlock(LogTransformer(), stateful=False), - NNBlock(Conv1dAE(SEQ_LEN, in_channels=2), SEQ_LEN), - registry=self.reg, - ) - block_pl.fit( - self.x_train, - nn__max_epochs=1, - nn__accelerator="cpu", - ) - out = block_pl.run(self.x_stream) - self.assertTupleEqual(self.x_stream.shape, out.shape) - - def test_pipeline_04(self): - block_pl = BlockPipeline( - PreprocessBlock(StandardScaler()), - NNBlock(TransformerAE(SEQ_LEN, n_features=2), SEQ_LEN), - ThresholdBlock(StdDevThreshold()), - registry=self.reg, - ) - block_pl.fit( - self.x_train, - nn__max_epochs=1, - nn__accelerator="cpu", - ) - out = block_pl.run(self.x_stream) - self.assertTupleEqual(self.x_stream.shape, out.shape) - for block in block_pl: - self.assertTrue(block.stateful) - - def test_pipeline_05(self): - block_pl = BlockPipeline( - PreprocessBlock(LogTransformer(), stateful=False), - NNBlock(SparseVanillaAE(seq_len=SEQ_LEN, n_features=2), SEQ_LEN), - PostprocessBlock(TanhNorm()), - registry=self.reg, - ) - block_pl.fit( - self.x_train, - nn__max_epochs=1, - ) - out = block_pl.run(self.x_stream) - self.assertTupleEqual(self.x_stream.shape, out.shape) - - def test_pipeline_persistence(self): - skeys = ["test"] - dkeys = ["pipeline"] - # Pipeline for saving - pl_1 = BlockPipeline( - PreprocessBlock(TanhScaler()), - NNBlock(SparseConv1dAE(seq_len=SEQ_LEN, in_channels=2), SEQ_LEN), - ThresholdBlock(StdDevThreshold()), - PostprocessBlock(TanhNorm()), - registry=self.reg, - ) - pl_1.fit( - self.x_train, - nn__accelerator="cpu", - nn__max_epochs=1, - ) - - _preweights = [] - with torch.no_grad(): - for params in pl_1[1].artifact.parameters(): - _preweights.append(torch.mean(params)) - - pl_1.save(skeys, dkeys) - - # Pipeline for loading - pl_2 = BlockPipeline( - PreprocessBlock(TanhScaler()), - NNBlock(SparseConv1dAE(seq_len=SEQ_LEN, in_channels=2), SEQ_LEN), - PostprocessBlock(TanhNorm()), - registry=self.reg, - ) - pl_2.load(skeys, dkeys) - - _postweights = [] - with torch.no_grad(): - for params in pl_2[1].artifact.parameters(): - _postweights.append(torch.mean(params)) - - self.assertListEqual(_preweights, _postweights) - out = pl_2(self.x_stream) - self.assertTupleEqual(self.x_stream.shape, out.shape) - - def test_pipeline_save_err(self): - block_pl = BlockPipeline( - PreprocessBlock(TanhScaler()), - NNBlock(VanillaAE(SEQ_LEN, n_features=2), SEQ_LEN), - PostprocessBlock(TanhNorm()), - ) - block_pl.fit(self.x_train, nn__max_epochs=1) - self.assertRaises(ValueError, block_pl.save, ["ml"], ["pl"]) - self.assertRaises(ValueError, block_pl.load, ["ml"], ["pl"]) - - def test_pipeline_fit_err(self): - block_pl = BlockPipeline( - PreprocessBlock(TanhScaler()), - NNBlock(VanillaAE(SEQ_LEN, n_features=2), SEQ_LEN), - PostprocessBlock(TanhNorm()), - ) - self.assertRaises(ValueError, block_pl.fit, self.x_train, max_epochs=1) - - @unittest.skip("Just for testing memory usage") - def test_memory_usage(self): - model = SparseConv1dAE(seq_len=SEQ_LEN, in_channels=2) - block_nn = NNBlock(model, SEQ_LEN) - block_pre = PreprocessBlock(TanhScaler()) - block_post = PostprocessBlock(TanhNorm()) - block_th = ThresholdBlock(StdDevThreshold()) - - pl = BlockPipeline( - block_pre, - block_nn, - block_th, - block_post, - registry=self.reg, - ) - - print(asizeof.asizeof(model) / 1024) - print(asizeof.asizeof(block_nn) / 1024) - print(asizeof.asizeof(block_pre) / 1024) - print(asizeof.asizeof(block_post) / 1024) - print(asizeof.asizeof(block_th) / 1024) - print(asizeof.asizeof(pl) / 1024) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/connectors/test_config.py b/tests/connectors/test_config.py deleted file mode 100644 index da592101..00000000 --- a/tests/connectors/test_config.py +++ /dev/null @@ -1,10 +0,0 @@ -import pytest -from numalogic.connectors.exceptions import RDSFetcherConfValidationException -from numalogic.connectors._config import RDSFetcherConf - - -def test_RDSFetcherConf_post_init_exception(): - with pytest.raises(RDSFetcherConfValidationException): - RDSFetcherConf( - dimensions=[], metrics=[], datasource="test", hash_query_type=True, hash_column_name="" - ) diff --git a/tests/__init__.py b/tests/test_connectors/__init__.py similarity index 100% rename from tests/__init__.py rename to tests/test_connectors/__init__.py diff --git a/tests/connectors/data.csv b/tests/test_connectors/data.csv similarity index 100% rename from tests/connectors/data.csv rename to tests/test_connectors/data.csv diff --git a/tests/blocks/__init__.py b/tests/test_connectors/rds/__init__.py similarity index 100% rename from tests/blocks/__init__.py rename to tests/test_connectors/rds/__init__.py diff --git a/tests/config/__init__.py b/tests/test_connectors/rds/db/__init__.py similarity index 100% rename from tests/config/__init__.py rename to tests/test_connectors/rds/db/__init__.py diff --git a/tests/connectors/rds/db/test_factory.py b/tests/test_connectors/rds/db/test_factory.py similarity index 66% rename from tests/connectors/rds/db/test_factory.py rename to tests/test_connectors/rds/db/test_factory.py index 24ee119e..d292f6a0 100644 --- a/tests/connectors/rds/db/test_factory.py +++ b/tests/test_connectors/rds/db/test_factory.py @@ -1,7 +1,7 @@ import pytest -from numalogic.connectors.rds.db.factory import RdsFactory -from numalogic.connectors.rds.db.mysql_fetcher import MysqlFetcher -from numalogic.connectors.utils.aws.exceptions import UnRecognizedDatabaseTypeException +from connectors import RdsFactory +from connectors import MysqlFetcher +from connectors import UnRecognizedDatabaseTypeException def test_get_db_handler_with_supported_db_type(): diff --git a/tests/connectors/rds/db/test_mysql_fetcher.py b/tests/test_connectors/rds/db/test_mysql_fetcher.py similarity index 94% rename from tests/connectors/rds/db/test_mysql_fetcher.py rename to tests/test_connectors/rds/db/test_mysql_fetcher.py index 920c9906..09be5d7c 100644 --- a/tests/connectors/rds/db/test_mysql_fetcher.py +++ b/tests/test_connectors/rds/db/test_mysql_fetcher.py @@ -1,13 +1,13 @@ import pymysql import pytest from unittest.mock import Mock, patch, MagicMock -from numalogic.connectors.utils.aws.config import DatabaseTypes, RDSConnectionConfig -from numalogic.connectors.rds.db.mysql_fetcher import MysqlFetcher +from connectors import DatabaseTypes, RDSConfig +from connectors import MysqlFetcher @pytest.fixture def mock_db_config(): - db_config = RDSConnectionConfig( + db_config = RDSConfig( endpoint="localhost", port=3306, database_username="username", @@ -35,7 +35,7 @@ def mock_db_config(): @pytest.fixture def mock_db_config_ssl_disabled(): - db_config = RDSConnectionConfig( + db_config = RDSConfig( endpoint="localhost", port=3306, database_username="username", @@ -58,7 +58,7 @@ def mock_db_config_ssl_disabled(): @pytest.fixture def setup_fetcher(): - rds_config = Mock() + rds_config = Mock() # Assuming that your RDSConfig class behaves like a normal python object kwargs = {"key": "value"} return MysqlFetcher(rds_config, **kwargs) diff --git a/tests/connectors/rds/test_base.py b/tests/test_connectors/rds/test_base.py similarity index 95% rename from tests/connectors/rds/test_base.py rename to tests/test_connectors/rds/test_base.py index cf4fe1e8..89010db8 100644 --- a/tests/connectors/rds/test_base.py +++ b/tests/test_connectors/rds/test_base.py @@ -4,18 +4,13 @@ import pytest from pandas import DataFrame, to_datetime -from numalogic.connectors._config import Pivot -from numalogic.connectors.rds._base import ( - RDSBase, - RDSConnectionConfig, - Boto3ClientManager, - format_dataframe, -) +from connectors import Pivot +from connectors import RDSBase, RDSConfig, Boto3ClientManager, format_dataframe mock_rds_base = create_autospec(RDSBase) -# Mock RDSConnectionConfig for testing +# Mock RDSConfig for testing class TestRDSBase(RDSBase): def get_connection(self): super().get_connection() @@ -30,7 +25,7 @@ def execute_query(self, query): @pytest.fixture(autouse=True) def mock_db_config(): - mock_db_config = RDSConnectionConfig() + mock_db_config = RDSConfig() mock_db_config.database_type = "db_type" return mock_db_config diff --git a/tests/connectors/rds/test_rds.py b/tests/test_connectors/rds/test_rds.py similarity index 79% rename from tests/connectors/rds/test_rds.py rename to tests/test_connectors/rds/test_rds.py index 46ee77d5..1ec58d2d 100644 --- a/tests/connectors/rds/test_rds.py +++ b/tests/test_connectors/rds/test_rds.py @@ -3,14 +3,14 @@ import pytest from pandas import DataFrame -from numalogic.connectors.utils.aws.config import RDSConnectionConfig -from numalogic.connectors.rds._rds import RDSFetcher +from connectors import RDSConfig +from connectors import RDSFetcher import pandas as pd @pytest.fixture(autouse=True) def mock_db_config(): - return RDSConnectionConfig() + return RDSConfig() @pytest.fixture(autouse=True) @@ -33,15 +33,15 @@ def test_fetch(mocker, data_fetcher): def test_execute_query(mocker): - rds_config = RDSConnectionConfig(database_type="mysql") + rds_config = RDSConfig(database_type="mysql") rds_fetcher = RDSFetcher(db_config=rds_config) mocker.patch.object(rds_fetcher.fetcher, "execute_query", return_value=DataFrame()) - result = rds_fetcher.fetch("SELECT * FROM table", datetime_column_name="eventdatetime") + result = rds_fetcher.fetch("SELECT * FROM table", datetime_field_name="eventdatetime") assert result.empty == DataFrame().empty def test_rds_fetcher_fetch(): - rds_config = RDSConnectionConfig(database_type="mysql") + rds_config = RDSConfig(database_type="mysql") rds_fetcher = RDSFetcher(db_config=rds_config) with patch.object(rds_fetcher.fetcher, "execute_query") as mock_query: mock_query.return_value = pd.DataFrame({"test": [1, 2, 3]}) diff --git a/tests/connectors/test_druid.py b/tests/test_connectors/test_druid.py similarity index 99% rename from tests/connectors/test_druid.py rename to tests/test_connectors/test_druid.py index bbd43f87..9561b412 100644 --- a/tests/connectors/test_druid.py +++ b/tests/test_connectors/test_druid.py @@ -15,8 +15,8 @@ from pydruid.utils.filters import Filter from numalogic._constants import TESTS_DIR -from numalogic.connectors._config import Pivot -from numalogic.connectors.druid import ( +from connectors import Pivot +from connectors import ( DruidFetcher, make_filter_pairs, build_params, diff --git a/tests/connectors/test_prometheus.py b/tests/test_connectors/test_prometheus.py similarity index 99% rename from tests/connectors/test_prometheus.py rename to tests/test_connectors/test_prometheus.py index 57e3afd0..3f65cbe1 100644 --- a/tests/connectors/test_prometheus.py +++ b/tests/test_connectors/test_prometheus.py @@ -6,7 +6,7 @@ from orjson import orjson from requests import Response -from numalogic.connectors import PrometheusFetcher +from connectors import PrometheusFetcher from numalogic.tools.exceptions import PrometheusFetcherError, PrometheusInvalidResponseError logging.basicConfig(level=logging.DEBUG) diff --git a/tests/connectors/test_redis.py b/tests/test_connectors/test_redis.py similarity index 89% rename from tests/connectors/test_redis.py rename to tests/test_connectors/test_redis.py index a10d18e7..db904743 100644 --- a/tests/connectors/test_redis.py +++ b/tests/test_connectors/test_redis.py @@ -4,7 +4,7 @@ from redis.sentinel import Sentinel import fakeredis -from numalogic.connectors.redis import get_redis_client +from connectors import get_redis_client server = fakeredis.FakeServer() fake_redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=True) diff --git a/tests/connectors/__init__.py b/tests/test_connectors/utils/__init__.py similarity index 100% rename from tests/connectors/__init__.py rename to tests/test_connectors/utils/__init__.py diff --git a/tests/connectors/rds/__init__.py b/tests/test_connectors/utils/aws/__init__.py similarity index 100% rename from tests/connectors/rds/__init__.py rename to tests/test_connectors/utils/aws/__init__.py diff --git a/tests/connectors/utils/aws/test_aws_boto3_client_manager.py b/tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py similarity index 94% rename from tests/connectors/utils/aws/test_aws_boto3_client_manager.py rename to tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py index cd432a4a..3afc4dd0 100644 --- a/tests/connectors/utils/aws/test_aws_boto3_client_manager.py +++ b/tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py @@ -2,9 +2,9 @@ import pytest from boto3 import Session -from numalogic.connectors.utils.aws.exceptions import UnRecognizedAWSClientException -from numalogic.connectors.utils.aws.boto3_client_manager import Boto3ClientManager -from numalogic.connectors.utils.aws.sts_client_manager import STSClientManager +from connectors import UnRecognizedAWSClientException +from connectors import Boto3ClientManager +from connectors import STSClientManager @pytest.fixture(autouse=True) diff --git a/tests/connectors/utils/aws/test_config.py b/tests/test_connectors/utils/aws/test_config.py similarity index 97% rename from tests/connectors/utils/aws/test_config.py rename to tests/test_connectors/utils/aws/test_config.py index f74e487b..d334c097 100644 --- a/tests/connectors/utils/aws/test_config.py +++ b/tests/test_connectors/utils/aws/test_config.py @@ -1,4 +1,4 @@ -from numalogic.connectors.utils.aws.config import ( +from connectors import ( DatabaseTypes, AWSConfig, SSLConfig, diff --git a/tests/connectors/utils/aws/test_db_configurations.py b/tests/test_connectors/utils/aws/test_db_configurations.py similarity index 82% rename from tests/connectors/utils/aws/test_db_configurations.py rename to tests/test_connectors/utils/aws/test_db_configurations.py index 0df8863a..08b71055 100644 --- a/tests/connectors/utils/aws/test_db_configurations.py +++ b/tests/test_connectors/utils/aws/test_db_configurations.py @@ -1,6 +1,6 @@ from unittest.mock import patch import pytest -import numalogic.connectors.utils.aws.db_configurations as db_configuration +import connectors.utils.aws.db_configurations as db_configuration from numalogic._constants import TESTS_DIR import os @@ -13,7 +13,7 @@ def test_load_db_conf_file_exists(): @patch( - "numalogic.connectors.utils.aws.db_configurations.OmegaConf.load", + "numalogic.test_connectors.utils.aws.db_configurations.OmegaConf.load", side_effect=FileNotFoundError(), ) def test_load_db_conf_file_not_exists(mock_load): diff --git a/tests/connectors/utils/aws/test_sts_client_manager.py b/tests/test_connectors/utils/aws/test_sts_client_manager.py similarity index 91% rename from tests/connectors/utils/aws/test_sts_client_manager.py rename to tests/test_connectors/utils/aws/test_sts_client_manager.py index ac184bcd..5fe4b50d 100644 --- a/tests/connectors/utils/aws/test_sts_client_manager.py +++ b/tests/test_connectors/utils/aws/test_sts_client_manager.py @@ -1,9 +1,9 @@ from unittest.mock import patch, MagicMock -from numalogic.connectors.utils.aws.sts_client_manager import STSClientManager +from connectors import STSClientManager from datetime import datetime, timedelta, timezone -@patch("numalogic.connectors.utils.aws.sts_client_manager.boto3.client") +@patch("numalogic.test_connectors.utils.aws.sts_client_manager.boto3.client") def test_STSClientManager(boto3_client_mock): # Prepare the mock methods mock_sts_client = MagicMock() diff --git a/tests/connectors/utils/test_enum.py b/tests/test_connectors/utils/test_enum.py similarity index 94% rename from tests/connectors/utils/test_enum.py rename to tests/test_connectors/utils/test_enum.py index 75e37b11..5d7f40be 100644 --- a/tests/connectors/utils/test_enum.py +++ b/tests/test_connectors/utils/test_enum.py @@ -1,4 +1,4 @@ -from numalogic.connectors.utils.enum import BaseEnum +from connectors import BaseEnum import logging logging.basicConfig(level=logging.DEBUG) diff --git a/tests/test_numalogic/__init__.py b/tests/test_numalogic/__init__.py new file mode 100644 index 00000000..5d8f4d8d --- /dev/null +++ b/tests/test_numalogic/__init__.py @@ -0,0 +1,18 @@ +import logging +import sys +import os +import warnings + +if not sys.warnoptions: + warnings.simplefilter("default", category=UserWarning) + os.environ["PYTHONWARNINGS"] = "default" + + +logger = logging.getLogger(__name__) +stream_handler = logging.StreamHandler() +stream_handler.setLevel(logging.INFO) + +formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") +stream_handler.setFormatter(formatter) + +logger.addHandler(stream_handler) diff --git a/tests/connectors/rds/db/__init__.py b/tests/test_numalogic/config/__init__.py similarity index 100% rename from tests/connectors/rds/db/__init__.py rename to tests/test_numalogic/config/__init__.py diff --git a/tests/config/test_config.py b/tests/test_numalogic/config/test_config.py similarity index 100% rename from tests/config/test_config.py rename to tests/test_numalogic/config/test_config.py diff --git a/tests/config/test_optdeps.py b/tests/test_numalogic/config/test_optdeps.py similarity index 100% rename from tests/config/test_optdeps.py rename to tests/test_numalogic/config/test_optdeps.py diff --git a/tests/connectors/utils/__init__.py b/tests/test_numalogic/models/__init__.py similarity index 100% rename from tests/connectors/utils/__init__.py rename to tests/test_numalogic/models/__init__.py diff --git a/tests/connectors/utils/aws/__init__.py b/tests/test_numalogic/models/autoencoder/__init__.py similarity index 100% rename from tests/connectors/utils/aws/__init__.py rename to tests/test_numalogic/models/autoencoder/__init__.py diff --git a/tests/models/autoencoder/test_trainer.py b/tests/test_numalogic/models/autoencoder/test_trainer.py similarity index 100% rename from tests/models/autoencoder/test_trainer.py rename to tests/test_numalogic/models/autoencoder/test_trainer.py diff --git a/tests/models/__init__.py b/tests/test_numalogic/models/autoencoder/variants/__init__.py similarity index 100% rename from tests/models/__init__.py rename to tests/test_numalogic/models/autoencoder/variants/__init__.py diff --git a/tests/models/autoencoder/variants/test_conv.py b/tests/test_numalogic/models/autoencoder/variants/test_conv.py similarity index 100% rename from tests/models/autoencoder/variants/test_conv.py rename to tests/test_numalogic/models/autoencoder/variants/test_conv.py diff --git a/tests/models/autoencoder/variants/test_lstm.py b/tests/test_numalogic/models/autoencoder/variants/test_lstm.py similarity index 100% rename from tests/models/autoencoder/variants/test_lstm.py rename to tests/test_numalogic/models/autoencoder/variants/test_lstm.py diff --git a/tests/models/autoencoder/variants/test_transformers.py b/tests/test_numalogic/models/autoencoder/variants/test_transformers.py similarity index 100% rename from tests/models/autoencoder/variants/test_transformers.py rename to tests/test_numalogic/models/autoencoder/variants/test_transformers.py diff --git a/tests/models/autoencoder/variants/test_vanilla.py b/tests/test_numalogic/models/autoencoder/variants/test_vanilla.py similarity index 100% rename from tests/models/autoencoder/variants/test_vanilla.py rename to tests/test_numalogic/models/autoencoder/variants/test_vanilla.py diff --git a/tests/models/autoencoder/__init__.py b/tests/test_numalogic/models/forecast/__init__.py similarity index 100% rename from tests/models/autoencoder/__init__.py rename to tests/test_numalogic/models/forecast/__init__.py diff --git a/tests/models/forecast/test_naive.py b/tests/test_numalogic/models/forecast/test_naive.py similarity index 93% rename from tests/models/forecast/test_naive.py rename to tests/test_numalogic/models/forecast/test_naive.py index fd90e1f2..2c10029b 100644 --- a/tests/models/forecast/test_naive.py +++ b/tests/test_numalogic/models/forecast/test_naive.py @@ -1,7 +1,10 @@ import unittest -from numalogic.synthetic.timeseries import SyntheticTSGenerator -from numalogic.models.forecast.variants import BaselineForecaster, SeasonalNaiveForecaster +from numalogic.synthetic import SyntheticTSGenerator +from numalogic.models.forecast.variants import ( + BaselineForecaster, + SeasonalNaiveForecaster, +) class TestBaselineForecaster(unittest.TestCase): diff --git a/tests/models/autoencoder/variants/__init__.py b/tests/test_numalogic/models/threshold/__init__.py similarity index 100% rename from tests/models/autoencoder/variants/__init__.py rename to tests/test_numalogic/models/threshold/__init__.py diff --git a/tests/models/threshold/test_maha.py b/tests/test_numalogic/models/threshold/test_maha.py similarity index 100% rename from tests/models/threshold/test_maha.py rename to tests/test_numalogic/models/threshold/test_maha.py diff --git a/tests/models/threshold/test_median.py b/tests/test_numalogic/models/threshold/test_median.py similarity index 100% rename from tests/models/threshold/test_median.py rename to tests/test_numalogic/models/threshold/test_median.py diff --git a/tests/models/threshold/test_static.py b/tests/test_numalogic/models/threshold/test_static.py similarity index 100% rename from tests/models/threshold/test_static.py rename to tests/test_numalogic/models/threshold/test_static.py diff --git a/tests/models/threshold/test_std.py b/tests/test_numalogic/models/threshold/test_std.py similarity index 100% rename from tests/models/threshold/test_std.py rename to tests/test_numalogic/models/threshold/test_std.py diff --git a/tests/models/forecast/__init__.py b/tests/test_numalogic/models/vae/__init__.py similarity index 100% rename from tests/models/forecast/__init__.py rename to tests/test_numalogic/models/vae/__init__.py diff --git a/tests/models/vae/test_conv.py b/tests/test_numalogic/models/vae/test_conv.py similarity index 100% rename from tests/models/vae/test_conv.py rename to tests/test_numalogic/models/vae/test_conv.py diff --git a/tests/models/threshold/__init__.py b/tests/test_numalogic/synthetic/__init__.py similarity index 100% rename from tests/models/threshold/__init__.py rename to tests/test_numalogic/synthetic/__init__.py diff --git a/tests/synthetic/test_anomalies.py b/tests/test_numalogic/synthetic/test_anomalies.py similarity index 97% rename from tests/synthetic/test_anomalies.py rename to tests/test_numalogic/synthetic/test_anomalies.py index 1e089c93..b4be086e 100644 --- a/tests/synthetic/test_anomalies.py +++ b/tests/test_numalogic/synthetic/test_anomalies.py @@ -27,7 +27,11 @@ def test_inject_contextual_anomalies(self, plot=False): train_df, test_df = ts_generator.train_test_split(ts_df, 1440) anomaly_generator = AnomalyGenerator( - train_df, anomaly_type="contextual", anomaly_sign="negative", mu=1, sigma=0.5 + train_df, + anomaly_type="contextual", + anomaly_sign="negative", + mu=1, + sigma=0.5, ) cols = ["s1", "s2"] outlier_df = anomaly_generator.inject_anomalies(test_df, cols=cols) diff --git a/tests/synthetic/test_sparsity.py b/tests/test_numalogic/synthetic/test_sparsity.py similarity index 100% rename from tests/synthetic/test_sparsity.py rename to tests/test_numalogic/synthetic/test_sparsity.py diff --git a/tests/synthetic/test_timeseries.py b/tests/test_numalogic/synthetic/test_timeseries.py similarity index 100% rename from tests/synthetic/test_timeseries.py rename to tests/test_numalogic/synthetic/test_timeseries.py diff --git a/tests/test_backtest.py b/tests/test_numalogic/test_backtest.py similarity index 100% rename from tests/test_backtest.py rename to tests/test_numalogic/test_backtest.py diff --git a/tests/models/vae/__init__.py b/tests/test_numalogic/tools/__init__.py similarity index 100% rename from tests/models/vae/__init__.py rename to tests/test_numalogic/tools/__init__.py diff --git a/tests/tools/test_data.py b/tests/test_numalogic/tools/test_data.py similarity index 100% rename from tests/tools/test_data.py rename to tests/test_numalogic/tools/test_data.py diff --git a/tests/registry/__init__.py b/tests/test_numalogic/transforms/__init__.py similarity index 100% rename from tests/registry/__init__.py rename to tests/test_numalogic/transforms/__init__.py diff --git a/tests/transforms/test_postprocess.py b/tests/test_numalogic/transforms/test_postprocess.py similarity index 100% rename from tests/transforms/test_postprocess.py rename to tests/test_numalogic/transforms/test_postprocess.py diff --git a/tests/transforms/test_transforms.py b/tests/test_numalogic/transforms/test_transforms.py similarity index 100% rename from tests/transforms/test_transforms.py rename to tests/test_numalogic/transforms/test_transforms.py diff --git a/tests/synthetic/__init__.py b/tests/test_registry/__init__.py similarity index 100% rename from tests/synthetic/__init__.py rename to tests/test_registry/__init__.py diff --git a/tests/registry/_mlflow_utils.py b/tests/test_registry/_mlflow_utils.py similarity index 100% rename from tests/registry/_mlflow_utils.py rename to tests/test_registry/_mlflow_utils.py diff --git a/tests/registry/test_cache.py b/tests/test_registry/test_cache.py similarity index 97% rename from tests/registry/test_cache.py rename to tests/test_registry/test_cache.py index 6a076e6e..54ec14ea 100644 --- a/tests/registry/test_cache.py +++ b/tests/test_registry/test_cache.py @@ -6,8 +6,8 @@ from freezegun import freeze_time from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import LocalLRUCache, ArtifactData, ArtifactCache -from numalogic.registry.artifact import _apply_jitter +from registry import LocalLRUCache, ArtifactData, ArtifactCache +from registry import _apply_jitter from numalogic.tools.exceptions import ConfigError diff --git a/tests/registry/test_dynamodb_registry.py b/tests/test_registry/test_dynamodb_registry.py similarity index 98% rename from tests/registry/test_dynamodb_registry.py rename to tests/test_registry/test_dynamodb_registry.py index dfc57f9a..ce75a88e 100644 --- a/tests/registry/test_dynamodb_registry.py +++ b/tests/test_registry/test_dynamodb_registry.py @@ -3,8 +3,8 @@ from moto import mock_dynamodb, mock_sts from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import LocalLRUCache, ArtifactData -from numalogic.registry import DynamoDBRegistry +from registry import LocalLRUCache, ArtifactData +from registry import DynamoDBRegistry ROLE = "qwertyuiopasdfghjklzxcvbnm" ENV = "dev" diff --git a/tests/registry/test_mlflow_registry.py b/tests/test_registry/test_mlflow_registry.py similarity index 99% rename from tests/registry/test_mlflow_registry.py rename to tests/test_registry/test_mlflow_registry.py index dcf41c37..af275319 100644 --- a/tests/registry/test_mlflow_registry.py +++ b/tests/test_registry/test_mlflow_registry.py @@ -10,10 +10,10 @@ from sklearn.preprocessing import StandardScaler from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import MLflowRegistry, ArtifactData, LocalLRUCache -from numalogic.registry.mlflow_registry import ModelStage +from registry import MLflowRegistry, ArtifactData, LocalLRUCache +from registry import ModelStage from numalogic.tools.exceptions import ModelVersionError -from tests.registry._mlflow_utils import ( +from tests.test_registry._mlflow_utils import ( model_sklearn, create_model, mock_log_model_pytorch, diff --git a/tests/registry/test_redis_registry.py b/tests/test_registry/test_redis_registry.py similarity index 96% rename from tests/registry/test_redis_registry.py rename to tests/test_registry/test_redis_registry.py index 7e51c3e6..01ca87d3 100644 --- a/tests/registry/test_redis_registry.py +++ b/tests/test_registry/test_redis_registry.py @@ -11,7 +11,7 @@ from sklearn.preprocessing import StandardScaler from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import RedisRegistry, LocalLRUCache, ArtifactData +from registry import RedisRegistry, LocalLRUCache, ArtifactData from numalogic.tools.exceptions import ModelKeyNotFound, RedisRegistryError from numalogic.tools.types import KeyedArtifact @@ -175,7 +175,7 @@ def test_load_latest_model_twice(self): artifact_data_1 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) artifact_data_2 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertTrue(self.registry.is_artifact_stale(artifact_data_1, 4)) - self.assertEqual("registry", artifact_data_1.extras["source"]) + self.assertEqual("test_registry", artifact_data_1.extras["source"]) with freeze_time(datetime.now() - timedelta(minutes=60)): self.assertEqual("cache", artifact_data_2.extras["source"]) @@ -187,10 +187,10 @@ def test_load_latest_cache_ttl_expire(self): ) registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) artifact_data_1 = registry.load(skeys=self.skeys, dkeys=self.dkeys) - self.assertEqual("registry", artifact_data_1.extras["source"]) + self.assertEqual("test_registry", artifact_data_1.extras["source"]) time.sleep(1) artifact_data_2 = registry.load(skeys=self.skeys, dkeys=self.dkeys) - self.assertEqual("registry", artifact_data_2.extras["source"]) + self.assertEqual("test_registry", artifact_data_2.extras["source"]) def test_multiple_save(self): self.registry.save_multiple( @@ -202,7 +202,7 @@ def test_multiple_save(self): **{"a": "b"} ) artifact_data = self.registry.load(skeys=self.skeys, dkeys=["AE"]) - self.assertEqual("registry", artifact_data.extras["source"]) + self.assertEqual("test_registry", artifact_data.extras["source"]) self.assertIsNotNone(artifact_data.artifact) def test_load_non_latest_model_twice(self): @@ -217,7 +217,7 @@ def test_load_non_latest_model_twice(self): artifact_data_2 = self.registry.load( skeys=self.skeys, dkeys=self.dkeys, latest=False, version=old_version ) - self.assertEqual("registry", artifact_data_1.extras["source"]) + self.assertEqual("test_registry", artifact_data_1.extras["source"]) self.assertEqual("cache", artifact_data_2.extras["source"]) def test_delete_version(self): diff --git a/tests/registry/test_serialize.py b/tests/test_registry/test_serialize.py similarity index 97% rename from tests/registry/test_serialize.py rename to tests/test_registry/test_serialize.py index 7dc4d394..35872475 100644 --- a/tests/registry/test_serialize.py +++ b/tests/test_registry/test_serialize.py @@ -7,7 +7,7 @@ from torchinfo import summary from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry._serialize import loads, dumps +from registry import loads, dumps LOGGER = logging.getLogger(__name__) diff --git a/tests/test_udfs/__init__.py b/tests/test_udfs/__init__.py new file mode 100644 index 00000000..5d8f4d8d --- /dev/null +++ b/tests/test_udfs/__init__.py @@ -0,0 +1,18 @@ +import logging +import sys +import os +import warnings + +if not sys.warnoptions: + warnings.simplefilter("default", category=UserWarning) + os.environ["PYTHONWARNINGS"] = "default" + + +logger = logging.getLogger(__name__) +stream_handler = logging.StreamHandler() +stream_handler.setLevel(logging.INFO) + +formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") +stream_handler.setFormatter(formatter) + +logger.addHandler(stream_handler) diff --git a/tests/udfs/resources/_config.yaml b/tests/test_udfs/resources/_config.yaml similarity index 100% rename from tests/udfs/resources/_config.yaml rename to tests/test_udfs/resources/_config.yaml diff --git a/tests/udfs/resources/_config2.yaml b/tests/test_udfs/resources/_config2.yaml similarity index 100% rename from tests/udfs/resources/_config2.yaml rename to tests/test_udfs/resources/_config2.yaml diff --git a/tests/udfs/resources/_config3.yaml b/tests/test_udfs/resources/_config3.yaml similarity index 100% rename from tests/udfs/resources/_config3.yaml rename to tests/test_udfs/resources/_config3.yaml diff --git a/tests/udfs/resources/_config4.yaml b/tests/test_udfs/resources/_config4.yaml similarity index 100% rename from tests/udfs/resources/_config4.yaml rename to tests/test_udfs/resources/_config4.yaml diff --git a/tests/udfs/resources/data/stream.json b/tests/test_udfs/resources/data/stream.json similarity index 100% rename from tests/udfs/resources/data/stream.json rename to tests/test_udfs/resources/data/stream.json diff --git a/tests/udfs/resources/rds_config.yaml b/tests/test_udfs/resources/rds_config.yaml similarity index 100% rename from tests/udfs/resources/rds_config.yaml rename to tests/test_udfs/resources/rds_config.yaml diff --git a/tests/udfs/resources/rds_config2.yaml b/tests/test_udfs/resources/rds_config2.yaml similarity index 100% rename from tests/udfs/resources/rds_config2.yaml rename to tests/test_udfs/resources/rds_config2.yaml diff --git a/tests/udfs/resources/rds_trainer_config_fetcher_conf.yaml b/tests/test_udfs/resources/rds_trainer_config_fetcher_conf.yaml similarity index 100% rename from tests/udfs/resources/rds_trainer_config_fetcher_conf.yaml rename to tests/test_udfs/resources/rds_trainer_config_fetcher_conf.yaml diff --git a/tests/udfs/resources/rds_trainer_config_fetcher_conf1.yaml b/tests/test_udfs/resources/rds_trainer_config_fetcher_conf1.yaml similarity index 100% rename from tests/udfs/resources/rds_trainer_config_fetcher_conf1.yaml rename to tests/test_udfs/resources/rds_trainer_config_fetcher_conf1.yaml diff --git a/tests/udfs/test_factory.py b/tests/test_udfs/test_factory.py similarity index 100% rename from tests/udfs/test_factory.py rename to tests/test_udfs/test_factory.py diff --git a/tests/udfs/test_inference.py b/tests/test_udfs/test_inference.py similarity index 96% rename from tests/udfs/test_inference.py rename to tests/test_udfs/test_inference.py index dc8eb0bd..7f8cd333 100644 --- a/tests/udfs/test_inference.py +++ b/tests/test_udfs/test_inference.py @@ -16,7 +16,7 @@ ScoreAdjustConf, ) from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import RedisRegistry, ArtifactData +from registry import RedisRegistry, ArtifactData from numalogic.tools.exceptions import RedisRegistryError from numalogic.udfs import StreamConf, InferenceUDF, MLPipelineConf from numalogic.udfs.entities import StreamPayload, Header, Status, TrainerPayload @@ -146,7 +146,7 @@ def test_inference_reg(udf, udf_args, mocker): "load", return_value=ArtifactData( artifact=VanillaAE(seq_len=12, n_features=2), - extras=dict(version="0", timestamp=time.time(), source="registry"), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), ) @@ -188,7 +188,7 @@ def test_inference_stale(udf, udf_args, mocker): extras=dict( version="1", timestamp=(datetime.now() - timedelta(hours=25)).timestamp(), - source="registry", + source="test_registry", ), metadata={}, ), @@ -229,7 +229,7 @@ def test_compute_err_01(udf, udf_args, mocker): "load", return_value=ArtifactData( artifact=VanillaAE(seq_len=12, n_features=2), - extras=dict(version="0", timestamp=time.time(), source="registry"), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), ) @@ -247,7 +247,7 @@ def test_compute_err_02(udf_with_adjust, udf_args, mocker): "load", return_value=ArtifactData( artifact=VanillaAE(seq_len=12, n_features=2), - extras=dict(version="0", timestamp=time.time(), source="registry"), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), ) @@ -265,8 +265,8 @@ def test_model_pass_error_01(udf, udf_args, mocker): RedisRegistry, "load", return_value=ArtifactData( - artifact=VanillaAE(seq_len=10, n_features=1), - extras=dict(version="0", timestamp=time.time(), source="registry"), + artifact=VanillaAE(seq_len=12, n_features=1), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), ) @@ -282,8 +282,8 @@ def test_model_pass_error_02(udf_with_adjust, udf_args, mocker): RedisRegistry, "load", return_value=ArtifactData( - artifact=VanillaAE(seq_len=10, n_features=1), - extras=dict(version="0", timestamp=time.time(), source="registry"), + artifact=VanillaAE(seq_len=12, n_features=1), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), ) diff --git a/tests/udfs/test_main.py b/tests/test_udfs/test_main.py similarity index 100% rename from tests/udfs/test_main.py rename to tests/test_udfs/test_main.py diff --git a/tests/udfs/test_numaflow.py b/tests/test_udfs/test_numaflow.py similarity index 100% rename from tests/udfs/test_numaflow.py rename to tests/test_udfs/test_numaflow.py diff --git a/tests/test_udfs/test_pipeline.py b/tests/test_udfs/test_pipeline.py new file mode 100644 index 00000000..1fc439f8 --- /dev/null +++ b/tests/test_udfs/test_pipeline.py @@ -0,0 +1,59 @@ +import logging +import os +import unittest +from datetime import datetime +from fakeredis import FakeServer, FakeStrictRedis +from omegaconf import OmegaConf +from orjson import orjson + +from numalogic._constants import TESTS_DIR +from numalogic.udfs._config import PipelineConf +from udfs.payloadtx import PayloadTransformer +from tests.test_udfs.utility import input_json_from_file + +logging.basicConfig(level=logging.DEBUG) +REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) +KEYS = ["service-mesh", "1", "2"] +DATUM = input_json_from_file( + os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json") +) + +DATUM_KW = { + "event_time": datetime.now(), + "watermark": datetime.now(), +} + + +class TestPipelineUDF(unittest.TestCase): + def setUp(self) -> None: + _given_conf = OmegaConf.load( + os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml") + ) + _given_conf_2 = OmegaConf.load( + os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml") + ) + schema = OmegaConf.structured(PipelineConf) + pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) + pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) + self.udf1 = PayloadTransformer(pl_conf=pl_conf) + self.udf2 = PayloadTransformer(pl_conf=pl_conf_2) + self.udf1.register_conf("druid-config", pl_conf.stream_confs["druid-config"]) + self.udf2.register_conf("druid-config", pl_conf_2.stream_confs["druid-config"]) + + def test_pipeline_1(self): + msgs = self.udf1(KEYS, DATUM) + self.assertEqual(2, len(msgs)) + for msg in msgs: + data_payload = orjson.loads(msg.value) + self.assertTrue(data_payload["pipeline_id"]) + + def test_pipeline_2(self): + msgs = self.udf2(KEYS, DATUM) + self.assertEqual(1, len(msgs)) + for msg in msgs: + data_payload = orjson.loads(msg.value) + self.assertTrue(data_payload["pipeline_id"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/udfs/test_postprocess.py b/tests/test_udfs/test_postprocess.py similarity index 88% rename from tests/udfs/test_postprocess.py rename to tests/test_udfs/test_postprocess.py index e0b5e853..164dfacb 100644 --- a/tests/udfs/test_postprocess.py +++ b/tests/test_udfs/test_postprocess.py @@ -13,8 +13,7 @@ from numalogic._constants import TESTS_DIR from numalogic.models.threshold import StdDevThreshold -from numalogic.registry import RedisRegistry, ArtifactData -from numalogic.transforms import TanhNorm +from registry import RedisRegistry, ArtifactData from numalogic.udfs import PipelineConf from numalogic.udfs.entities import Header, TrainerPayload, Status, OutputPayload from numalogic.udfs.postprocess import PostprocessUDF @@ -85,7 +84,7 @@ @pytest.fixture def conf(): - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) @@ -101,7 +100,7 @@ def artifact(): model = StdDevThreshold().fit(np.asarray([[0, 1], [1, 2]])) return ArtifactData( artifact=model, - extras=dict(version="0", timestamp=time.time(), source="registry"), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ) @@ -111,7 +110,7 @@ def bad_artifact(): model = StdDevThreshold() return ArtifactData( artifact=model, - extras=dict(version="0", timestamp=time.time(), source="registry"), + extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ) @@ -173,15 +172,6 @@ def test_postprocess_runtime_err_02(udf, mocker, bad_artifact): assert msgs[1].tags == ["staticthresh"] -def test_compute_without_postproc(udf, artifact): - y_unified, x_inferred = udf.compute(artifact.artifact, np.asarray(DATA["data"])) - assert isinstance(y_unified, float) - assert x_inferred.shape == (2,) - - -def test_compute_with_postproc(udf, artifact): - y_unified, x_inferred = udf.compute( - artifact.artifact, np.asarray(DATA["data"]), postproc_tx=TanhNorm() - ) - assert isinstance(y_unified, float) +def test_compute(udf, artifact): + x_inferred = udf.compute(artifact.artifact, np.asarray(DATA["data"])) assert x_inferred.shape == (2,) diff --git a/tests/udfs/test_preprocess.py b/tests/test_udfs/test_preprocess.py similarity index 88% rename from tests/udfs/test_preprocess.py rename to tests/test_udfs/test_preprocess.py index 2676670e..721efc9b 100644 --- a/tests/udfs/test_preprocess.py +++ b/tests/test_udfs/test_preprocess.py @@ -11,17 +11,19 @@ from pynumaflow.mapper import Datum from numalogic._constants import TESTS_DIR -from numalogic.registry import RedisRegistry +from registry import RedisRegistry from numalogic.tools.exceptions import ModelKeyNotFound from numalogic.udfs._config import PipelineConf from numalogic.udfs.entities import Status, Header, StreamPayload, TrainerPayload from numalogic.udfs.preprocess import PreprocessUDF -from tests.udfs.utility import input_json_from_file, store_in_redis +from tests.test_udfs.utility import input_json_from_file, store_in_redis logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file(os.path.join(TESTS_DIR, "udfs", "resources", "data", "stream.json")) +DATUM = input_json_from_file( + os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json") +) DATUM_KW = { "event_time": datetime.now(), @@ -32,8 +34,10 @@ @pytest.fixture def setup(): registry = RedisRegistry(REDIS_CLIENT) - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config.yaml")) - _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config2.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) + _given_conf_2 = OmegaConf.load( + os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml") + ) schema = OmegaConf.structured(PipelineConf) pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) @@ -111,7 +115,7 @@ def test_preprocess_run_time_error(setup, mocker): def test_preprocess_data_error(setup): udf1, _ = setup - with open(os.path.join(TESTS_DIR, "udfs", "resources", "data", "stream.json"), "rb") as f: + with open(os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json"), "rb") as f: stream = json.load(f) stream["data"] = stream["data"][:5] msg = udf1( diff --git a/tests/udfs/test_staticthresh.py b/tests/test_udfs/test_staticthresh.py similarity index 97% rename from tests/udfs/test_staticthresh.py rename to tests/test_udfs/test_staticthresh.py index a5db196e..a5d141f6 100644 --- a/tests/udfs/test_staticthresh.py +++ b/tests/test_udfs/test_staticthresh.py @@ -78,7 +78,7 @@ @pytest.fixture def conf() -> PipelineConf: - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) diff --git a/tests/udfs/test_trainer.py b/tests/test_udfs/test_trainer.py similarity index 97% rename from tests/udfs/test_trainer.py rename to tests/test_udfs/test_trainer.py index 86e19d52..d322ad38 100644 --- a/tests/udfs/test_trainer.py +++ b/tests/test_udfs/test_trainer.py @@ -16,8 +16,8 @@ from numalogic._constants import TESTS_DIR from numalogic.config import NumalogicConf, ModelInfo from numalogic.config import TrainerConf, LightningTrainerConf -from numalogic.connectors import RedisConf, DruidConf, DruidFetcherConf, PrometheusFetcher -from numalogic.connectors.druid import DruidFetcher +from connectors import RedisConf, DruidConf, DruidFetcherConf, PrometheusFetcher +from connectors import DruidFetcher from numalogic.tools.exceptions import ( ConfigNotFoundError, DruidFetcherError, @@ -58,9 +58,9 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config.yaml")) + conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) - conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config2.yaml")) + conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml")) conf_1 = OmegaConf.merge(schema, conf_1) conf_2 = OmegaConf.merge(schema, conf_2) self.pl_conf_1 = PipelineConf(**OmegaConf.merge(schema, conf_1)) @@ -444,7 +444,7 @@ def test_druid_from_config_1(self): with self.assertLogs(level="WARN") as log: self.udf1(self.keys, self.datum) self.assertLogs( - "WARNING:numalogic.udfs._logger:uuid='some-uuid' event='Caught " + "WARNING:numalogic.test_udfs._logger:uuid='some-uuid' event='Caught " "exception/error while fetching from source' udf_vertex='trainer' " "config_id='druid-config' pipeline_id='pipeline1' metadata={} " "keys=['5984175597303660107'] level='warning' " @@ -456,7 +456,7 @@ def test_druid_from_config_2(self): with self.assertLogs(level="WARN") as log: self.udf2(self.keys, self.datum) self.assertLogs( - "WARNING:numalogic.udfs._logger:uuid='some-uuid' event='Caught " + "WARNING:numalogic.test_udfs._logger:uuid='some-uuid' event='Caught " "exception/error while fetching from source' udf_vertex='trainer' " "config_id='druid-config' pipeline_id='pipeline1' metadata={} " "keys=['5984175597303660107'] level='warning'", @@ -570,7 +570,7 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config3.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config3.yaml")) self.conf = OmegaConf.merge(OmegaConf.structured(PipelineConf), conf) @patch.object(PromTrainerUDF, "fetch_data", Mock(return_value=_mock_mv_fetch_data())) diff --git a/tests/udfs/utility.py b/tests/test_udfs/utility.py similarity index 100% rename from tests/udfs/utility.py rename to tests/test_udfs/utility.py diff --git a/tests/tools/__init__.py b/tests/tools/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/transforms/__init__.py b/tests/transforms/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/udfs/__init__.py b/tests/udfs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/udfs/test_pipeline.py b/tests/udfs/test_pipeline.py deleted file mode 100644 index e4e0ee5e..00000000 --- a/tests/udfs/test_pipeline.py +++ /dev/null @@ -1,50 +0,0 @@ -import logging -import os -from datetime import datetime -from omegaconf import OmegaConf -from orjson import orjson -import pytest - -from numalogic._constants import TESTS_DIR -from numalogic.udfs import PipelineConf -from numalogic.udfs.payloadtx import PayloadTransformer -from tests.udfs.utility import input_json_from_file - -logging.basicConfig(level=logging.DEBUG) -KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file(os.path.join(TESTS_DIR, "udfs", "resources", "data", "stream.json")) - -DATUM_KW = { - "event_time": datetime.now(), - "watermark": datetime.now(), -} - - -@pytest.fixture -def setup(): - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config.yaml")) - _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config2.yaml")) - schema = OmegaConf.structured(PipelineConf) - pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) - pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) - udf1 = PayloadTransformer(pl_conf=pl_conf) - udf2 = PayloadTransformer(pl_conf=pl_conf_2) - udf1.register_conf("druid-config", pl_conf.stream_confs["druid-config"]) - udf2.register_conf("druid-config", pl_conf_2.stream_confs["druid-config"]) - return udf1, udf2 - - -def test_pipeline_1(setup): - msgs = setup[0](KEYS, DATUM) - assert 2 == len(msgs) - for msg in msgs: - data_payload = orjson.loads(msg.value) - assert data_payload["pipeline_id"] - - -def test_pipeline_2(setup): - msgs = setup[1](KEYS, DATUM) - assert 1 == len(msgs) - for msg in msgs: - data_payload = orjson.loads(msg.value) - assert data_payload["pipeline_id"] diff --git a/tests/udfs/test_rds_trainer.py b/tests/udfs/test_rds_trainer.py deleted file mode 100644 index 842e4fa6..00000000 --- a/tests/udfs/test_rds_trainer.py +++ /dev/null @@ -1,551 +0,0 @@ -import logging - -import pytest -from redis import RedisError -from numalogic.connectors import RDSFetcher -from numalogic.connectors.utils.aws.config import RDSConnectionConfig -from numalogic.tools.exceptions import RDSFetcherError, ConfigNotFoundError -from numalogic.udfs.tools import TrainMsgDeduplicator -from numalogic.udfs.trainer._rds import RDSTrainerUDF, build_query -import re -from datetime import datetime, timedelta -from numalogic._constants import TESTS_DIR -from numalogic.udfs.entities import TrainerPayload -from numalogic.udfs._config import load_pipeline_conf -from unittest.mock import patch, Mock -from numalogic.connectors._config import Pivot, RedisConf, RDSConf, RDSFetcherConf -from typing import Optional -import pandas as pd -import os -from fakeredis import FakeStrictRedis, FakeServer -from pynumaflow.mapper import Datum -from orjson import orjson -from omegaconf import OmegaConf -from numalogic.udfs import StreamConf, PipelineConf, MLPipelineConf -from numalogic.config import NumalogicConf, ModelInfo -from numalogic.config import TrainerConf, LightningTrainerConf -import time -from freezegun import freeze_time - -REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) - - -# @pytest.fixture -def mock_rds_fetch_data( - query, - datetime_column_name: str, - pivot: Optional[Pivot] = None, - group_by: Optional[list[str]] = None, -): - nrows = 5000 - """Mock rds fetch data.""" - return pd.read_csv( - os.path.join(TESTS_DIR, "resources", "data", "rds.csv"), - index_col="timestamp", - nrows=nrows, - ) - - -def mock_rds_fetch_data_1(nrows=5000): - """Mock rds fetch data to match druid scenarios.""" - return pd.read_csv( - os.path.join(TESTS_DIR, "resources", "data", "druid.csv"), - index_col="timestamp", - nrows=nrows, - ) - - -@pytest.fixture() -def mock_trainer_payload(): - return TrainerPayload( - uuid="979-98789798-98787", - config_id="fciPluginAppInteractions", - pipeline_id="metrics", - composite_keys=["pluginAssetId", "assetId", "interactionName"], - metrics=["failed", "degraded"], - ) - - -@pytest.fixture() -def mock_redis_client(): - return FakeStrictRedis(server=FakeServer()) - - -@pytest.fixture() -def mock_pipeline_conf(): - pipeline_conf = f"{TESTS_DIR}/udfs/resources/rds_trainer_config_fetcher_conf.yaml" - return load_pipeline_conf(pipeline_conf) - - -@pytest.fixture() -def mock_pipeline_conf_id_fetcher(): - pipeline_conf = f"{TESTS_DIR}/udfs/resources/rds_trainer_config_fetcher_conf1.yaml" - return load_pipeline_conf(pipeline_conf) - - -@pytest.fixture() -def mock_RDS_trainer_UDF(mock_pipeline_conf, mock_redis_client): - return RDSTrainerUDF(mock_redis_client, mock_pipeline_conf) - - -@pytest.fixture() -def payload(): - return { - "uuid": "some-uuid", - "config_id": "rds-config", - "pipeline_id": "pipeline1", - "composite_keys": ["5984175597303660107"], - "metrics": ["failed", "degraded"], - } - - -@pytest.fixture() -def udf1(mock_pipeline_conf, payload): - REDIS_CLIENT.flushall() - keys = payload["composite_keys"] - Datum( - keys=keys, - value=orjson.dumps(payload), - event_time=datetime.now(), - watermark=datetime.now(), - ) - conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "rds_config.yaml")) - schema = OmegaConf.structured(PipelineConf) - conf_1 = OmegaConf.merge(schema, conf_1) - PipelineConf(**OmegaConf.merge(schema, conf_1)) - return RDSTrainerUDF(REDIS_CLIENT, pl_conf=OmegaConf.to_object(conf_1)) - - -@pytest.fixture() -def datum_mock(mock_pipeline_conf, payload): - REDIS_CLIENT.flushall() - keys = payload["composite_keys"] - return Datum( - keys=keys, - value=orjson.dumps(payload), - event_time=datetime.now(), - watermark=datetime.now(), - ) - - -@pytest.fixture() -def udf2(mock_pipeline_conf, payload): - REDIS_CLIENT.flushall() - keys = payload["composite_keys"] - Datum( - keys=keys, - value=orjson.dumps(payload), - event_time=datetime.now(), - watermark=datetime.now(), - ) - schema = OmegaConf.structured(PipelineConf) - conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "udfs", "resources", "_config2.yaml")) - conf_2 = OmegaConf.merge(schema, conf_2) - PipelineConf(**OmegaConf.merge(schema, conf_2)) - return RDSTrainerUDF(REDIS_CLIENT, pl_conf=OmegaConf.to_object(conf_2)) - - -def test_build_query(mock_trainer_payload, mock_pipeline_conf): - # assumption of correct functionality - datetime_str = "04/23/24 13:55:26" - test_time = datetime.strptime(datetime_str, "%m/%d/%y %H:%M:%S") - query = build_query( - "foo", - True, - "123", - ["a", "b"], - ["c", "d"], - ["a"], - ["b"], - "time", - "hash", - 2.0, - 1.0, - reference_dt=test_time, - ).replace("\n", " ") - actual_query = re.sub(r"\s+", " ", query) - - expected_query = ( - "select time, a, b, c, d from foo where time >= '2024-04-23T10:55:26' " - "and time <= '2024-04-23T12:55:26' " - "and hash = '1692fcfff3e01e7ba8cffc2baadef5f5'" - ) - - assert actual_query.strip() == re.sub(r"\s+", " ", expected_query.replace("\n", " ")) - - # assumption of behavior when hash_query_type is False - with pytest.raises(NotImplementedError): - build_query( - "foo", False, "123", ["a", "b"], ["c", "d"], ["a"], ["b"], "time", "hash", 2.0, 1.0 - ) - - -def test_rds_trainer(mock_trainer_payload, mock_pipeline_conf, mock_RDS_trainer_UDF): - with patch.object(mock_RDS_trainer_UDF.data_fetcher, "fetch", new=mock_rds_fetch_data): - actual_df = mock_RDS_trainer_UDF.fetch_data(mock_trainer_payload) - actual_df_count_dict = actual_df.count().to_dict() - expected_df_count_dict = {"degraded": 4986, "failed": 5000, "success": 5000} - assert actual_df_count_dict == expected_df_count_dict - - -def test_rds_trainer_register_rds_fetcher_conf( - mock_trainer_payload, mock_pipeline_conf_id_fetcher, mock_redis_client -): - mock_RDS_trainer_UDF_obj = RDSTrainerUDF(mock_redis_client, mock_pipeline_conf_id_fetcher) - mock_RDS_trainer_UDF_obj.register_rds_fetcher_conf( - "fciPluginAppInteractions", "metrics", mock_RDS_trainer_UDF - ) - - assert mock_RDS_trainer_UDF_obj.pl_conf.rds_conf.connection_conf.database_username == "root" - - -def test_get_rds_fetcher_conf( - mock_trainer_payload, mock_pipeline_conf_id_fetcher, mock_redis_client -): - mock_RDS_trainer_UDF_obj = RDSTrainerUDF(mock_redis_client, mock_pipeline_conf_id_fetcher) - with pytest.raises(ConfigNotFoundError): - mock_RDS_trainer_UDF_obj.get_rds_fetcher_conf("fciPluginAppInteractions", "metrics1") - - -def test_trainer_do_train1(udf1, mocker, datum_mock, payload): - mocker.patch.object(RDSFetcher, "fetch", Mock(return_value=mock_rds_fetch_data_1())) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - metrics=["failed", "degraded"], - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ - ModelInfo(name="LogTransformer"), - ModelInfo(name="StandardScaler"), - ], - trainer=TrainerConf( - pltrainer_conf=LightningTrainerConf(max_epochs=1), - transforms=[ModelInfo(name="DataClipper", conf={"lower": [0, 0]})], - ), - ), - ) - } - ), - ) - time.time() - - keys = payload["composite_keys"] - - udf1(keys, datum_mock) - with freeze_time(datetime.now() + timedelta(days=2)): - udf1(keys, datum_mock) - assert 3 == REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::LATEST", - b"5984175597303660107::pipeline1:StdDevThreshold::LATEST", - b"5984175597303660107::pipeline1:LogTransformer:StandardScaler::LATEST", - ) - assert 3 == REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::1", - b"5984175597303660107::pipeline1:StdDevThreshold::1", - b"5984175597303660107::pipeline1:LogTransformer:StandardScaler::1", - ) - - -def test_trainer_do_not_train_1(udf1, mocker, payload, datum_mock): - mocker.patch.object(RDSFetcher, "fetch", Mock(return_value=mock_rds_fetch_data_1())) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - metrics=["failed", "degraded"], - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ - ModelInfo(name="LogTransformer"), - ModelInfo(name="StandardScaler"), - ], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - keys = payload["composite_keys"] - - udf1(keys, datum_mock) - udf1(keys, datum_mock) - assert 3 == REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::LATEST", - b"5984175597303660107::pipeline1:StdDevThreshold::LATEST", - b"5984175597303660107::pipeline1:LogTransformer:StandardScaler::LATEST", - ) - assert 0 == REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::1", - b"5984175597303660107:pipeline1::StdDevThreshold::1", - b"5984175597303660107:pipeline1::LogTransformer:StandardScaler::1", - ) - - -def test_trainer_do_not_train_3(mocker, udf1, datum_mock, payload): - mocker.patch.object(RDSFetcher, "fetch", Mock(return_value=mock_rds_fetch_data_1())) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ - ModelInfo(name="LogTransformer"), - ModelInfo(name="StandardScaler"), - ], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - - keys = payload["composite_keys"] - - TrainMsgDeduplicator(REDIS_CLIENT).ack_read([*keys, "pipeline1"], "some-uuid") - ts = datetime.strptime("2022-05-24 10:00:00", "%Y-%m-%d %H:%M:%S") - with freeze_time(ts + timedelta(minutes=15)): - udf1(keys, datum_mock) - assert 0 == REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::0", - b"5984175597303660107:pipeline1::StdDevThreshold::0", - b"5984175597303660107:pipeline1::LogTransformer:StandardScaler::0", - ) - - -def test_trainer_do_not_train_4(mocker, udf1, payload, datum_mock): - mocker.patch.object(RDSFetcher, "fetch", Mock(return_value=mock_rds_fetch_data_1(50))) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ - ModelInfo(name="LogTransformer"), - ModelInfo(name="StandardScaler"), - ], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - - keys = payload["composite_keys"] - - udf1(keys, datum_mock) - udf1(keys, datum_mock) - - -def test_trainer_conf_err(mock_RDS_trainer_UDF): - with pytest.raises(ConfigNotFoundError): - RDSTrainerUDF( - REDIS_CLIENT, - pl_conf=PipelineConf(redis_conf=RedisConf(url="redis://localhost:6379", port=0)), - ) - - -def test_trainer_data_insufficient(mocker, udf1, datum_mock, payload): - mocker.patch.object(RDSFetcher, "fetch", Mock(return_value=mock_rds_fetch_data_1(10))) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ModelInfo(name="StandardScaler", conf={})], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - keys = payload["composite_keys"] - - udf1(keys, datum_mock) - assert ( - REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::LATEST", - b"5984175597303660107::pipeline1:StdDevThreshold::LATEST", - b"5984175597303660107:pipeline1::StandardScaler::LATEST", - ) - == 0 - ) - - -def test_trainer_datafetcher_err(mocker, udf1, payload, datum_mock): - mocker.patch.object(RDSFetcher, "fetch", Mock(side_effect=RDSFetcherError)) - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo(name="VanillaAE", conf={"seq_len": 12, "n_features": 2}), - preprocess=[ModelInfo(name="StandardScaler", conf={})], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - - keys = payload["composite_keys"] - - udf1(keys, datum_mock) - assert ( - REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::LATEST", - b"5984175597303660107::pipeline1:StdDevThreshold::LATEST", - b"5984175597303660107:pipeline1::StandardScaler::LATEST", - ) - == 0 - ) - - -def test_trainer_datafetcher_err_and_train(mocker, udf1, payload, datum_mock): - mocker.patch.object( - RDSFetcher, "fetch", Mock(side_effect=[RDSFetcherError, mock_rds_fetch_data_1()]) - ) - ts = datetime.strptime("2022-05-24 10:00:00", "%Y-%m-%d %H:%M:%S") - keys = payload["composite_keys"] - with freeze_time(ts): - udf1.register_conf( - "rds-config", - StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - metrics=["failed", "degraded"], - numalogic_conf=NumalogicConf( - model=ModelInfo( - name="VanillaAE", conf={"seq_len": 12, "n_features": 2} - ), - preprocess=[ModelInfo(name="StandardScaler", conf={})], - trainer=TrainerConf(pltrainer_conf=LightningTrainerConf(max_epochs=1)), - ), - ) - } - ), - ) - udf1(keys, datum_mock) - with freeze_time(ts + timedelta(minutes=20)): - udf1(keys, datum_mock) - - assert ( - REDIS_CLIENT.exists( - b"5984175597303660107::pipeline1:VanillaAE::LATEST", - b"5984175597303660107::pipeline1:StdDevThreshold::LATEST", - b"5984175597303660107:pipeline1::StandardScaler::LATEST", - ) - == 2 - ) - - -def test_TrainMsgDeduplicator_exception_1(mocker, caplog, payload): - caplog.set_level(logging.INFO) - mocker.patch("redis.Redis.hset", Mock(side_effect=RedisError)) - train_dedup = TrainMsgDeduplicator(REDIS_CLIENT) - keys = payload["composite_keys"] - train_dedup.ack_read([*keys, "pipeline1"], "some-uuid") - assert "RedisError" in caplog.text - train_dedup.ack_train([*keys, "pipeline1"], "some-uuid") - assert "RedisError" in caplog.text - train_dedup.ack_insufficient_data([*keys, "pipeline1"], "some-uuid", train_records=180) - assert "RedisError" in caplog.text - - -def test_TrainMsgDeduplicator_exception_2(mocker, caplog, payload): - caplog.set_level(logging.INFO) - mocker.patch("redis.Redis.hset", Mock(side_effect=RedisError)) - train_dedup = TrainMsgDeduplicator(REDIS_CLIENT) - keys = payload["composite_keys"] - train_dedup.ack_read([*keys, "pipeline1"], "some-uuid") - assert "RedisError" in caplog.text - - -def test_rds_from_config_missing(datum_mock, payload): - pl_conf = PipelineConf( - stream_confs={ - "rds-config": StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo( - name="VanillaAE", conf={"seq_len": 12, "n_features": 2} - ), - preprocess=[ - ModelInfo(name="LogTransformer"), - ], - trainer=TrainerConf( - pltrainer_conf=LightningTrainerConf(max_epochs=1), - ), - ), - ) - } - ) - }, - rds_conf=RDSConf(connection_conf=RDSConnectionConfig(), delay_hrs=3), - ) - udf3 = RDSTrainerUDF(REDIS_CLIENT, pl_conf=pl_conf) - keys = payload["composite_keys"] - with pytest.raises(ConfigNotFoundError): - udf3(keys, datum_mock) - - -def test_rds_get_config_error(payload, datum_mock): - pl_conf = PipelineConf( - stream_confs={ - "rds-config": StreamConf( - ml_pipelines={ - "pipeline1": MLPipelineConf( - pipeline_id="pipeline1", - numalogic_conf=NumalogicConf( - model=ModelInfo( - name="VanillaAE", conf={"seq_len": 12, "n_features": 2} - ), - preprocess=[ - ModelInfo(name="LogTransformer"), - ], - trainer=TrainerConf( - pltrainer_conf=LightningTrainerConf(max_epochs=1), - ), - ), - ) - } - ) - }, - rds_conf=RDSConf( - connection_conf=RDSConnectionConfig(), - delay_hrs=3, - id_fetcher={ - "some-id-pipeline1": RDSFetcherConf( - datasource="some-datasource", - dimensions=["some-dimension"], - metrics=["some-metric"], - ) - }, - ), - ) - udf3 = RDSTrainerUDF(REDIS_CLIENT, pl_conf=pl_conf) - udf3.register_conf("rds-config", pl_conf.stream_confs["rds-config"]) - udf3.register_rds_fetcher_conf( - "some-id", "pipeline1", pl_conf.rds_conf.id_fetcher["some-id-pipeline1"] - ) - with pytest.raises(ConfigNotFoundError): - udf3.get_rds_fetcher_conf("different-config", "pipeline1") From 2970943b304b70f2fb5f24be1d46847281f95c10 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 21 May 2024 14:51:04 -0700 Subject: [PATCH 02/68] fix: add gru forecast models Signed-off-by: Avik Basu --- Makefile | 11 +- .../numalogic/models/forecast/__init__.py | 3 + .../numalogic/models/forecast/variants/rnn.py | 116 ++++++++++++++++++ src/core/numalogic/tools/data.py | 59 ++++++++- 4 files changed, 178 insertions(+), 11 deletions(-) create mode 100644 src/core/numalogic/models/forecast/variants/rnn.py diff --git a/Makefile b/Makefile index dd90768e..620e95b1 100644 --- a/Makefile +++ b/Makefile @@ -1,12 +1,3 @@ -# Check Python -PYTHON:=$(shell command -v python 2> /dev/null) -ifndef PYTHON -PYTHON:=$(shell command -v python3 2> /dev/null) -endif -ifndef PYTHON -$(error "Python is not available, please install.") -endif - clean: @rm -rf build dist .eggs *.egg-info @rm -rf .benchmarks .coverage coverage.xml htmlcov report.xml .tox @@ -23,7 +14,7 @@ lint: format # install all dependencies setup: - poetry install --with dev,torch,jupyter --all-extras + pip install -v -e '.