/
train.py
175 lines (139 loc) · 6.75 KB
/
train.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
# -*- coding: utf-8 -*-
import argparse
import time, gc
import pandas as pd
from lstm import train_and_test, preprocess, test
import numpy as np
import torch
import os
import pickle
from sklearn.model_selection import train_test_split
def main():
print("Predict Youtube cross genre")
directory = 'data/csv/'
'''df_data, y = preprocess_data(directory, 'train_news_twitter.csv')
df_test, test_y = preprocess_data(directory, 'youtube_train.csv')
train_and_test(df_data, y, df_test, test_y, 100, 'youtube')
print("Predict News cross genre")
directory = 'data/csv/'
df_data, y = preprocess_data(directory, 'train_youtube_twitter.csv')
df_test, test_y = preprocess_data(directory, 'news_train.csv')
train_and_test(df_data, y, df_test, test_y, 100, 'news')'''
print("Predict Twitter cross genre")
#directory = 'data/csv/'
#df_data, y = preprocess_data(directory, 'twitter_train.csv')
#df_test, test_y = preprocess_data(directory, 'twitter_train.csv')
#print("Shape of train and test: ", df_data.shape, df_test.shape)
#train_and_test(df_data, y, df_test, test_y, 100, 'twitter')
'''directory = 'data/csv/'
df_data, y, df_test, test_y = preprocess_data(directory, 'surprise_test.csv', split=True)
print("Shape of train and test: ", df_data.shape, df_test.shape)
train_and_test(df_data, y, df_test, test_y, 100, 'surprise')'''
#cross genre
'''model = 'models/news_model_cg_0.557.pt'
model = torch.load(model)
corpus = pickle.load(open('models/news_corpus_cg_0.557.pk', 'rb'))
corpus.batch_size = 16
model.batch_size = 16
df_test, test_y = preprocess_data(directory, 'twitter_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_twitter_2', test=False)
model = 'models/news_model_cg_0.557.pt'
model = torch.load(model)
corpus = pickle.load(open('models/news_corpus_cg_0.557.pk', 'rb'))
corpus.batch_size = 10
model.batch_size = 10
df_test, test_y = preprocess_data(directory, 'news_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_news_1', test=False)
model = 'models/youtube_model_cg_0.558.pt'
model = torch.load(model)
corpus = pickle.load(open('models/youtube_corpus_cg_0.558.pk', 'rb'))
corpus.batch_size = 2
model.batch_size = 2
df_test, test_y = preprocess_data(directory, 'youtube_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_youtube_1', test=False)'''
'''model = 'models/news_model_in.pt'
model = torch.load(model)
corpus = pickle.load(open('models/news_corpus_in.pk', 'rb'))
corpus.batch_size = 1
model.batch_size = 1
df_test, test_y = preprocess_data(directory, 'surprise_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_kb_1', test=False)'''
#in_genre
model = 'models/youtube_model_in.pt'
model = torch.load(model)
corpus = pickle.load(open('models/youtube_corpus_in.pk', 'rb'))
corpus.batch_size = 2
model.batch_size = 2
df_test, test_y = preprocess_data(directory, 'youtube_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_IN_youtube_1', test=False)
'''model = 'models/news_model_in.pt'
model = torch.load(model)
corpus = pickle.load(open('models/news_corpus_in.pk', 'rb'))
corpus.batch_size = 10
model.batch_size = 10
df_test, test_y = preprocess_data(directory, 'news_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_IN_news_1', test=False)
model = 'models/twitter_model_in.pt'
model = torch.load(model)
corpus = pickle.load(open('models/twitter_corpus_in.pk', 'rb'))
corpus.batch_size = 16
model.batch_size = 16
df_test, test_y = preprocess_data(directory, 'twitter_test.csv', predict=True)
test(df_test, test_y, model, corpus, 'IJS-KD_IN_twitter_1', test=False)'''
def preprocess_data(directory, input_file, delimiter="\t", predict=False, split=False):
# uncomment this to read data from csv
data_iterator = pd.read_csv(directory + input_file, encoding="utf-8", delimiter=delimiter, chunksize=1000)
df_data = pd.DataFrame()
for sub_data in data_iterator:
df_data = pd.concat([df_data, sub_data], axis=0)
gc.collect()
print("Data shape before preprocessing:", df_data.shape)
#df_data = df_data[:100]
df_data = preprocess(df_data)
df_data.to_csv(directory + "data_preprocessed.csv", encoding="utf8", sep="\t", index=False)
print(df_data.columns.tolist())
# shuffle the corpus and optionaly choose the chunk you want to use if you don't want to use the whole thing - will be much faster
df_data = df_data.sample(frac=1, random_state=1)
print("Data shape: ", df_data.shape)
if split:
df_train, df_test = train_test_split(df_data, test_size=0.1)
tags = df_train.gender
m_data = df_train[df_train['gender'] == 'M']
f_data = df_train[df_train['gender'] == 'F']
print('Males: ', m_data.shape, 'Females: ', f_data.shape)
df_train = df_train.drop(['gender'], axis=1)
y_train = np.array([0 if tmp_y=='M' else 1 for tmp_y in tags])
tags = df_test.gender
m_data = df_test[df_test['gender'] == 'M']
f_data = df_test[df_test['gender'] == 'F']
print('Males: ', m_data.shape, 'Females: ', f_data.shape)
df_test = df_test.drop(['gender'], axis=1)
y_test = np.array([0 if tmp_y == 'M' else 1 for tmp_y in tags])
print('All shape: ', df_train.shape, y_train.shape, df_test.shape, y_test.shape)
return df_train, y_train, df_test, y_test
else:
if predict:
tags = df_data.id
else:
tags = df_data.gender
m_data = df_data[df_data['gender'] == 'M']
f_data = df_data[df_data['gender'] == 'F']
print('Males: ', m_data.shape, 'Females: ', f_data.shape)
df_data = df_data.drop(['gender'], axis=1)
if not predict:
y = np.array([0 if tmp_y=='M' else 1 for tmp_y in tags])
else:
y = np.array([tmp_y for tmp_y in tags])
return df_data, y
if __name__ == '__main__':
start_time = time.time()
# run from command line
# e.g. python3 gender_classification.py --input './pan17-author-profiling-training-dataset-2017-03-10' --output results --language en
argparser = argparse.ArgumentParser(description='Clin gender evaluation')
argparser.add_argument('-c', '--input', dest='input', type=str,
default='data/weebit',
help='Choose input trainset')
# args = argparser.parse_args()
main()
print("--- Model creation in minutes ---", round(((time.time() - start_time) / 60), 2))
print("--- Training & Testing in minutes ---", round(((time.time() - start_time) / 60), 2))