solution

run.py

@@ -0,0 +1,48 @@
+from naivebayes import NaiveBayesTextClassifier
+from spacy.lang.en.stop_words import STOP_WORDS as en_stop
+
+naive_bayes = NaiveBayesTextClassifier(
+    categories=[0, 1],
+    stop_words=en_stop
+)
+
+with open('train/in.tsv', 'r', encoding='utf8') as f:
+    train = f.readlines()
+
+with open('train/expected.tsv', 'r', encoding='utf8') as f:
+    expected = f.readlines()
+
+for i in range(0, len(expected)):
+    expected[i] = int(expected[i])
+
+step = 20000
+start, end = 0, step
+
+for i in range(0, len(expected), step):
+    naive_bayes.train(train[start:end], expected[start:end])
+    if start + step < len(expected):
+        start += step
+    else:
+        start = 0
+    end = min(start + step, len(expected))
+
+
+with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
+    dev_0 = f.readlines()
+
+predicted_dev_0 = naive_bayes.classify(dev_0)
+
+with open('dev-0/out.tsv', 'wt') as f:
+    for p in predicted_dev_0:
+        f.write(str(p) + '\n')
+f.close()
+
+with open('test-A/in.tsv', 'r', encoding='utf8') as f:
+    test_A = f.readlines()
+
+predicted_test_A = naive_bayes.classify(test_A)
+
+with open('test-A/out.tsv', 'wt') as f:
+    for p in predicted_test_A:
+        f.write(str(p) + '\n')
+f.close()