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3 non-PAUSE user(s).

Ben Bullock

NAME

Text::Fuzzy - partial or fuzzy string matching using edit distances

SYNOPSIS

    use Text::Fuzzy;
    my $tf = Text::Fuzzy->new ('boboon');
    print "Distance is ", $tf->distance ('babboon'), "\n";
    # Prints "Distance is 2"
    my @words = qw/the quick brown fox jumped over the lazy dog/;
    my $nearest = $tf->nearest (\@words);
    print "Nearest array entry is ", $words[$nearest], "\n";
    # Prints "Nearest array entry is brown"
    

DESCRIPTION

This module calculates edit distances between words, and searches arrays and files to find the nearest entry by edit distance. It handles both byte strings and character strings (strings containing Unicode), treating each Unicode character as a single entity.

    use Text::Fuzzy;
    use utf8;
    my $tf = Text::Fuzzy->new ('あいうえお☺');
    print $tf->distance ('うえお☺'), "\n";
    # prints "2".
    

The default is the Levenshtein edit distance, which applies an equal weight of one to additions (cat -> cart), substitutions (cat -> cut), and deletions (carp -> cap). The Damerau-Levenshtein edit distance, which allows transpositions (salt -> slat) may also be selected.

METHODS

new

    my $tf = Text::Fuzzy->new ('bibbety bobbety boo');

Create a new Text::Fuzzy object from the supplied word.

The following parameters may be supplied:

max
    my $tf = Text::Fuzzy->new ('Cinderella', max => 3);

This sets the maximum edit distance allowed, in the same way as "set_max_distance".

    my $tf = Text::Fuzzy->new ('Cinderella');
    $tf->set_max_distance (3);

A negative maximum distance will switch off the maximum distance:

    my $tf = Text::Fuzzy->new ('Cinderella', max => -1);
no_exact
    my $tf = Text::Fuzzy->new ('slipper', no_exact => 1);

This switches on rejection of exact matches, in the same way as "no_exact":

    my $tf = Text::Fuzzy->new ('slipper');
    $tf->no_exact (1);
trans
    my $tf = Text::Fuzzy->new ('glass', trans => 1);

This switches on transpositions, in the same way as "transpositions_ok".

distance

    my $dist = $tf->distance ($word);

The return value is the edit distance to $word from the word used to create the object in "new".

    use Text::Fuzzy;
    my $cat = Text::Fuzzy->new ('cat');
    print $cat->distance ('cut'), "\n";
    # Prints 1
    print $cat->distance ('cart'), "\n";
    # Prints 1
    print $cat->distance ('catamaran'), "\n";
    # Prints 6
    use utf8;
    print $cat->distance ('γάτος'), "\n";
    # Prints 5

To know which edits are used to convert the words, use "distance_edits".

nearest

    my $index = $tf->nearest (\@words);
    my $nearest_word = $words[$index];

This returns the index of the nearest element in the array to the argument to "new".

It is possible to set a maximum edit distance, beyond which entries are rejected, using "set_max_distance" or the max parameter to "new". In this case, if none of the elements of @words are less than the maximum distance away from the word, $index is the undefined value, so when setting a maximum distance, check the return value of index using defined.

    use Text::Fuzzy;
    my $tf = Text::Fuzzy->new ('calamari', max => 1);
    my @words = qw/Have you ever kissed in the moonlight
                   In the grand and glorious
                   Gay notorious
                   South American Way?/;
    my $index = $tf->nearest (\@words);
    if (defined $index) {
        printf "Found at $index, distance was %d.\n",
        $tf->last_distance ();
    }
    else {
        print "Not found anywhere.\n";
    }
    
    

If there is more than one word with the same edit distance in @words, this returns the last one found, unless it is an exact match, in which case it returns the first one found. To get all matches, call it in array context:

    my @nearest = $tf->nearest (\@words);

If there are no matches within minimum distance, it returns an empty list. If there is one or more match, it returns the array offset of it, not the value itself.

    use Text::Fuzzy;
    
    my @funky_words = qw/nice funky rice gibbon lice graeme garden/;
    my $tf = Text::Fuzzy->new ('dice');
    my @nearest = $tf->nearest (\@funky_words);
    
    print "The nearest words are ";
    print join ", ", (map {$funky_words[$_]} @nearest);
    printf ", distance %d.\n", $tf->last_distance ();
    
    # Prints out "The nearest words are nice, rice, lice."
    
    

last_distance

    my $last_distance = $tf->last_distance ();

The distance from the previous match's closest match. This is used in conjunction with "nearest" to find the edit distance to the previous match.