[dev,jupyter]' --config-settings editable_mode=strict # test your application (tests in the tests/ directory) test: diff --git a/src/core/numalogic/models/forecast/__init__.py b/src/core/numalogic/models/forecast/__init__.py index e69de29b..e011d9b4 100644 --- a/src/core/numalogic/models/forecast/__init__.py +++ b/src/core/numalogic/models/forecast/__init__.py @@ -0,0 +1,3 @@ +from numalogic.models.forecast.variants.rnn import GRUForecaster, GRUIntervalForecaster + +__all__ = ["GRUForecaster", "GRUIntervalForecaster"] diff --git a/src/core/numalogic/models/forecast/variants/rnn.py b/src/core/numalogic/models/forecast/variants/rnn.py new file mode 100644 index 00000000..a9617505 --- /dev/null +++ b/src/core/numalogic/models/forecast/variants/rnn.py @@ -0,0 +1,116 @@ +import torch +from torch import nn, Tensor +import pytorch_lightning as pl +import torch.nn.functional as F +from torch.distributions import Normal + + +class GRUForecaster(pl.LightningModule): + """Multistep forecasting using GRU.""" + + def __init__( + self, + seq_len: int, + hidden_size: int = 32, + n_features: int = 1, + forecast_horizon: int = 15, + num_layers: int = 1, + weight_decay: float = 1e-5, + ): + super().__init__() + self.seq_len = seq_len + self.n_features = n_features + self.horizon = forecast_horizon + self.weight_decay = weight_decay + self.gru_1 = nn.GRU( + input_size=n_features, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + ) + self.gru_2 = nn.GRU( + input_size=hidden_size, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + ) + self.fc = nn.Linear(hidden_size, n_features) + + def forward(self, x: Tensor): + o, h = self.gru_1(x) + h = h[-1, :, :].unsqueeze(1).repeat(1, self.horizon, 1) + out, _ = self.gru_2(F.relu(h)) + return self.fc(F.relu(out)) + + def configure_optimizers(self) -> None: + return torch.optim.Adam(self.parameters(), lr=3e-4, weight_decay=self.weight_decay) + + def training_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: + x, y = batch + out = self.forward(x) + # print(out.shape, y.shape) + loss = F.mse_loss(out, y) + self.log("train_loss", loss, on_epoch=True, on_step=False) + return loss + + def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int | None) -> Tensor: + x, y = batch + return self.forward(x) + + +class GRUIntervalForecaster(pl.LightningModule): + """GRU Forecaster with interval prediction.""" + + def __init__( + self, + seq_len: int, + hidden_size: int = 32, + n_features: int = 1, + forecast_horizon: int = 15, + num_layers: int = 1, + weight_decay: float = 1e-5, + ): + super().__init__() + self.seq_len = seq_len + self.n_features = n_features + self.horizon = forecast_horizon + self.weight_decay = weight_decay + self.gru_1 = nn.GRU( + input_size=n_features, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + ) # L = 100 + self.gru_2 = nn.GRU( + input_size=hidden_size, + hidden_size=hidden_size, + num_layers=num_layers, + batch_first=True, + ) # L = 15 + self.mu = nn.Linear(hidden_size, n_features) + self.logvar = nn.Linear(hidden_size, n_features) + + def forward(self, x: Tensor): + o, h = self.gru_1(x) + h = h[-1, :, :].unsqueeze(1).repeat(1, self.horizon, 1) + out, _ = self.gru_2(F.relu(h)) + mu = self.mu(out) + logvar = F.softplus(self.logvar(out)) + return mu, logvar + + def configure_optimizers(self) -> None: + return torch.optim.Adam(self.parameters(), lr=3e-4, weight_decay=self.weight_decay) + + def training_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: + x, y = batch + mu, logvar = self.forward(x) + dist = Normal(mu, logvar.exp()) + out = dist.rsample() + # print(out.shape, y.shape) + loss = F.mse_loss(out, y) + self.log("train_loss", loss, on_epoch=True, on_step=False) + return loss + + def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int | None) -> Tensor: + x, y = batch + return self.forward(x) diff --git a/src/core/numalogic/tools/data.py b/src/core/numalogic/tools/data.py index 10f956c8..7f8acffd 100644 --- a/src/core/numalogic/tools/data.py +++ b/src/core/numalogic/tools/data.py @@ -128,7 +128,9 @@ def __init__(self, data: npt.NDArray[float], seq_len: int, stride: int = 1): self._data = data.astype(np.float32) self._stride = stride _LOGGER.info( - "StreamingDataset initialized with seq_len: %s, stride: %s", self._seq_len, self._stride + "StreamingDataset initialized with seq_len: %s, stride: %s", + self._seq_len, + self._stride, ) @property @@ -194,6 +196,61 @@ def __getitem__(self, idx: Union[int, slice]) -> npt.NDArray[float]: return self._data[(idx * self._stride) : (idx * self._stride) + self._seq_len] +class ForecastDataset(StreamingDataset): + """ + A Dataset for generating sequences for forecasting tasks. + + Args: + ---- + data: A numpy array containing the input data in the shape of (batch, num_features). + seq_len: Length of the sliding window sequences to be generated from the input data + horizon: The forecast horizon + stride: Stride to jump between sequences; defaults to 1 + """ + + def __init__(self, data: npt.NDArray[float], seq_len: int, horizon: int, stride: int = 1): + super().__init__(data, seq_len, stride=stride) + self.horizon = horizon + + def create_seq( + self, input_: npt.NDArray[float] + ) -> Generator[tuple[npt.NDArray[float], npt.NDArray[float]], None, None]: + r"""Yields sequences of specified length from the input data. + + Args: + ---- + input_: A numpy array containing the input data. + + Yields + ------ + A tuple of subarray of size (seq_len, num_features) + and forecast horizon of size (seq_len, num_features) + from the input data. + """ + idx = 0 + while idx < len(self._data) - self._seq_len - self.horizon + 1: + yield ( + input_[idx : idx + self._seq_len], + input_[idx + self._seq_len : idx + self._seq_len + self.horizon], + ) + idx += self._stride + + def __getitem__(self, idx: int) -> npt.NDArray[float]: + r"""Retrieves a sequence from the input data at the specified index.""" + if isinstance(idx, slice): + raise NotImplementedError("Slicing not supported!") + if idx >= len(self): + raise IndexError(f"{idx} out of bound!") + return ( + self._data[idx : idx + self._seq_len], + self._data[idx + self._seq_len : idx + self._seq_len + self.horizon], + ) + + def __len__(self) -> int: + r"""Returns the number of sequences that can be generated from the input data.""" + return (len(self._data) - self._seq_len - self.horizon) // self._stride + 1 + + class StreamingDataLoader(DataLoader): """ A DataLoader for convenience that uses StreamingDataset for handling time series data. From 8b03beb3f5186a501b5cf9f83a505acf73392207 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 21 May 2024 17:20:30 -0700 Subject: [PATCH 03/68] fix: typing Signed-off-by: Avik Basu --- src/core/numalogic/models/forecast/variants/rnn.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/core/numalogic/models/forecast/variants/rnn.py b/src/core/numalogic/models/forecast/variants/rnn.py index a9617505..be53e0d7 100644 --- a/src/core/numalogic/models/forecast/variants/rnn.py +++ b/src/core/numalogic/models/forecast/variants/rnn.py @@ -53,7 +53,7 @@ def training_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: self.log("train_loss", loss, on_epoch=True, on_step=False) return loss - def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int | None) -> Tensor: + def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: x, y = batch return self.forward(x) @@ -111,6 +111,6 @@ def training_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: self.log("train_loss", loss, on_epoch=True, on_step=False) return loss - def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int | None) -> Tensor: + def predict_step(self, batch: tuple[Tensor, Tensor], batch_idx: int) -> Tensor: x, y = batch return self.forward(x) From f6ec6a5dd0ec02f98108b992518462b7b1da4d18 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Wed, 29 May 2024 11:20:38 -0700 Subject: [PATCH 04/68] fix tests for libs Signed-off-by: Avik Basu --- .../numaflow-udfs/tests}/__init__.py | 0 .../tests}/resources/_config.yaml | 0 .../tests}/resources/_config2.yaml | 0 .../tests}/resources/_config3.yaml | 0 .../tests}/resources/_config4.yaml | 0 .../tests}/resources/data/stream.json | 0 .../tests}/resources/rds_config.yaml | 0 .../tests}/resources/rds_config2.yaml | 0 .../rds_trainer_config_fetcher_conf.yaml | 0 .../rds_trainer_config_fetcher_conf1.yaml | 0 .../numaflow-udfs/tests}/test_backtest.py | 0 .../numaflow-udfs/tests}/test_factory.py | 0 .../numaflow-udfs/tests}/test_inference.py | 0 .../numaflow-udfs/tests}/test_main.py | 0 .../numaflow-udfs/tests}/test_numaflow.py | 0 .../numaflow-udfs/tests}/test_pipeline.py | 10 +- .../numaflow-udfs/tests}/test_postprocess.py | 2 +- .../numaflow-udfs/tests}/test_preprocess.py | 12 +- .../numaflow-udfs/tests}/test_staticthresh.py | 2 +- .../numaflow-udfs/tests}/test_trainer.py | 10 +- .../numaflow-udfs/tests}/utility.py | 0 {src => apps/numaflow-udfs}/udfs/Dockerfile | 2 +- {src => apps/numaflow-udfs}/udfs/README.md | 0 {src => apps/numaflow-udfs}/udfs/__init__.py | 0 {src => apps/numaflow-udfs}/udfs/__main__.py | 0 {src => apps/numaflow-udfs}/udfs/_base.py | 0 {src => apps/numaflow-udfs}/udfs/_config.py | 0 {src => apps/numaflow-udfs}/udfs/_logger.py | 0 {src => apps/numaflow-udfs}/udfs/_metrics.py | 0 {src => apps/numaflow-udfs}/udfs/entities.py | 0 {src => apps/numaflow-udfs}/udfs/factory.py | 0 {src => apps/numaflow-udfs}/udfs/inference.py | 0 {src => apps/numaflow-udfs}/udfs/payloadtx.py | 0 .../numaflow-udfs}/udfs/postprocess.py | 0 .../numaflow-udfs}/udfs/preprocess.py | 0 .../numaflow-udfs}/udfs/staticthresh.py | 0 {src => apps/numaflow-udfs}/udfs/tools.py | 0 .../numaflow-udfs}/udfs/trainer/__init__.py | 0 .../numaflow-udfs}/udfs/trainer/_base.py | 0 .../numaflow-udfs}/udfs/trainer/_druid.py | 0 .../numaflow-udfs}/udfs/trainer/_prom.py | 0 .../numaflow-udfs}/udfs/trainer/_rds.py | 0 libs/connectors/nlconnectors/__init__.py | 17 + .../connectors/nlconnectors}/_base.py | 0 .../connectors/nlconnectors/config.py | 4 +- libs/connectors/nlconnectors/constants.py | 4 + .../connectors/nlconnectors/druid/__init__.py | 3 + .../connectors/nlconnectors}/druid/_druid.py | 37 +- .../nlconnectors}/druid/aggregators.py | 0 .../nlconnectors}/druid/postaggregator.py | 0 .../connectors/nlconnectors}/exceptions.py | 0 libs/connectors/nlconnectors/factory.py | 27 + .../connectors/nlconnectors}/prometheus.py | 2 +- libs/connectors/nlconnectors/rds/__init__.py | 3 + .../connectors/nlconnectors}/rds/_base.py | 10 +- .../connectors/nlconnectors}/rds/_rds.py | 14 +- .../nlconnectors/rds/db}/__init__.py | 0 .../nlconnectors}/rds/db/factory.py | 10 +- .../nlconnectors}/rds/db/mysql_fetcher.py | 4 +- .../connectors/nlconnectors}/redis.py | 6 +- .../nlconnectors/utils}/__init__.py | 0 .../nlconnectors/utils/aws}/__init__.py | 0 .../utils/aws/boto3_client_manager.py | 6 +- .../nlconnectors}/utils/aws/config.py | 2 +- .../utils/aws/db_configurations.py | 2 +- .../nlconnectors}/utils/aws/exceptions.py | 0 .../utils/aws/sts_client_manager.py | 0 .../connectors/nlconnectors}/utils/enum.py | 0 libs/connectors/pyproject.toml | 56 + .../connectors/tests}/__init__.py | 0 .../connectors/tests}/data.csv | 0 .../connectors/tests/rds}/__init__.py | 0 .../connectors/tests/rds/db}/__init__.py | 0 .../connectors/tests}/rds/db/test_factory.py | 6 +- .../tests}/rds/db/test_mysql_fetcher.py | 11 +- .../connectors/tests}/rds/test_base.py | 16 +- .../connectors/tests}/rds/test_rds.py | 12 +- .../connectors/tests}/resources/config.yaml | 0 .../tests}/resources/data/druid.csv | 0 .../resources/data/interactionstatus.csv | 0 .../tests}/resources/data/prom_default.csv | 0 .../tests}/resources/data/prom_mv.csv | 0 .../tests}/resources/data/raw_druid.csv | 0 .../connectors/tests}/resources/data/rds.csv | 0 .../tests}/resources/rds_db_config.yaml | 0 .../connectors/tests}/test_druid.py | 10 +- .../connectors/tests}/test_prometheus.py | 40 +- .../connectors/tests}/test_redis.py | 2 +- .../connectors/tests/utils}/__init__.py | 0 .../connectors/tests/utils/aws}/__init__.py | 0 .../aws/test_aws_boto3_client_manager.py | 23 +- .../tests}/utils/aws/test_config.py | 2 +- .../utils/aws/test_db_configurations.py | 10 +- .../utils/aws/test_sts_client_manager.py | 15 +- .../connectors/tests}/utils/test_enum.py | 5 +- {src => libs}/core/numalogic/__init__.py | 0 {src => libs}/core/numalogic/_constants.py | 0 .../core/numalogic/backtest/__init__.py | 0 .../core/numalogic/backtest/_constants.py | 0 .../core/numalogic/backtest/_prom.py | 0 {src => libs}/core/numalogic/base.py | 0 .../core/numalogic/config/__init__.py | 2 - .../core/numalogic/config/_config.py | 0 .../core/numalogic/config/factory.py | 70 +- .../core/numalogic/models}/__init__.py | 0 .../numalogic/models/autoencoder/__init__.py | 0 .../core/numalogic/models/autoencoder/base.py | 0 .../models/autoencoder/variants/__init__.py | 0 .../models/autoencoder/variants/conv.py | 0 .../models/autoencoder/variants/lstm.py | 0 .../autoencoder/variants/transformer.py | 0 .../models/autoencoder/variants/vanilla.py | 0 .../numalogic/models/forecast/__init__.py | 0 .../models/forecast/variants/__init__.py | 0 .../models/forecast/variants/naive.py | 0 .../numalogic/models/forecast/variants/rnn.py | 0 .../numalogic/models/threshold/__init__.py | 0 .../models/threshold/_mahalanobis.py | 0 .../numalogic/models/threshold/_median.py | 0 .../numalogic/models/threshold/_static.py | 0 .../core/numalogic/models/threshold/_std.py | 0 .../core/numalogic/models/vae/__init__.py | 0 .../core/numalogic/models/vae/base.py | 0 .../core/numalogic/models/vae/layer.py | 0 .../numalogic/models/vae/variants/__init__.py | 0 .../numalogic/models/vae/variants/conv.py | 0 .../core/numalogic/monitoring/__init__.py | 0 .../core/numalogic/monitoring/metrics.py | 0 .../core/numalogic/synthetic/__init__.py | 4 + .../core/numalogic/synthetic/anomalies.py | 0 .../core/numalogic/synthetic/sparsity.py | 0 .../core/numalogic/synthetic/timeseries.py | 0 .../core/numalogic/tools}/__init__.py | 0 .../core/numalogic/tools/aggregators.py | 0 .../core/numalogic/tools/callbacks.py | 0 {src => libs}/core/numalogic/tools/data.py | 0 .../core/numalogic/tools/exceptions.py | 0 {src => libs}/core/numalogic/tools/loss.py | 0 {src => libs}/core/numalogic/tools/trainer.py | 0 {src => libs}/core/numalogic/tools/types.py | 0 .../core/numalogic/transforms/__init__.py | 0 .../core/numalogic/transforms/_movavg.py | 0 .../core/numalogic/transforms/_postprocess.py | 0 .../core/numalogic/transforms/_scaler.py | 0 .../core/numalogic/transforms/_stateless.py | 0 {src => libs}/core/pyproject.toml | 6 +- .../test_udfs => libs/core/tests}/__init__.py | 0 .../core/tests}/config/__init__.py | 0 .../core/tests}/config/test_config.py | 0 .../core/tests}/models/__init__.py | 0 .../tests}/models/autoencoder/__init__.py | 0 .../tests}/models/autoencoder/test_trainer.py | 0 .../models/autoencoder/variants/__init__.py | 0 .../models/autoencoder/variants/test_conv.py | 0 .../models/autoencoder/variants/test_lstm.py | 0 .../autoencoder/variants/test_transformers.py | 0 .../autoencoder/variants/test_vanilla.py | 0 .../core/tests}/models/forecast/__init__.py | 0 .../core/tests}/models/forecast/test_naive.py | 0 .../core/tests}/models/threshold/__init__.py | 0 .../core/tests}/models/threshold/test_maha.py | 0 .../tests}/models/threshold/test_median.py | 0 .../tests}/models/threshold/test_static.py | 0 .../core/tests}/models/threshold/test_std.py | 0 .../core/tests}/models/vae/__init__.py | 0 .../core/tests}/models/vae/test_conv.py | 0 libs/core/tests/resources/config.yaml | 30 + libs/core/tests/resources/data/druid.csv | 14378 ++++++++++++++++ .../resources/data/interactionstatus.csv | 10001 +++++++++++ .../tests/resources/data/prom_default.csv | 122 + libs/core/tests/resources/data/prom_mv.csv | 362 + libs/core/tests/resources/data/raw_druid.csv | 2626 +++ libs/core/tests/resources/data/rds.csv | 14378 ++++++++++++++++ libs/core/tests/resources/rds_db_config.yaml | 17 + .../core/tests}/synthetic/__init__.py | 0 .../core/tests}/synthetic/test_anomalies.py | 0 .../core/tests}/synthetic/test_sparsity.py | 0 .../core/tests}/synthetic/test_timeseries.py | 0 .../core/tests}/tools/__init__.py | 0 .../core/tests}/tools/test_data.py | 0 .../core/tests}/transforms/__init__.py | 0 .../tests}/transforms/test_postprocess.py | 0 .../core/tests}/transforms/test_transforms.py | 0 libs/registry/nlregistry/__init__.py | 15 + .../registry/nlregistry}/__setup__.py | 4 +- .../registry/nlregistry}/_serialize.py | 0 .../registry/nlregistry}/artifact.py | 0 .../registry/nlregistry}/dynamodb_registry.py | 64 +- libs/registry/nlregistry/factory.py | 43 + .../registry/nlregistry}/localcache.py | 2 +- .../registry/nlregistry}/mlflow_registry.py | 47 +- .../registry/nlregistry}/redis_registry.py | 82 +- libs/registry/pyproject.toml | 52 + .../registry/tests}/__init__.py | 0 .../registry/tests}/_mlflow_utils.py | 0 .../registry/tests}/test_cache.py | 51 +- .../registry/tests}/test_dynamodb_registry.py | 12 +- .../registry/tests}/test_mlflow_registry.py | 255 +- .../registry/tests}/test_optdeps.py | 18 +- .../registry/tests}/test_redis_registry.py | 62 +- .../registry/tests}/test_serialize.py | 2 +- pyproject.toml | 4 - setup.py | 15 +- src/nlconnectors/connectors/__init__.py | 36 - src/nlconnectors/connectors/__setup__.py | 20 - src/nlconnectors/connectors/druid/__init__.py | 1 - src/nlconnectors/connectors/pyproject.toml | 79 - src/nlconnectors/connectors/rds/__init__.py | 3 - src/registry/__init__.py | 38 - 209 files changed, 42788 insertions(+), 510 deletions(-) rename {tests/test_connectors => apps/numaflow-udfs/tests}/__init__.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/_config.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/_config2.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/_config3.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/_config4.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/data/stream.json (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/rds_config.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/rds_config2.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/rds_trainer_config_fetcher_conf.yaml (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/resources/rds_trainer_config_fetcher_conf1.yaml (100%) rename {tests/test_numalogic => apps/numaflow-udfs/tests}/test_backtest.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_factory.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_inference.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_main.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_numaflow.py (100%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_pipeline.py (84%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_postprocess.py (98%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_preprocess.py (90%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_staticthresh.py (97%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/test_trainer.py (98%) rename {tests/test_udfs => apps/numaflow-udfs/tests}/utility.py (100%) rename {src => apps/numaflow-udfs}/udfs/Dockerfile (99%) rename {src => apps/numaflow-udfs}/udfs/README.md (100%) rename {src => apps/numaflow-udfs}/udfs/__init__.py (100%) rename {src => apps/numaflow-udfs}/udfs/__main__.py (100%) rename {src => apps/numaflow-udfs}/udfs/_base.py (100%) rename {src => apps/numaflow-udfs}/udfs/_config.py (100%) rename {src => apps/numaflow-udfs}/udfs/_logger.py (100%) rename {src => apps/numaflow-udfs}/udfs/_metrics.py (100%) rename {src => apps/numaflow-udfs}/udfs/entities.py (100%) rename {src => apps/numaflow-udfs}/udfs/factory.py (100%) rename {src => apps/numaflow-udfs}/udfs/inference.py (100%) rename {src => apps/numaflow-udfs}/udfs/payloadtx.py (100%) rename {src => apps/numaflow-udfs}/udfs/postprocess.py (100%) rename {src => apps/numaflow-udfs}/udfs/preprocess.py (100%) rename {src => apps/numaflow-udfs}/udfs/staticthresh.py (100%) rename {src => apps/numaflow-udfs}/udfs/tools.py (100%) rename {src => apps/numaflow-udfs}/udfs/trainer/__init__.py (100%) rename {src => apps/numaflow-udfs}/udfs/trainer/_base.py (100%) rename {src => apps/numaflow-udfs}/udfs/trainer/_druid.py (100%) rename {src => apps/numaflow-udfs}/udfs/trainer/_prom.py (100%) rename {src => apps/numaflow-udfs}/udfs/trainer/_rds.py (100%) create mode 100644 libs/connectors/nlconnectors/__init__.py rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/_base.py (100%) rename src/nlconnectors/connectors/_config.py => libs/connectors/nlconnectors/config.py (96%) create mode 100644 libs/connectors/nlconnectors/constants.py create mode 100644 libs/connectors/nlconnectors/druid/__init__.py rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/druid/_druid.py (91%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/druid/aggregators.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/druid/postaggregator.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/exceptions.py (100%) create mode 100644 libs/connectors/nlconnectors/factory.py rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/prometheus.py (99%) create mode 100644 libs/connectors/nlconnectors/rds/__init__.py rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/rds/_base.py (93%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/rds/_rds.py (88%) rename {src/core/numalogic/models => libs/connectors/nlconnectors/rds/db}/__init__.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/rds/db/factory.py (68%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/rds/db/mysql_fetcher.py (96%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/redis.py (96%) rename {src/core/numalogic/tools => libs/connectors/nlconnectors/utils}/__init__.py (100%) rename {src/nlconnectors/connectors/rds/db => libs/connectors/nlconnectors/utils/aws}/__init__.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/aws/boto3_client_manager.py (95%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/aws/config.py (99%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/aws/db_configurations.py (94%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/aws/exceptions.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/aws/sts_client_manager.py (100%) rename {src/nlconnectors/connectors => libs/connectors/nlconnectors}/utils/enum.py (100%) create mode 100644 libs/connectors/pyproject.toml rename {tests/test_numalogic => libs/connectors/tests}/__init__.py (100%) rename {tests/test_connectors => libs/connectors/tests}/data.csv (100%) rename {src/nlconnectors/connectors/utils => libs/connectors/tests/rds}/__init__.py (100%) rename {src/nlconnectors/connectors/utils/aws => libs/connectors/tests/rds/db}/__init__.py (100%) rename {tests/test_connectors => libs/connectors/tests}/rds/db/test_factory.py (70%) rename {tests/test_connectors => libs/connectors/tests}/rds/db/test_mysql_fetcher.py (94%) rename {tests/test_connectors => libs/connectors/tests}/rds/test_base.py (93%) rename {tests/test_connectors => libs/connectors/tests}/rds/test_rds.py (81%) rename {tests => libs/connectors/tests}/resources/config.yaml (100%) rename {tests => libs/connectors/tests}/resources/data/druid.csv (100%) rename {tests => libs/connectors/tests}/resources/data/interactionstatus.csv (100%) rename {tests => libs/connectors/tests}/resources/data/prom_default.csv (100%) rename {tests => libs/connectors/tests}/resources/data/prom_mv.csv (100%) rename {tests => libs/connectors/tests}/resources/data/raw_druid.csv (100%) rename {tests => libs/connectors/tests}/resources/data/rds.csv (100%) rename {tests => libs/connectors/tests}/resources/rds_db_config.yaml (100%) rename {tests/test_connectors => libs/connectors/tests}/test_druid.py (97%) rename {tests/test_connectors => libs/connectors/tests}/test_prometheus.py (94%) rename {tests/test_connectors => libs/connectors/tests}/test_redis.py (91%) rename {tests/test_connectors/rds => libs/connectors/tests/utils}/__init__.py (100%) rename {tests/test_connectors/rds/db => libs/connectors/tests/utils/aws}/__init__.py (100%) rename {tests/test_connectors => libs/connectors/tests}/utils/aws/test_aws_boto3_client_manager.py (85%) rename {tests/test_connectors => libs/connectors/tests}/utils/aws/test_config.py (97%) rename {tests/test_connectors => libs/connectors/tests}/utils/aws/test_db_configurations.py (66%) rename {tests/test_connectors => libs/connectors/tests}/utils/aws/test_sts_client_manager.py (77%) rename {tests/test_connectors => libs/connectors/tests}/utils/test_enum.py (92%) rename {src => libs}/core/numalogic/__init__.py (100%) rename {src => libs}/core/numalogic/_constants.py (100%) rename {src => libs}/core/numalogic/backtest/__init__.py (100%) rename {src => libs}/core/numalogic/backtest/_constants.py (100%) rename {src => libs}/core/numalogic/backtest/_prom.py (100%) rename {src => libs}/core/numalogic/base.py (100%) rename {src => libs}/core/numalogic/config/__init__.py (96%) rename {src => libs}/core/numalogic/config/_config.py (100%) rename {src => libs}/core/numalogic/config/factory.py (71%) rename {tests/test_connectors/utils => libs/core/numalogic/models}/__init__.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/__init__.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/base.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/variants/__init__.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/variants/conv.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/variants/lstm.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/variants/transformer.py (100%) rename {src => libs}/core/numalogic/models/autoencoder/variants/vanilla.py (100%) rename {src => libs}/core/numalogic/models/forecast/__init__.py (100%) rename {src => libs}/core/numalogic/models/forecast/variants/__init__.py (100%) rename {src => libs}/core/numalogic/models/forecast/variants/naive.py (100%) rename {src => libs}/core/numalogic/models/forecast/variants/rnn.py (100%) rename {src => libs}/core/numalogic/models/threshold/__init__.py (100%) rename {src => libs}/core/numalogic/models/threshold/_mahalanobis.py (100%) rename {src => libs}/core/numalogic/models/threshold/_median.py (100%) rename {src => libs}/core/numalogic/models/threshold/_static.py (100%) rename {src => libs}/core/numalogic/models/threshold/_std.py (100%) rename {src => libs}/core/numalogic/models/vae/__init__.py (100%) rename {src => libs}/core/numalogic/models/vae/base.py (100%) rename {src => libs}/core/numalogic/models/vae/layer.py 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rename {src => libs}/core/numalogic/tools/types.py (100%) rename {src => libs}/core/numalogic/transforms/__init__.py (100%) rename {src => libs}/core/numalogic/transforms/_movavg.py (100%) rename {src => libs}/core/numalogic/transforms/_postprocess.py (100%) rename {src => libs}/core/numalogic/transforms/_scaler.py (100%) rename {src => libs}/core/numalogic/transforms/_stateless.py (100%) rename {src => libs}/core/pyproject.toml (92%) rename {tests/test_udfs => libs/core/tests}/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/config/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/config/test_config.py (100%) rename {tests/test_numalogic => libs/core/tests}/models/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/models/autoencoder/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/models/autoencoder/test_trainer.py (100%) rename {tests/test_numalogic => libs/core/tests}/models/autoencoder/variants/__init__.py 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(100%) rename {tests/test_numalogic => libs/core/tests}/models/vae/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/models/vae/test_conv.py (100%) create mode 100644 libs/core/tests/resources/config.yaml create mode 100644 libs/core/tests/resources/data/druid.csv create mode 100644 libs/core/tests/resources/data/interactionstatus.csv create mode 100644 libs/core/tests/resources/data/prom_default.csv create mode 100644 libs/core/tests/resources/data/prom_mv.csv create mode 100644 libs/core/tests/resources/data/raw_druid.csv create mode 100644 libs/core/tests/resources/data/rds.csv create mode 100644 libs/core/tests/resources/rds_db_config.yaml rename {tests/test_numalogic => libs/core/tests}/synthetic/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/synthetic/test_anomalies.py (100%) rename {tests/test_numalogic => libs/core/tests}/synthetic/test_sparsity.py (100%) rename {tests/test_numalogic => libs/core/tests}/synthetic/test_timeseries.py (100%) rename {tests/test_numalogic => libs/core/tests}/tools/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/tools/test_data.py (100%) rename {tests/test_numalogic => libs/core/tests}/transforms/__init__.py (100%) rename {tests/test_numalogic => libs/core/tests}/transforms/test_postprocess.py (100%) rename {tests/test_numalogic => libs/core/tests}/transforms/test_transforms.py (100%) create mode 100644 libs/registry/nlregistry/__init__.py rename {src/registry => libs/registry/nlregistry}/__setup__.py (87%) rename {src/registry => libs/registry/nlregistry}/_serialize.py (100%) rename {src/registry => libs/registry/nlregistry}/artifact.py (100%) rename {src/registry => libs/registry/nlregistry}/dynamodb_registry.py (89%) create mode 100644 libs/registry/nlregistry/factory.py rename {src/registry => libs/registry/nlregistry}/localcache.py (98%) rename {src/registry => libs/registry/nlregistry}/mlflow_registry.py (89%) rename {src/registry => libs/registry/nlregistry}/redis_registry.py (87%) create mode 100644 libs/registry/pyproject.toml rename {tests/test_registry => libs/registry/tests}/__init__.py (100%) rename {tests/test_registry => libs/registry/tests}/_mlflow_utils.py (100%) rename {tests/test_registry => libs/registry/tests}/test_cache.py (71%) rename {tests/test_registry => libs/registry/tests}/test_dynamodb_registry.py (97%) rename {tests/test_registry => libs/registry/tests}/test_mlflow_registry.py (69%) rename {tests/test_numalogic/config => libs/registry/tests}/test_optdeps.py (77%) rename {tests/test_registry => libs/registry/tests}/test_redis_registry.py (85%) rename {tests/test_registry => libs/registry/tests}/test_serialize.py (97%) delete mode 100644 src/nlconnectors/connectors/__init__.py delete mode 100644 src/nlconnectors/connectors/__setup__.py delete mode 100644 src/nlconnectors/connectors/druid/__init__.py delete mode 100644 src/nlconnectors/connectors/pyproject.toml delete mode 100644 src/nlconnectors/connectors/rds/__init__.py delete mode 100644 src/registry/__init__.py diff --git a/tests/test_connectors/__init__.py b/apps/numaflow-udfs/tests/__init__.py similarity index 100% rename from tests/test_connectors/__init__.py rename to apps/numaflow-udfs/tests/__init__.py diff --git a/tests/test_udfs/resources/_config.yaml b/apps/numaflow-udfs/tests/resources/_config.yaml similarity index 100% rename from tests/test_udfs/resources/_config.yaml rename to apps/numaflow-udfs/tests/resources/_config.yaml diff --git a/tests/test_udfs/resources/_config2.yaml b/apps/numaflow-udfs/tests/resources/_config2.yaml similarity index 100% rename from tests/test_udfs/resources/_config2.yaml rename to apps/numaflow-udfs/tests/resources/_config2.yaml diff --git a/tests/test_udfs/resources/_config3.yaml b/apps/numaflow-udfs/tests/resources/_config3.yaml similarity index 100% rename from tests/test_udfs/resources/_config3.yaml rename to apps/numaflow-udfs/tests/resources/_config3.yaml diff --git a/tests/test_udfs/resources/_config4.yaml b/apps/numaflow-udfs/tests/resources/_config4.yaml similarity index 100% rename from tests/test_udfs/resources/_config4.yaml rename to apps/numaflow-udfs/tests/resources/_config4.yaml diff --git a/tests/test_udfs/resources/data/stream.json b/apps/numaflow-udfs/tests/resources/data/stream.json similarity index 100% rename from tests/test_udfs/resources/data/stream.json rename to apps/numaflow-udfs/tests/resources/data/stream.json diff --git a/tests/test_udfs/resources/rds_config.yaml b/apps/numaflow-udfs/tests/resources/rds_config.yaml similarity index 100% rename from tests/test_udfs/resources/rds_config.yaml rename to apps/numaflow-udfs/tests/resources/rds_config.yaml diff --git a/tests/test_udfs/resources/rds_config2.yaml b/apps/numaflow-udfs/tests/resources/rds_config2.yaml similarity index 100% rename from tests/test_udfs/resources/rds_config2.yaml rename to apps/numaflow-udfs/tests/resources/rds_config2.yaml diff --git a/tests/test_udfs/resources/rds_trainer_config_fetcher_conf.yaml b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml similarity index 100% rename from tests/test_udfs/resources/rds_trainer_config_fetcher_conf.yaml rename to apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml diff --git a/tests/test_udfs/resources/rds_trainer_config_fetcher_conf1.yaml b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml similarity index 100% rename from tests/test_udfs/resources/rds_trainer_config_fetcher_conf1.yaml rename to apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml diff --git a/tests/test_numalogic/test_backtest.py b/apps/numaflow-udfs/tests/test_backtest.py similarity index 100% rename from tests/test_numalogic/test_backtest.py rename to apps/numaflow-udfs/tests/test_backtest.py diff --git a/tests/test_udfs/test_factory.py b/apps/numaflow-udfs/tests/test_factory.py similarity index 100% rename from tests/test_udfs/test_factory.py rename to apps/numaflow-udfs/tests/test_factory.py diff --git a/tests/test_udfs/test_inference.py b/apps/numaflow-udfs/tests/test_inference.py similarity index 100% rename from tests/test_udfs/test_inference.py rename to apps/numaflow-udfs/tests/test_inference.py diff --git a/tests/test_udfs/test_main.py b/apps/numaflow-udfs/tests/test_main.py similarity index 100% rename from tests/test_udfs/test_main.py rename to apps/numaflow-udfs/tests/test_main.py diff --git a/tests/test_udfs/test_numaflow.py b/apps/numaflow-udfs/tests/test_numaflow.py similarity index 100% rename from tests/test_udfs/test_numaflow.py rename to apps/numaflow-udfs/tests/test_numaflow.py diff --git a/tests/test_udfs/test_pipeline.py b/apps/numaflow-udfs/tests/test_pipeline.py similarity index 84% rename from tests/test_udfs/test_pipeline.py rename to apps/numaflow-udfs/tests/test_pipeline.py index 1fc439f8..3b27f42b 100644 --- a/tests/test_udfs/test_pipeline.py +++ b/apps/numaflow-udfs/tests/test_pipeline.py @@ -14,9 +14,7 @@ logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file( - os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json") -) +DATUM = input_json_from_file(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json")) DATUM_KW = { "event_time": datetime.now(), @@ -26,11 +24,9 @@ class TestPipelineUDF(unittest.TestCase): def setUp(self) -> None: - _given_conf = OmegaConf.load( - os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml") - ) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) _given_conf_2 = OmegaConf.load( - os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml") + os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml") ) schema = OmegaConf.structured(PipelineConf) pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) diff --git a/tests/test_udfs/test_postprocess.py b/apps/numaflow-udfs/tests/test_postprocess.py similarity index 98% rename from tests/test_udfs/test_postprocess.py rename to apps/numaflow-udfs/tests/test_postprocess.py index 164dfacb..aa2eb9d1 100644 --- a/tests/test_udfs/test_postprocess.py +++ b/apps/numaflow-udfs/tests/test_postprocess.py @@ -84,7 +84,7 @@ @pytest.fixture def conf(): - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) diff --git a/tests/test_udfs/test_preprocess.py b/apps/numaflow-udfs/tests/test_preprocess.py similarity index 90% rename from tests/test_udfs/test_preprocess.py rename to apps/numaflow-udfs/tests/test_preprocess.py index 721efc9b..6935728b 100644 --- a/tests/test_udfs/test_preprocess.py +++ b/apps/numaflow-udfs/tests/test_preprocess.py @@ -21,9 +21,7 @@ logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file( - os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json") -) +DATUM = input_json_from_file(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json")) DATUM_KW = { "event_time": datetime.now(), @@ -34,10 +32,8 @@ @pytest.fixture def setup(): registry = RedisRegistry(REDIS_CLIENT) - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) - _given_conf_2 = OmegaConf.load( - os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml") - ) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) + _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml")) schema = OmegaConf.structured(PipelineConf) pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) @@ -115,7 +111,7 @@ def test_preprocess_run_time_error(setup, mocker): def test_preprocess_data_error(setup): udf1, _ = setup - with open(os.path.join(TESTS_DIR, "test_udfs", "resources", "data", "stream.json"), "rb") as f: + with open(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json"), "rb") as f: stream = json.load(f) stream["data"] = stream["data"][:5] msg = udf1( diff --git a/tests/test_udfs/test_staticthresh.py b/apps/numaflow-udfs/tests/test_staticthresh.py similarity index 97% rename from tests/test_udfs/test_staticthresh.py rename to apps/numaflow-udfs/tests/test_staticthresh.py index a5d141f6..67b1dc0c 100644 --- a/tests/test_udfs/test_staticthresh.py +++ b/apps/numaflow-udfs/tests/test_staticthresh.py @@ -78,7 +78,7 @@ @pytest.fixture def conf() -> PipelineConf: - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) diff --git a/tests/test_udfs/test_trainer.py b/apps/numaflow-udfs/tests/test_trainer.py similarity index 98% rename from tests/test_udfs/test_trainer.py rename to apps/numaflow-udfs/tests/test_trainer.py index d322ad38..7c16a219 100644 --- a/tests/test_udfs/test_trainer.py +++ b/apps/numaflow-udfs/tests/test_trainer.py @@ -58,9 +58,9 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config.yaml")) + conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) - conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config2.yaml")) + conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml")) conf_1 = OmegaConf.merge(schema, conf_1) conf_2 = OmegaConf.merge(schema, conf_2) self.pl_conf_1 = PipelineConf(**OmegaConf.merge(schema, conf_1)) @@ -444,7 +444,7 @@ def test_druid_from_config_1(self): with self.assertLogs(level="WARN") as log: self.udf1(self.keys, self.datum) self.assertLogs( - "WARNING:numalogic.test_udfs._logger:uuid='some-uuid' event='Caught " + "WARNING:numalogic.tests._logger:uuid='some-uuid' event='Caught " "exception/error while fetching from source' udf_vertex='trainer' " "config_id='druid-config' pipeline_id='pipeline1' metadata={} " "keys=['5984175597303660107'] level='warning' " @@ -456,7 +456,7 @@ def test_druid_from_config_2(self): with self.assertLogs(level="WARN") as log: self.udf2(self.keys, self.datum) self.assertLogs( - "WARNING:numalogic.test_udfs._logger:uuid='some-uuid' event='Caught " + "WARNING:numalogic.tests._logger:uuid='some-uuid' event='Caught " "exception/error while fetching from source' udf_vertex='trainer' " "config_id='druid-config' pipeline_id='pipeline1' metadata={} " "keys=['5984175597303660107'] level='warning'", @@ -570,7 +570,7 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf = OmegaConf.load(os.path.join(TESTS_DIR, "test_udfs", "resources", "_config3.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config3.yaml")) self.conf = OmegaConf.merge(OmegaConf.structured(PipelineConf), conf) @patch.object(PromTrainerUDF, "fetch_data", Mock(return_value=_mock_mv_fetch_data())) diff --git a/tests/test_udfs/utility.py b/apps/numaflow-udfs/tests/utility.py similarity index 100% rename from tests/test_udfs/utility.py rename to apps/numaflow-udfs/tests/utility.py diff --git a/src/udfs/Dockerfile b/apps/numaflow-udfs/udfs/Dockerfile similarity index 99% rename from src/udfs/Dockerfile rename to apps/numaflow-udfs/udfs/Dockerfile index ac0f352a..861f42cb 100644 --- a/src/udfs/Dockerfile +++ b/apps/numaflow-udfs/udfs/Dockerfile @@ -46,7 +46,7 @@ ENV VIRTUAL_ENV=/app/.venv COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} ENV PATH="$VIRTUAL_ENV/bin:$PATH" -COPY ../.. /app +COPY ../../.. /app WORKDIR /app ENTRYPOINT ["/usr/bin/dumb-init", "--"] diff --git a/src/udfs/README.md b/apps/numaflow-udfs/udfs/README.md similarity index 100% rename from src/udfs/README.md rename to apps/numaflow-udfs/udfs/README.md diff --git a/src/udfs/__init__.py b/apps/numaflow-udfs/udfs/__init__.py similarity index 100% rename from src/udfs/__init__.py rename to apps/numaflow-udfs/udfs/__init__.py diff --git a/src/udfs/__main__.py b/apps/numaflow-udfs/udfs/__main__.py similarity index 100% rename from src/udfs/__main__.py rename to apps/numaflow-udfs/udfs/__main__.py diff --git a/src/udfs/_base.py b/apps/numaflow-udfs/udfs/_base.py similarity index 100% rename from src/udfs/_base.py rename to apps/numaflow-udfs/udfs/_base.py diff --git a/src/udfs/_config.py b/apps/numaflow-udfs/udfs/_config.py similarity index 100% rename from src/udfs/_config.py rename to apps/numaflow-udfs/udfs/_config.py diff --git a/src/udfs/_logger.py b/apps/numaflow-udfs/udfs/_logger.py similarity index 100% rename from src/udfs/_logger.py rename to apps/numaflow-udfs/udfs/_logger.py diff --git a/src/udfs/_metrics.py b/apps/numaflow-udfs/udfs/_metrics.py similarity index 100% rename from src/udfs/_metrics.py rename to apps/numaflow-udfs/udfs/_metrics.py diff --git a/src/udfs/entities.py b/apps/numaflow-udfs/udfs/entities.py similarity index 100% rename from src/udfs/entities.py rename to apps/numaflow-udfs/udfs/entities.py diff --git a/src/udfs/factory.py b/apps/numaflow-udfs/udfs/factory.py similarity index 100% rename from src/udfs/factory.py rename to apps/numaflow-udfs/udfs/factory.py diff --git a/src/udfs/inference.py b/apps/numaflow-udfs/udfs/inference.py similarity index 100% rename from src/udfs/inference.py rename to apps/numaflow-udfs/udfs/inference.py diff --git a/src/udfs/payloadtx.py b/apps/numaflow-udfs/udfs/payloadtx.py similarity index 100% rename from src/udfs/payloadtx.py rename to apps/numaflow-udfs/udfs/payloadtx.py diff --git a/src/udfs/postprocess.py b/apps/numaflow-udfs/udfs/postprocess.py similarity index 100% rename from src/udfs/postprocess.py rename to apps/numaflow-udfs/udfs/postprocess.py diff --git a/src/udfs/preprocess.py b/apps/numaflow-udfs/udfs/preprocess.py similarity index 100% rename from src/udfs/preprocess.py rename to apps/numaflow-udfs/udfs/preprocess.py diff --git a/src/udfs/staticthresh.py b/apps/numaflow-udfs/udfs/staticthresh.py similarity index 100% rename from src/udfs/staticthresh.py rename to apps/numaflow-udfs/udfs/staticthresh.py diff --git a/src/udfs/tools.py b/apps/numaflow-udfs/udfs/tools.py similarity index 100% rename from src/udfs/tools.py rename to apps/numaflow-udfs/udfs/tools.py diff --git a/src/udfs/trainer/__init__.py b/apps/numaflow-udfs/udfs/trainer/__init__.py similarity index 100% rename from src/udfs/trainer/__init__.py rename to apps/numaflow-udfs/udfs/trainer/__init__.py diff --git a/src/udfs/trainer/_base.py b/apps/numaflow-udfs/udfs/trainer/_base.py similarity index 100% rename from src/udfs/trainer/_base.py rename to apps/numaflow-udfs/udfs/trainer/_base.py diff --git a/src/udfs/trainer/_druid.py b/apps/numaflow-udfs/udfs/trainer/_druid.py similarity index 100% rename from src/udfs/trainer/_druid.py rename to apps/numaflow-udfs/udfs/trainer/_druid.py diff --git a/src/udfs/trainer/_prom.py b/apps/numaflow-udfs/udfs/trainer/_prom.py similarity index 100% rename from src/udfs/trainer/_prom.py rename to apps/numaflow-udfs/udfs/trainer/_prom.py diff --git a/src/udfs/trainer/_rds.py b/apps/numaflow-udfs/udfs/trainer/_rds.py similarity index 100% rename from src/udfs/trainer/_rds.py rename to apps/numaflow-udfs/udfs/trainer/_rds.py diff --git a/libs/connectors/nlconnectors/__init__.py b/libs/connectors/nlconnectors/__init__.py new file mode 100644 index 00000000..d20ce517 --- /dev/null +++ b/libs/connectors/nlconnectors/__init__.py @@ -0,0 +1,17 @@ +from importlib.util import find_spec + +from nlconnectors.prometheus import PrometheusFetcher + +__all__ = [ + "PrometheusFetcher", +] + +if find_spec("boto3"): + from nlconnectors.rds import RDSFetcher # noqa: F401 + + __all__.append("RDSFetcher") + +if find_spec("pydruid"): + from nlconnectors.druid import DruidFetcher # noqa: F401 + + __all__.append("DruidFetcher") diff --git a/src/nlconnectors/connectors/_base.py b/libs/connectors/nlconnectors/_base.py similarity index 100% rename from src/nlconnectors/connectors/_base.py rename to libs/connectors/nlconnectors/_base.py diff --git a/src/nlconnectors/connectors/_config.py b/libs/connectors/nlconnectors/config.py similarity index 96% rename from src/nlconnectors/connectors/_config.py rename to libs/connectors/nlconnectors/config.py index 32dd71d3..2ae221bd 100644 --- a/src/nlconnectors/connectors/_config.py +++ b/libs/connectors/nlconnectors/config.py @@ -1,8 +1,8 @@ from dataclasses import dataclass, field from enum import IntEnum from typing import Optional -from numalogic.connectors.utils.aws.config import RDSConnectionConfig -from numalogic.connectors.exceptions import RDSFetcherConfValidationException +from nlconnectors.utils.aws.config import RDSConnectionConfig +from nlconnectors.exceptions import RDSFetcherConfValidationException class ConnectorType(IntEnum): diff --git a/libs/connectors/nlconnectors/constants.py b/libs/connectors/nlconnectors/constants.py new file mode 100644 index 00000000..6d143308 --- /dev/null +++ b/libs/connectors/nlconnectors/constants.py @@ -0,0 +1,4 @@ +import os + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +TESTS_DIR = os.path.join(BASE_DIR, "../tests") diff --git a/libs/connectors/nlconnectors/druid/__init__.py b/libs/connectors/nlconnectors/druid/__init__.py new file mode 100644 index 00000000..ecaa93bd --- /dev/null +++ b/libs/connectors/nlconnectors/druid/__init__.py @@ -0,0 +1,3 @@ +from nlconnectors.druid._druid import DruidFetcher, make_filter_pairs, build_params + +__all__ = ["DruidFetcher", "make_filter_pairs", "build_params"] diff --git a/src/nlconnectors/connectors/druid/_druid.py b/libs/connectors/nlconnectors/druid/_druid.py similarity index 91% rename from src/nlconnectors/connectors/druid/_druid.py rename to libs/connectors/nlconnectors/druid/_druid.py index 121baa4f..fdce34cf 100644 --- a/src/nlconnectors/connectors/druid/_druid.py +++ b/libs/connectors/nlconnectors/druid/_druid.py @@ -9,8 +9,8 @@ from pydruid.utils.dimensions import DimensionSpec from pydruid.utils.filters import Filter -from numalogic.connectors._base import DataFetcher -from numalogic.connectors._config import Pivot, FilterConf +from nlconnectors._base import DataFetcher +from nlconnectors.config import Pivot, FilterConf from typing import Optional, Final from numalogic.tools.exceptions import DruidFetcherError @@ -21,7 +21,9 @@ # TODO: pass dictionary of keys and values as dict -def make_filter_pairs(filter_keys: list[str], filter_values: list[str]) -> dict[str, str]: +def make_filter_pairs( + filter_keys: list[str], filter_values: list[str] +) -> dict[str, str]: """ Args: @@ -87,7 +89,10 @@ def build_params( """ _filter = Filter( type="and", - fields=[Filter(type="selector", dimension=k, value=v) for k, v in filter_pairs.items()], + fields=[ + Filter(type="selector", dimension=k, value=v) + for k, v in filter_pairs.items() + ], ) if static_filters: _LOGGER.debug("Static Filters are present!") @@ -96,12 +101,16 @@ def build_params( reference_dt = reference_dt or datetime.now(pytz.utc) end_dt = reference_dt - timedelta(hours=delay) - _LOGGER.debug("Querying with end_dt: %s, that is with delay of %s hrs", end_dt, delay) + _LOGGER.debug( + "Querying with end_dt: %s, that is with delay of %s hrs", end_dt, delay + ) start_dt = end_dt - timedelta(hours=hours) intervals = [f"{start_dt.isoformat()}/{end_dt.isoformat()}"] - dimension_specs = map(lambda d: DimensionSpec(dimension=d, output_name=d), dimensions) + dimension_specs = map( + lambda d: DimensionSpec(dimension=d, output_name=d), dimensions + ) return { "datasource": datasource, @@ -111,7 +120,11 @@ def build_params( "post_aggregations": post_aggregations or dict(), "filter": _filter, "dimensions": dimension_specs, - "context": {"timeout": TIMEOUT, "configIds": list(filter_pairs), "source": "numalogic"}, + "context": { + "timeout": TIMEOUT, + "configIds": list(filter_pairs), + "source": "numalogic", + }, } @@ -202,7 +215,10 @@ def fetch( pivoted_frames = [] for idx, column in enumerate(pivot.columns): _df = df.pivot_table( - index=pivot.index, columns=[column], values=pivot.value, aggfunc=pivot.agg[idx] + index=pivot.index, + columns=[column], + values=pivot.value, + aggfunc=pivot.agg[idx], ) pivoted_frames.append(_df) @@ -305,7 +321,10 @@ def chunked_fetch( pivoted_frames = [] for idx, column in enumerate(pivot.columns): _df = df.pivot_table( - index=pivot.index, columns=[column], values=pivot.value, aggfunc=pivot.agg[idx] + index=pivot.index, + columns=[column], + values=pivot.value, + aggfunc=pivot.agg[idx], ) pivoted_frames.append(_df) diff --git a/src/nlconnectors/connectors/druid/aggregators.py b/libs/connectors/nlconnectors/druid/aggregators.py similarity index 100% rename from src/nlconnectors/connectors/druid/aggregators.py rename to libs/connectors/nlconnectors/druid/aggregators.py diff --git a/src/nlconnectors/connectors/druid/postaggregator.py b/libs/connectors/nlconnectors/druid/postaggregator.py similarity index 100% rename from src/nlconnectors/connectors/druid/postaggregator.py rename to libs/connectors/nlconnectors/druid/postaggregator.py diff --git a/src/nlconnectors/connectors/exceptions.py b/libs/connectors/nlconnectors/exceptions.py similarity index 100% rename from src/nlconnectors/connectors/exceptions.py rename to libs/connectors/nlconnectors/exceptions.py diff --git a/libs/connectors/nlconnectors/factory.py b/libs/connectors/nlconnectors/factory.py new file mode 100644 index 00000000..59c71c1d --- /dev/null +++ b/libs/connectors/nlconnectors/factory.py @@ -0,0 +1,27 @@ +from typing import ClassVar + +from numalogic.config.factory import _ObjectFactory +from numalogic.tools.exceptions import UnknownConfigArgsError + + +class ConnectorFactory(_ObjectFactory): + """Factory class for data test_connectors.""" + + _CLS_SET: ClassVar[frozenset] = frozenset( + {"PrometheusFetcher", "DruidFetcher", "RDSFetcher"} + ) + + @classmethod + def get_cls(cls, name: str): + import nlconnectors as conn + + try: + return getattr(conn, name) + except AttributeError as err: + if name in cls._CLS_SET: + raise ImportError( + "Please install the required dependencies for the connector you want to use." + ) from err + raise UnknownConfigArgsError( + f"Invalid name provided for ConnectorFactory: {name}" + ) from None diff --git a/src/nlconnectors/connectors/prometheus.py b/libs/connectors/nlconnectors/prometheus.py similarity index 99% rename from src/nlconnectors/connectors/prometheus.py rename to libs/connectors/nlconnectors/prometheus.py index f6c092e5..b164be27 100644 --- a/src/nlconnectors/connectors/prometheus.py +++ b/libs/connectors/nlconnectors/prometheus.py @@ -21,7 +21,7 @@ import requests from orjson import JSONDecodeError -from connectors._base import DataFetcher +from nlconnectors._base import DataFetcher from numalogic.tools.exceptions import PrometheusFetcherError, PrometheusInvalidResponseError LOGGER = logging.getLogger(__name__) diff --git a/libs/connectors/nlconnectors/rds/__init__.py b/libs/connectors/nlconnectors/rds/__init__.py new file mode 100644 index 00000000..6b6e3f26 --- /dev/null +++ b/libs/connectors/nlconnectors/rds/__init__.py @@ -0,0 +1,3 @@ +from nlconnectors.rds._rds import RDSFetcher + +__all__ = ["RDSFetcher"] diff --git a/src/nlconnectors/connectors/rds/_base.py b/libs/connectors/nlconnectors/rds/_base.py similarity index 93% rename from src/nlconnectors/connectors/rds/_base.py rename to libs/connectors/nlconnectors/rds/_base.py index 5cf260b7..d370c2e6 100644 --- a/src/nlconnectors/connectors/rds/_base.py +++ b/libs/connectors/nlconnectors/rds/_base.py @@ -1,10 +1,10 @@ from abc import ABCMeta, abstractmethod from typing import Optional import pandas as pd -from numalogic.connectors.utils.aws.config import DatabaseServiceProvider, RDSConnectionConfig -from numalogic.connectors.utils.aws.boto3_client_manager import Boto3ClientManager +from nlconnectors.utils.aws.config import DatabaseServiceProvider, RDSConnectionConfig +from nlconnectors.utils.aws.boto3_client_manager import Boto3ClientManager import logging -from numalogic.connectors._config import Pivot +from nlconnectors.config import Pivot import time _LOGGER = logging.getLogger(__name__) @@ -40,7 +40,9 @@ def format_dataframe( """ _start_time = time.perf_counter() - df["timestamp"] = pd.to_datetime(df[datetime_column_name]).astype("int64") // 10**6 + df["timestamp"] = ( + pd.to_datetime(df[datetime_column_name]).astype("int64") // 10**6 + ) df.drop(columns=datetime_column_name, inplace=True) if group_by: df = df.groupby(by=group_by).sum().reset_index() diff --git a/src/nlconnectors/connectors/rds/_rds.py b/libs/connectors/nlconnectors/rds/_rds.py similarity index 88% rename from src/nlconnectors/connectors/rds/_rds.py rename to libs/connectors/nlconnectors/rds/_rds.py index 76024db6..e5f9cdcf 100644 --- a/src/nlconnectors/connectors/rds/_rds.py +++ b/libs/connectors/nlconnectors/rds/_rds.py @@ -1,11 +1,11 @@ from typing import Optional -from numalogic.connectors._base import DataFetcher -from numalogic.connectors._config import Pivot -from numalogic.connectors.rds._base import format_dataframe -from numalogic.connectors.utils.aws.config import RDSConnectionConfig +from nlconnectors._base import DataFetcher +from nlconnectors.config import Pivot +from nlconnectors.rds._base import format_dataframe +from nlconnectors.utils.aws.config import RDSConnectionConfig import logging import pandas as pd -from numalogic.connectors.rds.db.factory import RdsFactory +from nlconnectors.rds.db.factory import RdsFactory import time _LOGGER = logging.getLogger(__name__) @@ -28,7 +28,9 @@ def __init__(self, db_config: RDSConnectionConfig): super().