56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
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#!/usr/bin/env python3
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from datetime import date
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import os.path
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from random import choice
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from gensim.models import KeyedVectors
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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# This file contains a file generated by word2vec, in binary format
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BIN_FILE = os.path.join(
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BASE_DIR,
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'frwiki.skip.size500.win10.neg15.sample1e-5.min15.bin',
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)
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def main() -> None:
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# Open pre-trained dataset
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wv = KeyedVectors.load_word2vec_format(
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BIN_FILE,
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binary=True,
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)
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# Allowed words
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with open(os.path.join(BASE_DIR, 'lemmes.txt')) as f:
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words = [w for w in map(lambda w: w.replace('\n', ''), f.readlines())
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if not w.startswith('#')]
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# Choose a word to search
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word = choice(words)
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# Compute all similarity values
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similarities = {w: wv.similarity(word, w) for w in words}
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# Compute scores by order
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rank = sorted(similarities.items(), key=lambda item: -item[1])
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if not os.path.isdir(os.path.join(BASE_DIR, 'history')):
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os.mkdir(os.path.join(BASE_DIR, 'history'))
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# Store output
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today = date.today()
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filename = f'{today.year:04d}-{today.month:02d}-{today.day:02d}.txt'
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with open(os.path.join(BASE_DIR, 'history', filename), 'w') as f:
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for w, d in rank:
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f.write(f"{w} {100 * d:.02f}\n")
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if os.path.isfile(os.path.join(BASE_DIR, 'cemantix.txt')):
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os.unlink(os.path.join(BASE_DIR, 'cemantix.txt'))
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os.symlink(os.path.join(BASE_DIR, 'history', filename), 'cemantix.txt')
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if __name__ == '__main__':
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main()
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