AI-NaiveBayes
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## Installing with the CPAN shell
Alternatively, if your CPAN shell is set up, you should just be able to do:
% cpan AI::NaiveBayes
## Manual installation
As a last resort, you can manually install it. Download the tarball, untar it,
then build it:
% perl Makefile.PL
% make && make test
Then install it:
% make install
If your perl is system-managed, you can create a local::lib in your home
software and to any other program whose authors commit to using it.
You can use it for your programs, too.
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--- The Artistic License 1.0 ---
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},
"name" : "AI-NaiveBayes",
"no_index" : {
"directory" : [
"examples",
"t/lib"
]
},
"prereqs" : {
"configure" : {
"requires" : {
"ExtUtils::MakeMaker" : "0"
}
},
"develop" : {
"requires" : {
"Pod::Coverage::TrustPod" : "0",
"Test::Pod" : "1.41",
"Test::Pod::Coverage" : "1.08"
}
},
"runtime" : {
"requires" : {
"File::Find::Rule" : "0.32",
"List::Util" : "0",
"Moose" : "1.15",
"MooseX::Storage" : "0.25",
"perl" : "5.010",
"strict" : "0",
"warnings" : "0"
}
},
"test" : {
"requires" : {
"Test::More" : "0"
}
}
},
"release_status" : "stable",
"resources" : {
"repository" : {
"type" : "git",
"web" : "http://github.com/zby/AI-NaiveBayes"
}
},
"version" : "0.04",
"x_contributors" : [
"schweickism <schweickism@hotmail.com>",
"Zbigniew \u0141ukasiak <zzbbyy@gmail.com>"
],
---
abstract: 'A Bayesian classifier'
author:
- 'Zbigniew Lukasiak <zlukasiak@opera.com>'
- 'Tadeusz Sośnierz <tsosnierz@opera.com>'
- 'Ken Williams <ken@mathforum.org>'
build_requires:
Test::More: '0'
configure_requires:
ExtUtils::MakeMaker: '0'
dynamic_config: 0
generated_by: 'Dist::Zilla version 6.008, CPAN::Meta::Converter version 2.150005'
license: perl
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
version: '1.4'
name: AI-NaiveBayes
no_index:
directory:
- examples
- t/lib
requires:
File::Find::Rule: '0.32'
List::Util: '0'
Moose: '1.15'
MooseX::Storage: '0.25'
perl: '5.010'
strict: '0'
warnings: '0'
resources:
repository: http://github.com/zby/AI-NaiveBayes
version: '0.04'
x_contributors:
- 'schweickism <schweickism@hotmail.com>'
- 'Zbigniew Åukasiak <zzbbyy@gmail.com>'
x_serialization_backend: 'YAML::Tiny version 1.69'
sub classify {
my ($self, $newattrs) = @_;
$newattrs or die "Missing parameter for classify()";
my $m = $self->model;
# Note that we're using the log(prob) here. That's why we add instead of multiply.
my %scores = %{$m->{prior_probs}};
my %features;
while (my ($feature, $value) = each %$newattrs) {
next unless exists $m->{attributes}{$feature}; # Ignore totally unseen features
while (my ($label, $attributes) = each %{$m->{probs}}) {
my $score = ($attributes->{$feature} || $m->{smoother}{$label})*$value; # P($feature|$label)**$value
$scores{$label} += $score;
$features{$feature}{$label} = $score;
}
}
rescale(\%scores);
return AI::NaiveBayes::Classification->new( label_sums => \%scores, features => \%features );
}
sub rescale {
my ($scores) = @_;
# Scale everything back to a reasonable area in logspace (near zero), un-loggify, and normalize
my $total = 0;
my $max = max(values %$scores);
foreach (values %$scores) {
$_ = exp($_ - $max);
$total += $_**2;
}
$total = sqrt($total);
foreach (values %$scores) {
$_ /= $total;
}
}
__PACKAGE__->meta->make_immutable;
1;
__END__
my $classifier = AI::NaiveBayes->train(
{
attributes => {
sheep => 1, very => 1, valuable => 1, farming => 1
},
labels => ['farming']
},
{
attributes => {
vampires => 1, cannot => 1, see => 1, their => 1,
images => 1, mirrors => 1
},
labels => ['vampire']
},
);
# Classify a feature vector
my $result = $classifier->classify({bar => 3, blurp => 2});
# $result is now a AI::NaiveBayes::Classification object
my $best_category = $result->best_category;
=head1 DESCRIPTION
This module implements the classic "Naive Bayes" machine learning
algorithm. This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.
Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start
you can use the limited C<train> class method that trains the
classifier in a default way.
The classifier object is immutable.
It is a well-studied probabilistic algorithm often used in
automatic text categorization. Compared to other algorithms (kNN,
SVM, Decision Trees), it's pretty fast and reasonably competitive in
the quality of its results.
A paper by Fabrizio Sebastiani provides a really good introduction to
text categorization:
L<http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf>
=head1 METHODS
=over 4
=item new( model => $model )
settings.
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.
=item classify( HASHREF )
Classifies a feature-vector of the form:
{ feature1 => weight1, feature2 => weight2, ... }
The result is a C<AI::NaiveBayes::Classification> object.
=item rescale
Internal
=back
=head1 ATTRIBUTES
=over 4
=item model
certain string of words in a document, so we have:
P(words | cat) P(cat)
P(cat | words) = --------------------
P(words)
We have applied Bayes' Theorem because C<P(cat | words)> is a difficult
quantity to compute directly, but C<P(words | cat)> and C<P(cat)> are accessible
(see below).
The greater the expression above, the greater the probability that the given
document belongs to the given category. So we want to find the maximum
value. We write this as
P(words | cat) P(cat)
Best category = ArgMax -----------------------
cat in cats P(words)
Since C<P(words)> doesn't change over the range of categories, we can get rid
of it. That's good, because we didn't want to have to compute these values
anyway. So our new formula is:
Best category = ArgMax P(words | cat) P(cat)
cat in cats
Finally, we note that if C<w1, w2, ... wn> are the words in the document,
then this expression is equivalent to:
Best category = ArgMax P(w1|cat)*P(w2|cat)*...*P(wn|cat)*P(cat)
cat in cats
That's the formula I use in my document categorization code. The last
step is the only non-rigorous one in the derivation, and this is the
"naive" part of the Naive Bayes technique. It assumes that the
probability of each word appearing in a document is unaffected by the
presence or absence of each other word in the document. We assume
this even though we know this isn't true: for example, the word
"iodized" is far more likely to appear in a document that contains the
word "salt" than it is to appear in a document that contains the word
"subroutine". Luckily, as it turns out, making this assumption even
when it isn't true may have little effect on our results, as the
following paper by Pedro Domingos argues:
L<"http://www.cs.washington.edu/homes/pedrod/mlj97.ps.gz">
=head1 SEE ALSO
Algorithm::NaiveBayes (3), AI::Classifier::Text(3)
=head1 BASED ON
Much of the code and description is from L<Algorithm::NaiveBayes>.
lib/AI/NaiveBayes.pm view on Meta::CPAN
sub classify {
my ($self, $newattrs) = @_;
$newattrs or die "Missing parameter for classify()";
my $m = $self->model;
# Note that we're using the log(prob) here. That's why we add instead of multiply.
my %scores = %{$m->{prior_probs}};
my %features;
while (my ($feature, $value) = each %$newattrs) {
next unless exists $m->{attributes}{$feature}; # Ignore totally unseen features
while (my ($label, $attributes) = each %{$m->{probs}}) {
my $score = ($attributes->{$feature} || $m->{smoother}{$label})*$value; # P($feature|$label)**$value
$scores{$label} += $score;
$features{$feature}{$label} = $score;
}
}
rescale(\%scores);
return AI::NaiveBayes::Classification->new( label_sums => \%scores, features => \%features );
}
sub rescale {
my ($scores) = @_;
# Scale everything back to a reasonable area in logspace (near zero), un-loggify, and normalize
my $total = 0;
my $max = max(values %$scores);
foreach (values %$scores) {
$_ = exp($_ - $max);
$total += $_**2;
}
$total = sqrt($total);
foreach (values %$scores) {
$_ /= $total;
}
}
__PACKAGE__->meta->make_immutable;
1;
=pod
lib/AI/NaiveBayes.pm view on Meta::CPAN
my $classifier = AI::NaiveBayes->train(
{
attributes => {
sheep => 1, very => 1, valuable => 1, farming => 1
},
labels => ['farming']
},
{
attributes => {
vampires => 1, cannot => 1, see => 1, their => 1,
images => 1, mirrors => 1
},
labels => ['vampire']
},
);
# Classify a feature vector
my $result = $classifier->classify({bar => 3, blurp => 2});
# $result is now a AI::NaiveBayes::Classification object
my $best_category = $result->best_category;
=head1 DESCRIPTION
This module implements the classic "Naive Bayes" machine learning
algorithm. This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.
Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start
you can use the limited C<train> class method that trains the
classifier in a default way.
