mirror of
https://github.com/rspeer/wordfreq.git
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port test.py and test_chinese.py to pytest
This commit is contained in:
Robyn Speer
parent
8907423147
commit
75b4d62084
@ -1,5 +1,2 @@
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[nosetests]
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verbosity=2
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with-doctest=1
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with-coverage=0
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cover-package=wordfreq
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[aliases]
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test=pytest
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211
tests/test.py
211
tests/test.py
@ -2,57 +2,51 @@ from wordfreq import (
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word_frequency, available_languages, cB_to_freq,
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top_n_list, random_words, random_ascii_words, tokenize, lossy_tokenize
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)
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from nose.tools import (
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eq_, assert_almost_equal, assert_greater, raises, assert_not_equal
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)
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import pytest
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def test_freq_examples():
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# Stopwords are most common in the correct language
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assert_greater(word_frequency('the', 'en'),
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word_frequency('de', 'en'))
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assert_greater(word_frequency('de', 'es'),
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word_frequency('the', 'es'))
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assert word_frequency('the', 'en') > word_frequency('de', 'en')
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assert word_frequency('de', 'es') > word_frequency('the', 'es')
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# We get word frequencies from the 'large' list when available
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assert_greater(word_frequency('infrequency', 'en'), 0.)
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assert word_frequency('infrequency', 'en') > 0.
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def test_languages():
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# Make sure we get all the languages when looking for the default
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# 'best' wordlist
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avail = available_languages()
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assert_greater(len(avail), 32)
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assert len(avail) > 32
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# 'small' covers the same languages, but with some different lists
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avail_small = available_languages('small')
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eq_(len(avail_small), len(avail))
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assert_not_equal(avail_small, avail)
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assert len(avail_small) == len(avail)
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assert avail_small != avail
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# 'combined' is the same as 'small'
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avail_old_name = available_languages('combined')
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eq_(avail_old_name, avail_small)
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assert avail_old_name == avail_small
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# 'large' covers fewer languages
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avail_large = available_languages('large')
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assert_greater(len(avail_large), 12)
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assert_greater(len(avail), len(avail_large))
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assert len(avail_large) > 12
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assert len(avail) > len(avail_large)
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# Look up the digit '2' in the main word list for each language
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for lang in avail:
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assert_greater(word_frequency('2', lang), 0, lang)
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assert word_frequency('2', lang) > 0
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# Make up a weirdly verbose language code and make sure
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# we still get it
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new_lang_code = '%s-001-x-fake-extension' % lang.upper()
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assert_greater(word_frequency('2', new_lang_code), 0, new_lang_code)
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assert word_frequency('2', new_lang_code) > 0
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def test_minimums():
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eq_(word_frequency('esquivalience', 'en'), 0)
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eq_(word_frequency('esquivalience', 'en', minimum=1e-6), 1e-6)
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eq_(word_frequency('the', 'en', minimum=1), 1)
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assert word_frequency('esquivalience', 'en') == 0
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assert word_frequency('esquivalience', 'en', minimum=1e-6) == 1e-6
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assert word_frequency('the', 'en', minimum=1) == 1
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def test_most_common_words():
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@ -65,141 +59,135 @@ def test_most_common_words():
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"""
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return top_n_list(lang, 1)[0]
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eq_(get_most_common('ar'), 'في')
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eq_(get_most_common('de'), 'die')