__init__(db_config.endpoint) self.db_config = db_config factory_object = RdsFactory() - self.fetcher = factory_object.get_db_handler(db_config.database_type.lower())(db_config) + self.fetcher = factory_object.get_db_handler(db_config.database_type.lower())( + db_config + ) _LOGGER.info("Executing for database type: %s", self.fetcher.database_type) def fetch( diff --git a/src/core/numalogic/models/__init__.py b/libs/connectors/nlconnectors/rds/db/__init__.py similarity index 100% rename from src/core/numalogic/models/__init__.py rename to libs/connectors/nlconnectors/rds/db/__init__.py diff --git a/src/nlconnectors/connectors/rds/db/factory.py b/libs/connectors/nlconnectors/rds/db/factory.py similarity index 68% rename from src/nlconnectors/connectors/rds/db/factory.py rename to libs/connectors/nlconnectors/rds/db/factory.py index d41359f0..c2c3f915 100644 --- a/src/nlconnectors/connectors/rds/db/factory.py +++ b/libs/connectors/nlconnectors/rds/db/factory.py @@ -1,7 +1,7 @@ import logging -from connectors import DatabaseTypes -from connectors import UnRecognizedDatabaseTypeException +from nlconnectors.utils.aws.config import DatabaseTypes +from nlconnectors.utils.aws.exceptions import UnRecognizedDatabaseTypeException _LOGGER = logging.getLogger(__name__) @@ -27,8 +27,10 @@ def get_db_handler(cls, database_type: DatabaseTypes): """ if database_type == DatabaseTypes.MYSQL: - from connectors import MysqlFetcher + from nlconnectors.rds.db.mysql_fetcher import MysqlFetcher return MysqlFetcher - raise UnRecognizedDatabaseTypeException(f"database_type: {database_type} is not supported") + raise UnRecognizedDatabaseTypeException( + f"database_type: {database_type} is not supported" + ) diff --git a/src/nlconnectors/connectors/rds/db/mysql_fetcher.py b/libs/connectors/nlconnectors/rds/db/mysql_fetcher.py similarity index 96% rename from src/nlconnectors/connectors/rds/db/mysql_fetcher.py rename to libs/connectors/nlconnectors/rds/db/mysql_fetcher.py index cfa65a8f..961ff3e7 100644 --- a/src/nlconnectors/connectors/rds/db/mysql_fetcher.py +++ b/libs/connectors/nlconnectors/rds/db/mysql_fetcher.py @@ -2,12 +2,12 @@ from pymysql.cursors import Cursor -from numalogic.connectors.rds._base import RDSBase +from nlconnectors.rds._base import RDSBase import pymysql import pandas as pd import logging -from numalogic.connectors.utils.aws.config import DatabaseTypes, RDSConnectionConfig +from nlconnectors.utils.aws.config import DatabaseTypes, RDSConnectionConfig _LOGGER = logging.getLogger(__name__) diff --git a/src/nlconnectors/connectors/redis.py b/libs/connectors/nlconnectors/redis.py similarity index 96% rename from src/nlconnectors/connectors/redis.py rename to libs/connectors/nlconnectors/redis.py index 60c10e87..477084ce 100644 --- a/src/nlconnectors/connectors/redis.py +++ b/libs/connectors/nlconnectors/redis.py @@ -2,7 +2,7 @@ import os from typing import Optional -from connectors import RedisConf +from nlconnectors.config import RedisConf from numalogic.tools.exceptions import EnvVarNotFoundError, ConfigNotFoundError from numalogic.tools.types import redis_client_t from redis.backoff import ExponentialBackoff @@ -64,7 +64,7 @@ def get_redis_client( sentinel_kwargs=dict(password=password, **conn_kwargs), retry=retry, password=password, - **conn_kwargs + **conn_kwargs, ) if master_node: SENTINEL_CLIENT = sentinel.master_for(mastername) @@ -98,5 +98,5 @@ def get_redis_client_from_conf(redis_conf: RedisConf, **kwargs) -> redis_client_ redis_conf.port, password=os.getenv("REDIS_AUTH"), mastername=redis_conf.master_name, - **kwargs + **kwargs, ) diff --git a/src/core/numalogic/tools/__init__.py b/libs/connectors/nlconnectors/utils/__init__.py similarity index 100% rename from src/core/numalogic/tools/__init__.py rename to libs/connectors/nlconnectors/utils/__init__.py diff --git a/src/nlconnectors/connectors/rds/db/__init__.py b/libs/connectors/nlconnectors/utils/aws/__init__.py similarity index 100% rename from src/nlconnectors/connectors/rds/db/__init__.py rename to libs/connectors/nlconnectors/utils/aws/__init__.py diff --git a/src/nlconnectors/connectors/utils/aws/boto3_client_manager.py b/libs/connectors/nlconnectors/utils/aws/boto3_client_manager.py similarity index 95% rename from src/nlconnectors/connectors/utils/aws/boto3_client_manager.py rename to libs/connectors/nlconnectors/utils/aws/boto3_client_manager.py index 5f569df2..22de695c 100644 --- a/src/nlconnectors/connectors/utils/aws/boto3_client_manager.py +++ b/libs/connectors/nlconnectors/utils/aws/boto3_client_manager.py @@ -2,9 +2,9 @@ from boto3 import Session import logging -from numalogic.connectors.utils.aws.config import DatabaseServiceProvider, RDSConnectionConfig -from numalogic.connectors.utils.aws.exceptions import UnRecognizedAWSClientException -from numalogic.connectors.utils.aws.sts_client_manager import STSClientManager +from nlconnectors.utils.aws.config import DatabaseServiceProvider, RDSConnectionConfig +from nlconnectors.utils.aws.exceptions import UnRecognizedAWSClientException +from nlconnectors.utils.aws.sts_client_manager import STSClientManager logging.basicConfig(level=logging.INFO) _LOGGER = logging.getLogger(__name__) diff --git a/src/nlconnectors/connectors/utils/aws/config.py b/libs/connectors/nlconnectors/utils/aws/config.py similarity index 99% rename from src/nlconnectors/connectors/utils/aws/config.py rename to libs/connectors/nlconnectors/utils/aws/config.py index 2475be5e..a935367f 100644 --- a/src/nlconnectors/connectors/utils/aws/config.py +++ b/libs/connectors/nlconnectors/utils/aws/config.py @@ -1,7 +1,7 @@ from dataclasses import dataclass, field from typing import Optional -from connectors import BaseEnum +from nlconnectors.utils.enum import BaseEnum class DatabaseServiceProvider(str, BaseEnum): diff --git a/src/nlconnectors/connectors/utils/aws/db_configurations.py b/libs/connectors/nlconnectors/utils/aws/db_configurations.py similarity index 94% rename from src/nlconnectors/connectors/utils/aws/db_configurations.py rename to libs/connectors/nlconnectors/utils/aws/db_configurations.py index 867d45ad..bdebeb45 100644 --- a/src/nlconnectors/connectors/utils/aws/db_configurations.py +++ b/libs/connectors/nlconnectors/utils/aws/db_configurations.py @@ -2,7 +2,7 @@ from numalogic.tools.exceptions import ConfigNotFoundError from omegaconf import OmegaConf -from numalogic.connectors.utils.aws.config import RDSConnectionConfig +from nlconnectors.utils.aws.config import RDSConnectionConfig _LOGGER = logging.getLogger(__name__) diff --git a/src/nlconnectors/connectors/utils/aws/exceptions.py b/libs/connectors/nlconnectors/utils/aws/exceptions.py similarity index 100% rename from src/nlconnectors/connectors/utils/aws/exceptions.py rename to libs/connectors/nlconnectors/utils/aws/exceptions.py diff --git a/src/nlconnectors/connectors/utils/aws/sts_client_manager.py b/libs/connectors/nlconnectors/utils/aws/sts_client_manager.py similarity index 100% rename from src/nlconnectors/connectors/utils/aws/sts_client_manager.py rename to libs/connectors/nlconnectors/utils/aws/sts_client_manager.py diff --git a/src/nlconnectors/connectors/utils/enum.py b/libs/connectors/nlconnectors/utils/enum.py similarity index 100% rename from src/nlconnectors/connectors/utils/enum.py rename to libs/connectors/nlconnectors/utils/enum.py diff --git a/libs/connectors/pyproject.toml b/libs/connectors/pyproject.toml new file mode 100644 index 00000000..2625a73d --- /dev/null +++ b/libs/connectors/pyproject.toml @@ -0,0 +1,56 @@ +[build-system] +requires = ["setuptools >= 61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "nlconnectors" +version = "0.10.1a0" +description = "Data Connectors for Numalogic." +authors = [ + {name = "Numalogic Developers"} +] +readme = "README.md" +maintainers = [ + { name = "Avik Basu", email = "avikbasu93@gmail.com"}, + { name = "Vigith Maurice", email = "vigith@gmail.com"} +] +classifiers = [ + "Topic :: Software Development :: Libraries", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12" +] +requires-python = ">= 3.9" +dependencies = [ + "requests>=2.0", + "orjson>=3.9", + "pydruid>=0.6", + "boto3>=1.20", + "PyMySQL>=1.0", + "redis[hiredis]>=5.0", + "omegaconf>=2.3", +] + +[project.optional-dependencies] +dev = [ + "moto>=4.1", + "black>23.0", + "pytest>=7.0", + "pytest-cov>=4.0", + "ruff>=0.0.275", + "pre-commit>=3.3", + "pytest-mock>=3.12", + "freezegun>=1.2", + "deepdiff>=6.5.0", + "fakeredis>=2.10", +] + + +[project.urls] +Repository = "https://github.com/numaproj/numalogic" +Documentation = "https://numalogic.numaproj.io/" +Homepage = "https://numalogic.numaproj.io/" \ No newline at end of file diff --git a/tests/test_numalogic/__init__.py b/libs/connectors/tests/__init__.py similarity index 100% rename from tests/test_numalogic/__init__.py rename to libs/connectors/tests/__init__.py diff --git a/tests/test_connectors/data.csv b/libs/connectors/tests/data.csv similarity index 100% rename from tests/test_connectors/data.csv rename to libs/connectors/tests/data.csv diff --git a/src/nlconnectors/connectors/utils/__init__.py b/libs/connectors/tests/rds/__init__.py similarity index 100% rename from src/nlconnectors/connectors/utils/__init__.py rename to libs/connectors/tests/rds/__init__.py diff --git a/src/nlconnectors/connectors/utils/aws/__init__.py b/libs/connectors/tests/rds/db/__init__.py similarity index 100% rename from src/nlconnectors/connectors/utils/aws/__init__.py rename to libs/connectors/tests/rds/db/__init__.py diff --git a/tests/test_connectors/rds/db/test_factory.py b/libs/connectors/tests/rds/db/test_factory.py similarity index 70% rename from tests/test_connectors/rds/db/test_factory.py rename to libs/connectors/tests/rds/db/test_factory.py index d292f6a0..e713178c 100644 --- a/tests/test_connectors/rds/db/test_factory.py +++ b/libs/connectors/tests/rds/db/test_factory.py @@ -1,7 +1,7 @@ import pytest -from connectors import RdsFactory -from connectors import MysqlFetcher -from connectors import UnRecognizedDatabaseTypeException +from nlconnectors.rds._rds import RdsFactory +from nlconnectors.rds.db.mysql_fetcher import MysqlFetcher +from nlconnectors.utils.aws.exceptions import UnRecognizedDatabaseTypeException def test_get_db_handler_with_supported_db_type(): diff --git a/tests/test_connectors/rds/db/test_mysql_fetcher.py b/libs/connectors/tests/rds/db/test_mysql_fetcher.py similarity index 94% rename from tests/test_connectors/rds/db/test_mysql_fetcher.py rename to libs/connectors/tests/rds/db/test_mysql_fetcher.py index 09be5d7c..ab7e89f5 100644 --- a/tests/test_connectors/rds/db/test_mysql_fetcher.py +++ b/libs/connectors/tests/rds/db/test_mysql_fetcher.py @@ -1,8 +1,11 @@ import pymysql import pytest from unittest.mock import Mock, patch, MagicMock -from connectors import DatabaseTypes, RDSConfig -from connectors import MysqlFetcher +from nlconnectors.utils.aws.config import ( + DatabaseTypes, + RDSConnectionConfig as RDSConfig, +) +from nlconnectors.rds.db.mysql_fetcher import MysqlFetcher @pytest.fixture @@ -58,7 +61,9 @@ def mock_db_config_ssl_disabled(): @pytest.fixture def setup_fetcher(): - rds_config = Mock() # Assuming that your RDSConfig class behaves like a normal python object + rds_config = ( + Mock() + ) # Assuming that your RDSConfig class behaves like a normal python object kwargs = {"key": "value"} return MysqlFetcher(rds_config, **kwargs) diff --git a/tests/test_connectors/rds/test_base.py b/libs/connectors/tests/rds/test_base.py similarity index 93% rename from tests/test_connectors/rds/test_base.py rename to libs/connectors/tests/rds/test_base.py index 89010db8..cd4fd1e7 100644 --- a/tests/test_connectors/rds/test_base.py +++ b/libs/connectors/tests/rds/test_base.py @@ -4,8 +4,13 @@ import pytest from pandas import DataFrame, to_datetime -from connectors import Pivot -from connectors import RDSBase, RDSConfig, Boto3ClientManager, format_dataframe +from nlconnectors.config import Pivot +from nlconnectors.rds._base import ( + RDSBase, + Boto3ClientManager, + format_dataframe, + RDSConnectionConfig as RDSConfig, +) mock_rds_base = create_autospec(RDSBase) @@ -96,7 +101,8 @@ def test_format_dataframe(): df = format_dataframe(df, query, datetime_field_name) print(df.to_json()) assert ( - df["timestamp"] == to_datetime(expected_df["eventdatetime"]).astype("int64") // 10**6 + df["timestamp"] + == to_datetime(expected_df["eventdatetime"]).astype("int64") // 10**6 ).all() @@ -174,6 +180,8 @@ def test_format_dataframe_pivot(): group_by = ["timestamp", "ciStatus"] pivot = Pivot() pivot.columns = ["ciStatus"] - df = format_dataframe(df, query, datetime_field_name, group_by=group_by, pivot=pivot) + df = format_dataframe( + df, query, datetime_field_name, group_by=group_by, pivot=pivot + ) expected = [{"degraded": 8, "failed": 8, "success": 5, "timestamp": 1708527420000}] assert df.to_dict(orient="records") == expected diff --git a/tests/test_connectors/rds/test_rds.py b/libs/connectors/tests/rds/test_rds.py similarity index 81% rename from tests/test_connectors/rds/test_rds.py rename to libs/connectors/tests/rds/test_rds.py index 1ec58d2d..134a1ca2 100644 --- a/tests/test_connectors/rds/test_rds.py +++ b/libs/connectors/tests/rds/test_rds.py @@ -3,8 +3,8 @@ import pytest from pandas import DataFrame -from connectors import RDSConfig -from connectors import RDSFetcher +from nlconnectors.config import RDSConnectionConfig as RDSConfig +from nlconnectors import RDSFetcher import pandas as pd @@ -23,7 +23,9 @@ def test_init(data_fetcher, mock_db_config): def test_fetch(mocker, data_fetcher): - mock_data = pd.DataFrame(data={"col1": ["value1", "value2"], "col2": ["value3", "value4"]}) + mock_data = pd.DataFrame( + data={"col1": ["value1", "value2"], "col2": ["value3", "value4"]} + ) mocker.patch.object(RDSFetcher, "fetch", return_value=mock_data) @@ -36,7 +38,9 @@ def test_execute_query(mocker): rds_config = RDSConfig(database_type="mysql") rds_fetcher = RDSFetcher(db_config=rds_config) mocker.patch.object(rds_fetcher.fetcher, "execute_query", return_value=DataFrame()) - result = rds_fetcher.fetch("SELECT * FROM table", datetime_field_name="eventdatetime") + result = rds_fetcher.fetch( + "SELECT * FROM table", datetime_column_name="eventdatetime" + ) assert result.empty == DataFrame().empty diff --git a/tests/resources/config.yaml b/libs/connectors/tests/resources/config.yaml similarity index 100% rename from tests/resources/config.yaml rename to libs/connectors/tests/resources/config.yaml diff --git a/tests/resources/data/druid.csv b/libs/connectors/tests/resources/data/druid.csv similarity index 100% rename from tests/resources/data/druid.csv rename to libs/connectors/tests/resources/data/druid.csv diff --git a/tests/resources/data/interactionstatus.csv b/libs/connectors/tests/resources/data/interactionstatus.csv similarity index 100% rename from tests/resources/data/interactionstatus.csv rename to libs/connectors/tests/resources/data/interactionstatus.csv diff --git a/tests/resources/data/prom_default.csv b/libs/connectors/tests/resources/data/prom_default.csv similarity index 100% rename from tests/resources/data/prom_default.csv rename to libs/connectors/tests/resources/data/prom_default.csv diff --git a/tests/resources/data/prom_mv.csv b/libs/connectors/tests/resources/data/prom_mv.csv similarity index 100% rename from tests/resources/data/prom_mv.csv rename to libs/connectors/tests/resources/data/prom_mv.csv diff --git a/tests/resources/data/raw_druid.csv b/libs/connectors/tests/resources/data/raw_druid.csv similarity index 100% rename from tests/resources/data/raw_druid.csv rename to libs/connectors/tests/resources/data/raw_druid.csv diff --git a/tests/resources/data/rds.csv b/libs/connectors/tests/resources/data/rds.csv similarity index 100% rename from tests/resources/data/rds.csv rename to libs/connectors/tests/resources/data/rds.csv diff --git a/tests/resources/rds_db_config.yaml b/libs/connectors/tests/resources/rds_db_config.yaml similarity index 100% rename from tests/resources/rds_db_config.yaml rename to libs/connectors/tests/resources/rds_db_config.yaml diff --git a/tests/test_connectors/test_druid.py b/libs/connectors/tests/test_druid.py similarity index 97% rename from tests/test_connectors/test_druid.py rename to libs/connectors/tests/test_druid.py index 9561b412..b3dc1163 100644 --- a/tests/test_connectors/test_druid.py +++ b/libs/connectors/tests/test_druid.py @@ -14,9 +14,9 @@ from pydruid.utils.dimensions import DimensionSpec from pydruid.utils.filters import Filter -from numalogic._constants import TESTS_DIR -from connectors import Pivot -from connectors import ( +from nlconnectors.constants import TESTS_DIR +from nlconnectors.config import Pivot +from nlconnectors.druid import ( DruidFetcher, make_filter_pairs, build_params, @@ -161,7 +161,9 @@ def test_fetch_double_sketch(setup, mock_group_by_doubles_sketch): filter_values=["accounting.core.qbowebapp"], dimensions=["assetAlias", "env"], datasource="coredevx-rum-perf-metrics", - aggregations={"agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256)}, + aggregations={ + "agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256) + }, post_aggregations={ "postAgg0": _post_agg.QuantilesDoublesSketchToQuantile( output_name="agg0", field=postaggregator.Field("agg0"), fraction=0.9 diff --git a/tests/test_connectors/test_prometheus.py b/libs/connectors/tests/test_prometheus.py similarity index 94% rename from tests/test_connectors/test_prometheus.py rename to libs/connectors/tests/test_prometheus.py index 3f65cbe1..2fbc7148 100644 --- a/tests/test_connectors/test_prometheus.py +++ b/libs/connectors/tests/test_prometheus.py @@ -6,8 +6,11 @@ from orjson import orjson from requests import Response -from connectors import PrometheusFetcher -from numalogic.tools.exceptions import PrometheusFetcherError, PrometheusInvalidResponseError +from nlconnectors import PrometheusFetcher +from numalogic.tools.exceptions import ( + PrometheusFetcherError, + PrometheusInvalidResponseError, +) logging.basicConfig(level=logging.DEBUG) @@ -48,7 +51,10 @@ def _mock_query_range(): "numalogic": "true", "namespace": "sandbox-numalogic-demo", }, - "values": [[1656334767.73, "14.744611739611193"], [1656334797.73, "14.73040822323633"]], + "values": [ + [1656334767.73, "14.744611739611193"], + [1656334797.73, "14.73040822323633"], + ], } ] @@ -221,10 +227,16 @@ def test_fetch(self): filters={"namespace": "sandbox-numalogic-demo"}, ) self.assertEqual(df.shape, (2, 1)) - self.assertListEqual(df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"]) + self.assertListEqual( + df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"] + ) self.assertEqual(df.index.name, "timestamp") - @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_w_return_labels())) + @patch.object( + PrometheusFetcher, + "_api_query_range", + Mock(return_value=_mock_w_return_labels()), + ) def test_fetch_return_labels(self): metric = "namespace_app_rollouts_http_request_error_rate" df = self.fetcher.fetch( @@ -302,17 +314,25 @@ def test_fetch_response_err(self): filters={"namespace": "sandbox-numalogic-demo"}, ) - @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_query_range())) + @patch.object( + PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_query_range()) + ) def test_fetch_raw(self): df = self.fetcher.raw_fetch( query='namespace_asset_pod_cpu_utilization{namespace="sandbox-numalogic-demo"}', start=datetime.now() - timedelta(hours=1), ) self.assertEqual(df.shape, (2, 1)) - self.assertListEqual(df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"]) + self.assertListEqual( + df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"] + ) self.assertEqual(df.index.name, "timestamp") - @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_w_return_labels())) + @patch.object( + PrometheusFetcher, + "_api_query_range", + Mock(return_value=_mock_w_return_labels()), + ) def test_fetch_raw_return_labels(self): metric = "namespace_app_rollouts_http_request_error_rate" df = self.fetcher.raw_fetch( @@ -453,7 +473,9 @@ def test_start_end_err(self): end=datetime.now() - timedelta(hours=2), ) - @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_no_metric())) + @patch.object( + PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_no_metric()) + ) def test_no_metric_name_err(self): with self.assertRaises(PrometheusInvalidResponseError): self.fetcher.raw_fetch( diff --git a/tests/test_connectors/test_redis.py b/libs/connectors/tests/test_redis.py similarity index 91% rename from tests/test_connectors/test_redis.py rename to libs/connectors/tests/test_redis.py index db904743..a2ca1a99 100644 --- a/tests/test_connectors/test_redis.py +++ b/libs/connectors/tests/test_redis.py @@ -4,7 +4,7 @@ from redis.sentinel import Sentinel import fakeredis -from connectors import get_redis_client +from nlconnectors.redis import get_redis_client server = fakeredis.FakeServer() fake_redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=True) diff --git a/tests/test_connectors/rds/__init__.py b/libs/connectors/tests/utils/__init__.py similarity index 100% rename from tests/test_connectors/rds/__init__.py rename to libs/connectors/tests/utils/__init__.py diff --git a/tests/test_connectors/rds/db/__init__.py b/libs/connectors/tests/utils/aws/__init__.py similarity index 100% rename from tests/test_connectors/rds/db/__init__.py rename to libs/connectors/tests/utils/aws/__init__.py diff --git a/tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py b/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py similarity index 85% rename from tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py rename to libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py index 3afc4dd0..15a12f4e 100644 --- a/tests/test_connectors/utils/aws/test_aws_boto3_client_manager.py +++ b/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py @@ -2,9 +2,9 @@ import pytest from boto3 import Session -from connectors import UnRecognizedAWSClientException -from connectors import Boto3ClientManager -from connectors import STSClientManager +from nlconnectors.utils.aws.exceptions import UnRecognizedAWSClientException +from nlconnectors.utils.aws.boto3_client_manager import Boto3ClientManager +from nlconnectors.utils.aws.sts_client_manager import STSClientManager @pytest.fixture(autouse=True) @@ -93,7 +93,10 @@ def test_get_rds_token(rds_client_mock, boto3_client_manager_mock): assert rds_token == "test_token" rds_client_mock.generate_db_auth_token.assert_called_with( - DBHostname="test_endpoint", Port="test_port", DBUsername="username", Region="region" + DBHostname="test_endpoint", + Port="test_port", + DBUsername="username", + Region="region", ) @@ -103,7 +106,9 @@ def test_get_client_unrecognized(boto3_client_manager_mock): def test_get_client_rds(boto3_client_manager_mock, rds_client_mock, boto3_session_mock): - boto3_client_manager_mock.get_boto3_session = MagicMock(return_value=boto3_session_mock) + boto3_client_manager_mock.get_boto3_session = MagicMock( + return_value=boto3_session_mock + ) boto3_session_mock.client.return_value = rds_client_mock rds_client = boto3_client_manager_mock.get_client("rds") @@ -115,8 +120,12 @@ def test_get_client_rds(boto3_client_manager_mock, rds_client_mock, boto3_sessio assert rds_client == rds_client_mock -def test_get_client_athena(boto3_client_manager_mock, athena_client_mock, boto3_session_mock): - boto3_client_manager_mock.get_boto3_session = MagicMock(return_value=boto3_session_mock) +def test_get_client_athena( + boto3_client_manager_mock, athena_client_mock, boto3_session_mock +): + boto3_client_manager_mock.get_boto3_session = MagicMock( + return_value=boto3_session_mock + ) boto3_session_mock.client.return_value = athena_client_mock boto3_client_manager_mock.get_client("athena") diff --git a/tests/test_connectors/utils/aws/test_config.py b/libs/connectors/tests/utils/aws/test_config.py similarity index 97% rename from tests/test_connectors/utils/aws/test_config.py rename to libs/connectors/tests/utils/aws/test_config.py index d334c097..3bb61b47 100644 --- a/tests/test_connectors/utils/aws/test_config.py +++ b/libs/connectors/tests/utils/aws/test_config.py @@ -1,4 +1,4 @@ -from connectors import ( +from nlconnectors.utils.aws.config import ( DatabaseTypes, AWSConfig, SSLConfig, diff --git a/tests/test_connectors/utils/aws/test_db_configurations.py b/libs/connectors/tests/utils/aws/test_db_configurations.py similarity index 66% rename from tests/test_connectors/utils/aws/test_db_configurations.py rename to libs/connectors/tests/utils/aws/test_db_configurations.py index 08b71055..eb0db410 100644 --- a/tests/test_connectors/utils/aws/test_db_configurations.py +++ b/libs/connectors/tests/utils/aws/test_db_configurations.py @@ -1,7 +1,7 @@ from unittest.mock import patch import pytest -import connectors.utils.aws.db_configurations as db_configuration -from numalogic._constants import TESTS_DIR +import nlconnectors.utils.aws.db_configurations as db_configuration +from nlconnectors.constants import TESTS_DIR import os @@ -12,11 +12,7 @@ def test_load_db_conf_file_exists(): assert result is not None -@patch( - "numalogic.test_connectors.utils.aws.db_configurations.OmegaConf.load", - side_effect=FileNotFoundError(), -) -def test_load_db_conf_file_not_exists(mock_load): +def test_load_db_conf_file_not_exists(): path = "/path/doesnotexist/config.yaml" with pytest.raises(db_configuration.ConfigNotFoundError): db_configuration.load_db_conf(path) diff --git a/tests/test_connectors/utils/aws/test_sts_client_manager.py b/libs/connectors/tests/utils/aws/test_sts_client_manager.py similarity index 77% rename from tests/test_connectors/utils/aws/test_sts_client_manager.py rename to libs/connectors/tests/utils/aws/test_sts_client_manager.py index 5fe4b50d..cb44f6cd 100644 --- a/tests/test_connectors/utils/aws/test_sts_client_manager.py +++ b/libs/connectors/tests/utils/aws/test_sts_client_manager.py @@ -1,10 +1,11 @@ from unittest.mock import patch, MagicMock -from connectors import STSClientManager from datetime import datetime, timedelta, timezone +from nlconnectors.utils.aws.sts_client_manager import STSClientManager -@patch("numalogic.test_connectors.utils.aws.sts_client_manager.boto3.client") -def test_STSClientManager(boto3_client_mock): + +@patch("nlconnectors.utils.aws.sts_client_manager.boto3.client") +def test_sts_client_manager(boto3_client_mock): # Prepare the mock methods mock_sts_client = MagicMock() mock_sts_client.assume_role.return_value = { @@ -26,14 +27,18 @@ def test_STSClientManager(boto3_client_mock): mock_sts_client.assume_role.assert_called_once_with( RoleArn=role_arn, RoleSessionName=role_session_name, DurationSeconds=3600 ) - assert manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] + assert ( + manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] + ) # Test is_token_about_to_expire assert manager.is_token_about_to_expire() is False # Test get_credentials credentials = manager.get_credentials(role_arn, role_session_name) - assert manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] + assert ( + manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] + ) assert credentials == mock_sts_client.assume_role.return_value["Credentials"] # Test renew of credentials diff --git a/tests/test_connectors/utils/test_enum.py b/libs/connectors/tests/utils/test_enum.py similarity index 92% rename from tests/test_connectors/utils/test_enum.py rename to libs/connectors/tests/utils/test_enum.py index 5d7f40be..54d21f17 100644 --- a/tests/test_connectors/utils/test_enum.py +++ b/libs/connectors/tests/utils/test_enum.py @@ -1,10 +1,11 @@ -from connectors import BaseEnum import logging +from nlconnectors.utils.enum import BaseEnum + logging.basicConfig(level=logging.DEBUG) -class test_aws_init: +class TestAWSInit: def test_invalid_value_returns_false(self): # Arrange class MyEnum(BaseEnum): diff --git a/src/core/numalogic/__init__.py b/libs/core/numalogic/__init__.py similarity index 100% rename from src/core/numalogic/__init__.py rename to libs/core/numalogic/__init__.py diff --git a/src/core/numalogic/_constants.py b/libs/core/numalogic/_constants.py similarity index 100% rename from src/core/numalogic/_constants.py rename to libs/core/numalogic/_constants.py diff --git a/src/core/numalogic/backtest/__init__.py b/libs/core/numalogic/backtest/__init__.py similarity index 100% rename from src/core/numalogic/backtest/__init__.py rename to libs/core/numalogic/backtest/__init__.py diff --git a/src/core/numalogic/backtest/_constants.py b/libs/core/numalogic/backtest/_constants.py similarity index 100% rename from src/core/numalogic/backtest/_constants.py rename to libs/core/numalogic/backtest/_constants.py diff --git a/src/core/numalogic/backtest/_prom.py b/libs/core/numalogic/backtest/_prom.py similarity index 100% rename from src/core/numalogic/backtest/_prom.py rename to libs/core/numalogic/backtest/_prom.py diff --git a/src/core/numalogic/base.py b/libs/core/numalogic/base.py similarity index 100% rename from src/core/numalogic/base.py rename to libs/core/numalogic/base.py diff --git a/src/core/numalogic/config/__init__.py b/libs/core/numalogic/config/__init__.py similarity index 96% rename from src/core/numalogic/config/__init__.py rename to libs/core/numalogic/config/__init__.py index dd7e8aff..4c285449 100644 --- a/src/core/numalogic/config/__init__.py +++ b/libs/core/numalogic/config/__init__.py @@ -25,7 +25,6 @@ PreprocessFactory, PostprocessFactory, ThresholdFactory, - RegistryFactory, AggregatorFactory, ) @@ -39,7 +38,6 @@ "PreprocessFactory", "PostprocessFactory", "ThresholdFactory", - "RegistryFactory", "TrainerConf", "ScoreConf", "ScoreAdjustConf", diff --git a/src/core/numalogic/config/_config.py b/libs/core/numalogic/config/_config.py similarity index 100% rename from src/core/numalogic/config/_config.py rename to libs/core/numalogic/config/_config.py diff --git a/src/core/numalogic/config/factory.py b/libs/core/numalogic/config/factory.py similarity index 71% rename from src/core/numalogic/config/factory.py rename to libs/core/numalogic/config/factory.py index 14eb9a38..a4cfc5f8 100644 --- a/src/core/numalogic/config/factory.py +++ b/libs/core/numalogic/config/factory.py @@ -36,13 +36,20 @@ def get_cls(cls, name: str): try: return cls._CLS_MAP[name] except KeyError: - raise UnknownConfigArgsError(f"Invalid name provided for factory: {name}") from None + raise UnknownConfigArgsError( + f"Invalid name provided for factory: {name}" + ) from None class PreprocessFactory(_ObjectFactory): """Factory class to create preprocess instances.""" - from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler + from sklearn.preprocessing import ( + StandardScaler, + MinMaxScaler, + MaxAbsScaler, + RobustScaler, + ) from numalogic.transforms import ( LogTransformer, StaticPowerTransformer, @@ -147,65 +154,6 @@ class ModelFactory(_ObjectFactory): } -class RegistryFactory(_ObjectFactory): - """Factory class to create test_registry instances.""" - - _CLS_SET: ClassVar[frozenset] = frozenset( - {"RedisRegistry", "MLflowRegistry", "DynamoDBRegistry"} - ) - - def get_instance(self, object_info: Union[ModelInfo, RegistryInfo]): - import registry as reg - - try: - _cls = getattr(reg, object_info.name) - except AttributeError as err: - if object_info.name in self._CLS_SET: - raise ImportError( - "Please install the required dependencies for the test_registry you want to use." - ) from err - raise UnknownConfigArgsError( - f"Invalid model info instance provided: {object_info}" - ) from err - return _cls(**object_info.conf) - - @classmethod - def get_cls(cls, name: str): - import registry as reg - - try: - return getattr(reg, name) - except AttributeError as err: - if name in cls._CLS_SET: - raise ImportError( - "Please install the required dependencies for the test_registry you want to use." - ) from err - raise UnknownConfigArgsError( - f"Invalid name provided for RegistryFactory: {name}" - ) from None - - -class ConnectorFactory(_ObjectFactory): - """Factory class for data test_connectors.""" - - _CLS_SET: ClassVar[frozenset] = frozenset({"PrometheusFetcher", "DruidFetcher", "RDSFetcher"}) - - @classmethod - def get_cls(cls, name: str): - import connectors as conn - - try: - return getattr(conn, name) - except AttributeError as err: - if name in cls._CLS_SET: - raise ImportError( - "Please install the required dependencies for the connector you want to use." - ) from err - raise UnknownConfigArgsError( - f"Invalid name provided for ConnectorFactory: {name}" - ) from None - - class AggregatorFactory: """Factory class for aggregator functions.""" diff --git a/tests/test_connectors/utils/__init__.py b/libs/core/numalogic/models/__init__.py similarity index 100% rename from tests/test_connectors/utils/__init__.py rename to libs/core/numalogic/models/__init__.py diff --git a/src/core/numalogic/models/autoencoder/__init__.py b/libs/core/numalogic/models/autoencoder/__init__.py similarity index 100% rename from src/core/numalogic/models/autoencoder/__init__.py rename to libs/core/numalogic/models/autoencoder/__init__.py diff --git a/src/core/numalogic/models/autoencoder/base.py b/libs/core/numalogic/models/autoencoder/base.py similarity index 100% rename from src/core/numalogic/models/autoencoder/base.py rename to libs/core/numalogic/models/autoencoder/base.py diff --git a/src/core/numalogic/models/autoencoder/variants/__init__.py b/libs/core/numalogic/models/autoencoder/variants/__init__.py similarity index 100% rename from src/core/numalogic/models/autoencoder/variants/__init__.py rename to libs/core/numalogic/models/autoencoder/variants/__init__.py diff --git a/src/core/numalogic/models/autoencoder/variants/conv.py b/libs/core/numalogic/models/autoencoder/variants/conv.py similarity index 100% rename from src/core/numalogic/models/autoencoder/variants/conv.py rename to libs/core/numalogic/models/autoencoder/variants/conv.py diff --git a/src/core/numalogic/models/autoencoder/variants/lstm.py b/libs/core/numalogic/models/autoencoder/variants/lstm.py similarity index 100% rename from src/core/numalogic/models/autoencoder/variants/lstm.py rename to libs/core/numalogic/models/autoencoder/variants/lstm.py diff --git a/src/core/numalogic/models/autoencoder/variants/transformer.py b/libs/core/numalogic/models/autoencoder/variants/transformer.py similarity index 100% rename from src/core/numalogic/models/autoencoder/variants/transformer.py rename to libs/core/numalogic/models/autoencoder/variants/transformer.py diff --git a/src/core/numalogic/models/autoencoder/variants/vanilla.py b/libs/core/numalogic/models/autoencoder/variants/vanilla.py similarity index 100% rename from src/core/numalogic/models/autoencoder/variants/vanilla.py rename to libs/core/numalogic/models/autoencoder/variants/vanilla.py diff --git a/src/core/numalogic/models/forecast/__init__.py b/libs/core/numalogic/models/forecast/__init__.py similarity index 100% rename from src/core/numalogic/models/forecast/__init__.py rename to libs/core/numalogic/models/forecast/__init__.py diff --git a/src/core/numalogic/models/forecast/variants/__init__.py b/libs/core/numalogic/models/forecast/variants/__init__.py similarity index 100% rename from src/core/numalogic/models/forecast/variants/__init__.py rename to libs/core/numalogic/models/forecast/variants/__init__.py diff --git a/src/core/numalogic/models/forecast/variants/naive.py b/libs/core/numalogic/models/forecast/variants/naive.py similarity index 100% rename from src/core/numalogic/models/forecast/variants/naive.py rename to libs/core/numalogic/models/forecast/variants/naive.py diff --git a/src/core/numalogic/models/forecast/variants/rnn.py b/libs/core/numalogic/models/forecast/variants/rnn.py similarity index 100% rename from src/core/numalogic/models/forecast/variants/rnn.py rename to libs/core/numalogic/models/forecast/variants/rnn.py diff --git a/src/core/numalogic/models/threshold/__init__.py b/libs/core/numalogic/models/threshold/__init__.py similarity index 100% rename from src/core/numalogic/models/threshold/__init__.py rename to libs/core/numalogic/models/threshold/__init__.py diff --git a/src/core/numalogic/models/threshold/_mahalanobis.py b/libs/core/numalogic/models/threshold/_mahalanobis.py similarity index 100% rename from src/core/numalogic/models/threshold/_mahalanobis.py rename to libs/core/numalogic/models/threshold/_mahalanobis.py diff --git a/src/core/numalogic/models/threshold/_median.py b/libs/core/numalogic/models/threshold/_median.py similarity index 100% rename from src/core/numalogic/models/threshold/_median.py rename to libs/core/numalogic/models/threshold/_median.py diff --git a/src/core/numalogic/models/threshold/_static.py b/libs/core/numalogic/models/threshold/_static.py similarity index 100% rename from src/core/numalogic/models/threshold/_static.py rename to libs/core/numalogic/models/threshold/_static.py diff --git a/src/core/numalogic/models/threshold/_std.py b/libs/core/numalogic/models/threshold/_std.py similarity index 100% rename from src/core/numalogic/models/threshold/_std.py rename to libs/core/numalogic/models/threshold/_std.py diff --git a/src/core/numalogic/models/vae/__init__.py b/libs/core/numalogic/models/vae/__init__.py similarity index 100% rename from src/core/numalogic/models/vae/__init__.py rename to libs/core/numalogic/models/vae/__init__.py diff --git a/src/core/numalogic/models/vae/base.py b/libs/core/numalogic/models/vae/base.py similarity index 100% rename from src/core/numalogic/models/vae/base.py rename to libs/core/numalogic/models/vae/base.py diff --git a/src/core/numalogic/models/vae/layer.py b/libs/core/numalogic/models/vae/layer.py similarity index 100% rename from src/core/numalogic/models/vae/layer.py rename to libs/core/numalogic/models/vae/layer.py diff --git a/src/core/numalogic/models/vae/variants/__init__.py b/libs/core/numalogic/models/vae/variants/__init__.py similarity index 100% rename from src/core/numalogic/models/vae/variants/__init__.py rename to libs/core/numalogic/models/vae/variants/__init__.py diff --git a/src/core/numalogic/models/vae/variants/conv.py b/libs/core/numalogic/models/vae/variants/conv.py similarity index 100% rename from src/core/numalogic/models/vae/variants/conv.py rename to libs/core/numalogic/models/vae/variants/conv.py diff --git a/src/core/numalogic/monitoring/__init__.py b/libs/core/numalogic/monitoring/__init__.py similarity index 100% rename from src/core/numalogic/monitoring/__init__.py rename to libs/core/numalogic/monitoring/__init__.py diff --git a/src/core/numalogic/monitoring/metrics.py b/libs/core/numalogic/monitoring/metrics.py similarity index 100% rename from src/core/numalogic/monitoring/metrics.py rename to libs/core/numalogic/monitoring/metrics.py diff --git a/src/core/numalogic/synthetic/__init__.py b/libs/core/numalogic/synthetic/__init__.py similarity index 78% rename from src/core/numalogic/synthetic/__init__.py rename to libs/core/numalogic/synthetic/__init__.py index 43504fd5..5bc47b2b 100644 --- a/src/core/numalogic/synthetic/__init__.py +++ b/libs/core/numalogic/synthetic/__init__.py @@ -9,5 +9,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +from numalogic.synthetic.timeseries import SyntheticTSGenerator +from numalogic.synthetic.anomalies import AnomalyGenerator +from numalogic.synthetic.sparsity import SparsityGenerator + __all__ = ["SyntheticTSGenerator", "AnomalyGenerator", "SparsityGenerator"] diff --git a/src/core/numalogic/synthetic/anomalies.py b/libs/core/numalogic/synthetic/anomalies.py similarity index 100% rename from src/core/numalogic/synthetic/anomalies.py rename to libs/core/numalogic/synthetic/anomalies.py diff --git a/src/core/numalogic/synthetic/sparsity.py b/libs/core/numalogic/synthetic/sparsity.py similarity index 100% rename from src/core/numalogic/synthetic/sparsity.py rename to libs/core/numalogic/synthetic/sparsity.py diff --git a/src/core/numalogic/synthetic/timeseries.py b/libs/core/numalogic/synthetic/timeseries.py similarity index 100% rename from src/core/numalogic/synthetic/timeseries.py rename to libs/core/numalogic/synthetic/timeseries.py diff --git a/tests/test_connectors/utils/aws/__init__.py b/libs/core/numalogic/tools/__init__.py similarity index 100% rename from tests/test_connectors/utils/aws/__init__.py rename to libs/core/numalogic/tools/__init__.py diff --git a/src/core/numalogic/tools/aggregators.py b/libs/core/numalogic/tools/aggregators.py similarity index 100% rename from src/core/numalogic/tools/aggregators.py rename to libs/core/numalogic/tools/aggregators.py diff --git a/src/core/numalogic/tools/callbacks.py b/libs/core/numalogic/tools/callbacks.py similarity index 100% rename from src/core/numalogic/tools/callbacks.py rename to libs/core/numalogic/tools/callbacks.py diff --git a/src/core/numalogic/tools/data.py b/libs/core/numalogic/tools/data.py similarity index 100% rename from src/core/numalogic/tools/data.py rename to libs/core/numalogic/tools/data.py diff --git a/src/core/numalogic/tools/exceptions.py b/libs/core/numalogic/tools/exceptions.py similarity index 100% rename from src/core/numalogic/tools/exceptions.py rename to libs/core/numalogic/tools/exceptions.py diff --git a/src/core/numalogic/tools/loss.py b/libs/core/numalogic/tools/loss.py similarity index 100% rename from src/core/numalogic/tools/loss.py rename to libs/core/numalogic/tools/loss.py diff --git a/src/core/numalogic/tools/trainer.py b/libs/core/numalogic/tools/trainer.py similarity index 100% rename from src/core/numalogic/tools/trainer.py rename to libs/core/numalogic/tools/trainer.py diff --git a/src/core/numalogic/tools/types.py b/libs/core/numalogic/tools/types.py similarity index 100% rename from src/core/numalogic/tools/types.py rename to libs/core/numalogic/tools/types.py diff --git a/src/core/numalogic/transforms/__init__.py b/libs/core/numalogic/transforms/__init__.py similarity index 100% rename from src/core/numalogic/transforms/__init__.py rename to libs/core/numalogic/transforms/__init__.py diff --git a/src/core/numalogic/transforms/_movavg.py b/libs/core/numalogic/transforms/_movavg.py similarity index 100% rename from src/core/numalogic/transforms/_movavg.py rename to libs/core/numalogic/transforms/_movavg.py diff --git a/src/core/numalogic/transforms/_postprocess.py b/libs/core/numalogic/transforms/_postprocess.py similarity index 100% rename from src/core/numalogic/transforms/_postprocess.py rename to libs/core/numalogic/transforms/_postprocess.py diff --git a/src/core/numalogic/transforms/_scaler.py b/libs/core/numalogic/transforms/_scaler.py similarity index 100% rename from src/core/numalogic/transforms/_scaler.py rename to libs/core/numalogic/transforms/_scaler.py diff --git a/src/core/numalogic/transforms/_stateless.py b/libs/core/numalogic/transforms/_stateless.py similarity index 100% rename from src/core/numalogic/transforms/_stateless.py rename to libs/core/numalogic/transforms/_stateless.py diff --git a/src/core/pyproject.toml b/libs/core/pyproject.toml similarity index 92% rename from src/core/pyproject.toml rename to libs/core/pyproject.toml index f5c130cb..7ff2c145 100644 --- a/src/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic" -version = "0.10.1a0" +version = "0.11a0" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} @@ -21,7 +21,8 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11" + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" dependencies = [ @@ -30,6 +31,7 @@ dependencies = [ "scikit-learn>=1.3", "torch>=2.0", "pytorch-lightning>=2.0", + "omegaconf>=2.3", ] [project.optional-dependencies] diff --git a/tests/test_udfs/__init__.py b/libs/core/tests/__init__.py similarity index 100% rename from tests/test_udfs/__init__.py rename to libs/core/tests/__init__.py diff --git a/tests/test_numalogic/config/__init__.py b/libs/core/tests/config/__init__.py similarity index 100% rename from tests/test_numalogic/config/__init__.py rename to libs/core/tests/config/__init__.py diff --git a/tests/test_numalogic/config/test_config.py b/libs/core/tests/config/test_config.py similarity index 100% rename from tests/test_numalogic/config/test_config.py rename to libs/core/tests/config/test_config.py diff --git a/tests/test_numalogic/models/__init__.py b/libs/core/tests/models/__init__.py similarity index 100% rename from tests/test_numalogic/models/__init__.py rename to libs/core/tests/models/__init__.py diff --git a/tests/test_numalogic/models/autoencoder/__init__.py b/libs/core/tests/models/autoencoder/__init__.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/__init__.py rename to libs/core/tests/models/autoencoder/__init__.py diff --git a/tests/test_numalogic/models/autoencoder/test_trainer.py b/libs/core/tests/models/autoencoder/test_trainer.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/test_trainer.py rename to libs/core/tests/models/autoencoder/test_trainer.py diff --git a/tests/test_numalogic/models/autoencoder/variants/__init__.py b/libs/core/tests/models/autoencoder/variants/__init__.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/variants/__init__.py rename to libs/core/tests/models/autoencoder/variants/__init__.py diff --git a/tests/test_numalogic/models/autoencoder/variants/test_conv.py b/libs/core/tests/models/autoencoder/variants/test_conv.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/variants/test_conv.py rename to libs/core/tests/models/autoencoder/variants/test_conv.py diff --git a/tests/test_numalogic/models/autoencoder/variants/test_lstm.py b/libs/core/tests/models/autoencoder/variants/test_lstm.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/variants/test_lstm.py rename to libs/core/tests/models/autoencoder/variants/test_lstm.py diff --git a/tests/test_numalogic/models/autoencoder/variants/test_transformers.py b/libs/core/tests/models/autoencoder/variants/test_transformers.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/variants/test_transformers.py rename to libs/core/tests/models/autoencoder/variants/test_transformers.py diff --git a/tests/test_numalogic/models/autoencoder/variants/test_vanilla.py b/libs/core/tests/models/autoencoder/variants/test_vanilla.py similarity index 100% rename from tests/test_numalogic/models/autoencoder/variants/test_vanilla.py rename to libs/core/tests/models/autoencoder/variants/test_vanilla.py diff --git a/tests/test_numalogic/models/forecast/__init__.py b/libs/core/tests/models/forecast/__init__.py similarity index 100% rename from tests/test_numalogic/models/forecast/__init__.py rename to libs/core/tests/models/forecast/__init__.py diff --git a/tests/test_numalogic/models/forecast/test_naive.py b/libs/core/tests/models/forecast/test_naive.py similarity index 100% rename from tests/test_numalogic/models/forecast/test_naive.py rename to libs/core/tests/models/forecast/test_naive.py diff --git a/tests/test_numalogic/models/threshold/__init__.py b/libs/core/tests/models/threshold/__init__.py similarity index 100% rename from tests/test_numalogic/models/threshold/__init__.py rename to libs/core/tests/models/threshold/__init__.py diff --git a/tests/test_numalogic/models/threshold/test_maha.py b/libs/core/tests/models/threshold/test_maha.py similarity index 100% rename from tests/test_numalogic/models/threshold/test_maha.py rename to libs/core/tests/models/threshold/test_maha.py diff --git a/tests/test_numalogic/models/threshold/test_median.py b/libs/core/tests/models/threshold/test_median.py similarity index 100% rename from tests/test_numalogic/models/threshold/test_median.py rename to libs/core/tests/models/threshold/test_median.py diff --git a/tests/test_numalogic/models/threshold/test_static.py b/libs/core/tests/models/threshold/test_static.py similarity index 100% rename from tests/test_numalogic/models/threshold/test_static.py rename to libs/core/tests/models/threshold/test_static.py diff --git a/tests/test_numalogic/models/threshold/test_std.py b/libs/core/tests/models/threshold/test_std.py similarity index 100% rename from tests/test_numalogic/models/threshold/test_std.py rename to libs/core/tests/models/threshold/test_std.py diff --git a/tests/test_numalogic/models/vae/__init__.py b/libs/core/tests/models/vae/__init__.py similarity index 100% rename from tests/test_numalogic/models/vae/__init__.py rename to libs/core/tests/models/vae/__init__.py diff --git a/tests/test_numalogic/models/vae/test_conv.py b/libs/core/tests/models/vae/test_conv.py similarity index 100% rename from tests/test_numalogic/models/vae/test_conv.py rename to libs/core/tests/models/vae/test_conv.py diff --git a/libs/core/tests/resources/config.yaml b/libs/core/tests/resources/config.yaml new file mode 100644 index 00000000..22d8bf11 --- /dev/null +++ b/libs/core/tests/resources/config.yaml @@ -0,0 +1,30 @@ +model: + name: "SparseVanillaAE" + conf: + seq_len: 20 + n_features: 1 + encoder_layersizes: + - 16 + - 8 + decoder_layersizes: + - 8 + - 16 + dropout_p: 0.25 +trainer: + pltrainer_conf: + max_epochs: 40 +preprocess: + - name: "LogTransformer" + stateful: false + conf: + add_factor: 3 + - name: "StandardScaler" + conf: + with_mean: False +threshold: + name: "StdDevThreshold" +postprocess: + name: "TanhNorm" + stateful: false + conf: + scale_factor: 5 diff --git a/libs/core/tests/resources/data/druid.csv b/libs/core/tests/resources/data/druid.csv new file mode 100644 index 00000000..9e30343e --- /dev/null +++ b/libs/core/tests/resources/data/druid.csv @@ -0,0 +1,14378 @@ +timestamp,degraded,failed,success 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+aws_assume_role_arn: "arn:aws:iam::123456789:role/ml_iam_role" +aws_assume_role_session_name: "ml_pipeline_reader" +endpoint: "ml-rds-aurora-mysql-cluster.us-west-2.rds.amazonaws.com" +port: 3306 +database_name: "mldb" +database_username: "ml_user" +database_password: "" +database_connection_timeout: 10 +database_type: "mysql" +database_provider: "rds" +aws_region: "us-west-2" +aws_rds_use_iam: True +ssl_enabled: true +ssl: + ca: "/usr/bin/ml_data/us-west-2-bundle.pem" diff --git a/tests/test_numalogic/synthetic/__init__.py b/libs/core/tests/synthetic/__init__.py similarity index 100% rename from tests/test_numalogic/synthetic/__init__.py rename to libs/core/tests/synthetic/__init__.py diff --git a/tests/test_numalogic/synthetic/test_anomalies.py b/libs/core/tests/synthetic/test_anomalies.py similarity index 100% rename from tests/test_numalogic/synthetic/test_anomalies.py rename to libs/core/tests/synthetic/test_anomalies.py diff --git a/tests/test_numalogic/synthetic/test_sparsity.py b/libs/core/tests/synthetic/test_sparsity.py similarity index 100% rename from tests/test_numalogic/synthetic/test_sparsity.py rename to libs/core/tests/synthetic/test_sparsity.py diff --git a/tests/test_numalogic/synthetic/test_timeseries.py b/libs/core/tests/synthetic/test_timeseries.py similarity index 100% rename from tests/test_numalogic/synthetic/test_timeseries.py rename to libs/core/tests/synthetic/test_timeseries.py diff --git a/tests/test_numalogic/tools/__init__.py b/libs/core/tests/tools/__init__.py similarity index 100% rename from tests/test_numalogic/tools/__init__.py rename to libs/core/tests/tools/__init__.py diff --git a/tests/test_numalogic/tools/test_data.py b/libs/core/tests/tools/test_data.py similarity index 100% rename from tests/test_numalogic/tools/test_data.py rename to libs/core/tests/tools/test_data.py diff --git a/tests/test_numalogic/transforms/__init__.py b/libs/core/tests/transforms/__init__.py similarity index 100% rename from tests/test_numalogic/transforms/__init__.py rename to libs/core/tests/transforms/__init__.py diff --git a/tests/test_numalogic/transforms/test_postprocess.py b/libs/core/tests/transforms/test_postprocess.py similarity index 100% rename from tests/test_numalogic/transforms/test_postprocess.py rename to libs/core/tests/transforms/test_postprocess.py diff --git a/tests/test_numalogic/transforms/test_transforms.py b/libs/core/tests/transforms/test_transforms.py similarity index 100% rename from tests/test_numalogic/transforms/test_transforms.py rename to libs/core/tests/transforms/test_transforms.py diff --git a/libs/registry/nlregistry/__init__.py b/libs/registry/nlregistry/__init__.py new file mode 100644 index 00000000..2c40f7e4 --- /dev/null +++ b/libs/registry/nlregistry/__init__.py @@ -0,0 +1,15 @@ +from nlregistry.redis_registry import RedisRegistry +from nlregistry.mlflow_registry import MLflowRegistry +from nlregistry.dynamodb_registry import DynamoDBRegistry +from nlregistry.localcache import LocalLRUCache +from nlregistry.artifact import ArtifactManager, ArtifactData, ArtifactCache + +__all__ = [ + "RedisRegistry", + "MLflowRegistry", + "DynamoDBRegistry", + "LocalLRUCache", + "ArtifactManager", + "ArtifactData", + "ArtifactCache", +] diff --git a/src/registry/__setup__.py b/libs/registry/nlregistry/__setup__.py similarity index 87% rename from src/registry/__setup__.py rename to libs/registry/nlregistry/__setup__.py index 4e1e493c..836c27fe 100644 --- a/src/registry/__setup__.py +++ b/libs/registry/nlregistry/__setup__.py @@ -6,11 +6,11 @@ def setup_args(): name="numalogic-test_registry", version="0.10.a2", packages=find_packages( - where="src", + where="libs", include=["registry", "registry.