mirror of
https://github.com/rspeer/wordfreq.git
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253 lines
9.5 KiB
Python
253 lines
9.5 KiB
Python
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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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 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 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 len(avail) >= 34
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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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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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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 len(avail_large) >= 14
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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 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 word_frequency('2', new_lang_code) > 0
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def test_minimums():
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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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# If something causes the most common words in well-supported languages to
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# change, we should know.
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def get_most_common(lang):
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"""
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Return the single most common word in the language.
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"""
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return top_n_list(lang, 1)[0]
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assert get_most_common('ar') == 'في'
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assert get_most_common('bg') == 'на'
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assert get_most_common('bn') == 'না'
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assert get_most_common('ca') == 'de'
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assert get_most_common('cs') == 'a'
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assert get_most_common('da') == 'i'
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assert get_most_common('el') == 'και'
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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('fi') == 'ja'
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assert get_most_common('fil') == 'sa'
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assert get_most_common('fr') == 'de'
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assert get_most_common('gl') == 'de'
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assert get_most_common('he') == 'את'
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assert get_most_common('hi') == 'के'
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assert get_most_common('hu') == 'a'
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assert get_most_common('id') == 'yang'
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assert get_most_common('is') == 'og'
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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('ko') == '이'
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assert get_most_common('lt') == 'ir'
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assert get_most_common('lv') == 'un'
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assert get_most_common('mk') == 'на'
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assert get_most_common('ml') == 'ഒരു'
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assert get_most_common('ms') == 'yang'
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assert get_most_common('nb') == 'i'
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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('ro') == 'de'
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assert get_most_common('ru') == 'в'
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assert get_most_common('sh') == 'je'
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assert get_most_common('sk') == 'a'
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assert get_most_common('sl') == 'je'
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assert get_most_common('sv') == 'är'
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assert get_most_common('ta') == 'ஒரு'
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assert get_most_common('tr') == 've'
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assert get_most_common('uk') == 'в'
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assert get_most_common('ur') == 'کے'
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assert get_most_common('vi') == 'là'
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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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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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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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def test_failed_cB_conversion():
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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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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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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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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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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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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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assert tokenize("Surf's up 🌊🏄🏴'",'en') == ["surf's", "up", "🌊", "🏄", "🏴"]
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def test_casefolding():
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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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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 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, rel=0.01)
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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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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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assert random_ascii_words(nwords=4, lang='ja', bits_per_word=0) == '00 00 00 00'
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def test_not_enough_ascii():
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with pytest.raises(ValueError):
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random_ascii_words(lang='zh', bits_per_word=16)
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def test_arabic():
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# Remove tatweels
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assert tokenize('متــــــــعب', 'ar') == ['متعب']
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# Remove combining marks
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assert tokenize('حَرَكَات', 'ar') == ['حركات']
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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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# Try tokenizing Chinese text as English -- it should remain stuck together.
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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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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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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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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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assert tokenize('हिन्दी विक्षनरी', 'hi') == ['हिन्दी', 'विक्षनरी']
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# Remove vowel points in Hebrew
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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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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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assert tokenize('acelaşi', 'ro') == ['același']
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assert tokenize('ACELAŞI', 'ro') == ['același']
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