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diff --git a/docs/source/auto_examples/plot_barycenter_fgw.py b/docs/source/auto_examples/plot_barycenter_fgw.py deleted file mode 100644 index 77b0370..0000000 --- a/docs/source/auto_examples/plot_barycenter_fgw.py +++ /dev/null @@ -1,184 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================= -Plot graphs' barycenter using FGW -================================= - -This example illustrates the computation barycenter of labeled graphs using FGW - -Requires networkx >=2 - -.. [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 <titouan.vayer@irisa.fr> -# -# License: MIT License - -#%% load libraries -import numpy as np -import matplotlib.pyplot as plt -import networkx as nx -import math -from scipy.sparse.csgraph import shortest_path -import matplotlib.colors as mcol -from matplotlib import cm -from ot.gromov import fgw_barycenters -#%% Graph functions - - -def find_thresh(C, inf=0.5, sup=3, step=10): - """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix - and the original matrix. - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - inf : float - The beginning of the linesearch - sup : float - The end of the linesearch - step : integer - Number of thresholds tested - """ - dist = [] - search = np.linspace(inf, sup, step) - for thresh in search: - Cprime = sp_to_adjency(C, 0, thresh) - SC = shortest_path(Cprime, method='D') - SC[SC == float('inf')] = 100 - dist.append(np.linalg.norm(SC - C)) - return search[np.argmin(dist)], dist - - -def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. - All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - threshinf : float - The minimum value of distance from which the new value is set to 1 - threshsup : float - The maximum value of distance from which the new value is set to 1 - Returns - ------- - C : ndarray, shape (n_nodes,n_nodes) - The threshold matrix. Each element is in {0,1} - """ - H = np.zeros_like(C) - np.fill_diagonal(H, np.diagonal(C)) - C = C - H - C = np.minimum(np.maximum(C, threshinf), threshsup) - C[C == threshsup] = 0 - C[C != 0] = 1 - - return C - - -def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): - """ Create a noisy circular graph - """ - g = nx.Graph() - g.add_nodes_from(list(range(N))) - for i in range(N): - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) - else: - g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) - g.add_edge(i, i + 1) - if structure_noise: - randomint = np.random.randint(0, p) - if randomint == 0: - if i <= N - 3: - g.add_edge(i, i + 2) - if i == N - 2: - g.add_edge(i, 0) - if i == N - 1: - g.add_edge(i, 1) - g.add_edge(N, 0) - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) - else: - g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) - return g - - -def graph_colors(nx_graph, vmin=0, vmax=7): - cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) - cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') - cpick.set_array([]) - val_map = {} - for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): - val_map[k] = cpick.to_rgba(v) - colors = [] - for node in nx_graph.nodes(): - colors.append(val_map[node]) - return colors - -############################################################################## -# Generate data -# ------------- - -#%% circular dataset -# We build a dataset of noisy circular graphs. -# Noise is added on the structures by random connections and on the features by gaussian noise. - - -np.random.seed(30) -X0 = [] -for k in range(9): - X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) - -############################################################################## -# Plot data -# --------- - -#%% Plot graphs - -plt.figure(figsize=(8, 10)) -for i in range(len(X0)): - plt.subplot(3, 3, i + 1) - g = X0[i] - pos = nx.kamada_kawai_layout(g) - nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) -plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) -plt.show() - -############################################################################## -# Barycenter computation -# ---------------------- - -#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph -# Features distances are the euclidean distances -Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] -ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] -Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] -lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() -sizebary = 15 # we choose a barycenter with 15 nodes - -A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) - -############################################################################## -# Plot Barycenter -# ------------------------- - -#%% Create the barycenter -bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) -for i, v in enumerate(A.ravel()): - bary.add_node(i, attr_name=v) - -#%% -pos = nx.kamada_kawai_layout(bary) -nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) -plt.suptitle('Barycenter', fontsize=20) -plt.show() |