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eq_(get_most_common('en'), 'the')
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eq_(get_most_common('es'), 'de')
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eq_(get_most_common('fr'), 'de')
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eq_(get_most_common('it'), 'di')
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eq_(get_most_common('ja'), 'の')
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eq_(get_most_common('nl'), 'de')
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eq_(get_most_common('pl'), 'w')
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eq_(get_most_common('pt'), 'de')
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eq_(get_most_common('ru'), 'в')
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eq_(get_most_common('tr'), 'bir')
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eq_(get_most_common('zh'), '的')
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assert get_most_common('ar') == 'في'
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assert get_most_common('de') == 'die'
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assert get_most_common('en') == 'the'
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assert get_most_common('es') == 'de'
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assert get_most_common('fr') == 'de'
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assert get_most_common('it') == 'di'
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assert get_most_common('ja') == 'の'
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assert get_most_common('nl') == 'de'
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assert get_most_common('pl') == 'w'
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assert get_most_common('pt') == 'de'
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assert get_most_common('ru') == 'в'
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assert get_most_common('tr') == 'bir'
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assert get_most_common('zh') == '的'
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def test_language_matching():
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freq = word_frequency('的', 'zh')
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eq_(word_frequency('的', 'zh-TW'), freq)
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eq_(word_frequency('的', 'zh-CN'), freq)
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eq_(word_frequency('的', 'zh-Hant'), freq)
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eq_(word_frequency('的', 'zh-Hans'), freq)
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eq_(word_frequency('的', 'yue-HK'), freq)
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eq_(word_frequency('的', 'cmn'), freq)
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assert word_frequency('的', 'zh-TW') == freq
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assert word_frequency('的', 'zh-CN') == freq
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assert word_frequency('的', 'zh-Hant') == freq
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assert word_frequency('的', 'zh-Hans') == freq
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assert word_frequency('的', 'yue-HK') == freq
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assert word_frequency('的', 'cmn') == freq
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def test_cB_conversion():
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eq_(cB_to_freq(0), 1.)
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assert_almost_equal(cB_to_freq(-100), 0.1)
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assert_almost_equal(cB_to_freq(-600), 1e-6)
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assert cB_to_freq(0) == 1.
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assert cB_to_freq(-100) == pytest.approx(0.1)
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assert cB_to_freq(-600) == pytest.approx(1e-6)
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@raises(ValueError)
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def test_failed_cB_conversion():
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cB_to_freq(1)
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with pytest.raises(ValueError):
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cB_to_freq(1)
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def test_tokenization():
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# We preserve apostrophes within words, so "can't" is a single word in the
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# data
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eq_(tokenize("I don't split at apostrophes, you see.", 'en'),
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['i', "don't", 'split', 'at', 'apostrophes', 'you', 'see'])
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assert (
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tokenize("I don't split at apostrophes, you see.", 'en')
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== ['i', "don't", 'split', 'at', 'apostrophes', 'you', 'see']
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)
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eq_(tokenize("I don't split at apostrophes, you see.", 'en', include_punctuation=True),
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['i', "don't", 'split', 'at', 'apostrophes', ',', 'you', 'see', '.'])
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assert (
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tokenize("I don't split at apostrophes, you see.", 'en', include_punctuation=True)
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== ['i', "don't", 'split', 'at', 'apostrophes', ',', 'you', 'see', '.']
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)
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# Certain punctuation does not inherently split a word.
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eq_(tokenize("Anything is possible at zombo.com", 'en'),
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['anything', 'is', 'possible', 'at', 'zombo.com'])
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assert (
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tokenize("Anything is possible at zombo.com", 'en')
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== ['anything', 'is', 'possible', 'at', 'zombo.com']
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)
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# Splits occur after symbols, and at splitting punctuation such as hyphens.
