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