-
Notifications
You must be signed in to change notification settings - Fork 0
/
rateRegress.py
144 lines (110 loc) · 4.41 KB
/
rateRegress.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
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
import justTry
from sklearn.linear_model import LinearRegression, Lasso, Ridge
import numpy as np
import math
from copy import deepcopy
bus = justTry.getBusFeatVector()
userrevs = justTry.getUserReviews()
for business in bus:
business.dico["reviews"] = {}
bustab = {business.id:business for business in bus}
trainuserrevs = {}
testuserrevs = {}
count = 0
for user in userrevs.keys():
for review in userrevs[user]:
if count % 10 == 0:
if testuserrevs.get(user, None) is None:
testuserrevs[user] = {}
testuserrevs[user][review] = userrevs[user][review]
else:
if trainuserrevs.get(user, None) is None:
trainuserrevs[user] = {}
trainuserrevs[user][review] = userrevs[user][review]
count += 1
for user in trainuserrevs.keys():
for business in trainuserrevs[user].keys():
bustab[business].dico["reviews"][user] = trainuserrevs[user][business]
total = 0
count = 0
for user in trainuserrevs.keys():
for business in trainuserrevs[user].keys():
total += trainuserrevs[user][business]
count += 1
revavg = total/float(count)
def specialAvg(items, key):
nums = [items[item] for item in items.keys() if item != key]
if len(nums) > 0:
return sum(nums)/float(len(nums))
else:
return revavg
trainvec = []
for key in trainuserrevs.keys():
trainvecinternal = []
for business in trainuserrevs[key].keys():
busvec = bustab[business].vector
busvec[69] = specialAvg(bustab[business].dico["reviews"],key)
np.append(busvec, specialAvg(trainuserrevs[key], business))
trainvecinternal.append((key, business, trainuserrevs[key][business], busvec))
trainvec.append(trainvecinternal)
#trainvec = [[(key, business, trainuserrevs[key][business], np.append(bustab[business].vector,specialAvg(bustab[business],key))) for business in trainuserrevs[key].keys()] for key in trainuserrevs.keys()]
testvec = []
for key in testuserrevs.keys():
testvecinternal = []
for business in testuserrevs[key].keys():
busvec = bustab[business].vector
busvec[69] = specialAvg(bustab[business].dico["reviews"],key)
np.append(busvec, specialAvg(testuserrevs[key], business))
testvecinternal.append((key, business, testuserrevs[key][business], busvec))
testvec.append(testvecinternal)
#testvec = [[(key, business, testuserrevs[key][business], np.append(bustab[business].vector,revavg)) for business in testuserrevs[key].keys()] for key in testuserrevs.keys()]
actualtrainvec = []
map(actualtrainvec.extend, trainvec)
trainuserids = []
trainbusinessids = []
trainactualratings = []
trainvectors = []
for (user, business, actualrating, vec) in actualtrainvec:
trainuserids.append(user)
trainbusinessids.append(business)
trainactualratings.append(actualrating)
trainvectors.append(vec)
actualtestvec = []
map(actualtestvec.extend, testvec)
testuserids = []
testbusinessids = []
testactualratings = []
testvectors = []
for (user, business, actualrating, vec) in actualtestvec:
testuserids.append(user)
testbusinessids.append(business)
testactualratings.append(actualrating)
testvectors.append(vec)
xtrain = trainvectors
xtest = testvectors
ytrain = trainactualratings
ytest = testactualratings
lreg = LinearRegression()
lreg.fit(xtrain, ytrain, n_jobs=-1)
lasso = Lasso()
lasso.fit(xtrain, ytrain)
ridge = Ridge()
ridge.fit(xtrain, ytrain)
lregpredictions = lreg.predict(xtest)
lassopredictions = lasso.predict(xtest)
ridgepredictions = ridge.predict(xtest)
sqerrors = [(test - pred)**2 for test, pred in zip(ytest, lregpredictions)]
sqerror = sum(sqerrors)/float(len(sqerrors))
abserrors = [abs(test - pred) for test, pred in zip(ytest, lregpredictions)]
abserror = sum(abserrors)/float(len(abserrors))
print "Linear:", math.sqrt(sqerror), abserror
sqerrors = [(test - pred)**2 for test, pred in zip(ytest, lassopredictions)]
sqerror = sum(sqerrors)/float(len(sqerrors))
abserrors = [abs(test - pred) for test, pred in zip(ytest, lassopredictions)]
abserror = sum(abserrors)/float(len(abserrors))
print "Lasso:", math.sqrt(sqerror), abserror
sqerrors = [(test - pred)**2 for test, pred in zip(ytest, ridgepredictions)]
sqerror = sum(sqerrors)/float(len(sqerrors))
abserrors = [abs(test - pred) for test, pred in zip(ytest, ridgepredictions)]
abserror = sum(abserrors)/float(len(abserrors))
print "Ridge:", math.sqrt(sqerror), abserror