main.py

@@ -1,46 +0,0 @@
-import lzma
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.naive_bayes import BernoulliNB
-import numpy as np
-import csv
-
-
-def readInput(dir):
-    X = []
-    if 'xz' in dir:
-        with lzma.open(dir) as f:
-            for line in f:
-                X.append(line.decode('utf-8'))
-    else:
-        with open(dir) as f:
-            for line in f:
-                X. append(line.replace('\n',''))
-    return X
-
-def writeOutput(output, dir):
-    with open(dir, 'w', newline='') as f:
-        writer = csv.writer(f)
-        writer.writerows(output)
-
-
-if __name__ == '__main__':
-    # train
-    trainX = readInput('train/in.tsv.xz')
-    trainY = readInput('train/expected.tsv')
-    vectorizer = TfidfVectorizer()
-    trainX = vectorizer.fit_transform(trainX)
-    trainY = np.array(trainY)
-    bernoulli = BernoulliNB()
-    bernoulli.fit(trainX, trainY)
-
-    # dev-0
-    devX = readInput('dev-0/in.tsv.xz')
-    devX = vectorizer.transform(devX)
-    devPredicted = bernoulli.predict(devX)
-    writeOutput(devPredicted, 'dev-0/out.tsv')
-
-    # test-A
-    testX = readInput('test-A/in.tsv.xz')
-    testX = vectorizer.transform(testX)
-    testPredicted = bernoulli.predict(testX)
-    writeOutput(testPredicted, 'test-A/out.tsv')