for (diffItem_dislike,
diffRatings_dislike) in deviations_dislike.items():
if diffItem_dislike not in userRatings and userItem in deviations_dislike[
diffItem_dislike]:
freq_dislike = frequencies_dislike[diffItem_dislike][userItem]
tem2_rec.setdefault(diffItem_dislike, 0.0)
tem2_fre.setdefault(diffItem_dislike, 0)
# add to the running sum representing the numerator of the formula
tem2_rec[diffItem_dislike] += (
(diffRatings_dislike[userItem] + userRating) *
freq_dislike)
# keep a running sum of the frequency of diffItem
tem2_fre[diffItem_dislike] += freq_dislike
recommendations = dict(recommendations, **tem2_rec)
frequencies4s1 = dict(frequencies4s1, **tem2_fre)
recommendations = [(k, v / frequencies4s1[k])
for [k, v] in recommendations.items()]
# finally sort and return
recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse=True)
return recommendations
# test 02 using MovieLens DataSet
flag = True
from data_format.input_format import user_install_record_to_dict
user_dict = user_install_record_to_dict(
file('C:/Program Files/Geany/RecSys_Python/ml-100k/ua.test'),
training=flag)
get_deviation_likePolar(user_dict)
g = user_dict['1']
print "slopeone result: \n", slopeOne_biPolar_recommendations(g), "\n"
# for left in users:
# for right in users:
# #TODO
# pass
# else: #E distance
# pass
if __name__ == '__main__':
# -----Training Part-----
flag = True
from data_format.input_format import user_install_record_to_dict
user_dict = user_install_record_to_dict(file('../input/ml-100k/u.data'), training=flag)
# print user_dict
rec = UserBasedRecommendation(neighbor=5, user_rating_dict=user_dict, training=flag)
# print rec.get_all_users()
# print rec.get_active_users()
# one_user = rec.get_all_users()[0]
one_user = '******'
# for one_user in rec.get_all_users()[1:1]:
# print user_dict['216']
# nearest_neighbor = rec.get_nearest_neighbor(user=one_user, training=flag)
nearest_neighbor = rec.get_active_users()
# print rec.get_all_items(user='******')
# pearson_value_list = rec.get_pearson_value_list(user=one_user, users=nearest_neighbor, training=flag)
# print pearson_value_list
# print rec.get_nearest_neighbor(user=one_user)
# add to the running sum representing the numerator of the formula
tem1_rec[diffItem_like] += ((diffRatings_like[userItem] + userRating) * freq_like)
import copy
recommendations = copy.deepcopy(tem1_rec)
# keep a running sum of the frequency of diffItem
tem1_fre[diffItem_like] += freq_like
frequencies4s1 = copy.deepcopy(tem1_fre)
# for every item in our users_dislike dataset that the user didn't rate
for (diffItem_dislike, diffRatings_dislike) in deviations_dislike.items():
if diffItem_dislike not in userRatings and userItem in deviations_dislike[diffItem_dislike]:
freq_dislike = frequencies_dislike[diffItem_dislike][userItem]
tem2_rec.setdefault(diffItem_dislike, 0.0)
tem2_fre.setdefault(diffItem_dislike, 0)
# add to the running sum representing the numerator of the formula
tem2_rec[diffItem_dislike] += ((diffRatings_dislike[userItem] + userRating) * freq_dislike)
# keep a running sum of the frequency of diffItem
tem2_fre[diffItem_dislike] += freq_dislike
recommendations = dict(recommendations, **tem2_rec)
frequencies4s1 = dict(frequencies4s1, **tem2_fre)
recommendations = [(k, v / frequencies4s1[k]) for [k, v] in recommendations.items()]
# finally sort and return
recommendations.sort(key = lambda artistTuple: artistTuple[1], reverse = True)
return recommendations
# test 02 using MovieLens DataSet
flag = True
from data_format.input_format import user_install_record_to_dict
user_dict = user_install_record_to_dict(file('C:/Program Files/Geany/RecSys_Python/ml-100k/ua.test'), training=flag)
get_deviation_likePolar(user_dict)
g = user_dict['1']
print "slopeone result: \n", slopeOne_biPolar_recommendations(g), "\n"
"""
# get standard value
all_user = self.get_all_users()
for x in item_list:
input_list = []
for u in all_user:
if not self.user_rating[u].has_key(x):
input_list.append(None)
else:
input_list.append(self.user_rating[u][x])
normal_value = convert_values_normalization(input_list)
# update value
for i in len(normal_value):
if normal_value[i] != None:
self.user_rating[all_user[i]][x] = normal_value[i]
print "Use standard user rating."
if __name__ == '__main__':
from data_format.input_format import user_install_record_to_dict
flag = False
user_dict = user_install_record_to_dict(file('../input/ml-100k/u.data'), training=flag)
rec = ItemBasedRecommendation(neighbor=5, user_rating_dict=user_dict, training=flag)
# print rec.get_all_users()
print len(rec.get_all_items(user='******'))
# ibr.use_standard_user_rating()
# print ibr.user_rating
# pass
