| Search-ContextGraph documentation | Contained in the Search-ContextGraph distribution. |
Search::ContextGraph - spreading activation search engine
use Search::ContextGraph;
my $cg = Search::ContextGraph->new();
# first you add some documents, perhaps all at once...
my %docs = (
'first' => [ 'elephant', 'snake' ],
'second' => [ 'camel', 'pony' ],
'third' => { 'snake' => 2, 'constrictor' => 1 },
);
$cg->bulk_add( %docs );
# or in a loop...
foreach my $title ( keys %docs ) {
$cg->add( $title, $docs{$title} );
}
# or from a file...
my $cg = Search::ContextGraph->load_from_dir( "./myfiles" );
# you can store a graph object for later use
$cg->store( "stored.cng" );
# and retrieve it later...
my $cg = ContextGraph->retrieve( "stored.cng" );
# SEARCHING
# the easiest way
my @ranked_docs = $cg->simple_search( 'peanuts' );
# get back both related terms and docs for more power
my ( $docs, $words ) = $cg->search('snake');
# you can use a document as your query
my ( $docs, $words ) = $cg->find_similar('First Document');
# Or you can query on a combination of things
my ( $docs, $words ) =
$cg->mixed_search( { docs => [ 'First Document' ],
terms => [ 'snake', 'pony' ]
);
# Print out result set of returned documents
foreach my $k ( sort { $docs->{$b} <=> $docs->{$a} }
keys %{ $docs } ) {
print "Document $k had relevance ", $docs->{$k}, "\n";
}
# Reload it
my $new = Search::ContextGraph->retrieve( "filename" );
Spreading activation is a neat technique for building search engines that return accurate results for a query even when there is no exact keyword match. The engine works by building a data structure called a context graph, which is a giant network of document and term nodes. All document nodes are connected to the terms that occur in that document; similarly, every term node is connected to all of the document nodes that term occurs in. We search the graph by starting at a query node and distributing a set amount of energy to its neighbor nodes. Then we recurse, diminishing the energy at each stage, until this spreading energy falls below a given threshold. Each node keeps track of accumulated energy, and this serves as our measure of relevance.
This means that documents that have many words in common will appear similar to the search engine. Likewise, words that occur together in many documents will be perceived as semantically related. Especially with larger, coherent document collections, the search engine can be quite effective at recognizing synonyms and finding useful relationships between documents. You can read a full description of the algorithm at http://www.nitle.org/papers/Contextual_Network_Graphs.pdf.
The search engine gives expanded recall (relevant results even when there is no keyword match) without incurring the kind of computational and patent issues posed by latent semantic indexing (LSI). The technique used here was originally described in a 1981 dissertation by Scott Preece.
Object constructor. Possible parameters:
Rebalance the graph every time a change occurs. Default is true. Disable and do by hand using reweight_graph for better performance in graphs with frequent updates/additions/deletions.
Set this to 1 or 2 to turn on verbose debugging output
Set the maximum distance to spread energy out from the start node. Default is effectively unlimited. You can tweak it using set_max_depth. Comes in handy if you find searches are too slow.
When true, tells the module to use compiled C internals. This reduces memory requirements by about 60%, but actually runs a little slower than the pure Perl version. Don't bother to turn it on unless you have a huge graph. Default is pure Perl.
Initial energy to assign to a query node. Default is 100.
Minimal energy needed to propagate search along the graph. Default is 1.
Minimal energy needed for a node to enter the result set. Default is 1.
Load documents from a directory. Takes two arguments, a directory path and an optional parsing subroutine. If the parsing subroutine is passed an argument, it will use it to extract term tokens from the file. By default, the file is split on whitespace and stripped of numbers and punctuation.
Opens and loads a term-document matrix (TDM) file to initialize the graph. The TDM encodes information about term-to-document links. This is a legacy method mainly for the convenience of the module author. For notes on the proper file format, see the README file. =cut
Renames a document. Will return undef if the new name is already in use.
Loads a previously stored graph from disk, using Storable.
Accessor for node activation threshold value. This value determines how far energy can spread in the graph. Lower it to increase the number of results. Default is 1.
Accessor for auto reweight flag. If true, edge weights will be recalculated every time a document is added, updated or removed. This can significantly slow down large graphs. On by default.
Accessor for collection threshold value. This determines how much energy a node must have to make it into the result set. Lower it to increase the number of results. Default is 1.
Turns debugging on or off. 1 is verbose, 2 is very verbose, 0 is off.
Accessor for initial energy value at the query node. This controls how much energy gets poured into the graph at the start of the search. Increase this value to get more results from your queries.
You can tell the graph to cut off searches after a certain distance from the query node. This can speed up searches on very large graphs, and has little adverse effect, especially if you are interested in just the first few search results. Set this value to undef to restore the default (10^8).
Add a document to the search engine. Takes as arguments a unique doc identifier and a reference to an array or hash of words in the document. For example:
TITLE => { WORD1 => COUNT1, WORD2 => COUNT2 ... }
or
TITLE => [ WORD1, WORD2, WORD3 ]
Use bulk_add if you want to pass in a bunch of docs all at once.
