From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [MRG] Actually run sphinx-gallery (#146) * generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd --- docs/source/auto_examples/plot_fgw.py | 173 ---------------------------------- 1 file changed, 173 deletions(-) delete mode 100644 docs/source/auto_examples/plot_fgw.py (limited to 'docs/source/auto_examples/plot_fgw.py') diff --git a/docs/source/auto_examples/plot_fgw.py b/docs/source/auto_examples/plot_fgw.py deleted file mode 100644 index 43efc94..0000000 --- a/docs/source/auto_examples/plot_fgw.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -Plot Fused-gromov-Wasserstein -============================== - -This example illustrates the computation of FGW for 1D measures[18]. - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - -""" - -# Author: Titouan Vayer -# -# License: MIT License - -import matplotlib.pyplot as pl -import numpy as np -import ot -from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein - -############################################################################## -# Generate data -# --------- - -#%% parameters -# We create two 1D random measures -n = 20 # number of points in the first distribution -n2 = 30 # number of points in the second distribution -sig = 1 # std of first distribution -sig2 = 0.1 # std of second distribution - -np.random.seed(0) - -phi = np.arange(n)[:, None] -xs = phi + sig * np.random.randn(n, 1) -ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) - -phi2 = np.arange(n2)[:, None] -xt = phi2 + sig * np.random.randn(n2, 1) -yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) -yt = yt[::-1, :] - -p = ot.unif(n) -q = ot.unif(n2) - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.close(10) -pl.figure(10, (7, 7)) - -pl.subplot(2, 1, 1) - -pl.scatter(ys, xs, c=phi, s=70) -pl.ylabel('Feature value a', fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) -pl.xticks(()) -pl.yticks(()) -pl.subplot(2, 1, 2) -pl.scatter(yt, xt, c=phi2, s=70) -pl.xlabel('coordinates x/y', fontsize=25) -pl.ylabel('Feature value b', fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) -pl.yticks(()) -pl.tight_layout() -pl.show() - -############################################################################## -# Create structure matrices and across-feature distance matrix -# --------- - -#%% Structure matrices and across-features distance matrix -C1 = ot.dist(xs) -C2 = ot.dist(xt) -M = ot.dist(ys, yt) -w1 = ot.unif(C1.shape[0]) -w2 = ot.unif(C2.shape[0]) -Got = ot.emd([], [], M) - -############################################################################## -# Plot matrices -# --------- - -#%% -cmap = 'Reds' -pl.close(10) -pl.figure(10, (5, 5)) -fs = 15 -l_x = [0, 5, 10, 15] -l_y = [0, 5, 10, 15, 20, 25] -gs = pl.GridSpec(5, 5) - -ax1 = pl.subplot(gs[3:, :2]) - -pl.imshow(C1, cmap=cmap, interpolation='nearest') -pl.title("$C_1$", fontsize=fs) -pl.xlabel("$k$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(l_x) -pl.yticks(l_x) - -ax2 = pl.subplot(gs[:3, 2:]) - -pl.imshow(C2, cmap=cmap, interpolation='nearest') -pl.title("$C_2$", fontsize=fs) -pl.ylabel("$l$", fontsize=fs) -#pl.ylabel("$l$",fontsize=fs) -pl.xticks(()) -pl.yticks(l_y) -ax2.set_aspect('auto') - -ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) -pl.imshow(M, cmap=cmap, interpolation='nearest') -pl.yticks(l_x) -pl.xticks(l_y) -pl.ylabel("$i$", fontsize=fs) -pl.title("$M_{AB}$", fontsize=fs) -pl.xlabel("$j$", fontsize=fs) -pl.tight_layout() -ax3.set_aspect('auto') -pl.show() - -############################################################################## -# Compute FGW/GW -# --------- - -#%% Computing FGW and GW -alpha = 1e-3 - -ot.tic() -Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) -ot.toc() - -#%reload_ext WGW -Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) - -############################################################################## -# Visualize transport matrices -# --------- - -#%% visu OT matrix -cmap = 'Blues' -fs = 15 -pl.figure(2, (13, 5)) -pl.clf() -pl.subplot(1, 3, 1) -pl.imshow(Got, cmap=cmap, interpolation='nearest') -#pl.xlabel("$y$",fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(()) - -pl.title('Wasserstein ($M$ only)') - -pl.subplot(1, 3, 2) -pl.imshow(Gg, cmap=cmap, interpolation='nearest') -pl.title('Gromov ($C_1,C_2$ only)') -pl.xticks(()) -pl.subplot(1, 3, 3) -pl.imshow(Gwg, cmap=cmap, interpolation='nearest') -pl.title('FGW ($M+C_1,C_2$)') - -pl.xlabel("$j$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) - -pl.tight_layout() -pl.show() -- cgit v1.2.3