bayes solution one

main.py

@@ -0,0 +1,34 @@
+import numpy
+from sklearn.preprocessing import LabelEncoder
+from sklearn.naive_bayes import GaussianNB, MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+def trainModel(trainFileIn, trainFileExpected):
+    with open(trainFileExpected, 'r') as f:
+        expectedData = f.readlines()
+
+    with open(trainFileIn, 'r') as f:
+        inData = f.readlines()
+
+    expectedDataEncoded = LabelEncoder().fit_transform(expectedData)
+
+    pipeline = Pipeline(steps=[
+        ('tfidf', TfidfVectorizer()),
+        ('naive-bayes', MultinomialNB())
+    ])
+
+    return pipeline.fit(inData, expectedDataEncoded)
+
+def evaluateModel(model, inFile, outFile):
+    with open(inFile, 'r') as f:
+        inData = f.readlines()
+    prediction = model.predict(inData)
+    numpy.savetxt(outFile, prediction, fmt='%d', delimiter='\n')
+
+model = trainModel("train/in.tsv", "train/expected.tsv")
+evaluateModel(model, "dev-0/in.tsv", "dev-0/out.tsv")
+evaluateModel(model, "test-A/in.tsv", "test-A/out.tsv")
+
+