Added naive bayes

bayes2.py

@@ -4,10 +4,13 @@ import gzip
 from sklearn.pipeline import make_pipeline
 from sklearn.naive_bayes import MultinomialNB
 from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn import metrics
 
 # Read data
 dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None)
 test = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None)
+test_expected = pd.read_table(
+    'dev-0/expected.tsv', error_bad_lines=False, header=None)
 
 X_train = []
 y_train = []
@@ -25,9 +28,18 @@ y_train = np.asarray(y_train)
 X_dev = dev[0].values
 X_test = test[0].values
 
-print(type(y_train[0]))
-print(X_train[0])
-
 # Create model
 model = make_pipeline(TfidfVectorizer(), MultinomialNB())
 model.fit(X_train, y_train)
+
+# Predict
+dev_p = model.predict(X_dev)
+test_p = model.predict(X_test)
+
+# Accuracy
+score = metrics.accuracy_score(test_expected, dev_p)
+print("Accuracy:   %0.3f" % score)
+
+# Save to files
+dev_p.tofile('./dev-0/out.tsv', sep='\n')
+test_p.tofile('./test-A/out.tsv', sep='\n')