Adds a document from a file. By default, uses the PATH provided as the document identifier, and parses the file by splitting on whitespace. If a fancier title, or more elegant parsing behavior is desired, pass in named arguments as indicated. NAME can be any string, CODE should be a reference to a subroutine that takes one argument (the contents of the file) and returns an array of tokens, or a hash in the form TOKEN => COUNT, or a reference to the same.
Add documents to the graph in bulk. Takes as an argument a hash whose keys are document identifiers, and values are references to hashes in the form { WORD1 => COUNT, WORD2 => COUNT...} This method is faster than adding in documents one by one if you have auto_rebalance turned on.
Given a raw node, returns the degree (raw node means the node must be prefixed with 'D:' or 'T:' depending on type )
Remove a document from the graph. Takes a document identifier as an argument. Returns 1 if successful, undef otherwise.
Returns true if the document with identifier DOC is in the collection
Returns true if the term TERM is in the collection
Calculates the distance between two nodes of the same type (D or T) using the formula:
distance = ... =cut
Used for clustering using linear local embedding. Produces a similarity matrix in a format I'm too tired to document right now. LIMIT is the maximum number of neighbors to keep for each node.
Returns a list of neighbor nodes that all the given nodes share in common
Given a list of nodes, returns a hash of nearest nodes with relevance values, in the format NODE => RELEVANCE, for all nodes above the threshold value. (You probably want one of search, find_similar, or mixed_search instead).
Iterates through the graph, calculating edge weights and normalizing around nodes. This method is automatically called every time a document is added, removed, or updated, unless you turn the option off with auto_reweight(0). When adding a lot of docs, this can be time consuming, so either set auto_reweight to off or use the bulk_add method to add lots of docs at once
Given a document identifier and a word list, updates the information for that document in the graph. Returns the number of changes made
Returns a count of all documents that TERM occurs in. If no argument is provided, returns a document count for the entire collection.
Returns a sorted list of document identifiers that contain TERM, in ASCII-betical order. If no argument is given, returns a sorted document list for the whole collection.
Lists all of the neighbors of a node, together with edge weights connecting to them
Dumps internal state in term-document matrix (TDM) format, which looks like this:
A B C B C B C A B C B C B C A B C B C B C
Where each row represents a document, A is the number of terms in the document, B is the term node and C is the edge weight between the doc node and B. Mostly used as a legacy format by the module author. Doc and term nodes are printed in ASCII-betical sorted order, zero-based indexing. Up to you to keep track of the ID => title mappings, neener-neener! Use doc_list and term_list to get an equivalently sorted list
Returns a list of neighbor nodes of the same type (doc/doc, or term/term) two hops away.
Returns the number of unique terms in a document or, if no document is specified, in the entire collection.
Returns a sorted list of unique terms appearing in the document with identifier DOC, in ASCII-betical order. If no argument is given, returns a sorted term list for the whole collection.
Returns the total occurence count for a term, or if no argument is given, a word count for the entire collection. The word count is always greater than or equal to the term count.
Searches the graph for all of the words in @QUERY. Use find_similar if you want to do a document similarity instead, or mixed_search if you want to search on any combination of words and documents. Returns a pair of hashrefs: the first a reference to a hash of docs and relevance values, the second to a hash of words and relevance values.
This is the DWIM method - takes a query string as its argument, and returns an array of documents, sorted by relevance.
Given a list of patterns, searches for documents with matching titles
Given an array of document identifiers, performs a similarity search and returns a pair of hashrefs. First hashref is to a hash of docs and relevance values, second is to a hash of words and relevance values.
Combine all the nodes in @BAD into the node with identifier GOOD. First argument must be one of 'T' or 'D' to indicate term or document nodes. Used to combine synonyms in the graph.
Given a hashref in the form: { docs => [ 'Title 1', 'Title 2' ], terms => ['buffalo', 'fox' ], } } Runs a combined search on the terms and documents provided, and returns a pair of hashrefs. The first hashref is to a hash of docs and relevance values, second is to a hash of words and relevance values.
Stores the object to a file for later use. Not compatible (yet) with compiled XS version, which will give a fatal error.
Returns true if the nodes share an edge. Node names must be prefixed with 'D' or 'T' as appropriate.
Returns an array of connected components in the graph. Each component is a list of nodes that are mutually accessible by traveling along edges.
Maciej Ceglowski <maciej@ceglowski.com>
The spreading activation technique used here was originally discussed in a 1981 dissertation by Scott Preece, at the University of Illinois.
XS implementation thanks to Schuyler Erle.
Schuyler Erle Ken Williams Leon Brocard
Perl module: (C) 2003 Maciej Ceglowski
XS Implementation: (C) 2003 Maciej Ceglowski, Schuyler Erle
This program is free software, distributed under the GNU Public License. See LICENSE for details.
