task done

solution.py

@@ -0,0 +1,51 @@
+import numpy
+import lzma
+from sklearn.naive_bayes import MultinomialNB
+from sklearn import preprocessing
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+TEST_A = "test-A"
+DEV_0 = "dev-0"
+TRAIN_IN = "./train/in.tsv.xz"
+TRAIN_EXPECTED = "./train/expected.tsv"
+
+
+def open_file(path):
+    with open(path) as file:
+        return file.readlines()
+
+
+def open_xz(path):
+    with lzma.open(path, 'rt') as file:
+        return file.readlines()
+
+
+def get_model(train_in, train_expected):
+    label_encoder = preprocessing.LabelEncoder()
+    train_expected = label_encoder.fit_transform(train_expected)
+    pipeline = make_pipeline(TfidfVectorizer(), MultinomialNB())
+    model = pipeline.fit(train_in, train_expected)
+    return model
+
+
+def predict(train_test_in_path, train_in_path, train_expected_path):
+    train_in = open_xz(train_in_path)
+    train_expected = open_file(train_expected_path)
+    train_test_in = open_xz(train_test_in_path + '/in.tsv.xz')
+    model = get_model(train_in, train_expected)
+    prediction = model.predict(train_test_in)
+    return prediction
+
+
+def save_result(path, prediction):
+    numpy.savetxt(path + "/out.tsv", prediction, '%d')
+
+
+if __name__ == '__main__':
+    prediction_dev_0 = predict(DEV_0, TRAIN_IN, TRAIN_EXPECTED)
+    prediction_test_a = predict(TEST_A, TRAIN_IN, TRAIN_EXPECTED)
+
+    save_result(DEV_0, prediction_dev_0)
+    save_result(TEST_A, prediction_test_a)