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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os
print(os.listdir("../input"))

# Any results you write to the current directory are saved as output.


    

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import numpy as np
import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from scipy.sparse import hstack


    

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class_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']

train = pd.read_csv('../input/train.csv').fillna(' ')
test = pd.read_csv('../input/test.csv').fillna(' ')

train_text = train['comment_text']
test_text = test['comment_text']
all_text = pd.concat([train_text, test_text])


    

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print(all_text.shape)
print(all_text.head())


    

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word_vectorizer = TfidfVectorizer(
    sublinear_tf=True,
    strip_accents='unicode',
    analyzer='word',
    token_pattern=r'\w{1,}',
    stop_words='english',
    ngram_range=(1, 1),
    max_features=100)#10000
word_vectorizer.fit(all_text)
train_word_features = word_vectorizer.transform(train_text)
test_word_features = word_vectorizer.transform(test_text)


    

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print(train_word_features.shape)
print(train_word_features)


    

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char_vectorizer = TfidfVectorizer(
    sublinear_tf=True,
    strip_accents='unicode',
    analyzer='char',
    stop_words='english',
    ngram_range=(2, 6),
    max_features=500)#50000
char_vectorizer.fit(all_text)
train_char_features = char_vectorizer.transform(train_text)
test_char_features = char_vectorizer.transform(test_text)


    

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train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])


    

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train_features.shape


    

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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='sag')

    cv_score = np.mean(cross_val_score(classifier, train_features, train_target, cv=3, 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)))


    

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submission.to_csv('submission.csv', index=False)