Grammalecte  Check-in [7db21c89fd]

Overview
Comment:[graphspell][py] suggestion: use Jaro-Winkler
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Timelines: family | ancestors | descendants | both | graphspell | bdic_opt
Files: files | file ages | folders
SHA3-256: 7db21c89fd739ab350943e005913b071025e3b448eb89e6ebfba7a8b89886637
User & Date: olr on 2020-09-15 16:57:04
Other Links: branch diff | manifest | tags
Context
2020-09-15
17:04
[fr] update suggest tests Closed-Leaf check-in: a8c66433af user: olr tags: fr, bdic_opt
16:57
[graphspell][py] suggestion: use Jaro-Winkler check-in: 7db21c89fd user: olr tags: graphspell, bdic_opt
14:09
[graphspell][js] str_transform: fix cleanWord() check-in: 2bfe79e9aa user: olr tags: graphspell, bdic_opt
Changes
Hide Diffs Side-by-Side Diffs Ignore Whitespace Patch

Modified graphspell/char_player.py from [e2b351100d] to [24cf38a4cb]. 

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"""
List of similar chars
useful for suggestion mechanism
"""


dDistanceBetweenChars = {
    # dDistanceBetweenChars:
    # - with Jaro-Winkler, values between 1 and 10
    # - with Damerau-Levenshtein, values / 10 (between 0 and 1: 0.1, 0.2 ... 0.9)
    "a": {},
    "e": {"é": 0.5},
    "é": {"e": 0.5},
    "i": {"y": 0.2},
    "o": {},
    "u": {},
    "y": {"i": 0.3},
    "b": {"d": 0.8, "h": 0.9},
    "c": {"ç": 0.1, "k": 0.5, "q": 0.5, "s": 0.5, "x": 0.5, "z": 0.8},
    "d": {"b": 0.8},
    "f": {"v": 0.8},
    "g": {"j": 0.5},
    "h": {"b": 0.9},
    "j": {"g": 0.5, "i": 0.9},
    "k": {"c": 0.5, "q": 0.1, "x": 0.5},
    "l": {"i": 0.9},
    "m": {"n": 0.8},
    "n": {"m": 0.8, "r": 0.9},
    "p": {"q": 0.9},
    "q": {"c": 0.5, "k": 0.1, "p": 0.9},
    "r": {"n": 0.9, "j": 0.9},
    "s": {"c": 0.5, "ç": 0.1, "x": 0.5, "z": 0.5},
    "t": {"d": 0.9},
    "v": {"f": 0.8, "w": 0.1},
    "w": {"v": 0.1},
    "x": {"c": 0.5, "k": 0.5, "q": 0.5, "s": 0.5},
    "z": {"s": 0.5}
    #"a": {},
    "e": {"é": 5},
    "é": {"e": 5},
    "i": {"y": 2},
    #"o": {},
    #"u": {},
    "y": {"i": 3},
    "b": {"d": 8, "h": 9},
    "c": {"ç": 1, "k": 5, "q": 5, "s": 5, "x": 5, "z": 8},
    "d": {"b": 8},
    "f": {"v": 8},
    "g": {"j": 5},
    "h": {"b": 9},
    "j": {"g": 5, "i": 9},
    "k": {"c": 5, "q": 1, "x": 5},
    "l": {"i": 9},
    "m": {"n": 8},
    "n": {"m": 8, "r": 9},
    "p": {"q": 9},
    "q": {"c": 5, "k": 1, "p": 9},
    "r": {"n": 9, "j": 9},
    "s": {"c": 5, "ç": 1, "x": 5, "z": 5},
    "t": {"d": 9},
    "v": {"f": 8, "w": 1},
    "w": {"v": 1},
    "x": {"c": 5, "k": 5, "q": 5, "s": 5},
    "z": {"s": 5}
}


def distanceBetweenChars (c1, c2):
    "returns a float between 0 and 1"
    if c1 == c2:
        return 0
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# End of word

