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88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
from wordfreq import tokenize, word_frequency
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import pytest
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def test_tokens():
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# Let's test on some Chinese text that has unusual combinations of
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# syllables, because it is about an American vice-president.
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#
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# (He was the Chinese Wikipedia's featured article of the day when I
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# wrote this test.)
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hobart = '加勒特·霍巴特' # Garret Hobart, or "jiā lè tè huò bā tè".
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# He was the sixth American vice president to die in office.
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fact_simplified = '他是历史上第六位在任期内去世的美国副总统。'
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fact_traditional = '他是歷史上第六位在任期內去世的美國副總統。'
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# His name breaks into five pieces, with the only piece staying together
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# being the one that means 'Bart'. The dot is not included as a token.
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assert tokenize(hobart, 'zh') == ['加', '勒', '特', '霍', '巴特']
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assert tokenize(fact_simplified, 'zh') == [
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# he / is / history / in / #6 / counter for people
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'他', '是', '历史', '上', '第六', '位',
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# during / term of office / in / die
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'在', '任期', '内', '去世',
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# of / U.S. / deputy / president
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'的', '美国', '副', '总统'
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]
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# Jieba's original tokenizer knows a lot of names, it seems.
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assert tokenize(hobart, 'zh', external_wordlist=True) == ['加勒特', '霍巴特']
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# We get almost the same tokens from the sentence using Jieba's own
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# wordlist, but it tokenizes "in history" as two words and
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# "sixth person" as one.
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assert tokenize(fact_simplified, 'zh', external_wordlist=True) == [
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# he / is / history / in / sixth person
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'他', '是', '历史', '上', '第六位',
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# during / term of office / in / die
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'在', '任期', '内', '去世',
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# of / U.S. / deputy / president
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'的', '美国', '副', '总统'
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]
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# Check that Traditional Chinese works at all
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assert word_frequency(fact_traditional, 'zh') > 0
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# You get the same token lengths if you look it up in Traditional Chinese,
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# but the words are different
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simp_tokens = tokenize(fact_simplified, 'zh', include_punctuation=True)
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trad_tokens = tokenize(fact_traditional, 'zh', include_punctuation=True)
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assert ''.join(simp_tokens) == fact_simplified
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assert ''.join(trad_tokens) == fact_traditional
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simp_lengths = [len(token) for token in simp_tokens]
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trad_lengths = [len(token) for token in trad_tokens]
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assert simp_lengths == trad_lengths
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def test_combination():
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xiexie_freq = word_frequency('谢谢', 'zh') # "Thanks"
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assert word_frequency('谢谢谢谢', 'zh') == pytest.approx(xiexie_freq / 20, rel=0.01)
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def test_alternate_codes():
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# Tokenization of Chinese works when you use other language codes
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# that are not equal to 'zh'.
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tokens = ['谢谢', '谢谢']
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# Code with a region attached
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assert tokenize('谢谢谢谢', 'zh-CN') == tokens
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# Over-long codes for Chinese
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assert tokenize('谢谢谢谢', 'chi') == tokens
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assert tokenize('谢谢谢谢', 'zho') == tokens
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# Separate codes for Mandarin and Cantonese
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assert tokenize('谢谢谢谢', 'cmn') == tokens
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assert tokenize('谢谢谢谢', 'yue') == tokens
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def test_unreasonably_long():
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# This crashed earlier versions of wordfreq
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lots_of_ls = 'l' * 800
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assert word_frequency(lots_of_ls, 'zh') < 1e-300
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assert zipf_frequency(lots_of_ls, 'zh') == 0.
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