run.py

@@ -1,50 +0,0 @@
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LinearRegression
-
-with open('train/train.tsv', 'r', encoding='utf8') as f:
-    train_data = f.readlines()
-
-with open('train/meta.tsv', 'r', encoding='utf8') as f:
-    expected_years = f.readlines()
-
-for i, expected in enumerate(expected_years):
-    expected_years[i] = expected.split('\t')[5]
-#
-vectorizer = TfidfVectorizer(token_pattern=r"\b[a-zA-Z]+\b|[0-9]{4}[\.]?[a-z]{0,3}[\.]?")
-train = vectorizer.fit_transform(train_data)
-model = LinearRegression()
-model.fit(train, expected_years)
-
-with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
-    dev_0 = f.readlines()
-
-# prediction on test1 data
-dev_0 = vectorizer.transform(dev_0)
-predicted_dev_0 = model.predict(dev_0)
-
-with open('dev-0/out.tsv', 'wt') as f:
-    for p in predicted_dev_0:
-        f.write(str(p) + '\n')
-
-with open('dev-1/in.tsv', 'r', encoding='utf8') as f:
-    dev_1 = f.readlines()
-
-# prediction on test2 data
-dev_1 = vectorizer.transform(dev_1)
-predicted_dev_1 = model.predict(dev_1)
-
-with open('dev-1/out.tsv', 'wt') as f:
-    for p in predicted_dev_1:
-        f.write(str(p) + '\n')
-
-with open('test-A/in.tsv', 'r', encoding='utf8') as f:
-    test_A = f.readlines()
-
-test_A = vectorizer.transform(test_A)
-predicted_test_A = model.predict(test_A)
-
-with open('test-A/out.tsv', 'wt') as f:
-    for p in predicted_test_A:
-        f.write(str(p) + '\n')
-
-print("finished")