AI-XGBoost

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examples/basic.pl  view on Meta::CPAN

        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];

examples/capi.pl  view on Meta::CPAN

use 5.010;
use AI::XGBoost::CAPI qw(:all);

my $dtrain = XGDMatrixCreateFromFile('agaricus.txt.train');
my $dtest = XGDMatrixCreateFromFile('agaricus.txt.test');

my ($rows, $cols) = (XGDMatrixNumRow($dtrain), XGDMatrixNumCol($dtrain));
say "Train dimensions: $rows, $cols";

my $booster = XGBoosterCreate([$dtrain]);

for my $iter (0 .. 10) {
    XGBoosterUpdateOneIter($booster, $iter, $dtrain);
}

my $predictions = XGBoosterPredict($booster, $dtest);
# say join "\n", @$predictions;

XGBoosterFree($booster);
XGDMatrixFree($dtrain);
XGDMatrixFree($dtest);

examples/capi_dump_model.pl  view on Meta::CPAN

use AI::XGBoost::CAPI qw(:all);

my $dtrain = XGDMatrixCreateFromFile('agaricus.txt.train');
my $dtest = XGDMatrixCreateFromFile('agaricus.txt.test');

my $booster = XGBoosterCreate([$dtrain]);
XGBoosterUpdateOneIter($booster, 1, $dtrain);

my $json_model_with_stats = XGBoosterDumpModelEx($booster, "featmap.txt", 1, "json");

say Dumper $json_model_with_stats;

XGBoosterFree($booster);
XGDMatrixFree($dtrain);
XGDMatrixFree($dtest);

examples/capi_raw.pl  view on Meta::CPAN

my $silent = 0;
my ($dtrain, $dtest) = (0, 0);

AI::XGBoost::CAPI::RAW::XGDMatrixCreateFromFile('agaricus.txt.test', $silent, \$dtest);
AI::XGBoost::CAPI::RAW::XGDMatrixCreateFromFile('agaricus.txt.train', $silent, \$dtrain);

my ($rows, $cols) = (0, 0);
AI::XGBoost::CAPI::RAW::XGDMatrixNumRow($dtrain, \$rows);
AI::XGBoost::CAPI::RAW::XGDMatrixNumCol($dtrain, \$cols);
say "Dimensions: $rows, $cols";

my $booster = 0;

AI::XGBoost::CAPI::RAW::XGBoosterCreate( [$dtrain] , 1, \$booster);

for my $iter (0 .. 10) {
    AI::XGBoost::CAPI::RAW::XGBoosterUpdateOneIter($booster, $iter, $dtrain);
}

my $out_len = 0;
my $out_result = 0;

AI::XGBoost::CAPI::RAW::XGBoosterPredict($booster, $dtest, 0, 0, \$out_len, \$out_result);
my $ffi = FFI::Platypus->new();
my $predictions = $ffi->cast(opaque => "float[$out_len]", $out_result);

#say join "\n", @$predictions;

AI::XGBoost::CAPI::RAW::XGBoosterFree($booster);
AI::XGBoost::CAPI::RAW::XGDMatrixFree($dtrain);
AI::XGBoost::CAPI::RAW::XGDMatrixFree($dtest);

lib/AI/XGBoost.pm  view on Meta::CPAN

         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];

 use aliased 'AI::XGBoost::DMatrix';
 use AI::XGBoost qw(train);
 use Data::Dataset::Classic::Iris;
 
 # We are going to solve a multiple classification problem:
 #  determining plant species using a set of flower's measures 
 
 # XGBoost uses number for "class" so we are going to codify classes
 my %class = (

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

         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

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

=head1 SYNOPSIS

 use 5.010;
 use AI::XGBoost::CAPI qw(:all);
 
 my $dtrain = XGDMatrixCreateFromFile('agaricus.txt.train');
 my $dtest = XGDMatrixCreateFromFile('agaricus.txt.test');
 
 my ($rows, $cols) = (XGDMatrixNumRow($dtrain), XGDMatrixNumCol($dtrain));
 say "Train dimensions: $rows, $cols";
 
 my $booster = XGBoosterCreate([$dtrain]);
 
 for my $iter (0 .. 10) {
     XGBoosterUpdateOneIter($booster, $iter, $dtrain);
 }
 
 my $predictions = XGBoosterPredict($booster, $dtest);
 # say join "\n", @$predictions;
 
 XGBoosterFree($booster);
 XGDMatrixFree($dtrain);
 XGDMatrixFree($dtest);

=head1 DESCRIPTION

Perlified wrapper for the C API

=head2 Error handling

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

 
 my $silent = 0;
 my ($dtrain, $dtest) = (0, 0);
 
 AI::XGBoost::CAPI::RAW::XGDMatrixCreateFromFile('agaricus.txt.test', $silent, \$dtest);
 AI::XGBoost::CAPI::RAW::XGDMatrixCreateFromFile('agaricus.txt.train', $silent, \$dtrain);
 
 my ($rows, $cols) = (0, 0);
 AI::XGBoost::CAPI::RAW::XGDMatrixNumRow($dtrain, \$rows);
 AI::XGBoost::CAPI::RAW::XGDMatrixNumCol($dtrain, \$cols);
 say "Dimensions: $rows, $cols";
 
 my $booster = 0;
 
 AI::XGBoost::CAPI::RAW::XGBoosterCreate( [$dtrain] , 1, \$booster);
 
 for my $iter (0 .. 10) {
     AI::XGBoost::CAPI::RAW::XGBoosterUpdateOneIter($booster, $iter, $dtrain);
 }
 
 my $out_len = 0;
 my $out_result = 0;
 
 AI::XGBoost::CAPI::RAW::XGBoosterPredict($booster, $dtest, 0, 0, \$out_len, \$out_result);
 my $ffi = FFI::Platypus->new();
 my $predictions = $ffi->cast(opaque => "float[$out_len]", $out_result);
 
 #say join "\n", @$predictions;
 
 AI::XGBoost::CAPI::RAW::XGBoosterFree($booster);
 AI::XGBoost::CAPI::RAW::XGDMatrixFree($dtrain);
 AI::XGBoost::CAPI::RAW::XGDMatrixFree($dtest);

=head1 DESCRIPTION

Wrapper for the C API.

The doc for the methods is extracted from doxygen comments: https://github.com/dmlc/xgboost/blob/master/include/xgboost/c_api.h