first commit

run.py

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+import lzma
+import nltk
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
+    in_train = [x.strip().lower() for x in train_file.readlines()]
+
+with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
+    out_train = [int(x.strip()) for x in train_file.readlines()]
+
+with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
+    in_dev = [x.strip().lower() for x in dev_file.readlines()]
+
+with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
+    in_test = [x.strip().lower() for x in test_file.readlines()]
+
+tfidf_vectorizer=TfidfVectorizer(stop_words="english")
+IN_train = tfidf_vectorizer.fit_transform(in_train)
+classifier = MultinomialNB()
+y_pred = classifier.fit(IN_train, out_train)
+
+y_prediction = y_pred.predict(tfidf_vectorizer.transform(in_test))
+with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
+    for single_pred in y_prediction:
+        test_out_file.writelines(f"{str(single_pred)}\n")
+
+pred_dev = y_pred.predict(tfidf_vectorizer.transform(in_test))
+with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
+    for single_pred in pred_dev:
+        dev_out_file.writelines(f"{str(single_pred)}\n")