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2 Commits
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5f7fe04d90 |
5272
dev-0/out.tsv
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5272
dev-0/out.tsv
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47
run.py
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47
run.py
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import lzma
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from naivebayes import NaiveBayesTextClassifier
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from spacy.lang.en.stop_words import STOP_WORDS
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import numpy as np
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import pandas as pd
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np.max_length = 1200000
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def get_data(fname):
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with open(fname, 'r', encoding='utf8') as file:
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return file.readlines()
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def get_data_zipped(fname):
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with lzma.open(fname, 'r') as file:
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return file.readlines()
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def train_bayes(model, x, y, step=10000):
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start = 0
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end = step
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for _ in range(0, len(x), step):
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model.train(x[start:end], y[start:end])
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if start + step < len(x):
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start += step
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else:
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start = 0
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end = min(start + step, len(x))
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train_x = get_data_zipped('train/in.tsv.xz')
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train_y = get_data('train/expected.tsv')
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train_y = [int(y) for y in train_y]
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test_x = get_data_zipped('test-A/in.tsv.xz')
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dev_x = get_data_zipped('dev-0/in.tsv.xz')
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model = NaiveBayesTextClassifier(
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categories=[0, 1],
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stop_words=STOP_WORDS
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)
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train_bayes(model, train_x, train_y)
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predicted = model.classify(dev_x)
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predicted2 = model.classify(test_x)
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pd.DataFrame(predicted).to_csv('dev-0/out.tsv', sep='\t', header=None, encoding="utf-8", index=False)
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pd.DataFrame(predicted2).to_csv('test-A/out.tsv', sep='\t', header=None, encoding="utf-8", index=False)
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5152
test-A/out.tsv
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5152
test-A/out.tsv
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