28 lines
747 B
Python
28 lines
747 B
Python
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from sklearn.naive_bayes import GaussianNB
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import pandas as pd
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer
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r_in = './train/train.tsv'
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r_ind_ev = './dev-0/in.tsv'
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tsv_read = pd.read_table(r_in, error_bad_lines=False, sep='\t', header=None)
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tsv_read_dev = pd.read_table(r_ind_ev, error_bad_lines=False, sep='\t', header=None)
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y_train = tsv_read[0].values
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X_train = tsv_read[1].values
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X_dev = tsv_read_dev[0].values
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vectorizer = TfidfVectorizer()
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counts = vectorizer.fit_transform(X_train)
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classifier = MultinomialNB()
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classifier.fit(counts, y_train)
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counts2 = vectorizer.transform(X_dev)
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predictions = classifier.predict(counts2)
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predictions.tofile("./dev-0/out.tsv", sep='\n')
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