Merge tag '0.8.0' into dfsg/latest

4 files changed, 89 insertions, 51 deletions

diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py
index 3f81765..556e08f 100644
--- a/examples/gromov/plot_barycenter_fgw.py
+++ b/examples/gromov/plot_barycenter_fgw.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 =================================
-Plot graphs' barycenter using FGW
+Plot graphs barycenter using FGW
 =================================
 
 This example illustrates the computation barycenter of labeled graphs using
diff --git a/examples/gromov/plot_fgw.py b/examples/gromov/plot_fgw.py
index 97fe619..5475fb3 100644
--- a/examples/gromov/plot_fgw.py
+++ b/examples/gromov/plot_fgw.py
@@ -26,7 +26,7 @@ from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein
 
 ##############################################################################
 # Generate data
-# ---------
+# -------------
 
 #%% parameters
 # We create two 1D random measures
@@ -76,7 +76,7 @@ pl.show()
 
 ##############################################################################
 # Create structure matrices and across-feature distance matrix
-# ---------
+# ------------------------------------------------------------
 
 #%% Structure matrices and across-features distance matrix
 C1 = ot.dist(xs)
@@ -88,7 +88,7 @@ Got = ot.emd([], [], M)
 
 ##############################################################################
 # Plot matrices
-# ---------
+# -------------
 
 #%%
 cmap = 'Reds'
@@ -131,7 +131,7 @@ pl.show()
 
 ##############################################################################
 # Compute FGW/GW
-# ---------
+# --------------
 
 #%% Computing FGW and GW
 alpha = 1e-3
@@ -145,7 +145,7 @@ Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True,
 
 ##############################################################################
 # Visualize transport matrices
-# ---------
+# ----------------------------
 
 #%% visu OT matrix
 cmap = 'Blues'
diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py
index deb2f86..5a362cf 100644
--- a/examples/gromov/plot_gromov.py
+++ b/examples/gromov/plot_gromov.py
@@ -104,3 +104,37 @@ pl.imshow(gw, cmap='jet')
 pl.title('Entropic Gromov Wasserstein')

 

 pl.show()

+

+#############################################################################

+#

+# Compute GW with a scalable stochastic method with any loss function

+# ----------------------------------------------------------------------

+

+

+def loss(x, y):

+    return np.abs(x - y)

+

+

+pgw, plog = ot.gromov.pointwise_gromov_wasserstein(C1, C2, p, q, loss, max_iter=100,

+                                                   log=True)

+

+sgw, slog = ot.gromov.sampled_gromov_wasserstein(C1, C2, p, q, loss, epsilon=0.1, max_iter=100,

+                                                 log=True)

+

+print('Pointwise Gromov-Wasserstein distance estimated: ' + str(plog['gw_dist_estimated']))

+print('Variance estimated: ' + str(plog['gw_dist_std']))

+print('Sampled Gromov-Wasserstein distance: ' + str(slog['gw_dist_estimated']))

+print('Variance estimated: ' + str(slog['gw_dist_std']))

+

+

+pl.figure(1, (10, 5))

+

+pl.subplot(1, 2, 1)

+pl.imshow(pgw.toarray(), cmap='jet')

+pl.title('Pointwise Gromov Wasserstein')

+

+pl.subplot(1, 2, 2)

+pl.imshow(sgw, cmap='jet')

+pl.title('Sampled Gromov Wasserstein')

+

+pl.show()

diff --git a/examples/gromov/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py
index f6f031a..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

 

@@ -84,22 +86,24 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
 # The four distributions are constructed from 4 simple images

 

 

-def im2mat(I):

+def im2mat(img):

     """Converts and image to matrix (one pixel per line)"""

-    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))

+    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')