AI-XGBoost

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examples/iris.pl  view on Meta::CPAN

# XGBoost uses number for "class" so we are going to codify classes
my %class = (
    setosa => 0,
    versicolor => 1,
    virginica => 2
);

my $iris = Data::Dataset::Classic::Iris::get();

# Split train and test, label and features
my $train_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];
my $test_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];

sub transpose {
# Transposing without using PDL, Data::Table, Data::Frame or other modules
# to keep minimal dependencies
    my $array = shift;
    my @aux = ();
    for my $row (@$array) {
        for my $column (0 .. scalar @$row - 1) {

lib/AI/XGBoost.pm  view on Meta::CPAN

 
 # XGBoost uses number for "class" so we are going to codify classes
 my %class = (
     setosa => 0,
     versicolor => 1,
     virginica => 2
 );
 
 my $iris = Data::Dataset::Classic::Iris::get();
 
 # Split train and test, label and features
 my $train_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];
 my $test_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];
 
 sub transpose {
 # Transposing without using PDL, Data::Table, Data::Frame or other modules
 # to keep minimal dependencies
     my $array = shift;
     my @aux = ();
     for my $row (@$array) {
         for my $column (0 .. scalar @$row - 1) {

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

=head2 set_attr

Set a string attribute

=head2 get_attr

Get a string attribute

=head2 get_score

Get importance of each feature

=head3 Parameters

=over 4

=item importance_type

Type of importance. Valid values:

=over 4

=item weight

Number of times a feature is used to split the data across all trees

=item gain

Average gain of the feature when it is used in trees

=item cover

Average coverage of the feature when it is used in trees

=back

=item fmap

Name of feature map file

=back

=head2 get_dump

=head2 attributes

Returns all attributes of the booster as a HASHREF

=head2 TO_JSON

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

    _CheckCall(
                AI::XGBoost::CAPI::RAW::XGBoosterPredict( $booster,     $data_matrix, $option_mask,
                                                          $ntree_limit, \$out_len,    \$out_result
                )
    );
    my $ffi = FFI::Platypus->new();
    return $ffi->cast( opaque => "float[$out_len]", $out_result );
}

sub XGBoosterDumpModel {
    my ( $booster, $feature_map, $with_stats ) = @_;
    $feature_map //= "";
    $with_stats  //= 1;
    my $out_len    = 0;
    my $out_result = 0;
    _CheckCall(
           AI::XGBoost::CAPI::RAW::XGBoosterDumpModel( $booster, $feature_map, $with_stats, \$out_len, \$out_result ) );
    my $ffi = FFI::Platypus->new();
    $out_result = $ffi->cast( opaque => "opaque[$out_len]", $out_result );
    return [ map { $ffi->cast( opaque => "string", $_ ) } @$out_result ];
}

sub XGBoosterDumpModelEx {
    my ( $booster, $feature_map, $with_stats, $format ) = @_;
    $feature_map //= "";
    $with_stats  //= 1;
    my $out_len    = 0;
    my $out_result = 0;
    _CheckCall(
                AI::XGBoost::CAPI::RAW::XGBoosterDumpModelEx(
                                                   $booster, $feature_map, $with_stats, $format, \$out_len, \$out_result
                )
    );
    my $ffi = FFI::Platypus->new();
    $out_result = $ffi->cast( opaque => "opaque[$out_len]", $out_result );
    return [ map { $ffi->cast( opaque => "string", $_ ) } @$out_result ];
}

sub XGBoosterDumpModelWithFeatures {
    my ( $booster, $feature_names, $feature_types, $with_stats ) = @_;
    $with_stats //= 1;
    my $out_len                       = 0;
    my $out_result                    = 0;
    my $ffi                           = FFI::Platypus->new();
    my $number_of_features            = scalar @$feature_names;
    my $array_of_opaque_feature_names = [ map { $ffi->cast( string => "opaque", $_ ) } @$feature_names ];
    my $array_of_opaque_feature_types = [ map { $ffi->cast( string => "opaque", $_ ) } @$feature_types ];
    _CheckCall(
                AI::XGBoost::CAPI::RAW::XGBoosterDumpModelWithFeatures( $booster, $number_of_features,
                                                                        $array_of_opaque_feature_names,
                                                                        $array_of_opaque_feature_types,
                                                                        $with_stats, \$out_len, \$out_result
                )
    );
    $out_result = $ffi->cast( opaque => "opaque[$out_len]", $out_result );
    return [ map { $ffi->cast( opaque => "string", $_ ) } @$out_result ];
}

sub XGBoosterDumpModelExWithFeatures {
    my ( $booster, $feature_names, $feature_types, $with_stats, $format ) = @_;
    my $out_len                       = 0;
    my $out_result                    = 0;
    my $ffi                           = FFI::Platypus->new();
    my $number_of_features            = scalar @$feature_names;
    my $array_of_opaque_feature_names = [ map { $ffi->cast( string => "opaque", $_ ) } @$feature_names ];
    my $array_of_opaque_feature_types = [ map { $ffi->cast( string => "opaque", $_ ) } @$feature_types ];
    _CheckCall(
                AI::XGBoost::CAPI::RAW::XGBoosterDumpModelExWithFeatures( $booster, $number_of_features,
                                                                          $array_of_opaque_feature_names,
                                                                          $array_of_opaque_feature_types,
                                                                          $with_stats, $format, \$out_len, \$out_result
                )
    );
    $out_result = $ffi->cast( opaque => "opaque[$out_len]", $out_result );
    return [ map { $ffi->cast( opaque => "string", $_ ) } @$out_result ];
}

sub XGBoosterFree {
    my ($booster) = @_;
    _CheckCall( AI::XGBoost::CAPI::RAW::XGBoosterFree($booster) );

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

=item

1: output margin instead of transformed value

=item

2: output leaf index of trees instead of leaf value, note leaf index is unique per tree

=item

4: output feature contributions to individual predictions

=back

=item ntree_limit

limit number of trees used for prediction, this is only valid for boosted trees
when the parameter is set to 0, we will use all the trees

=back

lib/AI/XGBoost/CAPI/RAW.pm  view on Meta::CPAN

=item

1: output margin instead of transformed value

=item

2: output leaf index of trees instead of leaf value, note leaf index is unique per tree

=item

4: output feature contributions to individual predictions

=back

=item ntree_limit 

limit number of trees used for prediction, this is only valid for boosted trees
when the parameter is set to 0, we will use all the trees

=item out_len