[WIP] Fix gromov examples gallery (#444)

* maj gw/ srgw/ generic cg solver

* correct pep8 on current state

* fix bug previous tests

* fix pep8

* fix bug srGW constC in loss and gradient

* fix doc html

* fix doc html

* start updating test_optim.py

* update tests gromov and optim - plus fix gromov dependencies

* add symmetry feature to entropic gw

* add symmetry feature to entropic gw

* add exemple for sr(F)GW matchings

* small stuff

* remove (reg,M) from line-search/ complete srgw tests with backend

* remove backend repetitions / rename fG to costG/ fix innerlog to True

* fix pep8

* take comments into account / new nx parameters still to test

* factor (f)gw2 + test new backend parameters in ot.gromov + harmonize stopping criterions

* split gromov.py in ot/gromov/ + update test_gromov with helper_backend functions

* manual documentaion gromov

* remove circular autosummary

* trying stuff

* debug documentation

* alphabetic ordering of module

* merge into branch

* add note in entropic gw solvers

* fix exemples/gromov doc

* add fixed issue to releases.md

---------

Co-authored-by: Rémi Flamary <[email protected]>

Author: cedricvincentcuaz

RELEASES.md

@@ -46,7 +46,7 @@ PR #413)
 - Fix an issue where the parameter `stopThr` in `empirical_sinkhorn_divergence` was rendered useless by subcalls
   that explicitly specified `stopThr=1e-9` (Issue #421, PR #422).
 - Fixed a bug breaking an example where we would try to make an array of arrays of different shapes (Issue #424, PR #425)
-
+- Fixed an issue with the documentation gallery section (PR #444)
 
 ## 0.8.2
 

examples/gromov/plot_gromov_wasserstein_dictionary_learning.py

@@ -45,10 +45,11 @@
 import ot
 import networkx
 from networkx.generators.community import stochastic_block_model as sbm
-# %%
-# =============================================================================
+
+#############################################################################
+#
 # Generate a dataset composed of graphs following Stochastic Block models of 1, 2 and 3 clusters.
-# =============================================================================
+# ---------------------------------------------
 
 np.random.seed(42)
 
@@ -109,10 +110,10 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
 pl.tight_layout()
 pl.show()
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Estimate the gromov-wasserstein dictionary from the dataset
-# =============================================================================
+# ---------------------------------------------
 
 
 np.random.seed(0)
@@ -140,10 +141,10 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
 pl.tight_layout()
 pl.show()
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Visualization of the estimated dictionary atoms
-# =============================================================================
+# ---------------------------------------------
 
 
 # Continuous connections between nodes of the atoms are colored in shades of grey (1: dark / 2: white)
@@ -164,10 +165,11 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
     pl.axis("off")
 pl.tight_layout()
 pl.show()
-#%%
-# =============================================================================
+
+#############################################################################
+#
 # Visualization of the embedding space
-# =============================================================================
+# ---------------------------------------------
 
 unmixings = []
 reconstruction_errors = []
@@ -211,11 +213,11 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
 pl.legend(fontsize=11)
 pl.tight_layout()
 pl.show()
-# %%
-# =============================================================================
-# Endow the dataset with node features
-# =============================================================================
 
+#############################################################################
+#
+# Endow the dataset with node features
+# ---------------------------------------------
 # We follow this feature assignment on all nodes of a graph depending on its label/number of clusters
 # 1 cluster --> 0 as nodes feature
 # 2 clusters --> 1 as nodes feature
@@ -251,10 +253,11 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
     pl.axis("off")
 pl.tight_layout()
 pl.show()
-# %%
-# =============================================================================
+
+#############################################################################
+#
 # Estimate a Fused Gromov-Wasserstein dictionary from the dataset of attributed graphs
-# =============================================================================
+# ---------------------------------------------
 np.random.seed(0)
 ps = [ot.unif(C.shape[0]) for C in dataset]
 D = 3  # 6 atoms instead of 3
@@ -280,10 +283,10 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
 pl.tight_layout()
 pl.show()
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Visualization of the estimated dictionary atoms
-# =============================================================================
+# ---------------------------------------------
 
 pl.figure(7, (12, 8))
 pl.clf()
@@ -307,10 +310,10 @@ def plot_graph(x, C, binary=True, color='C0', s=None):
 pl.tight_layout()
 pl.show()
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Visualization of the embedding space
-# =============================================================================
+# ---------------------------------------------
 
 unmixings = []
 reconstruction_errors = []

examples/gromov/plot_semirelaxed_fgw.py

@@ -31,10 +31,10 @@
 import networkx
 from networkx.generators.community import stochastic_block_model as sbm
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Generate two graphs following Stochastic Block models of 2 and 3 clusters.
-# =============================================================================
+# ---------------------------------------------
 
 
 N2 = 20  # 2 communities
@@ -81,10 +81,11 @@
         weightedG3.add_edge(i, j, weight=weight_intra_G3)
     else:
         weightedG3.add_edge(i, j, weight=weight_inter_G3)
-# %%
-# =============================================================================
+
+#############################################################################
+#
 # Compute their semi-relaxed Gromov-Wasserstein divergences
-# =============================================================================
+# ---------------------------------------------
 
 # 0) GW(C2, h2, C3, h3) for reference
 OT, log = gromov_wasserstein(C2, C3, h2, h3, symmetric=True, log=True)
@@ -106,11 +107,11 @@
 print('srGW(C3, h3, C2) = ', srgw_32)
 
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Visualization of the semi-relaxed Gromov-Wasserstein matchings
-# =============================================================================
-
+# ---------------------------------------------
+#
 # We color nodes of the graph on the right - then project its node colors
 # based on the optimal transport plan from the srGW matching
 
@@ -222,10 +223,10 @@ def draw_transp_colored_srGW(G1, C1, G2, C2, part_G1,
 
 pl.show()
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Add node features
-# =============================================================================
+# ---------------------------------------------
 
 # We add node features with given mean - by clusters
 # and inversely proportional to clusters' intra-connectivity
@@ -238,10 +239,10 @@ def draw_transp_colored_srGW(G1, C1, G2, C2, part_G1,
 for i, c in enumerate(part_G3):
     F3[i, 0] = np.random.normal(loc=2. - c, scale=0.01)
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Compute their semi-relaxed Fused Gromov-Wasserstein divergences
-# =============================================================================
+# ---------------------------------------------
 
 alpha = 0.5
 # Compute pairwise euclidean distance between node features
@@ -268,11 +269,11 @@ def draw_transp_colored_srGW(G1, C1, G2, C2, part_G1,
 print('srGW(C2, F2, h2, C3, F3) = ', srfgw_23)
 print('srGW(C3, F3, h3, C2, F2) = ', srfgw_32)
 
-# %%
-# =============================================================================
+#############################################################################
+#
 # Visualization of the semi-relaxed Fused Gromov-Wasserstein matchings
-# =============================================================================
-
+# ---------------------------------------------
+#
 # We color nodes of the graph on the right - then project its node colors
 # based on the optimal transport plan from the srFGW matching
 # NB: colors refer to clusters - not to node features