set_max_distance

    # Set the max distance.
    $tf->set_max_distance (3);

Set the maximum edit distance of $tf. Set the maximum distance to a low value to improve the speed of searches over lists with "nearest", or to reject unlikely matches. When searching for a near match, anything with an edit distance of a value over the maximum is rejected without computing the exact distance. To compute exact distances, call this method without an argument:

    $tf->set_max_distance ();

The maximum edit distance is switched off, and whatever the nearest match is is accepted. A negative value also switches it off:

    $tf->set_max_distance (-1);

The object created by "new" has no maximum distance unless specified by the user.

    use Text::Fuzzy;
    my $tf = Text::Fuzzy->new ('nopqrstuvwxyz');
    # Prints 13, the correct value.
    print $tf->distance ('abcdefghijklm'), "\n";
    $tf->set_max_distance (10);
    # Prints 11, one more than the maximum distance, because the search
    # stopped when the distance was exceeded.
    print $tf->distance ('abcdefghijklm'), "\n";

transpositions_ok

    $tf->transpositions_ok (1);

A true value in the argument changes the type of edit distance used to allow transpositions, such as clam and calm. Initially transpositions are not allowed, giving the Levenshtein edit distance. If transpositions are used, the edit distance becomes the Damerau-Levenshtein edit distance. A false value disallows transpositions:

    $tf->transpositions_ok (0);

no_exact

    $tf->no_exact (1);

This is a flag to "nearest" which makes it ignore exact matches. For example,

    use Text::Fuzzy;
    
    my @words = qw/bibbity bobbity boo/;
    for my $word (@words) {
        my $tf = Text::Fuzzy->new ($word);
        $tf->no_exact (0);
        my $nearest = $tf->nearest (\@words);
        print "With exact, nearest to $word is $words[$nearest]\n";
        # Make "$word" not match itself.
        $tf->no_exact (1);
        my $nearest = $tf->nearest (\@words);
        print "Without exact, nearest to $word is $words[$nearest]\n";
    }

This prints

    With exact, nearest to bibbity is bibbity
    Without exact, nearest to bibbity is bobbity
    With exact, nearest to bobbity is bobbity
    Without exact, nearest to bobbity is bibbity
    With exact, nearest to boo is boo
    Without exact, nearest to boo is bobbity

This is for the case of searching over an array which contains the searched-for item itself.

scan_file

    my $nearest = $tf->scan_file ('/usr/share/dict/words');

Scan a file to find the nearest match to the word used in "new". This assumes that the file contains lines of text separated by newlines, and finds the closest match in the file. Its return value is a string rather than a line number. It cannot return an array of values. It does not currently support Unicode-encoded files.

FUNCTIONS

distance_edits

    my ($distance, $edits) = distance_edits ('piece', 'peace');

This returns the edit distance between the two arguments, and the edits necessary to transform the first one into the second one. $Edits is a string containing the four letters k, r, d, and i, for "keep", "replace", "delete", and "insert" respectively. For example, for "piece" and "peace", $edits contains "krrkk" for "keep, replace, replace, keep, keep".

This does not handle transpositions.

EXAMPLES

misspelt-web-page.cgi

The file examples/misspelt-web-page.cgi is an example of a CGI script which does something similar to the Apache mod_speling module, offering spelling corrections for mistyped URLs and sending the user to a correct page.

    use Text::Fuzzy;
    
    # The directory of files served by the web server.
    
    my $web_root = '/usr/local/www/data';
    
    # If the query is "http://www.example.com/abc/xyz.html", $path_info is
    # "abc/xyz.html".
    
    my $path_info = $ENV{REQUEST_URI};
    
    if (! defined $path_info) {
        fail ("No path info");
    }
    
    if ($0 =~ /$path_info/) {
        fail ("Don't redirect to self");
    }
    
    # This is the list of files under the main page.
    
    my @allfiles = get_all_files ($web_root, '');
    
    # This is our spelling search engine.
    
    my $tf = Text::Fuzzy->new ($path_info);
    
    my $nearest = $tf->nearest (\@allfiles, max => 5);
    
    if (defined $nearest) {
        redirect ($allfiles[$nearest]);
    }
    else {
        fail ("Nothing like $path_info was found");
    }
    exit;
    
    # Read all the files under "$root/$dir". This is recursive. The return
    # value is an array containing all files found.
    
    sub get_all_files
    {
        my ($root, $dir) = @_;
        my @allfiles;
        my $full_dir = "$root/$dir";
        if (! -d $full_dir) {
            fail ("$full_dir is not a directory");
        }
        opendir DIR, $full_dir or fail ("Can't open directory $full_dir: $!");
        my @files = grep !/^\./, readdir DIR;
        closedir DIR or fail ("Can't close $full_dir: $!");
        for my $file (@files) {
            my $dir_file = "$dir/$file";
            my $full_file = "$root/$dir_file";
            if (-d $full_file) {
                push @allfiles, get_all_files ($root, $dir_file);
            }
            else {
                push @allfiles, $dir_file;
            }
        }
        return @allfiles;
    }
    
