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work on rel. frequencies of numbers, and other features

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This commit is contained in:
Elia Robyn Lake 2022-02-18 11:33:28 -05:00
parent ef4d6fe0df
commit f800ff9bcc
13 changed files with 1136 additions and 63 deletions
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.flake8 Normal file
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[flake8]
max-line-length = 100

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mypy.ini Normal file
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[mypy]
python_version = 3.7
[mypy-ipadic]
ignore_missing_imports = True
[mypy-jieba]
ignore_missing_imports = True
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ignore_missing_imports = True
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ignore_missing_imports = True
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ignore_missing_imports = True
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ignore_missing_imports = True

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number-freq-notes.txt Normal file
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906 0.00000047
1006 0.00000038
1106 0.00000028
1206 0.00000028
1306 0.00000025
1406 0.00000022
1506 0.00000028
1606 0.00000052
1633 0.00000048
1678 0.00000048
1706 0.00000040
1733 0.00000047
1754 0.00000063
1778 0.00000111
1806 0.00000130
1823 0.00000135
1833 0.00000494
1856 0.00000325
1866 0.00000299
1876 0.00000290
1886 0.00000331
1896 0.00000423
1906 0.00000496
1916 0.00000715
1921 0.00000928
1926 0.00001130
1936 0.00001037
1946 0.00000921
1956 0.00001100
1966 0.00001800
1976 0.00002500
1986 0.00003500
1996 0.00007100
2006 0.00011704
2009 0.00013361
2011 0.00015103
2014 0.00016833
2015 0.00016840
2016 0.00014277
2017 0.00010960
2018 0.00005095
2019 0.00002800
2021 0.00000090
2023 0.00000042
2026 0.00000026
2036 0.00000014
2046 0.00000010

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poetry.lock generated Normal file
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5
pyproject.toml
View File

@ -10,7 +10,8 @@ python = "^3.7"
msgpack = ">= 1.0"
langcodes = ">= 3.0"
regex = ">= 2020.04.04"
ftfy = ">= 3.0"
ftfy = ">= 6.1"
mypy = "^0.931"
[tool.poetry.dev-dependencies]
pytest = "^6.2.5"
@ -20,6 +21,8 @@ ipadic = "^1.0.0"
mecab-ko-dic = "^1.0.0"
ipython = ">=7"
black = "^22.1.0"
flake8 = "^4.0.1"
types-setuptools = "^57.4.9"
[build-system]
requires = ["poetry-core>=1.0.0"]

62
wordfreq/__init__.py
View File

@ -1,5 +1,6 @@
from pkg_resources import resource_filename
from functools import lru_cache
from typing import List, Dict, Iterator, Tuple
import langcodes
import msgpack
import gzip
@ -12,7 +13,7 @@ import warnings
from .tokens import tokenize, simple_tokenize, 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.

5
wordfreq/chinese.py
View File

@ -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.

8
wordfreq/language_info.py
View File

@ -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:

16
wordfreq/mecab.py
View File

@ -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.

72
wordfreq/numbers.py Normal file
View File

@ -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)))

23
wordfreq/preprocess.py
View File

@ -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.

32
wordfreq/tokens.py
View File

@ -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]

3
wordfreq/transliterate.py
View File

@ -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.