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69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
from nose.tools import eq_, assert_almost_equal, assert_greater
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from wordfreq import tokenize, word_frequency
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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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eq_(
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tokenize(hobart, 'zh'),
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['加', '勒', '特', '霍', '巴特']
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)
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eq_(
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tokenize(fact_simplified, 'zh'),
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[
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# he / is / in history / #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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)
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# Jieba's original tokenizer knows a lot of names, it seems.
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eq_(
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tokenize(hobart, 'zh', external_wordlist=True),
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['加勒特', '霍巴特']
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)
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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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eq_(
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tokenize(fact_simplified, 'zh', external_wordlist=True),
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[
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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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)
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# You match the same tokens if you look it up in Traditional Chinese.
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eq_(tokenize(fact_simplified, 'zh'), tokenize(fact_traditional, 'zh'))
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assert_greater(word_frequency(fact_traditional, 'zh'), 0)
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def test_combination():
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xiexie_freq = word_frequency('谢谢', 'zh') # "Thanks"
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assert_almost_equal(
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word_frequency('谢谢谢谢', 'zh'),
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xiexie_freq / 20
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)
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