    # Print a "permanent redirect" to the respelt name, then exit.
    
    sub redirect
    {
        my ($url) = @_;
        print <<EOF;
    Status: 301
    Location: $url
    
    EOF
        exit;
    }
    
    # Print an error message for the sake of the requester, and print a
    # message to the error log, then exit.
    
    sub fail
    {
        my ($error) = @_;
        print <<EOF;
    Content-Type: text/plain
    
    $error
    EOF
        # Add the name of the program and the time to the error message,
        # otherwise the error log will get awfully confusing-looking.
        my $time = scalar gmtime ();
        print STDERR "$0: $time: $error\n";
        exit;
    }

See also http://www.lemoda.net/perl/perl-mod-speling/ for how to set up .htaccess to use the script.

spell-check.pl

The file examples/spell-check.pl is a spell checker. It uses a dictionary of words specified by a command-line option "-d":

    spell-check.pl -d /usr/dict/words file1.txt file2.txt

It prints out any words which look like spelling mistakes, using the dictionary.

    use Getopt::Long;
    use Text::Fuzzy;
    use Lingua::EN::PluralToSingular 'to_singular';
    
    # The location of the Unix dictionary.
    my $dict = '/usr/share/dict/words';
    
    # Default maximum edit distance. Five is quite a big number for a
    # spelling mistake.
    my $max = 5;
    
    GetOptions (
        "dict=s" => \$dict,
        "max=i" => \$max,
    );
    
    my @words;
    my %words;
    my $min_length = 4;
    read_dictionary ($dict, \@words, \%words);
    # Known mistakes, don't repeat.
    my %known;
    # Spell-check each file on the command line.
    for my $file (@ARGV) {
        open my $input, "<", $file or die "Can't open $file: $!";
        while (<$input>) {
            my @line = split /[^a-z']+/i, $_;
            for my $word (@line) {
                # Remove leading/trailing apostrophes.
                $word =~ s/^'|'$//g;
                my $clean_word = to_singular (lc $word);
                $clean_word =~ s/'s$//;
                if ($words{$clean_word} || $words{$word}) {
                    # It is in the dictionary.
                    next;
                }
                if (length $word < $min_length) {
                    # Very short words are ignored.
                    next;
                }
                if ($word eq uc $word) {
                    # Acronym like BBC, IRA, etc.
                    next;
                }
                if ($known{$clean_word}) {
                    # This word was already given to the user.
                    next;
                }
                if ($clean_word =~ /(.*)ed$/ || $clean_word =~ /(.*)ing/) {
                    my $stem = $1;
                    if ($words{$stem} || $words{"${stem}e"}) {
                        # Past/gerund of $stem/${stem}e
                        next;
                    }
                    # Test for doubled end consonants,
                    # e.g. "submitted"/"submit".
                    if ($stem =~ /([bcdfghjklmnpqrstvwxz])\1/) {
                        $stem =~ s/$1$//;
                        if ($words{$stem}) {
                            # Past/gerund of $stem/${stem}e
                            next;
                        }
                    }
                }
                my $tf = Text::Fuzzy->new ($clean_word, max => $max);
                my $nearest = $tf->nearest (\@words);
                # We have set a maximum distance to search for, so we need
                # to check whether $nearest is defined.
                if (defined $nearest) {
                    my $correction = $words[$nearest];
                    print "$file:$.: '$word' may be '$correction'.\n";
                    $known{$clean_word} = $correction;
                }
                else {
                    print "$file:$.: $word may be a spelling mistake.\n";
                    $known{$clean_word} = 1;
                }
            }
        }
        close $input or die $!;
    }
    
    exit;
    
    sub read_dictionary
    {
        my ($dict, $words_array, $words_hash) = @_;    
        open my $din, "<", $dict or die "Can't open dictionary $dict: $!";
        my @words;
        while (<$din>) {
            chomp;
            push @words, $_;
        }
        close $din or die $!;
        # Apostrophe words
    
        my @apo = qw/
    
                        let's I'll you'll he'll she'll they'll we'll I'm
                        you're he's she's it's we're they're I've they've
                        you've we've one's isn't aren't doesn't don't
                        won't wouldn't I'd you'd he'd we'd they'd
                        shouldn't couldn't didn't can't
    
                    /;
    
        # Irregular past participles.
        my @pp = qw/became/;
    
        push @words, @apo, @pp;
        for (@words) {
            push @$words_array, lc $_;
            $words_hash->{$_} = 1;
            $words_hash->{lc $_} = 1;
        }
    }

