mirror of
https://github.com/rspeer/wordfreq.git
synced 2024-12-25 10:15:23 +00:00
262 lines
8.8 KiB
Python
262 lines
8.8 KiB
Python
from wordfreq import (
|
||
word_frequency, available_languages, cB_to_freq,
|
||
top_n_list, random_words, random_ascii_words, tokenize, lossy_tokenize
|
||
)
|
||
from nose.tools import (
|
||
eq_, assert_almost_equal, assert_greater, raises, assert_not_equal
|
||
)
|
||
|
||
|
||
def test_freq_examples():
|
||
# Stopwords are most common in the correct language
|
||
assert_greater(word_frequency('the', 'en'),
|
||
word_frequency('de', 'en'))
|
||
|
||
assert_greater(word_frequency('de', 'es'),
|
||
word_frequency('the', 'es'))
|
||
|
||
|
||
# To test the reasonableness of the Twitter list, we want to look up a
|
||
# common word representing laughter in each language. The default for
|
||
# languages not listed here is 'haha'.
|
||
LAUGHTER_WORDS = {
|
||
'en': 'lol',
|
||
'hi': 'lol',
|
||
'cs': 'lol',
|
||
'ru': 'лол',
|
||
'zh': '笑',
|
||
'ja': '笑',
|
||
'ar': 'ﻪﻬﻬﻬﻫ',
|
||
'fa': 'خخخخ',
|
||
'ca': 'jaja',
|
||
'es': 'jaja',
|
||
'fr': 'ptdr',
|
||
'pt': 'kkkk',
|
||
'he': 'חחח',
|
||
'bg': 'ахаха',
|
||
'uk': 'хаха',
|
||
'bn': 'হা হা',
|
||
'mk': 'хаха'
|
||
}
|
||
|
||
|
||
def test_languages():
|
||
# Make sure we get all the languages when looking for the default
|
||
# 'best' wordlist
|
||
avail = available_languages()
|
||
assert_greater(len(avail), 32)
|
||
|
||
# 'small' covers the same languages, but with some different lists
|
||
avail_small = available_languages('small')
|
||
eq_(len(avail_small), len(avail))
|
||
assert_not_equal(avail_small, avail)
|
||
|
||
# 'combined' is the same as 'small'
|
||
avail_old_name = available_languages('combined')
|
||
eq_(avail_old_name, avail_small)
|
||
|
||
# 'large' covers fewer languages
|
||
avail_large = available_languages('large')
|
||
assert_greater(len(avail_large), 12)
|
||
assert_greater(len(avail), len(avail_large))
|
||
|
||
# Look up the digit '2' in the main word list for each language
|
||
for lang in avail:
|
||
assert_greater(word_frequency('2', lang), 0, lang)
|
||
|
||
# Make up a weirdly verbose language code and make sure
|
||
# we still get it
|
||
new_lang_code = '%s-001-x-fake-extension' % lang.upper()
|
||
assert_greater(word_frequency('2', new_lang_code), 0, new_lang_code)
|
||
|
||
|
||
def test_minimums():
|
||
eq_(word_frequency('esquivalience', 'en'), 0)
|
||
eq_(word_frequency('esquivalience', 'en', minimum=1e-6), 1e-6)
|
||
eq_(word_frequency('the', 'en', minimum=1), 1)
|
||
|
||
|
||
def test_most_common_words():
|
||
# If something causes the most common words in well-supported languages to
|
||
# change, we should know.
|
||
|
||
def get_most_common(lang):
|
||
"""
|
||
Return the single most common word in the language.
