[WIP] Graphical tweaks for GWB + fixed seed method for the partial gromov test (#376)

* GWB first solver version

* tests + example for gwb (untested) + free_bar doc fix

* improved doc, fixed minor bugs, better example visu

* minor doc + visu fixes

* plot GWB pep8 fix

* fixed partial gromov test reproductibility

* added an animation for the GWB visu

* added PR num

* minor doc fixes + better gwb logo

* GWB graphical tweaks + better seed method for partial gromov test

* fixed PR number

* refixed seed issue

* seed fix fix fix

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

Author: eloitanguy

RELEASES.md

@@ -4,14 +4,13 @@
 
 #### New features
 
-- Added Generalized Wasserstein Barycenter solver + example (PR #372)
+- Added Generalized Wasserstein Barycenter solver + example (PR #372), fixed graphical details on the example (PR #376)
 
 #### Closed issues
 
 - Fixed an issue where we could not ask TorchBackend to place a random tensor on GPU
   (Issue #371, PR #373)
 
-
 ## 0.8.2
 
 This releases introduces several new notable features. The less important

examples/barycenters/plot_generalized_free_support_barycenter.py

@@ -33,8 +33,8 @@
 # Input measures
 sub_sample_factor = 8
 I1 = pl.imread('../../data/redcross.png').astype(np.float64)[::sub_sample_factor, ::sub_sample_factor, 2]
-I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::sub_sample_factor, ::sub_sample_factor, 2]
-I3 = pl.imread('../../data/heart.png').astype(np.float64)[::sub_sample_factor, ::sub_sample_factor, 2]
+I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::-sub_sample_factor, ::sub_sample_factor, 2]
+I3 = pl.imread('../../data/heart.png').astype(np.float64)[::-sub_sample_factor, ::sub_sample_factor, 2]
 
 sz = I1.shape[0]
 UU, VV = np.meshgrid(np.arange(sz), np.arange(sz))
@@ -145,8 +145,11 @@ def _init():
 
 
 def _update_plot(i):
-    ax.view_init(elev=i, azim=4 * i)
+    if i < 45:
+        ax.view_init(elev=0, azim=4 * i)
+    else:
+        ax.view_init(elev=i - 45, azim=4 * i)
     return fig,
 
 
-ani = animation.FuncAnimation(fig, _update_plot, init_func=_init, frames=90, interval=50, blit=True, repeat_delay=2000)
+ani = animation.FuncAnimation(fig, _update_plot, init_func=_init, frames=136, interval=50, blit=True, repeat_delay=2000)

test/test_partial.py

@@ -137,7 +137,7 @@ def test_partial_wasserstein():
 
 
 def test_partial_gromov_wasserstein():
-    np.random.seed(42)
+    rng = np.random.RandomState(seed=42)
     n_samples = 20  # nb samples
     n_noise = 10  # nb of samples (noise)
 
@@ -150,11 +150,11 @@ def test_partial_gromov_wasserstein():
     mu_t = np.array([0, 0, 0])
     cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 
-    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
-    xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, rng)
+    xs = np.concatenate((xs, ((rng.rand(n_noise, 2) + 1) * 4)), axis=0)
     P = sp.linalg.sqrtm(cov_t)
-    xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-    xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
+    xt = rng.randn(n_samples, 3).dot(P) + mu_t
+    xt = np.concatenate((xt, ((rng.rand(n_noise, 3) + 1) * 10)), axis=0)
     xt2 = xs[::-1].copy()
 
     C1 = ot.dist(xs, xs)