s444356

run.py

@@ -0,0 +1,49 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+
+with open('train/train.tsv', 'r', encoding='utf8') as f:
+    train = f.readlines()
+
+with open('train/meta.tsv', 'r', encoding='utf8') as f:
+    expected = f.readlines()
+
+for i in range(0, len(expected)):
+    expected[i] = expected[i].split('\t')[5]
+
+vectorizer = TfidfVectorizer()
+train = vectorizer.fit_transform(train)
+model = LinearRegression()
+model.fit(train, expected)
+
+with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
+    dev_0 = f.readlines()
+
+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')
+f.close()
+
+with open('dev-1/in.tsv', 'r', encoding='utf8') as f:
+    dev_1 = f.readlines()
+
+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')
+f.close()
+
+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')
+f.close()