| Search-ContextGraph documentation | Contained in the Search-ContextGraph distribution. |
package Search::ContextGraph; use strict; use warnings; use Carp; use base "Storable"; use File::Find; use IO::Socket; our $VERSION = '0.15'; my $count = 0;
sub new { my ( $class, %params) = @_; # backwards compatible... *add_document = \&add; *add_documents = \&bulk_add; # plucene friendly *optimize = \&reweight_graph; *is_indexed = \&has_doc; # fail on all unknown paramters (helps fight typos) my @allowed = qw/debug auto_reweight use_global_weights max_depth START_ENERGY ACTIVATE_THRESHOLD COLLECT_THRESHOLD use_file xs/; my %check; $check{$_}++ foreach @allowed; my @forbidden; foreach my $k ( keys %params ) { push @forbidden, $k unless exists $check{$k}; } if ( @forbidden ) { croak "The following unrecognized parameters were detected: ", join ", ", @forbidden; } my $obj = bless { debug => 0, auto_reweight => 1, use_global_weights => 1, max_depth => 100000000, START_ENERGY => 100, ACTIVATE_THRESHOLD => 1, COLLECT_THRESHOLD => .2, %params, depth => 0, neighbors => {}, }, $class; if ( $obj->{use_file} ) { my %neighbors; use MLDBM qw/DB_File Storable/; use Fcntl; warn "Using MLDBM: $obj->{use_file}"; $obj->{neighbors} = tie %neighbors, 'MLDBM', $obj->{use_file} or die $!; #$obj->{neighbors} = \%neighbors; } return $obj; }
{ my $parse_sub; sub load_from_dir { my ( $class, $dir, $code ) = @_; croak "$dir is not a directory" unless -d $dir; require File::Find; unless ( defined $code and ref $code and ref $code eq 'CODE' ) { $code = sub { my $text = shift; $text =~ s/[^\w]/ /gs; my @toks = split /\s+/m, $text; return grep { length($_) > 1 } @toks; }; } $parse_sub = $code; my %docs; # Recursively open every file and provide the contents # to whatever parsing subroutine we're using my $reader = sub { my ( $parse ) = @_; return if /^\./; return unless -f $_; open my $fh, $_ or croak "Could not open file $File::Find::name: $!"; local $/; my $contents = <$fh>; close $fh or croak "failed to close filehandle"; my @words = $parse_sub->($contents); $docs{ $File::Find::name } = \@words; }; find( $reader , $dir ); my $self = __PACKAGE__->new(); $self->bulk_add( %docs ); return $self; } }
sub load_from_tdm { my ( $self, $file ) = @_; croak "TDM file $file does not exist" unless -f $file; return if $self->{'loaded'}; $self->_read_tdm( $file ); $self->{'loaded'} = 1; $self->reweight_graph(); }
sub rename { my ( $self, $old, $new ) = @_; croak "rename method needs two arguments" unless defined $old and defined $new; croak "document $old not found" unless exists $self->{neighbors}{ _nodeify('D', $old ) }; my $bad = _nodeify( 'D', $old ); my $good = _nodeify( 'D', $new ); return if exists $self->{neighbors}{$good}; my $s = $self->{neighbors}; foreach my $n ( keys %{ $s->{$bad} } ) { $s->{$good}{$n} = $s->{$n}{$good} = $s->{$bad}{$n}; delete $s->{$bad}{$n}; delete $s->{$n}{$bad}; } delete $s->{$bad}; return 1; }
sub retrieve { my ( $self, $file ) = @_; croak "Must provide a filename to retrieve graph" unless $file; croak "'$file' is not a file" unless -f $file; Storable::retrieve( $file ); }
sub get_activate_threshold { $_[0]->{'ACTIVATE_THRESHOLD'} } sub set_activate_threshold { my ( $self, $threshold ) = @_; croak "Can't set activate threshold to zero" unless $threshold; croak "Can't set activate threshold to negative value" unless $threshold > 0; $self->{'ACTIVATE_THRESHOLD'} = $_[1]; }
sub get_auto_reweight{ $_[0]->{auto_reweight} } sub set_auto_reweight{ $_[0]->{auto_reweight} = $_[0]->[1]; }
sub get_collect_threshold { return ( $_[0]->{'xs'} ? $_[0]->{Graph}->collectionThreshold : $_[0]->{'COLLECT_THRESHOLD'}) } sub set_collect_threshold { my ( $self, $newval ) = @_; $newval ||=0; $self->{Graph}->collectionThreshold( $newval ) if $self->{'xs'}; $self->{'COLLECT_THRESHOLD'} = $newval || 0; return 1; }
sub get_debug_mode { $_[0]->{debug} } sub set_debug_mode { my ( $self, $mode ) = @_; $self->{'debug'} = $mode; }
sub get_initial_energy { $_[0]->{'START_ENERGY'} } sub set_initial_energy { my ( $self, $start_energy ) = @_; croak "Can't set initial energy to zero" unless $start_energy; croak "Can't set initial energy to negative value" unless $start_energy > 0; $self->{'START_ENERGY'} = $start_energy ; }
sub get_max_depth { $_[0]->{max_depth} } sub set_max_depth { croak "Tried to set maximum depth to an undefined value" unless defined $_[1]; $_[0]->{max_depth} = $_[1] || 100000000 }
