main.py

@@ -1,43 +0,0 @@
-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

@@ -1 +0,0 @@
-Subproject commit 7b30f5b953ef02b62dfb4b8a158ea5f1218d11e7