forked from kubapok/retroc2
50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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with open('train/train.tsv', 'r', encoding='utf8') as f:
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train_data = f.readlines()
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with open('train/meta.tsv', 'r', encoding='utf8') as f:
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expected_years = f.readlines()
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for i, expected in enumerate(expected_years):
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expected_years[i] = expected.split('\t')[5]
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#
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vectorizer = TfidfVectorizer(token_pattern=r"\b[a-zA-Z]+\b|[0-9]{4}[\.]?[a-z]{0,3}[\.]?")
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train = vectorizer.fit_transform(train_data)
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model = LinearRegression()
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model.fit(train, expected_years)
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with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
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dev_0 = f.readlines()
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# prediction on test1 data
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dev_0 = vectorizer.transform(dev_0)
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predicted_dev_0 = model.predict(dev_0)
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with open('dev-0/out.tsv', 'wt') as f:
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for p in predicted_dev_0:
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f.write(str(p) + '\n')
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with open('dev-1/in.tsv', 'r', encoding='utf8') as f:
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dev_1 = f.readlines()
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# prediction on test2 data
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dev_1 = vectorizer.transform(dev_1)
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predicted_dev_1 = model.predict(dev_1)
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with open('dev-1/out.tsv', 'wt') as f:
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for p in predicted_dev_1:
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f.write(str(p) + '\n')
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with open('test-A/in.tsv', 'r', encoding='utf8') as f:
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test_A = f.readlines()
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test_A = vectorizer.transform(test_A)
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predicted_test_A = model.predict(test_A)
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with open('test-A/out.tsv', 'wt') as f:
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for p in predicted_test_A:
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f.write(str(p) + '\n')
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print("finished") |