dFinal1 = {
    "a": ("as", "at", "ant", "ah"),
    "A": ("AS", "AT", "ANT", "AH"),
    "c": ("ch",),
    "C": ("CH",),
    "c": ("ch", "que"),
    "C": ("CH", "QUE"),
    "e": ("et", "er", "ets", "ée", "ez", "ai", "ais", "ait", "ent", "eh"),
    "E": ("ET", "ER", "ETS", "ÉE", "EZ", "AI", "AIS", "AIT", "ENT", "EH"),
    "é": ("et", "er", "ets", "ée", "ez", "ai", "ais", "ait"),
    "É": ("ET", "ER", "ETS", "ÉE", "EZ", "AI", "AIS", "AIT"),
    "è": ("et", "er", "ets", "ée", "ez", "ai", "ais", "ait"),
    "È": ("ET", "ER", "ETS", "ÉE", "EZ", "AI", "AIS", "AIT"),
    "ê": ("et", "er", "ets", "ée", "ez", "ai", "ais", "ait"),
    "Ê": ("ET", "ER", "ETS", "ÉE", "EZ", "AI", "AIS", "AIT"),
    "ë": ("et", "er", "ets", "ée", "ez", "ai", "ais", "ait"),
    "Ë": ("ET", "ER", "ETS", "ÉE", "EZ", "AI", "AIS", "AIT"),
    "g": ("gh",),
    "G": ("GH",),
    "i": ("is", "it", "ie", "in"),
    "I": ("IS", "IT", "IE", "IN"),
    "k": ("que",),
    "K": ("QUE",),
    "n": ("nt", "nd", "ns", "nh"),
    "N": ("NT", "ND", "NS", "NH"),
    "o": ("aut", "ot", "os"),
    "O": ("AUT", "OT", "OS"),
    "ô": ("aut", "ot", "os"),
    "Ô": ("AUT", "OT", "OS"),
    "ö": ("aut", "ot", "os"),
︙

Modified graphspell/ibdawg.py from [bda5a789eb] to [d672255b46].

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import re
from functools import wraps
import time
import json
import binascii
import importlib
from collections import OrderedDict
from math import floor

#import logging
#logging.basicConfig(filename="suggestions.log", level=logging.DEBUG)

from . import str_transform as st
from . import char_player as cp
from .echo import echo
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        return result
    return wrapper


class SuggResult:
    """Structure for storing, classifying and filtering suggestions"""

    def __init__ (self, sWord, nDistLimit=-1):
    def __init__ (self, sWord, nSuggLimit=10, nDistLimit=-1):
        self.sWord = sWord
        self.sSimplifiedWord = st.simplifyWord(sWord)
        self.nDistLimit = nDistLimit  if nDistLimit >= 0  else  (len(sWord) // 3) + 1
        self.nMinDist = 1000
        # Temporary sets
        self.aSugg = set()
        self.dSugg = { 0: [],  1: [],  2: [] }
        self.aAllSugg = set()       # all found words even those refused
        self.aAllSugg = set()   # All suggestions, even the one rejected
        self.dGoodSugg = {}     # Acceptable suggestions
        self.dBestSugg = {}     # Best suggestions
        # Parameters
        self.nSuggLimit = nSuggLimit
        self.nSuggLimitExt = nSuggLimit + 2             # we add few entries in case suggestions merge after casing modifications
        self.nBestSuggLimit = floor(nSuggLimit * 1.5)   # n times the requested limit
        self.nGoodSuggLimit = nSuggLimit * 15           # n times the requested limit

    def addSugg (self, sSugg, nDeep=0):
        "add a suggestion"
        #logging.info((nDeep * "  ") + "__" + sSugg + "__")
        if sSugg in self.aAllSugg:
            return
        self.aAllSugg.add(sSugg)
        if sSugg not in self.aSugg:
            #nDist = min(st.distanceDamerauLevenshtein(self.sWord, sSugg), st.distanceDamerauLevenshtein(self.sSimplifiedWord, st.simplifyWord(sSugg)))
            nDist = int(st.distanceDamerauLevenshtein(self.sSimplifiedWord, st.simplifyWord(sSugg)))
            #logging.info((nDeep * "  ") + "__" + sSugg + "__ :" + self.sSimplifiedWord +"|"+ st.simplifyWord(sSugg) +" -> "+ str(nDist))
            if nDist <= self.nDistLimit:
                if " " in sSugg:
                    nDist += 1
                if nDist not in self.dSugg:
                    self.dSugg[nDist] = []
                self.dSugg[nDist].append(sSugg)
                self.aSugg.add(sSugg)
                if nDist < self.nMinDist:
                    self.nMinDist = nDist
                self.nDistLimit = min(self.nDistLimit, self.nMinDist+1)
        nDistJaro = 1 - st.distanceJaroWinkler(self.sSimplifiedWord, st.simplifyWord(sSugg))
        nDist = floor(nDistJaro * 10)
        if nDistJaro < .11:     # Best suggestions
            self.dBestSugg[sSugg] = round(nDistJaro*1000)
            if len(self.dBestSugg) > self.nBestSuggLimit:
                self.nDistLimit = -1  # make suggest() to end search
        elif nDistJaro < .33:   # Good suggestions
            self.dGoodSugg[sSugg] = round(nDistJaro*1000)
            if len(self.dGoodSugg) > self.nGoodSuggLimit:
                self.nDistLimit = -1  # make suggest() to end search
        else:
            if nDist < self.nMinDist:
                self.nMinDist = nDist
            self.nDistLimit = min(self.nDistLimit, self.nMinDist)
        if nDist <= self.nDistLimit:
            if nDist < self.nMinDist:
                self.nMinDist = nDist
            self.nDistLimit = min(self.nDistLimit, self.nMinDist+1)

