naive bayes solution

main.py

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+import pandas as pd
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.pipeline import make_pipeline
+
+train_set = pd.read_table('train/train.tsv.gz', error_bad_lines=False, header=None)
+dev_set = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None)
+test_set = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None)
+
+X_train = train_set[1].values
+y_train = train_set[0].values
+
+X_dev = dev_set[0].values
+X_test = test_set[0].values
+
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(X_train, y_train)
+
+dev_prediction = model.predict(X_dev)
+test_prediction = model.predict(X_test)
+
+dev_prediction.tofile('./dev-0/out.tsv', sep='\n')
+test_prediction.tofile('./test-A/out.tsv', sep='\n')