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lossy_tokenize from .language_info import get_language_info -from .preprocess import num_generic_digits +from .numbers import digit_freq logger = logging.getLogger(__name__) @@ -34,7 +35,7 @@ tokenize = tokenize simple_tokenize = simple_tokenize -def read_cBpack(filename): +def read_cBpack(filename: str) -> List[List[str]]: """ Read a file from an idiosyncratic format that we use for storing approximate word frequencies, called "cBpack". @@ -87,7 +88,7 @@ def read_cBpack(filename): return data[1:] -def available_languages(wordlist="best"): +def available_languages(wordlist: str = "best") -> Dict[str, str]: """ Given a wordlist name, return a dictionary of language codes to filenames, representing all the languages in which that wordlist is available. @@ -111,7 +112,9 @@ def available_languages(wordlist="best"): @lru_cache(maxsize=None) -def get_frequency_list(lang, wordlist="best", match_cutoff=None): +def get_frequency_list( + lang: str, wordlist: str = "best", match_cutoff: None = None +) -> List[List[str]]: """ Read the raw data from a wordlist file, returning it as a list of lists. (See `read_cBpack` for what this represents.) @@ -125,10 +128,9 @@ def get_frequency_list(lang, wordlist="best", match_cutoff=None): warnings.warn("The `match_cutoff` parameter is deprecated", DeprecationWarning) available = available_languages(wordlist) - # TODO: decrease the maximum distance. This distance is so high just - # because it allows a test where 'yue' matches 'zh', and maybe the - # distance between those is high because they shouldn't match. - best, _distance = langcodes.closest_match(lang, list(available), max_distance=70) + # the max_distance is high because we unify scripts, such as Traditional + # vs. Simplified Chinese, in one wordlist + best, _distance = langcodes.closest_match(lang, list(available), max_distance=60) if best == "und": raise LookupError("No wordlist %r available for language %r" % (wordlist, lang)) @@ -141,7 +143,7 @@ def get_frequency_list(lang, wordlist="best", match_cutoff=None): return read_cBpack(available[best]) -def cB_to_freq(cB): +def cB_to_freq(cB: int) -> float: """ Convert a word frequency from the logarithmic centibel scale that we use internally, to a proportion from 0 to 1. @@ -157,7 +159,7 @@ def cB_to_freq(cB): return 10 ** (cB / 100) -def cB_to_zipf(cB): +def cB_to_zipf(cB: int) -> float: """ Convert a word frequency from centibels to the Zipf scale (see `zipf_to_freq`). @@ -169,7 +171,7 @@ def cB_to_zipf(cB): return (cB + 900) / 100 -def zipf_to_freq(zipf): +def zipf_to_freq(zipf: float) -> float: """ Convert a word frequency from the Zipf scale to a proportion between 0 and 1. @@ -185,7 +187,7 @@ def zipf_to_freq(zipf): return 10**zipf / 1e9 -def freq_to_zipf(freq): +def freq_to_zipf(freq: float) -> float: """ Convert a word frequency from a proportion between 0 and 1 to the Zipf scale (see `zipf_to_freq`). @@ -194,7 +196,9 @@ def freq_to_zipf(freq): @lru_cache(maxsize=None) -def get_frequency_dict(lang, wordlist="best", match_cutoff=None): +def get_frequency_dict( + lang: str, wordlist: str = "best", match_cutoff: None = None +) -> Dict[str, float]: """ Get a word frequency list as a dictionary, mapping tokens to frequencies as floating-point probabilities. @@ -210,7 +214,7 @@ def get_frequency_dict(lang, wordlist="best", match_cutoff=None): return freqs -def iter_wordlist(lang, wordlist="best"): +def iter_wordlist(lang: str, wordlist: str = "best") -> Iterator[str]: """ Yield the words in a wordlist in approximate descending order of frequency. @@ -225,12 +229,12 @@ def iter_wordlist(lang, wordlist="best"): # This dict and inner