-
Notifications
You must be signed in to change notification settings - Fork 3
/
data_pp.py
187 lines (146 loc) · 6.12 KB
/
data_pp.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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Data pre-processing and preparation
"""
import pickle
import util
import pandas as pd
import sklearn.linear_model
import numpy as np
import scipy
def get_data_pk():
f = open('edata.pkl')
x = pickle.load(f)
f.close()
return x
def url_to_source(url):
try:
return url.split('/')[2]
except Exception:
if url.find('antiviral.gawker.com') > 0: return "antiviral.gawker.com"
if url.find('twitter') > 0: return "twitter"
print url
return -1
# appling a dic to a column
def apply_dic(data, dic, col_name):
f = lambda row: dic[row[col_name]]
return data.apply(f, axis = 1)
def process_data(data):
# add url/body/a_stances to data
(dic_url, dic_body, dic_as) = util.get_dic_aid()
#aid_url = pd.DataFrame( dic_url.items(), columns = ['articleId', 'url'])
#data = data.merge(aid_url)
#data.assign(url = data.apply(lambda row: dic_url[row['articleId']], axis = 1) )
data = data.assign(url = apply_dic(data, dic_url, 'articleId'))
data = data.assign(source = data.apply(lambda row: url_to_source(row['url']), axis = 1))
data = data.assign(body = apply_dic(data, dic_body, 'articleId'))
data = data.assign(astance = apply_dic(data, dic_as, 'articleId'))
# add claim truth label to data
dic_truth = util.get_dic_truth()
#cid_truth = pd.DataFrame( dic_truth.items(), columns = ['claimId', 'truth'])
#data = data.merge(cid_truth)
#data.assign(claimTruth = data.apply(lambda row: dic_truth[row['claimId']], axis = 1))
data = data.assign(claimTruth = apply_dic(data, dic_truth, 'claimId'))
# add counts to data
# get unique claims
claims = pd.Series(data.claimId).unique()
#claim_tab = pd.DataFrame({'claimId': claims, 'claimCount': range(1, len(claims)+1)})
dic_claims = {c: (i+1) for i, c in enumerate(claims)}
articles = pd.Series(data.articleId).unique()
#article_tab = pd.DataFrame({'articleId': articles, 'articleCount': range(1, len(articles)+1)})
dic_articles = {a: (i+1) for i, a in enumerate(articles)}
sources = pd.Series(data.source).unique()
#source_tab = pd.DataFrame({'source': sources, 'sourceCount': range(1, len(sources)+1)})
dic_sources = {s: (i+1) for i, s in enumerate(sources)}
data = data.assign(claimCount = apply_dic(data, dic_claims, 'claimId'))
data = data.assign(articleCount = apply_dic(data, dic_articles, 'articleId'))
data = data.assign(sourceCount = apply_dic(data, dic_sources, 'source'))
return data
def make_stan_input(data, X, data_test = None, X_test = None, mul_lr = False):
"""
data_test, X_test: include (unlabeled) test data
mul_lr: input for mul_lr model
"""
# data_all includes train and test data
if X_test != None:
data_all = pd.concat([data, data_test], ignore_index = True)
X_all = scipy.sparse.vstack([X, X_test])
else:
data_all = data
X_all = X
n = data_all.articleCount.max()
m = 1 # number of workers
k = data_all.claimCount.max()
o = data_all.sourceCount.max()
# make a list of triplets (claim, stance, souce)
# representing the connections between claims, stances and sources
# not including claim nor stance labels
nl = len(data_all)
list_claim = data_all.claimCount.values.tolist()
list_stance = data_all.articleCount.values.tolist()
list_source = data_all.sourceCount.values.tolist()
# stance labels
stance_dic = {'against': 1, 'observing': 2, 'for': 3}
stance_l = map(lambda x: stance_dic[x], data.articleHeadlineStance.values.tolist())
ns = len(stance_l)
stance_wid = [1] * ns
stance_iid = data.articleCount.tolist()
# claim labels
claim_dic = {'false': 1, 'unknown': 2, 'true': 3}
#claim_l = data.drop_duplicates(subset = 'claimCount').sort_values('claimCount').claimTruth
#claim_l = map(lambda x: claim_dic[x], claim_l)
(claims, claim_l) = util.extract_truth_labels(data)
claim_l = map(lambda x: claim_dic[x], claim_l)
nc = len(claim_l)
claim_wid = [1] * nc
claim_iid = claims
# source labels
no = 1
source_l = [1]
source_wid = [1]
source_iid = [1]
#clf = sklearn.linear_model.LogisticRegression(multi_class='multinomial',\
# solver='lbfgs', C = 1)
clf = sklearn.linear_model.LogisticRegression(penalty = 'l1')
clf.fit(X, data.articleHeadlineStance)
stance_mean = clf.intercept_ + X_all.toarray().dot( clf.coef_.T)
stance_mean = stance_mean[:, [0, 2, 1]]
res = {'n': n,
'm': m,
'k': k,
'o': o,
'nl': nl,
'list_claim': list_claim,
'list_stance': list_stance,
'list_source': list_source,
'ns': ns,
'stance_l': stance_l,
'stance_wid': stance_wid,
'stance_iid': stance_iid,
'nc': nc,
'claim_l': claim_l,
'claim_wid': claim_wid,
'claim_iid': claim_iid,
'no': no,
'source_l': source_l,
'source_wid': source_wid,
'source_iid': source_iid,
#'c': c,
'dim_s': 518,
'fs': X_all.toarray(),
'ws': clf.coef_.T,
'stance_intercept': clf.intercept_,
'ws_var': 1,
'stance_mean': stance_mean,
'source_score': np.zeros((o,)),
'source_score_var': 2,
'claim_intercept': np.zeros((3,))
}
if mul_lr:
res['n'] = 1489
res['fs'] = X.toarray()
#if X_test != None:
# stance_mean_test = clf.intercept_ + X_test.toarray().dot( clf.coef_.T)
# res['stance_mean_test'] = stance_mean_test
return res