20 lines
724 B
Python
20 lines
724 B
Python
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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 stop_words import get_stop_words
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import pandas as pd
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meh = TfidfVectorizer(stop_words = get_stop_words('polish'))
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linReg = LinearRegression()
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colnames = ['start_date', 'end_date', 'title', 'sort_title', 'data']
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t = pd.read_csv('test-A/in.tsv', sep = "\t", names = colnames)
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datFin = pd.read_csv('train/train.tsv', sep = "\t", names = colnames)
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date = (datFin['start_date'] + datFin['end_date']) / 2
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trn = meh.fit_transform(datFin['data'])
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linReg.fit(trn, date)
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r = meh.transform(t['data'])
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x = linReg.predict(r)
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with open('test-A/out.tsv', 'w') as file:
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for each in x:
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file.write("%f\n" % each)
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