*"], exclude=["tests"], ), - package_dir={"": "src"}, + package_dir={"": "libs"}, install_requires=[ "numalogic", "redis[hiredis]>=4.0", diff --git a/src/registry/_serialize.py b/libs/registry/nlregistry/_serialize.py similarity index 100% rename from src/registry/_serialize.py rename to libs/registry/nlregistry/_serialize.py diff --git a/src/registry/artifact.py b/libs/registry/nlregistry/artifact.py similarity index 100% rename from src/registry/artifact.py rename to libs/registry/nlregistry/artifact.py diff --git a/src/registry/dynamodb_registry.py b/libs/registry/nlregistry/dynamodb_registry.py similarity index 89% rename from src/registry/dynamodb_registry.py rename to libs/registry/nlregistry/dynamodb_registry.py index 20a996f2..282a6c37 100644 --- a/src/registry/dynamodb_registry.py +++ b/libs/registry/nlregistry/dynamodb_registry.py @@ -17,10 +17,10 @@ import boto3 from botocore.exceptions import ClientError -from registry import ArtifactManager, ArtifactCache, ArtifactData +from nlregistry.artifact import ArtifactManager, ArtifactCache, ArtifactData from numalogic.tools.exceptions import DynamoDBRegistryError from numalogic.tools.types import KEYS, artifact_t, META_VT -from registry import loads, dumps +from nlregistry._serialize import loads, dumps _LOGGER = logging.getLogger(__name__) @@ -48,13 +48,17 @@ def get_session(self, new_session=False) -> boto3.Session: if new_session: self.session = None - if (not self.session) or (time.time() - self.sess_start_time > self.__STS_RESET_SECS): + if (not self.session) or ( + time.time() - self.sess_start_time > self.__STS_RESET_SECS + ): self.session = self._get_sts_session() self.sess_start_time = time.time() return self.session def _get_sts_session(self): - _LOGGER.info("Getting session with role: %s and region: %s", self.role, self.region) + _LOGGER.info( + "Getting session with role: %s and region: %s", self.role, self.region + ) assumed_role_object = boto3.client("sts").assume_role( RoleArn=self.role, RoleSessionName="AssumeRoleSession1" ) @@ -94,7 +98,13 @@ class DynamoDBRegistry(ArtifactManager): >>> loaded_artifact = test_registry.load(skeys, dkeys). """ - __slots__ = ("table_name", "role", "connector", "models_to_retain", "cache_registry") + __slots__ = ( + "table_name", + "role", + "connector", + "models_to_retain", + "cache_registry", + ) def __init__( self, @@ -233,7 +243,9 @@ def load( ArtifactData instance. """ if (latest and version) or (not latest and not version): - raise ValueError("Either One of 'latest' or 'version' needed in load method call") + raise ValueError( + "Either One of 'latest' or 'version' needed in load method call" + ) table = self.connector.get_resource().Table(self.table_name) part_key = self.construct_key(skeys) @@ -269,11 +281,20 @@ def load( @staticmethod def __save_item( - table, part_key: str, sort_key: str, data: dict[str, Any], version: Optional[int] = None + table, + part_key: str, + sort_key: str, + data: dict[str, Any], + version: Optional[int] = None, ): try: response = table.put_item( - Item={"skey": part_key, "dkey": sort_key, "data": data, "version": version} + Item={ + "skey": part_key, + "dkey": sort_key, + "data": data, + "version": version, + } ) except ClientError as err: raise DynamoDBRegistryError(f"{err.__class__.__name__} raised") from err @@ -313,12 +334,19 @@ def save( # Save a copy of the artifact with the current version. self.__save_item( - table, part_key, self._construct_version_key(sort_key, version=version), artifact_data + table, + part_key, + self._construct_version_key(sort_key, version=version), + artifact_data, ) # Save the zeroth version (v0) as the one pointing to the current version. self.__save_item( - table, part_key, self._construct_version_key(sort_key), artifact_data, version=version + table, + part_key, + self._construct_version_key(sort_key), + artifact_data, + version=version, ) # Delete the stale model @@ -326,7 +354,9 @@ def save( if stale_version > 0: _LOGGER.info("Deleting stale version %s", stale_version) self.__delete_item( - table, part_key, self._construct_version_key(sort_key, version=stale_version) + table, + part_key, + self._construct_version_key(sort_key, version=stale_version), ) return str(version) @@ -339,7 +369,9 @@ def __delete_item(table, part_key: str, v_sort_key: str): raise DynamoDBRegistryError(f"{err.__class__.__name__} raised") from err return response - def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> Optional[dict[str, Any]]: + def delete( + self, skeys: KEYS, dkeys: KEYS, version: str + ) -> Optional[dict[str, Any]]: """ Deletes the artifact from dynamodb. Args: @@ -361,11 +393,15 @@ def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> Optional[dict[str, A if version == artifact_data.extras.get("version"): # Find if an older version exists prev_version = str(int(version) - 1) - artifact_data_prev = self.load(skeys, dkeys, latest=False, version=prev_version) + artifact_data_prev = self.load( + skeys, dkeys, latest=False, version=prev_version + ) if artifact_data_prev and prev_version != "0": # Update the zeroth record pointer to the previous version prev_item = self._pack_artifact_data( - artifact_data_prev.artifact, prev_version, **artifact_data_prev.metadata + artifact_data_prev.artifact, + prev_version, + **artifact_data_prev.metadata, ) self.__save_item( table, diff --git a/libs/registry/nlregistry/factory.py b/libs/registry/nlregistry/factory.py new file mode 100644 index 00000000..7261e8ff --- /dev/null +++ b/libs/registry/nlregistry/factory.py @@ -0,0 +1,43 @@ +from typing import ClassVar, Union + +from numalogic.config import RegistryInfo, ModelInfo +from numalogic.config.factory import _ObjectFactory +from numalogic.tools.exceptions import UnknownConfigArgsError + + +class RegistryFactory(_ObjectFactory): + """Factory class to create test_registry instances.""" + + _CLS_SET: ClassVar[frozenset] = frozenset( + {"RedisRegistry", "MLflowRegistry", "DynamoDBRegistry"} + ) + + def get_instance(self, object_info: Union[ModelInfo, RegistryInfo]): + import nlregistry as reg + + try: + _cls = getattr(reg, object_info.name) + except AttributeError as err: + if object_info.name in self._CLS_SET: + raise ImportError( + "Please install the required dependencies for the test_registry you want to use." + ) from err + raise UnknownConfigArgsError( + f"Invalid model info instance provided: {object_info}" + ) from err + return _cls(**object_info.conf) + + @classmethod + def get_cls(cls, name: str): + import nlregistry as reg + + try: + return getattr(reg, name) + except AttributeError as err: + if name in cls._CLS_SET: + raise ImportError( + "Please install the required dependencies for the test_registry you want to use." + ) from err + raise UnknownConfigArgsError( + f"Invalid name provided for RegistryFactory: {name}" + ) from None diff --git a/src/registry/localcache.py b/libs/registry/nlregistry/localcache.py similarity index 98% rename from src/registry/localcache.py rename to libs/registry/nlregistry/localcache.py index 14af55fc..4a49d779 100644 --- a/src/registry/localcache.py +++ b/libs/registry/nlregistry/localcache.py @@ -14,7 +14,7 @@ from typing import Optional from cachetools import TTLCache -from registry import ArtifactCache, ArtifactData +from nlregistry.artifact import ArtifactCache, ArtifactData from numalogic.tools.types import Singleton diff --git a/src/registry/mlflow_registry.py b/libs/registry/nlregistry/mlflow_registry.py similarity index 89% rename from src/registry/mlflow_registry.py rename to libs/registry/nlregistry/mlflow_registry.py index bd9360a5..3b5f693f 100644 --- a/src/registry/mlflow_registry.py +++ b/libs/registry/nlregistry/mlflow_registry.py @@ -24,8 +24,7 @@ from sklearn.base import BaseEstimator from torch import nn -from registry import ArtifactManager, ArtifactData -from registry import ArtifactCache +from nlregistry.artifact import ArtifactManager, ArtifactData, ArtifactCache from numalogic.tools.exceptions import ModelVersionError from numalogic.tools.types import artifact_t, KEYS, META_VT @@ -158,7 +157,9 @@ def load( model_key = self.construct_key(skeys, dkeys) if (latest and version) or (not latest and not version): - raise ValueError("Either One of 'latest' or 'version' needed in load method call") + raise ValueError( + "Either One of 'latest' or 'version' needed in load method call" + ) try: if latest: @@ -166,20 +167,30 @@ def load( if cached_artifact: _LOGGER.debug("Found cached artifact for key: %s", model_key) return cached_artifact - version_info = self.client.get_latest_versions(model_key, stages=[self.model_stage]) + version_info = self.client.get_latest_versions( + model_key, stages=[self.model_stage] + ) if not version_info: - raise ModelVersionError("Model version missing for key = %s" % model_key) + raise ModelVersionError( + "Model version missing for key = %s" % model_key + ) version_info = version_info[-1] else: version_info = self.client.get_model_version(model_key, version) - model, metadata = self.__load_artifacts(skeys, dkeys, version_info, artifact_type) + model, metadata = self.__load_artifacts( + skeys, dkeys, version_info, artifact_type + ) except RestException as mlflow_err: return self.__log_mlflow_err(mlflow_err, model_key) except ModelVersionError: - _LOGGER.exception("No Model found found in model stage: %s", self.model_stage) + _LOGGER.exception( + "No Model found found in model stage: %s", self.model_stage + ) return None except Exception: - _LOGGER.exception("Unexpected error while Registry loading with key: %s", model_key) + _LOGGER.exception( + "Unexpected error while Registry loading with key: %s", model_key + ) return None else: artifact_data = ArtifactData( @@ -196,7 +207,9 @@ def __log_mlflow_err(mlflow_err: RestException, model_key: str) -> None: _LOGGER.info("Model not found with key: %s", model_key) else: _LOGGER.exception( - "Mlflow error when loading a model with key: %s: %r", model_key, mlflow_err + "Mlflow error when loading a model with key: %s: %r", + model_key, + mlflow_err, ) def save( @@ -230,7 +243,9 @@ def save( mlflow.log_params(metadata) model_version = self.transition_stage(skeys=skeys, dkeys=dkeys) except Exception: - _LOGGER.exception("Unhandled error when saving a model with key: %s", model_key) + _LOGGER.exception( + "Unhandled error when saving a model with key: %s", model_key + ) return None else: _LOGGER.info("Successfully inserted model %s to Mlflow", model_key) @@ -307,7 +322,9 @@ def transition_stage(self, skeys: KEYS, dkeys: KEYS) -> Optional[ModelVersion]: # only keep "models_to_retain" number of models. self.__delete_stale_models(skeys=skeys, dkeys=dkeys) except RestException: - _LOGGER.exception("Error when transitioning a model: %s to different stage", model_name) + _LOGGER.exception( + "Error when transitioning a model: %s to different stage", model_name + ) return None else: _LOGGER.info("Successfully transitioned model to Production stage") @@ -315,7 +332,9 @@ def transition_stage(self, skeys: KEYS, dkeys: KEYS) -> Optional[ModelVersion]: def __delete_stale_models(self, skeys: KEYS, dkeys: KEYS): model_name = self.construct_key(skeys, dkeys) - list_model_versions = list(self.client.search_model_versions(f"name='{model_name}'")) + list_model_versions = list( + self.client.search_model_versions(f"name='{model_name}'") + ) if len(list_model_versions) > self.models_to_retain: models_to_delete = list_model_versions[self.models_to_retain :] for stale_model in models_to_delete: @@ -327,7 +346,9 @@ def __load_artifacts( ) -> tuple[artifact_t, dict[str, Any]]: model_key = self.construct_key(skeys, dkeys) handler = self.handler_from_type(artifact_type) - model = handler.load_model(model_uri=f"models:/{model_key}/{version_info.version}") + model = handler.load_model( + model_uri=f"models:/{model_key}/{version_info.version}" + ) _LOGGER.info("Successfully loaded model %s from Mlflow", model_key) run_info = mlflow.get_run(version_info.run_id) diff --git a/src/registry/redis_registry.py b/libs/registry/nlregistry/redis_registry.py similarity index 87% rename from src/registry/redis_registry.py rename to libs/registry/nlregistry/redis_registry.py index d221fe1e..4155ce35 100644 --- a/src/registry/redis_registry.py +++ b/libs/registry/nlregistry/redis_registry.py @@ -18,10 +18,22 @@ from redis.exceptions import RedisError -from registry import ArtifactManager, ArtifactData, ArtifactCache, _apply_jitter -from registry import loads, dumps +from nlregistry.artifact import ( + ArtifactManager, + ArtifactData, + ArtifactCache, + _apply_jitter, +) +from nlregistry._serialize import loads, dumps from numalogic.tools.exceptions import ModelKeyNotFound, RedisRegistryError -from numalogic.tools.types import artifact_t, redis_client_t, KEYS, META_T, META_VT, KeyedArtifact +from numalogic.tools.types import ( + artifact_t, + redis_client_t, + KEYS, + META_T, + META_VT, + KeyedArtifact, +) _LOGGER = logging.getLogger(__name__) @@ -44,7 +56,7 @@ class RedisRegistry(ArtifactManager): -------- >>> import redis >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.test_registry.redis_registry import RedisRegistry + >>> from nlregistry import RedisRegistry >>> ... >>> r = redis.Redis(host='127.0.0.1', port=6379) >>> test_registry = RedisRegistry(client=r) @@ -130,7 +142,9 @@ def _save_in_cache(self, key: str, artifact_data: ArtifactData) -> None: def _clear_cache(self, key: Optional[str] = None) -> Optional[ArtifactData]: if self.cache_registry: - return self.cache_registry.delete(key) if key else self.cache_registry.clear() + return ( + self.cache_registry.delete(key) if key else self.cache_registry.clear() + ) return None def __get_artifact_data( @@ -142,7 +156,9 @@ def __get_artifact_data( artifact_version, artifact_timestamp, serialized_metadata, - ) = self.client.hmget(name=model_key, keys=["artifact", "version", "timestamp", "metadata"]) + ) = self.client.hmget( + name=model_key, keys=["artifact", "version", "timestamp", "metadata"] + ) deserialized_artifact = loads(serialized_artifact) deserialized_metadata = None if serialized_metadata: @@ -180,13 +196,17 @@ def __load_latest_artifact(self, key: str) -> tuple[ArtifactData, bool]: if not self.client.exists(latest_key): raise ModelKeyNotFound(f"latest key: {latest_key}, Not Found !!!") model_key = self.client.get(latest_key) - _LOGGER.debug("latest key, %s, is pointing to the key : %s", latest_key, model_key) + _LOGGER.debug( + "latest key, %s, is pointing to the key : %s", latest_key, model_key + ) artifact, _ = self.__load_version_artifact( version=self.get_version(model_key.decode()), key=key ) return artifact, False - def __load_version_artifact(self, version: str, key: str) -> tuple[ArtifactData, bool]: + def __load_version_artifact( + self, version: str, key: str + ) -> tuple[ArtifactData, bool]: version_key = self.__construct_version_key(key, version) cached_artifact = self._load_from_cache(version_key) if cached_artifact: @@ -207,7 +227,9 @@ def __save_artifact( new_version_key = self.__construct_version_key(key, version) latest_key = self.__construct_latest_key(key) pipe.set(name=latest_key, value=new_version_key) - _LOGGER.debug("Setting latest key : %s ,to this new key = %s", latest_key, new_version_key) + _LOGGER.debug( + "Setting latest key : %s ,to this new key = %s", latest_key, new_version_key + ) serialized_metadata = orjson.dumps(metadata) if metadata else b"" serialized_artifact = dumps(deserialized_object=artifact) _cur_ts = int(time.time()) @@ -216,7 +238,9 @@ def __save_artifact( mapping={ "artifact": serialized_artifact, "version": version, - "timestamp": _apply_jitter(_cur_ts, self.jitter_sec, self.jitter_steps_sec), + "timestamp": _apply_jitter( + _cur_ts, self.jitter_sec, self.jitter_steps_sec + ), "metadata": serialized_metadata, }, ) @@ -253,7 +277,9 @@ def load( RedisRegistryError: If any redis error occurs. """ if (latest and version) or (not latest and not version): - raise ValueError("Either One of 'latest' or 'version' needed in load method call") + raise ValueError( + "Either One of 'latest' or 'version' needed in load method call" + ) key = self.construct_key(skeys, dkeys) try: if latest: @@ -266,7 +292,9 @@ def load( if not is_cached: if latest: _LOGGER.debug( - "Saving %s, in cache as %s", self.__construct_latest_key(key), key + "Saving %s, in cache as %s", + self.__construct_latest_key(key), + key, ) self._save_in_cache(self.__construct_latest_key(key), artifact_data) else: @@ -275,7 +303,9 @@ def load( self.__construct_version_key(key, version), key, ) - self._save_in_cache(self.__construct_version_key(key, version), artifact_data) + self._save_in_cache( + self.__construct_version_key(key, version), artifact_data + ) return artifact_data def save( @@ -313,10 +343,16 @@ def save( version_key = self.client.get(name=latest_key) version = int(self.get_version(version_key.decode())) + 1 _redis_pipe = ( - self.client.pipeline(transaction=self.transactional) if _pipe is None else _pipe + self.client.pipeline(transaction=self.transactional) + if _pipe is None + else _pipe ) new_version_key = self.__save_artifact( - pipe=_redis_pipe, artifact=artifact, key=key, version=str(version), **metadata + pipe=_redis_pipe, + artifact=artifact, + key=key, + version=str(version), + **metadata, ) _redis_pipe.expire(name=new_version_key, time=self.ttl) if _pipe is None: @@ -324,7 +360,9 @@ def save( except RedisError as err: raise RedisRegistryError(f"{err.__class__.__name__} raised") from err else: - _LOGGER.info("Model with the key = %s, saved successfully.", new_version_key) + _LOGGER.info( + "Model with the key = %s, saved successfully.", new_version_key + ) return str(version) def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: @@ -381,7 +419,9 @@ def is_artifact_stale(artifact_data: ArtifactData, freq_hr: int) -> bool: stale_ts = (datetime.now() - timedelta(hours=freq_hr)).timestamp() return stale_ts > artifact_ts - def __update_metadata(self, skeys: KEYS, dict_artifacts: dict[str, KeyedArtifact], metadata): + def __update_metadata( + self, skeys: KEYS, dict_artifacts: dict[str, KeyedArtifact], metadata + ): try: with self.client.pipeline(transaction=self.transactional) as pipe: pipe.multi() @@ -390,7 +430,9 @@ def __update_metadata(self, skeys: KEYS, dict_artifacts: dict[str, KeyedArtifact latest_key = self.__construct_latest_key(key) version_key = self.client.get(name=latest_key) pipe.hset( - name=version_key.decode(), key="metadata", value=orjson.dumps(metadata) + name=version_key.decode(), + key="metadata", + value=orjson.dumps(metadata), ) pipe.execute() except RedisError as err: @@ -426,7 +468,9 @@ def save_multiple( dict_artifacts=dict_artifacts, metadata={**{"artifact_versions": dict_model_ver}, **metadata}, ) - _LOGGER.info("Successfully saved all the artifacts with: %s", dict_model_ver) + _LOGGER.info( + "Successfully saved all the artifacts with: %s", dict_model_ver + ) except RedisError as err: raise RedisRegistryError(f"{err.__class__.__name__} raised") from err else: diff --git a/libs/registry/pyproject.toml b/libs/registry/pyproject.toml new file mode 100644 index 00000000..ffae0be1 --- /dev/null +++ b/libs/registry/pyproject.toml @@ -0,0 +1,52 @@ +[build-system] +requires = ["setuptools >= 61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "nlregistry" +version = "0.10.1a0" +description = "Collection of operational Machine Learning models and tools." +authors = [ + {name = "Numalogic Developers"} +] +readme = "README.md" +maintainers = [ + { name = "Avik Basu", email = "avikbasu93@gmail.com"}, + { name = "Vigith Maurice", email = "vigith@gmail.com"} +] +classifiers = [ + "Topic :: Software Development :: Libraries", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12" +] +requires-python = ">= 3.9" +dependencies = [ + "numalogic", + "redis[hiredis]>=4.0", + "mlflow-skinny>2.0", + "boto3>=1.20", +] + +[project.optional-dependencies] +dev = [ + "fakeredis>=2.10", + "moto[dynamodb,s3]>=5.0", + "black>23.0", + "pytest>=7.0", + "pytest-cov>=4.0", + "ruff>=0.0.275", + "pre-commit>=3.3", + "pytest-mock>=3.12", + "torchinfo" +] + + +[project.urls] +Repository = "https://github.com/numaproj/numalogic" +Documentation = "https://numalogic.numaproj.io/" +Homepage = "https://numalogic.numaproj.io/" \ No newline at end of file diff --git a/tests/test_registry/__init__.py b/libs/registry/tests/__init__.py similarity index 100% rename from tests/test_registry/__init__.py rename to libs/registry/tests/__init__.py diff --git a/tests/test_registry/_mlflow_utils.py b/libs/registry/tests/_mlflow_utils.py similarity index 100% rename from tests/test_registry/_mlflow_utils.py rename to libs/registry/tests/_mlflow_utils.py diff --git a/tests/test_registry/test_cache.py b/libs/registry/tests/test_cache.py similarity index 71% rename from tests/test_registry/test_cache.py rename to libs/registry/tests/test_cache.py index 54ec14ea..bb80a6d9 100644 --- a/tests/test_registry/test_cache.py +++ b/libs/registry/tests/test_cache.py @@ -6,14 +6,16 @@ from freezegun import freeze_time from numalogic.models.autoencoder.variants import VanillaAE -from registry import LocalLRUCache, ArtifactData, ArtifactCache -from registry import _apply_jitter +from nlregistry import LocalLRUCache, ArtifactData, ArtifactCache +from nlregistry.artifact import _apply_jitter from numalogic.tools.exceptions import ConfigError class TestArtifactCache(unittest.TestCase): def test_cache(self): - cache_reg = ArtifactCache(cachesize=2, ttl=2, jitter_sec=180, jitter_steps_sec=2) + cache_reg = ArtifactCache( + cachesize=2, ttl=2, jitter_sec=180, jitter_steps_sec=2 + ) with self.assertRaises(NotImplementedError): cache_reg.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) with self.assertRaises(NotImplementedError): @@ -30,11 +32,19 @@ def setUp(self): cache.clear() def test_cache_size(self): - cache_registry = LocalLRUCache(cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0) - cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) + cache_registry = LocalLRUCache( + cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0 + ) + cache_registry.save( + "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) + ) time.sleep(1) - cache_registry.save("m2", ArtifactData(VanillaAE(12, 1), metadata={}, extras={})) - cache_registry.save("m3", ArtifactData(VanillaAE(14, 1), metadata={}, extras={})) + cache_registry.save( + "m2", ArtifactData(VanillaAE(12, 1), metadata={}, extras={}) + ) + cache_registry.save( + "m3", ArtifactData(VanillaAE(14, 1), metadata={}, extras={}) + ) self.assertIsNone(cache_registry.load("m1")) self.assertIsInstance(cache_registry.load("m2"), ArtifactData) self.assertEqual(4, cache_registry.cachesize) @@ -53,20 +63,28 @@ def test_cache_overwrite(self): ) loaded_artifact = cache_registry.load("m1") - self.assertDictEqual({"version": "2", "source": "cache"}, loaded_artifact.extras) + self.assertDictEqual( + {"version": "2", "source": "cache"}, loaded_artifact.extras + ) def test_cache_ttl(self): ts = "2021-01-01 00:00:00" with freeze_time(ts): - cache_registry = LocalLRUCache(cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0) - cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) + cache_registry = LocalLRUCache( + cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0 + ) + cache_registry.save( + "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) + ) self.assertIsInstance(cache_registry.load("m1"), ArtifactData) time.sleep(1) self.assertIsNone(cache_registry.load("m1")) def test_singleton(self): cache_registry_1 = LocalLRUCache(cachesize=2, ttl=1) - cache_registry_1.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) + cache_registry_1.save( + "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) + ) self.assertIsInstance(cache_registry_1.load("m1"), ArtifactData) cache_registry_2 = LocalLRUCache(cachesize=3, ttl=3) @@ -74,13 +92,17 @@ def test_singleton(self): def test_delete(self): cache_registry = LocalLRUCache(cachesize=2, ttl=1) - cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) + cache_registry.save( + "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) + ) cache_registry.delete("m1") self.assertIsNone(cache_registry.load("m1")) def test_clear(self): cache_registry = LocalLRUCache(cachesize=2, ttl=1) - cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) + cache_registry.save( + "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) + ) cache_registry.clear() self.assertIsNone(cache_registry.load("m1")) @@ -100,7 +122,8 @@ def load_cache(idx): threads = [ Thread( - target=cache_reg.save, args=(f"key_{i}", ArtifactData(model, dict(key_idx=i), {})) + target=cache_reg.save, + args=(f"key_{i}", ArtifactData(model, dict(key_idx=i), {})), ) for i in range(n_threads) ] diff --git a/tests/test_registry/test_dynamodb_registry.py b/libs/registry/tests/test_dynamodb_registry.py similarity index 97% rename from tests/test_registry/test_dynamodb_registry.py rename to libs/registry/tests/test_dynamodb_registry.py index ce75a88e..ddd972cb 100644 --- a/tests/test_registry/test_dynamodb_registry.py +++ b/libs/registry/tests/test_dynamodb_registry.py @@ -1,17 +1,15 @@ import time import unittest -from moto import mock_dynamodb, mock_sts +from moto import mock_aws from numalogic.models.autoencoder.variants import VanillaAE -from registry import LocalLRUCache, ArtifactData -from registry import DynamoDBRegistry +from nlregistry import LocalLRUCache, ArtifactData, DynamoDBRegistry ROLE = "qwertyuiopasdfghjklzxcvbnm" ENV = "dev" -@mock_sts -@mock_dynamodb +@mock_aws class TestDynamoDBRegistry(unittest.TestCase): @classmethod def setUpClass(cls) -> None: @@ -276,7 +274,9 @@ def test_load_latest_version(self): try: self.registry.load(skeys, dkeys, latest=True, version="1") - self.fail("Should have raised ValueError, you cannot have both latest=True and version") + self.fail( + "Should have raised ValueError, you cannot have both latest=True and version" + ) except ValueError: pass diff --git a/tests/test_registry/test_mlflow_registry.py b/libs/registry/tests/test_mlflow_registry.py similarity index 69% rename from tests/test_registry/test_mlflow_registry.py rename to libs/registry/tests/test_mlflow_registry.py index af275319..0aeca85b 100644 --- a/tests/test_registry/test_mlflow_registry.py +++ b/libs/registry/tests/test_mlflow_registry.py @@ -5,15 +5,20 @@ from freezegun import freeze_time from mlflow import ActiveRun from mlflow.exceptions import RestException -from mlflow.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST, ErrorCode, RESOURCE_LIMIT_EXCEEDED +from mlflow.protos.databricks_pb2 import ( + RESOURCE_DOES_NOT_EXIST, + ErrorCode, + RESOURCE_LIMIT_EXCEEDED, +) from mlflow.store.entities import PagedList from sklearn.preprocessing import StandardScaler from numalogic.models.autoencoder.variants import VanillaAE -from registry import MLflowRegistry, ArtifactData, LocalLRUCache -from registry import ModelStage +from nlregistry.artifact import ArtifactData +from nlregistry.localcache import LocalLRUCache +from nlregistry.mlflow_registry import ModelStage, MLflowRegistry from numalogic.tools.exceptions import ModelVersionError -from tests.test_registry._mlflow_utils import ( +from tests._mlflow_utils import ( model_sklearn, create_model, mock_log_model_pytorch, @@ -55,11 +60,18 @@ def test_construct_key(self): @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_param", mock_log_state_dict) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version + ) def test_save_model(self): ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys @@ -71,9 +83,15 @@ def test_save_model(self): @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) def test_save_model_sklearn(self): model = self.model_sklearn ml = MLflowRegistry(TRACKING_URI) @@ -84,13 +102,21 @@ def test_save_model_sklearn(self): self.assertEqual(mock_status, status.status) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), + ) def test_load_model_when_pytorch_model_exist1(self): model = self.model ml = MLflowRegistry(TRACKING_URI) @@ -102,12 +128,20 @@ def test_load_model_when_pytorch_model_exist1(self): self.assertIsInstance(data.artifact, VanillaAE) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_empty_rundata()), + ) def test_load_model_when_pytorch_model_exist2(self): model = self.model ml = MLflowRegistry(TRACKING_URI, models_to_retain=2) @@ -121,10 +155,19 @@ def test_load_model_when_pytorch_model_exist2(self): @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_empty_rundata()), + ) @patch.object( MLflowRegistry, "load", @@ -148,11 +191,20 @@ def test_load_model_when_sklearn_model_exist(self): @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_empty_rundata()))) @patch("mlflow.active_run", Mock(return_value=return_empty_rundata())) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_model_version", mock_get_model_version_obj) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_empty_rundata()), + ) def test_load_model_with_version(self): model = self.model ml = MLflowRegistry(TRACKING_URI) @@ -163,14 +215,23 @@ def test_load_model_with_version(self): self.assertIsInstance(data.artifact, VanillaAE) self.assertEqual(data.metadata, {}) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch( "mlflow.tracking.MlflowClient.get_latest_versions", Mock(return_value=PagedList(items=[], token=None)), ) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_empty_rundata()), + ) def test_staging_model_load_error(self): ml = MLflowRegistry(TRACKING_URI, model_stage=ModelStage.STAGE) skeys = self.skeys @@ -178,11 +239,20 @@ def test_staging_model_load_error(self): ml.load(skeys=skeys, dkeys=dkeys) self.assertRaises(ModelVersionError) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version()) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_empty_rundata()), + ) def test_both_version_latest_model_with_version(self): ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys @@ -190,8 +260,14 @@ def test_both_version_latest_model_with_version(self): with self.assertRaises(ValueError): ml.load(skeys=skeys, dkeys=dkeys, latest=False) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version()) @patch("mlflow.pyfunc.load_model", Mock(side_effect=RuntimeError)) def test_load_model_when_no_model_01(self): @@ -202,8 +278,14 @@ def test_load_model_when_no_model_01(self): ml.load(skeys=fake_skeys, dkeys=fake_dkeys, artifact_type="pyfunc") self.assertTrue(log.output) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version()) @patch("mlflow.pytorch.load_model", Mock(side_effect=RuntimeError)) def test_load_model_when_no_model_02(self): @@ -217,7 +299,11 @@ def test_load_model_when_no_model_02(self): @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch( "mlflow.tracking.MlflowClient.transition_model_version_stage", - Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_LIMIT_EXCEEDED)})), + Mock( + side_effect=RestException( + {"error_code": ErrorCode.Name(RESOURCE_LIMIT_EXCEEDED)} + ) + ), ) def test_transition_stage_fail(self): fake_skeys = ["Fakemodel_"] @@ -227,8 +313,14 @@ def test_transition_stage_fail(self): ml.transition_stage(fake_skeys, fake_dkeys) self.assertTrue(log.output) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version()) def test_no_implementation(self): fake_skeys = ["Fakemodel_"] @@ -238,13 +330,21 @@ def test_no_implementation(self): ml.load(skeys=fake_skeys, dkeys=fake_dkeys, artifact_type="somerandom") self.assertTrue(log.output) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_params", mock_log_state_dict) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) - @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) + @patch( + "mlflow.tracking.MlflowClient.search_model_versions", + mock_list_of_model_version2, + ) @patch("mlflow.tracking.MlflowClient.delete_model_version", None) @patch("mlflow.pytorch.load_model", Mock(side_effect=RuntimeError)) def test_delete_model_when_model_exist(self): @@ -258,7 +358,10 @@ def test_delete_model_when_model_exist(self): ml.load(skeys=skeys, dkeys=dkeys) self.assertTrue(log.output) - @patch("mlflow.tracking.MlflowClient.delete_model_version", Mock(side_effect=RuntimeError)) + @patch( + "mlflow.tracking.MlflowClient.delete_model_version", + Mock(side_effect=RuntimeError), + ) def test_delete_model_when_no_model(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] @@ -279,13 +382,22 @@ def test_save_failed(self): ml.save(skeys=fake_skeys, dkeys=fake_dkeys, artifact=self.model) self.assertTrue(log.output) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.get_latest_versions", - Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)})), + Mock( + side_effect=RestException( + {"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)} + ) + ), + ) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), ) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) def test_load_no_model_found(self): ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys @@ -293,13 +405,22 @@ def test_load_no_model_found(self): data = ml.load(skeys=skeys, dkeys=dkeys, artifact_type="pytorch") self.assertIsNone(data) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.get_latest_versions", - Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)})), + Mock( + side_effect=RestException( + {"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)} + ) + ), + ) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), ) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) def test_load_other_mlflow_err(self): ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys @@ -307,13 +428,21 @@ def test_load_other_mlflow_err(self): self.assertIsNone(ml.load(skeys=skeys, dkeys=dkeys, artifact_type="pytorch")) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), + ) def test_is_model_stale_true(self): model = self.model ml = MLflowRegistry(TRACKING_URI) @@ -322,13 +451,21 @@ def test_is_model_stale_true(self): self.assertTrue(ml.is_artifact_stale(data, 12)) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), + ) def test_is_model_stale_false(self): model = self.model ml = MLflowRegistry(TRACKING_URI) @@ -350,18 +487,28 @@ def test_no_cache(self): def test_cache(self): cache_registry = LocalLRUCache() registry = MLflowRegistry(TRACKING_URI, cache_registry=cache_registry) - registry._save_in_cache("key", ArtifactData(artifact=self.model, extras={}, metadata={})) + registry._save_in_cache( + "key", ArtifactData(artifact=self.model, extras={}, metadata={}) + ) self.assertIsNotNone(registry._load_from_cache("key")) self.assertIsNotNone(registry._clear_cache("key")) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) + ) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) - @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) + @patch( + "mlflow.tracking.MlflowClient.transition_model_version_stage", + mock_transition_stage, + ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict()), + ) def test_cache_loading(self): cache_registry = LocalLRUCache(ttl=50000) ml = MLflowRegistry(TRACKING_URI, cache_registry=cache_registry) diff --git a/tests/test_numalogic/config/test_optdeps.py b/libs/registry/tests/test_optdeps.py similarity index 77% rename from tests/test_numalogic/config/test_optdeps.py rename to libs/registry/tests/test_optdeps.py index ec4d0379..7d0adc49 100644 --- a/tests/test_numalogic/config/test_optdeps.py +++ b/libs/registry/tests/test_optdeps.py @@ -24,28 +24,32 @@ def setUp(self) -> None: name="RedisRegistry", model_expiry_sec=10, extra_param=dict(ttl=50) ) - @patch("numalogic.config.factory.getattr", side_effect=AttributeError) + @patch("nlregistry.factory.getattr", side_effect=AttributeError) def test_not_installed_dep_01(self, _): - from numalogic.config.factory import RegistryFactory + from nlregistry.factory import RegistryFactory model_factory = RegistryFactory() server = fakeredis.FakeServer() redis_cli = fakeredis.FakeStrictRedis(server=server, decode_responses=False) with self.assertRaises(ImportError): - model_factory.get_cls("RedisRegistry")(redis_cli, **self.regconf.extra_param) + model_factory.get_cls("RedisRegistry")( + redis_cli, **self.regconf.extra_param + ) - @patch("numalogic.config.factory.getattr", side_effect=AttributeError) + @patch("nlregistry.factory.getattr", side_effect=AttributeError) def test_not_installed_dep_02(self, _): - from numalogic.config.factory import RegistryFactory + from nlregistry.factory import RegistryFactory model_factory = RegistryFactory() server = fakeredis.FakeServer() redis_cli = fakeredis.FakeStrictRedis(server=server, decode_responses=False) with self.assertRaises(ImportError): - model_factory.get_instance(self.regconf)(redis_cli, **self.regconf.extra_param) + model_factory.get_instance(self.regconf)( + redis_cli, **self.regconf.extra_param + ) def test_unknown_registry(self): - from numalogic.config.factory import RegistryFactory + from nlregistry.factory import RegistryFactory model_factory = RegistryFactory() reg_conf = RegistryInfo(name="UnknownRegistry", model_expiry_sec=1) diff --git a/tests/test_registry/test_redis_registry.py b/libs/registry/tests/test_redis_registry.py similarity index 85% rename from tests/test_registry/test_redis_registry.py rename to libs/registry/tests/test_redis_registry.py index 01ca87d3..6b11e8c7 100644 --- a/tests/test_registry/test_redis_registry.py +++ b/libs/registry/tests/test_redis_registry.py @@ -11,7 +11,7 @@ from sklearn.preprocessing import StandardScaler from numalogic.models.autoencoder.variants import VanillaAE -from registry import RedisRegistry, LocalLRUCache, ArtifactData +from nlregistry import RedisRegistry, LocalLRUCache, ArtifactData from numalogic.tools.exceptions import ModelKeyNotFound, RedisRegistryError from numalogic.tools.types import KeyedArtifact @@ -27,7 +27,9 @@ def setUpClass(cls) -> None: cls.skeys = ["test"] cls.dkeys = ["error"] server = fakeredis.FakeServer() - cls.redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) + cls.redis_client = fakeredis.FakeStrictRedis( + server=server, decode_responses=False + ) def setUp(self): self.cache = LocalLRUCache(cachesize=4, ttl=1) @@ -63,7 +65,10 @@ def test_save_model_without_metadata_cache_hit(self): data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertEqual(data.extras["version"], save_version) resave_version1 = self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model, **{"lr": 0.01} + skeys=self.skeys, + dkeys=self.dkeys, + artifact=self.pytorch_model, + **{"lr": 0.01}, ) resave_data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertEqual(save_version, "0") @@ -110,7 +115,10 @@ def test_load_model_without_metadata(self): def test_load_model_with_metadata(self): version = self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model, **{"lr": 0.01} + skeys=self.skeys, + dkeys=self.dkeys, + artifact=self.pytorch_model, + **{"lr": 0.01}, ) data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertIsNotNone(data.artifact) @@ -129,7 +137,9 @@ def test_load_model_with_version(self): version = self.registry.save( skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) - data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys, version=version, latest=False) + data = self.registry.load( + skeys=self.skeys, dkeys=self.dkeys, version=version, latest=False + ) self.assertIsNotNone(data.artifact) self.assertIsNone(data.metadata) self.assertEqual(data.extras["version"], version) @@ -137,20 +147,26 @@ def test_load_model_with_version(self): def test_check_if_model_stale_true(self): delta = datetime.now() - timedelta(days=5) with freeze_time(delta): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertTrue(self.registry.is_artifact_stale(data, 12)) def test_check_if_model_stale_false(self): delta = datetime.now() with freeze_time(delta): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) with freeze_time(delta + timedelta(hours=7)): data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertFalse(self.registry.is_artifact_stale(data, 8)) def test_check_if_model_stale_err(self): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) data.extras = None with self.assertRaises(RedisRegistryError): @@ -162,7 +178,9 @@ def test_both_version_latest_model_with_version(self): def test_load_model_with_wrong_version(self): with self.assertRaises(ModelKeyNotFound): - self.registry.load(skeys=self.skeys, dkeys=self.dkeys, version=str(100), latest=False) + self.registry.load( + skeys=self.skeys, dkeys=self.dkeys, version=str(100), latest=False + ) def test_load_model_when_no_model(self): with self.assertRaises(ModelKeyNotFound): @@ -170,7 +188,9 @@ def test_load_model_when_no_model(self): def test_load_latest_model_twice(self): with freeze_time(datetime.now() - timedelta(days=5)): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) artifact_data_1 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) artifact_data_2 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) @@ -199,7 +219,7 @@ def test_multiple_save(self): "AE": KeyedArtifact(dkeys=["AE"], artifact=VanillaAE(10)), "scaler": KeyedArtifact(dkeys=["scaler"], artifact=StandardScaler()), }, - **{"a": "b"} + **{"a": "b"}, ) artifact_data = self.registry.load(skeys=self.skeys, dkeys=["AE"]) self.assertEqual("test_registry", artifact_data.extras["source"]) @@ -209,7 +229,9 @@ def test_load_non_latest_model_twice(self): old_version = self.registry.save( skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) artifact_data_1 = self.registry.load( skeys=self.skeys, dkeys=self.dkeys, latest=False, version=old_version @@ -225,18 +247,24 @@ def test_delete_version(self): skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) with self.assertRaises(ModelKeyNotFound): - self.registry.delete(skeys=self.skeys, dkeys=self.dkeys, version=str(version)) + self.registry.delete( + skeys=self.skeys, dkeys=self.dkeys, version=str(version) + ) self.registry.load(skeys=self.skeys, dkeys=self.dkeys) def test_delete_model_not_in_registry(self): with self.assertRaises(ModelKeyNotFound): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) self.registry.delete(skeys=self.skeys, dkeys=self.dkeys, version=str(8)) @patch("redis.Redis.set", Mock(side_effect=ConnectionError)) def test_exception_call1(self): with self.assertRaises(RedisRegistryError): - self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) + self.registry.save( + skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model + ) @patch("redis.Redis.get", Mock(side_effect=InvalidResponse)) def test_exception_call2(self): @@ -253,5 +281,7 @@ def test_exception_call4(self): with self.assertRaises(RedisRegistryError): self.registry.save_multiple( skeys=self.skeys, - dict_artifacts={"AE": KeyedArtifact(dkeys=self.dkeys, artifact=VanillaAE(10))}, + dict_artifacts={ + "AE": KeyedArtifact(dkeys=self.dkeys, artifact=VanillaAE(10)) + }, ) diff --git a/tests/test_registry/test_serialize.py b/libs/registry/tests/test_serialize.py similarity index 97% rename from tests/test_registry/test_serialize.py rename to libs/registry/tests/test_serialize.py index 35872475..193e957d 100644 --- a/tests/test_registry/test_serialize.py +++ b/libs/registry/tests/test_serialize.py @@ -3,11 +3,11 @@ import timeit import unittest +from nlregistry._serialize import dumps, loads from sklearn.preprocessing import StandardScaler from torchinfo import summary from numalogic.models.autoencoder.variants import VanillaAE -from registry import loads, dumps LOGGER = logging.getLogger(__name__) diff --git a/pyproject.toml b/pyproject.toml index b087195a..80773558 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,3 @@ -[build-system] -requires = ["setuptools >= 61.0", "wheel"] -build-backend = "setuptools.build_meta" - [tool.black] line-length = 100 include = '\.pyi?$' diff --git a/setup.py b/setup.py index 66fba0ec..051bbf28 100644 --- a/setup.py +++ b/setup.py @@ -27,14 +27,13 @@ def cd(newdir): pkg_name = os.getenv("PKG_NAME", "numalogic") if pkg_name == "numalogic": - with cd("src/core"): + with cd("libs/core"): + setup() +elif pkg_name == "nlconnectors": + with cd("libs/connectors"): + setup() +elif pkg_name == "nlregistry": + with cd("libs/registry"): setup() - -elif pkg_name == "connectors": - setup_module = _load_py_module( - name="connectors_setup", location="src/test_connectors/__setup__.py" - ) -elif pkg_name == "registry": - setup_module = _load_py_module(name="registry_setup", location="src/test_registry/__setup__.py") else: raise ValueError(f"Invalid package name {pkg_name}") diff --git a/src/nlconnectors/connectors/__init__.py b/src/nlconnectors/connectors/__init__.py deleted file mode 100644 index ce072de1..00000000 --- a/src/nlconnectors/connectors/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -from importlib.util import find_spec - -from connectors import ( - RedisConf, - PrometheusConf, - ConnectorConf, - DruidConf, - DruidFetcherConf, - ConnectorType, - RDSConf, - RDSFetcherConf, -) -from connectors import PrometheusFetcher - -__all__ = [ - "RedisConf", - "PrometheusConf", - "ConnectorConf", - "DruidConf", - "DruidFetcherConf", - "ConnectorType", - "PrometheusFetcher", - "RDSFetcher", - "RDSConf", - "RDSFetcherConf", -] - -if find_spec("boto3"): - from numalogic.connectors.rds import RDSFetcher # noqa: F401 - - __all__.append("RDSFetcher") - -if find_spec("pydruid"): - from connectors import DruidFetcher # noqa: F401 - - __all__.append("DruidFetcher") diff --git a/src/nlconnectors/connectors/__setup__.py b/src/nlconnectors/connectors/__setup__.py deleted file mode 100644 index 5572c9c3..00000000 --- a/src/nlconnectors/connectors/__setup__.py +++ /dev/null @@ -1,20 +0,0 @@ -from setuptools import find_packages - - -def setup_args(): - return dict( - name="numalogic-test_connectors", - version="0.10.a1", - packages=find_packages( - where="src", - include=["test_connectors", "test_connectors.*"], - exclude=["tests"], - ), - package_dir={"": "src"}, - install_requires=[ - "numalogic", - "pydruid>=0.6", - "boto3>=1.20", - "PyMySQL>=1.0", - ], - ) diff --git a/src/nlconnectors/connectors/druid/__init__.py b/src/nlconnectors/connectors/druid/__init__.py deleted file mode 100644 index fe81e106..00000000 --- a/src/nlconnectors/connectors/druid/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__all__ = ["DruidFetcher", "make_filter_pairs", "build_params"] diff --git a/src/nlconnectors/connectors/pyproject.toml b/src/nlconnectors/connectors/pyproject.toml deleted file mode 100644 index f504988d..00000000 --- a/src/nlconnectors/connectors/pyproject.toml +++ /dev/null @@ -1,79 +0,0 @@ -[build-system] -requires = ["setuptools >= 61.0", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "numalogic-connectors" -version = "0.10.0" -description = "Collection of operational Machine Learning models and tools." -authors = [ - {name = "Numalogic Developers"} -] -readme = "README.md" -maintainers = [ - { name = "Avik Basu", email = "avikbasu93@gmail.com"}, - { name = "Vigith Maurice", email = "vigith@gmail.com"} -] -classifiers = [ - "Topic :: Software Development :: Libraries", - "License :: OSI Approved :: Apache Software License", - "Intended Audience :: Developers", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11" -] -requires-python = ">= 3.9" -dependencies = [ - "numpy>=1.22", - "pandas[performance]>=2.0", - "scikit-learn>=1.3", - "torch>=2.0", - "pytorch-lightning>=2.0", -] - -[project.urls] -Repository = "https://github.com/numaproj/numalogic" -Documentation = "https://numalogic.numaproj.io/" -Homepage = "https://numalogic.numaproj.io/" - - -[tool.black] -line-length = 100 -include = '\.pyi?$' -exclude = ''' -/( - \.eggs - | \.git - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | \.idea - | _build - | buck-out - | build - | dist - | examples/.*/venv - | tests/.*/setup.py -)/ -''' - -[tool.ruff] -line-length = 100 -src = ["numalogic", "tests"] -select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] -ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] -target-version = "py39" -show-fixes = true -show-source = true - -[tool.ruff.per-file-ignores] -"tests/*" = ["D101", "D103"] - -[tool.ruff.pylint] -max-args = 15 -max-statements = 30 - -[tool.ruff.pydocstyle] -convention = "numpy" diff --git a/src/nlconnectors/connectors/rds/__init__.py b/src/nlconnectors/connectors/rds/__init__.py deleted file mode 100644 index 498d4c59..00000000 --- a/src/nlconnectors/connectors/rds/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from connectors import RDSFetcher - -__all__ = ["RDSFetcher"] diff --git a/src/registry/__init__.py b/src/registry/__init__.py deleted file mode 100644 index f7d349ba..00000000 --- a/src/registry/__init__.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2022 The Numaproj Authors. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from registry import LocalLRUCache - - -__all__ = ["ArtifactManager", "ArtifactData", "ArtifactCache", "LocalLRUCache"] - - -try: - from registry import MLflowRegistry # noqa: F401 -except ImportError: - pass -else: - __all__.append("MLflowRegistry") - -try: - from registry import RedisRegistry # noqa: F401 -except ImportError: - pass -else: - __all__.append("RedisRegistry") - -try: - from registry import DynamoDBRegistry # noqa: F401 -except ImportError: - pass -else: - __all__.append("DynamoDBRegistry") From e0e670094a7cc7dada996f6d619dfb73358b0489 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Wed, 29 May 2024 11:21:32 -0700 Subject: [PATCH 05/68] first refactor Signed-off-by: Avik Basu --- .../rds_trainer_config_fetcher_conf.yaml | 3 +- .../rds_trainer_config_fetcher_conf1.yaml | 3 +- libs/connectors/nlconnectors/druid/_druid.py | 17 ++--- libs/connectors/nlconnectors/factory.py | 4 +- libs/connectors/nlconnectors/rds/_base.py | 4 +- libs/connectors/nlconnectors/rds/_rds.py | 4 +- .../connectors/nlconnectors/rds/db/factory.py | 4 +- libs/connectors/pyproject.toml | 2 +- .../tests/rds/db/test_mysql_fetcher.py | 4 +- libs/connectors/tests/rds/test_base.py | 7 +-- libs/connectors/tests/rds/test_rds.py | 8 +-- libs/connectors/tests/test_druid.py | 4 +- libs/connectors/tests/test_prometheus.py | 16 ++--- .../aws/test_aws_boto3_client_manager.py | 12 +--- .../tests/utils/aws/test_db_configurations.py | 1 - .../utils/aws/test_sts_client_manager.py | 8 +-- libs/core/numalogic/config/factory.py | 4 +- libs/core/numalogic/tools/trainer.py | 4 +- libs/core/pyproject.toml | 2 +- libs/registry/nlregistry/artifact.py | 2 +- libs/registry/nlregistry/dynamodb_registry.py | 20 ++---- libs/registry/nlregistry/mlflow_registry.py | 40 +++--------- libs/registry/nlregistry/redis_registry.py | 48 ++++---------- libs/registry/pyproject.toml | 2 +- libs/registry/tests/test_cache.py | 44 ++++--------- libs/registry/tests/test_dynamodb_registry.py | 4 +- libs/registry/tests/test_mlflow_registry.py | 62 +++++-------------- libs/registry/tests/test_optdeps.py | 8 +-- libs/registry/tests/test_redis_registry.py | 48 ++++---------- pyproject.toml | 1 - 30 files changed, 99 insertions(+), 291 deletions(-) diff --git a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml index 755fce02..0d5d5481 100644 --- a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml +++ b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml @@ -36,7 +36,7 @@ stream_confs: retry_sec: 600 pltrainer_conf: max_epochs: 40 - + redis_conf: url: "isbsvc-fci-redis-isbs-redis-svc.oss-analytics-numalogicosamfci-usw2-prd.svc" port: 26379 @@ -77,4 +77,3 @@ rds_conf: datetime_column_name: "eventdatetime" hash_query_type: True hash_column_name: model_md5_hash - diff --git a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml index 298a7a6d..b4554959 100644 --- a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml +++ b/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml @@ -35,7 +35,7 @@ stream_confs: pltrainer_conf: accelerator: cpu max_epochs: 1 - + redis_conf: url: "isbsvc-fci-redis-isbs-redis-svc.oss-analytics-numalogicosamfci-usw2-prd.svc" port: 26379 @@ -77,4 +77,3 @@ rds_conf: datetime_column_name: "eventdatetime" hash_query_type: True hash_column_name: model_md5_hash - diff --git a/libs/connectors/nlconnectors/druid/_druid.py b/libs/connectors/nlconnectors/druid/_druid.py index fdce34cf..0668d3b1 100644 --- a/libs/connectors/nlconnectors/druid/_druid.py +++ b/libs/connectors/nlconnectors/druid/_druid.py @@ -21,9 +21,7 @@ # TODO: pass dictionary of keys and values as dict -def make_filter_pairs( - filter_keys: list[str], filter_values: list[str] -) -> dict[str, str]: +def make_filter_pairs(filter_keys: list[str], filter_values: list[str]) -> dict[str, str]: """ Args: @@ -89,10 +87,7 @@ def build_params( """ _filter = Filter( type="and", - fields=[ - Filter(type="selector", dimension=k, value=v) - for k, v in filter_pairs.items() - ], + fields=[Filter(type="selector", dimension=k, value=v) for k, v in filter_pairs.items()], ) if static_filters: _LOGGER.debug("Static Filters are present!") @@ -101,16 +96,12 @@ def build_params( reference_dt = reference_dt or datetime.now(pytz.utc) end_dt = reference_dt - timedelta(hours=delay) - _LOGGER.debug( - "Querying with end_dt: %s, that is with delay of %s hrs", end_dt, delay - ) + _LOGGER.debug("Querying with end_dt: %s, that is with delay of %s hrs", end_dt, delay) start_dt = end_dt - timedelta(hours=hours) intervals = [f"{start_dt.isoformat()}/{end_dt.isoformat()}"] - dimension_specs = map( - lambda d: DimensionSpec(dimension=d, output_name=d), dimensions - ) + dimension_specs = map(lambda d: DimensionSpec(dimension=d, output_name=d), dimensions) return { "datasource": datasource, diff --git a/libs/connectors/nlconnectors/factory.py b/libs/connectors/nlconnectors/factory.py index 59c71c1d..3ce370d4 100644 --- a/libs/connectors/nlconnectors/factory.py +++ b/libs/connectors/nlconnectors/factory.py @@ -7,9 +7,7 @@ class ConnectorFactory(_ObjectFactory): """Factory class for data test_connectors.""" - _CLS_SET: ClassVar[frozenset] = frozenset( - {"PrometheusFetcher", "DruidFetcher", "RDSFetcher"} - ) + _CLS_SET: ClassVar[frozenset] = frozenset({"PrometheusFetcher", "DruidFetcher", "RDSFetcher"}) @classmethod def get_cls(cls, name: str): diff --git a/libs/connectors/nlconnectors/rds/_base.py b/libs/connectors/nlconnectors/rds/_base.py index d370c2e6..2559adb8 100644 --- a/libs/connectors/nlconnectors/rds/_base.py +++ b/libs/connectors/nlconnectors/rds/_base.py @@ -40,9 +40,7 @@ def format_dataframe( """ _start_time = time.perf_counter() - df["timestamp"] = ( - pd.to_datetime(df[datetime_column_name]).astype("int64") // 10**6 - ) + df["timestamp"] = pd.to_datetime(df[datetime_column_name]).astype("int64") // 10**6 df.drop(columns=datetime_column_name, inplace=True) if group_by: df = df.groupby(by=group_by).sum().reset_index() diff --git a/libs/connectors/nlconnectors/rds/_rds.py b/libs/connectors/nlconnectors/rds/_rds.py index e5f9cdcf..cdf5fca7 100644 --- a/libs/connectors/nlconnectors/rds/_rds.py +++ b/libs/connectors/nlconnectors/rds/_rds.py @@ -28,9 +28,7 @@ def __init__(self, db_config: RDSConnectionConfig): super().