44 lines
1.3 KiB
Python
44 lines
1.3 KiB
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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import numpy as np
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import csv
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lm_model = LinearRegression()
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tfidvectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
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train_nm = ['start_date', 'end_date', 'title', 'sort_title', 'data']
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train_nm_test = ['data']
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dataset = []
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processed = []
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new_text = ""
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train_file = pd.read_csv('train/train.tsv', sep="\t", names=train_nm)
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print('DONE20!')
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date = (train_file['start_date'] + train_file['end_date']) / 2
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print('DONE22!')
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vectorizer= tfidvectorizer.fit_transform(train_file['data'])
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print('DONE24!')
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lm_model.fit(vectorizer, date)
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print('DONE26!')
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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 = train_nm, sep = "\t")
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print('DONE31!')
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test_file= tfidvectorizer.transform(test['data'])
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test_file_predict = lm_model.predict(test_file)
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with open('test-A/out.tsv', 'w') as file:
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for i in test_file_predict:
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file.write("%f\n" % i)
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print('DONE38!')
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