AI-XGBoost
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examples/basic.pl  view on Meta::CPAN 

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];

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 strict;
use warnings;
use 5.010;
use Data::Dumper;
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 

use 5.010;
use AI::XGBoost::CAPI::RAW;
use FFI::Platypus;

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);

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) {
            push @{$aux[$column]}, $row->[$column];
        }
    }
    return \@aux;
}

$train_dataset = transpose($train_dataset);
$test_dataset = transpose($test_dataset);

my $train_label = [map {$class{$_}} @{$iris->{'species'}}];
my $test_label = [map {$class{$_}} @{$iris->{'species'}}];

my $train_data = DMatrix->From(matrix => $train_dataset, label => $train_label);
my $test_data = DMatrix->From(matrix => $test_dataset, label => $test_label);

# Multiclass problems need a diferent objective function and the number
#  of classes, in this case we are using 'multi:softprob' and
#  num_class => 3
my $booster = train(data => $train_data, number_of_rounds => 20, params => {
        max_depth => 3,
        eta => 0.3,
        silent => 1,
        objective => 'multi:softprob',
        num_class => 3
    });

my $predictions = $booster->predict(data => $test_data);

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

=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];

 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 = (
     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) {
             push @{$aux[$column]}, $row->[$column];
         }
     }
     return \@aux;
 }
 
 $train_dataset = transpose($train_dataset);
 $test_dataset = transpose($test_dataset);
 
 my $train_label = [map {$class{$_}} @{$iris->{'species'}}];
 my $test_label = [map {$class{$_}} @{$iris->{'species'}}];
 
 my $train_data = DMatrix->From(matrix => $train_dataset, label => $train_label);
 my $test_data = DMatrix->From(matrix => $test_dataset, label => $test_label);
 
 # Multiclass problems need a diferent objective function and the number
 #  of classes, in this case we are using 'multi:softprob' and
 #  num_class => 3
 my $booster = train(data => $train_data, number_of_rounds => 20, params => {
         max_depth => 3,
         eta => 0.3,
         silent => 1,
         objective => 'multi:softprob',
         num_class => 3
     });
 
 my $predictions = $booster->predict(data => $test_data);

=head1 DESCRIPTION

Perl wrapper for XGBoost library. 

The easiest way to use the wrapper is using C<train>, but beforehand 
you need the data to be used contained in a C<DMatrix> object

This is a work in progress, feedback, comments, issues, suggestion and
pull requests are welcome!!

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

=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

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

=head1 VERSION

version 0.11

=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

XGBoost c api functions returns some int to signal the presence/absence of error.
In this module that is achieved using Exceptions from L<Exception::Class>

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


version 0.11

=head1 SYNOPSIS

 use 5.010;
 use AI::XGBoost::CAPI::RAW;
 use FFI::Platypus;
 
 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

=head1 FUNCTIONS

=head2 XGBGetLastError



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