sub add { my ( $self, $title, $words ) = @_; croak "Please provide a word list" unless defined $words; croak "Word list is not a reference to an array or hash" unless ref $words and ref $words eq "HASH" or ref $words eq "ARRAY"; croak "Please provide a document identifier" unless defined $title; my $dnode = _nodeify( 'D', $title ); croak "Tried to add document with duplicate identifier: '$title'\n" if exists $self->{neighbors}{$dnode}; my @list; if ( ref $words eq 'ARRAY' ) { @list = @{$words}; } else { @list = keys %{$words}; } croak "Tried to add a document with no content" unless scalar @list; my @edges; foreach my $term ( @list ) { my $tnode = _nodeify( 'T', lc( $term ) ); # Local weight for the document my $lcount = ( ref $words eq 'HASH' ? $words->{$term} : 1 ); # Update number of docs this word occurs in my $gcount = ++$self->{term_count}{lc( $term )}; my $final_weight = 1; push @edges, [ $dnode, $tnode, $final_weight, $lcount ]; } $self->{reweight_flag} = 1; __normalize( \@edges );
# PURE PERL VERSION #} else { foreach my $e ( @edges ) { $self->{neighbors}{$e->[0]}{$e->[1]} = join ',', $e->[2], $e->[3]; $self->{neighbors}{$e->[1]}{$e->[0]} = join ',', $e->[2], $e->[3]; } #} #print "Reweighting graph\n"; $self->reweight_graph() if $self->{auto_reweight}; return 1; }
sub add_file { my ( $self, $path, %params ) = @_; croak "Invalid file '$path' provided to add_file method." unless defined $path and -f $path; my $title = ( exists $params{name} ? $params{name} : $path ); local $/; open my $fh, $path or croak "Unable to open $path: $!"; my $content = <$fh>; my $ref; if ( exists $params{parse} ) { croak "code provided is not a reference" unless ref $params{parse}; croak "code provided is not a subroutine" unless ref $params{parse} eq 'CODE'; $ref = $params{parse}->( $content ); croak "did not get an appropriate reference back after parsing" unless ref $ref and ref $ref =~ /(HASH|ARRAY)/; } else { my $code = sub { my $txt = shift; $txt =~ s/\W/ /g; my @toks = split m/\s+/, $txt; \@toks; }; $ref = $code->($content); } return unless $ref; $self->add( $title, $ref ); }
sub bulk_add { my ( $self, %incoming_docs ) = @_; # Disable graph rebalancing until we've added everything { local $self->{auto_reweight} = 0; foreach my $doc ( keys %incoming_docs ) { $self->add( $doc, $incoming_docs{$doc}); } } $self->reweight_graph() if $self->{auto_reweight}; }
sub degree { scalar keys %{$_[0]->{neighbors}{$_[1]}} }
sub delete { my ( $self, $type, $name ) = @_; croak "Must provide a node type to delete() method" unless defined $type; croak "Invalid type $type passed to delete method. Must be one of [TD]" unless $type =~ /^[TD]$/io; croak "Please provide a node name" unless defined $name; return unless defined $name; my $node = _nodeify( $type, $name); my $n = $self->{neighbors}; croak "Found a neighborless node $node" unless exists $n->{$node}; my @terms = keys %{ $n->{$node} }; warn "found ", scalar @terms, " neighbors attached to $node\n" if $self->{debug}; # Check to see if we have orphaned any terms foreach my $t ( @terms ) { delete $n->{$node}{$t}; delete $n->{$t}{$node}; if ( scalar keys %{ $n->{$t} } == 0 ) { warn "\tdeleting orphaned node $t" if $self->{debug}; my ( $subtype, $name ) = $t =~ /^(.):(.*)$/; #$self->delete( $subtype, $name ); delete $n->{$t}; } } delete $n->{$node}; $self->check_consistency(); $self->{reweight_flag} = 1; $self->reweight_graph if $self->{auto_reweight}; 1; }
sub has_doc { my ( $self, $doc ) = @_; carp "Received undefined value for has_doc" unless defined $doc; my $node = _nodeify( 'D', $doc ); return exists $self->{neighbors}{$node} || undef; }
sub has_term { my ( $self, $term ) = @_; carp "Received undefined value for has_term" unless defined $term; my $node = _nodeify( 'T', $term ); return exists $self->{neighbors}{$node} || undef; }
sub distance { my ( $self, $n1, $n2, $type ) = @_; croak unless $type; $type = lc( $type ); croak unless $type =~ /^[dt]$/; my $key = ( $type eq 't' ? 'terms' : 'documents' ); my @shared = $self->intersection( $key => [ $n1, $n2 ] ); return 0 unless @shared; #warn "Found ", scalar @shared, " nodes shared between $n1 and $n2\n"; my $node1 = _nodeify( $type, $n1 ); my $node2 = _nodeify( $type, $n2 ); # formula is w(t1,d1)/deg(d1) + w(t1,d2)/deg(d2) ... ) /deg( t1 ) #warn "Calculating distance\n"; my $sum1 = 0; my $sum2 = 0; foreach my $next ( @shared ) { my ( undef, $lcount1) = split m/,/, $self->{neighbors}{$node1}{$next}; my ( undef, $lcount2) = split m/,/, $self->{neighbors}{$node2}{$next}; my $degree = $self->degree( $next ); #warn "\t degree of $next is $degree\n"; my $elem1 = $lcount1 / $degree; $sum1 += $elem1; my $elem2 = $lcount2 / $degree; $sum2 += $elem2; } #warn "sum is $sum1, $sum2\n"; my $final = ($sum1 / $self->degree( $node1 )) + ( $sum2 / $self->degree( $node2 )); #warn "final is $final\n"; return $final; }
