run.py

@@ -1,58 +0,0 @@
-import lzma
-from naivebayes import NaiveBayesTextClassifier
-from spacy.lang.en.stop_words import STOP_WORDS as en_stop
-
-
-def get_data(fname):
-    with open(fname, 'r', encoding='utf8') as file:
-        return file.readlines()
-
-
-def get_data_zipped(fname):
-    with lzma.open(fname, 'r') as file:
-        return file.readlines()
-
-
-def train_bayes(model, x, y, step=15000):
-    start = 0
-    end = step
-
-    for _ in range(0, len(x), step):
-        model.train(x[start:end], y[start:end])
-        if start + step < len(x):
-            start += step
-        else:
-            start = 0
-        end = min(start + step, len(x))
-
-
-def write_file(fname, data):
-    with open(fname, 'wt') as f:
-        for d in data:
-            f.write(f'{str(d)}\n')
-
-def main():
-    train_x = get_data_zipped('train/in.tsv.xz')
-    train_y = get_data('train/expected.tsv')
-    # preprocessing
-    train_y = [int(y) for y in train_y]
-
-    test_x = get_data_zipped('test-A/in.tsv.xz')
-
-    dev_x = get_data_zipped('dev-0/in.tsv.xz')
-
-    model = NaiveBayesTextClassifier(
-        categories=[0, 1],
-        stop_words=en_stop
-    )
-
-    train_bayes(model, train_x, train_y)
-
-    predicted = model.classify(dev_x)
-    predicted_2= model.classify(test_x)
-
-    write_file('dev-0/out.tsv', predicted)
-    write_file('test-A/out.tsv', predicted_2)
-
-
-main()