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Commit feaecbe

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Merge pull request #63 from rflamary/prV0.5
[MRG] Release 0.5
2 parents 8f6c455 + 27f0a9f commit feaecbe

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‎.coveragerc‎

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[run]
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omit=
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ot/externals/*
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ot/externals/funcsigs.py
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ot/gpu/*

‎.travis.yml‎

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# command to install dependencies
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install:
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- pip install -r requirements.txt
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- pip install flake8 pytest
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- pip install flake8 pytest pytest-cov
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- pip install .
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# command to run tests + check syntax style
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script:
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- python setup.py develop
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- flake8 examples/ ot/ test/
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- python -m pytest -v test/
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- python -m pytest -v test/ --cov=ot
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# - py.test ot test

‎CONTRIBUTING.md‎

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a copy of the code under your GitHub user account. For more details on
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how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
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2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:
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2. Clone your fork of the POT repo from your GitHub account to your local disk:
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```bash
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$ git clone git@github.com:YourLogin/POT.git
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example script in the ``examples/`` folder. Have a look at other
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examples for reference. Examples should demonstrate why the new
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functionality is useful in practice and, if possible, compare it
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to other methods available in scikit-learn.
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to other methods available in POT.
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- Documentation and high-coverage tests are necessary for enhancements to be
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accepted. Bug-fixes or new features should be provided with
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See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
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- Please include your operating system type and version number, as well
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as your Python, scikit-learn, numpy, and scipy versions. This information
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as your Python, POT, numpy, and scipy versions. This information
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can be found by running the following code snippet:
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```python
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New contributor tips
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--------------------
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A great way to start contributing to scikit-learn is to pick an item
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from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy)
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A great way to start contributing to POT is to pick an item
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from the list of [Easy issues](https://github.com/rflamary/POT/issues?labels=Easy)
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in the issue tracker. Resolving these issues allow you to start
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contributing to the project without much prior knowledge. Your
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assistance in this area will be greatly appreciated by the more
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illustrating it.
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This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.
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This Contribution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.

‎README.md‎

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[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot)
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[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT)
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[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest)
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[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot)
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[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot)
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[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE)
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Some sub-modules require additional dependences which are discussed below
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* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with:
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* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
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```
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pip install pymanopt autograd
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```

‎RELEASES.md‎

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# POT Releases
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## 0.5.0 Year 2
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*Sep 2018*
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POT is 2 years old! This release brings numerous new features to the
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toolbox as listed below but also several bug correction.
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Among the new features, we can highlight a [non-regularized Gromov-Wasserstein
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solver](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb),
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a new [greedy variant of sinkhorn](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn),
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[non-regularized](https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter),
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[convolutional (2D)](https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb)
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and [free support](https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb)
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Wasserstein barycenters and [smooth](https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb)
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and [stochastic](https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization)
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implementation of entropic OT.
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POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of
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the unmaintained cudamat. Note that while we tried to keed changes to the
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minimum, the OTDA classes were deprecated. If you are happy with the cudamat
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implementation, we recommend you stay with stable release 0.4 for now.
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The code quality has also improved with 92% code coverage in tests that is now
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printed to the log in the Travis builds. The documentation has also been
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greatly improved with new modules and examples/notebooks.
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This new release is so full of new stuff and corrections thanks to the old
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and new POT contributors (you can see the list in the [readme](https://github.com/rflamary/POT/blob/master/README.md)).
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#### Features
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* Add non regularized Gromov-Wasserstein solver (PR #41)
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* Linear OT mapping between empirical distributions and 90\% test coverage (PR #42)
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* Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR #44)
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* Add Markdown format for Pipy (PR #45)
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* Test for Python 3.5 and 3.6 on Travis (PR #46)
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* Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (PR #47)
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* Rename dataset functions to be more sklearn compliant (PR #49)
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* Smooth and sparse Optimal transport implementation with entropic and quadratic regularization (PR #50)
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* Stochastic OT in the dual and semi-dual (PR #52 and PR #62)
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* Free support barycenters (PR #56)
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* Speed-up Sinkhorn function (PR #57 and PR #58)
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* Add convolutional Wassersein barycenters for 2D images (PR #64)
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* Add Greedy Sinkhorn variant (Greenkhorn) (PR #66)
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* Big ot.gpu update with cupy implementation (instead of un-maintained cudamat) (PR #67)
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#### Deprecation
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Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5
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(PR #48 and PR #67). The deprecation message has been for a year here since
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0.4 and it is time to pull the plug.
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#### Closed issues
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* Issue #35 : remove import plot from ot/__init__.py (See PR #41)
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* Issue #43 : Unusable parameter log for EMDTransport (See PR #44)
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* Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip
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## 0.4 Community edition
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*15 Sep 2017*
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‎docs/cache_nbrun‎

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{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
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{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}

‎docs/source/all.rst‎

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.. automodule:: ot.bregman
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:members:
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ot.smooth
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.. automodule:: ot.smooth
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