main.py

@@ -1,29 +0,0 @@
-import csv
-import pandas as pd
-from sklearn.linear_model import LinearRegression
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-from stop_words import get_stop_words
-
-col_names = ['start_date', 'end_date', 'title', 'source', 'content']
-
-train_set = pd.read_table('train/train.tsv.xz', error_bad_lines=False, header=None, names=col_names)
-dev_set = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None, names=col_names[4:], quoting=csv.QUOTE_NONE,)
-test_set = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, names=col_names[4:], quoting=csv.QUOTE_NONE,)
-
-X_train = train_set['content']
-y_train = (train_set['start_date'] + train_set['end_date']) / 2
-
-X_dev = dev_set['content']
-X_test = test_set['content']
-
-print('Trenowanie modelu...')
-model = make_pipeline(TfidfVectorizer(stop_words=get_stop_words('polish')), LinearRegression())
-model.fit(X_train, y_train)
-
-print('Predykcje...')
-dev_prediction = model.predict(X_dev)
-test_prediction = model.predict(X_test)
-
-dev_prediction.tofile('./dev-0/out.tsv', sep='\n')
-test_prediction.tofile('./test-A/out.tsv', sep='\n')