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lib/AI/XGBoost/Booster.pm  view on Meta::CPAN

    my $self = shift;
    my ( $name, $value ) = @_;
    XGBoosterSetAttr( $self->_handle, $name, $value );
    return $self;
}

sub get_attr {
    my $self = shift;
    my ($name) = @_;
    XGBoosterGetAttr( $self->_handle, $name );
}

sub get_score {
    my $self = shift;
    my %args = @_;
    my ( $fmap, $importance_type ) = @args{qw(fmap importance_type)};

    if ( $importance_type eq "weight" ) {
        my @trees = $self->get_dump;
    } else {

    }

}

sub get_dump {
    my $self = shift;
    return XGBoosterDumpModelEx( $self->_handle, "", 1, "text" );
}

sub attributes {
    my $self = shift;
    return { map { $_ => $self->get_attr($_) } @{ XGBoosterGetAttrNames( $self->_handle ) } };
}

sub TO_JSON {
    my $self = shift;
    my $trees = XGBoosterDumpModelEx( $self->_handle, "", 1, "json" );
    return "[" . join( ',', @$trees ) . "]";
}

sub BUILD {
    my $self = shift;
    my $args = shift;
    $self->_handle( XGBoosterCreate( [ map { $_->handle } @{ $args->{'cache'} } ] ) );
}

sub DEMOLISH {
    my $self = shift();
    XGBoosterFree( $self->_handle );
}

__PACKAGE__->meta->make_immutable();

1;

__END__

=pod

=encoding utf-8

=head1 NAME

AI::XGBoost::Booster - XGBoost main class for training, prediction and evaluation

=head1 VERSION

version 0.11

=head1 SYNOPSIS

 use 5.010;
 use aliased 'AI::XGBoost::DMatrix';
 use AI::XGBoost qw(train);
 
 # We are going to solve a binary classification problem:
 #  Mushroom poisonous or not
 
 my $train_data = DMatrix->From(file => 'agaricus.txt.train');
 my $test_data = DMatrix->From(file => 'agaricus.txt.test');
 
 # With XGBoost we can solve this problem using 'gbtree' booster
 #  and as loss function a logistic regression 'binary:logistic'
 #  (Gradient Boosting Regression Tree)
 # XGBoost Tree Booster has a lot of parameters that we can tune
 # (https://github.com/dmlc/xgboost/blob/master/doc/parameter.md)
 
 my $booster = train(data => $train_data, number_of_rounds => 10, params => {
         objective => 'binary:logistic',
         eta => 1.0,
         max_depth => 2,
         silent => 1
     });
 
 # For binay classification predictions are probability confidence scores in [0, 1]
 #  indicating that the label is positive (1 in the first column of agaricus.txt.test)
 my $predictions = $booster->predict(data => $test_data);
 
 say join "\n", @$predictions[0 .. 10];

=head1 DESCRIPTION

Booster objects control training, prediction and evaluation

Work In Progress, the API may change. Comments and suggestions are welcome!

=head1 METHODS

=head2 update

Update one iteration

=head3 Parameters

=over 4

=item iteration

Current iteration number