[WIP] Add debiased barycenter (Sinkhorn + convolutional sinkhorn) (#291)

* add debiased sinkhorn barycenter + make loops pythonic

* add debiased arg in tests

* add 1d and 2d examples of debiased barycenters

* fix doctest

* fix flake8

* pep8 + make func private + add convergence warnings

* remove rel paths + add rng + pylab to pyplot

* fix stopping criterion debiased

* pass alex

* change params with new API

* add logdomain barycenters + separate debiased API

* test new API

* fix jax read-only ?

* raise error for jax

* test catch jax error

* fix pytest catch error

* fix relative path

* fix flake8

* add warn arg everywhere

* fix ref number

* catch warnings in tests

* add contrib to readme + change ref number

* fix convolution example + gallery thumbnails

* increase coverage

* fix flake

Co-authored-by: Hicham Janati <hicham.janati@inria.fr>
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

1 files changed, 47 insertions, 43 deletions

diff --git a/examples/gromov/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py
index e2d88ba..7fe081f 100755
--- a/examples/gromov/plot_gromov_barycenter.py
+++ b/examples/gromov/plot_gromov_barycenter.py
@@ -13,11 +13,13 @@ computation in POT.
 #

 # License: MIT License

 

+import os

+from pathlib import Path

 

 import numpy as np

 import scipy as sp

 

-import matplotlib.pylab as pl

+from matplotlib import pyplot as plt

 from sklearn import manifold

 from sklearn.decomposition import PCA

 

@@ -89,17 +91,19 @@ def im2mat(img):
     return img.reshape((img.shape[0] * img.shape[1], img.shape[2]))

 

 

-square = pl.imread('../../data/square.png').astype(np.float64)[:, :, 2]

-cross = pl.imread('../../data/cross.png').astype(np.float64)[:, :, 2]

-triangle = pl.imread('../../data/triangle.png').astype(np.float64)[:, :, 2]

-star = pl.imread('../../data/star.png').astype(np.float64)[:, :, 2]

+this_file = os.path.realpath('__file__')

+data_path = os.path.join(Path(this_file).parent.parent.parent, 'data')

+

+square = plt.imread(os.path.join(data_path, 'square.png')).astype(np.float64)[:, :, 2]

+cross = plt.imread(os.path.join(data_path, 'cross.png')).astype(np.float64)[:, :, 2]

+triangle = plt.imread(os.path.join(data_path, 'triangle.png')).astype(np.float64)[:, :, 2]

+star = plt.imread(os.path.join(data_path, 'star.png')).astype(np.float64)[:, :, 2]

 

 shapes = [square, cross, triangle, star]

 

 S = 4

 xs = [[] for i in range(S)]

 

-

 for nb in range(4):

     for i in range(8):

         for j in range(8):

@@ -184,64 +188,64 @@ npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]
 npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]

 

 

-fig = pl.figure(figsize=(10, 10))

+fig = plt.figure(figsize=(10, 10))

 

-ax1 = pl.subplot2grid((4, 4), (0, 0))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax1 = plt.subplot2grid((4, 4), (0, 0))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')

 

-ax2 = pl.subplot2grid((4, 4), (0, 1))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax2 = plt.subplot2grid((4, 4), (0, 1))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')

 

-ax3 = pl.subplot2grid((4, 4), (0, 2))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax3 = plt.subplot2grid((4, 4), (0, 2))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')

 

-ax4 = pl.subplot2grid((4, 4), (0, 3))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax4 = plt.subplot2grid((4, 4), (0, 3))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')

 

-ax5 = pl.subplot2grid((4, 4), (1, 0))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax5 = plt.subplot2grid((4, 4), (1, 0))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')

 

-ax6 = pl.subplot2grid((4, 4), (1, 3))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax6 = plt.subplot2grid((4, 4), (1, 3))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')

 

-ax7 = pl.subplot2grid((4, 4), (2, 0))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax7 = plt.subplot2grid((4, 4), (2, 0))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')

 

-ax8 = pl.subplot2grid((4, 4), (2, 3))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax8 = plt.subplot2grid((4, 4), (2, 3))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')

 

-ax9 = pl.subplot2grid((4, 4), (3, 0))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax9 = plt.subplot2grid((4, 4), (3, 0))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')

 

-ax10 = pl.subplot2grid((4, 4), (3, 1))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax10 = plt.subplot2grid((4, 4), (3, 1))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')

 

-ax11 = pl.subplot2grid((4, 4), (3, 2))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax11 = plt.subplot2grid((4, 4), (3, 2))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')

 

-ax12 = pl.subplot2grid((4, 4), (3, 3))

-pl.xlim((-1, 1))

-pl.ylim((-1, 1))

+ax12 = plt.subplot2grid((4, 4), (3, 3))

+plt.xlim((-1, 1))

+plt.ylim((-1, 1))

 ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')