__init__(db_config.endpoint) self.db_config = db_config factory_object = RdsFactory() - self.fetcher = factory_object.get_db_handler(db_config.database_type.lower())( - db_config - ) + self.fetcher = factory_object.get_db_handler(db_config.database_type.lower())(db_config) _LOGGER.info("Executing for database type: %s", self.fetcher.database_type) def fetch( diff --git a/libs/connectors/nlconnectors/rds/db/factory.py b/libs/connectors/nlconnectors/rds/db/factory.py index c2c3f915..88f836d9 100644 --- a/libs/connectors/nlconnectors/rds/db/factory.py +++ b/libs/connectors/nlconnectors/rds/db/factory.py @@ -31,6 +31,4 @@ def get_db_handler(cls, database_type: DatabaseTypes): return MysqlFetcher - raise UnRecognizedDatabaseTypeException( - f"database_type: {database_type} is not supported" - ) + raise UnRecognizedDatabaseTypeException(f"database_type: {database_type} is not supported") diff --git a/libs/connectors/pyproject.toml b/libs/connectors/pyproject.toml index 2625a73d..eb87ed23 100644 --- a/libs/connectors/pyproject.toml +++ b/libs/connectors/pyproject.toml @@ -53,4 +53,4 @@ dev = [ [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" -Homepage = "https://numalogic.numaproj.io/" \ No newline at end of file +Homepage = "https://numalogic.numaproj.io/" diff --git a/libs/connectors/tests/rds/db/test_mysql_fetcher.py b/libs/connectors/tests/rds/db/test_mysql_fetcher.py index ab7e89f5..7b2f551c 100644 --- a/libs/connectors/tests/rds/db/test_mysql_fetcher.py +++ b/libs/connectors/tests/rds/db/test_mysql_fetcher.py @@ -61,9 +61,7 @@ def mock_db_config_ssl_disabled(): @pytest.fixture def setup_fetcher(): - rds_config = ( - Mock() - ) # Assuming that your RDSConfig class behaves like a normal python object + rds_config = Mock() # Assuming that your RDSConfig class behaves like a normal python object kwargs = {"key": "value"} return MysqlFetcher(rds_config, **kwargs) diff --git a/libs/connectors/tests/rds/test_base.py b/libs/connectors/tests/rds/test_base.py index cd4fd1e7..f64c115c 100644 --- a/libs/connectors/tests/rds/test_base.py +++ b/libs/connectors/tests/rds/test_base.py @@ -101,8 +101,7 @@ def test_format_dataframe(): df = format_dataframe(df, query, datetime_field_name) print(df.to_json()) assert ( - df["timestamp"] - == to_datetime(expected_df["eventdatetime"]).astype("int64") // 10**6 + df["timestamp"] == to_datetime(expected_df["eventdatetime"]).astype("int64") // 10**6 ).all() @@ -180,8 +179,6 @@ def test_format_dataframe_pivot(): group_by = ["timestamp", "ciStatus"] pivot = Pivot() pivot.columns = ["ciStatus"] - df = format_dataframe( - df, query, datetime_field_name, group_by=group_by, pivot=pivot - ) + df = format_dataframe(df, query, datetime_field_name, group_by=group_by, pivot=pivot) expected = [{"degraded": 8, "failed": 8, "success": 5, "timestamp": 1708527420000}] assert df.to_dict(orient="records") == expected diff --git a/libs/connectors/tests/rds/test_rds.py b/libs/connectors/tests/rds/test_rds.py index 134a1ca2..aad76f81 100644 --- a/libs/connectors/tests/rds/test_rds.py +++ b/libs/connectors/tests/rds/test_rds.py @@ -23,9 +23,7 @@ def test_init(data_fetcher, mock_db_config): def test_fetch(mocker, data_fetcher): - mock_data = pd.DataFrame( - data={"col1": ["value1", "value2"], "col2": ["value3", "value4"]} - ) + mock_data = pd.DataFrame(data={"col1": ["value1", "value2"], "col2": ["value3", "value4"]}) mocker.patch.object(RDSFetcher, "fetch", return_value=mock_data) @@ -38,9 +36,7 @@ def test_execute_query(mocker): rds_config = RDSConfig(database_type="mysql") rds_fetcher = RDSFetcher(db_config=rds_config) mocker.patch.object(rds_fetcher.fetcher, "execute_query", return_value=DataFrame()) - result = rds_fetcher.fetch( - "SELECT * FROM table", datetime_column_name="eventdatetime" - ) + result = rds_fetcher.fetch("SELECT * FROM table", datetime_column_name="eventdatetime") assert result.empty == DataFrame().empty diff --git a/libs/connectors/tests/test_druid.py b/libs/connectors/tests/test_druid.py index b3dc1163..2ae9c235 100644 --- a/libs/connectors/tests/test_druid.py +++ b/libs/connectors/tests/test_druid.py @@ -161,9 +161,7 @@ def test_fetch_double_sketch(setup, mock_group_by_doubles_sketch): filter_values=["accounting.core.qbowebapp"], dimensions=["assetAlias", "env"], datasource="coredevx-rum-perf-metrics", - aggregations={ - "agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256) - }, + aggregations={"agg0": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 256)}, post_aggregations={ "postAgg0": _post_agg.QuantilesDoublesSketchToQuantile( output_name="agg0", field=postaggregator.Field("agg0"), fraction=0.9 diff --git a/libs/connectors/tests/test_prometheus.py b/libs/connectors/tests/test_prometheus.py index 2fbc7148..95ecb8ca 100644 --- a/libs/connectors/tests/test_prometheus.py +++ b/libs/connectors/tests/test_prometheus.py @@ -227,9 +227,7 @@ def test_fetch(self): filters={"namespace": "sandbox-numalogic-demo"}, ) self.assertEqual(df.shape, (2, 1)) - self.assertListEqual( - df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"] - ) + self.assertListEqual(df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"]) self.assertEqual(df.index.name, "timestamp") @patch.object( @@ -314,18 +312,14 @@ def test_fetch_response_err(self): filters={"namespace": "sandbox-numalogic-demo"}, ) - @patch.object( - PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_query_range()) - ) + @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_query_range())) def test_fetch_raw(self): df = self.fetcher.raw_fetch( query='namespace_asset_pod_cpu_utilization{namespace="sandbox-numalogic-demo"}', start=datetime.now() - timedelta(hours=1), ) self.assertEqual(df.shape, (2, 1)) - self.assertListEqual( - df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"] - ) + self.assertListEqual(df.columns.to_list(), ["namespace_asset_pod_cpu_utilization"]) self.assertEqual(df.index.name, "timestamp") @patch.object( @@ -473,9 +467,7 @@ def test_start_end_err(self): end=datetime.now() - timedelta(hours=2), ) - @patch.object( - PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_no_metric()) - ) + @patch.object(PrometheusFetcher, "_api_query_range", Mock(return_value=_mock_no_metric())) def test_no_metric_name_err(self): with self.assertRaises(PrometheusInvalidResponseError): self.fetcher.raw_fetch( diff --git a/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py b/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py index 15a12f4e..4f9f29e3 100644 --- a/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py +++ b/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py @@ -106,9 +106,7 @@ def test_get_client_unrecognized(boto3_client_manager_mock): def test_get_client_rds(boto3_client_manager_mock, rds_client_mock, boto3_session_mock): - boto3_client_manager_mock.get_boto3_session = MagicMock( - return_value=boto3_session_mock - ) + boto3_client_manager_mock.get_boto3_session = MagicMock(return_value=boto3_session_mock) boto3_session_mock.client.return_value = rds_client_mock rds_client = boto3_client_manager_mock.get_client("rds") @@ -120,12 +118,8 @@ def test_get_client_rds(boto3_client_manager_mock, rds_client_mock, boto3_sessio assert rds_client == rds_client_mock -def test_get_client_athena( - boto3_client_manager_mock, athena_client_mock, boto3_session_mock -): - boto3_client_manager_mock.get_boto3_session = MagicMock( - return_value=boto3_session_mock - ) +def test_get_client_athena(boto3_client_manager_mock, athena_client_mock, boto3_session_mock): + boto3_client_manager_mock.get_boto3_session = MagicMock(return_value=boto3_session_mock) boto3_session_mock.client.return_value = athena_client_mock boto3_client_manager_mock.get_client("athena") diff --git a/libs/connectors/tests/utils/aws/test_db_configurations.py b/libs/connectors/tests/utils/aws/test_db_configurations.py index eb0db410..f3c1fe18 100644 --- a/libs/connectors/tests/utils/aws/test_db_configurations.py +++ b/libs/connectors/tests/utils/aws/test_db_configurations.py @@ -1,4 +1,3 @@ -from unittest.mock import patch import pytest import nlconnectors.utils.aws.db_configurations as db_configuration from nlconnectors.constants import TESTS_DIR diff --git a/libs/connectors/tests/utils/aws/test_sts_client_manager.py b/libs/connectors/tests/utils/aws/test_sts_client_manager.py index cb44f6cd..8bcfc409 100644 --- a/libs/connectors/tests/utils/aws/test_sts_client_manager.py +++ b/libs/connectors/tests/utils/aws/test_sts_client_manager.py @@ -27,18 +27,14 @@ def test_sts_client_manager(boto3_client_mock): mock_sts_client.assume_role.assert_called_once_with( RoleArn=role_arn, RoleSessionName=role_session_name, DurationSeconds=3600 ) - assert ( - manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] - ) + assert manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] # Test is_token_about_to_expire assert manager.is_token_about_to_expire() is False # Test get_credentials credentials = manager.get_credentials(role_arn, role_session_name) - assert ( - manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] - ) + assert manager.credentials == mock_sts_client.assume_role.return_value["Credentials"] assert credentials == mock_sts_client.assume_role.return_value["Credentials"] # Test renew of credentials diff --git a/libs/core/numalogic/config/factory.py b/libs/core/numalogic/config/factory.py index a4cfc5f8..5294feba 100644 --- a/libs/core/numalogic/config/factory.py +++ b/libs/core/numalogic/config/factory.py @@ -36,9 +36,7 @@ def get_cls(cls, name: str): try: return cls._CLS_MAP[name] except KeyError: - raise UnknownConfigArgsError( - f"Invalid name provided for factory: {name}" - ) from None + raise UnknownConfigArgsError(f"Invalid name provided for factory: {name}") from None class PreprocessFactory(_ObjectFactory): diff --git a/libs/core/numalogic/tools/trainer.py b/libs/core/numalogic/tools/trainer.py index 1322b20e..06c7d4c5 100644 --- a/libs/core/numalogic/tools/trainer.py +++ b/libs/core/numalogic/tools/trainer.py @@ -49,7 +49,7 @@ def __init__( enable_checkpointing: bool = False, enable_progress_bar: bool = False, enable_model_summary: bool = False, - **trainer_kw + **trainer_kw, ): if not sys.warnoptions: warnings.simplefilter("ignore", category=UserWarning) @@ -64,7 +64,7 @@ def __init__( enable_checkpointing=enable_checkpointing, enable_progress_bar=enable_progress_bar, enable_model_summary=enable_model_summary, - **trainer_kw + **trainer_kw, ) def predict(self, model: Optional[LightningModule] = None, unbatch=True, **kwargs) -> Tensor: diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 7ff2c145..2595137e 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -67,4 +67,4 @@ Homepage = "https://numalogic.numaproj.io/" #[tool.setuptools.packages.find] #include = ["numalogic*", "numalogic.*"] -#exclude = ["*egg-info*", "build*", "dist*", "docs*", "tests*"] \ No newline at end of file +#exclude = ["*egg-info*", "build*", "dist*", "docs*", "tests*"] diff --git a/libs/registry/nlregistry/artifact.py b/libs/registry/nlregistry/artifact.py index 9fe5d2a1..5d605462 100644 --- a/libs/registry/nlregistry/artifact.py +++ b/libs/registry/nlregistry/artifact.py @@ -74,7 +74,7 @@ def save( skeys: KEYS, dkeys: KEYS, artifact: Union[artifact_t, state_dict_t], - **metadata: META_VT + **metadata: META_VT, ) -> Any: r"""Saves the artifact into mlflow test_registry and updates version. diff --git a/libs/registry/nlregistry/dynamodb_registry.py b/libs/registry/nlregistry/dynamodb_registry.py index 282a6c37..673e0374 100644 --- a/libs/registry/nlregistry/dynamodb_registry.py +++ b/libs/registry/nlregistry/dynamodb_registry.py @@ -48,17 +48,13 @@ def get_session(self, new_session=False) -> boto3.Session: if new_session: self.session = None - if (not self.session) or ( - time.time() - self.sess_start_time > self.__STS_RESET_SECS - ): + if (not self.session) or (time.time() - self.sess_start_time > self.__STS_RESET_SECS): self.session = self._get_sts_session() self.sess_start_time = time.time() return self.session def _get_sts_session(self): - _LOGGER.info( - "Getting session with role: %s and region: %s", self.role, self.region - ) + _LOGGER.info("Getting session with role: %s and region: %s", self.role, self.region) assumed_role_object = boto3.client("sts").assume_role( RoleArn=self.role, RoleSessionName="AssumeRoleSession1" ) @@ -243,9 +239,7 @@ def load( ArtifactData instance. """ if (latest and version) or (not latest and not version): - raise ValueError( - "Either One of 'latest' or 'version' needed in load method call" - ) + raise ValueError("Either One of 'latest' or 'version' needed in load method call") table = self.connector.get_resource().Table(self.table_name) part_key = self.construct_key(skeys) @@ -369,9 +363,7 @@ def __delete_item(table, part_key: str, v_sort_key: str): raise DynamoDBRegistryError(f"{err.__class__.__name__} raised") from err return response - def delete( - self, skeys: KEYS, dkeys: KEYS, version: str - ) -> Optional[dict[str, Any]]: + def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> Optional[dict[str, Any]]: """ Deletes the artifact from dynamodb. Args: @@ -393,9 +385,7 @@ def delete( if version == artifact_data.extras.get("version"): # Find if an older version exists prev_version = str(int(version) - 1) - artifact_data_prev = self.load( - skeys, dkeys, latest=False, version=prev_version - ) + artifact_data_prev = self.load(skeys, dkeys, latest=False, version=prev_version) if artifact_data_prev and prev_version != "0": # Update the zeroth record pointer to the previous version prev_item = self._pack_artifact_data( diff --git a/libs/registry/nlregistry/mlflow_registry.py b/libs/registry/nlregistry/mlflow_registry.py index 3b5f693f..57c4a6d7 100644 --- a/libs/registry/nlregistry/mlflow_registry.py +++ b/libs/registry/nlregistry/mlflow_registry.py @@ -157,9 +157,7 @@ def load( model_key = self.construct_key(skeys, dkeys) if (latest and version) or (not latest and not version): - raise ValueError( - "Either One of 'latest' or 'version' needed in load method call" - ) + raise ValueError("Either One of 'latest' or 'version' needed in load method call") try: if latest: @@ -167,30 +165,20 @@ def load( if cached_artifact: _LOGGER.debug("Found cached artifact for key: %s", model_key) return cached_artifact - version_info = self.client.get_latest_versions( - model_key, stages=[self.model_stage] - ) + version_info = self.client.get_latest_versions(model_key, stages=[self.model_stage]) if not version_info: - raise ModelVersionError( - "Model version missing for key = %s" % model_key - ) + raise ModelVersionError("Model version missing for key = %s" % model_key) version_info = version_info[-1] else: version_info = self.client.get_model_version(model_key, version) - model, metadata = self.__load_artifacts( - skeys, dkeys, version_info, artifact_type - ) + model, metadata = self.__load_artifacts(skeys, dkeys, version_info, artifact_type) except RestException as mlflow_err: return self.__log_mlflow_err(mlflow_err, model_key) except ModelVersionError: - _LOGGER.exception( - "No Model found found in model stage: %s", self.model_stage - ) + _LOGGER.exception("No Model found found in model stage: %s", self.model_stage) return None except Exception: - _LOGGER.exception( - "Unexpected error while Registry loading with key: %s", model_key - ) + _LOGGER.exception("Unexpected error while Registry loading with key: %s", model_key) return None else: artifact_data = ArtifactData( @@ -243,9 +231,7 @@ def save( mlflow.log_params(metadata) model_version = self.transition_stage(skeys=skeys, dkeys=dkeys) except Exception: - _LOGGER.exception( - "Unhandled error when saving a model with key: %s", model_key - ) + _LOGGER.exception("Unhandled error when saving a model with key: %s", model_key) return None else: _LOGGER.info("Successfully inserted model %s to Mlflow", model_key) @@ -322,9 +308,7 @@ def transition_stage(self, skeys: KEYS, dkeys: KEYS) -> Optional[ModelVersion]: # only keep "models_to_retain" number of models. self.__delete_stale_models(skeys=skeys, dkeys=dkeys) except RestException: - _LOGGER.exception( - "Error when transitioning a model: %s to different stage", model_name - ) + _LOGGER.exception("Error when transitioning a model: %s to different stage", model_name) return None else: _LOGGER.info("Successfully transitioned model to Production stage") @@ -332,9 +316,7 @@ def transition_stage(self, skeys: KEYS, dkeys: KEYS) -> Optional[ModelVersion]: def __delete_stale_models(self, skeys: KEYS, dkeys: KEYS): model_name = self.construct_key(skeys, dkeys) - list_model_versions = list( - self.client.search_model_versions(f"name='{model_name}'") - ) + list_model_versions = list(self.client.search_model_versions(f"name='{model_name}'")) if len(list_model_versions) > self.models_to_retain: models_to_delete = list_model_versions[self.models_to_retain :] for stale_model in models_to_delete: @@ -346,9 +328,7 @@ def __load_artifacts( ) -> tuple[artifact_t, dict[str, Any]]: model_key = self.construct_key(skeys, dkeys) handler = self.handler_from_type(artifact_type) - model = handler.load_model( - model_uri=f"models:/{model_key}/{version_info.version}" - ) + model = handler.load_model(model_uri=f"models:/{model_key}/{version_info.version}") _LOGGER.info("Successfully loaded model %s from Mlflow", model_key) run_info = mlflow.get_run(version_info.run_id) diff --git a/libs/registry/nlregistry/redis_registry.py b/libs/registry/nlregistry/redis_registry.py index 4155ce35..3b2f88ba 100644 --- a/libs/registry/nlregistry/redis_registry.py +++ b/libs/registry/nlregistry/redis_registry.py @@ -142,9 +142,7 @@ def _save_in_cache(self, key: str, artifact_data: ArtifactData) -> None: def _clear_cache(self, key: Optional[str] = None) -> Optional[ArtifactData]: if self.cache_registry: - return ( - self.cache_registry.delete(key) if key else self.cache_registry.clear() - ) + return self.cache_registry.delete(key) if key else self.cache_registry.clear() return None def __get_artifact_data( @@ -156,9 +154,7 @@ def __get_artifact_data( artifact_version, artifact_timestamp, serialized_metadata, - ) = self.client.hmget( - name=model_key, keys=["artifact", "version", "timestamp", "metadata"] - ) + ) = self.client.hmget(name=model_key, keys=["artifact", "version", "timestamp", "metadata"]) deserialized_artifact = loads(serialized_artifact) deserialized_metadata = None if serialized_metadata: @@ -196,17 +192,13 @@ def __load_latest_artifact(self, key: str) -> tuple[ArtifactData, bool]: if not self.client.exists(latest_key): raise ModelKeyNotFound(f"latest key: {latest_key}, Not Found !!!") model_key = self.client.get(latest_key) - _LOGGER.debug( - "latest key, %s, is pointing to the key : %s", latest_key, model_key - ) + _LOGGER.debug("latest key, %s, is pointing to the key : %s", latest_key, model_key) artifact, _ = self.__load_version_artifact( version=self.get_version(model_key.decode()), key=key ) return artifact, False - def __load_version_artifact( - self, version: str, key: str - ) -> tuple[ArtifactData, bool]: + def __load_version_artifact(self, version: str, key: str) -> tuple[ArtifactData, bool]: version_key = self.__construct_version_key(key, version) cached_artifact = self._load_from_cache(version_key) if cached_artifact: @@ -227,9 +219,7 @@ def __save_artifact( new_version_key = self.__construct_version_key(key, version) latest_key = self.__construct_latest_key(key) pipe.set(name=latest_key, value=new_version_key) - _LOGGER.debug( - "Setting latest key : %s ,to this new key = %s", latest_key, new_version_key - ) + _LOGGER.debug("Setting latest key : %s ,to this new key = %s", latest_key, new_version_key) serialized_metadata = orjson.dumps(metadata) if metadata else b"" serialized_artifact = dumps(deserialized_object=artifact) _cur_ts = int(time.time()) @@ -238,9 +228,7 @@ def __save_artifact( mapping={ "artifact": serialized_artifact, "version": version, - "timestamp": _apply_jitter( - _cur_ts, self.jitter_sec, self.jitter_steps_sec - ), + "timestamp": _apply_jitter(_cur_ts, self.jitter_sec, self.jitter_steps_sec), "metadata": serialized_metadata, }, ) @@ -277,9 +265,7 @@ def load( RedisRegistryError: If any redis error occurs. """ if (latest and version) or (not latest and not version): - raise ValueError( - "Either One of 'latest' or 'version' needed in load method call" - ) + raise ValueError("Either One of 'latest' or 'version' needed in load method call") key = self.construct_key(skeys, dkeys) try: if latest: @@ -303,9 +289,7 @@ def load( self.__construct_version_key(key, version), key, ) - self._save_in_cache( - self.__construct_version_key(key, version), artifact_data - ) + self._save_in_cache(self.__construct_version_key(key, version), artifact_data) return artifact_data def save( @@ -343,9 +327,7 @@ def save( version_key = self.client.get(name=latest_key) version = int(self.get_version(version_key.decode())) + 1 _redis_pipe = ( - self.client.pipeline(transaction=self.transactional) - if _pipe is None - else _pipe + self.client.pipeline(transaction=self.transactional) if _pipe is None else _pipe ) new_version_key = self.__save_artifact( pipe=_redis_pipe, @@ -360,9 +342,7 @@ def save( except RedisError as err: raise RedisRegistryError(f"{err.__class__.__name__} raised") from err else: - _LOGGER.info( - "Model with the key = %s, saved successfully.", new_version_key - ) + _LOGGER.info("Model with the key = %s, saved successfully.", new_version_key) return str(version) def delete(self, skeys: KEYS, dkeys: KEYS, version: str) -> None: @@ -419,9 +399,7 @@ def is_artifact_stale(artifact_data: ArtifactData, freq_hr: int) -> bool: stale_ts = (datetime.now() - timedelta(hours=freq_hr)).timestamp() return stale_ts > artifact_ts - def __update_metadata( - self, skeys: KEYS, dict_artifacts: dict[str, KeyedArtifact], metadata - ): + def __update_metadata(self, skeys: KEYS, dict_artifacts: dict[str, KeyedArtifact], metadata): try: with self.client.pipeline(transaction=self.transactional) as pipe: pipe.multi() @@ -468,9 +446,7 @@ def save_multiple( dict_artifacts=dict_artifacts, metadata={**{"artifact_versions": dict_model_ver}, **metadata}, ) - _LOGGER.info( - "Successfully saved all the artifacts with: %s", dict_model_ver - ) + _LOGGER.info("Successfully saved all the artifacts with: %s", dict_model_ver) except RedisError as err: raise RedisRegistryError(f"{err.__class__.__name__} raised") from err else: diff --git a/libs/registry/pyproject.toml b/libs/registry/pyproject.toml index ffae0be1..620b5e68 100644 --- a/libs/registry/pyproject.toml +++ b/libs/registry/pyproject.toml @@ -49,4 +49,4 @@ dev = [ [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" -Homepage = "https://numalogic.numaproj.io/" \ No newline at end of file +Homepage = "https://numalogic.numaproj.io/" diff --git a/libs/registry/tests/test_cache.py b/libs/registry/tests/test_cache.py index bb80a6d9..8ffe4753 100644 --- a/libs/registry/tests/test_cache.py +++ b/libs/registry/tests/test_cache.py @@ -13,9 +13,7 @@ class TestArtifactCache(unittest.TestCase): def test_cache(self): - cache_reg = ArtifactCache( - cachesize=2, ttl=2, jitter_sec=180, jitter_steps_sec=2 - ) + cache_reg = ArtifactCache(cachesize=2, ttl=2, jitter_sec=180, jitter_steps_sec=2) with self.assertRaises(NotImplementedError): cache_reg.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) with self.assertRaises(NotImplementedError): @@ -32,19 +30,11 @@ def setUp(self): cache.clear() def test_cache_size(self): - cache_registry = LocalLRUCache( - cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0 - ) - cache_registry.save( - "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) - ) + cache_registry = LocalLRUCache(cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0) + cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) time.sleep(1) - cache_registry.save( - "m2", ArtifactData(VanillaAE(12, 1), metadata={}, extras={}) - ) - cache_registry.save( - "m3", ArtifactData(VanillaAE(14, 1), metadata={}, extras={}) - ) + cache_registry.save("m2", ArtifactData(VanillaAE(12, 1), metadata={}, extras={})) + cache_registry.save("m3", ArtifactData(VanillaAE(14, 1), metadata={}, extras={})) self.assertIsNone(cache_registry.load("m1")) self.assertIsInstance(cache_registry.load("m2"), ArtifactData) self.assertEqual(4, cache_registry.cachesize) @@ -63,28 +53,20 @@ def test_cache_overwrite(self): ) loaded_artifact = cache_registry.load("m1") - self.assertDictEqual( - {"version": "2", "source": "cache"}, loaded_artifact.extras - ) + self.assertDictEqual({"version": "2", "source": "cache"}, loaded_artifact.extras) def test_cache_ttl(self): ts = "2021-01-01 00:00:00" with freeze_time(ts): - cache_registry = LocalLRUCache( - cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0 - ) - cache_registry.save( - "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) - ) + cache_registry = LocalLRUCache(cachesize=2, ttl=1, jitter_sec=0, jitter_steps_sec=0) + cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) self.assertIsInstance(cache_registry.load("m1"), ArtifactData) time.sleep(1) self.assertIsNone(cache_registry.load("m1")) def test_singleton(self): cache_registry_1 = LocalLRUCache(cachesize=2, ttl=1) - cache_registry_1.save( - "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) - ) + cache_registry_1.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) self.assertIsInstance(cache_registry_1.load("m1"), ArtifactData) cache_registry_2 = LocalLRUCache(cachesize=3, ttl=3) @@ -92,17 +74,13 @@ def test_singleton(self): def test_delete(self): cache_registry = LocalLRUCache(cachesize=2, ttl=1) - cache_registry.save( - "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) - ) + cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) cache_registry.delete("m1") self.assertIsNone(cache_registry.load("m1")) def test_clear(self): cache_registry = LocalLRUCache(cachesize=2, ttl=1) - cache_registry.save( - "m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={}) - ) + cache_registry.save("m1", ArtifactData(VanillaAE(10, 1), metadata={}, extras={})) cache_registry.clear() self.assertIsNone(cache_registry.load("m1")) diff --git a/libs/registry/tests/test_dynamodb_registry.py b/libs/registry/tests/test_dynamodb_registry.py index ddd972cb..6d95ff19 100644 --- a/libs/registry/tests/test_dynamodb_registry.py +++ b/libs/registry/tests/test_dynamodb_registry.py @@ -274,9 +274,7 @@ def test_load_latest_version(self): try: self.registry.load(skeys, dkeys, latest=True, version="1") - self.fail( - "Should have raised ValueError, you cannot have both latest=True and version" - ) + self.fail("Should have raised ValueError, you cannot have both latest=True and version") except ValueError: pass diff --git a/libs/registry/tests/test_mlflow_registry.py b/libs/registry/tests/test_mlflow_registry.py index 0aeca85b..d81dbae7 100644 --- a/libs/registry/tests/test_mlflow_registry.py +++ b/libs/registry/tests/test_mlflow_registry.py @@ -60,18 +60,14 @@ def test_construct_key(self): @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_param", mock_log_state_dict) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage, ) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) - @patch( - "mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version - ) + @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version) def test_save_model(self): ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys @@ -102,9 +98,7 @@ def test_save_model_sklearn(self): self.assertEqual(mock_status, status.status) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) @patch( @@ -128,9 +122,7 @@ def test_load_model_when_pytorch_model_exist1(self): self.assertIsInstance(data.artifact, VanillaAE) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.transition_model_version_stage", @@ -299,11 +291,7 @@ def test_load_model_when_no_model_02(self): @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch( "mlflow.tracking.MlflowClient.transition_model_version_stage", - Mock( - side_effect=RestException( - {"error_code": ErrorCode.Name(RESOURCE_LIMIT_EXCEEDED)} - ) - ), + Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_LIMIT_EXCEEDED)})), ) def test_transition_stage_fail(self): fake_skeys = ["Fakemodel_"] @@ -330,9 +318,7 @@ def test_no_implementation(self): ml.load(skeys=fake_skeys, dkeys=fake_dkeys, artifact_type="somerandom") self.assertTrue(log.output) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_params", mock_log_state_dict) @@ -382,17 +368,11 @@ def test_save_failed(self): ml.save(skeys=fake_skeys, dkeys=fake_dkeys, artifact=self.model) self.assertTrue(log.output) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.get_latest_versions", - Mock( - side_effect=RestException( - {"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)} - ) - ), + Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)})), ) @patch( "mlflow.tracking.MlflowClient.get_run", @@ -405,17 +385,11 @@ def test_load_no_model_found(self): data = ml.load(skeys=skeys, dkeys=dkeys, artifact_type="pytorch") self.assertIsNone(data) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch( "mlflow.tracking.MlflowClient.get_latest_versions", - Mock( - side_effect=RestException( - {"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)} - ) - ), + Mock(side_effect=RestException({"error_code": ErrorCode.Name(RESOURCE_DOES_NOT_EXIST)})), ) @patch( "mlflow.tracking.MlflowClient.get_run", @@ -428,9 +402,7 @@ def test_load_other_mlflow_err(self): self.assertIsNone(ml.load(skeys=skeys, dkeys=dkeys, artifact_type="pytorch")) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) @patch( @@ -451,9 +423,7 @@ def test_is_model_stale_true(self): self.assertTrue(ml.is_artifact_stale(data, 12)) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) @patch( @@ -487,16 +457,12 @@ def test_no_cache(self): def test_cache(self): cache_registry = LocalLRUCache() registry = MLflowRegistry(TRACKING_URI, cache_registry=cache_registry) - registry._save_in_cache( - "key", ArtifactData(artifact=self.model, extras={}, metadata={}) - ) + registry._save_in_cache("key", ArtifactData(artifact=self.model, extras={}, metadata={})) self.assertIsNotNone(registry._load_from_cache("key")) self.assertIsNotNone(registry._clear_cache("key")) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())) - ) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) @patch( diff --git a/libs/registry/tests/test_optdeps.py b/libs/registry/tests/test_optdeps.py index 7d0adc49..dda02411 100644 --- a/libs/registry/tests/test_optdeps.py +++ b/libs/registry/tests/test_optdeps.py @@ -32,9 +32,7 @@ def test_not_installed_dep_01(self, _): server = fakeredis.FakeServer() redis_cli = fakeredis.FakeStrictRedis(server=server, decode_responses=False) with self.assertRaises(ImportError): - model_factory.get_cls("RedisRegistry")( - redis_cli, **self.regconf.extra_param - ) + model_factory.get_cls("RedisRegistry")(redis_cli, **self.regconf.extra_param) @patch("nlregistry.factory.getattr", side_effect=AttributeError) def test_not_installed_dep_02(self, _): @@ -44,9 +42,7 @@ def test_not_installed_dep_02(self, _): server = fakeredis.FakeServer() redis_cli = fakeredis.FakeStrictRedis(server=server, decode_responses=False) with self.assertRaises(ImportError): - model_factory.get_instance(self.regconf)( - redis_cli, **self.regconf.extra_param - ) + model_factory.get_instance(self.regconf)(redis_cli, **self.regconf.extra_param) def test_unknown_registry(self): from nlregistry.factory import RegistryFactory diff --git a/libs/registry/tests/test_redis_registry.py b/libs/registry/tests/test_redis_registry.py index 6b11e8c7..627e732b 100644 --- a/libs/registry/tests/test_redis_registry.py +++ b/libs/registry/tests/test_redis_registry.py @@ -27,9 +27,7 @@ def setUpClass(cls) -> None: cls.skeys = ["test"] cls.dkeys = ["error"] server = fakeredis.FakeServer() - cls.redis_client = fakeredis.FakeStrictRedis( - server=server, decode_responses=False - ) + cls.redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) def setUp(self): self.cache = LocalLRUCache(cachesize=4, ttl=1) @@ -137,9 +135,7 @@ def test_load_model_with_version(self): version = self.registry.save( skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) - data = self.registry.load( - skeys=self.skeys, dkeys=self.dkeys, version=version, latest=False - ) + data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys, version=version, latest=False) self.assertIsNotNone(data.artifact) self.assertIsNone(data.metadata) self.assertEqual(data.extras["version"], version) @@ -147,26 +143,20 @@ def test_load_model_with_version(self): def test_check_if_model_stale_true(self): delta = datetime.now() - timedelta(days=5) with freeze_time(delta): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertTrue(self.registry.is_artifact_stale(data, 12)) def test_check_if_model_stale_false(self): delta = datetime.now() with freeze_time(delta): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) with freeze_time(delta + timedelta(hours=7)): data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) self.assertFalse(self.registry.is_artifact_stale(data, 8)) def test_check_if_model_stale_err(self): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) data = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) data.extras = None with self.assertRaises(RedisRegistryError): @@ -178,9 +168,7 @@ def test_both_version_latest_model_with_version(self): def test_load_model_with_wrong_version(self): with self.assertRaises(ModelKeyNotFound): - self.registry.load( - skeys=self.skeys, dkeys=self.dkeys, version=str(100), latest=False - ) + self.registry.load(skeys=self.skeys, dkeys=self.dkeys, version=str(100), latest=False) def test_load_model_when_no_model(self): with self.assertRaises(ModelKeyNotFound): @@ -188,9 +176,7 @@ def test_load_model_when_no_model(self): def test_load_latest_model_twice(self): with freeze_time(datetime.now() - timedelta(days=5)): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) artifact_data_1 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) artifact_data_2 = self.registry.load(skeys=self.skeys, dkeys=self.dkeys) @@ -229,9 +215,7 @@ def test_load_non_latest_model_twice(self): old_version = self.registry.save( skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) artifact_data_1 = self.registry.load( skeys=self.skeys, dkeys=self.dkeys, latest=False, version=old_version @@ -247,24 +231,18 @@ def test_delete_version(self): skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model ) with self.assertRaises(ModelKeyNotFound): - self.registry.delete( - skeys=self.skeys, dkeys=self.dkeys, version=str(version) - ) + self.registry.delete(skeys=self.skeys, dkeys=self.dkeys, version=str(version)) self.registry.load(skeys=self.skeys, dkeys=self.dkeys) def test_delete_model_not_in_registry(self): with self.assertRaises(ModelKeyNotFound): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) self.registry.delete(skeys=self.skeys, dkeys=self.dkeys, version=str(8)) @patch("redis.Redis.set", Mock(side_effect=ConnectionError)) def test_exception_call1(self): with self.assertRaises(RedisRegistryError): - self.registry.save( - skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model - ) + self.registry.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.pytorch_model) @patch("redis.Redis.get", Mock(side_effect=InvalidResponse)) def test_exception_call2(self): @@ -281,7 +259,5 @@ def test_exception_call4(self): with self.assertRaises(RedisRegistryError): self.registry.save_multiple( skeys=self.skeys, - dict_artifacts={ - "AE": KeyedArtifact(dkeys=self.dkeys, artifact=VanillaAE(10)) - }, + dict_artifacts={"AE": KeyedArtifact(dkeys=self.dkeys, artifact=VanillaAE(10))}, ) diff --git a/pyproject.toml b/pyproject.toml index 80773558..8d342bbe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,4 +37,3 @@ max-statements = 30 [tool.ruff.pydocstyle] convention = "numpy" - From 052bf86dcd7a3314d395188308d4094467d19743 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 30 May 2024 13:09:09 -0700 Subject: [PATCH 06/68] app folder refactor Signed-off-by: Avik Basu --- .../numaflow-udfs/.dockerignore | 2 +- apps/numaflow-udfs/{udfs => }/Dockerfile | 2 +- log.conf => apps/numaflow-udfs/log.conf | 0 apps/numaflow-udfs/pyproject.toml | 75 +++++++++++++++++++ libs/connectors/pyproject.toml | 4 +- libs/registry/pyproject.toml | 4 +- setup.py | 4 +- 7 files changed, 83 insertions(+), 8 deletions(-) rename .dockerignore => apps/numaflow-udfs/.dockerignore (96%) rename apps/numaflow-udfs/{udfs => }/Dockerfile (99%) rename log.conf => apps/numaflow-udfs/log.conf (100%) create mode 100644 apps/numaflow-udfs/pyproject.toml diff --git a/.dockerignore b/apps/numaflow-udfs/.dockerignore similarity index 96% rename from .dockerignore rename to apps/numaflow-udfs/.dockerignore index a225db57..6adf0f99 100644 --- a/.dockerignore +++ b/apps/numaflow-udfs/.dockerignore @@ -1,5 +1,5 @@ tests -.github +../../.github .gitignore .hack .ruff_cache diff --git a/apps/numaflow-udfs/udfs/Dockerfile b/apps/numaflow-udfs/Dockerfile similarity index 99% rename from apps/numaflow-udfs/udfs/Dockerfile rename to apps/numaflow-udfs/Dockerfile index 861f42cb..ac0f352a 100644 --- a/apps/numaflow-udfs/udfs/Dockerfile +++ b/apps/numaflow-udfs/Dockerfile @@ -46,7 +46,7 @@ ENV VIRTUAL_ENV=/app/.venv COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} ENV PATH="$VIRTUAL_ENV/bin:$PATH" -COPY ../../.. /app +COPY ../.. /app WORKDIR /app ENTRYPOINT ["/usr/bin/dumb-init", "--"] diff --git a/log.conf b/apps/numaflow-udfs/log.conf similarity index 100% rename from log.conf rename to apps/numaflow-udfs/log.conf diff --git a/apps/numaflow-udfs/pyproject.toml b/apps/numaflow-udfs/pyproject.toml new file mode 100644 index 00000000..fdffe3a2 --- /dev/null +++ b/apps/numaflow-udfs/pyproject.toml @@ -0,0 +1,75 @@ +[tool.poetry] +name = "numaflow-udfs" +version = "0.11a0" +description = "" +authors = ["Avik Basu "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "~3.11" +numalogic = "~0.11.0" +numalogic-connectors = "*" +numalogic-registry = "*" +pynumaflow = "~0.6" +structlog = "^24.1.0" +prometheus_client = "^0.18.0" + +[tool.poetry.group.dev.dependencies] +matplotlib = "^3.4.2" +black = "^23.0" +pytest = "^7.1" +pytest-cov = "^4.0" +torchinfo = "^1.7.2" +ruff = "~0.0.275" +pre-commit = "^3.3.1" +fakeredis = "^2.11.2" +freezegun = "^1.2.2" +pympler = "^1.0.1" +moto = "~4.1" +deepdiff = "^6.5.0" +pytest-mock = "^3.12.0" + + +[tool.black] +line-length = 100 +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | \.idea + | _build + | buck-out + | build + | dist + | examples/.*/venv + | tests/.*/setup.py +)/ +''' + +[tool.ruff] +line-length = 100 +src = ["numalogic", "tests"] +select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] +ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +target-version = "py311" +show-fixes = true +show-source = true + +[tool.ruff.per-file-ignores] +"tests/*" = ["D101", "D103"] + +[tool.ruff.pylint] +max-args = 15 +max-statements = 30 + +[tool.ruff.pydocstyle] +convention = "numpy" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/connectors/pyproject.toml b/libs/connectors/pyproject.toml index eb87ed23..4171dfe5 100644 --- a/libs/connectors/pyproject.toml +++ b/libs/connectors/pyproject.toml @@ -3,8 +3,8 @@ requires = ["setuptools >= 61.0", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "nlconnectors" -version = "0.10.1a0" +name = "numalogic-connectors" +version = "0.11a0" description = "Data Connectors for Numalogic." authors = [ {name = "Numalogic Developers"} diff --git a/libs/registry/pyproject.toml b/libs/registry/pyproject.toml index 620b5e68..bb1ea2c7 100644 --- a/libs/registry/pyproject.toml +++ b/libs/registry/pyproject.toml @@ -3,8 +3,8 @@ requires = ["setuptools >= 61.0", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "nlregistry" -version = "0.10.1a0" +name = "numalogic-registry" +version = "0.11a0" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} diff --git a/setup.py b/setup.py index 051bbf28..5b17b378 100644 --- a/setup.py +++ b/setup.py @@ -29,10 +29,10 @@ def cd(newdir): if pkg_name == "numalogic": with cd("libs/core"): setup() -elif pkg_name == "nlconnectors": +elif pkg_name == "numalogic-connectors": with cd("libs/connectors"): setup() -elif pkg_name == "nlregistry": +elif pkg_name == "numalogic-registry": with cd("libs/registry"): setup() else: From 286d8d0831dba703bdeb039165bc0ad7045e485b Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 31 May 2024 13:31:37 -0700 Subject: [PATCH 07/68] chore: numa-udf refactor Signed-off-by: Avik Basu --- .../.dockerignore | 0 apps/{numaflow-udfs => numa-udfs}/Dockerfile | 2 - .../udfs => numa-udfs}/README.md | 0 apps/{numaflow-udfs => numa-udfs}/log.conf | 0 apps/numa-udfs/poetry.lock | 3812 ++++ .../pyproject.toml | 12 +- .../udfs => numa-udfs/src}/__init__.py | 18 +- .../udfs => numa-udfs/src}/__main__.py | 6 +- .../udfs => numa-udfs/src}/_base.py | 2 +- .../udfs => numa-udfs/src}/_config.py | 2 +- .../udfs => numa-udfs/src}/_logger.py | 0 .../udfs => numa-udfs/src}/_metrics.py | 0 .../numa-udfs/src}/backtest/__init__.py | 2 +- .../numa-udfs/src}/backtest/_constants.py | 0 .../numa-udfs/src}/backtest/_prom.py | 10 +- apps/numa-udfs/src/constants.py | 11 + .../udfs => numa-udfs/src}/entities.py | 0 .../udfs => numa-udfs/src}/factory.py | 14 +- .../udfs => numa-udfs/src}/inference.py | 16 +- .../udfs => numa-udfs/src}/payloadtx.py | 6 +- .../udfs => numa-udfs/src}/postprocess.py | 16 +- .../udfs => numa-udfs/src}/preprocess.py | 17 +- .../udfs => numa-udfs/src}/staticthresh.py | 6 +- .../udfs => numa-udfs/src}/tools.py | 10 +- apps/numa-udfs/src/trainer/__init__.py | 6 + .../udfs => numa-udfs/src}/trainer/_base.py | 15 +- .../udfs => numa-udfs/src}/trainer/_druid.py | 14 +- .../udfs => numa-udfs/src}/trainer/_prom.py | 12 +- .../udfs => numa-udfs/src}/trainer/_rds.py | 12 +- .../tests/__init__.py | 0 .../tests/resources/_config.yaml | 0 .../tests/resources/_config2.yaml | 0 .../tests/resources/_config3.yaml | 0 .../tests/resources/_config4.yaml | 0 apps/numa-udfs/tests/resources/config.yaml | 30 + apps/numa-udfs/tests/resources/data/druid.csv | 14378 ++++++++++++++++ .../tests/resources/data/prom_mv.csv | 362 + .../tests/resources/data/stream.json | 0 .../tests/resources/rds_config.yaml | 0 .../tests/resources/rds_config2.yaml | 0 .../tests/resources/rds_db_config.yaml | 17 + .../rds_trainer_config_fetcher_conf.yaml | 0 .../rds_trainer_config_fetcher_conf1.yaml | 0 .../tests/test_backtest.py | 4 +- .../tests/test_factory.py | 2 +- .../tests/test_inference.py | 10 +- .../tests/test_main.py | 16 +- .../tests/test_numaflow.py | 2 +- .../tests/test_pipeline.py | 16 +- .../tests/test_postprocess.py | 15 +- .../tests/test_preprocess.py | 20 +- .../tests/test_staticthresh.py | 10 +- .../tests/test_trainer.py | 18 +- .../tests/utility.py | 0 apps/numaflow-udfs/udfs/trainer/__init__.py | 6 - 55 files changed, 18770 insertions(+), 157 deletions(-) rename apps/{numaflow-udfs => numa-udfs}/.dockerignore (100%) rename apps/{numaflow-udfs => numa-udfs}/Dockerfile (90%) rename apps/{numaflow-udfs/udfs => numa-udfs}/README.md (100%) rename apps/{numaflow-udfs => numa-udfs}/log.conf (100%) create mode 100644 apps/numa-udfs/poetry.lock rename apps/{numaflow-udfs => numa-udfs}/pyproject.toml (77%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/__init__.py (56%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/__main__.py (87%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/_base.py (98%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/_config.py (99%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/_logger.py (100%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/_metrics.py (100%) rename {libs/core/numalogic => apps/numa-udfs/src}/backtest/__init__.py (83%) rename {libs/core/numalogic => apps/numa-udfs/src}/backtest/_constants.py (100%) rename {libs/core/numalogic => apps/numa-udfs/src}/backtest/_prom.py (98%) create mode 100644 apps/numa-udfs/src/constants.py rename apps/{numaflow-udfs/udfs => numa-udfs/src}/entities.py (100%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/factory.py (90%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/inference.py (94%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/payloadtx.py (92%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/postprocess.py (97%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/preprocess.py (95%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/staticthresh.py (96%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/tools.py (98%) create mode 100644 apps/numa-udfs/src/trainer/__init__.py rename apps/{numaflow-udfs/udfs => numa-udfs/src}/trainer/_base.py (97%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/trainer/_druid.py (93%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/trainer/_prom.py (92%) rename apps/{numaflow-udfs/udfs => numa-udfs/src}/trainer/_rds.py (96%) rename apps/{numaflow-udfs => numa-udfs}/tests/__init__.py (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/_config.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/_config2.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/_config3.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/_config4.yaml (100%) create mode 100644 apps/numa-udfs/tests/resources/config.yaml create mode 100644 apps/numa-udfs/tests/resources/data/druid.csv create mode 100644 apps/numa-udfs/tests/resources/data/prom_mv.csv rename apps/{numaflow-udfs => numa-udfs}/tests/resources/data/stream.json (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/rds_config.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/rds_config2.yaml (100%) create mode 100644 apps/numa-udfs/tests/resources/rds_db_config.yaml rename apps/{numaflow-udfs => numa-udfs}/tests/resources/rds_trainer_config_fetcher_conf.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/resources/rds_trainer_config_fetcher_conf1.yaml (100%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_backtest.py (95%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_factory.py (95%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_inference.py (96%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_main.py (78%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_numaflow.py (98%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_pipeline.py (74%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_postprocess.py (91%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_preprocess.py (83%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_staticthresh.py (91%) rename apps/{numaflow-udfs => numa-udfs}/tests/test_trainer.py (97%) rename apps/{numaflow-udfs => numa-udfs}/tests/utility.py (100%) delete mode 100644 apps/numaflow-udfs/udfs/trainer/__init__.py diff --git a/apps/numaflow-udfs/.dockerignore b/apps/numa-udfs/.dockerignore similarity index 100% rename from apps/numaflow-udfs/.dockerignore rename to apps/numa-udfs/.dockerignore diff --git a/apps/numaflow-udfs/Dockerfile b/apps/numa-udfs/Dockerfile similarity index 90% rename from apps/numaflow-udfs/Dockerfile rename to apps/numa-udfs/Dockerfile index ac0f352a..6959e481 100644 --- a/apps/numaflow-udfs/Dockerfile +++ b/apps/numa-udfs/Dockerfile @@ -25,8 +25,6 @@ WORKDIR /app COPY poetry.lock pyproject.toml ./ RUN poetry install --without dev --no-root --extras "${INSTALL_EXTRAS}" \ - && poetry run pip install --no-cache-dir "torch>=2.0,<3.0" --index-url https://download.pytorch.org/whl/cpu \ - && poetry run pip install --no-cache-dir "lightning[pytorch]" \ && rm -rf $POETRY_CACHE_DIR \ && pip cache purge \ && apt-get purge -y --auto-remove build-essential diff 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"pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[metadata] +lock-version = "2.0" +python-versions = "~3.11" +content-hash = "31a6172c431287a1d2ec2d827755cd4dbbf7da532038caf7a842b7e404a307b5" diff --git a/apps/numaflow-udfs/pyproject.toml b/apps/numa-udfs/pyproject.toml similarity index 77% rename from apps/numaflow-udfs/pyproject.toml rename to apps/numa-udfs/pyproject.toml index fdffe3a2..07b8904c 100644 --- a/apps/numaflow-udfs/pyproject.toml +++ b/apps/numa-udfs/pyproject.toml @@ -1,15 +1,19 @@ [tool.poetry] -name = "numaflow-udfs" +name = "numa-udfs" version = "0.11a0" description = "" authors = ["Avik Basu "] readme = "README.md" +packages = [{include = "src"}] [tool.poetry.dependencies] python = "~3.11" -numalogic = "~0.11.0" -numalogic-connectors = "*" -numalogic-registry = "*" +#numalogic = "~0.11.0" +numalogic = {path = "../../dist/numalogic-0.11a0-py3-none-any.whl"} +#numalogic-connectors = "*" +numalogic-connectors = {path = "../../libs/connectors/dist/numalogic_connectors-0.11a0-py3-none-any.whl"} +#numalogic-registry = "*" +numalogic-registry = {path = "../../libs/registry/dist/numalogic_registry-0.11a0-py3-none-any.whl"} pynumaflow = "~0.6" structlog = "^24.1.0" prometheus_client = "^0.18.0" diff --git a/apps/numaflow-udfs/udfs/__init__.py b/apps/numa-udfs/src/__init__.py similarity index 56% rename from apps/numaflow-udfs/udfs/__init__.py rename to apps/numa-udfs/src/__init__.py index 98492c2e..e1ec6ed9 100644 --- a/apps/numaflow-udfs/udfs/__init__.py +++ b/apps/numa-udfs/src/__init__.py @@ -2,15 +2,15 @@ from logging import config as logconf import os -from numalogic._constants import BASE_DIR -from numalogic.udfs._base import NumalogicUDF -from numalogic.udfs._config import StreamConf, PipelineConf, MLPipelineConf, load_pipeline_conf -from numalogic.udfs.factory import UDFFactory, ServerFactory -from numalogic.udfs.payloadtx import PayloadTransformer -from numalogic.udfs.inference import InferenceUDF -from numalogic.udfs.postprocess import PostprocessUDF -from numalogic.udfs.preprocess import PreprocessUDF -from numalogic.udfs.trainer import TrainerUDF, PromTrainerUDF, DruidTrainerUDF, RDSTrainerUDF +from src.constants import BASE_DIR +from src._base import NumalogicUDF +from src._config import StreamConf, PipelineConf, MLPipelineConf, load_pipeline_conf +from src.factory import UDFFactory, ServerFactory +from src.payloadtx import PayloadTransformer +from src.inference import InferenceUDF +from src.postprocess import PostprocessUDF +from src.preprocess import PreprocessUDF +from src.trainer import TrainerUDF, PromTrainerUDF, DruidTrainerUDF, RDSTrainerUDF def set_logger() -> None: diff --git a/apps/numaflow-udfs/udfs/__main__.py b/apps/numa-udfs/src/__main__.py similarity index 87% rename from apps/numaflow-udfs/udfs/__main__.py rename to apps/numa-udfs/src/__main__.py index f03fb20a..90d490a3 100644 --- a/apps/numaflow-udfs/udfs/__main__.py +++ b/apps/numa-udfs/src/__main__.py @@ -3,10 +3,10 @@ import sys from typing import Final -from numalogic._constants import DEFAULT_BASE_CONF_PATH, DEFAULT_APP_CONF_PATH, DEFAULT_METRICS_PORT -from connectors import get_redis_client_from_conf +from src.constants import DEFAULT_BASE_CONF_PATH, DEFAULT_APP_CONF_PATH, DEFAULT_METRICS_PORT +from nlconnectors.redis import get_redis_client_from_conf from numalogic.monitoring import start_metrics_server -from numalogic.udfs import load_pipeline_conf, UDFFactory, ServerFactory, set_logger +from src import load_pipeline_conf, UDFFactory, ServerFactory, set_logger LOGGER = logging.getLogger(__name__) diff --git a/apps/numaflow-udfs/udfs/_base.py b/apps/numa-udfs/src/_base.py similarity index 98% rename from apps/numaflow-udfs/udfs/_base.py rename to apps/numa-udfs/src/_base.py index e295b0e4..4358d811 100644 --- a/apps/numaflow-udfs/udfs/_base.py +++ b/apps/numa-udfs/src/_base.py @@ -17,7 +17,7 @@ from numalogic.tools.exceptions import ConfigNotFoundError from numalogic.tools.types import artifact_t -from numalogic.udfs._config import StreamConf, PipelineConf, MLPipelineConf +from src._config import StreamConf, PipelineConf, MLPipelineConf _DEFAULT_CONF_ID = "default" diff --git a/apps/numaflow-udfs/udfs/_config.py b/apps/numa-udfs/src/_config.py similarity index 99% rename from apps/numaflow-udfs/udfs/_config.py rename to apps/numa-udfs/src/_config.py index 989e84ce..d52abd84 100644 --- a/apps/numaflow-udfs/udfs/_config.py +++ b/apps/numa-udfs/src/_config.py @@ -6,7 +6,7 @@ from numalogic.config import NumalogicConf, RegistryInfo -from connectors import ( +from nlconnectors.config import ( ConnectorType, RedisConf, PrometheusConf, diff --git a/apps/numaflow-udfs/udfs/_logger.py b/apps/numa-udfs/src/_logger.py similarity index 100% rename from apps/numaflow-udfs/udfs/_logger.py rename to apps/numa-udfs/src/_logger.py diff --git a/apps/numaflow-udfs/udfs/_metrics.py b/apps/numa-udfs/src/_metrics.py similarity index 100% rename from apps/numaflow-udfs/udfs/_metrics.py rename to apps/numa-udfs/src/_metrics.py diff --git a/libs/core/numalogic/backtest/__init__.py b/apps/numa-udfs/src/backtest/__init__.py similarity index 83% rename from libs/core/numalogic/backtest/__init__.py rename to apps/numa-udfs/src/backtest/__init__.py index 5a504966..74bf75f0 100644 --- a/libs/core/numalogic/backtest/__init__.py +++ b/apps/numa-udfs/src/backtest/__init__.py @@ -1,5 +1,5 @@ from importlib.util import find_spec -from numalogic.backtest._prom import PromBacktester, OutDataFrames +from src.backtest._prom import PromBacktester, OutDataFrames def _validate_req_pkgs(): diff --git a/libs/core/numalogic/backtest/_constants.py b/apps/numa-udfs/src/backtest/_constants.py similarity index 100% rename from libs/core/numalogic/backtest/_constants.py rename to apps/numa-udfs/src/backtest/_constants.py diff --git a/libs/core/numalogic/backtest/_prom.py b/apps/numa-udfs/src/backtest/_prom.py similarity index 98% rename from libs/core/numalogic/backtest/_prom.py rename to apps/numa-udfs/src/backtest/_prom.py index 0b8e2d09..4897d781 100644 --- a/libs/core/numalogic/backtest/_prom.py +++ b/apps/numa-udfs/src/backtest/_prom.py @@ -22,8 +22,8 @@ from numpy.typing import NDArray from omegaconf import OmegaConf -from numalogic._constants import BASE_DIR -from numalogic.backtest._constants import DEFAULT_SEQUENCE_LEN +from src.constants import BASE_DIR +from src.backtest._constants import DEFAULT_SEQUENCE_LEN from numalogic.config import ( NumalogicConf, ModelFactory, @@ -31,11 +31,11 @@ PostprocessFactory, ThresholdFactory, ) -from connectors import ConnectorType -from connectors import PrometheusFetcher +from nlconnectors.config import ConnectorType +from nlconnectors import PrometheusFetcher from numalogic.tools.data import StreamingDataset, inverse_window from numalogic.tools.types import artifact_t -from numalogic.udfs import UDFFactory, StreamConf, MLPipelineConf +from src import UDFFactory, StreamConf, MLPipelineConf DEFAULT_OUTPUT_DIR = os.path.join(BASE_DIR, ".btoutput") LOGGER = logging.getLogger(__name__) diff --git a/apps/numa-udfs/src/constants.py b/apps/numa-udfs/src/constants.py new file mode 100644 index 00000000..d768eeb4 --- /dev/null +++ b/apps/numa-udfs/src/constants.py @@ -0,0 +1,11 @@ +import os + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +TESTS_DIR = os.path.join(BASE_DIR, "../tests") + +BASE_CONF_DIR = os.path.join(BASE_DIR, "config") + +DEFAULT_BASE_CONF_PATH = os.path.join(BASE_CONF_DIR, "default-configs", "config.yaml") +DEFAULT_APP_CONF_PATH = os.path.join(BASE_CONF_DIR, "app-configs", "config.yaml") +DEFAULT_METRICS_PORT = 8490 +NUMALOGIC_METRICS = "numalogic_metrics" diff --git a/apps/numaflow-udfs/udfs/entities.py b/apps/numa-udfs/src/entities.py similarity index 100% rename from apps/numaflow-udfs/udfs/entities.py rename to apps/numa-udfs/src/entities.py diff --git a/apps/numaflow-udfs/udfs/factory.py b/apps/numa-udfs/src/factory.py similarity index 90% rename from apps/numaflow-udfs/udfs/factory.py rename to apps/numa-udfs/src/factory.py index 4d7c4a3b..fa47452b 100644 --- a/apps/numaflow-udfs/udfs/factory.py +++ b/apps/numa-udfs/src/factory.py @@ -19,13 +19,13 @@ class UDFFactory: """Factory class to fetch the right UDF.""" - from numalogic.udfs import NumalogicUDF - from udfs.payloadtx import PayloadTransformer - from numalogic.udfs.inference import InferenceUDF - from numalogic.udfs.postprocess import PostprocessUDF - from numalogic.udfs.preprocess import PreprocessUDF - from numalogic.udfs.trainer import DruidTrainerUDF, PromTrainerUDF, RDSTrainerUDF - from numalogic.udfs.staticthresh import StaticThresholdUDF + from src import NumalogicUDF + from src.payloadtx import PayloadTransformer + from src.inference import InferenceUDF + from src.postprocess import PostprocessUDF + from src.preprocess import PreprocessUDF + from src.trainer import DruidTrainerUDF, PromTrainerUDF, RDSTrainerUDF + from src.staticthresh import StaticThresholdUDF nl_udf_t = TypeVar("nl_udf_t", bound=NumalogicUDF, covariant=True) diff --git a/apps/numaflow-udfs/udfs/inference.py b/apps/numa-udfs/src/inference.py similarity index 94% rename from apps/numaflow-udfs/udfs/inference.py rename to apps/numa-udfs/src/inference.py index ba1ff807..11a840b1 100644 --- a/apps/numaflow-udfs/udfs/inference.py +++ b/apps/numa-udfs/src/inference.py @@ -9,21 +9,21 @@ from orjson import orjson from pynumaflow.mapper import Messages, Datum, Message -from numalogic.config import RegistryFactory -from registry import LocalLRUCache, ArtifactData +from nlregistry.factory import RegistryFactory +from nlregistry import LocalLRUCache, ArtifactData from numalogic.tools.types import artifact_t, redis_client_t -from numalogic.udfs._base import NumalogicUDF -from numalogic.udfs._config import PipelineConf -from numalogic.udfs._logger import configure_logger, log_data_payload_values -from numalogic.udfs._metrics import ( +from src._base import NumalogicUDF +from src._config import PipelineConf +from src._logger import configure_logger, log_data_payload_values +from src._metrics import ( RUNTIME_ERROR_COUNTER, MSG_PROCESSED_COUNTER, MSG_IN_COUNTER, UDF_TIME, _increment_counter, ) -from numalogic.udfs.entities import StreamPayload, Status -from numalogic.udfs.tools import ( +from src.entities import StreamPayload, Status +from src.tools import ( _load_artifact, _update_info_metric, get_trainer_message, diff --git a/apps/numaflow-udfs/udfs/payloadtx.py b/apps/numa-udfs/src/payloadtx.py similarity index 92% rename from apps/numaflow-udfs/udfs/payloadtx.py rename to apps/numa-udfs/src/payloadtx.py index 7251a433..3f844afb 100644 --- a/apps/numaflow-udfs/udfs/payloadtx.py +++ b/apps/numa-udfs/src/payloadtx.py @@ -6,9 +6,9 @@ from pynumaflow.mapper import Datum, Messages, Message from numalogic.tools.types import artifact_t -from numalogic.udfs import NumalogicUDF -from numalogic.udfs._config import PipelineConf -from numalogic.udfs._logger import configure_logger, log_data_payload_values +from src import NumalogicUDF +from src._config import PipelineConf +from src._logger import configure_logger, log_data_payload_values _struct_log = configure_logger() diff --git a/apps/numaflow-udfs/udfs/postprocess.py b/apps/numa-udfs/src/postprocess.py similarity index 97% rename from apps/numaflow-udfs/udfs/postprocess.py rename to apps/numa-udfs/src/postprocess.py index ed86f5b9..88d6643d 100644 --- a/apps/numaflow-udfs/udfs/postprocess.py +++ b/apps/numa-udfs/src/postprocess.py @@ -10,27 +10,27 @@ from numalogic.config import ( PostprocessFactory, - RegistryFactory, ScoreConf, AggregatorFactory, AggregatorConf, ) +from nlregistry.factory import RegistryFactory from numalogic.models.threshold import SigmoidThreshold -from registry import LocalLRUCache +from nlregistry import LocalLRUCache from