    def getSuggestions (self, nSuggLimit=10):
    def getSuggestions (self):
        "return a list of suggestions"
        # we sort the better results with the original word
        lRes = []
        bFirstListSorted = False
        for nDist, lSugg in self.dSugg.items():
            if nDist > self.nDistLimit:
                break
            if not bFirstListSorted and len(lSugg) > 1:
                lSugg.sort(key=lambda sSugg: st.distanceDamerauLevenshtein(self.sWord, sSugg))
                bFirstListSorted = True
        if len(self.dBestSugg) > 0:
            # sort only with simplified words
            lResTmp = sorted(self.dBestSugg.items(), key=lambda x: x[1])
            #print(nDist, "|".join(lSugg))
            #for sSugg in lSugg:
            for i in range(min(self.nSuggLimitExt, len(lResTmp))):
            #    print(sSugg, st.distanceDamerauLevenshtein(self.sWord, sSugg))
            lRes.extend(lSugg)
            if len(lRes) > nSuggLimit:
                break
                lRes.append(lResTmp[i][0])
        if len(lRes) < self.nSuggLimitExt:
            # sort with simplified words and original word
            lResTmp = sorted(self.dGoodSugg.items(), key=lambda x: ((1-st.distanceJaroWinkler(self.sWord, x[0]))*10, x[1]))
            for i in range(min(self.nSuggLimitExt, len(lResTmp))):
                lRes.append(lResTmp[i][0])
        # casing
        if self.sWord.isupper():
            lRes = list(OrderedDict.fromkeys(map(lambda sSugg: sSugg.upper(), lRes))) # use dict, when Python 3.6+
        elif self.sWord[0:1].isupper():
            # dont’ use <.istitle>
            lRes = list(OrderedDict.fromkeys(map(lambda sSugg: sSugg[0:1].upper()+sSugg[1:], lRes))) # use dict, when Python 3.6+
        return lRes[:nSuggLimit]
        return lRes[:self.nSuggLimit]

    def reset (self):
        "clear data"
        self.aSugg.clear()
        self.dSugg.clear()
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        sSfx = ""
        if self.lexicographer:
            sPfx, sWord, sSfx = self.lexicographer.split(sWord)
        nMaxSwitch = max(len(sWord) // 3, 1)
        nMaxDel = len(sWord) // 5
        nMaxHardRepl = max((len(sWord) - 5) // 4, 1)
        nMaxJump = max(len(sWord) // 4, 1)
        oSuggResult = SuggResult(sWord)
        oSuggResult = SuggResult(sWord, nSuggLimit)
        sWord = st.cleanWord(sWord)
        if bSplitTrailingNumbers:
            self._splitTrailingNumbers(oSuggResult, sWord)
        self._splitSuggest(oSuggResult, sWord)
        self._suggest(oSuggResult, sWord, nMaxSwitch, nMaxDel, nMaxHardRepl, nMaxJump)
        aSugg = oSuggResult.getSuggestions(nSuggLimit)
        aSugg = oSuggResult.getSuggestions()
        if self.lexicographer:
            aSugg = self.lexicographer.filterSugg(aSugg)
        if sSfx or sPfx:
            # we add what we removed
            return list(map(lambda sSug: sPfx + sSug + sSfx, aSugg))
        return aSugg
︙

Modified graphspell/str_transform.py from [e27e70d363] to [98c57fa9ba]. 