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eq_(tokenize('😂test', 'en'), ['😂', 'test'])
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eq_(tokenize("flip-flop", 'en'), ['flip', 'flop'])
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eq_(tokenize('this text has... punctuation :)', 'en', include_punctuation=True),
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['this', 'text', 'has', '...', 'punctuation', ':)'])
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assert tokenize('😂test', 'en') == ['😂', 'test']
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assert tokenize("flip-flop", 'en') == ['flip', 'flop']
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assert (
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tokenize('this text has... punctuation :)', 'en', include_punctuation=True)
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== ['this', 'text', 'has', '...', 'punctuation', ':)']
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)
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# Multi-codepoint emoji sequences such as 'medium-skinned woman with headscarf'
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# and 'David Bowie' stay together, because our Unicode segmentation algorithm
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# is up to date
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eq_(tokenize('emoji test 🧕🏽', 'en'), ['emoji', 'test', '🧕🏽'])
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eq_(tokenize("👨🎤 Planet Earth is blue, and there's nothing I can do 🌎🚀", 'en'),
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['👨🎤', 'planet', 'earth', 'is', 'blue', 'and', "there's",
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'nothing', 'i', 'can', 'do', '🌎', '🚀'])
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assert tokenize('emoji test 🧕🏽', 'en') == ['emoji', 'test', '🧕🏽']
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assert (
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tokenize("👨🎤 Planet Earth is blue, and there's nothing I can do 🌎🚀", 'en')
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== ['👨🎤', 'planet', 'earth', 'is', 'blue', 'and', "there's",
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'nothing', 'i', 'can', 'do', '🌎', '🚀']
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)
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# Water wave, surfer, flag of California (indicates ridiculously complete support
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# for Unicode 10 and Emoji 5.0)
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eq_(tokenize("Surf's up 🌊🏄🏴'",'en'),
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["surf's", "up", "🌊", "🏄", "🏴"])
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assert tokenize("Surf's up 🌊🏄🏴'",'en') == ["surf's", "up", "🌊", "🏄", "🏴"]
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def test_casefolding():
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eq_(tokenize('WEISS', 'de'), ['weiss'])
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eq_(tokenize('weiß', 'de'), ['weiss'])
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eq_(tokenize('İstanbul', 'tr'), ['istanbul'])
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eq_(tokenize('SIKISINCA', 'tr'), ['sıkısınca'])
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assert tokenize('WEISS', 'de') == ['weiss']
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assert tokenize('weiß', 'de') == ['weiss']
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assert tokenize('İstanbul', 'tr') == ['istanbul']
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assert tokenize('SIKISINCA', 'tr') == ['sıkısınca']
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def test_number_smashing():
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eq_(tokenize('"715 - CRΣΣKS" by Bon Iver', 'en'),
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['715', 'crσσks', 'by', 'bon', 'iver'])
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eq_(lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en'),
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['000', 'crσσks', 'by', 'bon', 'iver'])
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eq_(lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en', include_punctuation=True),
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['"', '000', '-', 'crσσks', '"', 'by', 'bon', 'iver'])
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eq_(lossy_tokenize('1', 'en'), ['1'])
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eq_(lossy_tokenize('3.14', 'en'), ['0.00'])
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eq_(lossy_tokenize('24601', 'en'), ['00000'])
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eq_(word_frequency('24601', 'en'), word_frequency('90210', 'en'))
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assert tokenize('"715 - CRΣΣKS" by Bon Iver', 'en') == ['715', 'crσσks', 'by', 'bon', 'iver']
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assert lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en') == ['000', 'crσσks', 'by', 'bon', 'iver']
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assert (
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lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en', include_punctuation=True)
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== ['"', '000', '-', 'crσσks', '"', 'by', 'bon', 'iver']
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)
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assert lossy_tokenize('1', 'en') == ['1']
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assert lossy_tokenize('3.14', 'en') == ['0.00']
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assert lossy_tokenize('24601', 'en') == ['00000']
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assert word_frequency('24601', 'en') == word_frequency('90210', 'en')
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def test_phrase_freq():
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ff = word_frequency("flip-flop", 'en')
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assert_greater(ff, 0)
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assert_almost_equal(
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1.0 / ff,
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1.0 / word_frequency('flip', 'en') + 1.0 / word_frequency('flop', 'en')
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)
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assert ff > 0
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phrase_freq = 1.0 / word_frequency('flip', 'en') + 1.0 / word_frequency('flop', 'en')
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assert 1.0 / ff == pytest.approx(phrase_freq)
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def test_not_really_random():
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# If your xkcd-style password comes out like this, maybe you shouldn't
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# use it
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eq_(random_words(nwords=4, lang='en', bits_per_word=0),
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'the the the the')
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assert random_words(nwords=4, lang='en', bits_per_word=0) == 'the the the the'
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# This not only tests random_ascii_words, it makes sure we didn't end
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# up with 'eos' as a very common Japanese word
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eq_(random_ascii_words(nwords=4, lang='ja', bits_per_word=0),
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'1 1 1 1')
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assert random_ascii_words(nwords=4, lang='ja', bits_per_word=0) == '1 1 1 1'
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@raises(ValueError)
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def test_not_enough_ascii():
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random_ascii_words(lang='zh', bits_per_word=14)
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with pytest.raises(ValueError):
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random_ascii_words(lang='zh', bits_per_word=14)
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def test_arabic():
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# Remove tatweels
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eq_(
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tokenize('متــــــــعب', 'ar'),
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['متعب']
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)
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assert tokenize('متــــــــعب', 'ar') == ['متعب']
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# Remove combining marks
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eq_(
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tokenize('حَرَكَات', 'ar'),
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['حركات']
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)
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assert tokenize('حَرَكَات', 'ar') == ['حركات']
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eq_(
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tokenize('\ufefb', 'ar'), # An Arabic ligature...
