Rapprocher des noms d’entreprises mal orthographiés

"""
Rapprocher deux noms d'entreprises : normaliser, retirer la forme juridique,
puis Jaro-Winkler.

Niveau N0. Déterministe, bibliothèque standard seule. Deux décisions portent
tout le résultat.

D'abord, la forme juridique est retirée plutôt que comparée.
« Boulangerie Martin SARL » et « Boulangerie Martin SAS » sont un seul nom
commercial sous deux statuts ; laisser SARL et SAS dans les chaînes les
éloignerait pour une raison dont personne n'a à se soucier.

Ensuite, Jaro-Winkler plutôt qu'une distance d'édition ordinaire. Il
récompense un début commun, ce qui convient à des noms qui diffèrent par la
fin : une forme, une ville, un reste de ponctuation après la même marque.
"""

import re
import unicodedata

# Legal forms, French and foreign. This is business knowledge, not example
# data. No "spa": it is also a word of trading names, and dropping it would
# merge "Nordic Spa" with "Nordic SA".
LEGAL_FORMS = frozenset(("sarl sas sasu sa eurl sci snc scop selarl gie ltd limited "
                         "plc gmbh ag inc llc corp bv nv srl").split())

# The forms a French filing also writes in front of the name. "sa" is not among
# them: it is an article in Catalan and Corsican trading names, and dropping a
# leading one would merge "Sa Nostra" with "Nostra". Nor is "nv", for the same
# reason in "NV Energy".
LEADING_FORMS = frozenset("sarl sas sasu eurl sci snc scop selarl gie".split())

# What a company register writes short. Expanded before anything is compared,
# because "Ets Martin" and "Établissements Martin" are one company and Jaro
# scores them 0.68.
ABBREVIATIONS = {
    "ets": "etablissements", "etab": "etablissements", "etabs": "etablissements",
    "ste": "societe", "stes": "societes", "cie": "compagnie",
}

# Winkler looks at the first four characters only, and gives back a tenth of
# the score Jaro withheld for each of them that both names share.
PREFIX_LENGTH, PREFIX_SCALING = 4, 0.1


# Latin diacritics only. Dropping every combining mark would take the vowels of
# Devanagari and Thai with it, and "कमल उद्योग" and "कोमल उद्योग" would score 1.0.
_LATIN_MARKS = re.compile(r"[\u0300-\u036f]")


def _words(text: str) -> list[str]:
    r"""
    Runs of letters, digits and the marks that belong to them.

    The marks have to be kept, which a plain `\w` would not do: a Devanagari
    vowel sign is a combining mark, and dropping it would empty half the name.
    """
    found, current = [], []
    for char in text:
        if char.isalnum() or unicodedata.category(char).startswith("M"):
            current.append(char)
        elif current:
            found.append("".join(current))
            current = []
    return found + ["".join(current)] if current else found


def normalise(name: str) -> str:
    """Lowercase, expand the abbreviations, strip accents and punctuation, drop the legal form."""
    folded = _LATIN_MARKS.sub("", unicodedata.normalize("NFKD", name.lower()))
    # "&" is the same word as "et" in a company name, and punctuation is about
    # to be dropped, which would make the two spellings differ by one word.
    plain = folded.replace("&", " et ")
    # Letters and digits of every script: a Cyrillic or Japanese name is kept,
    # not emptied into a string that would match every other emptied name.
    words = [ABBREVIATIONS.get(w, w) for w in _words(plain)]
    kept = list(words)
    if len(kept) > 1 and kept[0] in LEADING_FORMS:
        kept = kept[1:]
    while len(kept) > 1 and kept[-1] in LEGAL_FORMS:
        kept = kept[:-1]
    # A name made of nothing but a legal form keeps it. Emptying it would make
    # it match every other emptied name perfectly, which is worse than useless.
    return " ".join(kept or words)


def _jaro(a: str, b: str) -> float:
    """
    Share of characters found on both sides, discounted by their disorder.

    A character counts as found only if its twin sits less than half the length
    of the longer name away: at most that half, minus one. That window is what separates Jaro from a plain count
    of common letters.
    """
    if a == b:
        return 1.0
    if not a or not b:
        return 0.0
    window = max(max(len(a), len(b)) // 2 - 1, 0)
    hit_a, hit_b = [False] * len(a), [False] * len(b)
    for i, char in enumerate(a):
        for j in range(max(0, i - window), min(len(b), i + window + 1)):
            if not hit_b[j] and b[j] == char:
                hit_a[i] = hit_b[j] = True
                break
    matched_a = [c for c, hit in zip(a, hit_a) if hit]
    matched_b = [c for c, hit in zip(b, hit_b) if hit]
    if not matched_a:
        return 0.0
    m = len(matched_a)
    swaps = sum(x != y for x, y in zip(matched_a, matched_b)) // 2
    return (m / len(a) + m / len(b) + (m - swaps) / m) / 3


def jaro_winkler(a: str, b: str) -> float:
    """Jaro, raised towards 1 in proportion to the shared opening."""
    score = _jaro(a, b)
    prefix = 0
    for x, y in zip(a[:PREFIX_LENGTH], b[:PREFIX_LENGTH]):
        if x != y:
            break
        prefix += 1
    return score + prefix * PREFIX_SCALING * (1 - score)


def similarity(left: str, right: str) -> float:
    """Similarity of two company names, from 0 to 1."""
    return jaro_winkler(normalise(left), normalise(right))

