Recommender_System/Network_Based_Recommendation_System.py at master · AAbasinejad/Recommender_System · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123

import time
import pprint as pp
import networkx as nx

import Network_Based_Recommendation_System_FUNCTIONS as homework_2


################################################################################################################
################################################################################################################
################################################################################################################

print
print "Current time: " + str(time.asctime(time.localtime()))
print
print
print


sum_of_AVEARGE_R_PRECISION_for_PERSONAL_recommendation = 0.


number_of_training_set_test_set_instances = 5
for index in xrange(1, number_of_training_set_test_set_instances + 1):
	training_set_file = './input_data/u'+str(index)+'_base_homework_format.txt'
	test_set_file     = './input_data/u'+str(index)+'_test_homework_format.txt'

	print
	print
	print "-----------------------------------------------"
	print "Current data: "
	print "              " + training_set_file
	print "              " + test_set_file
	print


	pp.pprint("Load Test Graph.")
	print "Current time: " + str(time.asctime(time.localtime()))
	test_graph_users_items = homework_2.create_graph_set_of_users_set_of_items(test_set_file)
	print " #Users in Test Graph= " + str(len(test_graph_users_items['users']))
	print " #Items in Test Graph= " + str(len(test_graph_users_items['items']))
	print " #Nodes in Test Graph= " + str(len(test_graph_users_items['graph']))
	print " #Edges in Test Graph= " + str(test_graph_users_items['graph'].number_of_edges())
	print "Current time: " + str(time.asctime(time.localtime()))
	print
	print


	pp.pprint("Load Training Graph.")
	print "Current time: " + str(time.asctime(time.localtime()))
	training_graph_users_items = homework_2.create_graph_set_of_users_set_of_items(training_set_file)
	print " #Users in Training Graph= " + str(len(training_graph_users_items['users']))
	print " #Items in Training Graph= " + str(len(training_graph_users_items['items']))
	print " #Nodes in Training Graph= " + str(len(training_graph_users_items['graph']))
	print " #Edges in Training Graph= " + str(training_graph_users_items['graph'].number_of_edges())
	print "Current time: " + str(time.asctime(time.localtime()))
	print
	print


	pp.pprint("Create Item-Item-Weighted Graph.")
	print "Current time: " + str(time.asctime(time.localtime()))
	item_item_graph = homework_2.create_item_item_graph(training_graph_users_items)
	print " #Nodes in Item-Item Graph= " + str(len(item_item_graph))
	print " #Edges in Item-Item Graph= " + str(item_item_graph.number_of_edges())
	print "Current time: " + str(time.asctime(time.localtime()))
	print
	print


	### Conversion of the 'Item-Item-Graph' to a scipy sparse matrix representation.
	### This reduces a lot the PageRank running time ;)
	print
	print " Conversion of the 'Item-Item-Graph' to a scipy sparse matrix representation."
	N = len(item_item_graph)
	nodelist = item_item_graph.nodes()
	M = nx.to_scipy_sparse_matrix(item_item_graph, nodelist=nodelist, weight='weight', dtype=float)
	print " Done."
	print
	#################################################################################################


	sum_of_R_PRECISION_for_PERSONAL_recommendation = 0.

	print
	print "Start Personal Recommandation for each user in the Test Set."
	print "Current time: " + str(time.asctime(time.localtime()))
	### Run recommandation for each user in the TEST set.
	for current_user_id in test_graph_users_items['users']:

		# Personal recommendation for the current user using PersonalizedPageRank
		preference_vector = homework_2.create_preference_vector_for_teleporting(current_user_id, training_graph_users_items)
		personalized_pagerank_vector_of_items = homework_2.pagerank(M, N, nodelist, alpha=0.85, personalization=preference_vector)
		sorted_list_of_recommended_items_form_PERSONAL_recommendation = homework_2.create_ranked_list_of_recommended_items(personalized_pagerank_vector_of_items, current_user_id, training_graph_users_items)
		current_R_PRECISION_for_PERSONAL_recommendation = homework_2.r_precision(current_user_id, sorted_list_of_recommended_items_form_PERSONAL_recommendation, test_graph_users_items)

		sum_of_R_PRECISION_for_PERSONAL_recommendation += current_R_PRECISION_for_PERSONAL_recommendation

	print "Current time: " + str(time.asctime(time.localtime()))
	print "Done."


	avg_R_PRECISION_for_PERSONAL_recommendation = sum_of_R_PRECISION_for_PERSONAL_recommendation / float(len(test_graph_users_items['users']))


	sum_of_AVEARGE_R_PRECISION_for_PERSONAL_recommendation += avg_R_PRECISION_for_PERSONAL_recommendation


average_R_PRECISION_for_PERSONAL_recommendation_over_all_training_set_test_set_couples = sum_of_AVEARGE_R_PRECISION_for_PERSONAL_recommendation / number_of_training_set_test_set_instances

print
print
print "  average_R_PRECISION_for_PERSONAL_recommendation_over_all_training_set_test_set_couples = " + str(average_R_PRECISION_for_PERSONAL_recommendation_over_all_training_set_test_set_couples)
print

print
print
print "Current time: " + str(time.asctime(time.localtime()))
print
print "Done ;)"
print