|
||
"""
|
||
return top_n_list(lang, 1)[0]
|
||
|
||
eq_(get_most_common('ar'), 'في')
|
||
eq_(get_most_common('de'), 'die')
|
||
eq_(get_most_common('en'), 'the')
|
||
eq_(get_most_common('es'), 'de')
|
||
eq_(get_most_common('fr'), 'de')
|
||
eq_(get_most_common('it'), 'di')
|
||
eq_(get_most_common('ja'), 'の')
|
||
eq_(get_most_common('nl'), 'de')
|
||
eq_(get_most_common('pl'), 'w')
|
||
eq_(get_most_common('pt'), 'de')
|
||
eq_(get_most_common('ru'), 'в')
|
||
eq_(get_most_common('tr'), 'bir')
|
||
eq_(get_most_common('zh'), '的')
|
||
|
||
|
||
def test_language_matching():
|
||
freq = word_frequency('的', 'zh')
|
||
eq_(word_frequency('的', 'zh-TW'), freq)
|
||
eq_(word_frequency('的', 'zh-CN'), freq)
|
||
eq_(word_frequency('的', 'zh-Hant'), freq)
|
||
eq_(word_frequency('的', 'zh-Hans'), freq)
|
||
eq_(word_frequency('的', 'yue-HK'), freq)
|
||
eq_(word_frequency('的', 'cmn'), freq)
|
||
|
||
|
||
def test_cB_conversion():
|
||
eq_(cB_to_freq(0), 1.)
|
||
assert_almost_equal(cB_to_freq(-100), 0.1)
|
||
assert_almost_equal(cB_to_freq(-600), 1e-6)
|
||
|
||
|
||
@raises(ValueError)
|
||
def test_failed_cB_conversion():
|
||
cB_to_freq(1)
|
||
|
||
|
||
def test_tokenization():
|
||
# We preserve apostrophes within words, so "can't" is a single word in the
|
||
# data
|
||
eq_(tokenize("I don't split at apostrophes, you see.", 'en'),
|
||
['i', "don't", 'split', 'at', 'apostrophes', 'you', 'see'])
|
||
|
||
eq_(tokenize("I don't split at apostrophes, you see.", 'en', include_punctuation=True),
|
||
['i', "don't", 'split', 'at', 'apostrophes', ',', 'you', 'see', '.'])
|
||
|
||
# Certain punctuation does not inherently split a word.
|
||
eq_(tokenize("Anything is possible at zombo.com", 'en'),
|
||
['anything', 'is', 'possible', 'at', 'zombo.com'])
|
||
|
||
# Splits occur after symbols, and at splitting punctuation such as hyphens.
|
||
eq_(tokenize('😂test', 'en'), ['😂', 'test'])
|
||
|
||
eq_(tokenize("flip-flop", 'en'), ['flip', 'flop'])
|
||
|
||
eq_(tokenize('this text has... punctuation :)', 'en', include_punctuation=True),
|
||
['this', 'text', 'has', '...', 'punctuation', ':)'])
|
||
|
||
# Multi-codepoint emoji sequences such as 'medium-skinned woman with headscarf'
|
||
# and 'David Bowie' stay together, because our Unicode segmentation algorithm
|
||
# is up to date
|
||
eq_(tokenize('emoji test 🧕🏽', 'en'), ['emoji', 'test', '🧕🏽'])
|
||
|
||
eq_(tokenize("👨🎤 Planet Earth is blue, and there's nothing I can do 🌎🚀", 'en'),
|
||
['👨🎤', 'planet', 'earth', 'is', 'blue', 'and', "there's",
|
||
'nothing', 'i', 'can', 'do', '🌎', '🚀'])
|
||
|
||
# Water wave, surfer, flag of California (indicates ridiculously complete support
|
||
# for Unicode 10 and Emoji 5.0)
|
||
eq_(tokenize("Surf's up 🌊🏄🏴'",'en'),
|
||
["surf's", "up", "🌊", "🏄", "🏴"])
|
||
|
||
|
||
def test_casefolding():
|
||
eq_(tokenize('WEISS', 'de'), ['weiss'])
|
||
eq_(tokenize('weiß', 'de'), ['weiss'])
|
||
eq_(tokenize('İstanbul', 'tr'), ['istanbul'])
|
||
eq_(tokenize('SIKISINCA', 'tr'), ['sıkısınca'])
|
||
|
||
|
||
def test_number_smashing():
|
||
eq_(tokenize('"715 - CRΣΣKS" by Bon Iver', 'en'),
|
||