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['\u0644\u0627'] # ...that is affected by NFKC normalization
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)
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# An Arabic ligature that is affected by NFKC normalization
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assert tokenize('\ufefb', 'ar') == ['\u0644\u0627']
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def test_ideographic_fallback():
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@ -207,29 +195,28 @@ def test_ideographic_fallback():
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#
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# More complex examples like this, involving the multiple scripts of Japanese,
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# are in test_japanese.py.
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eq_(tokenize('中国文字', 'en'), ['中国文字'])
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assert tokenize('中国文字', 'en') == ['中国文字']
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def test_other_languages():
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# Test that we leave Thai letters stuck together. If we had better Thai support,
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# we would actually split this into a three-word phrase.
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eq_(tokenize('การเล่นดนตรี', 'th'), ['การเล่นดนตรี'])
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eq_(tokenize('"การเล่นดนตรี" means "playing music"', 'en'),
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['การเล่นดนตรี', 'means', 'playing', 'music'])
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assert tokenize('การเล่นดนตรี', 'th') == ['การเล่นดนตรี']
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assert tokenize('"การเล่นดนตรี" means "playing music"', 'en') == ['การเล่นดนตรี', 'means', 'playing', 'music']
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# Test Khmer, a script similar to Thai
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eq_(tokenize('សូមស្វាគមន៍', 'km'), ['សូមស្វាគមន៍'])
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assert tokenize('សូមស្វាគមន៍', 'km') == ['សូមស្វាគមន៍']
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# Test Hindi -- tokens split where there are spaces, and not where there aren't
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eq_(tokenize('हिन्दी विक्षनरी', 'hi'), ['हिन्दी', 'विक्षनरी'])
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assert tokenize('हिन्दी विक्षनरी', 'hi') == ['हिन्दी', 'विक्षनरी']
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# Remove vowel points in Hebrew
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eq_(tokenize('דֻּגְמָה', 'he'), ['דגמה'])
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assert tokenize('דֻּגְמָה', 'he') == ['דגמה']
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# Deal with commas, cedillas, and I's in Turkish
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eq_(tokenize('kișinin', 'tr'), ['kişinin'])
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eq_(tokenize('KİȘİNİN', 'tr'), ['kişinin'])
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assert tokenize('kișinin', 'tr') == ['kişinin']
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assert tokenize('KİȘİNİN', 'tr') == ['kişinin']
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# Deal with cedillas that should be commas-below in Romanian
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eq_(tokenize('acelaşi', 'ro'), ['același'])
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eq_(tokenize('ACELAŞI', 'ro'), ['același'])
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assert tokenize('acelaşi', 'ro') == ['același']
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assert tokenize('ACELAŞI', 'ro') == ['același']
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@ -1,5 +1,5 @@
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from nose.tools import eq_, assert_almost_equal, assert_greater
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from wordfreq import tokenize, word_frequency
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import pytest
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def test_tokens():
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@ -17,64 +17,49 @@ def test_tokens():
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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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assert tokenize(hobart, 'zh') == ['加', '勒', '特', '霍', '巴特']
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eq_(
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tokenize(fact_simplified, 'zh'),
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[
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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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)
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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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eq_(
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tokenize(hobart, 'zh', external_wordlist=True),
|
||||
['加勒特', '霍巴特']
|
||||
)
|
||||
assert tokenize(hobart, 'zh', external_wordlist=True) == ['加勒特', '霍巴特']