The classifier object is immutable.
It is a well-studied probabilistic algorithm often used in
automatic text categorization. Compared to other algorithms (kNN,
SVM, Decision Trees), it's pretty fast and reasonably competitive in
the quality of its results.
A paper by Fabrizio Sebastiani provides a really good introduction to
text categorization:
L<http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf>
=head1 METHODS
=over 4
=item new( model => $model )
lib/AI/NaiveBayes.pm view on Meta::CPAN
settings.
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.
=item classify( HASHREF )
Classifies a feature-vector of the form:
{ feature1 => weight1, feature2 => weight2, ... }
The result is a C<AI::NaiveBayes::Classification> object.
=item rescale
Internal
=back
=head1 ATTRIBUTES
=over 4
=item model
lib/AI/NaiveBayes.pm view on Meta::CPAN
certain string of words in a document, so we have:
P(words | cat) P(cat)
P(cat | words) = --------------------
P(words)
We have applied Bayes' Theorem because C<P(cat | words)> is a difficult
quantity to compute directly, but C<P(words | cat)> and C<P(cat)> are accessible
(see below).
The greater the expression above, the greater the probability that the given
document belongs to the given category. So we want to find the maximum
value. We write this as
P(words | cat) P(cat)
Best category = ArgMax -----------------------
cat in cats P(words)
Since C<P(words)> doesn't change over the range of categories, we can get rid
of it. That's good, because we didn't want to have to compute these values
anyway. So our new formula is:
Best category = ArgMax P(words | cat) P(cat)
cat in cats
Finally, we note that if C<w1, w2, ... wn> are the words in the document,
then this expression is equivalent to:
Best category = ArgMax P(w1|cat)*P(w2|cat)*...*P(wn|cat)*P(cat)
cat in cats
That's the formula I use in my document categorization code. The last
step is the only non-rigorous one in the derivation, and this is the
"naive" part of the Naive Bayes technique. It assumes that the
probability of each word appearing in a document is unaffected by the
presence or absence of each other word in the document. We assume
this even though we know this isn't true: for example, the word
"iodized" is far more likely to appear in a document that contains the
word "salt" than it is to appear in a document that contains the word
"subroutine". Luckily, as it turns out, making this assumption even
when it isn't true may have little effect on our results, as the
following paper by Pedro Domingos argues:
L<"http://www.cs.washington.edu/homes/pedrod/mlj97.ps.gz">
=head1 SEE ALSO
Algorithm::NaiveBayes (3), AI::Classifier::Text(3)
=head1 BASED ON
Much of the code and description is from L<Algorithm::NaiveBayes>.
lib/AI/NaiveBayes/Classification.pm view on Meta::CPAN
package AI::NaiveBayes::Classification;
$AI::NaiveBayes::Classification::VERSION = '0.04';
use strict;
use warnings;
use 5.010;
use Moose;
has features => (is => 'ro', isa => 'HashRef[HashRef]', required => 1);
has label_sums => (is => 'ro', isa => 'HashRef', required => 1);
has best_category => (is => 'ro', isa => 'Str', lazy_build => 1);
sub _build_best_category {
my $self = shift;
my $sc = $self->label_sums;
my ($best_cat, $best_score) = each %$sc;
while (my ($key, $val) = each %$sc) {
($best_cat, $best_score) = ($key, $val) if $val > $best_score;
}
return $best_cat;
}
sub find_predictors{
my $self = shift;
my $best_cat = $self->best_category;
my $features = $self->features;
my @predictors;
for my $feature ( keys %$features ) {
for my $cat ( keys %{ $features->{$feature } } ){
next if $cat eq $best_cat;
push @predictors, [ $feature, $features->{$feature}{$best_cat} - $features->{$feature}{$cat} ];
}
}
@predictors = sort { abs( $b->[1] ) <=> abs( $a->[1] ) } @predictors;
return $best_cat, @predictors;
}
__PACKAGE__->meta->make_immutable;
1;
=pod
=encoding UTF-8
=head1 NAME
AI::NaiveBayes::Classification - The result of a bayesian classification
=head1 VERSION
version 0.04
=head1 SYNOPSIS
my $result = $classifier->classify({bar => 3, blurp => 2});
# $result is an AI::NaiveBayes::Classification object
say $result->best_category;
my $predictors = $result->find_predictors;
=head1 DESCRIPTION
AI::NaiveBayes::Classification represents the result of a bayesian classification,
produced by AI::NaiveBayes classifier.
=head1 METHODS
=over 4
=item C<best_category()>
Returns a string being a label that suits given document the best.
