Merge pull request #63 from rflamary/prV0.5

[MRG] Release 0.5

Author: rflamary

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+
+omit=
+	ot/externals/*
+	ot/externals/funcsigs.py	
+	ot/gpu/*

.travis.yml

@@ -26,11 +26,11 @@ before_script: # configure a headless display to test plot generation
 # command to install dependencies
 install:
   - pip install -r requirements.txt
-  - pip install flake8 pytest
+  - pip install flake8 pytest pytest-cov
   - pip install .
 # command to run tests + check syntax style
 script:
   - python setup.py develop
   - flake8 examples/ ot/ test/
-  - python -m pytest -v test/
+  - python -m pytest -v test/ --cov=ot
   # - py.test ot test

CONTRIBUTING.md

@@ -17,7 +17,7 @@ GitHub, clone, and develop on a branch. Steps:
    a copy of the code under your GitHub user account. For more details on
    how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
 
-2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:
+2. Clone your fork of the POT repo from your GitHub account to your local disk:
 
    ```bash
    $ git clone [email protected]:YourLogin/POT.git
@@ -84,7 +84,7 @@ following rules before you submit a pull request:
    example script in the ``examples/`` folder. Have a look at other
    examples for reference. Examples should demonstrate why the new
    functionality is useful in practice and, if possible, compare it
-   to other methods available in scikit-learn.
+   to other methods available in POT.
 
 -  Documentation and high-coverage tests are necessary for enhancements to be
    accepted. Bug-fixes or new features should be provided with 
@@ -145,7 +145,7 @@ following rules before submitting:
    See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
 
 -  Please include your operating system type and version number, as well
-   as your Python, scikit-learn, numpy, and scipy versions. This information
+   as your Python, POT, numpy, and scipy versions. This information
    can be found by running the following code snippet:
 
   ```python
@@ -165,8 +165,8 @@ following rules before submitting:
 New contributor tips
 --------------------
 
-A great way to start contributing to scikit-learn is to pick an item
-from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy)
+A great way to start contributing to POT is to pick an item
+from the list of [Easy issues](https://github.com/rflamary/POT/issues?labels=Easy)
 in the issue tracker. Resolving these issues allow you to start
 contributing to the project without much prior knowledge. Your
 assistance in this area will be greatly appreciated by the more
@@ -201,4 +201,4 @@ method does to the data and a figure (coming from an example)
 illustrating it.
 
 
-This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.
+This Contribution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.

README.md

@@ -4,6 +4,7 @@
 [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot)
 [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT)
 [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot)
 [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot)
 [![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE)
 
@@ -79,7 +80,7 @@ Note that for easier access the module is name ot instead of pot.
 
 Some sub-modules require additional dependences which are discussed below
 
-* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with:
+* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
 ```
 pip install pymanopt autograd
 ```

RELEASES.md

@@ -1,5 +1,64 @@
 # POT Releases
 
+
+## 0.5.0 Year 2
+*Sep 2018*
+
+POT is 2 years old! This release brings numerous new features to the 
+toolbox as listed below but also several bug correction.
+
+Among the new features, we can highlight a [non-regularized Gromov-Wasserstein 
+solver](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb), 
+a new [greedy variant of sinkhorn](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn),  
+[non-regularized](https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter), 
+[convolutional (2D)](https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb)
+and [free support](https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb)
+ Wasserstein barycenters and [smooth](https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb) 
+ and [stochastic](https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization) 
+implementation of entropic OT.
+
+POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of 
+the unmaintained cudamat. Note that while we tried to keed changes to the 
+minimum, the OTDA classes were deprecated. If you are happy with the cudamat 
+implementation, we recommend you stay with stable release 0.4 for now.
+
+The code quality has also improved with 92% code coverage in tests that is now 
+printed to the log in the Travis builds. The documentation has also been 
+greatly improved with new modules and examples/notebooks.
+
+This new release is so full of new stuff and corrections thanks to the old
+and new POT contributors (you can see the list in the [readme](https://github.com/rflamary/POT/blob/master/README.md)).
+
+#### Features
+
+* Add non regularized Gromov-Wasserstein solver  (PR #41)
+* Linear OT mapping between empirical distributions and 90\% test coverage (PR #42)
+* Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR #44)
+* Add Markdown format for Pipy (PR #45)
+* Test for Python 3.5 and 3.6 on Travis (PR #46)
+* Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (PR #47)
+* Rename dataset functions to be more sklearn compliant (PR #49)
+* Smooth and sparse Optimal transport implementation with entropic and quadratic regularization (PR #50)
+* Stochastic OT in the dual and semi-dual (PR #52 and PR #62)
+* Free support barycenters (PR #56)
+* Speed-up Sinkhorn function (PR #57 and PR #58)
+* Add convolutional Wassersein barycenters for 2D images (PR #64) 
+* Add Greedy Sinkhorn variant (Greenkhorn) (PR #66)
+* Big ot.gpu update with cupy implementation (instead of un-maintained cudamat) (PR #67)
+
+#### Deprecation
+
+Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5 
+(PR #48 and PR #67). The deprecation message has been for a year here since 
+0.4 and it is time to pull the plug.
+
+#### Closed issues
+
+* Issue #35 : remove import plot from ot/__init__.py (See PR #41)
+* Issue #43 : Unusable parameter log for EMDTransport (See PR #44)
+* Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip 
+
+
 ## 0.4 Community edition
 *15 Sep 2017*
 

docs/cache_nbrun

@@ -1 +1 @@
-{"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"}
+{"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

@@ -19,11 +19,6 @@ ot.bregman
 
 .. automodule:: ot.bregman
    :members:
-   
-ot.smooth
------
-.. automodule:: ot.smooth
-   :members:
 
 ot.smooth
 -----