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logistic_Count.py
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logistic_Count.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Apr 22 14:21:39 2018
@author: Hongtao Liu
"""
import datetime
start = datetime.datetime.now()
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from scipy.sparse import hstack
class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
train = pd.read_csv('train.csv',nrows=30000).fillna(' ')
#test = pd.read_csv('test.csv').fillna(' ')
train_text = train['comment_text']
#test_text = test['comment_text']
#all_text = pd.concat([train_text, test_text])
all_text=train_text
word_vectorizer = CountVectorizer(
# sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
stop_words='english',
ngram_range=(1, 1),
max_features=10000,binary=True)
word_vectorizer.fit(all_text)
train_word_features = word_vectorizer.transform(train_text)
#test_word_features = word_vectorizer.transform(test_text)
#char_vectorizer = CountVectorizer(
# sublinear_tf=True,
# strip_accents='unicode',
# analyzer='char',
# stop_words='english',
# ngram_range=(2, 6),
# max_features=50000)
#char_vectorizer.fit(all_text)
#train_char_features = char_vectorizer.transform(train_text)
#test_char_features = char_vectorizer.transform(test_text)
#train_features = hstack([train_char_features, train_word_features])
#train_features=hstack([train_char_features])
train_features=hstack([train_word_features])
#test_features = hstack([test_char_features, test_word_features])
scores = []
#submission = pd.DataFrame.from_dict({'id': test['id']})
for class_name in class_names:
train_target = train[class_name]
classifier = LogisticRegression(C=0.1, solver= 'liblinear')
cv_score = np.mean(cross_val_score(classifier, train_features, train_target, cv=5, scoring='roc_auc'))
scores.append(cv_score)
print('CV score for class {} is {}'.format(class_name, cv_score))
classifier.fit(train_features, train_target)
# submission[class_name] = classifier.predict_proba(test_features)[:, 1]
print('Total CV score is {}'.format(np.mean(scores)))
end = datetime.datetime.now()
print (end-start)
#submission.to_csv('submission.csv', index=False)