=item C<find_predictors()>
This method returns the C<best_category()>, as well as the list of all the predictors
along with their influence on the best category selected. So the second value
returned is a list of array references, where each one contains a string being a
single feature and a number describing its influence on the result. So the
second part of the result may look like this:
(
[ 'activities', 1.2511540632952 ],
[ 'over', -1.0269523272981 ],
[ 'provide', 0.8280157033269 ],
[ 'natural', 0.7361042359385 ],
[ 'against', -0.6923354975173 ],
)
=back
lib/AI/NaiveBayes/Classification.pm view on Meta::CPAN
This software is copyright (c) 2012 by Opera Software ASA.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.
=cut
__END__
# ABSTRACT: The result of a bayesian classification
lib/AI/NaiveBayes/Learner.pm view on Meta::CPAN
use 5.010;
use List::Util qw( min sum );
use Moose;
use AI::NaiveBayes;
has attributes => (is => 'ro', isa => 'HashRef', default => sub { {} }, clearer => '_clear_attrs');
has labels => (is => 'ro', isa => 'HashRef', default => sub { {} }, clearer => '_clear_labels');
has examples => (is => 'ro', isa => 'Int', default => 0, clearer => '_clear_examples');
has features_kept => (is => 'ro', predicate => 'limit_features');
has classifier_class => ( is => 'ro', isa => 'Str', default => 'AI::NaiveBayes' );
sub add_example {
my ($self, %params) = @_;
for ('attributes', 'labels') {
die "Missing required '$_' parameter" unless exists $params{$_};
}
$self->{examples}++;
lib/AI/NaiveBayes/Learner.pm view on Meta::CPAN
# P(attr|label) = $count/$label_tokens (simple)
# P(attr|label) = ($count + 1)/($label_tokens + $vocab_size) (with smoothing)
# log P(attr|label) = log($count + 1) - log($label_tokens + $vocab_size)
my $denominator = log($label_tokens + $vocab_size);
while (my ($attribute, $count) = each %{ $labels->{$label}{attributes} }) {
$model->{probs}{$label}{$attribute} = log($count + 1) - $denominator;
}
if ($self->limit_features) {
my %old = %{$model->{probs}{$label}};
my @features = sort { abs($old{$a}) <=> abs($old{$b}) } keys(%old);
my $limit = min($self->features_kept, 0+@features);
if ($limit < 1) {
$limit = int($limit * keys(%old));
}
my @top = @features[0..$limit-1];
my %kept = map { $_ => $old{$_} } @top;
$model->{probs}{$label} = \%kept;
}
}
my $classifier_class = $self->classifier_class;
return $classifier_class->new( model => $model );
}
sub add_hash {
my ($first, $second) = @_;
lib/AI/NaiveBayes/Learner.pm view on Meta::CPAN
=head1 NAME
AI::NaiveBayes::Learner - Build AI::NaiveBayes classifier from a set of training examples.
=head1 VERSION
version 0.04
=head1 SYNOPSIS
my $learner = AI::NaiveBayes::Learner->new(features_kept => 0.5);
$learner->add_example(
attributes => { sheep => 1, very => 1, valuable => 1, farming => 1 },
labels => ['farming']
);
my $classifier = $learner->classifier;
=head1 DESCRIPTION
This is a trainer of AI::NaiveBayes classifiers. It saves information passed
by the C<add_example> method from
training data into internal structures and then constructs a classifier when
the C<classifier> method is called.
=head1 ATTRIBUTES
=over 4
=item C<features_kept>
Indicates how many features should remain after calculating probabilities. By
default all of them will be kept. For C<features_kept> > 1, C<features_kept> of
features will be preserved. For values lower than 1, a specified fraction of
features will be kept (e.g. top 20% of features for C<features_kept> = 0.2).
The rest of the attributes is for class' internal usage, and thus not
documented.
=item C<classifier_class>
The class of the classifier to be created. By default it is
C<AI::NaiveBayes>
=back
=head1 METHODS
=over 4
=item C<add_example( attributes => HASHREF, labels => LIST )>
Saves the information from a training example into internal data structures.
C<attributes> should be of the form of
{ feature1 => weight1, feature2 => weight2, ... }
C<labels> should be a list of strings denoting one or more classes to which the example belongs.
=item C<classifier()>
Creates an AI::NaiveBayes classifier based on the data accumulated before.
