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RunExample.py
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#! python3
# -*- coding: utf-8 -*-
import argparse
import datetime
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity
from Item_basedCF import *
from ThreadWithReturn import *
from User_basedCF import *
MovieLensData = {
1: 'Datas/ml-100k/u.data',
2: 'Datas/ml-1M/ratings.dat',
3: 'Datas/ml-10M100K/ratings.dat',
4: 'Datas/ml-20m/ratings.csv'
}
def parseargs():
parser = argparse.ArgumentParser()
parser.add_argument("--ratings",
type=str,
default='ml-100k',
help="Ratings file")
parser.add_argument("--testsize",
type=float,
default=0.2,
help="Percentage of test data")
return parser.parse_args()
if __name__ == '__main__':
myparser = parseargs()
startTime = datetime.datetime.now()
MyData = LoadMovieLensData(myparser.ratings)
MyUBCF = UBCollaborativeFilter()
MyIBCF = IBCollaborativeFilter()
train_data, test_data = train_test_split(MyData, test_size=myparser.testsize)
print(type(train_data))
print(MyData.head())
n_users = MyData.user_id.max()
n_items = MyData.item_id.max()
print('Number of users = ' + str(n_users) + ' | Number of movies = ' + str(n_items))
test1 = ThreadWithReturnValue(target=DataFrame2Matrix, args=(n_users, n_items, train_data))
test2 = ThreadWithReturnValue(target=DataFrame2Matrix, args=(n_users, n_items, test_data))
test1.start()
test2.start()
train_data_matrix = test1.join()
test_data_matrix = test2.join()
MyUBCF.train_data_matrix = train_data_matrix
MyIBCF.train_data_matrix = train_data_matrix
MyUBCF.test_data_matrix = test_data_matrix
MyIBCF.test_data_matrix = test_data_matrix
# 皮尔逊相关系数
# MyUBCF.SimilityMatrix = np.corrcoef(train_data_matrix)
# MyIBCF.SimilityMatrix = np.corrcoef(train_data_matrix.T)
# # 余弦相似度
MyUBCF.SimilityMatrix = cosine_similarity(train_data_matrix)
# MyIBCF.SimilityMatrix = cosine_similarity(train_data_matrix.T)
# 按X轴方向获取非0元素均值，如果某行所有元素为0返回nan，横着，求对应用户所有电影得平均分
MyUBCF.UserMeanMatrix = np.true_divide(MyUBCF.train_data_matrix.sum(1), (MyUBCF.train_data_matrix != 0).sum(1))
# 按Y轴方向获取非0元素均值，如果某行所有元素为0返回nan，竖着，求该物品得所有用户平均分
# MyIBCF.ItemMeanMatrix = numpy.true_divide(MyUBCF.train_data_matrix.sum(0), (MyUBCF.train_data_matrix != 0).sum(0))
# MyIBCF.ItemMeanMatrix[np.isnan(MyIBCF.ItemMeanMatrix)] = 0
KList = [10, 20, 30, 40, 50, 60]
for i in range(len(KList)):
MyUBCF.truerating = []
MyUBCF.predictions = []
# MyIBCF.truerating = []
# MyIBCF.predictions = []
medTime = datetime.datetime.now()
print((medTime - startTime).seconds)
t1 = Thread(target=MyUBCF.doEvaluate, args=(test_data_matrix, KList[i]))
# t2 = Thread(target=MyIBCF.doEvaluate, args=(test_data_matrix, KList[i]))
t1.start()
# t2.start()
t1.join()
# t2.join()
endTime = datetime.datetime.now()
print("Cost time:%d seconds" % (endTime - startTime).seconds)
Savetxt("Docs/%s/UBCF %s %1.1f.txt" % (myparser.ratings, myparser.ratings, myparser.testsize),
"UBCF K=%d\tRMSE:%f\tMAE:%f\t" % (KList[i], MyUBCF.RMSE[KList[i]], MyUBCF.MAE[KList[i]]))
# Savetxt('Docs/%s/IBCF %s %1.1f.txt' % (myparser.ratings, myparser.ratings, myparser.testsize),"IBCF K=%d\tRMSE:%f\tMAE:%f\t" % (KList[i], MyIBCF.RMSE[KList[i]], MyIBCF.MAE[KList[i]]))
# Check performance by plotting train and test errors
plt.plot(KList, list(MyUBCF.RMSE.values()), marker='o', label='RMSE')
plt.plot(KList, list(MyUBCF.MAE.values()), marker='v', label='MAE')
plt.title('The Error of UBCF in MovieLens ' + myparser.ratings)
plt.xlabel('K')
plt.ylabel('value')
plt.legend()
plt.grid()
plt.savefig('Docs/%s/UBCF %s %1.1f.png' % (myparser.ratings, myparser.ratings, myparser.testsize))
plt.show()
plt.gcf().clear()
# Check performance by plotting train and test errors
# plt.plot(KList, list(MyIBCF.RMSE.values()), marker='o', label='RMSE')
# plt.plot(KList, list(MyIBCF.MAE.values()), marker='v', label='MAE')
# plt.title('The Error of IBCF in MovieLens ' + myparser.ratings)
# plt.xlabel('K')
# plt.ylabel('value')
# plt.legend()
# plt.grid()
# plt.savefig('Docs/%s/IBCF %s %1.1f.png' % (myparser.ratings, myparser.ratings, myparser.testsize))
# plt.show()