wordfreq/tests/test_general.py

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from wordfreq import (
word_frequency,
available_languages,
cB_to_freq,
top_n_list,
random_words,
random_ascii_words,
tokenize,
lossy_tokenize,
)
import pytest
def test_freq_examples():
# Stopwords are most common in the correct language
assert word_frequency("the", "en") > word_frequency("de", "en")
assert word_frequency("de", "es") > word_frequency("the", "es")
# We get word frequencies from the 'large' list when available
assert word_frequency("infrequency", "en") > 0.0
def test_languages():
# Make sure we get all the languages when looking for the default
# 'best' wordlist
avail = available_languages()
assert len(avail) >= 34
# 'small' covers the same languages, but with some different lists
avail_small = available_languages("small")
assert len(avail_small) == len(avail)
assert avail_small != avail
# 'combined' is the same as 'small'
avail_old_name = available_languages("combined")
assert avail_old_name == avail_small
# 'large' covers fewer languages
avail_large = available_languages("large")
assert len(avail_large) >= 14
assert len(avail) > len(avail_large)
# Look up the digit '2' in the main word list for each language
for lang in avail:
assert word_frequency("2", lang) > 0
# Make up a weirdly verbose language code and make sure
# we still get it
new_lang_code = "%s-001-x-fake-ext" % lang.upper()
assert word_frequency("2", new_lang_code) > 0
def test_minimums():
assert word_frequency("esquivalience", "en") == 0
assert word_frequency("esquivalience", "en", minimum=1e-6) == 1e-6
assert 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]
assert get_most_common("ar") == "في"
assert get_most_common("bg") == "на"
assert get_most_common("bn") == "না"
assert get_most_common("ca") == "de"
assert get_most_common("cs") == "a"
assert get_most_common("da") == "i"
assert get_most_common("el") == "και"
assert get_most_common("de") == "die"
assert get_most_common("en") == "the"
assert get_most_common("es") == "de"
assert get_most_common("fi") == "ja"
assert get_most_common("fil") == "sa"
assert get_most_common("fr") == "de"
assert get_most_common("he") == "את"
assert get_most_common("hi") == "के"
assert get_most_common("hu") == "a"
assert get_most_common("id") == "yang"
assert get_most_common("is") == "og"
assert get_most_common("it") == "di"
assert get_most_common("ja") == ""
assert get_most_common("ko") == ""
assert get_most_common("lt") == "ir"
assert get_most_common("lv") == "un"
assert get_most_common("mk") == "на"
assert get_most_common("ms") == "yang"
assert get_most_common("nb") == "i"
assert get_most_common("nl") == "de"
assert get_most_common("pl") == "w"
assert get_most_common("pt") == "de"
assert get_most_common("ro") == "de"
assert get_most_common("ru") == "в"
assert get_most_common("sh") == "je"
assert get_most_common("sk") == "a"
assert get_most_common("sl") == "je"
assert get_most_common("sv") == "är"
assert get_most_common("ta") == "ஒரு"
assert get_most_common("tr") == "ve"
assert get_most_common("uk") == "в"
assert get_most_common("ur") == "کے"
assert get_most_common("vi") == ""
assert get_most_common("zh") == ""
def test_language_matching():
freq = word_frequency("", "zh")
assert word_frequency("", "zh-TW") == freq
assert word_frequency("", "zh-CN") == freq
assert word_frequency("", "zh-Hant") == freq
assert word_frequency("", "zh-Hans") == freq
assert word_frequency("", "yue-CN") == freq
assert word_frequency("", "cmn") == freq
def test_cB_conversion():
assert cB_to_freq(0) == 1.0
assert cB_to_freq(-100) == pytest.approx(0.1)
assert cB_to_freq(-600) == pytest.approx(1e-6)
def test_failed_cB_conversion():
with pytest.raises(ValueError):
cB_to_freq(1)
def test_tokenization():
# We preserve apostrophes within words, so "can't" is a single word in the
# data
assert tokenize("I don't split at apostrophes, you see.", "en") == [
"i",
"don't",
"split",
"at",
"apostrophes",
"you",
"see",
]
assert 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.