numalogic.tools.aggregators import aggregate_window, aggregate_features from numalogic.tools.types import redis_client_t, artifact_t -from numalogic.udfs import NumalogicUDF -from numalogic.udfs._config import PipelineConf, MLPipelineConf -from numalogic.udfs._logger import configure_logger, log_data_payload_values -from numalogic.udfs._metrics import ( +from src import NumalogicUDF +from src._config import PipelineConf, MLPipelineConf +from src._logger import configure_logger, log_data_payload_values +from src._metrics import ( RUNTIME_ERROR_COUNTER, MSG_PROCESSED_COUNTER, MSG_IN_COUNTER, UDF_TIME, _increment_counter, ) -from numalogic.udfs.entities import StreamPayload, Header, Status, OutputPayload -from numalogic.udfs.tools import _load_artifact, get_trainer_message, get_static_thresh_message +from src.entities import StreamPayload, Header, Status, OutputPayload +from src.tools import _load_artifact, get_trainer_message, get_static_thresh_message # TODO: move to config LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", "3600")) diff --git a/apps/numaflow-udfs/udfs/preprocess.py b/apps/numa-udfs/src/preprocess.py similarity index 95% rename from apps/numaflow-udfs/udfs/preprocess.py rename to apps/numa-udfs/src/preprocess.py index 64f55ea5..4c3ee5f4 100644 --- a/apps/numaflow-udfs/udfs/preprocess.py +++ b/apps/numa-udfs/src/preprocess.py @@ -10,9 +10,10 @@ from sklearn.pipeline import make_pipeline from numalogic._constants import NUMALOGIC_METRICS -from numalogic.config import PreprocessFactory, RegistryFactory -from numalogic.udfs._logger import configure_logger, log_data_payload_values -from numalogic.udfs._metrics import ( +from numalogic.config import PreprocessFactory +from nlregistry.factory import RegistryFactory +from src._logger import configure_logger, log_data_payload_values +from src._metrics import ( DATASHAPE_ERROR_COUNTER, MSG_DROPPED_COUNTER, SOURCE_COUNTER, @@ -22,12 +23,12 @@ UDF_TIME, _increment_counter, ) -from registry import LocalLRUCache +from nlregistry import LocalLRUCache from numalogic.tools.types import redis_client_t, artifact_t -from numalogic.udfs import NumalogicUDF -from numalogic.udfs._config import PipelineConf -from numalogic.udfs.entities import Status, Header -from numalogic.udfs.tools import ( +from src import NumalogicUDF +from src._config import PipelineConf +from src.entities import Status, Header +from src.tools import ( make_stream_payload, get_df, _load_artifact, diff --git a/apps/numaflow-udfs/udfs/staticthresh.py b/apps/numa-udfs/src/staticthresh.py similarity index 96% rename from apps/numaflow-udfs/udfs/staticthresh.py rename to apps/numa-udfs/src/staticthresh.py index 1be7e3ab..2829376f 100644 --- a/apps/numaflow-udfs/udfs/staticthresh.py +++ b/apps/numa-udfs/src/staticthresh.py @@ -6,11 +6,11 @@ from numalogic.config import AggregatorFactory, ScoreAdjustConf, AggregatorConf from numalogic.models.threshold import SigmoidThreshold from numalogic.tools.aggregators import aggregate_window, aggregate_features -from numalogic.udfs import NumalogicUDF, PipelineConf +from src import NumalogicUDF, PipelineConf import numpy.typing as npt -from numalogic.udfs._logger import configure_logger, log_data_payload_values -from numalogic.udfs.entities import StreamPayload, OutputPayload +from src._logger import configure_logger, log_data_payload_values +from src.entities import StreamPayload, OutputPayload SCORE_PREFIX = os.getenv("SCORE_PREFIX", "unified") diff --git a/apps/numaflow-udfs/udfs/tools.py b/apps/numa-udfs/src/tools.py similarity index 98% rename from apps/numaflow-udfs/udfs/tools.py rename to apps/numa-udfs/src/tools.py index 3734c910..1b64eefd 100644 --- a/apps/numaflow-udfs/udfs/tools.py +++ b/apps/numa-udfs/src/tools.py @@ -8,13 +8,13 @@ from pandas import DataFrame from pynumaflow.mapper import Message -from registry import ArtifactManager, ArtifactData +from nlregistry import ArtifactManager, ArtifactData from numalogic.tools.exceptions import RedisRegistryError from numalogic.tools.types import KEYS, redis_client_t -from numalogic.udfs._config import StreamConf -from numalogic.udfs._logger import configure_logger -from numalogic.udfs.entities import StreamPayload, TrainerPayload -from numalogic.udfs._metrics import ( +from src._config import StreamConf +from src._logger import configure_logger +from src.entities import StreamPayload, TrainerPayload +from src._metrics import ( SOURCE_COUNTER, MODEL_INFO, REDIS_ERROR_COUNTER, diff --git a/apps/numa-udfs/src/trainer/__init__.py b/apps/numa-udfs/src/trainer/__init__.py new file mode 100644 index 00000000..65e91339 --- /dev/null +++ b/apps/numa-udfs/src/trainer/__init__.py @@ -0,0 +1,6 @@ +from src.trainer._base import TrainerUDF +from src.trainer._prom import PromTrainerUDF +from src.trainer._druid import DruidTrainerUDF +from src.trainer._rds import RDSTrainerUDF + +__all__ = ["TrainerUDF", "PromTrainerUDF", "DruidTrainerUDF", "RDSTrainerUDF"] diff --git a/apps/numaflow-udfs/udfs/trainer/_base.py b/apps/numa-udfs/src/trainer/_base.py similarity index 97% rename from apps/numaflow-udfs/udfs/trainer/_base.py rename to apps/numa-udfs/src/trainer/_base.py index bba37aec..973b59e7 100644 --- a/apps/numaflow-udfs/udfs/trainer/_base.py +++ b/apps/numa-udfs/src/trainer/_base.py @@ -10,16 +10,17 @@ from sklearn.pipeline import make_pipeline from torch.utils.data import DataLoader -from numalogic.config import PreprocessFactory, ModelFactory, ThresholdFactory, RegistryFactory +from numalogic.config import PreprocessFactory, ModelFactory, ThresholdFactory +from nlregistry.factory import RegistryFactory from numalogic.config._config import NumalogicConf, ModelInfo from numalogic.models.autoencoder import TimeseriesTrainer from numalogic.tools.data import StreamingDataset from numalogic.tools.exceptions import ConfigNotFoundError, RedisRegistryError from numalogic.tools.types import redis_client_t, artifact_t, KEYS, KeyedArtifact -from numalogic.udfs import NumalogicUDF -from numalogic.udfs._config import PipelineConf -from numalogic.udfs._logger import configure_logger, log_data_payload_values -from numalogic.udfs._metrics import ( +from src import NumalogicUDF +from src._config import PipelineConf +from src._logger import configure_logger, log_data_payload_values +from src._metrics import ( REDIS_ERROR_COUNTER, INSUFFICIENT_DATA_COUNTER, NAN_SUMMARY, @@ -31,8 +32,8 @@ _increment_counter, _add_summary, ) -from numalogic.udfs.entities import TrainerPayload -from numalogic.udfs.tools import TrainMsgDeduplicator +from src.entities import TrainerPayload +from src.tools import TrainMsgDeduplicator import torch _struct_log = configure_logger() diff --git a/apps/numaflow-udfs/udfs/trainer/_druid.py b/apps/numa-udfs/src/trainer/_druid.py similarity index 93% rename from apps/numaflow-udfs/udfs/trainer/_druid.py rename to apps/numa-udfs/src/trainer/_druid.py index 815372e4..2e4955a1 100644 --- a/apps/numaflow-udfs/udfs/trainer/_druid.py +++ b/apps/numa-udfs/src/trainer/_druid.py @@ -3,21 +3,21 @@ import pandas as pd -from numalogic.config.factory import ConnectorFactory -from connectors import DruidFetcherConf +from nlconnectors.factory import ConnectorFactory +from nlconnectors.config import DruidFetcherConf from numalogic.tools.exceptions import ConfigNotFoundError, DruidFetcherError from numalogic.tools.types import redis_client_t -from numalogic.udfs._config import PipelineConf -from numalogic.udfs._logger import configure_logger -from numalogic.udfs.entities import TrainerPayload -from numalogic.udfs._metrics import ( +from src._config import PipelineConf +from src._logger import configure_logger +from src.entities import TrainerPayload +from src._metrics import ( FETCH_EXCEPTION_COUNTER, DATAFRAME_SHAPE_SUMMARY, FETCH_TIME_SUMMARY, _increment_counter, _add_summary, ) -from numalogic.udfs.trainer._base import TrainerUDF +from src.trainer._base import TrainerUDF _struct_log = configure_logger() diff --git a/apps/numaflow-udfs/udfs/trainer/_prom.py b/apps/numa-udfs/src/trainer/_prom.py similarity index 92% rename from apps/numaflow-udfs/udfs/trainer/_prom.py rename to apps/numa-udfs/src/trainer/_prom.py index 07c4e549..eeea1edf 100644 --- a/apps/numaflow-udfs/udfs/trainer/_prom.py +++ b/apps/numa-udfs/src/trainer/_prom.py @@ -5,20 +5,20 @@ import pandas as pd import pytz -from numalogic.config.factory import ConnectorFactory +from nlconnectors.factory import ConnectorFactory from numalogic.tools.exceptions import ConfigNotFoundError, PrometheusFetcherError from numalogic.tools.types import redis_client_t -from numalogic.udfs._config import PipelineConf -from numalogic.udfs._logger import configure_logger -from numalogic.udfs.entities import TrainerPayload -from numalogic.udfs._metrics import ( +from src._config import PipelineConf +from src._logger import configure_logger +from src.entities import TrainerPayload +from src._metrics import ( DATAFRAME_SHAPE_SUMMARY, FETCH_EXCEPTION_COUNTER, FETCH_TIME_SUMMARY, _add_summary, _increment_counter, ) -from numalogic.udfs.trainer._base import TrainerUDF +from src.trainer._base import TrainerUDF _struct_log = configure_logger() diff --git a/apps/numaflow-udfs/udfs/trainer/_rds.py b/apps/numa-udfs/src/trainer/_rds.py similarity index 96% rename from apps/numaflow-udfs/udfs/trainer/_rds.py rename to apps/numa-udfs/src/trainer/_rds.py index ae5e1010..64a381b3 100644 --- a/apps/numaflow-udfs/udfs/trainer/_rds.py +++ b/apps/numa-udfs/src/trainer/_rds.py @@ -3,20 +3,20 @@ from typing import Optional import pandas as pd import hashlib -from numalogic.config.factory import ConnectorFactory -from numalogic.connectors import RDSFetcherConf +from nlconnectors.factory import ConnectorFactory +from nlconnectors.config import RDSFetcherConf from numalogic.tools.exceptions import ConfigNotFoundError, RDSFetcherError from numalogic.tools.types import redis_client_t -from numalogic.udfs._config import PipelineConf -from numalogic.udfs.entities import TrainerPayload -from numalogic.udfs._metrics import ( +from src._config import PipelineConf +from src.entities import TrainerPayload +from src._metrics import ( FETCH_EXCEPTION_COUNTER, DATAFRAME_SHAPE_SUMMARY, FETCH_TIME_SUMMARY, _increment_counter, _add_summary, ) -from numalogic.udfs.trainer._base import TrainerUDF +from src.trainer._base import TrainerUDF from datetime import datetime, timedelta import pytz diff --git a/apps/numaflow-udfs/tests/__init__.py b/apps/numa-udfs/tests/__init__.py similarity index 100% rename from apps/numaflow-udfs/tests/__init__.py rename to apps/numa-udfs/tests/__init__.py diff --git a/apps/numaflow-udfs/tests/resources/_config.yaml b/apps/numa-udfs/tests/resources/_config.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/_config.yaml rename to apps/numa-udfs/tests/resources/_config.yaml diff --git a/apps/numaflow-udfs/tests/resources/_config2.yaml b/apps/numa-udfs/tests/resources/_config2.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/_config2.yaml rename to apps/numa-udfs/tests/resources/_config2.yaml diff --git a/apps/numaflow-udfs/tests/resources/_config3.yaml b/apps/numa-udfs/tests/resources/_config3.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/_config3.yaml rename to apps/numa-udfs/tests/resources/_config3.yaml diff --git a/apps/numaflow-udfs/tests/resources/_config4.yaml b/apps/numa-udfs/tests/resources/_config4.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/_config4.yaml rename to apps/numa-udfs/tests/resources/_config4.yaml diff --git a/apps/numa-udfs/tests/resources/config.yaml b/apps/numa-udfs/tests/resources/config.yaml new file mode 100644 index 00000000..22d8bf11 --- /dev/null +++ b/apps/numa-udfs/tests/resources/config.yaml @@ -0,0 +1,30 @@ +model: + name: "SparseVanillaAE" + conf: + seq_len: 20 + n_features: 1 + encoder_layersizes: + - 16 + - 8 + decoder_layersizes: + - 8 + - 16 + dropout_p: 0.25 +trainer: + pltrainer_conf: + max_epochs: 40 +preprocess: + - name: "LogTransformer" + stateful: false + conf: + add_factor: 3 + - name: "StandardScaler" + conf: + with_mean: False +threshold: + name: "StdDevThreshold" +postprocess: + name: "TanhNorm" + stateful: false + conf: + scale_factor: 5 diff --git a/apps/numa-udfs/tests/resources/data/druid.csv b/apps/numa-udfs/tests/resources/data/druid.csv new file mode 100644 index 00000000..9e30343e --- /dev/null +++ b/apps/numa-udfs/tests/resources/data/druid.csv @@ -0,0 +1,14378 @@ +timestamp,degraded,failed,success +1688772480000,94.0,248.0,340525.0 +1688772540000,85.0,227.0,334020.0 +1688772600000,76.0,219.0,334196.0 +1688772660000,65.0,247.0,328733.0 +1688772720000,69.0,225.0,327688.0 +1688772780000,75.0,261.0,331595.0 +1688772840000,79.0,234.0,322085.0 +1688772900000,103.0,288.0,324940.0 +1688772960000,99.0,249.0,321774.0 +1688773020000,66.0,186.0,320672.0 +1688773080000,131.0,282.0,323292.0 +1688773140000,71.0,323.0,320816.0 +1688773200000,94.0,319.0,318706.0 +1688773260000,84.0,227.0,317228.0 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20:35:34,2.921040883251412,0.0,14.496589429450758 +2023-11-20 20:36:04,2.871738323652133,0.0,14.507501775568182 +2023-11-20 20:36:34,3.886006446191664,0.0,14.396621241714016 +2023-11-20 20:37:04,2.7965638775764528,0.0,14.519061464251894 +2023-11-20 20:37:34,2.807809234141304,0.0,14.471065636837121 +2023-11-20 20:38:04,5.156425463950802,0.0,14.509351325757576 +2023-11-20 20:38:34,2.7560258993963447,0.0,14.690607244318182 +2023-11-20 20:39:04,4.58522349264438,0.0,14.510091145833334 +2023-11-20 20:39:34,2.26578641378564,0.0,14.356116092566287 +2023-11-20 20:40:04,3.052977799820426,0.0,14.506114612926137 +2023-11-20 20:40:34,2.8158795499276668,0.0,14.382287227746213 +2023-11-20 20:41:04,5.002862033264017,0.0,14.512310606060606 +2023-11-20 20:41:34,2.4904873101456806,0.0,14.521280924479166 +2023-11-20 20:42:04,3.186575309605419,0.0,14.443599816524621 diff --git a/apps/numaflow-udfs/tests/resources/data/stream.json b/apps/numa-udfs/tests/resources/data/stream.json similarity index 100% rename from apps/numaflow-udfs/tests/resources/data/stream.json rename to apps/numa-udfs/tests/resources/data/stream.json diff --git a/apps/numaflow-udfs/tests/resources/rds_config.yaml b/apps/numa-udfs/tests/resources/rds_config.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/rds_config.yaml rename to apps/numa-udfs/tests/resources/rds_config.yaml diff --git a/apps/numaflow-udfs/tests/resources/rds_config2.yaml b/apps/numa-udfs/tests/resources/rds_config2.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/rds_config2.yaml rename to apps/numa-udfs/tests/resources/rds_config2.yaml diff --git a/apps/numa-udfs/tests/resources/rds_db_config.yaml b/apps/numa-udfs/tests/resources/rds_db_config.yaml new file mode 100644 index 00000000..951ef7e1 --- /dev/null +++ b/apps/numa-udfs/tests/resources/rds_db_config.yaml @@ -0,0 +1,17 @@ + + +aws_assume_role_arn: "arn:aws:iam::123456789:role/ml_iam_role" +aws_assume_role_session_name: "ml_pipeline_reader" +endpoint: "ml-rds-aurora-mysql-cluster.us-west-2.rds.amazonaws.com" +port: 3306 +database_name: "mldb" +database_username: "ml_user" +database_password: "" +database_connection_timeout: 10 +database_type: "mysql" +database_provider: "rds" +aws_region: "us-west-2" +aws_rds_use_iam: True +ssl_enabled: true +ssl: + ca: "/usr/bin/ml_data/us-west-2-bundle.pem" diff --git a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml b/apps/numa-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml rename to apps/numa-udfs/tests/resources/rds_trainer_config_fetcher_conf.yaml diff --git a/apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml b/apps/numa-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml similarity index 100% rename from apps/numaflow-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml rename to apps/numa-udfs/tests/resources/rds_trainer_config_fetcher_conf1.yaml diff --git a/apps/numaflow-udfs/tests/test_backtest.py b/apps/numa-udfs/tests/test_backtest.py similarity index 95% rename from apps/numaflow-udfs/tests/test_backtest.py rename to apps/numa-udfs/tests/test_backtest.py index 58b73775..4aafeb0f 100644 --- a/apps/numaflow-udfs/tests/test_backtest.py +++ b/apps/numa-udfs/tests/test_backtest.py @@ -4,8 +4,8 @@ import pytest from omegaconf import OmegaConf -from numalogic._constants import TESTS_DIR -from numalogic.backtest import PromBacktester, OutDataFrames +from src.constants import TESTS_DIR +from src.backtest import PromBacktester, OutDataFrames from numalogic.config import ( NumalogicConf, ModelInfo, diff --git a/apps/numaflow-udfs/tests/test_factory.py b/apps/numa-udfs/tests/test_factory.py similarity index 95% rename from apps/numaflow-udfs/tests/test_factory.py rename to apps/numa-udfs/tests/test_factory.py index db22db53..7f8bb3da 100644 --- a/apps/numaflow-udfs/tests/test_factory.py +++ b/apps/numa-udfs/tests/test_factory.py @@ -2,7 +2,7 @@ from fakeredis import FakeRedis, FakeServer -from numalogic.udfs import UDFFactory, ServerFactory, PipelineConf +from src import UDFFactory, ServerFactory, PipelineConf class TestUDFFactory(unittest.TestCase): diff --git a/apps/numaflow-udfs/tests/test_inference.py b/apps/numa-udfs/tests/test_inference.py similarity index 96% rename from apps/numaflow-udfs/tests/test_inference.py rename to apps/numa-udfs/tests/test_inference.py index 7f8cd333..75747509 100644 --- a/apps/numaflow-udfs/tests/test_inference.py +++ b/apps/numa-udfs/tests/test_inference.py @@ -16,10 +16,10 @@ ScoreAdjustConf, ) from numalogic.models.autoencoder.variants import VanillaAE -from registry import RedisRegistry, ArtifactData +from nlregistry import RedisRegistry, ArtifactData from numalogic.tools.exceptions import RedisRegistryError -from numalogic.udfs import StreamConf, InferenceUDF, MLPipelineConf -from numalogic.udfs.entities import StreamPayload, Header, Status, TrainerPayload +from src import StreamConf, InferenceUDF, MLPipelineConf +from src.entities import StreamPayload, Header, Status, TrainerPayload REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] @@ -265,7 +265,7 @@ def test_model_pass_error_01(udf, udf_args, mocker): RedisRegistry, "load", return_value=ArtifactData( - artifact=VanillaAE(seq_len=12, n_features=1), + artifact=VanillaAE(seq_len=10, n_features=1), extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), @@ -282,7 +282,7 @@ def test_model_pass_error_02(udf_with_adjust, udf_args, mocker): RedisRegistry, "load", return_value=ArtifactData( - artifact=VanillaAE(seq_len=12, n_features=1), + artifact=VanillaAE(seq_len=15, n_features=1), extras=dict(version="0", timestamp=time.time(), source="test_registry"), metadata={}, ), diff --git a/apps/numaflow-udfs/tests/test_main.py b/apps/numa-udfs/tests/test_main.py similarity index 78% rename from apps/numaflow-udfs/tests/test_main.py rename to apps/numa-udfs/tests/test_main.py index 2504f5b0..b74d8ef9 100644 --- a/apps/numaflow-udfs/tests/test_main.py +++ b/apps/numa-udfs/tests/test_main.py @@ -4,31 +4,31 @@ from omegaconf import OmegaConf from pynumaflow.mapper import Mapper, MultiProcMapper -from numalogic._constants import TESTS_DIR +from src.constants import TESTS_DIR from numalogic.tools.exceptions import ConfigNotFoundError -BASE_CONFIG_PATH = f"{TESTS_DIR}/udfs/resources/_config3.yaml" -APP_CONFIG_PATH = f"{TESTS_DIR}/udfs/resources/_config4.yaml" +BASE_CONFIG_PATH = f"{TESTS_DIR}/resources/_config3.yaml" +APP_CONFIG_PATH = f"{TESTS_DIR}/resources/_config4.yaml" REDIS_AUTH = "123" class TestMainScript(unittest.TestCase): @patch.dict("os.environ", {"BASE_CONF_PATH": BASE_CONFIG_PATH, "REDIS_AUTH": REDIS_AUTH}) def test_init_server_01(self): - from numalogic.udfs.__main__ import init_server + from src.__main__ import init_server server = init_server("preprocess", "sync") self.assertIsInstance(server, Mapper) @patch.dict("os.environ", {"BASE_CONF_PATH": BASE_CONFIG_PATH, "REDIS_AUTH": REDIS_AUTH}) def test_init_server_02(self): - from numalogic.udfs.__main__ import init_server + from src.__main__ import init_server server = init_server("inference", "multiproc") self.assertIsInstance(server, MultiProcMapper) def test_conf_loader(self): - from numalogic.udfs import load_pipeline_conf + from src import load_pipeline_conf plconf = load_pipeline_conf(BASE_CONFIG_PATH, APP_CONFIG_PATH) base_conf = OmegaConf.load(BASE_CONFIG_PATH) @@ -40,7 +40,7 @@ def test_conf_loader(self): ) def test_conf_loader_appconf_not_exist(self): - from numalogic.udfs import load_pipeline_conf + from src import load_pipeline_conf app_conf_path = "_random.yaml" plconf = load_pipeline_conf(BASE_CONFIG_PATH, app_conf_path) @@ -49,7 +49,7 @@ def test_conf_loader_appconf_not_exist(self): self.assertListEqual(list(plconf.stream_confs), list(base_conf["stream_confs"])) def test_conf_loader_err(self): - from numalogic.udfs import load_pipeline_conf + from src import load_pipeline_conf with self.assertRaises(ConfigNotFoundError): load_pipeline_conf("_random1.yaml", "_random2.yaml") diff --git a/apps/numaflow-udfs/tests/test_numaflow.py b/apps/numa-udfs/tests/test_numaflow.py similarity index 98% rename from apps/numaflow-udfs/tests/test_numaflow.py rename to apps/numa-udfs/tests/test_numaflow.py index 88cf0ab7..4eabf8ca 100644 --- a/apps/numaflow-udfs/tests/test_numaflow.py +++ b/apps/numa-udfs/tests/test_numaflow.py @@ -5,7 +5,7 @@ from pynumaflow.mapper import Datum, Messages, Message from numalogic.tools.types import artifact_t -from numalogic.udfs import NumalogicUDF +from src import NumalogicUDF class DummyUDF(NumalogicUDF): diff --git a/apps/numaflow-udfs/tests/test_pipeline.py b/apps/numa-udfs/tests/test_pipeline.py similarity index 74% rename from apps/numaflow-udfs/tests/test_pipeline.py rename to apps/numa-udfs/tests/test_pipeline.py index 3b27f42b..08a8f37c 100644 --- a/apps/numaflow-udfs/tests/test_pipeline.py +++ b/apps/numa-udfs/tests/test_pipeline.py @@ -6,15 +6,15 @@ from omegaconf import OmegaConf from orjson import orjson -from numalogic._constants import TESTS_DIR -from numalogic.udfs._config import PipelineConf -from udfs.payloadtx import PayloadTransformer -from tests.test_udfs.utility import input_json_from_file +from src.constants import TESTS_DIR +from src._config import PipelineConf +from src.payloadtx import PayloadTransformer +from tests.utility import input_json_from_file logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json")) +DATUM = input_json_from_file(os.path.join(TESTS_DIR, "resources", "data", "stream.json")) DATUM_KW = { "event_time": datetime.now(), @@ -24,10 +24,8 @@ class TestPipelineUDF(unittest.TestCase): def setUp(self) -> None: - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) - _given_conf_2 = OmegaConf.load( - os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml") - ) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config.yaml")) + _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config2.yaml")) schema = OmegaConf.structured(PipelineConf) pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) diff --git a/apps/numaflow-udfs/tests/test_postprocess.py b/apps/numa-udfs/tests/test_postprocess.py similarity index 91% rename from apps/numaflow-udfs/tests/test_postprocess.py rename to apps/numa-udfs/tests/test_postprocess.py index aa2eb9d1..842ef518 100644 --- a/apps/numaflow-udfs/tests/test_postprocess.py +++ b/apps/numa-udfs/tests/test_postprocess.py @@ -11,12 +11,12 @@ from orjson import orjson from pynumaflow.mapper import Datum -from numalogic._constants import TESTS_DIR +from src.constants import TESTS_DIR from numalogic.models.threshold import StdDevThreshold -from registry import RedisRegistry, ArtifactData -from numalogic.udfs import PipelineConf -from numalogic.udfs.entities import Header, TrainerPayload, Status, OutputPayload -from numalogic.udfs.postprocess import PostprocessUDF +from nlregistry import RedisRegistry, ArtifactData +from src import PipelineConf +from src.entities import Header, TrainerPayload, Status, OutputPayload +from src.postprocess import PostprocessUDF logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) @@ -84,7 +84,7 @@ @pytest.fixture def conf(): - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) @@ -173,5 +173,6 @@ def test_postprocess_runtime_err_02(udf, mocker, bad_artifact): def test_compute(udf, artifact): - x_inferred = udf.compute(artifact.artifact, np.asarray(DATA["data"])) + score, x_inferred = udf.compute(artifact.artifact, np.asarray(DATA["data"])) assert x_inferred.shape == (2,) + assert isinstance(score, float) diff --git a/apps/numaflow-udfs/tests/test_preprocess.py b/apps/numa-udfs/tests/test_preprocess.py similarity index 83% rename from apps/numaflow-udfs/tests/test_preprocess.py rename to apps/numa-udfs/tests/test_preprocess.py index 6935728b..8d8f830a 100644 --- a/apps/numaflow-udfs/tests/test_preprocess.py +++ b/apps/numa-udfs/tests/test_preprocess.py @@ -10,18 +10,18 @@ from pynumaflow.mapper import Datum -from numalogic._constants import TESTS_DIR -from registry import RedisRegistry +from src.constants import TESTS_DIR +from nlregistry import RedisRegistry from numalogic.tools.exceptions import ModelKeyNotFound -from numalogic.udfs._config import PipelineConf -from numalogic.udfs.entities import Status, Header, StreamPayload, TrainerPayload -from numalogic.udfs.preprocess import PreprocessUDF -from tests.test_udfs.utility import input_json_from_file, store_in_redis +from src._config import PipelineConf +from src.entities import Status, Header, StreamPayload, TrainerPayload +from src.preprocess import PreprocessUDF +from tests.utility import input_json_from_file, store_in_redis logging.basicConfig(level=logging.DEBUG) REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) KEYS = ["service-mesh", "1", "2"] -DATUM = input_json_from_file(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json")) +DATUM = input_json_from_file(os.path.join(TESTS_DIR, "resources", "data", "stream.json")) DATUM_KW = { "event_time": datetime.now(), @@ -32,8 +32,8 @@ @pytest.fixture def setup(): registry = RedisRegistry(REDIS_CLIENT) - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) - _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config.yaml")) + _given_conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config2.yaml")) schema = OmegaConf.structured(PipelineConf) pl_conf = PipelineConf(**OmegaConf.merge(schema, _given_conf)) pl_conf_2 = PipelineConf(**OmegaConf.merge(schema, _given_conf_2)) @@ -111,7 +111,7 @@ def test_preprocess_run_time_error(setup, mocker): def test_preprocess_data_error(setup): udf1, _ = setup - with open(os.path.join(TESTS_DIR, "tests", "resources", "data", "stream.json"), "rb") as f: + with open(os.path.join(TESTS_DIR, "resources", "data", "stream.json"), "rb") as f: stream = json.load(f) stream["data"] = stream["data"][:5] msg = udf1( diff --git a/apps/numaflow-udfs/tests/test_staticthresh.py b/apps/numa-udfs/tests/test_staticthresh.py similarity index 91% rename from apps/numaflow-udfs/tests/test_staticthresh.py rename to apps/numa-udfs/tests/test_staticthresh.py index 67b1dc0c..d5221fd6 100644 --- a/apps/numaflow-udfs/tests/test_staticthresh.py +++ b/apps/numa-udfs/tests/test_staticthresh.py @@ -8,10 +8,10 @@ from orjson import orjson from pynumaflow.mapper import Datum -from numalogic._constants import TESTS_DIR -from numalogic.udfs import PipelineConf -from numalogic.udfs.entities import Status, Header, OutputPayload -from numalogic.udfs.staticthresh import StaticThresholdUDF +from src.constants import TESTS_DIR +from src import PipelineConf +from src.entities import Status, Header, OutputPayload +from src.staticthresh import StaticThresholdUDF logging.basicConfig(level=logging.DEBUG) KEYS = ["service-mesh", "1", "2"] @@ -78,7 +78,7 @@ @pytest.fixture def conf() -> PipelineConf: - _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) + _given_conf = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) return PipelineConf(**OmegaConf.merge(schema, _given_conf)) diff --git a/apps/numaflow-udfs/tests/test_trainer.py b/apps/numa-udfs/tests/test_trainer.py similarity index 97% rename from apps/numaflow-udfs/tests/test_trainer.py rename to apps/numa-udfs/tests/test_trainer.py index 7c16a219..9a4742e7 100644 --- a/apps/numaflow-udfs/tests/test_trainer.py +++ b/apps/numa-udfs/tests/test_trainer.py @@ -13,19 +13,19 @@ from pynumaflow.mapper import Datum from redis import RedisError -from numalogic._constants import TESTS_DIR +from src.constants import TESTS_DIR from numalogic.config import NumalogicConf, ModelInfo from numalogic.config import TrainerConf, LightningTrainerConf -from connectors import RedisConf, DruidConf, DruidFetcherConf, PrometheusFetcher -from connectors import DruidFetcher +from nlconnectors.config import RedisConf, DruidConf, DruidFetcherConf +from nlconnectors import DruidFetcher, PrometheusFetcher from numalogic.tools.exceptions import ( ConfigNotFoundError, DruidFetcherError, PrometheusFetcherError, ) -from numalogic.udfs import StreamConf, PipelineConf, MLPipelineConf -from numalogic.udfs.tools import TrainMsgDeduplicator -from numalogic.udfs.trainer import DruidTrainerUDF, PromTrainerUDF +from src import StreamConf, PipelineConf, MLPipelineConf +from src.tools import TrainMsgDeduplicator +from src.trainer import DruidTrainerUDF, PromTrainerUDF REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) @@ -58,9 +58,9 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config.yaml")) + conf_1 = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config.yaml")) schema = OmegaConf.structured(PipelineConf) - conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config2.yaml")) + conf_2 = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config2.yaml")) conf_1 = OmegaConf.merge(schema, conf_1) conf_2 = OmegaConf.merge(schema, conf_2) self.pl_conf_1 = PipelineConf(**OmegaConf.merge(schema, conf_1)) @@ -570,7 +570,7 @@ def setUp(self): event_time=datetime.now(), watermark=datetime.now(), ) - conf = OmegaConf.load(os.path.join(TESTS_DIR, "tests", "resources", "_config3.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_DIR, "resources", "_config3.yaml")) self.conf = OmegaConf.merge(OmegaConf.structured(PipelineConf), conf) @patch.object(PromTrainerUDF, "fetch_data", Mock(return_value=_mock_mv_fetch_data())) diff --git a/apps/numaflow-udfs/tests/utility.py b/apps/numa-udfs/tests/utility.py similarity index 100% rename from apps/numaflow-udfs/tests/utility.py rename to apps/numa-udfs/tests/utility.py diff --git a/apps/numaflow-udfs/udfs/trainer/__init__.py b/apps/numaflow-udfs/udfs/trainer/__init__.py deleted file mode 100644 index e2ff3f49..00000000 --- a/apps/numaflow-udfs/udfs/trainer/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from numalogic.udfs.trainer._base import TrainerUDF -from numalogic.udfs.trainer._prom import PromTrainerUDF -from numalogic.udfs.trainer._druid import DruidTrainerUDF -from numalogic.udfs.trainer._rds import RDSTrainerUDF - -__all__ = ["TrainerUDF", "PromTrainerUDF", "DruidTrainerUDF", "RDSTrainerUDF"] From 3e0aaba58fe5f841b1aa9f196886ded77765d36f Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Mon, 3 Jun 2024 15:40:34 -0700 Subject: [PATCH 08/68] feat: add dev containers to numa-udf Signed-off-by: Avik Basu --- apps/numa-udfs/.devcontainer/Dockerfile | 49 ++++ .../numa-udfs/.devcontainer/devcontainer.json | 7 + apps/numa-udfs/Dockerfile | 15 +- apps/numa-udfs/poetry.lock | 274 +++++++++--------- apps/numa-udfs/pyproject.toml | 6 +- 5 files changed, 205 insertions(+), 146 deletions(-) create mode 100644 apps/numa-udfs/.devcontainer/Dockerfile create mode 100644 apps/numa-udfs/.devcontainer/devcontainer.json diff --git a/apps/numa-udfs/.devcontainer/Dockerfile b/apps/numa-udfs/.devcontainer/Dockerfile new file mode 100644 index 00000000..3d5ab353 --- /dev/null +++ b/apps/numa-udfs/.devcontainer/Dockerfile @@ -0,0 +1,49 @@ +#################################################################################################### +# builder: install needed dependencies and setup virtual environment +#################################################################################################### + +ARG PYTHON_VERSION=3.11 +FROM python:${PYTHON_VERSION}-bookworm AS builder + +ARG POETRY_VERSION=1.6 + +ENV POETRY_NO_INTERACTION=1 \ + POETRY_VIRTUALENVS_IN_PROJECT=1 \ + POETRY_VIRTUALENVS_CREATE=1 \ + POETRY_CACHE_DIR=/tmp/poetry_cache \ + POETRY_VERSION=${POETRY_VERSION} \ + POETRY_HOME="/opt/poetry" \ + PATH="$POETRY_HOME/bin:$PATH" + +RUN apt-get update \ + && apt-get install --no-install-recommends -y build-essential \ + && apt-get clean && rm -rf /var/lib/apt/lists/* \ + && pip install --no-cache-dir poetry==$POETRY_VERSION + +WORKDIR /app + +COPY numalogic-0.11a0-py3-none-any.whl /app +COPY numalogic_registry-0.11a0-py3-none-any.whl /app +COPY numalogic_connectors-0.11a0-py3-none-any.whl /app + +COPY poetry.lock pyproject.toml ./ + +RUN poetry install --without dev --no-root \ + && rm -rf $POETRY_CACHE_DIR \ + && pip cache purge \ + && apt-get purge -y --auto-remove build-essential + +#################################################################################################### +# runtime: used for running the udf vertices +#################################################################################################### +FROM python:${PYTHON_VERSION}-bookworm AS runtime + +ENV VIRTUAL_ENV=/app/.venv +COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} +ENV PATH="$VIRTUAL_ENV/bin:$PATH" + +COPY ../.. /app +WORKDIR /app + +ENTRYPOINT ["/usr/bin/dumb-init", "--"] +EXPOSE 5000 diff --git a/apps/numa-udfs/.devcontainer/devcontainer.json b/apps/numa-udfs/.devcontainer/devcontainer.json new file mode 100644 index 00000000..65a266a6 --- /dev/null +++ b/apps/numa-udfs/.devcontainer/devcontainer.json @@ -0,0 +1,7 @@ +{ + "name": "Numalogic UDFs", + "build": { + "dockerfile": "Dockerfile", + "context": "..", + }, +} diff --git a/apps/numa-udfs/Dockerfile b/apps/numa-udfs/Dockerfile index 6959e481..d84ad02d 100644 --- a/apps/numa-udfs/Dockerfile +++ b/apps/numa-udfs/Dockerfile @@ -3,10 +3,9 @@ #################################################################################################### ARG PYTHON_VERSION=3.11 -FROM python:${PYTHON_VERSION}-slim-bookworm AS builder +FROM python:${PYTHON_VERSION}-bookworm AS builder ARG POETRY_VERSION=1.6 -ARG INSTALL_EXTRAS ENV POETRY_NO_INTERACTION=1 \ POETRY_VIRTUALENVS_IN_PROJECT=1 \ @@ -16,15 +15,17 @@ ENV POETRY_NO_INTERACTION=1 \ POETRY_HOME="/opt/poetry" \ PATH="$POETRY_HOME/bin:$PATH" -RUN apt-get update \ - && apt-get install --no-install-recommends -y build-essential \ - && apt-get clean && rm -rf /var/lib/apt/lists/* \ - && pip install --no-cache-dir poetry==$POETRY_VERSION +RUN pip install --no-cache-dir poetry==$POETRY_VERSION WORKDIR /app + +COPY numalogic-0.11a0-py3-none-any.whl /app +COPY numalogic_registry-0.11a0-py3-none-any.whl /app +COPY numalogic_connectors-0.11a0-py3-none-any.whl /app + COPY poetry.lock pyproject.toml ./ -RUN poetry install --without dev --no-root --extras "${INSTALL_EXTRAS}" \ +RUN poetry install --without dev --no-root \ && rm -rf $POETRY_CACHE_DIR \ && pip cache purge \ && apt-get purge -y --auto-remove build-essential diff --git a/apps/numa-udfs/poetry.lock b/apps/numa-udfs/poetry.lock index 6c2fddf1..b49d0382 100644 --- a/apps/numa-udfs/poetry.lock +++ b/apps/numa-udfs/poetry.lock @@ -209,17 +209,17 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "boto3" -version = "1.34.116" +version = "1.34.118" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = 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python-versions = "~3.11" -content-hash = "31a6172c431287a1d2ec2d827755cd4dbbf7da532038caf7a842b7e404a307b5" +content-hash = "0b78839a425cb747d18016a758fdbf009b71c7a0734d0d19f93ddb02fa626e92" diff --git a/apps/numa-udfs/pyproject.toml b/apps/numa-udfs/pyproject.toml index 07b8904c..eda13327 100644 --- a/apps/numa-udfs/pyproject.toml +++ b/apps/numa-udfs/pyproject.toml @@ -9,11 +9,11 @@ packages = [{include = "src"}] [tool.poetry.dependencies] python = "~3.11" #numalogic = "~0.11.0" -numalogic = {path = "../../dist/numalogic-0.11a0-py3-none-any.whl"} +numalogic = {path = "numalogic-0.11a0-py3-none-any.whl"} #numalogic-connectors = "*" -numalogic-connectors = {path = "../../libs/connectors/dist/numalogic_connectors-0.11a0-py3-none-any.whl"} +numalogic-connectors = {path = "numalogic_connectors-0.11a0-py3-none-any.whl"} #numalogic-registry = "*" -numalogic-registry = {path = "../../libs/registry/dist/numalogic_registry-0.11a0-py3-none-any.whl"} +numalogic-registry = {path = "numalogic_registry-0.11a0-py3-none-any.whl"} pynumaflow = "~0.6" structlog = "^24.1.0" prometheus_client = "^0.18.0" From ec7d5be6d551adf62b7ca56a53f3a5f22883cd3e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Mon, 3 Jun 2024 19:18:52 -0700 Subject: [PATCH 09/68] chore: use requirements dir Signed-off-by: Avik Basu --- .../nlconnectors/__init__.py | 0 .../nlconnectors/_base.py | 0 .../nlconnectors/config.py | 0 .../nlconnectors/constants.py | 0 .../nlconnectors/druid/__init__.py | 0 .../nlconnectors/druid/_druid.py | 0 .../nlconnectors/druid/aggregators.py | 0 .../nlconnectors/druid/postaggregator.py | 0 .../nlconnectors/exceptions.py | 0 .../nlconnectors/factory.py | 0 .../nlconnectors/prometheus.py | 0 .../nlconnectors/rds/__init__.py | 0 .../nlconnectors/rds/_base.py | 0 .../nlconnectors/rds/_rds.py | 0 .../nlconnectors/rds/db/__init__.py | 0 .../nlconnectors/rds/db/factory.py | 0 .../nlconnectors/rds/db/mysql_fetcher.py | 0 .../nlconnectors/redis.py | 0 .../nlconnectors/utils/__init__.py | 0 .../nlconnectors/utils/aws/__init__.py | 0 .../utils/aws/boto3_client_manager.py | 0 .../nlconnectors/utils/aws/config.py | 0 .../utils/aws/db_configurations.py | 0 .../nlconnectors/utils/aws/exceptions.py | 0 .../utils/aws/sts_client_manager.py | 0 .../nlconnectors/utils/enum.py | 0 .../pyproject.toml | 50 +++++++------- .../tests/__init__.py | 0 .../tests/data.csv | 0 .../tests/rds/__init__.py | 0 .../tests/rds/db/__init__.py | 0 .../tests/rds/db/test_factory.py | 0 .../tests/rds/db/test_mysql_fetcher.py | 0 .../tests/rds/test_base.py | 0 .../tests/rds/test_rds.py | 0 .../tests/resources/config.yaml | 0 .../tests/resources/data/druid.csv | 0 .../resources/data/interactionstatus.csv | 0 .../tests/resources/data/prom_default.csv | 0 .../tests/resources/data/prom_mv.csv | 0 .../tests/resources/data/raw_druid.csv | 0 .../tests/resources/data/rds.csv | 0 .../tests/resources/rds_db_config.yaml | 0 .../tests/test_druid.py | 0 .../tests/test_prometheus.py | 0 .../tests/test_redis.py | 0 .../tests/utils/__init__.py | 0 .../tests/utils/aws/__init__.py | 0 .../aws/test_aws_boto3_client_manager.py | 0 .../tests/utils/aws/test_config.py | 0 .../tests/utils/aws/test_db_configurations.py | 0 .../utils/aws/test_sts_client_manager.py | 0 .../tests/utils/test_enum.py | 0 libs/numalogic-core/makefile | 22 +++++++ .../numalogic/__init__.py | 0 .../numalogic/_constants.py | 0 .../numalogic/base.py | 0 .../numalogic/config/__init__.py | 0 .../numalogic/config/_config.py | 0 .../numalogic/config/factory.py | 0 .../numalogic/models/__init__.py | 0 .../numalogic/models/autoencoder/__init__.py | 0 .../numalogic/models/autoencoder/base.py | 0 .../models/autoencoder/variants/__init__.py | 5 +- .../models/autoencoder/variants/conv.py | 0 .../models/autoencoder/variants/lstm.py | 0 .../autoencoder/variants/transformer.py | 0 .../models/autoencoder/variants/vanilla.py | 0 .../numalogic/models/forecast/__init__.py | 0 .../models/forecast/variants/__init__.py | 0 .../models/forecast/variants/naive.py | 0 .../numalogic/models/forecast/variants/rnn.py | 0 .../numalogic/models/threshold/__init__.py | 0 .../models/threshold/_mahalanobis.py | 0 .../numalogic/models/threshold/_median.py | 0 .../numalogic/models/threshold/_static.py | 0 .../numalogic/models/threshold/_std.py | 0 .../numalogic/models/vae/__init__.py | 0 .../numalogic/models/vae/base.py | 0 .../numalogic/models/vae/layer.py | 0 .../numalogic/models/vae/variants/__init__.py | 0 .../numalogic/models/vae/variants/conv.py | 0 .../numalogic/monitoring/__init__.py | 0 .../numalogic/monitoring/metrics.py | 0 .../numalogic/synthetic/__init__.py | 0 .../numalogic/synthetic/anomalies.py | 0 .../numalogic/synthetic/sparsity.py | 0 .../numalogic/synthetic/timeseries.py | 0 .../numalogic/tools/__init__.py | 0 .../numalogic/tools/aggregators.py | 0 .../numalogic/tools/callbacks.py | 0 .../numalogic/tools/data.py | 0 .../numalogic/tools/exceptions.py | 0 .../numalogic/tools/loss.py | 0 .../numalogic/tools/trainer.py | 0 .../numalogic/tools/types.py | 0 .../numalogic/transforms/__init__.py | 0 .../numalogic/transforms/_movavg.py | 0 .../numalogic/transforms/_postprocess.py | 0 .../numalogic/transforms/_scaler.py | 0 .../numalogic/transforms/_stateless.py | 0 libs/{core => numalogic-core}/pyproject.toml | 66 +++++++++---------- .../requirements/requirements-dev.txt | 8 +++ .../requirements/requirements-nb.txt | 5 ++ .../requirements/requirements.txt | 6 ++ .../tests/__init__.py | 0 .../tests/config/__init__.py | 0 .../tests/config/test_config.py | 0 .../tests/models/__init__.py | 0 .../tests/models/autoencoder/__init__.py | 0 .../tests/models/autoencoder/test_trainer.py | 0 .../models/autoencoder/variants/__init__.py | 0 .../models/autoencoder/variants/test_conv.py | 0 .../models/autoencoder/variants/test_lstm.py | 0 .../autoencoder/variants/test_transformers.py | 0 .../autoencoder/variants/test_vanilla.py | 0 .../tests/models/forecast/__init__.py | 0 .../tests/models/forecast/test_naive.py | 0 .../tests/models/threshold/__init__.py | 0 .../tests/models/threshold/test_maha.py | 0 .../tests/models/threshold/test_median.py | 0 .../tests/models/threshold/test_static.py | 0 .../tests/models/threshold/test_std.py | 0 .../tests/models/vae/__init__.py | 0 .../tests/models/vae/test_conv.py | 0 .../tests/resources/config.yaml | 0 .../tests/resources/data/druid.csv | 0 .../resources/data/interactionstatus.csv | 0 .../tests/resources/data/prom_default.csv | 0 .../tests/resources/data/prom_mv.csv | 0 .../tests/resources/data/raw_druid.csv | 0 .../tests/resources/data/rds.csv | 0 .../tests/resources/rds_db_config.yaml | 0 .../tests/synthetic/__init__.py | 0 .../tests/synthetic/test_anomalies.py | 0 .../tests/synthetic/test_sparsity.py | 0 .../tests/synthetic/test_timeseries.py | 0 .../tests/tools/__init__.py | 0 .../tests/tools/test_data.py | 0 .../tests/transforms/__init__.py | 0 .../tests/transforms/test_postprocess.py | 0 .../tests/transforms/test_transforms.py | 0 .../nlregistry/__init__.py | 0 .../nlregistry/_serialize.py | 0 .../nlregistry/artifact.py | 0 .../nlregistry/dynamodb_registry.py | 0 .../nlregistry/factory.py | 0 .../nlregistry/localcache.py | 0 .../nlregistry/mlflow_registry.py | 0 .../nlregistry/redis_registry.py | 0 .../pyproject.toml | 0 .../tests/__init__.py | 0 .../tests/_mlflow_utils.py | 0 .../tests/test_cache.py | 0 .../tests/test_dynamodb_registry.py | 0 .../tests/test_mlflow_registry.py | 0 .../tests/test_optdeps.py | 0 .../tests/test_redis_registry.py | 0 .../tests/test_serialize.py | 0 libs/registry/nlregistry/__setup__.py | 20 ------ 160 files changed, 104 insertions(+), 78 deletions(-) rename libs/{connectors => numalogic-connectors}/nlconnectors/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/_base.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/config.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/constants.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/druid/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/druid/_druid.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/druid/aggregators.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/druid/postaggregator.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/exceptions.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/factory.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/prometheus.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/_base.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/_rds.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/db/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/db/factory.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/rds/db/mysql_fetcher.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/redis.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/__init__.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/boto3_client_manager.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/config.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/db_configurations.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/exceptions.py (100%) rename libs/{connectors => numalogic-connectors}/nlconnectors/utils/aws/sts_client_manager.py (100%) rename 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b/libs/numalogic-connectors/nlconnectors/exceptions.py similarity index 100% rename from libs/connectors/nlconnectors/exceptions.py rename to libs/numalogic-connectors/nlconnectors/exceptions.py diff --git a/libs/connectors/nlconnectors/factory.py b/libs/numalogic-connectors/nlconnectors/factory.py similarity index 100% rename from libs/connectors/nlconnectors/factory.py rename to libs/numalogic-connectors/nlconnectors/factory.py diff --git a/libs/connectors/nlconnectors/prometheus.py b/libs/numalogic-connectors/nlconnectors/prometheus.py similarity index 100% rename from libs/connectors/nlconnectors/prometheus.py rename to libs/numalogic-connectors/nlconnectors/prometheus.py diff --git a/libs/connectors/nlconnectors/rds/__init__.py b/libs/numalogic-connectors/nlconnectors/rds/__init__.py similarity index 100% rename from libs/connectors/nlconnectors/rds/__init__.py rename to libs/numalogic-connectors/nlconnectors/rds/__init__.py diff --git a/libs/connectors/nlconnectors/rds/_base.py b/libs/numalogic-connectors/nlconnectors/rds/_base.py similarity index 100% rename from libs/connectors/nlconnectors/rds/_base.py rename to libs/numalogic-connectors/nlconnectors/rds/_base.py diff --git a/libs/connectors/nlconnectors/rds/_rds.py b/libs/numalogic-connectors/nlconnectors/rds/_rds.py similarity index 100% rename from libs/connectors/nlconnectors/rds/_rds.py rename to libs/numalogic-connectors/nlconnectors/rds/_rds.py diff --git a/libs/connectors/nlconnectors/rds/db/__init__.py b/libs/numalogic-connectors/nlconnectors/rds/db/__init__.py similarity index 100% rename from libs/connectors/nlconnectors/rds/db/__init__.py rename to libs/numalogic-connectors/nlconnectors/rds/db/__init__.py diff --git a/libs/connectors/nlconnectors/rds/db/factory.py b/libs/numalogic-connectors/nlconnectors/rds/db/factory.py similarity index 100% rename from libs/connectors/nlconnectors/rds/db/factory.py rename to libs/numalogic-connectors/nlconnectors/rds/db/factory.py diff --git a/libs/connectors/nlconnectors/rds/db/mysql_fetcher.py b/libs/numalogic-connectors/nlconnectors/rds/db/mysql_fetcher.py similarity index 100% rename from libs/connectors/nlconnectors/rds/db/mysql_fetcher.py rename to libs/numalogic-connectors/nlconnectors/rds/db/mysql_fetcher.py diff --git a/libs/connectors/nlconnectors/redis.py b/libs/numalogic-connectors/nlconnectors/redis.py similarity index 100% rename from libs/connectors/nlconnectors/redis.py rename to libs/numalogic-connectors/nlconnectors/redis.py diff --git a/libs/connectors/nlconnectors/utils/__init__.py b/libs/numalogic-connectors/nlconnectors/utils/__init__.py similarity index 100% rename from libs/connectors/nlconnectors/utils/__init__.py rename to libs/numalogic-connectors/nlconnectors/utils/__init__.py diff --git a/libs/connectors/nlconnectors/utils/aws/__init__.py b/libs/numalogic-connectors/nlconnectors/utils/aws/__init__.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/__init__.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/__init__.py diff --git a/libs/connectors/nlconnectors/utils/aws/boto3_client_manager.py b/libs/numalogic-connectors/nlconnectors/utils/aws/boto3_client_manager.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/boto3_client_manager.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/boto3_client_manager.py diff --git a/libs/connectors/nlconnectors/utils/aws/config.py b/libs/numalogic-connectors/nlconnectors/utils/aws/config.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/config.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/config.py diff --git a/libs/connectors/nlconnectors/utils/aws/db_configurations.py b/libs/numalogic-connectors/nlconnectors/utils/aws/db_configurations.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/db_configurations.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/db_configurations.py diff --git a/libs/connectors/nlconnectors/utils/aws/exceptions.py b/libs/numalogic-connectors/nlconnectors/utils/aws/exceptions.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/exceptions.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/exceptions.py diff --git a/libs/connectors/nlconnectors/utils/aws/sts_client_manager.py b/libs/numalogic-connectors/nlconnectors/utils/aws/sts_client_manager.py similarity index 100% rename from libs/connectors/nlconnectors/utils/aws/sts_client_manager.py rename to libs/numalogic-connectors/nlconnectors/utils/aws/sts_client_manager.py diff --git a/libs/connectors/nlconnectors/utils/enum.py b/libs/numalogic-connectors/nlconnectors/utils/enum.py similarity index 100% rename from libs/connectors/nlconnectors/utils/enum.py rename to libs/numalogic-connectors/nlconnectors/utils/enum.py diff --git a/libs/connectors/pyproject.toml b/libs/numalogic-connectors/pyproject.toml similarity index 61% rename from libs/connectors/pyproject.toml rename to libs/numalogic-connectors/pyproject.toml index 4171dfe5..2756eeea 100644 --- a/libs/connectors/pyproject.toml +++ b/libs/numalogic-connectors/pyproject.toml @@ -25,32 +25,38 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dependencies = [ - "requests>=2.0", - "orjson>=3.9", - "pydruid>=0.6", - "boto3>=1.20", - "PyMySQL>=1.0", - "redis[hiredis]>=5.0", - "omegaconf>=2.3", -] +dynamic = ["dependencies", "optional-dependencies"] -[project.optional-dependencies] -dev = [ - "moto>=4.1", - "black>23.0", - "pytest>=7.0", - "pytest-cov>=4.0", - "ruff>=0.0.275", - "pre-commit>=3.3", - "pytest-mock>=3.12", - "freezegun>=1.2", - "deepdiff>=6.5.0", - "fakeredis>=2.10", -] +# "requests>=2.0", +# "orjson>=3.9", +# "pydruid>=0.6", +# "boto3>=1.20", +# "PyMySQL>=1.0", +# "redis[hiredis]>=5.0", +# "omegaconf>=2.3", +#] + +#[project.optional-dependencies] +#dev = [ +# "moto>=4.1", +# "black>23.0", +# "pytest>=7.0", +# "pytest-cov>=4.0", +# "ruff>=0.0.275", +# "pre-commit>=3.3", +# "pytest-mock>=3.12", +# "freezegun>=1.2", +# "deepdiff>=6.5.0", +# "fakeredis>=2.10", +#] [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" + + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements/requirements.txt"]} +optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }} \ No newline at end of file diff --git a/libs/connectors/tests/__init__.py b/libs/numalogic-connectors/tests/__init__.py similarity index 100% rename from libs/connectors/tests/__init__.py rename to libs/numalogic-connectors/tests/__init__.py diff --git a/libs/connectors/tests/data.csv b/libs/numalogic-connectors/tests/data.csv similarity index 100% rename from libs/connectors/tests/data.csv rename to libs/numalogic-connectors/tests/data.csv diff --git a/libs/connectors/tests/rds/__init__.py b/libs/numalogic-connectors/tests/rds/__init__.py similarity index 100% rename from libs/connectors/tests/rds/__init__.py rename to libs/numalogic-connectors/tests/rds/__init__.py diff --git a/libs/connectors/tests/rds/db/__init__.py b/libs/numalogic-connectors/tests/rds/db/__init__.py similarity index 100% rename from libs/connectors/tests/rds/db/__init__.py rename to libs/numalogic-connectors/tests/rds/db/__init__.py diff --git a/libs/connectors/tests/rds/db/test_factory.py b/libs/numalogic-connectors/tests/rds/db/test_factory.py similarity index 100% rename from libs/connectors/tests/rds/db/test_factory.py rename to libs/numalogic-connectors/tests/rds/db/test_factory.py diff --git a/libs/connectors/tests/rds/db/test_mysql_fetcher.py b/libs/numalogic-connectors/tests/rds/db/test_mysql_fetcher.py similarity index 100% rename from libs/connectors/tests/rds/db/test_mysql_fetcher.py rename to libs/numalogic-connectors/tests/rds/db/test_mysql_fetcher.py diff --git a/libs/connectors/tests/rds/test_base.py b/libs/numalogic-connectors/tests/rds/test_base.py similarity index 100% rename from libs/connectors/tests/rds/test_base.py rename to libs/numalogic-connectors/tests/rds/test_base.py diff --git a/libs/connectors/tests/rds/test_rds.py b/libs/numalogic-connectors/tests/rds/test_rds.py similarity index 100% rename from libs/connectors/tests/rds/test_rds.py rename to libs/numalogic-connectors/tests/rds/test_rds.py diff --git a/libs/connectors/tests/resources/config.yaml b/libs/numalogic-connectors/tests/resources/config.yaml similarity index 100% rename from libs/connectors/tests/resources/config.yaml rename to libs/numalogic-connectors/tests/resources/config.yaml diff --git a/libs/connectors/tests/resources/data/druid.csv b/libs/numalogic-connectors/tests/resources/data/druid.csv similarity index 100% rename from libs/connectors/tests/resources/data/druid.csv rename to libs/numalogic-connectors/tests/resources/data/druid.csv diff --git a/libs/connectors/tests/resources/data/interactionstatus.csv b/libs/numalogic-connectors/tests/resources/data/interactionstatus.csv similarity index 100% rename from libs/connectors/tests/resources/data/interactionstatus.csv rename to libs/numalogic-connectors/tests/resources/data/interactionstatus.csv diff --git a/libs/connectors/tests/resources/data/prom_default.csv b/libs/numalogic-connectors/tests/resources/data/prom_default.csv similarity index 100% rename from libs/connectors/tests/resources/data/prom_default.csv rename to libs/numalogic-connectors/tests/resources/data/prom_default.csv diff --git a/libs/connectors/tests/resources/data/prom_mv.csv b/libs/numalogic-connectors/tests/resources/data/prom_mv.csv similarity index 100% rename from libs/connectors/tests/resources/data/prom_mv.csv rename to libs/numalogic-connectors/tests/resources/data/prom_mv.csv diff --git a/libs/connectors/tests/resources/data/raw_druid.csv b/libs/numalogic-connectors/tests/resources/data/raw_druid.csv similarity index 100% rename from libs/connectors/tests/resources/data/raw_druid.csv rename to libs/numalogic-connectors/tests/resources/data/raw_druid.csv diff --git a/libs/connectors/tests/resources/data/rds.csv b/libs/numalogic-connectors/tests/resources/data/rds.csv similarity index 100% rename from libs/connectors/tests/resources/data/rds.csv rename to libs/numalogic-connectors/tests/resources/data/rds.csv diff --git a/libs/connectors/tests/resources/rds_db_config.yaml b/libs/numalogic-connectors/tests/resources/rds_db_config.yaml similarity index 100% rename from libs/connectors/tests/resources/rds_db_config.yaml rename to libs/numalogic-connectors/tests/resources/rds_db_config.yaml diff --git a/libs/connectors/tests/test_druid.py b/libs/numalogic-connectors/tests/test_druid.py similarity index 100% rename from libs/connectors/tests/test_druid.py rename to libs/numalogic-connectors/tests/test_druid.py diff --git a/libs/connectors/tests/test_prometheus.py b/libs/numalogic-connectors/tests/test_prometheus.py similarity index 100% rename from libs/connectors/tests/test_prometheus.py rename to libs/numalogic-connectors/tests/test_prometheus.py diff --git a/libs/connectors/tests/test_redis.py b/libs/numalogic-connectors/tests/test_redis.py similarity index 100% rename from libs/connectors/tests/test_redis.py rename to libs/numalogic-connectors/tests/test_redis.py diff --git a/libs/connectors/tests/utils/__init__.py b/libs/numalogic-connectors/tests/utils/__init__.py similarity index 100% rename from libs/connectors/tests/utils/__init__.py rename to libs/numalogic-connectors/tests/utils/__init__.py diff --git a/libs/connectors/tests/utils/aws/__init__.py b/libs/numalogic-connectors/tests/utils/aws/__init__.py similarity index 100% rename from libs/connectors/tests/utils/aws/__init__.py rename to libs/numalogic-connectors/tests/utils/aws/__init__.py diff --git a/libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py b/libs/numalogic-connectors/tests/utils/aws/test_aws_boto3_client_manager.py similarity index 100% rename from libs/connectors/tests/utils/aws/test_aws_boto3_client_manager.py rename to libs/numalogic-connectors/tests/utils/aws/test_aws_boto3_client_manager.py diff --git a/libs/connectors/tests/utils/aws/test_config.py b/libs/numalogic-connectors/tests/utils/aws/test_config.py similarity index 100% rename from libs/connectors/tests/utils/aws/test_config.py rename to libs/numalogic-connectors/tests/utils/aws/test_config.py diff --git a/libs/connectors/tests/utils/aws/test_db_configurations.py b/libs/numalogic-connectors/tests/utils/aws/test_db_configurations.py similarity index 100% rename from libs/connectors/tests/utils/aws/test_db_configurations.py rename to libs/numalogic-connectors/tests/utils/aws/test_db_configurations.py diff --git a/libs/connectors/tests/utils/aws/test_sts_client_manager.py b/libs/numalogic-connectors/tests/utils/aws/test_sts_client_manager.py similarity index 100% rename from libs/connectors/tests/utils/aws/test_sts_client_manager.py rename to libs/numalogic-connectors/tests/utils/aws/test_sts_client_manager.py diff --git a/libs/connectors/tests/utils/test_enum.py b/libs/numalogic-connectors/tests/utils/test_enum.py similarity index 100% rename from libs/connectors/tests/utils/test_enum.py rename to libs/numalogic-connectors/tests/utils/test_enum.py diff --git a/libs/numalogic-core/makefile b/libs/numalogic-core/makefile new file mode 100644 index 00000000..beb07f58 --- /dev/null +++ b/libs/numalogic-core/makefile @@ -0,0 +1,22 @@ +clean: + @rm -rf build dist .eggs *.egg-info + @rm -rf .benchmarks .coverage coverage.xml htmlcov report.xml .tox + @find . -type d -name '.mypy_cache' -exec rm -rf {} + + @find . -type d -name '__pycache__' -exec rm -rf {} + + @find . -type d -name '*pytest_cache*' -exec rm -rf {} + + @find . -type f -name "*.py[co]" -exec rm -rf {} + + +format: clean + black libs/ apps/ examples/ tests/ + +lint: format + ruff check --fix . + +setup: + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + pip install -r requirements/requirements-nb.txt + +# test your application (tests in the tests/ directory) +test: + poetry run pytest -v tests/ diff --git a/libs/core/numalogic/__init__.py b/libs/numalogic-core/numalogic/__init__.py similarity index 100% rename from libs/core/numalogic/__init__.py rename to libs/numalogic-core/numalogic/__init__.py diff --git a/libs/core/numalogic/_constants.py b/libs/numalogic-core/numalogic/_constants.py similarity index 100% rename from libs/core/numalogic/_constants.py rename to libs/numalogic-core/numalogic/_constants.py diff --git a/libs/core/numalogic/base.py b/libs/numalogic-core/numalogic/base.py similarity index 100% rename from libs/core/numalogic/base.py rename to libs/numalogic-core/numalogic/base.py diff --git a/libs/core/numalogic/config/__init__.py b/libs/numalogic-core/numalogic/config/__init__.py similarity index 100% rename from libs/core/numalogic/config/__init__.py rename to libs/numalogic-core/numalogic/config/__init__.py diff --git a/libs/core/numalogic/config/_config.py b/libs/numalogic-core/numalogic/config/_config.py similarity index 100% rename from libs/core/numalogic/config/_config.py rename to libs/numalogic-core/numalogic/config/_config.py diff --git a/libs/core/numalogic/config/factory.py b/libs/numalogic-core/numalogic/config/factory.py similarity index 100% rename from libs/core/numalogic/config/factory.py rename to libs/numalogic-core/numalogic/config/factory.py diff --git a/libs/core/numalogic/models/__init__.py b/libs/numalogic-core/numalogic/models/__init__.py similarity index 100% rename from libs/core/numalogic/models/__init__.py rename to libs/numalogic-core/numalogic/models/__init__.py diff --git a/libs/core/numalogic/models/autoencoder/__init__.py b/libs/numalogic-core/numalogic/models/autoencoder/__init__.