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Merge pull request apachecn#514 from cclauss/patch-1
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谢谢小哥哥 | RS-itemcf.py: print() is a function in Python 3
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@jiangzhonglian
jiangzhonglian authored May 21, 2019
2 parents c1fafcd + af2f246 commit 8db88fd
Showing 1 changed file with 21 additions and 20 deletions.
41 changes: 21 additions & 20 deletions src/py3.x/16.RecommenderSystems/RS-itemcf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
参考地址：https://github.com/Lockvictor/MovieLens-RecSys
更新地址：https://github.com/apachecn/AiLearning
'''
from __future__ import print_function
import sys
import math
import random
Expand All @@ -33,8 +34,8 @@ def __init__(self):
self.movie_popular = {}
self.movie_count = 0

print >> sys.stderr, 'Similar movie number = %d' % self.n_sim_movie
print >> sys.stderr, 'Recommended movie number = %d' % self.n_rec_movie
print('Similar movie number = %d' % self.n_sim_movie, file=sys.stderr)
print('Recommended movie number = %d' % self.n_rec_movie, file=sys.stderr)

@staticmethod
def loadfile(filename):
Expand All @@ -49,9 +50,9 @@ def loadfile(filename):
for i, line in enumerate(fp):
yield line.strip('\r\n')
if i > 0 and i % 100000 == 0:
print >> sys.stderr, 'loading %s(%s)' % (filename, i)
print('loading %s(%s)' % (filename, i), file=sys.stderr)
fp.close()
print >> sys.stderr, 'load %s success' % filename
print('load %s success' % filename, file=sys.stderr)

def generate_dataset(self, filename, pivot=0.7):
"""loadfile(加载文件，将数据集按照7:3 进行随机拆分)
Expand Down Expand Up @@ -81,14 +82,14 @@ def generate_dataset(self, filename, pivot=0.7):
self.testset[user][movie] = int(rating)
testset_len += 1

print >> sys.stderr, '分离训练集和测试集成功'
print >> sys.stderr, 'train set = %s' % trainset_len
print >> sys.stderr, 'test set = %s' % testset_len
print('分离训练集和测试集成功', file=sys.stderr)
print('train set = %s' % trainset_len, file=sys.stderr)
print('test set = %s' % testset_len, file=sys.stderr)

def calc_movie_sim(self):
"""calc_movie_sim(计算用户之间的相似度)"""

print >> sys.stderr, 'counting movies number and popularity...'
print('counting movies number and popularity...', file=sys.stderr)

# 统计在所有的用户中，不同电影的总出现次数， user, movies
for _, movies in self.trainset.items():
Expand All @@ -98,15 +99,15 @@ def calc_movie_sim(self):
self.movie_popular[movie] = 0
self.movie_popular[movie] += 1

print >> sys.stderr, 'count movies number and popularity success'
print('count movies number and popularity success', file=sys.stderr)

# save the total number of movies
self.movie_count = len(self.movie_popular)
print >> sys.stderr, 'total movie number = %d' % self.movie_count
print('total movie number = %d' % self.movie_count, file=sys.stderr)

# 统计在相同用户时，不同电影同时出现的次数
itemsim_mat = self.movie_sim_mat
print >> sys.stderr, 'building co-rated users matrix...'
print('building co-rated users matrix...', file=sys.stderr)
# user, movies
for _, movies in self.trainset.items():
for m1 in movies:
Expand All @@ -116,10 +117,10 @@ def calc_movie_sim(self):
itemsim_mat.setdefault(m1, {})
itemsim_mat[m1].setdefault(m2, 0)
itemsim_mat[m1][m2] += 1
print >> sys.stderr, 'build co-rated users matrix success'
print('build co-rated users matrix success', file=sys.stderr)

# calculate similarity matrix
print >> sys.stderr, 'calculating movie similarity matrix...'
print('calculating movie similarity matrix...', file=sys.stderr)
simfactor_count = 0
PRINT_STEP = 2000000
for m1, related_movies in itemsim_mat.items():
Expand All @@ -130,10 +131,10 @@ def calc_movie_sim(self):
simfactor_count += 1
# 打印进度条
if simfactor_count % PRINT_STEP == 0:
print >> sys.stderr, 'calculating movie similarity factor(%d)' % simfactor_count
print('calculating movie similarity factor(%d)' % simfactor_count, file=sys.stderr)

print >> sys.stderr, 'calculate movie similarity matrix(similarity factor) success'
print >> sys.stderr, 'Total similarity factor number = %d' % simfactor_count
print('calculate movie similarity matrix(similarity factor) success', file=sys.stderr)
print('Total similarity factor number = %d' % simfactor_count, file=sys.stderr)

# @profile
def recommend(self, user):
Expand Down Expand Up @@ -167,7 +168,7 @@ def recommend(self, user):

def evaluate(self):
''' return precision, recall, coverage and popularity '''
print >> sys.stderr, 'Evaluation start...'
print('Evaluation start...', file=sys.stderr)

# 返回top N的推荐结果
N = self.n_rec_movie
Expand All @@ -185,7 +186,7 @@ def evaluate(self):
# 参考地址：http://blog.csdn.net/churximi/article/details/51648388
for i, user in enumerate(self.trainset):
if i > 0 and i % 500 == 0:
print >> sys.stderr, 'recommended for %d users' % i
print('recommended for %d users' % i, file=sys.stderr)
test_movies = self.testset.get(user, {})
rec_movies = self.recommend(user)

Expand All @@ -204,8 +205,8 @@ def evaluate(self):
coverage = len(all_rec_movies) / (1.0 * self.movie_count)
popularity = popular_sum / (1.0 * rec_count)

print >> sys.stderr, 'precision=%.4f \t recall=%.4f \t coverage=%.4f \t popularity=%.4f' % (
precision, recall, coverage, popularity)
print('precision=%.4f \t recall=%.4f \t coverage=%.4f \t popularity=%.4f' % (
precision, recall, coverage, popularity), file=sys.stderr)


if __name__ == '__main__':
Expand Down

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