sub distance_matrix { my ( $self, $type, $limit ) = @_; croak "Must provide type argument to distance_matrix()" unless defined $type; croak "must provide limit" unless $limit; my @nodes; if ( lc( $type ) eq 'd' ) { @nodes = $self->doc_list(); } elsif ( lc( $type ) eq 't' ) { @nodes = $self->term_list(); } else { croak "Unsupported type $type"; } my @ret; my $count = 0; foreach my $from ( @nodes ) { warn $from, " - $count\n"; $count++; my $index = -1; my @found; foreach my $to ( @nodes ) { $index++; next if $from eq $to; my $dist = $self->distance( $from, $to, $type ); push @found, [ $index, $dist ] if $dist; #print( $index++, ' ', $dist, " " ) if $dist; } my @sorted = sort { $b->[1] <=> $a->[1] } @found; my @final = splice ( @sorted, 0, $limit ); push @ret, join " ", ( map { join ' ', $_->[0], substr($_->[1], 0, 7) } sort { $a->[0] <=> $b->[0] } @final), "\n"; #print "\n"; } return join "\n", @ret; }
sub intersection { my ( $self, %nodes ) = @_; my @nodes; if ( exists $nodes{documents} ) { push @nodes, map { _nodeify( 'D', $_ ) } @{ $nodes{documents}}; } if ( exists $nodes{terms} ) { push @nodes, map { _nodeify( 'T', $_ ) } @{ $nodes{terms}}; } my %seen; foreach my $n ( @nodes ) { my @neighbors = $self->_neighbors( $n ); $seen{ $_ }++ foreach @neighbors; } return map { s/^[DT]://; $_ } grep { $seen{$_} == scalar @nodes } keys %seen; }
sub raw_search { my ( $self, @query ) = @_; $self->_clear(); foreach ( @query ) { $self->_energize( $_, $self->{'START_ENERGY'}); } my $results_ref = $self->_collect(); return $results_ref; }
sub reweight_graph { my ( $self ) = @_; my $n = $self->{neighbors}; #shortcut my $doc_count = $self->doc_count(); #print "Renormalizing for doc count $doc_count\n" if $self->{debug}; foreach my $node ( keys %{$n} ) { next unless $node =~ /^D:/o; warn "reweighting at node $node\n" if $self->{debug} > 1; my @terms = keys %{ $n->{$node} }; my @edges; foreach my $t ( @terms ) { my $pair = $n->{$node}{$t}; my ( undef, $lcount ) = split /,/, $pair; ( my $term = $t ) =~ s/^T://; croak "did not receive a local count" unless $lcount; my $weight; if ( $self->{use_global_weights} ) { my $gweight = log( $doc_count / $self->doc_count( $term ) ) + 1; my $lweight = log( $lcount ) + 1; $weight = ( $gweight * $lweight ); } else { $weight = log( $lcount ) + 1; } push @edges, [ $node, $t, $weight, $lcount ]; } __normalize( \@edges ); foreach my $e ( @edges ) { my $pair = join ',', $e->[2], $e->[3]; $n->{$node}{$e->[1]} = $n->{$e->[1]}{$node} = $pair; } } $self->{reweight_flag} = 0; return 1; }
sub update { my ( $self, $id, $words ) = @_; croak "update not implemented in XS" if $self->{xs}; croak "Must provide a document identifier to update_document" unless defined $id; my $dnode = _nodeify( 'D', $id ); return unless exists $self->{neighbors}{$dnode}; croak "must provide a word list " unless defined $words and ref $words and ( ref $words eq 'HASH' or ref $words eq 'ARRAY' ); my $n = $self->{neighbors}{$dnode}; # Get the current word list my @terms = keys %{ $n }; if ( ref $words eq 'ARRAY' ) { my %words; $words{$_}++ foreach @$words; $words = \%words; } local $self->{auto_reweight} = 0; my $must_reweight = 0; my %seen; foreach my $term ( keys %{$words} ) { my $t = _nodeify( 'T', $term ); if ( exists $n->{$t} ){ # Update the local count, if necessary my $curr_val = $n->{$t}; my ( undef, $loc ) = split m/,/, $curr_val; unless ( $loc == $words->{$term} ) { $n->{$t} = join ',', 1, $words->{$term}; $must_reweight++; } } else { $n->{$t} = $self->{neighbors}{$t}{$dnode} = join ',', 1, $words->{$term}; $must_reweight++; } $seen{$t}++; } # Check for deleted words foreach my $t ( @terms ) { $must_reweight++ unless exists $seen{$t}; } $self->reweight_graph() if $must_reweight; return $must_reweight; }
sub doc_count { my ( $self, $term ) = @_; if ( defined $term ) { $term = _nodeify( 'T', $term ) unless $term =~ /^T:/; my $node = $self->{neighbors}{$term}; return 0 unless defined $node; return scalar keys %{$node}; } else { return scalar grep /D:/, keys %{ $self->{'neighbors'} }; } }
