2021-05-12 21:22:41 +02:00
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import csv
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2021-05-12 21:00:14 +02:00
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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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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col_names = ['start_date', 'end_date', 'title', 'source', 'content']
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train_set = pd.read_table('train/train.tsv.xz', error_bad_lines=False, header=None, names=col_names)
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2021-05-12 21:22:41 +02:00
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dev_set = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None, names=col_names[4:], quoting=csv.QUOTE_NONE,)
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test_set = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, names=col_names[4:], quoting=csv.QUOTE_NONE,)
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2021-05-12 21:00:14 +02:00
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X_train = train_set['content']
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y_train = (train_set['start_date'] + train_set['end_date']) / 2
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X_dev = dev_set['content']
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X_test = test_set['content']
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print('Trenowanie modelu...')
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model = make_pipeline(TfidfVectorizer(), LinearRegression())
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model.fit(X_train, y_train)
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print('Predykcje...')
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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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