first

main.py

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+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+from stop_words import get_stop_words
+import pandas as pd
+import numpy as np
+import csv
+
+
+lm_model = LinearRegression()
+tfidvectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
+    
+    
+train_nm = ['start_date', 'end_date', 'title', 'sort_title', 'data']
+train_nm_test = ['data']
+
+dataset = []
+processed = []
+new_text = ""
+    
+train_file = pd.read_csv('train/train.tsv', sep="\t", names=train_nm)
+print('DONE20!')
+date = (train_file['start_date'] + train_file['end_date']) / 2
+print('DONE22!')
+vectorizer= tfidvectorizer.fit_transform(train_file['data'])
+print('DONE24!')
+lm_model.fit(vectorizer, date)
+print('DONE26!')
+   
+dev_0 = pd.read_csv("dev-0/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE)
+dev_1 = pd.read_csv("dev-1/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE,)
+test = pd.read_csv("test-A/in.tsv", names = train_nm, sep = "\t")
+print('DONE31!')
+
+
+test_file= tfidvectorizer.transform(test['data'])
+test_file_predict = lm_model.predict(test_file)
+with open('test-A/out.tsv', 'w') as file:
+    for i in test_file_predict:
+        file.write("%f\n" % i)
+
+print('DONE38!')
+
+

python-stop-words

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+Subproject commit 7b30f5b953ef02b62dfb4b8a158ea5f1218d11e7