39 lines
898 B
Python
39 lines
898 B
Python
import numpy as np
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from sklearn.preprocessing import LabelEncoder
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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def Create_model(X_tsv, Y_tsv):
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with open(X_tsv) as f:
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X = f.readlines()
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with open(Y_tsv) as f:
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Y = f.readlines()
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Y = LabelEncoder().fit_transform(Y)
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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return pipeline.fit(X, Y)
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def predict(model, X_tsv, file_name):
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with open(X_tsv) as f:
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X = f.readlines()
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prediction = model.predict(X)
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np.savetxt(file_name, prediction, fmt='%d')
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def main():
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model = Create_model("train/in.tsv", "train/expected.tsv")
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predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
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predict(model, "test-A/in.tsv", "test-A/out.tsv")
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if __name__ == '__main__':
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main() |