New translations accuracy_diff_entropy.py (Chinese Simplified)

lang/zh/gklearn/experiments/papers/PRL_2020/accuracy_diff_entropy.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Oct  5 16:08:33 2020
+
+@author: ljia
+
+This script compute classification accuracy of each geaph kernel on datasets 
+with different entropy of degree distribution.
+"""
+from utils import Graph_Kernel_List, cross_validate
+import numpy as np
+import logging
+
+num_nodes = 40
+half_num_graphs = 100
+
+
+def generate_graphs():
+# 	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+# 	gsyzer = GraphSynthesizer()
+# 	graphs = gsyzer.unified_graphs(num_graphs=1000, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=0, seed=None, directed=False)
+# 	return graphs
+	import networkx as nx
+	
+	degrees11 = [5] * num_nodes
+# 	degrees12 = [2] * num_nodes
+	degrees12 = [5] * num_nodes
+	degrees21 = list(range(1, 11)) * 6
+# 	degrees22 = [5 * i for i in list(range(1, 11)) * 6]
+	degrees22 = list(range(1, 11)) * 6
+	
+	# method 1
+	graphs11 = [nx.configuration_model(degrees11, create_using=nx.Graph) for i in range(half_num_graphs)]
+	graphs12 = [nx.configuration_model(degrees12, create_using=nx.Graph) for i in range(half_num_graphs)]
+	
+	# method 2: can easily generate isomorphic graphs.
+# 	graphs11 = [nx.random_regular_graph(2, num_nodes, seed=None) for i in range(half_num_graphs)]
+# 	graphs12 = [nx.random_regular_graph(10, num_nodes, seed=None) for i in range(half_num_graphs)]
+	
+	# Add node labels.
+	for g in graphs11:
+		for n in g.nodes():
+			g.nodes[n]['atom'] = 0
+	for g in graphs12:
+		for n in g.nodes():
+			g.nodes[n]['atom'] = 1
+		
+	graphs1 = graphs11 + graphs12
+
+	# method 1: the entorpy of the two classes is not the same.
+	graphs21 = [nx.configuration_model(degrees21, create_using=nx.Graph) for i in range(half_num_graphs)]
+	graphs22 = [nx.configuration_model(degrees22, create_using=nx.Graph) for i in range(half_num_graphs)]	
+	
+# 	# method 2: tooo slow, and may fail.
+# 	graphs21 = [nx.random_degree_sequence_graph(degrees21, seed=None, tries=100) for i in range(half_num_graphs)]
+# 	graphs22 = [nx.random_degree_sequence_graph(degrees22, seed=None, tries=100) for i in range(half_num_graphs)]	
+
+# 	# method 3: no randomness.
+# 	graphs21 = [nx.havel_hakimi_graph(degrees21, create_using=None) for i in range(half_num_graphs)]
+# 	graphs22 = [nx.havel_hakimi_graph(degrees22, create_using=None) for i in range(half_num_graphs)]
+
+# 	# method 4:
+# 	graphs21 = [nx.configuration_model(degrees21, create_using=nx.Graph) for i in range(half_num_graphs)]
+# 	graphs22 = [nx.degree_sequence_tree(degrees21, create_using=nx.Graph) for i in range(half_num_graphs)]	
+	
+# 	# method 5: the entorpy of the two classes is not the same.
+# 	graphs21 = [nx.expected_degree_graph(degrees21, seed=None, selfloops=False) for i in range(half_num_graphs)]
+# 	graphs22 = [nx.expected_degree_graph(degrees22, seed=None, selfloops=False) for i in range(half_num_graphs)]	
+	
+# 	# method 6: seems there is no randomness0
+# 	graphs21 = [nx.random_powerlaw_tree(num_nodes, gamma=3, seed=None, tries=10000) for i in range(half_num_graphs)]
+# 	graphs22 = [nx.random_powerlaw_tree(num_nodes, gamma=3, seed=None, tries=10000) for i in range(half_num_graphs)]	
+
+	# Add node labels.
