-
Notifications
You must be signed in to change notification settings - Fork 1
/
main.py
84 lines (70 loc) · 3.25 KB
/
main.py
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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 13 15:18:28 2018
@author: baifrank
"""
import numpy as np
import pandas as pd
from LFM_decom import dataset
from LFM_decom import utils
if __name__ == "__main__":
path = "/Users/baifrank/Desktop/recomm_output"
R_train,R_test = dataset.load_data(path+'/ua_base_df_continuous.txt',path+'/ua_test_df_continuous.txt')
#path_1m = "/Users/baifrank/Desktop/recomm_output/ml_1m_output"
#R_train,R_test = dataset.load_data(path_1m+'/ua_base_df_continuous.txt',path_1m+'/ua.test.txt')
R_train_ndarray = R_train.toarray()
R_test_ndarray = R_test.toarray()
###############################################################################
##设置不同的迭代次数和隐因子个数，计算误差//set several iterations and number of latent factors
##无bias的情况//with out bias
l_mae_train,l_mse_train,l_mae_test,l_mse_test = [],[],[],[]
l_factor = [2,10,50,100,200]
for k_factor in l_factor:
b,c = utils.decompose_LFM_bias(R_train_ndarray,n_factor=k_factor,n_iter=200,lr=0.01,lam=0.01,bias=False,shrinkage=0)
R_pre = np.dot(b,c)
mae_train,mse_train = utils.compute_error(R_train_ndarray,R_pre)
mae_test,mse_test = utils.compute_error(R_test_ndarray,R_pre)
l_mae_train.append(mae_train)
l_mse_train.append(mse_train)
l_mae_test.append(mae_test)
l_mse_test.append(mse_test)
df_error = pd.DataFrame([l_mae_train,l_mse_train,l_mae_test,l_mse_test])
df_error.index = ["mae_train","mse_train","mae_test","mse_test"]
df_error.columns = l_factor
print(df_error)
##有bias的情况//with bias
l_mae_train,l_mse_train,l_mae_test,l_mse_test = [],[],[],[]
l_factor = [2,10,50,100,200]
for k_factor in l_factor:
U,V,B_u,B_i,Mu = utils.decompose_LFM_bias(R_train_ndarray,n_factor=k_factor,n_iter=200,lr=0.01,lam=0.01,bias=True,shrinkage=0)
R_pre = np.dot(U,V) + B_u.reshape((len(B_u),1)) + B_i.reshape((1,len(B_i))) + Mu
mae_train,mse_train = utils.compute_error(R_train_ndarray,R_pre)
mae_test,mse_test = utils.compute_error(R_test_ndarray,R_pre)
l_mae_train.append(mae_train)
l_mse_train.append(mse_train)
l_mae_test.append(mae_test)
l_mse_test.append(mse_test)
df_error = pd.DataFrame([l_mae_train,l_mse_train,l_mae_test,l_mse_test])
df_error.index = ["mae_train","mse_train","mae_test","mse_test"]
df_error.columns = l_factor
print(df_error)
##作折线图，绘制不同factor数量下的误差//make line chart（x:number of factors,y:error-mae and mse）
import matplotlib.pyplot as plt
'''
x = l_factor
y_mae_train = l_mae_train
y_mae_test = l_mae_test
y_mse_train = l_mse_train
y_mse_test = l_mse_test
plt.plot(x, y_mse_train, marker='o', mec='r', mfc='w',label=u'train set')
plt.plot(x, y_mse_test, marker='*', ms=10,label=u'test set')
plt.legend() # 让图例生效//for legend
#plt.xticks(x, names, rotation=45)
#plt.margins(0)
plt.subplots_adjust(bottom=0.15)
plt.xlabel(u"num_factor") #X轴标签//label of x-axis
plt.ylabel("mse") #Y轴标签//label of y-axis
plt.title("LFM(bias)'s mse") #标题//title
plt.show()
'''