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dev-0/meta.tsv
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dev-0/meta.tsv
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dev-0/out.tsv
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dev-0/out.tsv
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dev-1/meta.tsv
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dev-1/meta.tsv
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linear-regression.py
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linear-regression.py
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from re import L
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import pandas as pd
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import numpy as np
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import csv
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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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from sklearn.feature_extraction.text import TfidfVectorizer
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def linear_regression():
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# odczyt z plików
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colnames_train = ['start_date', 'end_date', 'title', 'sort_title', 'data']
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colnames_test = ['data']
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train = pd.read_csv("train/train.tsv", names = colnames_train, sep = "\t")
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dev_0 = pd.read_csv("dev-0/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE)
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dev_1 = pd.read_csv("dev-1/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE,)
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test = pd.read_csv("test-A/in.tsv", names = colnames_test, sep = "\t")
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# stworzenie instancji TFIDF i regresji liniowej
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tf = TfidfVectorizer(stop_words=get_stop_words('polish'))
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lin_reg = LinearRegression()
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# wydobycie daty
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date = (train['start_date'] + train['end_date']) / 2
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# regresja liniowa
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train_vec = tf.fit_transform(train['data'])
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lin_reg.fit(train_vec, date)
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# predykcja dla dev-0
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evaluate_dev = tf.transform(dev_0['data'])
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prediction_dev = lin_reg.predict(evaluate_dev)
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pd.DataFrame(prediction_dev).to_csv('dev-0/out2.tsv', sep = "\t", index = False, header = False)
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# predykcja dla test-A
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evaluate_test = tf.transform(test['data'])
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prediction_test = lin_reg.predict(evaluate_test)
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pd.DataFrame(prediction_test).to_csv('test-A/out2.tsv', sep = "\t", index = False, header = False)
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return None
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if __name__ == "__main__":
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linear_regression()
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# geval: 21.80
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test-A/out.tsv
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test-A/out.tsv
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train/meta.tsv
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train/meta.tsv
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train/train.tsv
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train/train.tsv
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