sub doc_list { my ( $self, $term ) = @_; my $t; if ( defined $term and $term !~ /T:/) { $t = _nodeify( 'T', $term ); } my $hash = ( defined $term ? $self->{neighbors}{$t} : $self->{neighbors} ); sort map { s/^D://o; $_ } grep /^D:/, keys %{ $hash }; } sub dump { my ( $self ) = @_; my @docs = $self->doc_list(); foreach my $d ( @docs ) { print $self->dump_node( $d ); } }
sub dump_node { my ( $self, $node ) = @_; my @lines; push @lines, join "\t", "COUNT", "WEIGHT", "NEIGHBOR"; foreach my $n ( keys %{ $self->{neighbors}{$node} } ) { my $v = $self->{neighbors}{$node}{$n}; my ( $weight, $count ) = split /,/, $v; push @lines, join "\t", $count, substr( $weight, 0, 8 ), $n; } return @lines; }
sub dump_tdm { my ( $self, $file ) = @_; my $counter = 0; my %lookup; $lookup{$_} = $counter++ foreach $self->term_list; my @docs = $self->doc_list; my $fh; if ( defined $file ) { open $fh, "> $file" or croak "Could not open TDM output file: $!"; } else { *fh = *STDOUT; } foreach my $doc ( @docs ) { my $n = $self->{neighbors}{$doc}; my $row_count = scalar keys %{$n}; print $fh $row_count; foreach my $t ( sort keys %{$doc} ) { my $index = $lookup{$t}; my ( $weight, undef ) = split m/,/, $n->{$t}; print $fh ' ', $index, ' ', $weight; } print $fh "\n"; } }
sub near_neighbors { my ( $self, $name, $type ) = @_; my $node = _nodeify( $type, $name ); my $n = $self->{neighbors}{$node}; my %found; foreach my $next ( keys %{$n} ) { foreach my $mynext ( keys %{ $self->{neighbors}{$next} }){ $found{$mynext}++; } } delete $found{$node}; return keys %found; }
sub term_count { my ( $self, $doc ) = @_; if ( defined $doc ) { my $node = $self->{neighbors}{ _nodeify( 'D', $doc) }; return 0 unless defined $node; return scalar keys %{$node}; } else { return scalar grep /T:/, keys %{ $self->{neighbors} }; } }
sub term_list { my ( $self, $doc ) = @_; my $node = ( defined $doc ? $self->{neighbors}{ _nodeify( 'D', $doc) } : $self->{neighbors} ); sort map { s/^T://o; $_ } grep /^T:/, keys %{ $node }; }
sub word_count { my ( $self, $term ) = @_; my $n = $self->{neighbors}; # shortcut my $count = 0; my @terms; if ( defined $term ) { push @terms, $term; } else { @terms = $self->term_list(); } foreach my $term (@terms ) { $term = _nodeify( 'T', $term) unless $term =~/^T:/o; foreach my $doc ( keys %{ $n->{$term} } ) { ( undef, my $lcount ) = split /,/, $n->{$term}{$doc}; $count += $lcount; } } return $count; }
sub search { my ( $self, @query ) = @_; my @nodes = _nodeify( 'T', @query ); my $results = $self->raw_search( @nodes ); my ($docs, $words) = _partition( $results ); return ( $docs, $words); }
sub simple_search { my ( $self, $query ) = @_; my @words = map { s/\W+//g; lc($_) } split m/\s+/, $query; my @nodes = _nodeify( 'T', @words ); my $results = $self->raw_search( @nodes ); my ($docs, $words) = _partition( $results ); my @sorted_docs = sort { $docs->{$b} <=> $docs->{$a} } keys %{$docs}; return @sorted_docs; }
sub find_by_title { my ( $self, @titles ) = @_; my @found; my @docs = $self->doc_list(); my $pattern = join '|', @titles; my $match_me = qr/$pattern/i; #warn $match_me, "\n"; foreach my $d ( @docs ) { # warn $d, "\n"; push @found, $d if $d =~ $match_me; } return @found; }
sub find_similar { my ( $self, @docs ) = @_; my @nodes = _nodeify( 'D', @docs ); my $results = $self->raw_search( @nodes ); my ($docs, $words) = _partition( $results ); return ( $docs, $words); }
sub merge { my ( $self, $type, $good, @bad ) = @_; croak "must provide a type argument to merge" unless defined $type; croak "Invalid type argument $type to merge [must be one of (D,T)]" unless $type =~ /^[DT]/io; my $target = _nodeify( $type, $good ); my @sources = _nodeify( $type, @bad ); my $tnode = $self->{neighbors}{$target}; foreach my $bad_node ( @sources ) { #print "Examining $bad_node\n"; next if $bad_node eq $target; my %neighbors = %{$self->{neighbors}{$bad_node}}; foreach my $n ( keys %neighbors ) { #print "\t $target ($bad_node) neighbor $n\n"; if ( exists $self->{neighbors}{$target}{$n} ) { #print "\t\t$n has link to $bad_node\n"; # combine the local counts for the term members of the edge my $curr_val = $tnode->{$n}; my $aug_val = $self->{neighbors}{$bad_node}{$n}; my ($w1, $c1) = split m/,/, $curr_val; my ($w2, $c2) = split m/,/, $aug_val; my $new_count = $c1 + $c2; $curr_val =~ s/,\d+$/,$new_count/; $tnode->{$n} = $curr_val; } else { die "sanity check failed for existence test" if exists $self->{neighbors}{$target}{$n}; my $val = $self->{neighbors}{$bad_node}{$n}; #print "\tno existing link -- reassigning $target -- $n\n"; # reassign the current value of this edge $self->{neighbors}{$n}{$target} = $val; $self->{neighbors}{$target}{$n} = $val; } delete $self->{neighbors}{$bad_node}{$n}; delete $self->{neighbors}{$n}{$bad_node}; } delete $self->{neighbors}{$bad_node}; } }