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/__init__.py rename to libs/numalogic-core/numalogic/models/autoencoder/__init__.py diff --git a/libs/core/numalogic/models/autoencoder/base.py b/libs/numalogic-core/numalogic/models/autoencoder/base.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/base.py rename to libs/numalogic-core/numalogic/models/autoencoder/base.py diff --git a/libs/core/numalogic/models/autoencoder/variants/__init__.py b/libs/numalogic-core/numalogic/models/autoencoder/variants/__init__.py similarity index 82% rename from libs/core/numalogic/models/autoencoder/variants/__init__.py rename to libs/numalogic-core/numalogic/models/autoencoder/variants/__init__.py index 3d0ae748..fe8c4ef2 100644 --- a/libs/core/numalogic/models/autoencoder/variants/__init__.py +++ b/libs/numalogic-core/numalogic/models/autoencoder/variants/__init__.py @@ -1,7 +1,10 @@ from numalogic.models.autoencoder.variants.vanilla import VanillaAE, SparseVanillaAE, MultichannelAE from numalogic.models.autoencoder.variants.conv import Conv1dAE, SparseConv1dAE from numalogic.models.autoencoder.variants.lstm import LSTMAE, SparseLSTMAE -from numalogic.models.autoencoder.variants.transformer import TransformerAE, SparseTransformerAE +from numalogic.models.autoencoder.variants.transformer import ( + TransformerAE, + SparseTransformerAE, +) from numalogic.models.autoencoder.base import BaseAE diff --git a/libs/core/numalogic/models/autoencoder/variants/conv.py b/libs/numalogic-core/numalogic/models/autoencoder/variants/conv.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/variants/conv.py rename to libs/numalogic-core/numalogic/models/autoencoder/variants/conv.py diff --git a/libs/core/numalogic/models/autoencoder/variants/lstm.py b/libs/numalogic-core/numalogic/models/autoencoder/variants/lstm.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/variants/lstm.py rename to libs/numalogic-core/numalogic/models/autoencoder/variants/lstm.py diff --git a/libs/core/numalogic/models/autoencoder/variants/transformer.py b/libs/numalogic-core/numalogic/models/autoencoder/variants/transformer.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/variants/transformer.py rename to libs/numalogic-core/numalogic/models/autoencoder/variants/transformer.py diff --git a/libs/core/numalogic/models/autoencoder/variants/vanilla.py b/libs/numalogic-core/numalogic/models/autoencoder/variants/vanilla.py similarity index 100% rename from libs/core/numalogic/models/autoencoder/variants/vanilla.py rename to libs/numalogic-core/numalogic/models/autoencoder/variants/vanilla.py diff --git a/libs/core/numalogic/models/forecast/__init__.py b/libs/numalogic-core/numalogic/models/forecast/__init__.py similarity index 100% rename from libs/core/numalogic/models/forecast/__init__.py rename to libs/numalogic-core/numalogic/models/forecast/__init__.py diff --git a/libs/core/numalogic/models/forecast/variants/__init__.py b/libs/numalogic-core/numalogic/models/forecast/variants/__init__.py similarity index 100% rename from libs/core/numalogic/models/forecast/variants/__init__.py rename to libs/numalogic-core/numalogic/models/forecast/variants/__init__.py diff --git a/libs/core/numalogic/models/forecast/variants/naive.py b/libs/numalogic-core/numalogic/models/forecast/variants/naive.py similarity index 100% rename from libs/core/numalogic/models/forecast/variants/naive.py rename to libs/numalogic-core/numalogic/models/forecast/variants/naive.py diff --git a/libs/core/numalogic/models/forecast/variants/rnn.py b/libs/numalogic-core/numalogic/models/forecast/variants/rnn.py similarity index 100% rename from libs/core/numalogic/models/forecast/variants/rnn.py rename to libs/numalogic-core/numalogic/models/forecast/variants/rnn.py diff --git a/libs/core/numalogic/models/threshold/__init__.py b/libs/numalogic-core/numalogic/models/threshold/__init__.py similarity index 100% rename from libs/core/numalogic/models/threshold/__init__.py rename to libs/numalogic-core/numalogic/models/threshold/__init__.py diff --git a/libs/core/numalogic/models/threshold/_mahalanobis.py b/libs/numalogic-core/numalogic/models/threshold/_mahalanobis.py similarity index 100% rename from libs/core/numalogic/models/threshold/_mahalanobis.py rename to libs/numalogic-core/numalogic/models/threshold/_mahalanobis.py diff --git a/libs/core/numalogic/models/threshold/_median.py b/libs/numalogic-core/numalogic/models/threshold/_median.py similarity index 100% rename from libs/core/numalogic/models/threshold/_median.py rename to libs/numalogic-core/numalogic/models/threshold/_median.py diff --git a/libs/core/numalogic/models/threshold/_static.py b/libs/numalogic-core/numalogic/models/threshold/_static.py similarity index 100% rename from libs/core/numalogic/models/threshold/_static.py rename to libs/numalogic-core/numalogic/models/threshold/_static.py diff --git a/libs/core/numalogic/models/threshold/_std.py b/libs/numalogic-core/numalogic/models/threshold/_std.py similarity index 100% rename from libs/core/numalogic/models/threshold/_std.py rename to libs/numalogic-core/numalogic/models/threshold/_std.py diff --git a/libs/core/numalogic/models/vae/__init__.py b/libs/numalogic-core/numalogic/models/vae/__init__.py similarity index 100% rename from libs/core/numalogic/models/vae/__init__.py rename to libs/numalogic-core/numalogic/models/vae/__init__.py diff --git a/libs/core/numalogic/models/vae/base.py b/libs/numalogic-core/numalogic/models/vae/base.py similarity index 100% rename from libs/core/numalogic/models/vae/base.py rename to libs/numalogic-core/numalogic/models/vae/base.py diff --git a/libs/core/numalogic/models/vae/layer.py b/libs/numalogic-core/numalogic/models/vae/layer.py similarity index 100% rename from libs/core/numalogic/models/vae/layer.py rename to libs/numalogic-core/numalogic/models/vae/layer.py diff --git a/libs/core/numalogic/models/vae/variants/__init__.py b/libs/numalogic-core/numalogic/models/vae/variants/__init__.py similarity index 100% rename from libs/core/numalogic/models/vae/variants/__init__.py rename to libs/numalogic-core/numalogic/models/vae/variants/__init__.py diff --git a/libs/core/numalogic/models/vae/variants/conv.py b/libs/numalogic-core/numalogic/models/vae/variants/conv.py similarity index 100% rename from libs/core/numalogic/models/vae/variants/conv.py rename to libs/numalogic-core/numalogic/models/vae/variants/conv.py diff --git a/libs/core/numalogic/monitoring/__init__.py b/libs/numalogic-core/numalogic/monitoring/__init__.py similarity index 100% rename from libs/core/numalogic/monitoring/__init__.py rename to libs/numalogic-core/numalogic/monitoring/__init__.py diff --git a/libs/core/numalogic/monitoring/metrics.py b/libs/numalogic-core/numalogic/monitoring/metrics.py similarity index 100% rename from libs/core/numalogic/monitoring/metrics.py rename to libs/numalogic-core/numalogic/monitoring/metrics.py diff --git a/libs/core/numalogic/synthetic/__init__.py b/libs/numalogic-core/numalogic/synthetic/__init__.py similarity index 100% rename from libs/core/numalogic/synthetic/__init__.py rename to libs/numalogic-core/numalogic/synthetic/__init__.py diff --git a/libs/core/numalogic/synthetic/anomalies.py b/libs/numalogic-core/numalogic/synthetic/anomalies.py similarity index 100% rename from libs/core/numalogic/synthetic/anomalies.py rename to libs/numalogic-core/numalogic/synthetic/anomalies.py diff --git a/libs/core/numalogic/synthetic/sparsity.py b/libs/numalogic-core/numalogic/synthetic/sparsity.py similarity index 100% rename from libs/core/numalogic/synthetic/sparsity.py rename to libs/numalogic-core/numalogic/synthetic/sparsity.py diff --git a/libs/core/numalogic/synthetic/timeseries.py b/libs/numalogic-core/numalogic/synthetic/timeseries.py similarity index 100% rename from libs/core/numalogic/synthetic/timeseries.py rename to libs/numalogic-core/numalogic/synthetic/timeseries.py diff --git a/libs/core/numalogic/tools/__init__.py b/libs/numalogic-core/numalogic/tools/__init__.py similarity index 100% rename from libs/core/numalogic/tools/__init__.py rename to libs/numalogic-core/numalogic/tools/__init__.py diff --git a/libs/core/numalogic/tools/aggregators.py b/libs/numalogic-core/numalogic/tools/aggregators.py similarity index 100% rename from libs/core/numalogic/tools/aggregators.py rename to libs/numalogic-core/numalogic/tools/aggregators.py diff --git a/libs/core/numalogic/tools/callbacks.py b/libs/numalogic-core/numalogic/tools/callbacks.py similarity index 100% rename from libs/core/numalogic/tools/callbacks.py rename to libs/numalogic-core/numalogic/tools/callbacks.py diff --git a/libs/core/numalogic/tools/data.py b/libs/numalogic-core/numalogic/tools/data.py similarity index 100% rename from libs/core/numalogic/tools/data.py rename to libs/numalogic-core/numalogic/tools/data.py diff --git a/libs/core/numalogic/tools/exceptions.py b/libs/numalogic-core/numalogic/tools/exceptions.py similarity index 100% rename from libs/core/numalogic/tools/exceptions.py rename to libs/numalogic-core/numalogic/tools/exceptions.py diff --git a/libs/core/numalogic/tools/loss.py b/libs/numalogic-core/numalogic/tools/loss.py similarity index 100% rename from libs/core/numalogic/tools/loss.py rename to libs/numalogic-core/numalogic/tools/loss.py diff --git a/libs/core/numalogic/tools/trainer.py b/libs/numalogic-core/numalogic/tools/trainer.py similarity index 100% rename from libs/core/numalogic/tools/trainer.py rename to libs/numalogic-core/numalogic/tools/trainer.py diff --git a/libs/core/numalogic/tools/types.py b/libs/numalogic-core/numalogic/tools/types.py similarity index 100% rename from libs/core/numalogic/tools/types.py rename to libs/numalogic-core/numalogic/tools/types.py diff --git a/libs/core/numalogic/transforms/__init__.py b/libs/numalogic-core/numalogic/transforms/__init__.py similarity index 100% rename from libs/core/numalogic/transforms/__init__.py rename to libs/numalogic-core/numalogic/transforms/__init__.py diff --git a/libs/core/numalogic/transforms/_movavg.py b/libs/numalogic-core/numalogic/transforms/_movavg.py similarity index 100% rename from libs/core/numalogic/transforms/_movavg.py rename to libs/numalogic-core/numalogic/transforms/_movavg.py diff --git a/libs/core/numalogic/transforms/_postprocess.py b/libs/numalogic-core/numalogic/transforms/_postprocess.py similarity index 100% rename from libs/core/numalogic/transforms/_postprocess.py rename to libs/numalogic-core/numalogic/transforms/_postprocess.py diff --git a/libs/core/numalogic/transforms/_scaler.py b/libs/numalogic-core/numalogic/transforms/_scaler.py similarity index 100% rename from libs/core/numalogic/transforms/_scaler.py rename to libs/numalogic-core/numalogic/transforms/_scaler.py diff --git a/libs/core/numalogic/transforms/_stateless.py b/libs/numalogic-core/numalogic/transforms/_stateless.py similarity index 100% rename from libs/core/numalogic/transforms/_stateless.py rename to libs/numalogic-core/numalogic/transforms/_stateless.py diff --git a/libs/core/pyproject.toml b/libs/numalogic-core/pyproject.toml similarity index 56% rename from libs/core/pyproject.toml rename to libs/numalogic-core/pyproject.toml index 2595137e..0c57d2f9 100644 --- a/libs/core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -25,33 +25,34 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dependencies = [ - "numpy>=1.22", - "pandas[performance]>=2.0", - "scikit-learn>=1.3", - "torch>=2.0", - "pytorch-lightning>=2.0", - "omegaconf>=2.3", -] - -[project.optional-dependencies] -dev = [ - "matplotlib>=3.4", - "black>23.0", - "pytest>=7.0", - "pytest-cov>=4.0", - "torchinfo>=1.7", - "ruff>=0.0.275", - "pre-commit>=3.3", - "pytest-mock>=3.12", -] -jupyter = [ - "jupyter>=1.0", - "ipykernel>=6.15", - "ipympl>=0.9", - "ipython-autotime>=0.3", - "black[jupyter]" -] +dynamic = ["dependencies", "optional-dependencies"] +#dependencies = [ +# "numpy>=1.22", +# "pandas[performance]>=2.0", +# "scikit-learn>=1.3", +# "torch>=2.0", +# "pytorch-lightning>=2.0", +# "omegaconf>=2.3", +#] +# +#[project.optional-dependencies] +#dev = [ +# "matplotlib>=3.4", +# "black>23.0", +# "pytest>=7.0", +# "pytest-cov>=4.0", +# "torchinfo>=1.7", +# "ruff>=0.0.275", +# "pre-commit>=3.3", +# "pytest-mock>=3.12", +#] +#jupyter = [ +# "jupyter>=1.0", +# "ipykernel>=6.15", +# "ipympl>=0.9", +# "ipython-autotime>=0.3", +# "black[jupyter]" +#] [project.urls] @@ -60,11 +61,6 @@ Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" -#[tool.setuptools] -## packages = ["numalogic"] -#package-dir = {"numalogic" = "../numalogic"} - - -#[tool.setuptools.packages.find] -#include = ["numalogic*", "numalogic.*"] -#exclude = ["*egg-info*", "build*", "dist*", "docs*", "tests*"] +[tool.setuptools.dynamic] +dependencies = {file = ["requirements/requirements.txt"]} +optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }, jupyter = { file = ["requirements/requirements-nb.txt"] }} diff --git a/libs/numalogic-core/requirements/requirements-dev.txt b/libs/numalogic-core/requirements/requirements-dev.txt new file mode 100644 index 00000000..4ea603bc --- /dev/null +++ b/libs/numalogic-core/requirements/requirements-dev.txt @@ -0,0 +1,8 @@ +matplotlib>=3.4 +black>23.0 +pytest>=7.0 +pytest-cov>=4.0 +torchinfo>=1.7 +ruff>=0.0.275 +pre-commit>=3.3 +pytest-mock>=3.12 diff --git a/libs/numalogic-core/requirements/requirements-nb.txt b/libs/numalogic-core/requirements/requirements-nb.txt new file mode 100644 index 00000000..310d9b0d --- /dev/null +++ b/libs/numalogic-core/requirements/requirements-nb.txt @@ -0,0 +1,5 @@ +jupyter>=1.0 +ipykernel>=6.15 +ipympl>=0.9 +ipython-autotime>=0.3 +black[jupyter] diff --git a/libs/numalogic-core/requirements/requirements.txt b/libs/numalogic-core/requirements/requirements.txt new file mode 100644 index 00000000..a99a87a9 --- /dev/null +++ b/libs/numalogic-core/requirements/requirements.txt @@ -0,0 +1,6 @@ +numpy>=1.22 +pandas[performance]>=2.0 +scikit-learn>=1.3 +torch>=2.0 +pytorch-lightning>=2.0 +omegaconf>=2.3 \ No newline at end of file diff --git a/libs/core/tests/__init__.py b/libs/numalogic-core/tests/__init__.py similarity index 100% rename from libs/core/tests/__init__.py rename to libs/numalogic-core/tests/__init__.py diff --git a/libs/core/tests/config/__init__.py b/libs/numalogic-core/tests/config/__init__.py similarity index 100% rename from libs/core/tests/config/__init__.py rename to libs/numalogic-core/tests/config/__init__.py diff --git a/libs/core/tests/config/test_config.py b/libs/numalogic-core/tests/config/test_config.py similarity index 100% rename from libs/core/tests/config/test_config.py rename to libs/numalogic-core/tests/config/test_config.py diff --git a/libs/core/tests/models/__init__.py b/libs/numalogic-core/tests/models/__init__.py similarity index 100% rename from libs/core/tests/models/__init__.py rename to libs/numalogic-core/tests/models/__init__.py diff --git a/libs/core/tests/models/autoencoder/__init__.py b/libs/numalogic-core/tests/models/autoencoder/__init__.py similarity index 100% rename from libs/core/tests/models/autoencoder/__init__.py rename to libs/numalogic-core/tests/models/autoencoder/__init__.py diff --git a/libs/core/tests/models/autoencoder/test_trainer.py b/libs/numalogic-core/tests/models/autoencoder/test_trainer.py similarity index 100% rename from libs/core/tests/models/autoencoder/test_trainer.py rename to libs/numalogic-core/tests/models/autoencoder/test_trainer.py diff --git a/libs/core/tests/models/autoencoder/variants/__init__.py b/libs/numalogic-core/tests/models/autoencoder/variants/__init__.py similarity index 100% rename from libs/core/tests/models/autoencoder/variants/__init__.py rename to libs/numalogic-core/tests/models/autoencoder/variants/__init__.py diff --git a/libs/core/tests/models/autoencoder/variants/test_conv.py b/libs/numalogic-core/tests/models/autoencoder/variants/test_conv.py similarity index 100% rename from libs/core/tests/models/autoencoder/variants/test_conv.py rename to libs/numalogic-core/tests/models/autoencoder/variants/test_conv.py diff --git a/libs/core/tests/models/autoencoder/variants/test_lstm.py b/libs/numalogic-core/tests/models/autoencoder/variants/test_lstm.py similarity index 100% rename from libs/core/tests/models/autoencoder/variants/test_lstm.py rename to libs/numalogic-core/tests/models/autoencoder/variants/test_lstm.py diff --git a/libs/core/tests/models/autoencoder/variants/test_transformers.py b/libs/numalogic-core/tests/models/autoencoder/variants/test_transformers.py similarity index 100% rename from libs/core/tests/models/autoencoder/variants/test_transformers.py rename to libs/numalogic-core/tests/models/autoencoder/variants/test_transformers.py diff --git a/libs/core/tests/models/autoencoder/variants/test_vanilla.py b/libs/numalogic-core/tests/models/autoencoder/variants/test_vanilla.py similarity index 100% rename from libs/core/tests/models/autoencoder/variants/test_vanilla.py rename to libs/numalogic-core/tests/models/autoencoder/variants/test_vanilla.py diff --git a/libs/core/tests/models/forecast/__init__.py b/libs/numalogic-core/tests/models/forecast/__init__.py similarity index 100% rename from libs/core/tests/models/forecast/__init__.py rename to libs/numalogic-core/tests/models/forecast/__init__.py diff --git a/libs/core/tests/models/forecast/test_naive.py b/libs/numalogic-core/tests/models/forecast/test_naive.py similarity index 100% rename from libs/core/tests/models/forecast/test_naive.py rename to libs/numalogic-core/tests/models/forecast/test_naive.py diff --git a/libs/core/tests/models/threshold/__init__.py b/libs/numalogic-core/tests/models/threshold/__init__.py similarity index 100% rename from libs/core/tests/models/threshold/__init__.py rename to libs/numalogic-core/tests/models/threshold/__init__.py diff --git a/libs/core/tests/models/threshold/test_maha.py b/libs/numalogic-core/tests/models/threshold/test_maha.py similarity index 100% rename from libs/core/tests/models/threshold/test_maha.py rename to libs/numalogic-core/tests/models/threshold/test_maha.py diff --git a/libs/core/tests/models/threshold/test_median.py b/libs/numalogic-core/tests/models/threshold/test_median.py similarity index 100% rename from libs/core/tests/models/threshold/test_median.py rename to libs/numalogic-core/tests/models/threshold/test_median.py diff --git a/libs/core/tests/models/threshold/test_static.py b/libs/numalogic-core/tests/models/threshold/test_static.py similarity index 100% rename from libs/core/tests/models/threshold/test_static.py rename to libs/numalogic-core/tests/models/threshold/test_static.py diff --git a/libs/core/tests/models/threshold/test_std.py b/libs/numalogic-core/tests/models/threshold/test_std.py similarity index 100% rename from libs/core/tests/models/threshold/test_std.py rename to libs/numalogic-core/tests/models/threshold/test_std.py diff --git a/libs/core/tests/models/vae/__init__.py b/libs/numalogic-core/tests/models/vae/__init__.py similarity index 100% rename from libs/core/tests/models/vae/__init__.py rename to libs/numalogic-core/tests/models/vae/__init__.py diff --git a/libs/core/tests/models/vae/test_conv.py b/libs/numalogic-core/tests/models/vae/test_conv.py similarity index 100% rename from libs/core/tests/models/vae/test_conv.py rename to libs/numalogic-core/tests/models/vae/test_conv.py diff --git a/libs/core/tests/resources/config.yaml b/libs/numalogic-core/tests/resources/config.yaml similarity index 100% rename from libs/core/tests/resources/config.yaml rename to libs/numalogic-core/tests/resources/config.yaml diff --git a/libs/core/tests/resources/data/druid.csv b/libs/numalogic-core/tests/resources/data/druid.csv similarity index 100% rename from libs/core/tests/resources/data/druid.csv rename to libs/numalogic-core/tests/resources/data/druid.csv diff --git a/libs/core/tests/resources/data/interactionstatus.csv b/libs/numalogic-core/tests/resources/data/interactionstatus.csv similarity index 100% rename from libs/core/tests/resources/data/interactionstatus.csv rename to libs/numalogic-core/tests/resources/data/interactionstatus.csv diff --git a/libs/core/tests/resources/data/prom_default.csv b/libs/numalogic-core/tests/resources/data/prom_default.csv similarity index 100% rename from libs/core/tests/resources/data/prom_default.csv rename to libs/numalogic-core/tests/resources/data/prom_default.csv diff --git a/libs/core/tests/resources/data/prom_mv.csv b/libs/numalogic-core/tests/resources/data/prom_mv.csv similarity index 100% rename from libs/core/tests/resources/data/prom_mv.csv rename to libs/numalogic-core/tests/resources/data/prom_mv.csv diff --git a/libs/core/tests/resources/data/raw_druid.csv b/libs/numalogic-core/tests/resources/data/raw_druid.csv similarity index 100% rename from libs/core/tests/resources/data/raw_druid.csv rename to libs/numalogic-core/tests/resources/data/raw_druid.csv diff --git a/libs/core/tests/resources/data/rds.csv b/libs/numalogic-core/tests/resources/data/rds.csv similarity index 100% rename from libs/core/tests/resources/data/rds.csv rename to libs/numalogic-core/tests/resources/data/rds.csv diff --git a/libs/core/tests/resources/rds_db_config.yaml b/libs/numalogic-core/tests/resources/rds_db_config.yaml similarity index 100% rename from libs/core/tests/resources/rds_db_config.yaml rename to libs/numalogic-core/tests/resources/rds_db_config.yaml diff --git a/libs/core/tests/synthetic/__init__.py b/libs/numalogic-core/tests/synthetic/__init__.py similarity index 100% rename from libs/core/tests/synthetic/__init__.py rename to libs/numalogic-core/tests/synthetic/__init__.py diff --git a/libs/core/tests/synthetic/test_anomalies.py b/libs/numalogic-core/tests/synthetic/test_anomalies.py similarity index 100% rename from libs/core/tests/synthetic/test_anomalies.py rename to libs/numalogic-core/tests/synthetic/test_anomalies.py diff --git a/libs/core/tests/synthetic/test_sparsity.py b/libs/numalogic-core/tests/synthetic/test_sparsity.py similarity index 100% rename from libs/core/tests/synthetic/test_sparsity.py rename to libs/numalogic-core/tests/synthetic/test_sparsity.py diff --git a/libs/core/tests/synthetic/test_timeseries.py b/libs/numalogic-core/tests/synthetic/test_timeseries.py similarity index 100% rename from libs/core/tests/synthetic/test_timeseries.py rename to libs/numalogic-core/tests/synthetic/test_timeseries.py diff --git a/libs/core/tests/tools/__init__.py b/libs/numalogic-core/tests/tools/__init__.py similarity index 100% rename from libs/core/tests/tools/__init__.py rename to libs/numalogic-core/tests/tools/__init__.py diff --git a/libs/core/tests/tools/test_data.py b/libs/numalogic-core/tests/tools/test_data.py similarity index 100% rename from libs/core/tests/tools/test_data.py rename to libs/numalogic-core/tests/tools/test_data.py diff --git a/libs/core/tests/transforms/__init__.py b/libs/numalogic-core/tests/transforms/__init__.py similarity index 100% rename from libs/core/tests/transforms/__init__.py rename to libs/numalogic-core/tests/transforms/__init__.py diff --git a/libs/core/tests/transforms/test_postprocess.py b/libs/numalogic-core/tests/transforms/test_postprocess.py similarity index 100% rename from libs/core/tests/transforms/test_postprocess.py rename to libs/numalogic-core/tests/transforms/test_postprocess.py diff --git a/libs/core/tests/transforms/test_transforms.py b/libs/numalogic-core/tests/transforms/test_transforms.py similarity index 100% rename from libs/core/tests/transforms/test_transforms.py rename to libs/numalogic-core/tests/transforms/test_transforms.py diff --git a/libs/registry/nlregistry/__init__.py b/libs/numalogic-registry/nlregistry/__init__.py similarity index 100% rename from libs/registry/nlregistry/__init__.py rename to libs/numalogic-registry/nlregistry/__init__.py diff --git a/libs/registry/nlregistry/_serialize.py b/libs/numalogic-registry/nlregistry/_serialize.py similarity index 100% rename from libs/registry/nlregistry/_serialize.py rename to libs/numalogic-registry/nlregistry/_serialize.py diff --git a/libs/registry/nlregistry/artifact.py b/libs/numalogic-registry/nlregistry/artifact.py similarity index 100% rename from libs/registry/nlregistry/artifact.py rename to libs/numalogic-registry/nlregistry/artifact.py diff --git a/libs/registry/nlregistry/dynamodb_registry.py b/libs/numalogic-registry/nlregistry/dynamodb_registry.py similarity index 100% rename from libs/registry/nlregistry/dynamodb_registry.py rename to libs/numalogic-registry/nlregistry/dynamodb_registry.py diff --git a/libs/registry/nlregistry/factory.py b/libs/numalogic-registry/nlregistry/factory.py similarity index 100% rename from libs/registry/nlregistry/factory.py rename to libs/numalogic-registry/nlregistry/factory.py diff --git a/libs/registry/nlregistry/localcache.py b/libs/numalogic-registry/nlregistry/localcache.py similarity index 100% rename from libs/registry/nlregistry/localcache.py rename to libs/numalogic-registry/nlregistry/localcache.py diff --git a/libs/registry/nlregistry/mlflow_registry.py b/libs/numalogic-registry/nlregistry/mlflow_registry.py similarity index 100% rename from libs/registry/nlregistry/mlflow_registry.py rename to libs/numalogic-registry/nlregistry/mlflow_registry.py diff --git a/libs/registry/nlregistry/redis_registry.py b/libs/numalogic-registry/nlregistry/redis_registry.py similarity index 100% rename from libs/registry/nlregistry/redis_registry.py rename to libs/numalogic-registry/nlregistry/redis_registry.py diff --git a/libs/registry/pyproject.toml b/libs/numalogic-registry/pyproject.toml similarity index 100% rename from libs/registry/pyproject.toml rename to libs/numalogic-registry/pyproject.toml diff --git a/libs/registry/tests/__init__.py b/libs/numalogic-registry/tests/__init__.py similarity index 100% rename from libs/registry/tests/__init__.py rename to libs/numalogic-registry/tests/__init__.py diff --git a/libs/registry/tests/_mlflow_utils.py b/libs/numalogic-registry/tests/_mlflow_utils.py similarity index 100% rename from libs/registry/tests/_mlflow_utils.py rename to libs/numalogic-registry/tests/_mlflow_utils.py diff --git a/libs/registry/tests/test_cache.py b/libs/numalogic-registry/tests/test_cache.py similarity index 100% rename from libs/registry/tests/test_cache.py rename to libs/numalogic-registry/tests/test_cache.py diff --git a/libs/registry/tests/test_dynamodb_registry.py b/libs/numalogic-registry/tests/test_dynamodb_registry.py similarity index 100% rename from libs/registry/tests/test_dynamodb_registry.py rename to libs/numalogic-registry/tests/test_dynamodb_registry.py diff --git a/libs/registry/tests/test_mlflow_registry.py b/libs/numalogic-registry/tests/test_mlflow_registry.py similarity index 100% rename from libs/registry/tests/test_mlflow_registry.py rename to libs/numalogic-registry/tests/test_mlflow_registry.py diff --git a/libs/registry/tests/test_optdeps.py b/libs/numalogic-registry/tests/test_optdeps.py similarity index 100% rename from libs/registry/tests/test_optdeps.py rename to libs/numalogic-registry/tests/test_optdeps.py diff --git a/libs/registry/tests/test_redis_registry.py b/libs/numalogic-registry/tests/test_redis_registry.py similarity index 100% rename from libs/registry/tests/test_redis_registry.py rename to libs/numalogic-registry/tests/test_redis_registry.py diff --git a/libs/registry/tests/test_serialize.py b/libs/numalogic-registry/tests/test_serialize.py similarity index 100% rename from libs/registry/tests/test_serialize.py rename to libs/numalogic-registry/tests/test_serialize.py diff --git a/libs/registry/nlregistry/__setup__.py b/libs/registry/nlregistry/__setup__.py deleted file mode 100644 index 836c27fe..00000000 --- a/libs/registry/nlregistry/__setup__.py +++ /dev/null @@ -1,20 +0,0 @@ -from setuptools import find_packages - - -def setup_args(): - return dict( - name="numalogic-test_registry", - version="0.10.a2", - packages=find_packages( - where="libs", - include=["registry", "registry.*"], - exclude=["tests"], - ), - package_dir={"": "libs"}, - install_requires=[ - "numalogic", - "redis[hiredis]>=4.0", - "mlflow-skinny>2.0", - "boto3>=1.20", - ], - ) From 8f03f3305b2b4f465c08668db34e63a73af63dcc Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 11:06:21 -0700 Subject: [PATCH 10/68] chore: more refactor Signed-off-by: Avik Basu --- Makefile | 9 +++---- libs/numalogic-connectors/makefile | 22 +++++++++++++++++ .../requirements/requirements-dev.txt | 10 ++++++++ .../requirements/requirements.txt | 8 +++++++ libs/numalogic-registry/makefile | 22 +++++++++++++++++ libs/numalogic-registry/pyproject.toml | 24 ++++--------------- .../requirements/requirements-dev.txt | 9 +++++++ .../requirements/requirements.txt | 4 ++++ setup.py | 6 ++--- 9 files changed, 88 insertions(+), 26 deletions(-) create mode 100644 libs/numalogic-connectors/makefile create mode 100644 libs/numalogic-connectors/requirements/requirements-dev.txt create mode 100644 libs/numalogic-connectors/requirements/requirements.txt create mode 100644 libs/numalogic-registry/makefile create mode 100644 libs/numalogic-registry/requirements/requirements-dev.txt create mode 100644 libs/numalogic-registry/requirements/requirements.txt diff --git a/Makefile b/Makefile index 620e95b1..0aa6958a 100644 --- a/Makefile +++ b/Makefile @@ -7,14 +7,15 @@ clean: @find . -type f -name "*.py[co]" -exec rm -rf {} + format: clean - poetry run black numalogic/ examples/ tests/ + black libs/ apps/ examples/ tests/ lint: format - poetry run ruff check --fix . + ruff check --fix . -# install all dependencies setup: - pip install -v -e '.[dev,jupyter]' --config-settings editable_mode=strict + PKG_NAME=numalogic pip install -v -e '.[dev,jupyter]' --config-settings editable_mode=strict + PKG_NAME=numalogic-connectors pip install -v -e '.[dev]' --config-settings editable_mode=strict + PKG_NAME=numalogic-registry pip install -v -e '.[dev]' --config-settings editable_mode=strict # test your application (tests in the tests/ directory) test: diff --git a/libs/numalogic-connectors/makefile b/libs/numalogic-connectors/makefile new file mode 100644 index 00000000..32674495 --- /dev/null +++ b/libs/numalogic-connectors/makefile @@ -0,0 +1,22 @@ +clean: + @rm -rf build dist .eggs *.egg-info + @rm -rf .benchmarks .coverage coverage.xml htmlcov report.xml .tox + @find . -type d -name '.mypy_cache' -exec rm -rf {} + + @find . -type d -name '__pycache__' -exec rm -rf {} + + @find . -type d -name '*pytest_cache*' -exec rm -rf {} + + @find . -type f -name "*.py[co]" -exec rm -rf {} + + +format: clean + black libs/ apps/ examples/ tests/ + +lint: format + ruff check --fix . + +setup: + pip install ../numalogic-core + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + +# test your application (tests in the tests/ directory) +test: + poetry run pytest -v tests/ diff --git a/libs/numalogic-connectors/requirements/requirements-dev.txt b/libs/numalogic-connectors/requirements/requirements-dev.txt new file mode 100644 index 00000000..86c30b4a --- /dev/null +++ b/libs/numalogic-connectors/requirements/requirements-dev.txt @@ -0,0 +1,10 @@ +moto>=4.1 +black>23.0 +pytest>=7.0 +pytest-cov>=4.0 +ruff>=0.0.275 +pre-commit>=3.3 +pytest-mock>=3.12 +freezegun>=1.2 +deepdiff>=6.5.0 +fakeredis>=2.10 \ No newline at end of file diff --git a/libs/numalogic-connectors/requirements/requirements.txt b/libs/numalogic-connectors/requirements/requirements.txt new file mode 100644 index 00000000..c0683430 --- /dev/null +++ b/libs/numalogic-connectors/requirements/requirements.txt @@ -0,0 +1,8 @@ +numalogic +requests>=2.0 +orjson>=3.9 +pydruid>=0.6 +boto3>=1.20 +PyMySQL>=1.0 +redis[hiredis]>=5.0 +omegaconf>=2.3 \ No newline at end of file diff --git a/libs/numalogic-registry/makefile b/libs/numalogic-registry/makefile new file mode 100644 index 00000000..f22973ba --- /dev/null +++ b/libs/numalogic-registry/makefile @@ -0,0 +1,22 @@ +clean: + @rm -rf build dist .eggs *.egg-info + @rm -rf .benchmarks .coverage coverage.xml htmlcov report.xml .tox + @find . -type d -name '.mypy_cache' -exec rm -rf {} + + @find . -type d -name '__pycache__' -exec rm -rf {} + + @find . -type d -name '*pytest_cache*' -exec rm -rf {} + + @find . -type f -name "*.py[co]" -exec rm -rf {} + + +format: clean + black libs/ apps/ examples/ tests/ + +lint: format + ruff check --fix . + +setup: + pip install -e ../numalogic-core + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + +# test your application (tests in the tests/ directory) +test: + poetry run pytest -v tests/ diff --git a/libs/numalogic-registry/pyproject.toml b/libs/numalogic-registry/pyproject.toml index bb1ea2c7..ffb4ba00 100644 --- a/libs/numalogic-registry/pyproject.toml +++ b/libs/numalogic-registry/pyproject.toml @@ -25,28 +25,14 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dependencies = [ - "numalogic", - "redis[hiredis]>=4.0", - "mlflow-skinny>2.0", - "boto3>=1.20", -] - -[project.optional-dependencies] -dev = [ - "fakeredis>=2.10", - "moto[dynamodb,s3]>=5.0", - "black>23.0", - "pytest>=7.0", - "pytest-cov>=4.0", - "ruff>=0.0.275", - "pre-commit>=3.3", - "pytest-mock>=3.12", - "torchinfo" -] +dynamic = ["dependencies", "optional-dependencies"] [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements/requirements.txt"]} +optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }} diff --git a/libs/numalogic-registry/requirements/requirements-dev.txt b/libs/numalogic-registry/requirements/requirements-dev.txt new file mode 100644 index 00000000..69dd2278 --- /dev/null +++ b/libs/numalogic-registry/requirements/requirements-dev.txt @@ -0,0 +1,9 @@ +fakeredis>=2.10 +moto[dynamodb,s3]>=5.0 +black>23.0 +pytest>=7.0 +pytest-cov>=4.0 +ruff>=0.0.275 +pre-commit>=3.3 +pytest-mock>=3.12 +torchinfo diff --git a/libs/numalogic-registry/requirements/requirements.txt b/libs/numalogic-registry/requirements/requirements.txt new file mode 100644 index 00000000..249ef282 --- /dev/null +++ b/libs/numalogic-registry/requirements/requirements.txt @@ -0,0 +1,4 @@ +numalogic +redis[hiredis]>=4.0 +mlflow-skinny>2.0 +boto3>=1.20 \ No newline at end of file diff --git a/setup.py b/setup.py index 5b17b378..042f43e1 100644 --- a/setup.py +++ b/setup.py @@ -27,13 +27,13 @@ def cd(newdir): pkg_name = os.getenv("PKG_NAME", "numalogic") if pkg_name == "numalogic": - with cd("libs/core"): + with cd("libs/numalogic-core"): setup() elif pkg_name == "numalogic-connectors": - with cd("libs/connectors"): + with cd("libs/numalogic-connectors"): setup() elif pkg_name == "numalogic-registry": - with cd("libs/registry"): + with cd("libs/numalogic-registry"): setup() else: raise ValueError(f"Invalid package name {pkg_name}") From 9bae7bd0615fd6c91a4302b91c372abab840dd07 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 13:48:16 -0700 Subject: [PATCH 11/68] chore: add more requirements Signed-off-by: Avik Basu --- libs/numalogic-connectors/requirements/requirements-dev.txt | 2 +- libs/numalogic-connectors/requirements/requirements.txt | 2 +- libs/numalogic-registry/requirements/requirements.txt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/numalogic-connectors/requirements/requirements-dev.txt b/libs/numalogic-connectors/requirements/requirements-dev.txt index 86c30b4a..08f069ee 100644 --- a/libs/numalogic-connectors/requirements/requirements-dev.txt +++ b/libs/numalogic-connectors/requirements/requirements-dev.txt @@ -7,4 +7,4 @@ pre-commit>=3.3 pytest-mock>=3.12 freezegun>=1.2 deepdiff>=6.5.0 -fakeredis>=2.10 \ No newline at end of file +fakeredis>=2.10 diff --git a/libs/numalogic-connectors/requirements/requirements.txt b/libs/numalogic-connectors/requirements/requirements.txt index c0683430..dc199287 100644 --- a/libs/numalogic-connectors/requirements/requirements.txt +++ b/libs/numalogic-connectors/requirements/requirements.txt @@ -5,4 +5,4 @@ pydruid>=0.6 boto3>=1.20 PyMySQL>=1.0 redis[hiredis]>=5.0 -omegaconf>=2.3 \ No newline at end of file +omegaconf>=2.3 diff --git a/libs/numalogic-registry/requirements/requirements.txt b/libs/numalogic-registry/requirements/requirements.txt index 249ef282..45f75436 100644 --- a/libs/numalogic-registry/requirements/requirements.txt +++ b/libs/numalogic-registry/requirements/requirements.txt @@ -1,4 +1,4 @@ numalogic redis[hiredis]>=4.0 mlflow-skinny>2.0 -boto3>=1.20 \ No newline at end of file +boto3>=1.20 From 24093682e1cbee5d0122691df511599d36720bd0 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 14:24:58 -0700 Subject: [PATCH 12/68] fix: constants Signed-off-by: Avik Basu --- apps/numa-udfs/src/__init__.py | 4 ++-- apps/numa-udfs/src/backtest/__init__.py | 2 ++ apps/numa-udfs/src/backtest/_prom.py | 4 ++-- apps/numa-udfs/src/constants.py | 6 ++++-- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/apps/numa-udfs/src/__init__.py b/apps/numa-udfs/src/__init__.py index e1ec6ed9..57caae0c 100644 --- a/apps/numa-udfs/src/__init__.py +++ b/apps/numa-udfs/src/__init__.py @@ -2,7 +2,7 @@ from logging import config as logconf import os -from src.constants import BASE_DIR +from src.constants import SRC_DIR, LOG_CONF_PATH from src._base import NumalogicUDF from src._config import StreamConf, PipelineConf, MLPipelineConf, load_pipeline_conf from src.factory import UDFFactory, ServerFactory @@ -16,7 +16,7 @@ def set_logger() -> None: """Sets the logger for the UDFs.""" logconf.fileConfig( - fname=os.path.join(BASE_DIR, "log.conf"), + fname=LOG_CONF_PATH, disable_existing_loggers=False, ) if os.getenv("DEBUG", "false").lower() == "true": diff --git a/apps/numa-udfs/src/backtest/__init__.py b/apps/numa-udfs/src/backtest/__init__.py index 74bf75f0..aa4a49e4 100644 --- a/apps/numa-udfs/src/backtest/__init__.py +++ b/apps/numa-udfs/src/backtest/__init__.py @@ -1,3 +1,5 @@ +# TODO move this subpackage to numalogic core library + from importlib.util import find_spec from src.backtest._prom import PromBacktester, OutDataFrames diff --git a/apps/numa-udfs/src/backtest/_prom.py b/apps/numa-udfs/src/backtest/_prom.py index 4897d781..b62426fb 100644 --- a/apps/numa-udfs/src/backtest/_prom.py +++ b/apps/numa-udfs/src/backtest/_prom.py @@ -22,7 +22,7 @@ from numpy.typing import NDArray from omegaconf import OmegaConf -from src.constants import BASE_DIR +from src.constants import SRC_DIR from src.backtest._constants import DEFAULT_SEQUENCE_LEN from numalogic.config import ( NumalogicConf, @@ -37,7 +37,7 @@ from numalogic.tools.types import artifact_t from src import UDFFactory, StreamConf, MLPipelineConf -DEFAULT_OUTPUT_DIR = os.path.join(BASE_DIR, ".btoutput") +DEFAULT_OUTPUT_DIR = os.path.join(SRC_DIR, ".btoutput") LOGGER = logging.getLogger(__name__) diff --git a/apps/numa-udfs/src/constants.py b/apps/numa-udfs/src/constants.py index d768eeb4..63ab56ad 100644 --- a/apps/numa-udfs/src/constants.py +++ b/apps/numa-udfs/src/constants.py @@ -1,11 +1,13 @@ import os -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -TESTS_DIR = os.path.join(BASE_DIR, "../tests") +SRC_DIR = os.path.dirname(os.path.abspath(__file__)) +BASE_DIR = os.path.dirname(SRC_DIR) +TESTS_DIR = os.path.join(BASE_DIR, "tests") BASE_CONF_DIR = os.path.join(BASE_DIR, "config") DEFAULT_BASE_CONF_PATH = os.path.join(BASE_CONF_DIR, "default-configs", "config.yaml") DEFAULT_APP_CONF_PATH = os.path.join(BASE_CONF_DIR, "app-configs", "config.yaml") +LOG_CONF_PATH = os.path.join(SRC_DIR, "log.conf") DEFAULT_METRICS_PORT = 8490 NUMALOGIC_METRICS = "numalogic_metrics" From 574ed70de26187eedba1ce226b0690aadcfe723f Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 14:59:25 -0700 Subject: [PATCH 13/68] chore: remove comments Signed-off-by: Avik Basu --- libs/numalogic-core/pyproject.toml | 29 ----------------------------- 1 file changed, 29 deletions(-) diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 0c57d2f9..93a10827 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -26,41 +26,12 @@ classifiers = [ ] requires-python = ">= 3.9" dynamic = ["dependencies", "optional-dependencies"] -#dependencies = [ -# "numpy>=1.22", -# "pandas[performance]>=2.0", -# "scikit-learn>=1.3", -# "torch>=2.0", -# "pytorch-lightning>=2.0", -# "omegaconf>=2.3", -#] -# -#[project.optional-dependencies] -#dev = [ -# "matplotlib>=3.4", -# "black>23.0", -# "pytest>=7.0", -# "pytest-cov>=4.0", -# "torchinfo>=1.7", -# "ruff>=0.0.275", -# "pre-commit>=3.3", -# "pytest-mock>=3.12", -#] -#jupyter = [ -# "jupyter>=1.0", -# "ipykernel>=6.15", -# "ipympl>=0.9", -# "ipython-autotime>=0.3", -# "black[jupyter]" -#] - [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" - [tool.setuptools.dynamic] dependencies = {file = ["requirements/requirements.txt"]} optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }, jupyter = { file = ["requirements/requirements-nb.txt"] }} From e03df9ac899e5513a1932a97fac7e768f67626c9 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 15:15:32 -0700 Subject: [PATCH 14/68] fix: log file Signed-off-by: Avik Basu --- apps/numa-udfs/.devcontainer/Dockerfile | 2 +- apps/numa-udfs/src/constants.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/numa-udfs/.devcontainer/Dockerfile b/apps/numa-udfs/.devcontainer/Dockerfile index 3d5ab353..26073812 100644 --- a/apps/numa-udfs/.devcontainer/Dockerfile +++ b/apps/numa-udfs/.devcontainer/Dockerfile @@ -28,7 +28,7 @@ COPY numalogic_connectors-0.11a0-py3-none-any.whl /app COPY poetry.lock pyproject.toml ./ -RUN poetry install --without dev --no-root \ +RUN poetry install --with dev \ && rm -rf $POETRY_CACHE_DIR \ && pip cache purge \ && apt-get purge -y --auto-remove build-essential diff --git a/apps/numa-udfs/src/constants.py b/apps/numa-udfs/src/constants.py index 63ab56ad..516e7cf1 100644 --- a/apps/numa-udfs/src/constants.py +++ b/apps/numa-udfs/src/constants.py @@ -8,6 +8,6 @@ DEFAULT_BASE_CONF_PATH = os.path.join(BASE_CONF_DIR, "default-configs", "config.yaml") DEFAULT_APP_CONF_PATH = os.path.join(BASE_CONF_DIR, "app-configs", "config.yaml") -LOG_CONF_PATH = os.path.join(SRC_DIR, "log.conf") +LOG_CONF_PATH = os.path.join(BASE_DIR, "log.conf") DEFAULT_METRICS_PORT = 8490 NUMALOGIC_METRICS = "numalogic_metrics" From 5d2be4d36b2f7c10e1464fe6f619ab8913f42dc6 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 4 Jun 2024 15:39:56 -0700 Subject: [PATCH 15/68] chore: add precommit Signed-off-by: Avik Basu --- libs/numalogic-core/.pre-commit-config.yaml | 31 +++++++++++++++ libs/numalogic-core/makefile | 2 +- libs/numalogic-core/pyproject.toml | 44 +++++++++++++++++++++ 3 files changed, 76 insertions(+), 1 deletion(-) create mode 100644 libs/numalogic-core/.pre-commit-config.yaml diff --git a/libs/numalogic-core/.pre-commit-config.yaml b/libs/numalogic-core/.pre-commit-config.yaml new file mode 100644 index 00000000..7b6f181e --- /dev/null +++ b/libs/numalogic-core/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +default_language_version: + python: python3.9 +repos: +- repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + language_version: python3.9 + args: [--config=pyproject.toml, --diff, --color ] +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.14 + hooks: + - id: ruff + args: [ --fix ] + - id: ruff-format +- repo: https://github.com/adamchainz/blacken-docs + rev: "1.13.0" + hooks: + - id: blacken-docs + additional_dependencies: + - black==22.12.0 +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-toml + - id: check-added-large-files + - id: check-ast + - id: check-case-conflict + - id: check-docstring-first diff --git a/libs/numalogic-core/makefile b/libs/numalogic-core/makefile index beb07f58..eee4f338 100644 --- a/libs/numalogic-core/makefile +++ b/libs/numalogic-core/makefile @@ -7,7 +7,7 @@ clean: @find . -type f -name "*.py[co]" -exec rm -rf {} + format: clean - black libs/ apps/ examples/ tests/ + black numalogic/ tests/ lint: format ruff check --fix . diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 93a10827..7a31d94c 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -35,3 +35,47 @@ Homepage = "https://numalogic.numaproj.io/" [tool.setuptools.dynamic] dependencies = {file = ["requirements/requirements.txt"]} optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }, jupyter = { file = ["requirements/requirements-nb.txt"] }} + +[tool.pytest.ini_options] +log_cli = true +log_cli_level = "INFO" + +[tool.black] +line-length = 100 +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | \.idea + | _build + | buck-out + | build + | dist + | examples/.*/venv + | tests/.*/setup.py +)/ +''' + +[tool.ruff] +line-length = 100 +src = ["numalogic", "tests"] +lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] +lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +target-version = "py39" +show-fixes = true +output-format = "full" + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D101", "D103"] + +[tool.ruff.lint.pylint] +max-args = 15 +max-statements = 30 + +[tool.ruff.lint.pydocstyle] +convention = "numpy" From 71390efc1bac7f52e51bd8bd5f156ba51c296dbd Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:09:41 -0700 Subject: [PATCH 16/68] chore: github actions Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 24 ++++++++---------------- pytest.ini | 3 --- 2 files changed, 8 insertions(+), 19 deletions(-) delete mode 100644 pytest.ini diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index be49353b..a6373f14 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,32 +7,24 @@ on: branches: [ "main", "release/*" ] jobs: - build: + numaloigic-core: name: Python version runs-on: ubuntu-latest strategy: fail-fast: false matrix: - python-version: ["3.9", "3.10", "3.11"] - + python-version: ["3.10", "3.11"] steps: - - uses: actions/checkout@v3 - - - name: Install poetry - run: pipx install poetry==1.6.1 - + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - cache: 'poetry' - - name: Install dependencies + working-directory: ./libs/numalogic_core run: | - poetry env use ${{ matrix.python-version }} - poetry install --all-extras --with dev - poetry run pip install "torch<3.0" -i https://download.pytorch.org/whl/cpu - poetry run pip install "pytorch-lightning<3.0" - - - name: Test with pytest + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + - name: Run tests + working-directory: ./libs/numalogic_core run: make test diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index 11c72fa2..00000000 --- a/pytest.ini +++ /dev/null @@ -1,3 +0,0 @@ -[pytest] -log_cli = true -log_cli_level = INFO From 92671198d80ee2f8cdfa9ba7136db52418e5de32 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:14:58 -0700 Subject: [PATCH 17/68] fix workflow file Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a6373f14..ee2d763a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -23,8 +23,8 @@ jobs: - name: Install dependencies working-directory: ./libs/numalogic_core run: | - pip install -r requirements/requirements.txt - pip install -r requirements/requirements-dev.txt + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt - name: Run tests working-directory: ./libs/numalogic_core run: make test From 4bd78f6ee7ee3631211fd49b4a0c230dfa575e5c Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:16:23 -0700 Subject: [PATCH 18/68] fix workflow file Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ee2d763a..3631b839 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -21,7 +21,7 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install dependencies - working-directory: ./libs/numalogic_core + working-directory: ./libs/numalogic-core run: | pip install -r requirements/requirements.txt pip install -r requirements/requirements-dev.txt From fafe46a5c0685eedfb54258cf34dd4872cee18f7 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:21:07 -0700 Subject: [PATCH 19/68] pip cache Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3631b839..78ce312b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -20,11 +20,12 @@ jobs: uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} + cache: 'pip' - name: Install dependencies working-directory: ./libs/numalogic-core run: | pip install -r requirements/requirements.txt pip install -r requirements/requirements-dev.txt - name: Run tests - working-directory: ./libs/numalogic_core + working-directory: ./libs/numalogic-core run: make test From a52ed60933ebd183e6357b4c602ad9a13ecbb9ab Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:26:45 -0700 Subject: [PATCH 20/68] fix: test run Signed-off-by: Avik Basu --- libs/numalogic-core/makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/numalogic-core/makefile b/libs/numalogic-core/makefile index eee4f338..67fb24a1 100644 --- a/libs/numalogic-core/makefile +++ b/libs/numalogic-core/makefile @@ -19,4 +19,4 @@ setup: # test your application (tests in the tests/ directory) test: - poetry run pytest -v tests/ + pytest -v tests/ From ec603507c98490adb6712b9586fac932a6fb2863 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:38:20 -0700 Subject: [PATCH 21/68] fix: connectors ci Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 26 ++++++++++++++++++++++++-- libs/numalogic-connectors/makefile | 4 ++-- 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 78ce312b..94694ada 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -8,7 +8,7 @@ on: jobs: numaloigic-core: - name: Python version + name: numalogic-core runs-on: ubuntu-latest strategy: fail-fast: false @@ -17,7 +17,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -29,3 +29,25 @@ jobs: - name: Run tests working-directory: ./libs/numalogic-core run: make test + numalogic-connectors: + name: numalogic-conectors + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [ "3.10", "3.11" ] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + - name: Install dependencies + working-directory: ./libs/numalogic-connectors + run: | + pip install ../numalogic-core + pip install -r requirements/requirements.txt + - name: Run tests + working-directory: ./libs/numalogic-connectors + run: make test diff --git a/libs/numalogic-connectors/makefile b/libs/numalogic-connectors/makefile index 32674495..2d208422 100644 --- a/libs/numalogic-connectors/makefile +++ b/libs/numalogic-connectors/makefile @@ -7,7 +7,7 @@ clean: @find . -type f -name "*.py[co]" -exec rm -rf {} + format: clean - black libs/ apps/ examples/ tests/ + black nlconnectors/ tests/ lint: format ruff check --fix . @@ -19,4 +19,4 @@ setup: # test your application (tests in the tests/ directory) test: - poetry run pytest -v tests/ + pytest -v tests/ From 49016bc7f626cda23635d444511d5884e5ae5b32 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:48:24 -0700 Subject: [PATCH 22/68] trying to fix tests Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 94694ada..72eab859 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,6 +10,9 @@ jobs: numaloigic-core: name: numalogic-core runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-core strategy: fail-fast: false matrix: @@ -31,6 +34,9 @@ jobs: run: make test numalogic-connectors: name: numalogic-conectors + defaults: + run: + working-directory: ./libs/numalogic-connectors runs-on: ubuntu-latest strategy: fail-fast: false @@ -44,10 +50,10 @@ jobs: python-version: ${{ matrix.python-version }} cache: 'pip' - name: Install dependencies - working-directory: ./libs/numalogic-connectors run: | pip install ../numalogic-core pip install -r requirements/requirements.txt + - name: Echo + run: pwd - name: Run tests - working-directory: ./libs/numalogic-connectors run: make test From 5b28c32fad2464a30e67d5ba3f4cef0209924ee9 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 11:51:42 -0700 Subject: [PATCH 23/68] fix tests Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 72eab859..9a9636b4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -50,9 +50,7 @@ jobs: python-version: ${{ matrix.python-version }} cache: 'pip' - name: Install dependencies - run: | - pip install ../numalogic-core - pip install -r requirements/requirements.txt + run: make setup - name: Echo run: pwd - name: Run tests From c04daeb868298f91fb6a3fbb5ff46e7a587c761e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:09:50 -0700 Subject: [PATCH 24/68] wkflw: registry Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 23 +++++++++++++++++++++++ libs/numalogic-registry/makefile | 6 +++--- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 9a9636b4..39a74737 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -55,3 +55,26 @@ jobs: run: pwd - name: Run tests run: make test + numalogic-registry: + name: numalogic-registry + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-registry + strategy: + fail-fast: false + matrix: + python-version: [ "3.10", "3.11" ] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + - name: Install dependencies + working-directory: ./libs/numalogic-registry + run: make setup + - name: Run tests + working-directory: ./libs/numalogic-registry + run: make test diff --git a/libs/numalogic-registry/makefile b/libs/numalogic-registry/makefile index f22973ba..a3ba7976 100644 --- a/libs/numalogic-registry/makefile +++ b/libs/numalogic-registry/makefile @@ -7,16 +7,16 @@ clean: @find . -type f -name "*.py[co]" -exec rm -rf {} + format: clean - black libs/ apps/ examples/ tests/ + black nlregistry/ tests/ lint: format ruff check --fix . setup: - pip install -e ../numalogic-core + pip install ../numalogic-core pip install -r requirements/requirements.txt pip install -r requirements/requirements-dev.txt # test your application (tests in the tests/ directory) test: - poetry run pytest -v tests/ + pytest -v tests/ From 101d175aef87f7981afe0724ffd511a930e4d8be Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:13:53 -0700 Subject: [PATCH 25/68] wkflw: dependency fix Signed-off-by: Avik Basu --- libs/numalogic-registry/requirements/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/numalogic-registry/requirements/requirements.txt b/libs/numalogic-registry/requirements/requirements.txt index 45f75436..be808cfe 100644 --- a/libs/numalogic-registry/requirements/requirements.txt +++ b/libs/numalogic-registry/requirements/requirements.txt @@ -2,3 +2,4 @@ numalogic redis[hiredis]>=4.0 mlflow-skinny>2.0 boto3>=1.20 +orjson>=3.9 From a1e5d63b41267f63b06296198eb21c7a11e63ab4 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:22:12 -0700 Subject: [PATCH 26/68] wkflw: dependency fix Signed-off-by: Avik Basu --- libs/numalogic-registry/requirements/requirements-dev.