run.py

@@ -1,47 +0,0 @@
-import lzma
-from naivebayes import NaiveBayesTextClassifier
-from spacy.lang.en.stop_words import STOP_WORDS
-import numpy as np
-import pandas as pd
-
-np.max_length = 1200000
-
-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=10000):
-    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))
-
-train_x = get_data_zipped('train/in.tsv.xz')
-train_y = get_data('train/expected.tsv')
-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=STOP_WORDS
-)
-
-train_bayes(model, train_x, train_y)
-
-predicted = model.classify(dev_x)
-predicted2 = model.classify(test_x)
-
-pd.DataFrame(predicted).to_csv('dev-0/out.tsv', sep='\t', header=None, encoding="utf-8", index=False)
-pd.DataFrame(predicted2).to_csv('test-A/out.tsv', sep='\t', header=None, encoding="utf-8", index=False)