assert 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.
assert tokenize("😂test", "en") == ["😂", "test"]
assert tokenize("flip-flop", "en") == ["flip", "flop"]
assert 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
assert tokenize("emoji test 🧕🏽", "en") == ["emoji", "test", "🧕🏽"]
assert 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)
assert tokenize("Surf's up 🌊🏄🏴󠁵󠁳󠁣󠁡󠁿'", "en") == ["surf's", "up", "🌊", "🏄", "🏴󠁵󠁳󠁣󠁡󠁿"]
def test_casefolding():
assert tokenize("WEISS", "de") == ["weiss"]
assert tokenize("weiß", "de") == ["weiss"]
assert tokenize("İstanbul", "tr") == ["istanbul"]
assert tokenize("SIKISINCA", "tr") == ["sıkısınca"]
def test_normalization():
assert tokenize('"715 - CRΣΣKS" by Bon Iver', "en") == [
"715",
"crσσks",
"by",
"bon",
"iver",
]
assert lossy_tokenize('"715 - CRΣΣKS" by Bon Iver', "en") == [
"715",
"crσσks",
"by",
"bon",
"iver",
]
def test_uncurl_quotes():
assert lossy_tokenize("lets", "en") == ["let's"]
assert word_frequency("lets", "en") == word_frequency("let's", "en")
def test_phrase_freq():
ff = word_frequency("flip-flop", "en")
assert ff > 0
phrase_freq = 1.0 / word_frequency("flip", "en") + 1.0 / word_frequency(
"flop", "en"
)
assert 1.0 / ff == pytest.approx(phrase_freq, rel=0.01)
def test_not_really_random():
# If your xkcd-style password comes out like this, maybe you shouldn't
# use it
assert 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
assert random_ascii_words(nwords=4, lang="ja", bits_per_word=0) == "1 1 1 1"
def test_not_enough_ascii():
with pytest.raises(ValueError):
random_ascii_words(lang="zh", bits_per_word=16)
def test_arabic():
# Remove tatweels
assert tokenize("متــــــــعب", "ar") == ["متعب"]
# Remove combining marks
assert tokenize("حَرَكَات", "ar") == ["حركات"]
# An Arabic ligature that is affected by NFKC normalization
assert tokenize("\ufefb", "ar") == ["\u0644\u0627"]
def test_ideographic_fallback():
# Try tokenizing Chinese text as English -- it should remain stuck together.
#
# More complex examples like this, involving the multiple scripts of Japanese,
# are in test_japanese.py.
assert tokenize("中国文字", "en") == ["中国文字"]
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.
assert tokenize("การเล่นดนตรี", "th") == ["การเล่นดนตรี"]
assert tokenize('"การเล่นดนตรี" means "playing music"', "en") == [
"การเล่นดนตรี",
"means",
"playing",
"music",
]
# Test Khmer, a script similar to Thai
assert tokenize("សូមស្វាគមន៍", "km") == ["សូមស្វាគមន៍"]
# Test Hindi -- tokens split where there are spaces, and not where there aren't
assert tokenize("हिन्दी विक्षनरी", "hi") == ["हिन्दी", "विक्षनरी"]
# Remove vowel points in Hebrew
assert tokenize("דֻּגְמָה", "he") == ["דגמה"]
# Deal with commas, cedillas, and I's in Turkish
assert tokenize("kișinin", "tr") == ["kişinin"]
assert tokenize("KİȘİNİN", "tr") == ["kişinin"]
# Deal with cedillas that should be commas-below in Romanian
assert tokenize("acelaşi", "ro") == ["același"]
assert tokenize("ACELAŞI", "ro") == ["același"]