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"""
Operations on strings:
- calculate distance between two strings
- transform strings with transformation codes
"""

import unicodedata
import re

from .char_player import distanceBetweenChars
from .char_player import distanceBetweenChars, dDistanceBetweenChars


#### N-GRAMS

def getNgrams (sWord, n=2):
    "return a list of Ngrams strings"
    return [ sWord[i:i+n]  for i in range(len(sWord)-n+1) ]
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    sWord = sWord.lower().translate(_xTransCharsForSimplification)
    sNewWord = ""
    for i, c in enumerate(sWord, 1):
        if c != sWord[i:i+1] or (c == 'e' and sWord[i:i+2] != "ee"):  # exception for <e> to avoid confusion between crée / créai
            sNewWord += c
    return sNewWord.replace("eau", "o").replace("au", "o").replace("ai", "éi").replace("ei", "é").replace("ph", "f")


def cleanWord (sWord):
    "remove letters repeated more than 2 times"
    return re.sub("(.)\\1{2,}", '\\1\\1', sWord)


_xTransNumbersToExponent = str.maketrans({
    "0": "⁰", "1": "¹", "2": "²", "3": "³", "4": "⁴", "5": "⁵", "6": "⁶", "7": "⁷", "8": "⁸", "9": "⁹"
})

def numbersToExponent (sWord):
    "convert numeral chars to exponant chars"
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                d[i,   j-1] + 1,        # Insertion
                d[i-1, j-1] + nCost,    # Substitution
            )
            if i and j and s1[i] == s2[j-1] and s1[i-1] == s2[j]:
                d[i, j] = min(d[i, j], d[i-2, j-2] + nCost)     # Transposition
    return d[nLen1-1, nLen2-1]


def distanceJaroWinkler (a, b, boost = .666):
    # https://github.com/thsig/jaro-winkler-JS
    #if (a == b): return 1.0
    a_len = len(a)
    b_len = len(b)
    nMax = max(a_len, b_len)
    a_flag = [None for _ in range(nMax)]
    b_flag = [None for _ in range(nMax)]
    search_range = (max(a_len, b_len) // 2) - 1
    minv = min(a_len, b_len)

    # Looking only within the search range, count and flag the matched pairs.
    Num_com = 0
    yl1 = b_len - 1
    for i in range(a_len):
        lowlim = i - search_range  if i >= search_range  else 0
        hilim  = i + search_range  if (i + search_range) <= yl1  else yl1
        for j in range(lowlim, hilim+1):
            if b_flag[j] != 1 and a[j:j+1] == b[i:i+1]:
                a_flag[j] = 1
                b_flag[i] = 1
                Num_com += 1
                break

    # Return if no characters in common
    if Num_com == 0:
        return 0.0

    # Count the number of transpositions
    k = 0
    N_trans = 0
    for i in range(a_len):
        if a_flag[i] == 1:
            for j in range(k, b_len):
                if b_flag[j] == 1:
                    k = j + 1
                    break
            if a[i] != b[j]:
                N_trans += 1
    N_trans = N_trans // 2

    # Adjust for similarities in nonmatched characters
    N_simi = 0
    if minv > Num_com:
        for i in range(a_len):
            if not a_flag[i]:
                for j in range(b_len):
                    if not b_flag[j]:
                        if a[i] in dDistanceBetweenChars and b[j] in dDistanceBetweenChars[a[i]]:
                            N_simi += dDistanceBetweenChars[a[i]][b[j]]
                            b_flag[j] = 2
                            break

    Num_sim = (N_simi / 10.0) + Num_com

    # Main weight computation
    weight = Num_sim / a_len + Num_sim / b_len + (Num_com - N_trans) / Num_com
    weight = weight / 3

    # Continue to boost the weight if the strings are similar
    if weight > boost:
        # Adjust for having up to the first 4 characters in common
        j = 4  if minv >= 4  else minv
        i = 0
        while i < j  and a[i] == b[i]:
            i += 1
        if i:
            weight += i * 0.1 * (1.0 - weight)
        # Adjust for long strings.
        # After agreeing beginning chars, at least two more must agree
        # and the agreeing characters must be more than half of the
        # remaining characters.
        if minv > 4  and  Num_com > i + 1  and  2 * Num_com >= minv + i:
            weight += (1 - weight) * ((Num_com - i - 1) / (a_len * b_len - i*2 + 2))
    return weight


def distanceSift4 (s1, s2, nMaxOffset=5):
    "implementation of general Sift4."
    # https://siderite.blogspot.com/2014/11/super-fast-and-accurate-string-distance.html
    if not s1:
        return len(s2)
    if not s2:
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Fossil 2.24 [8be0372c10] 2024-04-23 13:25:26
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