Can now compute accuracies over the union of temporal and normal features

Author: classicCoder16

compute_union.py

@@ -0,0 +1,87 @@
+import snap
+import numpy as np
+import sys
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neural_network import MLPClassifier
+from weight_evolution import EvolModel
+from train_graph import Train_Graph
+from test_graph import Test_Graph
+from feature_extractors import *
+
+def get_all_features(feature_funcs, max_scc, train_examples, test_examples):
+	all_train_features = []
+	all_test_features = []
+	for func in feature_funcs:
+		print 'Extracting features with', func
+		all_train_features.append(get_features(max_scc, train_examples, func))
+		all_test_features.append(get_features(max_scc, test_examples, func))
+	# Transpose since sklearn takes nsamples, nfeatures shape
+	all_train_features = np.array(all_train_features).T
+	all_test_features = np.array(all_test_features).T
+	return all_train_features, all_test_features
+
+def get_features(max_scc, examples, func):
+	all_features = []
+	for ex in examples:
+		result = func(max_scc, ex[0], ex[1])
+		all_features.append(result)
+	return all_features
+
+def print_metrics(gt, pred):
+	print 'Accuracy:', sklearn.metrics.accuracy_score(gt, pred)
+	print 'Precision:', sklearn.metrics.precision_score(gt, pred)
+	print 'Recall:', sklearn.metrics.recall_score(gt, pred)
+	print 'F1 Score:', sklearn.metrics.f1_score(gt, pred)
+
+def test_classifiers(train_examples, train_labels, test_examples, test_labels):
+	knn = KNeighborsClassifier()
+	logistic = LogisticRegression()
+	rf = RandomForestClassifier(n_estimators=100)
+	my_nn = MLPClassifier(hidden_layer_sizes = (100, 50, 50))
+	bliss_model = EvolModel()
+	models = [bliss_model, knn, logistic, rf, my_nn]
+	for model in models:
+		print 'Training model', model
+		model.fit(train_examples, train_labels)
+		preds = model.predict(test_examples)
+		gt = [elem for elem in test_labels]
+		print ''
+		print 'Evaluating Testing Set:'
+		print_metrics(gt, preds)
+
+		print ''
+		print 'Evaluating Training Set:'
+		preds_train = model.predict(train_examples)
+		gt_train = [elem for elem in train_labels]
+		print_metrics(gt_train, preds_train)
+
+def main(temp_train_feats, temp_train_ex, temp_test_feats, temp_test_ex, graph_file):
+	train_graph_obj = Train_Graph(graph_file_root=graph_file)
+	graph = train_graph_obj.pgraph
+	train_examples = temp_train_ex[:, 0].tolist()
+	train_labels = temp_train_ex[:, 1].tolist()
+	test_examples = temp_test_ex[:, 0].tolist()
+	test_labels = temp_test_ex[:, 1].tolist()
+	feature_funcs = [preferential_attachment]
+	# feature_funcs = [get_graph_distance, get_ev_centr_sum, get_page_rank_sum, \
+	# 				preferential_attachment, get_2_hops, get_degree_sum, \
+	# 				std_nbr_degree_sum, mean_nbr_deg_sum, adamic_adar_2, \
+	# 				common_neighbors_2]
+	print 'Extracting features'
+	norm_train_features, norm_test_features = get_all_features(feature_funcs, graph, train_examples, test_examples)
+	all_train_feats = np.hstack([norm_train_features, temp_train_feats])
+	all_test_feats = np.hstack([norm_test_features, temp_test_feats])
+	all_train_feats = sklearn.preprocessing.scale(all_train_feats)
+	all_test_feats = sklearn.preprocessing.scale(all_test_feats)
+	print 'Testing Classifiers'
+	test_classifiers(all_train_features, train_labels, all_test_features, test_labels)
+
+if __name__=='__main__':
+	temp_train_feats = np.load(sys.argv[1])
+	temp_train_ex = np.load(sys.argv[2])
+	temp_test_feats = np.load(sys.argv[3])
+	temp_test_ex = np.load(sys.argv[4])
+	graph_file = np.load(sys.argv[5])
+	main(temp_train_feats, temp_train_ex, temp_test_feats, temp_test_ex, graph_file)