Because the usual Unix dictionary doesn't have plurals, it uses Lingua::EN::PluralToSingular, to convert nouns into singular forms. Unfortunately it still misses past participles and past tenses of verbs.

extract-kana.pl

The file examples/extract-kana.pl extracts the kana entries from "edict", a freely-available Japanese to English electronic dictionary, and does some fuzzy searches on them. It requires a local copy of the file to run. This script demonstrates the use of Unicode searches with Text::Fuzzy.

    use Lingua::JA::Moji ':all';
    use Text::Fuzzy;
    use utf8;
    binmode STDOUT, ":utf8";
    my $infile = '/home/ben/data/edrdg/edict';
    open my $in, "<:encoding(EUC-JP)", $infile or die $!;
    my @kana;
    while (<$in>) {
        my $kana;
        if (/\[(\p{InKana}+)\]/) {
            $kana = $1;
        }
        elsif (/^(\p{InKana}+)/) {
            $kana = $1;
        }
        if ($kana) {
            $kana = kana2katakana ($kana);
            push @kana, $kana;
        }
    }
    printf "Starting fuzzy searches over %d lines.\n", scalar @kana;
    search ('ウオソウコ');
    search ('アイウエオカキクケコバビブベボハヒフヘホ');
    search ('アルベルトアインシュタイン');
    search ('バババブ');
    search ('バババブアルベルト');
    exit;
    
    sub search
    {
        my ($silly) = @_;
        my $max = 10;
        my $search = Text::Fuzzy->new ($silly, max => $max);
        my $n = $search->nearest (\@kana);
        if (defined $n) {
            printf "$silly nearest is $kana[$n] (distance %d)\n",
                $search->last_distance ();
        }
        else {
            printf "Nothing like '$silly' was found within $max edits.\n";
        }
    }
    

SUPPORT

Reporting a bug

There is a bug tracker for the module at https://github.com/benkasminbullock/Text-Fuzzy/issues

Mailing list

There is a mailing list at Google Groups at https://groups.google.com/group/textfuzzy

Testing

The CPAN tester results are at http://www.cpantesters.org/distro/T/Text-Fuzzy.html. The ActiveState tester results are at http://code.activestate.com/ppm/Text-Fuzzy/.

PRIVATE METHODS

The general user should not need any of these methods. They are not expected to be useful for the general user. They may be useful in benchmarking the module and checking its correctness.

no_alphabet

    $tf->no_alphabet (1);

This turns off alphabetizing of the string. Alphabetizing is a filter where the intersection of all the characters in the two strings is computed, and if the alphabetical difference of the two strings is greater than the maximum distance, the match is rejected without applying the dynamic programming algorithm. This increases speed, because the dynamic programming algorithm is slow.

The alphabetizing should not ever reject anything which is a legitimate match, and it should make the program run faster in almost every case. The only envisaged uses of switching this off are checking that the algorithm is working correctly, and benchmarking performance.

get_trans

    my $trans_ok = $tf->get_trans ();

This returns the value set by "transpositions_ok".

unicode_length

    my $length = $tf->unicode_length ();

This returns the length in characters (not bytes) of the string used in "new". If the string is not marked as Unicode, it returns the undefined value. In the following, $l1 should be equal to $l2.

    use utf8;
    my $word = 'ⅅⅆⅇⅈⅉ';
    my $l1 = length $word;
    my $tf = Text::Fuzzy->new ($word);
    my $l2 = $tf->unicode_length ();

ualphabet_rejections

    my $rejected = $tf->ualphabet_rejections ();

After running "nearest" over an array, this returns the number of entries of the array which were rejected using only the Unicode alphabet. Its value is reset to zero each time "nearest" is called.

alphabet_rejections

    my $rejected = $tf->alphabet_rejections ();

After running "nearest" over an array, this returns the number of entries of the array which were rejected using only the non-Unicode alphabet. Its value is reset to zero each time "nearest" is called.

length_rejections

    my $rejected = $tf->length_rejections ();

After running "nearest" over an array, this returns the number of entries of the array which were rejected because the length difference between them and the target string was larger than the maximum distance allowed.

get_max_distance

    # Get the maximum edit distance.
    print "The max distance is ", $tf->get_max_distance (), "\n";

Get the maximum edit distance of $tf. The maximum distance may be set with "set_max_distance".

ACKNOWLEDGEMENTS

The edit distance including transpositions was contributed by Nick Logan (UGEXE). Some of the tests in t/trans.t are taken from the Text::Levenshtein::Damerau::XS module.

AUTHOR

Ben Bullock, <bkb@cpan.org>

COPYRIGHT & LICENCE

This package and associated files are copyright (C) 2012-2013 Ben Bullock.

You can use, copy, modify and redistribute this package and associated files under the Perl Artistic Licence or the GNU General Public Licence.




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