app.py

@@ -1,28 +0,0 @@
-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 makePrediction(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')
-
-
-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)
-
-
-makePrediction('./dev-0/')
-makePrediction('./test-A/')