25 lines
1008 B
Python
25 lines
1008 B
Python
#!/usr/bin/env python
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# coding: utf-8
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.metrics import accuracy_score
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from sklearn.feature_extraction.text import CountVectorizer
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import lzma
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X_train = lzma.open("train/in.tsv.xz", mode='rt', encoding='utf-8').readlines()
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y_train = open('train/expected.tsv').readlines()
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X_dev0 = lzma.open("dev-0/in.tsv.xz", mode='rt', encoding='utf-8').readlines()
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y_expected_dev0 = open("dev-0/expected.tsv", "r").readlines()
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X_test = lzma.open("test-A/in.tsv.xz", mode='rt', encoding='utf-8').readlines()
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count_vect = CountVectorizer()
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X_train_counts = count_vect.fit_transform(X_train)
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X_dev0_counts = count_vect.transform(X_dev0)
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X_test_counts = count_vect.transform(X_test)
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clf = MultinomialNB().fit(X_train_counts, y_train)
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y_predicted_dev0_MNB = clf.predict(X_dev0_counts)
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y_predicted_test_MNB = clf.predict(X_test_counts)
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open("dev-0/out.tsv", mode='w').writelines(y_predicted_dev0_MNB)
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open("test-A/out.tsv", mode='w').writelines(y_predicted_test_MNB)
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