From cc799a5ddc95e2114f6586512c87d4124ed76012 Mon Sep 17 00:00:00 2001 From: slamitza Date: Sun, 10 Dec 2023 22:33:19 +0100 Subject: [PATCH] Fixes --- .../python/experimental/mcmc/BUILD | 2 + tfp_nightly.egg-info/PKG-INFO | 244 ---- tfp_nightly.egg-info/SOURCES.txt | 1068 ----------------- tfp_nightly.egg-info/dependency_links.txt | 1 - tfp_nightly.egg-info/not-zip-safe | 1 - tfp_nightly.egg-info/requires.txt | 14 - tfp_nightly.egg-info/top_level.txt | 1 - 7 files changed, 2 insertions(+), 1329 deletions(-) delete mode 100644 tfp_nightly.egg-info/PKG-INFO delete mode 100644 tfp_nightly.egg-info/SOURCES.txt delete mode 100644 tfp_nightly.egg-info/dependency_links.txt delete mode 100644 tfp_nightly.egg-info/not-zip-safe delete mode 100644 tfp_nightly.egg-info/requires.txt delete mode 100644 tfp_nightly.egg-info/top_level.txt diff --git a/tensorflow_probability/python/experimental/mcmc/BUILD b/tensorflow_probability/python/experimental/mcmc/BUILD index aa2843bfb9..a96087e35c 100644 --- a/tensorflow_probability/python/experimental/mcmc/BUILD +++ b/tensorflow_probability/python/experimental/mcmc/BUILD @@ -18,6 +18,8 @@ # //tensorflow_probability/python/internal/auto_batching # internally. +# Placeholder: py_library +# Placeholder: py_test load( "//tensorflow_probability/python:build_defs.bzl", "multi_substrate_py_library", diff --git a/tfp_nightly.egg-info/PKG-INFO b/tfp_nightly.egg-info/PKG-INFO deleted file mode 100644 index 96ea3cdd82..0000000000 --- a/tfp_nightly.egg-info/PKG-INFO +++ /dev/null @@ -1,244 +0,0 @@ -Metadata-Version: 2.1 -Name: tfp-nightly -Version: 0.24.0.dev0 -Summary: Probabilistic modeling and statistical inference in TensorFlow -Home-page: http://github.com/tensorflow/probability -Author: Google LLC -Author-email: no-reply@google.com -License: Apache 2.0 -Keywords: tensorflow probability statistics bayesian machine learning -Platform: UNKNOWN -Classifier: Development Status :: 4 - Beta -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: Education -Classifier: Intended Audience :: Science/Research -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.9 -Classifier: Programming Language :: Python :: 3.10 -Classifier: Programming Language :: Python :: 3.11 -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Scientific/Engineering :: Mathematics -Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence -Classifier: Topic :: Software Development -Classifier: Topic :: Software Development :: Libraries -Classifier: Topic :: Software Development :: Libraries :: Python Modules -Requires-Python: >=3.9 -Description-Content-Type: text/markdown -Provides-Extra: jax -Provides-Extra: tfds -License-File: LICENSE - -# TensorFlow Probability - -TensorFlow Probability is a library for probabilistic reasoning and statistical -analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow -Probability provides integration of probabilistic methods with deep networks, -gradient-based inference via automatic differentiation, and scalability to -large datasets and models via hardware acceleration (e.g., GPUs) and distributed -computation. - -__TFP also works as "Tensor-friendly Probability" in pure JAX!__: -`from tensorflow_probability.substrates import jax as tfp` -- -Learn more [here](https://www.tensorflow.org/probability/examples/TensorFlow_Probability_on_JAX). - -Our probabilistic machine learning tools are structured as follows. - -__Layer 0: TensorFlow.__ Numerical operations. In particular, the LinearOperator -class enables matrix-free implementations that can exploit special structure -(diagonal, low-rank, etc.) for efficient computation. It is built and maintained -by the TensorFlow Probability team and is now part of -[`tf.linalg`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/ops/linalg) -in core TF. - -__Layer 1: Statistical Building Blocks__ - -* Distributions ([`tfp.distributions`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions)): - A large collection of probability - distributions and related statistics with batch and - [broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) - semantics. See the - [Distributions Tutorial](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). -* Bijectors ([`tfp.bijectors`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/bijectors)): - Reversible and composable transformations of random variables. Bijectors - provide a rich class of transformed distributions, from classical examples - like the - [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) - to sophisticated deep learning models such as - [masked autoregressive