=back
t/01-learner.t view on Meta::CPAN
use AI::NaiveBayes::Learner;
ok(1); # If we made it this far, we're loaded.
my $learner = AI::NaiveBayes::Learner->new();
# Populate
$learner->add_example( attributes => _hash(qw(sheep very valuable farming)),
labels => ['farming'] );
is $learner->{labels}{farming}{count}, 1;
$learner->add_example( attributes => _hash(qw(farming requires many kinds animals)),
labels => ['farming'] );
is $learner->{labels}{farming}{count}, 2;
is keys %{$learner->{labels}}, 1;
$learner->add_example( attributes => _hash(qw(vampires drink blood vampires may staked)),
labels => ['vampire'] );
is $learner->{labels}{vampire}{count}, 1;
$learner->add_example( attributes => _hash(qw(vampires cannot see their images mirrors)),
labels => ['vampire'] );
is $learner->{labels}{vampire}{count}, 2;
is keys %{$learner->{labels}}, 2;
# features_kept > 1
$learner = AI::NaiveBayes::Learner->new(features_kept => 5);
$learner->add_example( attributes => _hash(qw(one two three four)),
labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(five six seven eight)),
labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(one two three four five)),
labels => ['farming'] );
my $model = $learner->classifier->model;
is keys %{$model->{probs}{farming}}, 5, '5 features kept';
is join(" ", sort { $a cmp $b } keys %{$model->{probs}{farming}}), 'five four one three two';
# features_kept < 1
$learner = AI::NaiveBayes::Learner->new(features_kept => 0.5);
$learner->add_example( attributes => _hash(qw(one two three four)),
labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(five six seven eight)),
labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(one two three four)),
labels => ['farming'] );
$model = $learner->classifier->model;
is keys %{$model->{probs}{farming}}, 4, 'half features kept';
is join(" ", sort { $a cmp $b } keys %{$model->{probs}{farming}}), 'four one three two';
sub _hash { +{ map {$_,1} @_ } }
t/02-predict.t view on Meta::CPAN
use Test::More tests => 12;
use AI::NaiveBayes;
use AI::NaiveBayes::Learner;
ok(1); # If we made it this far, we're loaded.
my $lr = AI::NaiveBayes::Learner->new();
# Populate
$lr->add_example( attributes => _hash(qw(sheep very valuable farming)),
labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(farming requires many kinds animals)),
labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(vampires drink blood vampires may staked)),
labels => ['vampire'] );
$lr->add_example( attributes => _hash(qw(vampires cannot see their images mirrors)),
labels => ['vampire'] );
my $classifier = $lr->classifier;
ok $classifier;
# Predict
my $s = $classifier->classify( _hash(qw(i would like to begin farming sheep)) );
my $h = $s->label_sums;
ok $h;
ok $h->{farming} > 0.5;
ok $h->{vampire} < 0.5;
$s = $classifier->classify( _hash(qw(i see that many vampires may have eaten my beautiful daughter's blood)) );
$h = $s->label_sums;
ok $h;
ok $h->{farming} < 0.5;
ok $h->{vampire} > 0.5;
# Find predictors
my $p = $classifier->classify( _hash( qw(i would like to begin farming sheep)) );
my( $best_cat, @predictors ) = $p->find_predictors();
is( $best_cat, 'farming', 'Best category' );
is( scalar @predictors, 2, 'farming and sheep - two predictors' );
is( $predictors[0][0], 'farming', 'Farming is the best predictor' );
# Prior probs
$lr = AI::NaiveBayes::Learner->new();
# Populate
$lr->add_example( attributes => _hash(qw(sheep very valuable farming)),
labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(farming requires many kinds animals)),
labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(good soil)),
labels => ['farming'] );
$lr->add_example( attributes => _hash(qw(vampires drink blood vampires may staked)),
labels => ['vampire'] );
$classifier = $lr->classifier;
# Predict
$s = $classifier->classify( _hash(qw(jakis tekst po polsku)) );
$h = $s->label_sums;
ok(abs( 3 - $h->{farming} / $h->{vampire} ) < 0.01, 'Prior probabillities' );
t/default_training.t view on Meta::CPAN
use Test::More tests => 2;
use AI::NaiveBayes;
ok(1); # If we made it this far, we're loaded.
my $classifier = AI::NaiveBayes->train(
{
attributes => _hash(qw(sheep very valuable farming)),
labels => ['farming']
},
{
attributes => _hash(qw(vampires cannot see their images mirrors)),
labels => ['vampire']
},
);
isa_ok( $classifier, 'AI::NaiveBayes' );
################################################################
sub _hash { +{ map {$_,1} @_ } }
( run in 1.646 second using v1.01-cache-2.11-cpan-49f99fa48dc )