File Coverage

blib/lib/KSx/Search/RegexpTermQuery.pm

Criterion	Covered	Total	%
statement	42	126	33.3
branch	4	26	15.3
condition	2	14	14.2
subroutine	11	21	52.3
pod	2	2	100.0
total	61	189	32.2

use strict;
use warnings;

package KSx::Search::RegexpTermQuery;
use base qw( KinoSearch::Search::Query );

our $VERSION = '0.05';

use Hash::Util::FieldHash::Compat 'fieldhashes';
fieldhashes \my( %re, %prefix, %field );

sub new {
    my ($package, %args) = @_;

    my $re = delete $args{regexp};
    my $field = delete $args{field};

    my $self = $package->SUPER::new(%args);

    $re{$self} = $re;
    $field{$self} = $field;

    # get the literal prefix of the regexp, if any.
    if($re{$self} =~
        m<^
            (?:    # prefix for qr//'s, without allowing /i :
                \(\? ([a-hj-z]*) (?:-[a-z]*)?:
            )?
            (\\[GA]|\^) # anchor
            ([^#\$()*+.?[\]\\^]+) # literal pat (no metachars or comments)
        >x
    ) {{
        my ($mod,$anchor,$prefix) = ($1||'',$2,$3);
	$anchor eq '^' and $mod =~ /m/ and last;
	for($prefix) {
            $mod =~ /x/ and s/\s+//g;
        }
        $prefix{$self} = $prefix;
    }}

    $self;
}

#sub extract_terms {
#    my $self = shift;
#    return @{ $self->{terms} };
#}

sub make_compiler {
    return KSx::Search::RegexpTermCompiler->new(
        parent => @_
    );
}


package KSx::Search::RegexpTermCompiler;
use base qw( KinoSearch::Search::Compiler );

use Hash::Util::FieldHash::Compat 'fieldhashes';

fieldhashes \my ( %idf, %raw_impact, #%plists,
                  %terms,
                  %query_norm_factor, % normalized_impact, %tfs );
sub new {
    my($pack, %args) = @_;

    my $searcher = $args{searchable};
    my $reader      = $searcher->get_reader;
    my $lex_reader  = $reader->fetch("KinoSearch::Index::LexiconReader");
    my $post_reader = $reader->fetch("KinoSearch::Index::PostingsReader");

    # Retrieve the correct Similarity for the Query's field.
    my $sim = $args{similarity} =
        $searcher->get_schema->fetch_sim($field{$args{parent}});

    my $self = $pack->SUPER::new(%args);

    my $parent = $args{parent};

    # Get a lexicon and find our place therein
    my( $re, $prefix ) = ($re{$parent}, $prefix{$parent});
    ref $re eq 'Regexp' or $re = qr/$re/; # avoid repetitive recompilation
    my $lexcn = $lex_reader->lexicon( field => $field{$parent} );
    $lexcn->seek(defined $prefix ? $prefix : '');

    # iterate through it, stopping at terms that match
    my @terms; #my @plists;
    my %hits; # The keys are the doc nums; the values the tfs.

    while () {
        my $term = get_term $lexcn;

        # sift out unwanted terms
        last if defined $prefix and index( $term, $prefix ) != 0;
        next unless $term =~ $re;

        # for terms that match...

        push @terms, $term;

        # We have to iterate through the documents in each posting list,
        # recording the doc numbers, so we can calc the doc freq later on.
        # E.g., if there are two documents, one containing ‘dog’ and ‘dot,’
        # and the other containing just ‘dog,’ and the re is /^do.*/, then
        # the doc freq has to be 2, since the re matches two docs. The doc
        # freqs of the individual terms are 1 and 2, so we can’t add or
        # average them.
        my $plist = $post_reader->posting_list(
	                              term => $term,
	                              field => $field{$parent},
	                          );
	my $posting; my $weight;
        while (my $doc_num = $plist ->next) {
            # For efficiency’s sake, we’ll collect the results now, to
            # avoid iterating through postings (the slowest part of search-
            # ing) more than once, even though this code probably belongs
            # in RegexpTermScorer
            my $posting ||= $plist->get_posting;
            $hits{$doc_num} +=
             $weight ||= $posting->get_freq * $posting->get_weight
        }