['715', 'crσσks', 'by', 'bon', 'iver'])
|
||
eq_(lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en'),
|
||
['000', 'crσσks', 'by', 'bon', 'iver'])
|
||
eq_(lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', 'en', include_punctuation=True),
|
||
['"', '000', '-', 'crσσks', '"', 'by', 'bon', 'iver'])
|
||
eq_(lossy_tokenize('1', 'en'), ['1'])
|
||
eq_(lossy_tokenize('3.14', 'en'), ['0.00'])
|
||
eq_(lossy_tokenize('24601', 'en'), ['00000'])
|
||
eq_(word_frequency('24601', 'en'), word_frequency('90210', 'en'))
|
||
|
||
|
||
def test_phrase_freq():
|
||
ff = word_frequency("flip-flop", 'en')
|
||
assert_greater(ff, 0)
|
||
assert_almost_equal(
|
||
1.0 / ff,
|
||
1.0 / word_frequency('flip', 'en') + 1.0 / word_frequency('flop', 'en')
|
||
)
|
||
|
||
|
||
def test_not_really_random():
|
||
# If your xkcd-style password comes out like this, maybe you shouldn't
|
||
# use it
|
||
eq_(random_words(nwords=4, lang='en', bits_per_word=0),
|
||
'the the the the')
|
||
|
||
# This not only tests random_ascii_words, it makes sure we didn't end
|
||
# up with 'eos' as a very common Japanese word
|
||
eq_(random_ascii_words(nwords=4, lang='ja', bits_per_word=0),
|
||
'1 1 1 1')
|
||
|
||
|
||
@raises(ValueError)
|
||
def test_not_enough_ascii():
|
||
random_ascii_words(lang='zh', bits_per_word=14)
|
||
|
||
|
||
def test_arabic():
|
||
# Remove tatweels
|
||
eq_(
|
||
tokenize('متــــــــعب', 'ar'),
|
||
['متعب']
|
||
)
|
||
|
||
# Remove combining marks
|
||
eq_(
|
||
tokenize('حَرَكَات', 'ar'),
|
||
['حركات']
|
||
)
|
||
|
||
eq_(
|
||
tokenize('\ufefb', 'ar'), # An Arabic ligature...
|
||
['\u0644\u0627'] # ...that is affected by NFKC normalization
|
||
)
|
||
|
||
|
||
def test_ideographic_fallback():
|
||
# Try tokenizing Chinese text as English -- it should remain stuck together.
|
||
eq_(tokenize('中国文字', 'en'), ['中国文字'])
|
||
|
||
# When Japanese is tagged with the wrong language, it will be split
|
||
# at script boundaries.
|
||
ja_text = 'ひらがなカタカナromaji'
|
||
eq_(
|
||
tokenize(ja_text, 'en'),
|
||
['ひらがな', 'カタカナ', 'romaji']
|
||
)
|
||
|
||
|
||
def test_other_languages():
|
||
# Test that we leave Thai letters stuck together. If we had better Thai support,
|
||
# we would actually split this into a three-word phrase.
|
||
eq_(tokenize('การเล่นดนตรี', 'th'), ['การเล่นดนตรี'])
|
||
eq_(tokenize('"การเล่นดนตรี" means "playing music"', 'en'),
|
||
['การเล่นดนตรี', 'means', 'playing', 'music'])
|
||
|
||
# Test Khmer, a script similar to Thai
|
||
eq_(tokenize('សូមស្វាគមន៍', 'km'), ['សូមស្វាគមន៍'])
|
||
|
||
# Test Hindi -- tokens split where there are spaces, and not where there aren't
|
||
eq_(tokenize('हिन्दी विक्षनरी', 'hi'), ['हिन्दी', 'विक्षनरी'])
|
||
|
||
# Remove vowel points in Hebrew
|
||
eq_(tokenize('דֻּגְמָה', 'he'), ['דגמה'])
|
||
|
||
# Deal with commas, cedillas, and I's in Turkish
|
||
eq_(tokenize('kișinin', 'tr'), ['kişinin'])
|
||
eq_(tokenize('KİȘİNİN', 'tr'), ['kişinin'])
|
||
|
||
# Deal with cedillas that should be commas-below in Romanian
|
||
eq_(tokenize('acelaşi', 'ro'), ['același'])
|
||
eq_(tokenize('ACELAŞI', 'ro'), ['același'])
|