function are used to implement a "drop everything" cache # for word_frequency(); the overheads of lru_cache() are comparable to the time # it takes to look up frequencies from scratch, so something faster is needed. -_wf_cache = {} +_wf_cache: Dict[Tuple[str, str, str, float], float] = {} -def _word_frequency(word, lang, wordlist, minimum): +def _word_frequency(word: str, lang: str, wordlist: str, minimum: float) -> float: tokens = lossy_tokenize(word, lang) - digits = num_generic_digits(word) + dfreq = digit_freq(word) if not tokens: return minimum @@ -245,7 +249,7 @@ def _word_frequency(word, lang, wordlist, minimum): # If any word is missing, just return the default value return minimum # spread the frequency of digits over all digit combinations - freq = freqs[token] / (10.0**digits) + freq = freqs[token] one_over_result += 1.0 / freq freq = 1.0 / one_over_result @@ -266,7 +270,9 @@ def _word_frequency(word, lang, wordlist, minimum): return round(unrounded, leading_zeroes + 3) -def word_frequency(word, lang, wordlist="best", minimum=0.0): +def word_frequency( + word: str, lang: str, wordlist: str = "best", minimum: float = 0.0 +) -> float: """ Get the frequency of `word` in the language with code `lang`, from the specified `wordlist`. @@ -293,7 +299,7 @@ def word_frequency(word, lang, wordlist="best", minimum=0.0): return _wf_cache[args] -def zipf_frequency(word, lang, wordlist="best", minimum=0.0): +def zipf_frequency(word: str, lang: str, wordlist: str = "best", minimum: float = 0.0): """ Get the frequency of `word`, in the language with code `lang`, on the Zipf scale. @@ -321,7 +327,9 @@ def zipf_frequency(word, lang, wordlist="best", minimum=0.0): @lru_cache(maxsize=100) -def top_n_list(lang, n, wordlist="best", ascii_only=False): +def top_n_list( + lang: str, n: int, wordlist: str = "best", ascii_only: bool = False +) -> List[str]: """ Return a frequency list of length `n` in descending order of frequency. This list contains words from `wordlist`, of the given language. @@ -337,8 +345,12 @@ def top_n_list(lang, n, wordlist="best", ascii_only=False): def random_words( - lang="en", wordlist="best", nwords=5, bits_per_word=12, ascii_only=False -): + lang: str = "en", + wordlist: str = "best", + nwords: int = 5, + bits_per_word: int = 12, + ascii_only: bool = False, +) -> str: """ Returns a string of random, space separated words. @@ -362,7 +374,9 @@ def random_words( return " ".join([random.choice(choices) for i in range(nwords)]) -def random_ascii_words(lang="en", wordlist="best", nwords=5, bits_per_word=12): +def random_ascii_words( + lang: str = "en", wordlist: str = "best", nwords: int = 5, bits_per_word: int = 12 +) -> str: """ Returns a string of random, space separated, ASCII words. diff --git a/wordfreq/chinese.py b/wordfreq/chinese.py index 8b30095..73fefa0 100644 --- a/wordfreq/chinese.py +++ b/wordfreq/chinese.py @@ -1,4 +1,5 @@ from pkg_resources import resource_filename +from typing import List import jieba import msgpack import gzip @@ -17,7 +18,7 @@ jieba_tokenizer = None jieba_orig_tokenizer = None -def simplify_chinese(text): +def simplify_chinese(text: str) -> str: """ Convert Chinese text character-by-character to Simplified Chinese, for the purpose of looking up word frequencies. @@ -31,7 +32,7 @@ def simplify_chinese(text): return text.translate(SIMPLIFIED_MAP).casefold() -def jieba_tokenize(text, external_wordlist=False): +def jieba_tokenize(text: str, external_wordlist: bool = False) -> List[str]: """ Tokenize the given text into tokens whose word frequencies can probably be looked up. This uses Jieba, a word-frequency-based tokenizer. diff --git a/wordfreq/language_info.py