predictions

main.py

@@ -0,0 +1,34 @@
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import pandas as pd
+
+train = pd.read_csv('train/train.tsv', sep='\t', header=None, error_bad_lines=False)
+X_train = train[0].astype(str).tolist()
+Y_train = train[1].astype(str).tolist()
+
+naive_b = MultinomialNB()
+count_vec = CountVectorizer()
+
+Y_train=count_vec.fit_transform(Y_train)
+naive_b.fit(Y_train, X_train)
+
+dev = pd.read_csv('dev-0/in.tsv', sep='\n', header=None)
+X_dev = dev[0].astype(str).tolist()
+Y_dev = count_vec.transform(X_dev)
+dev_predict = naive_b.predict(Y_dev)
+dev_out = open('dev-0/out.tsv', 'w')
+
+for p in dev_predict:
+    dev_out.write(p + '\n')
+
+test = pd.read_csv('test-A/in.tsv', sep='\n', header=None)
+X_test = test[0].astype(str).tolist()
+Y_test = count_vec.transform(X_test)
+test_predict = naive_b.predict(Y_test)
+test_out = open('test-A/out.tsv', 'w')
+
+for p in test_predict:
+    test_out.write(p + '\n')
+
+