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/numalogic-registry/requirements/requirements-dev.txt b/libs/numalogic-registry/requirements/requirements-dev.txt index 69dd2278..e4bd66c7 100644 --- a/libs/numalogic-registry/requirements/requirements-dev.txt +++ b/libs/numalogic-registry/requirements/requirements-dev.txt @@ -7,3 +7,4 @@ ruff>=0.0.275 pre-commit>=3.3 pytest-mock>=3.12 torchinfo +freezegun>=1.4 From 4f0bc9d7305587082b5c202a18bc4dff67932605 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:36:24 -0700 Subject: [PATCH 27/68] wkflw: lint Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 40 ++++++++++++++++++++++++++++++++------ 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index a60becbd..cd0b33bb 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -7,20 +7,48 @@ on: branches: [ "main", "release/*" ] jobs: - black: - name: Black format + numalogic-core: + name: numalogic-core runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-core steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: psf/black@stable with: options: "--check --verbose" version: "~= 23.3" - - ruff: + - uses: chartboost/ruff-action@v1 + with: + version: "0.0.275" + numalogic-connectors: + name: numalogic-connectors runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-connectors steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 + - uses: psf/black@stable + with: + options: "--check --verbose" + version: "~= 23.3" + - uses: chartboost/ruff-action@v1 + with: + version: "0.0.275" + numalogic-registry: + name: numalogic-registry + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-registry + steps: + - uses: actions/checkout@v4 + - uses: psf/black@stable + with: + options: "--check --verbose" + version: "~= 23.3" - uses: chartboost/ruff-action@v1 with: version: "0.0.275" From 2ee3f3673c044a01838657414e70fa5ffdabb9a7 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:39:27 -0700 Subject: [PATCH 28/68] wkflw: ruff version Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index cd0b33bb..42cd56d5 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -51,4 +51,4 @@ jobs: version: "~= 23.3" - uses: chartboost/ruff-action@v1 with: - version: "0.0.275" + version: 0.4.7 From 8992df3dae736d911e377bb4e748b633908e6bb8 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 12:52:38 -0700 Subject: [PATCH 29/68] fix: ruff version Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 42cd56d5..f44dc770 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -21,7 +21,7 @@ jobs: version: "~= 23.3" - uses: chartboost/ruff-action@v1 with: - version: "0.0.275" + version: 0.4.7 numalogic-connectors: name: numalogic-connectors runs-on: ubuntu-latest @@ -36,7 +36,7 @@ jobs: version: "~= 23.3" - uses: chartboost/ruff-action@v1 with: - version: "0.0.275" + version: 0.4.7 numalogic-registry: name: numalogic-registry runs-on: ubuntu-latest From fffe0cca84587e1345ebb38862645c3c5f6c3d69 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 13:44:10 -0700 Subject: [PATCH 30/68] wkfl: lint Signed-off-by: Avik Basu --- libs/numalogic-core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 7a31d94c..e22a90f6 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -63,7 +63,7 @@ exclude = ''' [tool.ruff] line-length = 100 -src = ["numalogic", "tests"] +include = ["pyproject.toml", "numalogic/**/*.py", "tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] target-version = "py39" From ed54f8b036fdd80c79adc9b16fb1cf067e193319 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 13:59:35 -0700 Subject: [PATCH 31/68] tmp test lint Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 1 + libs/numalogic-core/pyproject.toml | 1 + pyproject.toml | 19 ------------------- 3 files changed, 2 insertions(+), 19 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index f44dc770..50998d29 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -21,6 +21,7 @@ jobs: version: "~= 23.3" - uses: chartboost/ruff-action@v1 with: + src: '.' version: 0.4.7 numalogic-connectors: name: numalogic-connectors diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index e22a90f6..faafe7a4 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -63,6 +63,7 @@ exclude = ''' [tool.ruff] line-length = 100 +src = ["numalogic", "tests"] include = ["pyproject.toml", "numalogic/**/*.py", "tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] diff --git a/pyproject.toml b/pyproject.toml index 8d342bbe..760d48fb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,22 +18,3 @@ exclude = ''' | tests/.*/setup.py )/ ''' - -[tool.ruff] -line-length = 100 -src = ["numalogic", "tests"] -select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] -ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] -target-version = "py39" -show-fixes = true -show-source = true - -[tool.ruff.per-file-ignores] -"tests/*" = ["D101", "D103"] - -[tool.ruff.pylint] -max-args = 15 -max-statements = 30 - -[tool.ruff.pydocstyle] -convention = "numpy" From ab68a6ee1b4311d90d93c78821294a52167d2d8a Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:01:45 -0700 Subject: [PATCH 32/68] verbose Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 50998d29..65d41a3d 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -22,6 +22,7 @@ jobs: - uses: chartboost/ruff-action@v1 with: src: '.' + args: 'check -v' version: 0.4.7 numalogic-connectors: name: numalogic-connectors From e54795a5227c41326036494b3a4d4d99b94cad17 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:08:10 -0700 Subject: [PATCH 33/68] tmp lint Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 65d41a3d..cd26f1ca 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -22,7 +22,7 @@ jobs: - uses: chartboost/ruff-action@v1 with: src: '.' - args: 'check -v' + args: 'check -v --config pyproject.toml' version: 0.4.7 numalogic-connectors: name: numalogic-connectors From afc5a01a7ad993652d3580874430c2c30e956450 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:11:20 -0700 Subject: [PATCH 34/68] fix lint Signed-off-by: Avik Basu --- libs/numalogic-core/pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index faafe7a4..e026d838 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -63,8 +63,8 @@ exclude = ''' [tool.ruff] line-length = 100 -src = ["numalogic", "tests"] -include = ["pyproject.toml", "numalogic/**/*.py", "tests/**/*.py"] +# src = ["numalogic", "tests"] +include = ["pyproject.toml", "numalogic/libs/numalogic-core/**/*.py", "numalogic/libs/numalogic-core/tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] target-version = "py39" From 4814188822c7c1d08a119710ef044dc992988e45 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:14:10 -0700 Subject: [PATCH 35/68] try lint Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index cd26f1ca..dcfb6ac2 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -22,7 +22,7 @@ jobs: - uses: chartboost/ruff-action@v1 with: src: '.' - args: 'check -v --config pyproject.toml' + args: 'check -v --config libs/numalogic-core/pyproject.toml' version: 0.4.7 numalogic-connectors: name: numalogic-connectors From 9b31bdbb159ecfcc5f0100cf7bf7c471efbaa321 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:17:09 -0700 Subject: [PATCH 36/68] files include Signed-off-by: Avik Basu --- libs/numalogic-core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index e026d838..62c8d047 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -64,7 +64,7 @@ exclude = ''' [tool.ruff] line-length = 100 # src = ["numalogic", "tests"] -include = ["pyproject.toml", "numalogic/libs/numalogic-core/**/*.py", "numalogic/libs/numalogic-core/tests/**/*.py"] +# include = ["pyproject.toml", "numalogic/libs/numalogic-core/**/*.py", "numalogic/libs/numalogic-core/tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] target-version = "py39" From 486d5e02e42ca5c9c124baef64d485a40698382e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:20:19 -0700 Subject: [PATCH 37/68] debug Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index dcfb6ac2..c1113fc5 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -19,6 +19,8 @@ jobs: with: options: "--check --verbose" version: "~= 23.3" + - name: Echo + run: pwd - uses: chartboost/ruff-action@v1 with: src: '.' From 290b05c197d8339afb3d43e04979811bfa8258a4 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:46:37 -0700 Subject: [PATCH 38/68] avoid ruff hook Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 19 +++++++++++++------ libs/numalogic-core/pyproject.toml | 2 +- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index c1113fc5..6b294381 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -19,13 +19,20 @@ jobs: with: options: "--check --verbose" version: "~= 23.3" - - name: Echo - run: pwd - - uses: chartboost/ruff-action@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 with: - src: '.' - args: 'check -v --config libs/numalogic-core/pyproject.toml' - version: 0.4.7 + python-version: ${{ matrix.python-version }} + cache: 'pip' + - name: Install dependencies + run: | + pip install ruff==0.4.7 + ruff check -v . +# - uses: chartboost/ruff-action@v1 +# with: +# src: '.' +# args: 'check -v --config libs/numalogic-core/pyproject.toml' +# version: 0.4.7 numalogic-connectors: name: numalogic-connectors runs-on: ubuntu-latest diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 62c8d047..428df52c 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -63,7 +63,7 @@ exclude = ''' [tool.ruff] line-length = 100 -# src = ["numalogic", "tests"] +src = ["numalogic", "tests"] # include = ["pyproject.toml", "numalogic/libs/numalogic-core/**/*.py", "numalogic/libs/numalogic-core/tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] From f40a658478f77ce0f3afc86ad14bae4dc7a00c27 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:49:51 -0700 Subject: [PATCH 39/68] fix: lint for all libs Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 29 ++++++++++++++++++----------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 6b294381..ab8b75e0 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -19,20 +19,15 @@ jobs: with: options: "--check --verbose" version: "~= 23.3" - - name: Set up Python ${{ matrix.python-version }} + - name: Set up Python uses: actions/setup-python@v5 with: - python-version: ${{ matrix.python-version }} + python-version: 3.11 cache: 'pip' - name: Install dependencies run: | pip install ruff==0.4.7 ruff check -v . -# - uses: chartboost/ruff-action@v1 -# with: -# src: '.' -# args: 'check -v --config libs/numalogic-core/pyproject.toml' -# version: 0.4.7 numalogic-connectors: name: numalogic-connectors runs-on: ubuntu-latest @@ -45,9 +40,15 @@ jobs: with: options: "--check --verbose" version: "~= 23.3" - - uses: chartboost/ruff-action@v1 + - name: Set up Python + uses: actions/setup-python@v5 with: - version: 0.4.7 + python-version: 3.11 + cache: 'pip' + - name: Install dependencies + run: | + pip install ruff==0.4.7 + ruff check -v . numalogic-registry: name: numalogic-registry runs-on: ubuntu-latest @@ -60,6 +61,12 @@ jobs: with: options: "--check --verbose" version: "~= 23.3" - - uses: chartboost/ruff-action@v1 + - name: Set up Python + uses: actions/setup-python@v5 with: - version: 0.4.7 + python-version: 3.11 + cache: 'pip' + - name: Install dependencies + run: | + pip install ruff==0.4.7 + ruff check -v . From b7abb0ae0ffd566f939f577c798d883f04732504 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 14:53:35 -0700 Subject: [PATCH 40/68] fix: lint Signed-off-by: Avik Basu --- .github/workflows/lint.yml | 6 ++-- libs/numalogic-connectors/pyproject.toml | 45 +++++++++++------------- libs/numalogic-core/pyproject.toml | 1 - libs/numalogic-registry/pyproject.toml | 19 ++++++++++ 4 files changed, 42 insertions(+), 29 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index ab8b75e0..76bbd4d1 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -24,7 +24,7 @@ jobs: with: python-version: 3.11 cache: 'pip' - - name: Install dependencies + - name: Ruff run: | pip install ruff==0.4.7 ruff check -v . @@ -45,7 +45,7 @@ jobs: with: python-version: 3.11 cache: 'pip' - - name: Install dependencies + - name: Ruff run: | pip install ruff==0.4.7 ruff check -v . @@ -66,7 +66,7 @@ jobs: with: python-version: 3.11 cache: 'pip' - - name: Install dependencies + - name: Ruff run: | pip install ruff==0.4.7 ruff check -v . diff --git a/libs/numalogic-connectors/pyproject.toml b/libs/numalogic-connectors/pyproject.toml index 2756eeea..adc8d36f 100644 --- a/libs/numalogic-connectors/pyproject.toml +++ b/libs/numalogic-connectors/pyproject.toml @@ -27,30 +27,6 @@ classifiers = [ requires-python = ">= 3.9" dynamic = ["dependencies", "optional-dependencies"] -# "requests>=2.0", -# "orjson>=3.9", -# "pydruid>=0.6", -# "boto3>=1.20", -# "PyMySQL>=1.0", -# "redis[hiredis]>=5.0", -# "omegaconf>=2.3", -#] - -#[project.optional-dependencies] -#dev = [ -# "moto>=4.1", -# "black>23.0", -# "pytest>=7.0", -# "pytest-cov>=4.0", -# "ruff>=0.0.275", -# "pre-commit>=3.3", -# "pytest-mock>=3.12", -# "freezegun>=1.2", -# "deepdiff>=6.5.0", -# "fakeredis>=2.10", -#] - - [project.urls] Repository = "https://github.com/numaproj/numalogic" Documentation = "https://numalogic.numaproj.io/" @@ -59,4 +35,23 @@ Homepage = "https://numalogic.numaproj.io/" [tool.setuptools.dynamic] dependencies = {file = ["requirements/requirements.txt"]} -optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }} \ No newline at end of file +optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }} + +[tool.ruff] +line-length = 100 +src = ["numalogic-connectors", "tests"] +lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] +lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +target-version = "py39" +show-fixes = true +output-format = "full" + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D101", "D103"] + +[tool.ruff.lint.pylint] +max-args = 15 +max-statements = 30 + +[tool.ruff.lint.pydocstyle] +convention = "numpy" diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 428df52c..7a31d94c 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -64,7 +64,6 @@ exclude = ''' [tool.ruff] line-length = 100 src = ["numalogic", "tests"] -# include = ["pyproject.toml", "numalogic/libs/numalogic-core/**/*.py", "numalogic/libs/numalogic-core/tests/**/*.py"] lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] target-version = "py39" diff --git a/libs/numalogic-registry/pyproject.toml b/libs/numalogic-registry/pyproject.toml index ffb4ba00..4b5941c9 100644 --- a/libs/numalogic-registry/pyproject.toml +++ b/libs/numalogic-registry/pyproject.toml @@ -36,3 +36,22 @@ Homepage = "https://numalogic.numaproj.io/" [tool.setuptools.dynamic] dependencies = {file = ["requirements/requirements.txt"]} optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }} + +[tool.ruff] +line-length = 100 +src = ["numalogic-registry", "tests"] +lint.select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] +lint.ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +target-version = "py39" +show-fixes = true +output-format = "full" + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D101", "D103"] + +[tool.ruff.lint.pylint] +max-args = 15 +max-statements = 30 + +[tool.ruff.lint.pydocstyle] +convention = "numpy" From e696bf069f4c2167fbe949e5caa5c136cbaf8c33 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 15:02:09 -0700 Subject: [PATCH 41/68] fix: lint for connectors Signed-off-by: Avik Basu --- libs/numalogic-connectors/nlconnectors/config.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/libs/numalogic-connectors/nlconnectors/config.py b/libs/numalogic-connectors/nlconnectors/config.py index 2ae221bd..99c935aa 100644 --- a/libs/numalogic-connectors/nlconnectors/config.py +++ b/libs/numalogic-connectors/nlconnectors/config.py @@ -6,6 +6,8 @@ class ConnectorType(IntEnum): + """Enum for connector types.""" + redis = 0 prometheus = 1 druid = 2 @@ -14,11 +16,15 @@ class ConnectorType(IntEnum): @dataclass class ConnectorConf: + """Base class for connector configuration.""" + url: str @dataclass class PrometheusConf(ConnectorConf): + """Prometheus' configuration class.""" + pushgateway: str = "" scrape_interval: int = 30 return_labels: list[str] = field(default_factory=list) @@ -26,6 +32,8 @@ class PrometheusConf(ConnectorConf): @dataclass class RedisConf(ConnectorConf): + """Redis' configuration class.""" + port: int expiry: int = 300 master_name: str = "mymaster" @@ -33,6 +41,8 @@ class RedisConf(ConnectorConf): @dataclass class Pivot: + """Pivot configuration for Druid fetcher.""" + index: str = "timestamp" columns: list[str] = field(default_factory=list) value: list[str] = field(default_factory=lambda: ["count"]) @@ -41,12 +51,16 @@ class Pivot: @dataclass class FilterConf: + """Filter configuration for Druid fetcher.""" + inclusion_filters: Optional[list[dict]] = None exclusion_filters: Optional[list[dict]] = None @dataclass class DruidFetcherConf: + """Druid fetcher configuration.""" + datasource: str static_filters: Optional[FilterConf] = None dimensions: list[str] = field(default_factory=list) From 3303a7dc8113909c9d83248dc3469258de754468 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 15:02:39 -0700 Subject: [PATCH 42/68] fix: precommit Signed-off-by: Avik Basu --- .../.pre-commit-config.yaml | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 libs/numalogic-connectors/.pre-commit-config.yaml diff --git a/libs/numalogic-connectors/.pre-commit-config.yaml b/libs/numalogic-connectors/.pre-commit-config.yaml new file mode 100644 index 00000000..ebcfff78 --- /dev/null +++ b/libs/numalogic-connectors/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +default_language_version: + python: python3.9 +repos: +- repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + language_version: python3.9 + args: [--config=pyproject.toml, --diff, --color ] +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.7 + hooks: + - id: ruff + args: [ --fix ] + - id: ruff-format +- repo: https://github.com/adamchainz/blacken-docs + rev: "1.13.0" + hooks: + - id: blacken-docs + additional_dependencies: + - black==22.12.0 +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-toml + - id: check-added-large-files + - id: check-ast + - id: check-case-conflict + - id: check-docstring-first From d106fb73cef1777462d5bb0312242e66937c3b61 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 15:32:00 -0700 Subject: [PATCH 43/68] registry lint Signed-off-by: Avik Basu --- .../.pre-commit-config.yaml | 31 +++++++++++++++++++ .../nlregistry/dynamodb_registry.py | 4 +-- libs/numalogic-registry/nlregistry/factory.py | 6 ++-- .../nlregistry/mlflow_registry.py | 6 ++-- .../nlregistry/redis_registry.py | 9 ++++-- 5 files changed, 47 insertions(+), 9 deletions(-) create mode 100644 libs/numalogic-registry/.pre-commit-config.yaml diff --git a/libs/numalogic-registry/.pre-commit-config.yaml b/libs/numalogic-registry/.pre-commit-config.yaml new file mode 100644 index 00000000..ee7dcd51 --- /dev/null +++ b/libs/numalogic-registry/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +default_language_version: + python: python3.9 +repos: +- repo: https://github.com/psf/black + rev: 24.4.2 + hooks: + - id: black + language_version: python3.9 + args: [--config=pyproject.toml, --diff, --color ] +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.8 + hooks: + - id: ruff + args: [ --fix ] + - id: ruff-format +- repo: https://github.com/adamchainz/blacken-docs + rev: "1.16.0" + hooks: + - id: blacken-docs + additional_dependencies: + - black==22.12.0 +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-toml + - id: check-added-large-files + - id: check-ast + - id: check-case-conflict + - id: check-docstring-first diff --git a/libs/numalogic-registry/nlregistry/dynamodb_registry.py b/libs/numalogic-registry/nlregistry/dynamodb_registry.py index 673e0374..7af92375 100644 --- a/libs/numalogic-registry/nlregistry/dynamodb_registry.py +++ b/libs/numalogic-registry/nlregistry/dynamodb_registry.py @@ -85,9 +85,9 @@ class DynamoDBRegistry(ArtifactManager): Examples -------- >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.test_registry import DynamoDBRegistry + >>> from nlregistry import DynamoDBRegistry >>> ... - >>> test_registry = DynamoDBRegistry(table="mytable", role="arn:aws:iam::1234567890:role/role-name") + >>> test_registry = DynamoDBRegistry(table="mytable", role="arn:aws:iam::123:role/name") >>> skeys, dkeys = ("mymetric", "ae"), ("vanilla", "seq10") >>> model = VanillaAE(seq_len=10) >>> test_registry.save(skeys, dkeys, artifact=model, **{'lr': 0.01}) diff --git a/libs/numalogic-registry/nlregistry/factory.py b/libs/numalogic-registry/nlregistry/factory.py index 7261e8ff..c3cbc502 100644 --- a/libs/numalogic-registry/nlregistry/factory.py +++ b/libs/numalogic-registry/nlregistry/factory.py @@ -20,7 +20,8 @@ def get_instance(self, object_info: Union[ModelInfo, RegistryInfo]): except AttributeError as err: if object_info.name in self._CLS_SET: raise ImportError( - "Please install the required dependencies for the test_registry you want to use." + "Please install the required dependencies for the test_registry " + "you want to use." ) from err raise UnknownConfigArgsError( f"Invalid model info instance provided: {object_info}" @@ -36,7 +37,8 @@ def get_cls(cls, name: str): except AttributeError as err: if name in cls._CLS_SET: raise ImportError( - "Please install the required dependencies for the test_registry you want to use." + "Please install the required dependencies for the test_registry " + "you want to use." ) from err raise UnknownConfigArgsError( f"Invalid name provided for RegistryFactory: {name}" diff --git a/libs/numalogic-registry/nlregistry/mlflow_registry.py b/libs/numalogic-registry/nlregistry/mlflow_registry.py index 57c4a6d7..18efcd4c 100644 --- a/libs/numalogic-registry/nlregistry/mlflow_registry.py +++ b/libs/numalogic-registry/nlregistry/mlflow_registry.py @@ -140,7 +140,9 @@ def load( version: Optional[str] = None, artifact_type: str = "pytorch", ) -> Optional[ArtifactData]: - """Load the artifact from the test_registry. The artifact is loaded from the cache if available. + """Load the artifact from the test_registry. + + The artifact is loaded from the cache if available. Args: ---- @@ -167,7 +169,7 @@ def load( return cached_artifact version_info = self.client.get_latest_versions(model_key, stages=[self.model_stage]) if not version_info: - raise ModelVersionError("Model version missing for key = %s" % model_key) + raise ModelVersionError(f"Model version missing for key = {model_key}") version_info = version_info[-1] else: version_info = self.client.get_model_version(model_key, version) diff --git a/libs/numalogic-registry/nlregistry/redis_registry.py b/libs/numalogic-registry/nlregistry/redis_registry.py index 3b2f88ba..777392a0 100644 --- a/libs/numalogic-registry/nlregistry/redis_registry.py +++ b/libs/numalogic-registry/nlregistry/redis_registry.py @@ -241,10 +241,13 @@ def load( latest: bool = True, version: Optional[str] = None, ) -> Optional[ArtifactData]: - """Loads the artifact from redis test_registry. Either latest or version (one of the arguments) - is needed to load the respective artifact. + """Loads the artifact from redis test_registry. - If cache test_registry is provided, it will first check the cache test_registry for the artifact. + Either latest or version (one of the arguments) + is needed to load the respective artifact. + + If cache test_registry is provided, it will first check the cache + test_registry for the artifact. If latest is passed, latest key is saved otherwise version call saves the respective version artifact in cache. From e490097e145ee58ca29719082755cf2244ecbe9c Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 15:40:38 -0700 Subject: [PATCH 44/68] wkflw: fix mkdocs Signed-off-by: Avik Basu --- .github/workflows/gh-pages.yaml | 2 +- Makefile | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/gh-pages.yaml b/.github/workflows/gh-pages.yaml index 2e3e7665..47b6b7f0 100644 --- a/.github/workflows/gh-pages.yaml +++ b/.github/workflows/gh-pages.yaml @@ -19,7 +19,7 @@ jobs: - name: Setup Python uses: actions/setup-python@v4 with: - python-version: 3.9 + python-version: 3.11 - name: build run: make docs - name: deploy diff --git a/Makefile b/Makefile index 0aa6958a..65b3236c 100644 --- a/Makefile +++ b/Makefile @@ -35,7 +35,7 @@ tag: git tag -s -a $(VERSION) -m "Release $(VERSION)" /usr/local/bin/mkdocs: - $(PYTHON) -m pip install mkdocs==1.3.0 mkdocs_material==8.3.9 + pip install mkdocs==1.3.0 mkdocs_material==8.3.9 # docs From 75a6cc5ed37938dc3c5d29aab2ade38a1c28972a Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 6 Jun 2024 17:49:45 -0700 Subject: [PATCH 45/68] try: coverage Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 2 -- .github/workflows/coverage.yml | 35 +++++++++++++--------------------- 2 files changed, 13 insertions(+), 24 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 39a74737..98a93349 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -25,12 +25,10 @@ jobs: python-version: ${{ matrix.python-version }} cache: 'pip' - name: Install dependencies - working-directory: ./libs/numalogic-core run: | pip install -r requirements/requirements.txt pip install -r requirements/requirements-dev.txt - name: Run tests - working-directory: ./libs/numalogic-core run: make test numalogic-connectors: name: numalogic-conectors diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 19ab4bb8..f97ccd6c 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -8,35 +8,26 @@ on: jobs: build: - name: Codecov + name: Codecov numalogic-core runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.9"] - + defaults: + run: + working-directory: ./libs/numalogic-core steps: - - uses: actions/checkout@v3 - - - name: Install poetry - run: pipx install poetry==1.6.1 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 with: - python-version: ${{ matrix.python-version }} - cache: 'poetry' - + python-version: 3.9 + cache: 'pip' - name: Install dependencies + working-directory: ./libs/numalogic-core run: | - poetry env use ${{ matrix.python-version }} - poetry install --all-extras --with dev - poetry run pip install "torch<3.0" -i https://download.pytorch.org/whl/cpu - poetry run pip install "pytorch-lightning<3.0" + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt - name: Run Coverage - run: | - poetry run pytest --cov-report=xml --cov=numalogic --cov-config .coveragerc tests/ -sq + run: pytest --cov-report=xml --cov=numalogic-core --cov-config .coveragerc tests/ -sq - name: Upload Coverage uses: codecov/codecov-action@v4 From 348b4dcc0bff1573ce3471179d7b3f8d8f194cbd Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 10:51:58 -0700 Subject: [PATCH 46/68] try coverage fix Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 2 +- .codecov.yml => libs/numalogic-core/.codecov.yml | 0 .coveragerc => libs/numalogic-core/.coveragerc | 0 3 files changed, 1 insertion(+), 1 deletion(-) rename .codecov.yml => libs/numalogic-core/.codecov.yml (100%) rename .coveragerc => libs/numalogic-core/.coveragerc (100%) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index f97ccd6c..08345808 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -8,7 +8,7 @@ on: jobs: build: - name: Codecov numalogic-core + name: numalogic-core runs-on: ubuntu-latest defaults: run: diff --git a/.codecov.yml b/libs/numalogic-core/.codecov.yml similarity index 100% rename from .codecov.yml rename to libs/numalogic-core/.codecov.yml diff --git a/.coveragerc b/libs/numalogic-core/.coveragerc similarity index 100% rename from .coveragerc rename to libs/numalogic-core/.coveragerc From 5a2bfc0e056980ffc8722668c44f53d71c597813 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 11:08:17 -0700 Subject: [PATCH 47/68] try coverage for all libs Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 60 +++++++++++++++++++++++++- libs/numalogic-connectors/.codecov.yml | 10 +++++ libs/numalogic-connectors/.coveragerc | 5 +++ libs/numalogic-registry/.codecov.yml | 10 +++++ libs/numalogic-registry/.coveragerc | 5 +++ 5 files changed, 89 insertions(+), 1 deletion(-) create mode 100644 libs/numalogic-connectors/.codecov.yml create mode 100644 libs/numalogic-connectors/.coveragerc create mode 100644 libs/numalogic-registry/.codecov.yml create mode 100644 libs/numalogic-registry/.coveragerc diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 08345808..3212953f 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -7,7 +7,7 @@ on: branches: [ "main" ] jobs: - build: + numalogic-core: name: numalogic-core runs-on: ubuntu-latest defaults: @@ -36,3 +36,61 @@ jobs: files: ./coverage.xml fail_ci_if_error: true verbose: true + numalogic-connectors: + name: numalogic-connectors + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-connectors + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.9 + cache: 'pip' + - name: Install dependencies + working-directory: ./libs/numalogic-connectors + run: | + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + + - name: Run Coverage + run: pytest --cov-report=xml --cov=numalogic-connectors --cov-config .coveragerc tests/ -sq + + - name: Upload Coverage + uses: codecov/codecov-action@v4 + with: + token: ${{ secrets.CODECOV_TOKEN }} + files: ./coverage.xml + fail_ci_if_error: true + verbose: true + numalogic-registry: + name: numalogic-registry + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./libs/numalogic-registry + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.9 + cache: 'pip' + - name: Install dependencies + working-directory: ./libs/numalogic-registry + run: | + pip install -r requirements/requirements.txt + pip install -r requirements/requirements-dev.txt + + - name: Run Coverage + run: pytest --cov-report=xml --cov=numalogic-registry --cov-config .coveragerc tests/ -sq + + - name: Upload Coverage + uses: codecov/codecov-action@v4 + with: + token: ${{ secrets.CODECOV_TOKEN }} + files: ./coverage.xml + fail_ci_if_error: true + verbose: true diff --git a/libs/numalogic-connectors/.codecov.yml b/libs/numalogic-connectors/.codecov.yml new file mode 100644 index 00000000..19eae8b6 --- /dev/null +++ b/libs/numalogic-connectors/.codecov.yml @@ -0,0 +1,10 @@ +coverage: + status: + project: + default: + target: auto + threshold: 3% + patch: + default: + target: auto + threshold: 20% diff --git a/libs/numalogic-connectors/.coveragerc b/libs/numalogic-connectors/.coveragerc new file mode 100644 index 00000000..75145cc8 --- /dev/null +++ b/libs/numalogic-connectors/.coveragerc @@ -0,0 +1,5 @@ +[run] +branch = True +parallel = True +source = numalogic-connectors +omit = tests/* diff --git a/libs/numalogic-registry/.codecov.yml b/libs/numalogic-registry/.codecov.yml new file mode 100644 index 00000000..19eae8b6 --- /dev/null +++ b/libs/numalogic-registry/.codecov.yml @@ -0,0 +1,10 @@ +coverage: + status: + project: + default: + target: auto + threshold: 3% + patch: + default: + target: auto + threshold: 20% diff --git a/libs/numalogic-registry/.coveragerc b/libs/numalogic-registry/.coveragerc new file mode 100644 index 00000000..71d81b39 --- /dev/null +++ b/libs/numalogic-registry/.coveragerc @@ -0,0 +1,5 @@ +[run] +branch = True +parallel = True +source = numalogic-registry +omit = tests/* From baaa38fdbab6103cdc727e769fb2d98d851376f3 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 11:10:57 -0700 Subject: [PATCH 48/68] try codecov fix Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 3212953f..b6dea792 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -51,10 +51,7 @@ jobs: cache: 'pip' - name: Install dependencies working-directory: ./libs/numalogic-connectors - run: | - pip install -r requirements/requirements.txt - pip install -r requirements/requirements-dev.txt - + run: make setup - name: Run Coverage run: pytest --cov-report=xml --cov=numalogic-connectors --cov-config .coveragerc tests/ -sq @@ -80,10 +77,7 @@ jobs: cache: 'pip' - name: Install dependencies working-directory: ./libs/numalogic-registry - run: | - pip install -r requirements/requirements.txt - pip install -r requirements/requirements-dev.txt - + run: make setup - name: Run Coverage run: pytest --cov-report=xml --cov=numalogic-registry --cov-config .coveragerc tests/ -sq From b9843d874a8f025733bb778bfe92ab9683adabc3 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 14:32:49 -0700 Subject: [PATCH 49/68] tmp try path Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index b6dea792..9f6a9c2f 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -36,6 +36,7 @@ jobs: files: ./coverage.xml fail_ci_if_error: true verbose: true + working-directory: ./libs/numalogic-core numalogic-connectors: name: numalogic-connectors runs-on: ubuntu-latest From a40e2cea4f1610ebaf6bcf5b759cdccff6998bc6 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 14:44:00 -0700 Subject: [PATCH 50/68] tmp try Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 9f6a9c2f..52a8371f 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -27,7 +27,7 @@ jobs: pip install -r requirements/requirements-dev.txt - name: Run Coverage - run: pytest --cov-report=xml --cov=numalogic-core --cov-config .coveragerc tests/ -sq + run: pytest --cov-report=xml --cov=numalogic --cov-config .coveragerc tests/ -sq - name: Upload Coverage uses: codecov/codecov-action@v4 From c493dc5166e5f3600f777ceb03b1ae1f5b79bef0 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 14:52:40 -0700 Subject: [PATCH 51/68] tmp: try Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 52a8371f..3a5d8d5f 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -37,6 +37,7 @@ jobs: fail_ci_if_error: true verbose: true working-directory: ./libs/numalogic-core + name: numalogic-core numalogic-connectors: name: numalogic-connectors runs-on: ubuntu-latest @@ -54,7 +55,7 @@ jobs: working-directory: ./libs/numalogic-connectors run: make setup - name: Run Coverage - run: pytest --cov-report=xml --cov=numalogic-connectors --cov-config .coveragerc tests/ -sq + run: pytest --cov-report=xml --cov=nlconnectors --cov-config .coveragerc tests/ -sq - name: Upload Coverage uses: codecov/codecov-action@v4 @@ -63,6 +64,8 @@ jobs: files: ./coverage.xml fail_ci_if_error: true verbose: true + working-directory: ./libs/numalogic-connectors + name: numalogic-connectors numalogic-registry: name: numalogic-registry runs-on: ubuntu-latest @@ -80,7 +83,7 @@ jobs: working-directory: ./libs/numalogic-registry run: make setup - name: Run Coverage - run: pytest --cov-report=xml --cov=numalogic-registry --cov-config .coveragerc tests/ -sq + run: pytest --cov-report=xml --cov=nlregistry --cov-config .coveragerc tests/ -sq - name: Upload Coverage uses: codecov/codecov-action@v4 @@ -89,3 +92,5 @@ jobs: files: ./coverage.xml fail_ci_if_error: true verbose: true + working-directory: ./libs/numalogic-registry + name: numalogic-registry From 95f06b5650ab19523bb182ff28f0a799f8fb72d8 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 15:05:40 -0700 Subject: [PATCH 52/68] try numa udf builds Signed-off-by: Avik Basu --- .github/workflows/apps.yml | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 .github/workflows/apps.yml diff --git a/.github/workflows/apps.yml b/.github/workflows/apps.yml new file mode 100644 index 00000000..6a0e83ce --- /dev/null +++ b/.github/workflows/apps.yml @@ -0,0 +1,21 @@ +name: Numalogic Apps CI + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +jobs: + numalogic-udfs: + name: numalogic-core + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./apps/numa-udfs + steps: + - uses: actions/checkout@v4 + - name: Build and run dev container task + uses: devcontainers/ci@v0.3 + with: + runCmd: pytest --cov-report=xml --cov=src tests/ -sq From 3ec0831df5015e4da1ea0cdbf17e3e30b4ee4e44 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 15:09:15 -0700 Subject: [PATCH 53/68] tmp: try subfolder Signed-off-by: Avik Basu --- .github/workflows/apps.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/apps.yml b/.github/workflows/apps.yml index 6a0e83ce..3e1ff939 100644 --- a/.github/workflows/apps.yml +++ b/.github/workflows/apps.yml @@ -18,4 +18,5 @@ jobs: - name: Build and run dev container task uses: devcontainers/ci@v0.3 with: + subFolder: ./apps/numa-udfs runCmd: pytest --cov-report=xml --cov=src tests/ -sq From 7b2ffbb3f51c72ec5ac72bab6383522cf459685a Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 15:11:34 -0700 Subject: [PATCH 54/68] tmp pkg wheels Signed-off-by: Avik Basu --- .../numa-udfs/numalogic-0.11a0-py3-none-any.whl | Bin 0 -> 66788 bytes ...numalogic_connectors-0.11a0-py3-none-any.whl | Bin 0 -> 25505 bytes .../numalogic_registry-0.11a0-py3-none-any.whl | Bin 0 -> 19600 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 apps/numa-udfs/numalogic-0.11a0-py3-none-any.whl create mode 100644 apps/numa-udfs/numalogic_connectors-0.11a0-py3-none-any.whl create mode 100644 apps/numa-udfs/numalogic_registry-0.11a0-py3-none-any.whl diff --git 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z(F)2xC3`>=@JK|4;z?)PWdZ%;fMhx8YfBy(X|uDG^nWaXfMj5Wl95|oS4aMc8mmH5BQ%r26G;?lpW(sxXn_*V!^U;hT4O(Qn| literal 0 HcmV?d00001 From d088cb0a0a703cc76fb0378f43075fc45f334dc6 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Fri, 7 Jun 2024 15:16:03 -0700 Subject: [PATCH 55/68] try fix docker build error Signed-off-by: Avik Basu --- .github/workflows/apps.yml | 2 +- apps/numa-udfs/pyproject.toml | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/apps.yml b/.github/workflows/apps.yml index 3e1ff939..f1445636 100644 --- a/.github/workflows/apps.yml +++ b/.github/workflows/apps.yml @@ -8,7 +8,7 @@ on: jobs: numalogic-udfs: - name: numalogic-core + name: numalogic-udfs runs-on: ubuntu-latest defaults: run: diff --git a/apps/numa-udfs/pyproject.toml b/apps/numa-udfs/pyproject.toml index eda13327..00a20076 100644 --- a/apps/numa-udfs/pyproject.toml +++ b/apps/numa-udfs/pyproject.toml @@ -4,7 +4,6 @@ version = "0.11a0" description = "" authors = ["Avik Basu "] readme = "README.md" -packages = [{include = "src"}] [tool.poetry.dependencies] python = "~3.11" From 1f7c75ac780ad7fa1015ab3939baa2e8993cdb7e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 14:58:25 -0700 Subject: [PATCH 56/68] try: testpypi Signed-off-by: Avik Basu --- .github/workflows/pypi.yml | 46 ++++++++++++++---------------- libs/numalogic-core/pyproject.toml | 2 +- 2 files changed, 23 insertions(+), 25 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 0d9b5d23..8fed91f1 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -5,35 +5,33 @@ on: tags: - v* jobs: - pypi_publish: + numalogic-core: if: github.repository == 'numaproj/numalogic' runs-on: ubuntu-latest environment: production - strategy: - fail-fast: false - matrix: - python-version: [ "3.11" ] + defaults: + run: + working-directory: ./libs/numalogic-core - name: Publish to PyPi + name: Build distribution steps: - - uses: actions/checkout@v4 - - - name: Install poetry - run: pipx install poetry==1.6.1 - - - name: Set up Python ${{ matrix.python-version }} + - uses: actions/checkout@v5 + - name: Set up Python uses: actions/setup-python@v5 with: - python-version: ${{ matrix.python-version }} - cache: 'poetry' - - - name: Install dependencies - run: | - poetry env use ${{ matrix.python-version }} - poetry install - - - name: Build dist - run: poetry build - + python-version: "3.11" + cache: 'pip' + - name: Install build & twine + run: >- + pip install + build twine + --user + - name: Build wheel + run: python -m build + - name: Store the distribution packages + uses: actions/upload-artifact@v3 + with: + name: python-package-distributions + path: dist/ - name: Publish - run: poetry publish -u __token__ -p ${{ secrets.PYPI_PASSWORD }} + run: twine upload -r testpypi -p ${{ secrets.TEST_PYPI_PASSWORD }} dist/* diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 7a31d94c..6cc0ce47 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic" -version = "0.11a0" +version = "0.12.0.dev1" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} From 6321f80395299e1e1a331737a78d4f4afad39455 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 15:00:01 -0700 Subject: [PATCH 57/68] tmp: fix Signed-off-by: Avik Basu --- .github/workflows/pypi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 8fed91f1..660fbe34 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -15,7 +15,7 @@ jobs: name: Build distribution steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: From 4866d1f36da9824d00fda6f24cd8cf23882dcae1 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 15:20:08 -0700 Subject: [PATCH 58/68] tmp: test pypi publish for all Signed-off-by: Avik Basu --- .github/workflows/pypi.yml | 59 +++++++++++++++++++++++- libs/numalogic-connectors/pyproject.toml | 2 +- libs/numalogic-core/pyproject.toml | 2 +- libs/numalogic-registry/pyproject.toml | 2 +- 4 files changed, 61 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 660fbe34..fa40fda1 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -12,7 +12,64 @@ jobs: defaults: run: working-directory: ./libs/numalogic-core - + name: Build distribution + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: 'pip' + - name: Install build & twine + run: >- + pip install + build twine + --user + - name: Build wheel + run: python -m build + - name: Store the distribution packages + uses: actions/upload-artifact@v3 + with: + name: python-package-distributions + path: dist/ + - name: Publish + run: twine upload -r testpypi -p ${{ secrets.TEST_PYPI_PASSWORD }} dist/* + numalogic-connectors: + if: github.repository == 'numaproj/numalogic' + runs-on: ubuntu-latest + environment: production + defaults: + run: + working-directory: ./libs/numalogic-connectors + name: Build distribution + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: 'pip' + - name: Install build & twine + run: >- + pip install + build twine + --user + - name: Build wheel + run: python -m build + - name: Store the distribution packages + uses: actions/upload-artifact@v3 + with: + name: python-package-distributions + path: dist/ + - name: Publish + run: twine upload -r testpypi -p ${{ secrets.TEST_PYPI_PASSWORD }} dist/* + numalogic-registry: + if: github.repository == 'numaproj/numalogic' + runs-on: ubuntu-latest + environment: production + defaults: + run: + working-directory: ./libs/numalogic-registry name: Build distribution steps: - uses: actions/checkout@v4 diff --git a/libs/numalogic-connectors/pyproject.toml b/libs/numalogic-connectors/pyproject.toml index adc8d36f..5d624029 100644 --- a/libs/numalogic-connectors/pyproject.toml +++ b/libs/numalogic-connectors/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic-connectors" -version = "0.11a0" +version = "0.12.0.dev1" description = "Data Connectors for Numalogic." authors = [ {name = "Numalogic Developers"} diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 6cc0ce47..3603c1df 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -25,7 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dynamic = ["dependencies", "optional-dependencies"] +dynamic = ["", "dependencies", "optional-dependencies"] [project.urls] Repository = "https://github.com/numaproj/numalogic" diff --git a/libs/numalogic-registry/pyproject.toml b/libs/numalogic-registry/pyproject.toml index 4b5941c9..ab3c41a0 100644 --- a/libs/numalogic-registry/pyproject.toml +++ b/libs/numalogic-registry/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic-registry" -version = "0.11a0" +version = "0.12.0.dev1" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} From 326dce09a9c48e0a88436bd6d0ec1367a1cf8030 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 15:31:07 -0700 Subject: [PATCH 59/68] try: dynamic version Signed-off-by: Avik Basu --- .github/workflows/pypi.yml | 6 +++--- libs/numalogic-core/pyproject.toml | 5 +++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index fa40fda1..d8c8a9bf 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -12,7 +12,7 @@ jobs: defaults: run: working-directory: ./libs/numalogic-core - name: Build distribution + name: numalogic-core steps: - uses: actions/checkout@v4 - name: Set up Python @@ -41,7 +41,7 @@ jobs: defaults: run: working-directory: ./libs/numalogic-connectors - name: Build distribution + name: numalogic-connectors steps: - uses: actions/checkout@v4 - name: Set up Python @@ -70,7 +70,7 @@ jobs: defaults: run: working-directory: ./libs/numalogic-registry - name: Build distribution + name: numalogic-registry steps: - uses: actions/checkout@v4 - name: Set up Python diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 3603c1df..5fc75d8b 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic" -version = "0.12.0.dev1" +# version = "0.12.0.dev1" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} @@ -25,7 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dynamic = ["", "dependencies", "optional-dependencies"] +dynamic = ["version", "dependencies", "optional-dependencies"] [project.urls] Repository = "https://github.com/numaproj/numalogic" @@ -33,6 +33,7 @@ Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" [tool.setuptools.dynamic] +version = {file = "../VERSION"} dependencies = {file = ["requirements/requirements.txt"]} optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }, jupyter = { file = ["requirements/requirements-nb.txt"] }} From 7027f0d10cf8e08f5671a35f9b31e627b4a493f0 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 15:47:33 -0700 Subject: [PATCH 60/68] use individual version Signed-off-by: Avik Basu --- libs/numalogic-core/pyproject.toml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 5fc75d8b..6cc0ce47 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic" -# version = "0.12.0.dev1" +version = "0.12.0.dev1" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} @@ -25,7 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3.12" ] requires-python = ">= 3.9" -dynamic = ["version", "dependencies", "optional-dependencies"] +dynamic = ["dependencies", "optional-dependencies"] [project.urls] Repository = "https://github.com/numaproj/numalogic" @@ -33,7 +33,6 @@ Documentation = "https://numalogic.numaproj.io/" Homepage = "https://numalogic.numaproj.io/" [tool.setuptools.dynamic] -version = {file = "../VERSION"} dependencies = {file = ["requirements/requirements.txt"]} optional-dependencies = {dev = { file = ["requirements/requirements-dev.txt"] }, jupyter = { file = ["requirements/requirements-nb.txt"] }} From f7bb6a7a5c656d57f3fd6f686cd1d3bed4e61720 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 16:08:05 -0700 Subject: [PATCH 61/68] tmp: try publish Signed-off-by: Avik Basu --- libs/numalogic-connectors/pyproject.toml | 2 +- libs/numalogic-core/pyproject.toml | 2 +- libs/numalogic-registry/pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/numalogic-connectors/pyproject.toml b/libs/numalogic-connectors/pyproject.toml index 5d624029..6ea8c651 100644 --- a/libs/numalogic-connectors/pyproject.toml +++ b/libs/numalogic-connectors/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic-connectors" -version = "0.12.0.dev1" +version = "0.12.0.dev2" description = "Data Connectors for Numalogic." authors = [ {name = "Numalogic Developers"} diff --git a/libs/numalogic-core/pyproject.toml b/libs/numalogic-core/pyproject.toml index 6cc0ce47..65e5fdbc 100644 --- a/libs/numalogic-core/pyproject.toml +++ b/libs/numalogic-core/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic" -version = "0.12.0.dev1" +version = "0.12.0.dev2" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} diff --git a/libs/numalogic-registry/pyproject.toml b/libs/numalogic-registry/pyproject.toml index ab3c41a0..0ae36bdd 100644 --- a/libs/numalogic-registry/pyproject.toml +++ b/libs/numalogic-registry/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "numalogic-registry" -version = "0.12.0.dev1" +version = "0.12.0.dev2" description = "Collection of operational Machine Learning models and tools." authors = [ {name = "Numalogic Developers"} From 52d8e3e0b69e5285244a009202ef836d6e7bccde Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 16:21:14 -0700 Subject: [PATCH 62/68] tmp: try testpypi pkgs Signed-off-by: Avik Basu --- apps/numa-udfs/.devcontainer/Dockerfile | 6 +- apps/numa-udfs/Dockerfile | 6 +- apps/numa-udfs/poetry.lock | 415 +++++++++++------------- apps/numa-udfs/pyproject.toml | 16 +- 4 files changed, 210 insertions(+), 233 deletions(-) diff --git a/apps/numa-udfs/.devcontainer/Dockerfile b/apps/numa-udfs/.devcontainer/Dockerfile index 26073812..a62c8369 100644 --- a/apps/numa-udfs/.devcontainer/Dockerfile +++ b/apps/numa-udfs/.devcontainer/Dockerfile @@ -22,9 +22,9 @@ RUN apt-get update \ WORKDIR /app -COPY numalogic-0.11a0-py3-none-any.whl /app -COPY numalogic_registry-0.11a0-py3-none-any.whl /app -COPY numalogic_connectors-0.11a0-py3-none-any.whl /app +#COPY numalogic-0.11a0-py3-none-any.whl /app +#COPY numalogic_registry-0.11a0-py3-none-any.whl /app +#COPY numalogic_connectors-0.11a0-py3-none-any.whl /app COPY poetry.lock pyproject.toml ./ diff --git a/apps/numa-udfs/Dockerfile b/apps/numa-udfs/Dockerfile index d84ad02d..a4b2791d 100644 --- a/apps/numa-udfs/Dockerfile +++ b/apps/numa-udfs/Dockerfile @@ -19,9 +19,9 @@ RUN pip install --no-cache-dir poetry==$POETRY_VERSION WORKDIR /app -COPY numalogic-0.11a0-py3-none-any.whl /app -COPY numalogic_registry-0.11a0-py3-none-any.whl /app -COPY numalogic_connectors-0.11a0-py3-none-any.whl /app +#COPY numalogic-0.11a0-py3-none-any.whl /app +#COPY numalogic_registry-0.11a0-py3-none-any.whl /app +#COPY numalogic_connectors-0.11a0-py3-none-any.whl /app COPY poetry.lock pyproject.toml ./ diff --git a/apps/numa-udfs/poetry.lock b/apps/numa-udfs/poetry.lock index b49d0382..04ac2418 100644 --- a/apps/numa-udfs/poetry.lock +++ b/apps/numa-udfs/poetry.lock @@ -209,17 +209,17 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "boto3" -version = "1.34.118" +version = "1.34.126" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.34.118-py3-none-any.whl", hash = "sha256:e9edaf979fbe59737e158f2f0f3f0861ff1d61233f18f6be8ebb483905f24587"}, - {file = "boto3-1.34.118.tar.gz", hash = "sha256:4eb8019421cb664a6fcbbee6152aa95a28ce8bbc1c4ee263871c09cdd58bf8ee"}, + {file = "boto3-1.34.126-py3-none-any.whl", hash = "sha256:7f676daef674fe74f34ce4063228eccc6e60c811f574720e31f230296c4bf29a"}, + {file = "boto3-1.34.126.tar.gz", hash = "sha256:7e8418b47dd43954a9088d504541bed8a42b6d06e712d02befba134c1c4d7c6d"}, ] [package.dependencies] -botocore = ">=1.34.118,<1.35.0" +botocore = ">=1.34.126,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -228,13 +228,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.118" +version = "1.34.126" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = 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"numalogic_connectors-0.11a0-py3-none-any.whl"} +numalogic-registry = { version = "0.12.0.dev2", source = "testpypi" } +# numalogic-registry = {path = "numalogic_registry-0.11a0-py3-none-any.whl"} pynumaflow = "~0.6" structlog = "^24.1.0" prometheus_client = "^0.18.0" @@ -32,6 +32,10 @@ moto = "~4.1" deepdiff = "^6.5.0" pytest-mock = "^3.12.0" +[[tool.poetry.source]] +name = "testpypi" +url = "https://test.pypi.org/simple/" +priority = "supplemental" [tool.black] line-length = 100 From 0d59672cb78224b41e28d69e30e04728d939827d Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 16:25:35 -0700 Subject: [PATCH 63/68] fix: dockerfiles Signed-off-by: Avik Basu --- apps/numa-udfs/.devcontainer/Dockerfile | 6 +----- apps/numa-udfs/Dockerfile | 3 +-- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/apps/numa-udfs/.devcontainer/Dockerfile b/apps/numa-udfs/.devcontainer/Dockerfile index a62c8369..8e4ddf67 100644 --- a/apps/numa-udfs/.devcontainer/Dockerfile +++ b/apps/numa-udfs/.devcontainer/Dockerfile @@ -27,11 +27,7 @@ WORKDIR /app #COPY numalogic_connectors-0.11a0-py3-none-any.whl /app COPY poetry.lock pyproject.toml ./ - -RUN poetry install --with dev \ - && rm -rf $POETRY_CACHE_DIR \ - && pip cache purge \ - && apt-get purge -y --auto-remove build-essential +RUN poetry install --with dev #################################################################################################### # runtime: used for running the udf vertices diff --git a/apps/numa-udfs/Dockerfile b/apps/numa-udfs/Dockerfile index a4b2791d..efcdc8d7 100644 --- a/apps/numa-udfs/Dockerfile +++ b/apps/numa-udfs/Dockerfile @@ -27,8 +27,7 @@ COPY poetry.lock pyproject.toml ./ RUN poetry install --without dev --no-root \ && rm -rf $POETRY_CACHE_DIR \ - && pip cache purge \ - && apt-get purge -y --auto-remove build-essential + && pip cache purge #################################################################################################### # runtime: used for running the udf vertices From 13db065b676baa8635bbd025554160793c2190ec Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 16:34:58 -0700 Subject: [PATCH 64/68] fix: orjson version Signed-off-by: Avik Basu --- apps/numa-udfs/poetry.lock | 64 ++++++++++++++++++++++++++++------- apps/numa-udfs/pyproject.toml | 3 +- 2 files changed, 54 insertions(+), 13 deletions(-) diff --git a/apps/numa-udfs/poetry.lock b/apps/numa-udfs/poetry.lock index 04ac2418..807b55a7 100644 --- a/apps/numa-udfs/poetry.lock +++ b/apps/numa-udfs/poetry.lock @@ -2457,21 +2457,61 @@ dev = ["black", "mypy", "pytest"] [[package]] name = "orjson" -version = "3.10.5" +version = "3.9.15" description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy" optional = false python-versions = ">=3.8" files = [ - {file = "orjson-3.10.5-cp310-none-win32.whl", hash = "sha256:be79e2393679eda6a590638abda16d167754393f5d0850dcbca2d0c3735cebe2"}, - {file = "orjson-3.10.5-cp310-none-win_amd64.whl", hash = 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b4ac7fad..c273ae12 100644 --- a/apps/numa-udfs/pyproject.toml +++ b/apps/numa-udfs/pyproject.toml @@ -13,7 +13,7 @@ numalogic-connectors = { version = "0.12.0.dev2", source = "testpypi" } # numalogic-connectors = {path = "numalogic_connectors-0.11a0-py3-none-any.whl"} numalogic-registry = { version = "0.12.0.dev2", source = "testpypi" } # numalogic-registry = {path = "numalogic_registry-0.11a0-py3-none-any.whl"} -pynumaflow = "~0.6" +pynumaflow = "~0.7" structlog = "^24.1.0" prometheus_client = "^0.18.0" orjson = "~3.9" From 222a13fed966d7326640a4f312d17296b06e19c1 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 16:59:37 -0700 Subject: [PATCH 67/68] fix: numa-udf coverage Signed-off-by: Avik Basu --- .github/workflows/apps.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/apps.yml b/.github/workflows/apps.yml index 0d359794..c0567a2c 100644 --- a/.github/workflows/apps.yml +++ b/.github/workflows/apps.yml @@ -31,12 +31,11 @@ jobs: cache: 'poetry' - name: Install dependencies - run: | - poetry env use 3.11 - poetry install --with dev + run: poetry install --no-root --with dev - name: Run Coverage run: | poetry run pytest --cov-report=xml --cov=src --cov-config .coveragerc tests/ -sq + - name: Upload Coverage uses: codecov/codecov-action@v4 with: @@ -44,3 +43,5 @@ jobs: files: ./coverage.xml fail_ci_if_error: true verbose: true + working-directory: ./apps/numa-udfs + name: numalogic-udfs From b624da99ad3bd0f12fd8433607ed83afb6727618 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 13 Jun 2024 17:07:53 -0700 Subject: [PATCH 68/68] chore: add devcontainer ci Signed-off-by: Avik Basu --- .github/workflows/apps.yml | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/.github/workflows/apps.yml b/.github/workflows/apps.yml index c0567a2c..35f5605e 100644 --- a/.github/workflows/apps.yml +++ b/.github/workflows/apps.yml @@ -1,4 +1,4 @@ -name: Numalogic Apps CI +name: UDFs on: push: @@ -7,20 +7,14 @@ on: branches: [ "main" ] jobs: - numalogic-udfs: - name: numalogic-udfs + coverage: + name: coverage runs-on: ubuntu-latest defaults: run: working-directory: ./apps/numa-udfs steps: - uses: actions/checkout@v4 -# - name: Build and run dev container task -# uses: devcontainers/ci@v0.3 -# with: -# subFolder: ./apps/numa-udfs -# runCmd: pytest --cov-report=xml --cov=src tests/ -sq - - name: Install poetry run: pipx install poetry==1.8 @@ -45,3 +39,17 @@ jobs: verbose: true working-directory: ./apps/numa-udfs name: numalogic-udfs + + devcontainer: + name: coverage + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./apps/numa-udfs + steps: + - uses: actions/checkout@v4 + - name: Build and run dev container task + uses: devcontainers/ci@v0.3 + with: + subFolder: ./apps/numa-udfs + runCmd: pytest tests/