+	for g in graphs21:
+		for n in g.nodes():
+			g.nodes[n]['atom'] = 0
+	for g in graphs22:
+		for n in g.nodes():
+			g.nodes[n]['atom'] = 1
+
+	graphs2 = graphs21 + graphs22
+	
+# 	# check for isomorphism.
+# 	iso_mat1 = np.zeros((len(graphs1), len(graphs1)))
+# 	num1 = 0
+# 	num2 = 0
+# 	for i in range(len(graphs1)):
+# 		for j in range(i + 1, len(graphs1)):
+# 			 if nx.is_isomorphic(graphs1[i], graphs1[j]):
+# 				 iso_mat1[i, j] = 1
+# 				 iso_mat1[j, i] = 1
+# 				 num1 += 1
+# 				 print('iso:', num1, ':', i, ',', j)
+# 			 else:
+# 				 num2 += 1
+# 				 print('not iso:', num2, ':', i, ',', j)
+# 				 
+# 	iso_mat2 = np.zeros((len(graphs2), len(graphs2)))
+# 	num1 = 0
+# 	num2 = 0
+# 	for i in range(len(graphs2)):
+# 		for j in range(i + 1, len(graphs2)):
+# 			 if nx.is_isomorphic(graphs2[i], graphs2[j]):
+# 				 iso_mat2[i, j] = 1
+# 				 iso_mat2[j, i] = 1
+# 				 num1 += 1
+# 				 print('iso:', num1, ':', i, ',', j)
+# 			 else:
+# 				 num2 += 1
+# 				 print('not iso:', num2, ':', i, ',', j)
+		
+	return graphs1, graphs2
+
+
+def get_infos(graph):
+	from gklearn.utils import Dataset
+	ds = Dataset()
+	ds.load_graphs(graph)
+	infos = ds.get_dataset_infos(keys=['all_degree_entropy', 'ave_node_degree'])
+	infos['ave_degree_entropy'] = np.mean(infos['all_degree_entropy'])
+	print(infos['ave_degree_entropy'], ',', infos['ave_node_degree'])
+	return infos
+
+
+def xp_accuracy_diff_entropy():
+	
+	# Generate graphs.
+	graphs1, graphs2 = generate_graphs()
+
+	
+	# Compute entropy of degree distribution of the generated graphs.
+	info11 = get_infos(graphs1[0:half_num_graphs])
+	info12 = get_infos(graphs1[half_num_graphs:])
+	info21 = get_infos(graphs2[0:half_num_graphs])
+	info22 = get_infos(graphs2[half_num_graphs:])
+
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/accuracy_diff_entropy/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	accuracies = {}
+	confidences = {}
+	
+	for kernel_name in Graph_Kernel_List:
+		print()
+		print('Kernel:', kernel_name)
+		
+		accuracies[kernel_name] = []
+		confidences[kernel_name] = []
+		for set_i, graphs in enumerate([graphs1, graphs2]):
+			print()
+			print('Graph set', set_i)
+			
+			tmp_graphs = [g.copy() for g in graphs]
+			targets = [0] * half_num_graphs + [1] * half_num_graphs
+			
+			accuracy = 'error'
+			confidence = 'error'
+			try:
+				accuracy, confidence = cross_validate(tmp_graphs, targets, kernel_name, ds_name=str(set_i), output_dir=save_dir) #, n_jobs=1)
+			except Exception as exp:
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('\n' + kernel_name + ', ' + str(set_i) + ':')
+				print(repr(exp))
+			accuracies[kernel_name].append(accuracy)
+			confidences[kernel_name].append(confidence)
+			
+			pickle.dump(accuracy, open(save_dir + 'accuracy.' + kernel_name + '.' + str(set_i) + '.pkl', 'wb'))
+			pickle.dump(confidence, open(save_dir + 'confidence.' + kernel_name + '.' + str(set_i) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(accuracies, open(save_dir + 'accuracies.pkl', 'wb'))	
+	pickle.dump(confidences, open(save_dir + 'confidences.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_accuracy_diff_entropy()