initial commit

app.py

@@ -0,0 +1,31 @@
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.preprocessing import LabelEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+import numpy as np
+
+
+def getResult(filePath):
+    with open(filePath + 'in.tsv') as input:
+        predictData = input.readlines()
+        pred = model.predict(predictData)
+        with open(filePath + "out.tsv", "w") as result:
+            for prediction in pred:
+                result.write(str(prediction) + '\n')
+
+
+# trainInData = getData('./train/in.tsv')
+# trainExpData = getData('./train/expected.tsv')
+
+expectedTraining = open('./train/expected.tsv').readlines()
+inTraining = open('./train/in.tsv').readlines()
+
+afterTransform = LabelEncoder().fit_transform(expectedTraining)
+pipeline = Pipeline(
+    steps=[('tfidf', TfidfVectorizer()), ('naive-bayes', MultinomialNB())])
+
+model = pipeline.fit(inTraining, afterTransform)
+
+
+getResult('./dev-0/')
+getResult('./test-A/')