Added naive bayes

bayes2.py

@@ -0,0 +1,45 @@
+import pandas as pd
+import numpy as np
+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 = []
+
+with gzip.open('train/train.tsv.gz', 'r') as f:
+    for l in f:
+        line = l.decode('UTF-8').replace("\n", "").split("\t")
+        y_train.append(int(line[0]))
+        X_train.append(str(line[1:]))
+
+# Convert to unified types
+X_train = np.asarray(X_train)
+y_train = np.asarray(y_train)
+
+X_dev = dev[0].values
+X_test = test[0].values
+
+# 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')