ver 1 - 10000 train set

main.py

@@ -0,0 +1,29 @@
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+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:])
+test_set = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, names=col_names[4:])
+
+train_set = train_set.head(10000)
+
+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(), 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')