    } continue {
        last unless  $lexcn->next ;
    }
    my $doc_freq = scalar keys %hits;

    # Save the hits and terms for later
#    $plists{$self} = \@plists;
    $tfs{$self} = \%hits;
    $terms{$self} = \@terms;

    # Calculate and store the IDF
    my $max_doc = $searcher->doc_max;
    my $idf = $idf{$self} = $max_doc
    ?    1 + log( $max_doc / ( 1 + $doc_freq ) )
    :    1
    ;

    $raw_impact{$self} = $idf * $parent->get_boost;

    # make final preparations
    $self->perform_query_normalization($searcher);

    $self;
}

sub perform_query_normalization {
# copied from KinoSearch::Search::Weight originally
    my ( $self, $searcher ) = @_;
    my $sim = $self->get_similarity;

    my $factor = $self->sum_of_squared_weights;    # factor = ( tf_q * idf_t )
    $factor = $sim->query_norm($factor);           # factor /= norm_q
    $self->normalize($factor);                     # impact *= factor
}

sub get_value { shift->get_parent->get_boost }

sub sum_of_squared_weights { $raw_impact{+shift}**2 }

sub normalize { # copied from TermQuery
    my ( $self, $query_norm_factor ) = @_;
    $query_norm_factor{$self} = $query_norm_factor;

    # Multiply raw impact by ( tf_q * idf_q / norm_q )
    #
    # Note: factoring in IDF a second time is correct.  See formula.
    $normalized_impact{$self}
        = $raw_impact{$self} * $idf{$self} * $query_norm_factor;
}

sub make_matcher {
    my $self = shift;

    return KSx::Search::RegexpTermScorer->new(
#        posting_lists => $plists{$self},
        @_,
        compiler      => $self,
    );
}

sub highlight_spans {  # plagiarised form of TermWeight’s routine
    my ($self, %args) = @_;
    my $doc_vector = $args{doc_vec};
    my $field_name = $args{field};
    return if $field{$self->get_parent} ne $field_name;
    my $searcher   = $args{searcher};
    my $terms      = $terms{$self};

    require KinoSearch::Search::Span;

    my @posits;
    my $weight_val = $self->get_value;
    for (@$terms) {
        my $term_vector
            = $doc_vector->term_vector( field => $field_name, term => $_ );
        next unless defined $term_vector;
        my $starts = $term_vector->get_start_offsets->to_arrayref;
        my $ends   = $term_vector->get_end_offsets->to_arrayref;
        while (@$starts) {
            my $start = shift @$starts;
            push @posits, KinoSearch::Search::Span->new(
                offset => $start,
                length   => shift(@$ends)-$start,
                weight       => $weight_val,
            );
        }
    }

    return \@posits;
}


package KSx::Search::RegexpTermScorer;
use base 'KinoSearch::Search::Matcher';

use Hash::Util::FieldHash::Compat 'fieldhashes';
fieldhashes\my(  %doc_nums, %pos, %wv,  %sim, %compiler );

sub new {
	my ($class, %args) = @_;
#	my $plists = delete $args{posting_lists};
	my $compiler   = delete $args{compiler};
	my $reader     = delete $args{reader};
	my $need_score = delete $args{need_score};
	my $self   = $class->SUPER::new(%args);
	$sim{$self} = $compiler->get_similarity;

	my $tfs = $tfs{$compiler};
	$doc_nums{$self} = [ sort { $a <=> $b } keys %$tfs ];

	$pos{$self} = -1;
	$wv {$self} = $compiler->get_value;
	$compiler{$self} = $compiler;

	$self
}

sub next {
	my $self = shift;
	my $doc_nums = $doc_nums{$self};
	return 0 if $pos{$self} >= $#$doc_nums;
	return $$doc_nums[ ++$pos{$self} ];
}

sub get_doc_num {
	my $self = shift;
	my $pos = $pos{$self};
	my $doc_nums = $doc_nums{$self};
	return $pos < scalar @$doc_nums ? $$doc_nums[$pos] : 0;
}

sub score {
	my $self = shift;
	my $pos = $pos{$self};
	my $doc_nums = $doc_nums{$self};
	return $wv{$self} * $sim{$self}->tf(
	  $tfs{$compiler{$self}}{$$doc_nums[$pos]}
	);
}


1;

__END__