38 lines
869 B
Python
38 lines
869 B
Python
import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from sklearn.pipeline import make_pipeline
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def load_data(path):
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return pd.read_csv(path, sep='\t', header=None)
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def write_res(data, path):
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with open(path, 'w') as f:
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for line in data:
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f.write(f'{line}\n')
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print(f"Data written {path}/out.tsv")
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def main():
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train = load_data('train/train.tsv')
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text = train[4]
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year = train.apply(lambda row: ((row[0] + row[1])/2), axis=1)
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model = make_pipeline(TfidfVectorizer(), LinearRegression())
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model.fit(text, year)
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for path in ['dev-0', 'dev-1', 'test-A']:
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in_df = load_data(f'{path}/in.tsv')
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predict = model.predict(in_df[0])
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write_res(predict, f'{path}/out.tsv')
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if __name__ == '__main__':
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main() |