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DfmPredictionRate.py
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# -*- coding: utf-8 -*-
import sys
import os
from DivideData import DivideData
from operator import itemgetter
class DfsPredictionRate(object):
def __init__(self):
self.train_set = {}
self.test_set = {}
self.sim_matrix = {}
self.average_matrix = {}
self.prediction_rate_matrix = {}
def generate_data_matrix(self, train_set_file, test_set_file, sim_file):
for line in DivideData.loadfile(train_set_file):
user, movie, rate, _ = line.split('::')
self.train_set.setdefault(user, {})
self.train_set[user][movie] = float(rate)
for line in DivideData.loadfile(test_set_file):
user, movie, rate, _ = line.split('::')
self.test_set.setdefault(user, {})
self.test_set[user][movie] = float(rate)
for line in DivideData.loadfile(sim_file):
forward_user, backward_user, sim = line.split('::')
self.sim_matrix.setdefault(forward_user, {})
self.sim_matrix[forward_user][backward_user] = float(sim)
def generate_average_matrix(self):
for user in self.train_set:
for movie, rate in self.train_set[user].iteritems():
self.average_matrix.setdefault(user, 0)
self.average_matrix[user] += rate / len(self.train_set[user])
def dfs_prediction_one_user_rate(self, target_user, target_item, n=150):
fen_zi, fen_mu = float(0), float(0)
for user, sim in sorted(self.sim_matrix[target_user].iteritems(), key=itemgetter(1), reverse=True)[:n]:
if target_item in self.train_set[user]:
fen_zi += (self.train_set[user][target_item] - self.average_matrix[user]) * self.sim_matrix[target_user][user]
fen_mu += self.sim_matrix[target_user][user]
if fen_mu == 0:
return 0
else:
return fen_zi / fen_mu
def dfs_prediction(self):
for user in self.test_set:
for movie in self.test_set[user]:
self.prediction_rate_matrix.setdefault(user, {})
self.prediction_rate_matrix[user][movie] = self.average_matrix[user] + self.dfs_prediction_one_user_rate(user, movie)
if self.prediction_rate_matrix[user][movie] > 5:
self.prediction_rate_matrix[user][movie] = 5
print >> sys.stderr, '预测评分成功'
Prediction_rate_matrix = DivideData.transform_data_structure(self.prediction_rate_matrix)
DivideData.export_file('ml-1m/prediction_rate_cos_upsim_dfm.csv', Prediction_rate_matrix)
'''
更改相似度文件后,记得更改导出的文件名
'''
print >> sys.stderr, '导出预测评分数据成功'
if __name__ == '__main__':
train_set_file = os.path.join('ml-1m', 'train_set.csv')
test_set_file = os.path.join('ml-1m', 'test_set.csv')
sim_file = os.path.join('ml-1m', 'COS_UPsim.csv')
'''
通过换不同的相似度文件,实现不同相似度算法进行的评分预测
'''
dfspr = DfsPredictionRate()
dfspr.generate_data_matrix(train_set_file, test_set_file, sim_file)
dfspr.generate_average_matrix()
dfspr.dfs_prediction()