|
||||
|
||||
# We get almost the same tokens from the sentence using Jieba's own
|
||||
# wordlist, but it tokenizes "in history" as two words and
|
||||
# "sixth person" as one.
|
||||
eq_(
|
||||
tokenize(fact_simplified, 'zh', external_wordlist=True),
|
||||
[
|
||||
# he / is / history / in / sixth person
|
||||
'他', '是', '历史', '上', '第六位',
|
||||
# during / term of office / in / die
|
||||
'在', '任期', '内', '去世',
|
||||
# of / U.S. / deputy / president
|
||||
'的', '美国', '副', '总统'
|
||||
]
|
||||
)
|
||||
assert tokenize(fact_simplified, 'zh', external_wordlist=True) == [
|
||||
# he / is / history / in / sixth person
|
||||
'他', '是', '历史', '上', '第六位',
|
||||
# during / term of office / in / die
|
||||
'在', '任期', '内', '去世',
|
||||
# of / U.S. / deputy / president
|
||||
'的', '美国', '副', '总统'
|
||||
]
|
||||
|
||||
# Check that Traditional Chinese works at all
|
||||
assert_greater(word_frequency(fact_traditional, 'zh'), 0)
|
||||
assert word_frequency(fact_traditional, 'zh') > 0
|
||||
|
||||
# You get the same token lengths if you look it up in Traditional Chinese,
|
||||
# but the words are different
|
||||
simp_tokens = tokenize(fact_simplified, 'zh', include_punctuation=True)
|
||||
trad_tokens = tokenize(fact_traditional, 'zh', include_punctuation=True)
|
||||
eq_(''.join(simp_tokens), fact_simplified)
|
||||
eq_(''.join(trad_tokens), fact_traditional)
|
||||
assert ''.join(simp_tokens) == fact_simplified
|
||||
assert ''.join(trad_tokens) == fact_traditional
|
||||
simp_lengths = [len(token) for token in simp_tokens]
|
||||
trad_lengths = [len(token) for token in trad_tokens]
|
||||
eq_(simp_lengths, trad_lengths)
|
||||
assert simp_lengths == trad_lengths
|
||||
|
||||
|
||||
def test_combination():
|
||||
xiexie_freq = word_frequency('谢谢', 'zh') # "Thanks"
|
||||
assert_almost_equal(
|
||||
word_frequency('谢谢谢谢', 'zh'),
|
||||
xiexie_freq / 20
|
||||
)
|
||||
assert word_frequency('谢谢谢谢', 'zh') == pytest.approx(xiexie_freq / 20)
|
||||
|
||||
|
||||
def test_alternate_codes():
|
||||
@ -83,12 +68,12 @@ def test_alternate_codes():
|
||||
tokens = ['谢谢', '谢谢']
|
||||
|
||||
# Code with a region attached
|
||||
eq_(tokenize('谢谢谢谢', 'zh-CN'), tokens)
|
||||
assert tokenize('谢谢谢谢', 'zh-CN') == tokens
|
||||
|
||||
# Over-long codes for Chinese
|
||||
eq_(tokenize('谢谢谢谢', 'chi'), tokens)
|
||||
eq_(tokenize('谢谢谢谢', 'zho'), tokens)
|
||||
assert tokenize('谢谢谢谢', 'chi') == tokens
|
||||
assert tokenize('谢谢谢谢', 'zho') == tokens
|
||||
|
||||
# Separate codes for Mandarin and Cantonese
|
||||
eq_(tokenize('谢谢谢谢', 'cmn'), tokens)
|
||||
eq_(tokenize('谢谢谢谢', 'yue'), tokens)
|
||||
assert tokenize('谢谢谢谢', 'cmn') == tokens
|
||||
assert tokenize('谢谢谢谢', 'yue') == tokens
|
||||
|
||||
Loading…
Reference in New Issue
Block a user