/**
 * Rapprocher deux noms d'entreprises : normaliser, retirer la forme juridique,
 * puis Jaro-Winkler.
 *
 * Niveau N0. Déterministe, sans dépendance. Deux décisions portent tout le
 * résultat.
 *
 * D'abord, la forme juridique est retirée plutôt que comparée.
 * « Boulangerie Martin SARL » et « Boulangerie Martin SAS » sont un seul nom
 * commercial sous deux statuts ; laisser SARL et SAS dans les chaînes les
 * éloignerait pour une raison dont personne n'a à se soucier.
 *
 * Ensuite, Jaro-Winkler plutôt qu'une distance d'édition ordinaire. Il
 * récompense un début commun, ce qui convient à des noms qui diffèrent par la
 * fin : une forme, une ville, un reste de ponctuation après la même marque.
 *
 * Écrit en entier plutôt que tiré d'un paquet : l'algorithme tient en trente
 * lignes, et c'est l'argument de cette fiche.
 */

// Legal forms, French and foreign. This is business knowledge, not example
// data. No "spa": it is also a word of trading names, and dropping it would
// merge "Nordic Spa" with "Nordic SA".
const LEGAL_FORMS = new Set(
  'sarl sas sasu sa eurl sci snc scop selarl gie ltd limited plc gmbh ag inc llc corp bv nv srl'.split(' '),
);

// The forms a French filing also writes in front of the name. "sa" is not among
// them: it is an article in Catalan and Corsican trading names, and dropping a
// leading one would merge "Sa Nostra" with "Nostra". Nor is "nv", for the same
// reason in "NV Energy".
const LEADING_FORMS = new Set('sarl sas sasu eurl sci snc scop selarl gie'.split(' '));

// What a company register writes short. Expanded before anything is compared,
// because "Ets Martin" and "Établissements Martin" are one company and Jaro
// scores them 0.68.
const ABBREVIATIONS = {
  ets: 'etablissements', etab: 'etablissements', etabs: 'etablissements',
  ste: 'societe', stes: 'societes', cie: 'compagnie',
};

// Winkler looks at the first four characters only, and gives back a tenth of
// the score Jaro withheld for each of them that both names share.
const PREFIX_LENGTH = 4;
const PREFIX_SCALING = 0.1;

/** Lowercase, expand the abbreviations, strip accents and punctuation, drop the legal form. */
export function normalise(name) {
  // Latin diacritics only. Dropping every combining mark would take the vowels
  // of Devanagari and Thai with it, and "कमल उद्योग" and "कोमल उद्योग" would score 1.0.
  const folded = name.toLowerCase().normalize('NFKD').replace(/[\u0300-\u036f]/gu, '');
  // "&" is the same word as "et" in a company name, and punctuation is about to
  // be dropped, which would make the two spellings differ by one word.
  const plain = folded.replaceAll('&', ' et ');
  // Letters, digits and the marks that belong to them, in every script: a
  // Cyrillic, Japanese or Devanagari name is kept, not emptied into a string
  // that would match every other emptied name.
  const words = (plain.match(/[\p{L}\p{N}\p{M}]+/gu) ?? []).map((w) => ABBREVIATIONS[w] ?? w);
  let kept = [...words];
  if (kept.length > 1 && LEADING_FORMS.has(kept[0])) kept = kept.slice(1);
  while (kept.length > 1 && LEGAL_FORMS.has(kept.at(-1))) kept = kept.slice(0, -1);
  // A name made of nothing but a legal form keeps it. Emptying it would make
  // it match every other emptied name perfectly, which is worse than useless.
  return (kept.length ? kept : words).join(' ');
}

/**
 * Share of characters found on both sides, discounted by their disorder.
 *
 * A character counts as found only if its twin sits less than half the length
 * of the longer name away: at most that half, minus one. That window is what separates Jaro from a plain count of
 * common letters.
 */
function jaro(a, b) {
  if (!a.length || !b.length) return 0;
  const window = Math.max(Math.floor(Math.max(a.length, b.length) / 2) - 1, 0);
  const hitA = new Array(a.length).fill(false);
  const hitB = new Array(b.length).fill(false);
  for (let i = 0; i < a.length; i += 1) {
    for (let j = Math.max(0, i - window); j < Math.min(b.length, i + window + 1); j += 1) {
      if (!hitB[j] && b[j] === a[i]) {
        hitA[i] = true;
        hitB[j] = true;
        break;
      }
    }
  }
  const matchedA = a.filter((_, i) => hitA[i]);
  const matchedB = b.filter((_, j) => hitB[j]);
  const m = matchedA.length;
  if (!m) return 0;
  const swaps = Math.floor(matchedA.filter((c, i) => c !== matchedB[i]).length / 2);
  return (m / a.length + m / b.length + (m - swaps) / m) / 3;
}

/** Jaro, raised towards 1 in proportion to the shared opening. */
export function jaroWinkler(left, right) {
  // Code points, as Python counts them, not UTF-16 units.
  const [a, b] = [[...left], [...right]];
  const score = left === right ? 1 : jaro(a, b);
  let prefix = 0;
  while (prefix < PREFIX_LENGTH && prefix < a.length && prefix < b.length && a[prefix] === b[prefix]) {
    prefix += 1;
  }
  return score + prefix * PREFIX_SCALING * (1 - score);
}

/** Similarity of two company names, from 0 to 1. */
export function similarity(left, right) {
  return jaroWinkler(normalise(left), normalise(right));
}