b/wordfreq/language_info.py index 3e48e1f..2856eb8 100644 --- a/wordfreq/language_info.py +++ b/wordfreq/language_info.py @@ -1,6 +1,6 @@ from functools import lru_cache from langcodes import Language, closest_match - +from typing import List, Union # Text in scripts written without spaces has to be handled specially in our # tokenization regex (see TOKEN_RE in tokens.py). Also, when one of these is @@ -44,7 +44,9 @@ EXTRA_JAPANESE_CHARACTERS = "ー々〻〆" # happens in ConceptNet. -def _language_in_list(language, targets, max_distance=10): +def _language_in_list( + language: Language, targets: List[str], max_distance: int = 10 +) -> bool: """ A helper function to determine whether this language matches one of the target languages, with a match score above a certain threshold. @@ -57,7 +59,7 @@ def _language_in_list(language, targets, max_distance=10): @lru_cache(maxsize=None) -def get_language_info(language): +def get_language_info(language: Union[str, Language]) -> dict: """ Looks up the things we need to know about how to handle text in a given language. This will return a dictionary with the following fields: diff --git a/wordfreq/mecab.py b/wordfreq/mecab.py index 8dd19f7..e1db124 100644 --- a/wordfreq/mecab.py +++ b/wordfreq/mecab.py @@ -1,14 +1,10 @@ -from pkg_resources import resource_filename import MeCab import unicodedata -import os + +from typing import Dict, List -# MeCab has fixed-sized buffers for many things, including the dictionary path -MAX_PATH_LENGTH = 58 - - -def make_mecab_analyzer(lang): +def make_mecab_analyzer(lang: str) -> MeCab.Tagger: """ Get a MeCab analyzer object, given the language code of the language to analyze. @@ -22,14 +18,14 @@ def make_mecab_analyzer(lang): return MeCab.Tagger(ipadic.MECAB_ARGS) else: - raise ValueError("Can't run MeCab on language {lang}".format(lang)) + raise ValueError(f"Can't run MeCab on language {lang}") # The constructed analyzers will go in this dictionary. -MECAB_ANALYZERS = {} +MECAB_ANALYZERS: Dict[str, MeCab.Tagger] = {} -def mecab_tokenize(text, lang): +def mecab_tokenize(text: str, lang: str) -> List[str]: """ Use the mecab-python3 package to tokenize the given text. The `lang` must be 'ja' for Japanese or 'ko' for Korean. diff --git a/wordfreq/numbers.py b/wordfreq/numbers.py new file mode 100644 index 0000000..4dd4bee --- /dev/null +++ b/wordfreq/numbers.py @@ -0,0 +1,72 @@ +from .preprocess import MULTI_DIGIT_RE + +# Frequencies of leading digits, according to Benford's law, sort of. +# Benford's law doesn't describe numbers with leading zeroes, because "007" +# and "7" are the same number, but for us they should have different frequencies. +# I added an estimate for the frequency of numbers with leading zeroes. +DIGIT_FREQS = [0.009, 0.300, 0.175, 0.124, 0.096, 0.078, 0.066, 0.057, 0.050, 0.045] + +# Suppose you have a token NNNN, a 4-digit number representing a year. We're making +# a probability distribution of P(token=NNNN) | P(token is 4 digits). +# +# We do this with a piecewise exponential function whose peak is a plateau covering +# the years 2019 to 2039. +# +# YEAR_LOG_PEAK is chosen by experimentation to make this probability add up to about +# .994. Here, that represents P(token represents a year) | P(token is 4 digits). +# The other .006 represents P(token does not represent a year) | P(token is 4 digits). + +YEAR_LOG_PEAK = -1.875 +NOT_YEAR_PROB = 0.006 +REFERENCE_YEAR = 2019 +PLATEAU_WIDTH = 20 + + +def benford_freq(text: str) -> float: + first_digit = int(text[0]) + return DIGIT_FREQS[first_digit] / 10 ** (len(text) - 1) + + +def year_freq(text: str) -> float: + year = int(text) + + # Fitting a line to the curve seen at + # https://twitter.com/r_speer/status/1493715982887571456. + + if year <= REFERENCE_YEAR: + year_log_freq = YEAR_LOG_PEAK - 0.0083 * (REFERENCE_YEAR - year) + + # It's no longer 2019, which is when the Google Books data was last collected. + # It's 2022 as I write this, and possibly even later as you're using it. Years + # keep happening. + # + # So, we'll just keep the expected frequency of the "present" year constant for + # 20 years. + + elif REFERENCE_YEAR < year <= REFERENCE_YEAR + PLATEAU_WIDTH: + year_log_freq = YEAR_LOG_PEAK + + # Fall off quickly to catch up with the actual frequency of future years + # (it's low). This curve is made up to fit with the made-up "present" data above. + else: + year_log_freq = YEAR_LOG_PEAK - 0.2 * (year - (REFERENCE_YEAR + PLATEAU_WIDTH)) + + year_prob = 10.0**year_log_freq + + # If this token _doesn't_ represent a year, then use the Benford frequency + # distribution. + not_year_prob = NOT_YEAR_PROB * benford_freq(text) + return year_prob + not_year_prob + + +def digit_freq(text: str) -> float: + freq = 1.0 + for match in MULTI_DIGIT_RE.findall(text): + if len(match) == 4: + freq *= year_freq(match) + else: + freq *= benford_freq(match) + return freq + + +print(sum(digit_freq("%04d" % year) for year in range(0, 10000))) diff --git a/wordfreq/preprocess.py b/wordfreq/preprocess.py index 88342c8..e7db588 100644 --- a/wordfreq/preprocess.py +++ b/wordfreq/preprocess.py @@ -3,6 +3,7 @@ import unicodedata from .language_info import get_language_info from .transliterate import transliterate +from langcodes import Language MARK_RE = regex.compile(r"[\p{Mn}\N{ARABIC TATWEEL}]", regex.V1) @@ -10,7 +11,7 @@ DIGIT_RE = regex.compile(r"\d") MULTI_DIGIT_RE = regex.compile(r"\d[\d.,]+") -def preprocess_text(text, language): +def preprocess_text(text: str, language: Language) -> str: """ This function applies pre-processing steps that convert forms of words considered equivalent into one standardized form. @@ -196,7 +197,7 @@ def preprocess_text(text, language): return text -def remove_marks(text): +def remove_marks(text: str) -> str: """ Remove decorations from words in abjad scripts: @@ -208,7 +209,7 @@ def remove_marks(text): return MARK_RE.sub("", text) -def casefold_with_i_dots(text): +def casefold_with_i_dots(text: str) -> str: """ Convert capital I's and capital dotted İ's to lowercase in the way that's appropriate for Turkish and related languages, then case-fold @@ -218,7 +219,7 @@ def casefold_with_i_dots(text): return text.casefold() -def commas_to_cedillas(text): +def commas_to_cedillas(text: str) -> str: """ Convert s and t with commas (ș and ț) to cedillas (ş and ţ), which is preferred in Turkish. @@ -235,7 +236,7 @@ def commas_to_cedillas(text): ) -def cedillas_to_commas(text): +def cedillas_to_commas(text: str) -> str: """ Convert s and t with cedillas (ş and ţ) to commas (ș and ț), which is preferred in Romanian. @@ -252,7 +253,7 @@ def cedillas_to_commas(text): ) -def _sub_zeroes(match): +def _sub_zeroes(match: regex.Match) -> str: """ Given a regex match, return what it matched with digits replaced by zeroes. @@ -260,15 +261,7 @@ def _sub_zeroes(match): return DIGIT_RE.sub("0", match.group(0)) -def num_generic_digits(text): - """ - Determine how many "generic digits" are in the text (digits that we - replace with 0 to combine numbers of the same length). - """ - return sum([len(match) for match in MULTI_DIGIT_RE.findall(text)]) - - -def smash_numbers(text): +def smash_numbers(text: str) -> str: """ Replace sequences of multiple digits with zeroes, so we don't need to distinguish the frequencies of thousands of numbers. diff --git a/wordfreq/tokens.py b/wordfreq/tokens.py index 238a2df..37acf03 100644 --- a/wordfreq/tokens.py +++ b/wordfreq/tokens.py @@ -2,6 +2,7 @@ import regex import unicodedata import logging import langcodes +from typing import List from ftfy.fixes import uncurl_quotes from .language_info import ( @@ -20,7 +21,7 @@ _WARNED_LANGUAGES = set() logger = logging.getLogger(__name__) -def _make_spaceless_expr(): +def _make_spaceless_expr() -> str: scripts = sorted(SPACELESS_SCRIPTS) pieces = [r"\p{IsIdeo}"] + [ r"\p{Script=%s}" % script_code for script_code in scripts @@ -179,7 +180,7 @@ TOKEN_RE_WITH_PUNCTUATION = regex.compile( PUNCT_RE = regex.compile(r"[\p{punct}]+") -def simple_tokenize(text, include_punctuation=False): +def simple_tokenize(text: str, include_punctuation: bool = False) -> List[str]: """ Tokenize the given text using a straightforward, Unicode-aware token expression. @@ -214,7 +215,12 @@ def simple_tokenize(text, include_punctuation=False): return [token.strip("'").casefold() for token in TOKEN_RE.findall(text)] -def tokenize(text, lang, include_punctuation=False, external_wordlist=False): +def tokenize( + text: str, + lang: str, + include_punctuation: bool = False, + external_wordlist: bool = False, +) -> List[str]: """ Tokenize this text in a way that's relatively simple but appropriate for the language. Strings that are looked up in wordfreq will be run through @@ -255,15 +261,20 @@ def tokenize(text, lang, include_punctuation=False, external_wordlist=False): text = preprocess_text(text, language) if info["tokenizer"] == "mecab": - from wordfreq.mecab import mecab_tokenize as _mecab_tokenize + from wordfreq.mecab import mecab_tokenize + + _mecab_tokenize = mecab_tokenize # Get just the language code out of the Language object, so we can # use it to select a MeCab dictionary + assert language.language is not None tokens = _mecab_tokenize(text, language.language) if not include_punctuation: tokens = [token for token in tokens if not PUNCT_RE.match(token)] elif info["tokenizer"] == "jieba": - from wordfreq.chinese import jieba_tokenize as _jieba_tokenize + from wordfreq.chinese import jieba_tokenize + + _jieba_tokenize = jieba_tokenize tokens = _jieba_tokenize(text, external_wordlist=external_wordlist) if not include_punctuation: @@ -285,7 +296,12 @@ def tokenize(text, lang, include_punctuation=False, external_wordlist=False): return tokens -def lossy_tokenize(text, lang, include_punctuation=False, external_wordlist=False): +def lossy_tokenize( + text: str, + lang: str, + include_punctuation: bool = False, + external_wordlist: bool = False, +) -> List[str]: """ Get a list of tokens for this text, with largely the same results and options as `tokenize`, but aggressively normalize some text in a lossy way @@ -312,7 +328,9 @@ def lossy_tokenize(text, lang, include_punctuation=False, external_wordlist=Fals tokens = tokenize(text, lang, include_punctuation, external_wordlist) if info["lookup_transliteration"] == "zh-Hans": - from wordfreq.chinese import simplify_chinese as _simplify_chinese + from wordfreq.chinese import simplify_chinese + + _simplify_chinese = simplify_chinese tokens = [_simplify_chinese(token) for token in tokens] diff --git a/wordfreq/transliterate.py b/wordfreq/transliterate.py index 788f14d..594fbc7 100644 --- a/wordfreq/transliterate.py +++ b/wordfreq/transliterate.py @@ -1,4 +1,5 @@ -# This table comes from https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping.py, +# This table comes from +# https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping.py, # from the 'cyrtranslit' module. We originally had to reimplement it because # 'cyrtranslit' didn't work in Python 3; now it does, but we've made the table # more robust than the one in cyrtranslit.