application_vnd.jupyter.stderr_output_1.png
from sklearn.model_selection import train_test_split

import numpy as np

import numpy as np # linear algebra

import pandas as pd

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

train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
list_sentences_train = train['comment_text'].fillna('unknown').values
list_classes = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
y = train[list_classes].values
list_sentences_test = test['comment_text'].fillna('unknown').values

from sklearn.model_selection import train_test_split
print('Positive Labels ')
any_category_positive = np.sum(y, 1)
print(pd.value_counts(any_category_positive))
X_t_train, X_t_test, y_train, y_test = train_test_split(X_t, y, test_size=0.1)
print('Training:', X_t_train.shape)
print('Testing:', X_t_test.shape)