Update run.py

run.py

@@ -0,0 +1,48 @@
+import lzma
+# import re
+from sklearn.feature_extraction.text import CountVectorizer
+import csv
+from sklearn.naive_bayes import GaussianNB
+
+# def get_str_cleaned(str_dirty):
+#         punctuation = '!"#$%&\'()*+,-./:;<=>?@[\\\\]^_`{|}~'
+#         new_str = str_dirty.lower()
+#         new_str = re.sub(' +', ' ', new_str)
+#         for char in punctuation:
+#             new_str = new_str.replace(char, '')
+#             new_str = new_str.replace('\n', '')
+#         return new_str
+
+# with open('train/expected.tsv') as f:
+#     trainY = list(csv.reader(f))
+
+trainX = []
+trainY = []
+testX = []
+testY = []
+
+with lzma.open('train/in.tsv.xz') as f:
+    for line in f:
+        # X_train.append(get_str_cleaned(line.decode('utf-8')))
+        trainX.append(line.decode('utf-8'))
+
+with open('train/expected.tsv') as f:
+    for line in f:
+        trainY.append(line)
+
+vectorizer = CountVectorizer()
+trainX = vectorizer.fit_transform(trainX)
+
+model = GaussianNB()
+model.fit(trainX, trainY)
+
+with open('dev-0/in.tsv') as f:
+    for line in f:
+        testX.append(line.decode('utf-8'))
+    # testX = list(csv.reader(f))
+
+predictedY = model.predict(testX)
+print(predictedY)
+
+# with open('dev-0/expected.tsv') as f:
+#     expectedY = list(csv.reader(f))