flows](https://arxiv.org/abs/1705.07057). - -__Layer 2: Model Building__ - -* Joint Distributions (e.g., [`tfp.distributions.JointDistributionSequential`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/distributions/joint_distribution_sequential.py)): - Joint distributions over one or more possibly-interdependent distributions. - For an introduction to modeling with TFP's `JointDistribution`s, check out - [this colab](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Modeling_with_JointDistribution.ipynb) -* Probabilistic Layers ([`tfp.layers`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/layers)): - Neural network layers with uncertainty over the functions they represent, - extending TensorFlow Layers. - -__Layer 3: Probabilistic Inference__ - -* Markov chain Monte Carlo ([`tfp.mcmc`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/mcmc)): - Algorithms for approximating integrals via sampling. Includes - [Hamiltonian Monte Carlo](https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo), - random-walk Metropolis-Hastings, and the ability to build custom transition - kernels. -* Variational Inference ([`tfp.vi`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/vi)): - Algorithms for approximating integrals via optimization. -* Optimizers ([`tfp.optimizer`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/optimizer)): - Stochastic optimization methods, extending TensorFlow Optimizers. Includes - [Stochastic Gradient Langevin Dynamics](http://www.icml-2011.org/papers/398_icmlpaper.pdf). -* Monte Carlo ([`tfp.monte_carlo`](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/monte_carlo)): - Tools for computing Monte Carlo expectations. - -TensorFlow Probability is under active development. Interfaces may change at any -time. - -## Examples - -See [`tensorflow_probability/examples/`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/) -for end-to-end examples. It includes tutorial notebooks such as: - -* [Linear Mixed Effects Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb). - A hierarchical linear model for sharing statistical strength across examples. -* [Eight Schools](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Eight_Schools.ipynb). - A hierarchical normal model for exchangeable treatment effects. -* [Hierarchical Linear Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb). - Hierarchical linear models compared among TensorFlow Probability, R, and Stan. -* [Bayesian Gaussian Mixture Models](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Bayesian_Gaussian_Mixture_Model.ipynb). - Clustering with a probabilistic generative model. -* [Probabilistic Principal Components Analysis](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_PCA.ipynb). - Dimensionality reduction with latent variables. -* [Gaussian Copulas](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Copula.ipynb). - Probability distributions for capturing dependence across random variables. -* [TensorFlow Distributions: A Gentle Introduction](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Distributions_Tutorial.ipynb). - Introduction to TensorFlow Distributions. -* [Understanding TensorFlow Distributions Shapes](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb). - How to distinguish between samples, batches, and events for arbitrarily shaped - probabilistic computations. -* [TensorFlow Probability Case Study: Covariance Estimation](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.ipynb). - A user's case study in applying TensorFlow Probability to estimate covariances. - -It also includes example scripts such as: - - Representation learning with a latent code and variational inference. -* [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/vq_vae.py). - Discrete representation learning with vector quantization. -* [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/disentangled_vae.py) - Disentangled representation learning over sequences with variational inference. -* [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/bayesian_neural_network.py). - Neural networks with uncertainty over their weights. -* [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/logistic_regression.py). - Bayesian inference for binary classification. - -## Installation - -For additional details on installing TensorFlow, guidance installing -prerequisites, and (optionally) setting up virtual environments, see the -[TensorFlow installation guide](https://www.tensorflow.org/install). - -### Stable Builds - -To install the latest stable version, run the following: - -```shell -# Notes: - -# - The `--upgrade` flag ensures you'll get the latest version. -# - The `--user` flag ensures the packages are installed to your user directory -# rather than the system directory. -# - TensorFlow 2 packages require a pip >= 19.0 -python -m pip install --upgrade --user pip -python -m pip install --upgrade --user tensorflow tensorflow_probability -``` - -For CPU-only usage (and a smaller install), install with `tensorflow-cpu`. - -To use a pre-2.0 version of TensorFlow, run: - -```shell -python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9" -``` - -Note: Since [TensorFlow](https://www.tensorflow.org/install) is *not* included -as a dependency of the TensorFlow Probability package (in `setup.py`), you must -explicitly install the TensorFlow package (`tensorflow` or `tensorflow-cpu`). -This allows us to maintain one package instead of separate packages for CPU and -GPU-enabled TensorFlow. See the -[TFP release notes](https://github.com/tensorflow/probability/releases) for more -details about dependencies between TensorFlow and TensorFlow Probability. - - -### Nightly Builds - -There are also nightly builds of TensorFlow Probability under the pip package -`tfp-nightly`, which depends on one of `tf-nightly` or `tf-nightly-cpu`. -Nightly builds include newer features, but may be less stable than the -versioned releases. Both stable and nightly docs are available -[here](https://www.tensorflow.org/probability/api_docs/python/tfp?version=nightly). - -```shell -python -m pip install --upgrade --user tf-nightly tfp-nightly -``` - -### Installing from Source - -You can also install from source. This requires the [Bazel]( -https://bazel.build/) build system. It is highly recommended that you install -the nightly build of TensorFlow (`tf-nightly`) before trying to build -TensorFlow Probability from source. - -```shell -# sudo apt-get install bazel git python-pip # Ubuntu; others, see above links. -python -m pip install --upgrade --user tf-nightly -git clone https://github.com/tensorflow/probability.git -cd probability -bazel build --copt=-O3 --copt=-march=native :pip_pkg -PKGDIR=$(mktemp -d) -./bazel-bin/pip_pkg $PKGDIR -python -m pip install --upgrade --user $PKGDIR/*.whl -``` - -## Community - -As part of TensorFlow, we're committed to fostering an open and welcoming -environment. - -* [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow): Ask - or answer technical questions. -* [GitHub](https://github.com/tensorflow/probability/issues): Report bugs or - make feature requests. -* [TensorFlow Blog](https://blog.tensorflow.org/): Stay up to date on content - from the TensorFlow team and best articles from the community. -* [Youtube Channel](http://youtube.com/tensorflow/): Follow TensorFlow shows. -* [tfprobability@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfprobability): - Open mailing list for discussion and questions. - -See the [TensorFlow Community](https://www.tensorflow.org/community/) page for -more details. Check out our latest publicity here: - -+ [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow]( - https://www.youtube.com/watch?v=BjUkL8DFH5Q) -+ [Introducing TensorFlow Probability]( - https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) - -## Contributing - -We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md) -for a guide on how to contribute. This project adheres to TensorFlow's -[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to -uphold this code. - -## References - -If you use TensorFlow Probability in a paper, please cite: - -+ _TensorFlow Distributions._ Joshua V. Dillon, Ian Langmore, Dustin Tran, -Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt -Hoffman, Rif A. Saurous. -[arXiv preprint arXiv:1711.10604, 2017](https://arxiv.org/abs/1711.10604). - -(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.) - - diff --git a/tfp_nightly.egg-info/SOURCES.txt b/tfp_nightly.egg-info/SOURCES.txt deleted file mode 100644 index 8645f5fa31..0000000000 --- a/tfp_nightly.egg-info/SOURCES.txt +++ /dev/null @@ -1,1068 +0,0 @@ -LICENSE -README.md -setup.py -tensorflow_probability/__init__.py -tensorflow_probability/python/__init__.py -tensorflow_probability/python/version.py -tensorflow_probability/python/bijectors/__init__.py -tensorflow_probability/python/bijectors/absolute_value.py -tensorflow_probability/python/bijectors/absolute_value_test.py -tensorflow_probability/python/bijectors/ascending.py -tensorflow_probability/python/bijectors/ascending_test.py -tensorflow_probability/python/bijectors/batch_normalization.py 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