-
Notifications
You must be signed in to change notification settings - Fork 0
/
part-1.py
141 lines (115 loc) · 6.04 KB
/
part-1.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
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from glmnet import ElasticNet
from scipy import stats, special
from sklearn.linear_model import LinearRegression, ElasticNetCV
from sklearn.model_selection import cross_val_predict
def inverse_box_cox(y, ld, additive):
return special.inv_boxcox(y, ld) - additive
def plot_predictions(predictions, label, image_name, label_name):
plt.figure()
plt.plot(predictions, label - predictions, 'r.')
slope, intercept = np.polyfit(predictions, label - predictions, 1)
abline_values = [slope * i + intercept for i in predictions]
plt.plot(predictions, abline_values, 'b')
plt.xlabel("fitted ")
plt.ylabel("residual")
plt.title(label_name)
# plt.show()
plt.savefig(image_name)
plt.close('all')
def l1_l2_regression(alpha):
m = ElasticNet(n_splits=20, scoring='r2', alpha=alpha)
m.fit(music_features, box_latitude_label)
lat_r_squared = m.score(music_features, box_latitude_label)
print('GLMNET L1 L2 alpha {} latitude r2 {}'.format(alpha, lat_r_squared))
plot_predictions(inverse_box_cox(m.predict(music_features), lambda_lat, 90), latitude_label,
'l1_l2_latitude_residual_{}.png'.format(alpha),
'residual vs fitted latitude for l1_l2 \n regression alpha {}'.format(alpha))
m.fit(music_features, box_longitude_label)
lon_r_squared = m.score(music_features, box_longitude_label)
print('GLMNET L1 L2 alpha {} longitude r2 {}'.format(alpha, lon_r_squared))
plot_predictions(inverse_box_cox(m.predict(music_features), lambda_lon, 180), longitude_label,
'l1_l2_longitude_residual_{}.png'.format(alpha),
'residual vs fitted longitude for l1_l2 \n regression alpha {}'.format(alpha))
def linear_regression():
# global latitude_r_squared, longitude_r_squared
m = LinearRegression()
m.fit(music_features, latitude_label)
latitude_r_squared = m.score(music_features, latitude_label)
print('Linear regression latitude r2 {}'.format(latitude_r_squared))
plot_predictions(cross_val_predict(m, music_features, latitude_label,
cv=20), latitude_label, 'latitude_residual.png', 'residual vs fitted latitude')
m.fit(music_features, longitude_label)
longitude_r_squared = m.score(music_features, longitude_label)
print('Linear regression longitude r2 {}'.format(longitude_r_squared))
plot_predictions(cross_val_predict(m, music_features, longitude_label,
cv=20), longitude_label, 'longitude_residual.png',
'residual vs fitted longitude')
def box_cox_regression():
m = LinearRegression()
m.fit(music_features, box_latitude_label)
lat_r_squared = m.score(music_features, box_latitude_label)
print('Box Cox Linear regression latitude r2 {}'.format(lat_r_squared))
plot_predictions(
inverse_box_cox(m.predict(music_features), lambda_lat, 90),
latitude_label,
'box_latitude_residual.png',
'residual vs fitted boc cox latitude')
m.fit(music_features, box_longitude_label)
lon_r_squared = m.score(music_features, box_longitude_label)
print('Box Cox Linear regression longitude r2 {}'.format(lon_r_squared))
plot_predictions(
inverse_box_cox(m.predict(music_features), lambda_lon, 180),
longitude_label,
'box_longitude_residual.png',
'residual vs fitted boxcox longitude')
def glmnet_box():
m1 = ElasticNet(n_splits=20, scoring='r2', alpha=0)
m1.fit(music_features, box_latitude_label)
lat_r_squared = m1.score(music_features, box_latitude_label)
print('GLMNET ridge lattitude r2 {}'.format(lat_r_squared))
plot_predictions(inverse_box_cox(m1.predict(music_features), lambda_lat, 90), latitude_label,
'ridge_latitude_residual.png',
'residual vs fitted latitude for Ridge')
m1.fit(music_features, box_longitude_label)
lon_r_squared = m1.score(music_features, box_longitude_label)
print('GLMNET ridge longitude r2 {}'.format(lon_r_squared))
plot_predictions(inverse_box_cox(m1.predict(music_features), lambda_lon, 180), longitude_label,
'ridge_longitude_residual.png',
'residual vs fitted longitude for Ridge regression')
def glmnet_lasso():
m = ElasticNet(n_splits=20, scoring='r2', alpha=1)
m.fit(music_features, box_latitude_label)
latitude_r_squared = m.score(music_features, box_latitude_label)
print('GLMNET lasso latitude r2 {}'.format(latitude_r_squared))
plot_predictions(inverse_box_cox(m.predict(music_features), lambda_lat, 90), latitude_label,
'lasso_latitude_residual.png',
'residual vs fitted latitude for lasso regression')
m.fit(music_features, box_longitude_label)
longitude_r_squared = m.score(music_features, box_longitude_label)
print('GLMNET lasso longitude r2 {}'.format(longitude_r_squared))
plot_predictions(inverse_box_cox(m.predict(music_features), lambda_lon, 180), longitude_label,
'lasso_longitude_residual.png',
'residual vs fitted longitude for lasso regression')
if __name__ == '__main__':
all_data = pd.read_csv('./Geographical Original of Music/default_plus_chromatic_features_1059_tracks.txt',
names=None,
na_values=['?'], sep=',')
latitude_label = all_data.iloc[:, all_data.shape[1] - 2:all_data.shape[1] - 1].values.reshape(-1)
longitude_label = all_data.iloc[:, all_data.shape[1] - 1].values.reshape(-1)
music_features = all_data.iloc[:, 0:all_data.shape[1] - 2].values
linear_regression()
box_latitude_label, lambda_lat = stats.boxcox(latitude_label + 90)
box_longitude_label, lambda_lon = stats.boxcox(longitude_label + 180)
box_cox_regression()
# GLMNET For Ridge
glmnet_box()
# GLMNET for lasso
glmnet_lasso()
# GLMNET for variable alphas
l1_l2_regression(0.1)
l1_l2_regression(0.5)
l1_l2_regression(0.8)
print('done')