NAME

String::FuzzyIndex - Fuzzy index routine based on Smith-Waterman algorithm

SYNOPSIS

use String::FuzzyIndex;

my @fzmatch = fzindex($haystack, $needle);

my @fzmatch = fzindex($haystack, $needle, :s_id(6), :s_ocr(3), :s_gap(-1), :s_nid(-1));
my @fzmatch = fzindex($haystack, $needle, :ignore_diacritics);
my @fzmatch = fzindex($haystack, $needle, :glossover_ocrerrors);

DESCRIPTION

`String::FuzzyIndex` is a pure Raku module for fuzzy string matchingi based on the Smith-Waterman algorithm. It provides a single subroutine named fzindex.

SUBROUTINES

fzindex

The subroutine <fzindex> locates the best match(es) of a needle string in a haystack string and returns the best-match data in the form of a List. In case no best match was found, the List is empty. Otherwise the returned List contains, for each best match, an eight-element sub-List describing that match.

The structure of a sub-`List` is as follows:

Similarity scoring scheme

The operation of fzindex is based on the Smith-Waterman algorithm. This algorithm is used to identify the best match(es) by performing so-called 'local sequence alignment', i.e. by comparing segments of the haystack and the needle of all possible lengths, and optimizing a similarity measure.

In the process, individual characters from the haystack and the needle are pairwise compared and scored based on how similar they are. If two characters are identical, their alignment is assigned the similarity score s_id. If they are not identical, their alignment is assigned the similarity score s_nid, unless the options ignore_diacritics and/or :glossover_ocrerrors are used (see below).

The alignment of haystack and needle segments may also call for the acknowledgement of gaps: positions in an aligned segment that correspond to neither identical nor non-identical elements. To this end, fzindex implements a linear gap penalty scheme according to which each such gap is assigned a gap penalty s_gap. In the traceback buffers reported for a match, gaps are denoted with a dash ('-').

When working with strings extracted from OCRed data, mismatches in the alignment of segments may arise from the improper recognition of diacritics and/or the mixup of two characters. A 'ç' may, for instance, have been recognized as a 'c' and so frustrate alignment. Similarly, an 'I' is commonly OCRed as an 'l', or an '|'. fzindex offers two options to help mitigate such OCR-related issues:

• :ignore_diacritics

Causes diacritics to be ignored in assessing whether two characters are identical. Hence, the pairwise comparison of 'ç' and 'c' results in assignment of the similarity score `s_id` rather than the score s_nid.

• :glossover_ocrerrors

Causes the alignment of two characters that are commonly confused in OCR to be assigned the score s_ocr rather than the score s_nid.

The default scores, which may be changed using correspondingly named arguments, are as follows:

Intended use The Raku built-in index localizes the first perfect match of a needle within a haystack. It is fast, but when it comes up empty, one may be left wondering if either the haystack or the needle perhaps contains a typo, spelling variation, OCR error or the like that caused index to report the absence of the needle from the haystack. In such a case, fzindex may be used as a backup for index.

However, since index is comparably fast, fzindex internally executes an index-based (actually: indices-based) match attempt before starting a local sequence alignment attempt.

Because of this, there is generally no need to implement the use of fzindex as an actual backup to the use of the index built-in. Instead, the fzindex routine may be used instead of the built-in index. Such use of fzindex obviously comes at a performance penalty, but has the advantages that (i) all perfect matches are reported at once, and (ii) any best fuzzy matches are reported in case no perfect matches exist.

Optimization

The Raku implementation of the Smith-Waterman algorithm is naturally relatively slow, and in the present version of String::FuzzyIndex no significant optimization attempt has been made. Should you need fuzzy string matching for a large number of lengthy strings, you are probably better off with an implementation in a low level language like C.

That said, some performance enhancing code has already been tested and is likely to appear in future versions.

Example

use String::FuzzyIndex;

my $haystack  = 'The squeeky weel gets the grease.';
my $needle    = 'squeaky wheel';

my @r = fzindex($haystack, $needle);

say "{@r.elems} match(es) found.";
for @r -> ($score, $hsp, $hep, $nsp, $nep, $htb, $ntb, $stb) {
    say "Match score               : ", $score;
    say "Matching haystack portion : ", $haystack.substr: $hsp, ($hep-$hsp+1);
    say "Matching needle portion   : ", $needle.substr:   $nsp, ($nep-$nsp+1);
    say "Scoring traceback buffer  : ", $stb;
    say "Haystack traceback buffer : ", $htb;
    say "Needle traceback buffer   : ", $ntb;
}

1 match(es) found.
Match score               : 0.820513
Matching haystack portion : squeeky weel
Matching needle portion   : squeaky wheel
Scoring traceback buffer  : (6 12 18 24 23 29 35 41 47 46 52 58 64)
Haystack traceback buffer : (s q u e e k y   w - e e l)
Needle traceback buffer   : (s q u e a k y   w h e e l)

AUTHOR

threadless-screw

COPYRIGHT AND LICENSE

Copyright 2019 threadless-screw

Copyright 2024 Raku Community

This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.