/
regression_methods.py
68 lines (60 loc) · 2.36 KB
/
regression_methods.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
from sklearn.linear_model import SGDRegressor, LinearRegression, Lasso, LassoLars
from sklearn import svm, tree
import numpy as np
import scipy
from sklearn import cross_validation, metrics
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
def Linear_Regression(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = LinearRegression()
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val+= metrics.mean_squared_error(y_test, y_pred)
print y_pred, y_test
return val/3
# print "Linear_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)
def Lars_Lasso(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = LassoLars(alpha=.1)
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val+= metrics.mean_squared_error(y_test, y_pred)
return val/3
# print "Lasso_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)
def Lasso_Regression(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = Lasso(alpha=0.1)
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val+= metrics.mean_squared_error(y_test, y_pred)
return val/3
# print "Lasso_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)
def SVM_Regression(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = svm.SVR()
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val+= metrics.mean_squared_error(y_test, y_pred)
return val/3
# print "SVM_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)
def SGD_Regression(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15,n_iter=5)
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val += metrics.mean_squared_error(y_test, y_pred)
return val/3
# print "SGD_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)