sub mixed_search { my ( $self, $incoming ) = @_; croak "must provide hash ref to mixed_search method" unless defined $incoming && ref( $incoming ) && ref( $incoming ) eq 'HASH'; my $tref = $incoming->{'terms'} || []; my $dref = $incoming->{'docs'} || []; my @dnodes = _nodeify( 'D', @{$dref} ); my @tnodes = _nodeify( 'T', @{$tref} ); my $results = $self->raw_search( @dnodes, @tnodes ); my ($docs, $words) = _partition( $results ); return ( $docs, $words); }
sub store { my ( $self, @args ) = @_; if ( $self->{'xs'} ) { croak "Cannot store object when running in XS mode."; } else { $self->SUPER::nstore(@args); } } # Partition - internal method. # Takes a result set and splits it into two hashrefs - one for # words and one for documents sub _partition { my ( $e ) = @_; my ( $docs, $words ); foreach my $k ( sort { $e->{$b} <=> $e->{$a} } keys %{ $e } ) { (my $name = $k ) =~ s/^[DT]://o; $k =~ /^D:/ ? $docs->{$name} = $e->{$k} : $words->{$name} = $e->{$k} ; } return ( $docs, $words ); } # return a list of all neighbor nodes sub _neighbors { my ( $self, $node ) = @_; return unless exists $self->{neighbors}{$node}; return keys %{ $self->{neighbors}{$node} }; } sub _nodeify { my ( $prefix, @list ) = @_; my @nodes; foreach my $item ( @list ) { push @nodes, uc($prefix).':'.$item; } ( wantarray ? @nodes : $nodes[0] ); } sub _read_tdm { my ( $self, $file ) = @_; print "Loading TDM...\n" if $self->{'debug'} > 1; croak "File does not exist" unless -f $file; open my $fh, $file or croak "Could not open $file: $!"; for ( 1..4 ){ my $skip = <$fh>; } my %neighbors; my $doc = 0; ######### XS VERSION ############## if ( $self->{'xs'} ) { my $map = $self->{'node_map'}; # shortcut alias while (<$fh>) { chomp; my $dindex = $self->_add_node( "D:$doc", 2 ); #warn "Added node $doc\n"; my ( $count, %vals ) = split; while ( my ( $term, $edge ) = each %vals ) { $self->{'term_count'}{$term}++; my $tnode = "T:$term"; my $tindex = ( defined $map->{$tnode} ? $map->{$tnode} : $self->_add_node( $tnode, 1 ) ); $self->{Graph}->set_edge( $dindex, $tindex, $edge ); } $doc++; } ####### PURE PERL VERSION ########## } else { while (<$fh>) { chomp; my $dnode = "D:$doc"; my ( $count, %vals ) = split; while ( my ( $term, $edge ) = each %vals ) { $self->{'term_count'}{$term}++; my $tnode = "T:$term"; $neighbors{$dnode}{$tnode} = $edge.',1'; $neighbors{$tnode}{$dnode} = $edge.',1'; } $doc++; } $self->{'neighbors'} = \%neighbors; } print "Loaded.\n" if $self->{'debug'} > 1; $self->{'from_TDM'} = 1; $self->{'doc_count'} = $doc; } # XS version only # # This sub maintains a mapping between node names and integer index # values. sub _add_node { my ( $self, $node_name, $type ) = @_; croak "Must provide a type" unless $type; croak "Must provide a node name" unless $node_name; croak "This node already exists" if $self->{'node_map'}{$node_name}; my $new_id = $self->{'next_free_id'}++; $self->{'node_map'}{$node_name} = $new_id; $self->{'id_map'}[$new_id] = $node_name; $self->{'Graph'}->add_node( $new_id, $type ); return $new_id; } # # INTERNAL METHODS # # each node should have the same number of inbound # and outbound links sub check_consistency { my ( $self ) = @_; my %inbound; my %outbound; foreach my $node ( keys %{$self->{neighbors}} ) { next unless $node =~ /^[DT]:/; # for MLDBM compatibility $outbound{$node} = scalar keys %{$self->{neighbors}{$node}}; foreach my $neighbor ( keys %{ $self->{neighbors}{$node} } ) { $inbound{$neighbor}++; } } my $in = scalar keys %inbound; my $out = scalar keys %outbound; carp "number of nodes with inbound links ($in) does not match number of nodes with outbound links ( $out )" unless scalar keys %inbound == scalar keys %outbound; foreach my $node ( keys %inbound ) { $outbound{$node} ||= 0; carp "$node has $inbound{$node} inbound links, $outbound{$node} outbound links\n" unless $inbound{$node} == $outbound{$node}; } }
sub have_edge { my ( $self, $node1, $node2 ) = @_; return exists $self->{neighbors}{$node1}{$node2}; } { my %visited; my %component; my $depth;
