-
Notifications
You must be signed in to change notification settings - Fork 0
/
timeline_scraper.py
149 lines (122 loc) · 5.27 KB
/
timeline_scraper.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
144
145
146
147
148
149
import pandas as pd
import kanye_periods
#from py_genius import Genius
from vocabulary_builder import buildVocabulary
from sklearn.model_selection import train_test_split
from nltk.stem import PorterStemmer as ps
import numpy as np
import sys
ARTIST = "Kanye West"
ps = ps()
'''
# Given a song 'title' returns the period it belongs to
def set_period(title):
print("Song: ", title)
result = gen.search(title)['response']['hits']
for i in range(len(result)):
if(result[i]['result']['primary_artist']['name'] == ARTIST):
album = gen.get_song(result[i]['result']['id'])['response']['song']['album']
album = album if not album else album['name']
if(album in kanye_periods.PERIOD_1):
return 0
elif(album in kanye_periods.PERIOD_2):
return 1
elif(album in kanye_periods.PERIOD_3):
return 2
else:
return None
raw = pd.read_csv('data_scraping/finaldata.csv', delimiter="|")
artist_data = raw[raw['Artist'] == ARTIST]
gen = Genius('thRsqBz-IHPVCj0IU6_VNpRjRs9o2HGNxN_WAAoXCyOJPuRbhyb0MSJGNFcTnnlQ')
artist_data['Artist'] = artist_data['Title'].apply(set_period)
artist_data = artist_data.dropna(subset = ['Artist'])
artist_data.to_csv(ARTIST + '_t_data.csv', sep="|", index=False)
'''
def normalize(data):
bias = data[:, 0]
m, n = data.shape
means = (data.sum(axis=0) * 1.0 / m)
data = data - means
variance = np.square(data).sum(axis=0) * 1.0 / m
variance[variance == 0] = 1
data /= np.sqrt(variance)
data[:, 0] = bias
return data
def split_data(data, test_size):
train_data = pd.DataFrame(columns=['Artist', 'Title', 'Lyrics'])
test_data = pd.DataFrame(columns=['Artist', 'Title', 'Lyrics'])
for i in range(len(kanye_periods.PERIODS)):
artist_data = data[data['Artist'] == i]
artist_train, artist_test = train_test_split(artist_data, test_size=test_size, random_state=420)
train_data = train_data.append(artist_train)
test_data = test_data.append(artist_test)
return train_data, test_data
def featureExtractor(raw_data, filename, vocab, lower=0, upper=20000, TF='regular', verbose=0):
# setup
# set of all words that appear in the song
processed_data = []
#TF = 'binary'
K = 0.5
for data_pt in raw_data:
label = data_pt[0]
print label
vocab_dict = dict.fromkeys(vocab, 0)
words = data_pt[2].decode('utf-8').split()
lyrics = [ps.stem(word) for word in words]
def wordFrequencies(vocab_dict, lyrics):
for word in lyrics:
if word in vocab_dict:
vocab_dict[word] += 1
if TF == 'binary':
for word in lyrics:
if word in vocab_dict:
vocab_dict[word] = 1
elif TF == 'regular':
wordFrequencies(vocab_dict, lyrics)
elif TF == 'log':
wordFrequencies(vocab_dict, lyrics)
for word in lyrics:
if word in vocab_dict:
vocab_dict[word] = np.log(1 + vocab_dict[word])
elif TF == 'norm':
wordFrequencies(vocab_dict, lyrics)
max_freq = max(vocab_dict.values())
for word in lyrics:
if word in vocab_dict:
vocab_dict[word] = K + ((1 - K) * (vocab_dict[word] / max_freq))
phi = ([1] + list(vocab_dict.values()))
processed_data.append([label] + phi)
if(TF != 'binary'):
processed_data = np.array(processed_data)
x = processed_data[:, 2:]
y = processed_data[:, 0:2]
x = normalize(x)
processed_data = np.append(y, x, axis=1)
processed_df = pd.DataFrame(processed_data)
print(processed_df.head())
processed_df.to_csv(filename + '_' + TF + '.csv', index=False)
def initialize_data_sets(raw_data_path):
data = pd.read_csv(raw_data_path, delimiter='|')
train_data, test_data = split_data(data, .15)
train_data, dev_data = split_data(train_data, .125)
pd.DataFrame(train_data, columns=["Artist", "Title", "Lyrics"]).to_csv('t_kanye_train.csv', sep="|", index=False)
pd.DataFrame(dev_data, columns=["Artist", "Title", "Lyrics"]).to_csv('t_kanye_dev.csv', sep="|", index=False)
pd.DataFrame(test_data, columns=["Artist", "Title", "Lyrics"]).to_csv('t_kanye_test.csv', sep="|", index=False)
def main():
initialize_data_sets('Kanye West_t_data.csv')
train_data = pd.read_csv('t_kanye_train.csv', delimiter='|').as_matrix()
dev_data = pd.read_csv('t_kanye_dev.csv', delimiter='|').as_matrix()
test_data = pd.read_csv('t_kanye_test.csv', delimiter='|').as_matrix()
if len(sys.argv) > 1:
lower = sys.argv[1]
upper = sys.argv[2]
TF = sys.argv[3] if len(sys.argv) > 3 else 'regular'
strain = 'kanye_train_' + str(lower) + '-' + str(upper)
sdev = 'kanye_dev_' + str(lower) + '-' + str(upper)
stest = 'kanye_test_' + str(lower) + '-' + str(upper)
vocab = buildVocabulary(10, 1000, 't_kanye_train.csv')
featureExtractor(train_data, strain, vocab, lower, upper, TF)
featureExtractor(dev_data, sdev, vocab, lower, upper, TF, vocab)
featureExtractor(test_data, stest, vocab, lower, upper, TF, vocab)
if __name__ == "__main__":
main()