New translations check_gm_gstsp.py (French)

lang/fr/notebooks/utils/check_gm_gstsp.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+compute gm with load_data.py and test them. 
+Created on Wed Sep 19 16:12:13 2018
+
+@author: ljia
+"""
+
+"""Shortest-Path graph kernel.
+Python implementation based on: "Shortest-path kernels on graphs", by
+Borgwardt, K.M.; Kriegel, H.-P., in Data Mining, Fifth IEEE
+International Conference on , vol., no., pp.8 pp.-, 27-30 Nov. 2005
+doi: 10.1109/ICDM.2005.132
+Author : Sandro Vega-Pons, Emanuele Olivetti
+"""
+
+import sys
+sys.path.insert(0, "../../")
+import numpy as np
+import networkx as nx
+from gklearn.utils.graphfiles import loadDataset
+import matplotlib.pyplot as plt
+from numpy.linalg import eig
+
+
+class GK_SP:
+    """
+    Shorthest path graph kernel.
+    """
+
+    def compare(self, g_1, g_2, verbose=False):
+        """Compute the kernel value (similarity) between two graphs.
+        Parameters
+        ----------
+        g1 : networkx.Graph
+            First graph.
+        g2 : networkx.Graph
+            Second graph.
+        Returns
+        -------
+        k : The similarity value between g1 and g2.
+        """
+        # Diagonal superior matrix of the floyd warshall shortest
+        # paths:
+        fwm1 = np.array(nx.floyd_warshall_numpy(g_1))
+        fwm1 = np.where(fwm1 == np.inf, 0, fwm1)
+        fwm1 = np.where(fwm1 == np.nan, 0, fwm1)
+        fwm1 = np.triu(fwm1, k=1)
+        bc1 = np.bincount(fwm1.reshape(-1).astype(int))
+
+        fwm2 = np.array(nx.floyd_warshall_numpy(g_2))
+        fwm2 = np.where(fwm2 == np.inf, 0, fwm2)
+        fwm2 = np.where(fwm2 == np.nan, 0, fwm2)
+        fwm2 = np.triu(fwm2, k=1)
+        bc2 = np.bincount(fwm2.reshape(-1).astype(int))
+
+        # Copy into arrays with the same length the non-zero shortests
+        # paths:
+        v1 = np.zeros(max(len(bc1), len(bc2)) - 1)
+        v1[range(0, len(bc1)-1)] = bc1[1:]
+
+        v2 = np.zeros(max(len(bc1), len(bc2)) - 1)
+        v2[range(0, len(bc2)-1)] = bc2[1:]
+
+        return np.sum(v1 * v2)
+
+    def compare_normalized(self, g_1, g_2, verbose=False):
+        """Compute the normalized kernel value between two graphs.
+        A normalized version of the kernel is given by the equation:
+        k_norm(g1, g2) = k(g1, g2) / sqrt(k(g1,g1) * k(g2,g2))
+        Parameters
+        ----------
+        g1 : networkx.Graph
+            First graph.
+        g2 : networkx.Graph
+            Second graph.
+        Returns
+        -------
+        k : The similarity value between g1 and g2.
+        """
+        return self.compare(g_1, g_2) / (np.sqrt(self.compare(g_1, g_1) *
+                                                 self.compare(g_2, g_2)))
+
+    def compare_list(self, graph_list, verbose=False):
+        """Compute the all-pairs kernel values for a list of graphs.
+        This function can be used to directly compute the kernel
+        matrix for a list of graphs. The direct computation of the
+        kernel matrix is faster than the computation of all individual
+        pairwise kernel values.
+        Parameters
+        ----------
+        graph_list: list
+            A list of graphs (list of networkx graphs)
+        Return
+        ------
+        K: numpy.array, shape = (len(graph_list), len(graph_list))
+        The similarity matrix of all graphs in graph_list.
+        """
+        n = len(graph_list)
+        k = np.zeros((n, n))
+        for i in range(n):
+            for j in range(i, n):
+                k[i, j] = self.compare(graph_list[i], graph_list[j])
+                k[j, i] = k[i, j]
+
+        k_norm = np.zeros(k.shape)
+        for i in range(k.shape[0]):
+            for j in range(k.shape[1]):
+                k_norm[i, j] = k[i, j] / np.sqrt(k[i, i] * k[j, j])
+
+        return k_norm
+
+
+ds_name = 'PAH'
+datafile = '../../datasets/PAH/dataset.ds'
+dataset, y = loadDataset(datafile, filename_y=None, extra_params=None)
+gk_sp = GK_SP()
+x = gk_sp.compare_list(dataset)
+np.savez('../check_gm/' + ds_name + '.gm.jstsp', gms=x)
+
+plt.imshow(x)
+plt.colorbar()
+plt.savefig('../check_gm/' + ds_name + '.gm.jstsp.eps', format='eps', dpi=300)
+# print(np.transpose(x))
+print('if symmetric: ', np.array_equal(x, np.transpose(x)))
+
+print('diag: ', np.diag(x))
+print('sum diag < 0.1: ', np.sum(np.diag(x) < 0.1))
+print('min, max diag: ', min(np.diag(x)), max(np.diag(x)))
+print('mean x: ', np.mean(np.mean(x)))
+
+[lamnda, v] = eig(x)
+print('min, max lambda: ', min(lamnda), max(lamnda))