sub connected_components { my ( $self ) = @_; %visited = (); # clear any old info %component = (); my $n = $self->{neighbors}; my @node_list = keys %{$n}; my @components; while ( @node_list ) { my $start = shift @node_list; next if exists $visited{$start}; last unless $start; warn "Visiting neighbors for $start\n"; visit_neighbors( $n, $start ); push @components, [ keys %component ]; %component = (); } warn "Found ", scalar @components, " connected components\n"; return @components; } sub visit_neighbors { my ( $g, $l ) = @_; return if $visited{$l}; $depth++; $visited{$l}++; $component{$l}++; warn $depth, " $l\n"; my @neigh = keys %{ $g->{$l} }; foreach my $n ( @neigh ) { visit_neighbors( $g, $n ); } $depth--; } } # Wipe the graph free of stored energies sub _clear { my ( $self ) = @_; $self->{'energy'} = undef; } # Gather the stored energy values from the graph sub _collect { my ( $self ) = @_; my $e = $self->{'energy'}; my $result = {}; foreach my $k ( keys %{$self->{'energy'}} ) { next unless $e->{$k} > $self->{'COLLECT_THRESHOLD'}; $result->{$k} = $e->{$k}; } return $result; } # Assign a starting energy ENERGY to NODE, and recursively distribute the # energy to neighbor nodes. Singleton nodes get special treatment sub _energize { my ( $self, $node, $energy ) = @_; return unless defined $self->{neighbors}{$node}; my $orig = $self->{energy}{$node} || 0; $self->{energy}->{$node} += $energy; return if ( $self->{depth} == $self->{max_depth} ); $self->{depth}++; if ( $self->{'debug'} > 1 ) { print ' ' x $self->{'depth'}; print "$node: energizing $orig + $energy\n"; } my $n = $self->{neighbors}; #sleep 1; my $degree = scalar keys %{ $n->{$node} }; if ( $degree == 0 ) { carp "WARNING: reached a node without neighbors: $node at search depth $self->{depth}\n"; $self->{depth}--; return; } my $subenergy = $energy / (log($degree)+1); # At singleton nodes (words that appear in only one document, for example) # Don't spread energy any further. This avoids a "reflection" back and # forth from singleton nodes to their neighbors. if ( $degree == 1 and $energy < $self->{'START_ENERGY'} ) { #do nothing } elsif ( $subenergy > $self->{ACTIVATE_THRESHOLD} ) { print ' ' x $self->{'depth'}, "$node: propagating subenergy $subenergy to $degree neighbors\n" if $self->{'debug'} > 1; foreach my $neighbor ( keys %{ $n->{$node} } ) { my $pair = $n->{$node}{$neighbor}; my ( $edge, undef ) = split /,/, $pair; my $weighted_energy = $subenergy * $edge; print ' ' x $self->{'depth'}, " edge $edge ($node, $neighbor)\n" if $self->{'debug'} > 1; $self->_energize( $neighbor, $weighted_energy ); } } $self->{'depth'}--; return 1; } # Given an array, normalize using cosine normalization sub __normalize { my ( $arr ) = @_; croak "Must provide array ref to __normalize" unless defined $arr and ref $arr and ref $arr eq 'ARRAY'; my $sum; $sum += $_->[2] foreach @{$arr}; $_->[2]/= $sum foreach @{$arr}; return 1; } sub DESTROY { undef $_[0]->{Graph} } 1; __END__ package Search::ContextGraph::SQLite; use DBI; use strict; use warnings; our %hash; sub TIEHASH { my ( $class ) = @_; my $self = {}; warn "Creating tied hash\n"; my $dbh = DBI->connect("dbi:SQLite:dbname=test.db","",""); if ( !-f "test.db" ) { my $sql = $dbh->do( "drop table edges; create table edges ( source char(100), sink char(100), weight float )" ); my $sql = $dbh->do( "create index source on edges(source)" ); my $sql = $dbh->do( "create index sink on edges(sink)" ); my $sql = $dbh->do( "create unique index edge on edges(source, sink)" ); } $self->{dbh} = $dbh; bless $self, $class; } sub FETCH { my ( $self, $key ) = @_; $self->{$key}; } sub STORE { my ( $self, $key, $value ) = @_; print "Storing key $key, value $value\n"; #print ref $value, "\n"; print "Hash has ", scalar %{$value}, "values\n"; foreach my $k ( keys %{$value} ) { print "\t$k $value->{$k}\n"; } $self->{$key} = $value } sub EXISTS { my ( $self, $key ) = @_; exists $self->{$key}; } sub DELETE { my ( $self, $key ) = @_; delete $self->{$key}; } sub FIRSTKEY { my ( $self ) = @_; my $a = keys %{ $self }; each %{ $self }; } sub NEXTKEY { my ( $self ) = @_; each %{ $self }; } sub DESTROY {} 1; package Search::ContextGraph::TieWrapper; use strict; use warnings; our %hash; sub TIEHASH { my ( $class ) = @_; my $self = {}; bless $self, $class; } sub FETCH { my ( $self, $key ) = @_; $self->{$key}; } sub STORE { my ( $self, $key, $value ) = @_; $self->{$key} = $value } sub EXISTS { my ( $self, $key ) = @_; exists $self->{$key}; } sub DELETE { my ( $self, $key ) = @_; delete $self->{$key}; } sub FIRSTKEY { my ( $self ) = @_; my $a = keys %{ $self }; each %{ $self }; } sub NEXTKEY { my ( $self ) = @_; each %{ $self }; } sub DESTROY {} 1;