AI-XGBoost
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# This file was automatically generated by Dist::Zilla::Plugin::ModuleBuild v6.008.
use strict;
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"dist_author" => [
"Pablo Rodr\x{ed}guez Gonz\x{e1}lez <pablo.rodriguez.gonzalez\@gmail.com>"
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---
abstract: 'Perl wrapper for XGBoost library L<https://github.com/dmlc/xgboost>'
author:
- 'Pablo RodrÃguez González <pablo.rodriguez.gonzalez@gmail.com>'
build_requires:
Module::Build: '0.28'
Test::More: '0'
Test::Most: '0'
configure_requires:
Module::Build: '0.28'
dynamic_config: 0
generated_by: 'Dist::Zilla version 6.008, CPAN::Meta::Converter version 2.150005'
license: apache
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
version: '1.4'
name: AI-XGBoost
requires:
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resources:
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homepage: https://github.com/pablrod/p5-AI-XGBoost
repository: https://github.com/pablrod/p5-AI-XGBoost.git
version: '0.11'
x_contributors:
- 'Ruben <me@ruben.tech>'
x_serialization_backend: 'YAML::Tiny version 1.69'
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examples/basic.pl view on Meta::CPAN
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_raw.pl view on Meta::CPAN
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
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) {
lib/AI/XGBoost.pm view on Meta::CPAN
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) {
lib/AI/XGBoost.pm view on Meta::CPAN
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!!
XGBoost library is used via L<Alien::XGBoost>. That means downloading,
compiling and installing if it's not available in your system.
=head1 FUNCTIONS
=head2 train
Performs gradient boosting using the data and parameters passed
lib/AI/XGBoost.pm view on Meta::CPAN
=head2 VERSIONS
=over 4
=item 0.2
Full C API "easy" to use, with PDL support as L<AI::XGBoost::CAPI>
Easy means clients don't have to use L<FFI::Platypus> or modules dealing
with C structures
=item 0.25
Alien package for libxgboost.so/xgboost.dll
=item 0.3
Object oriented API Moose based with DMatrix and Booster classes
=item 0.4
Complete object oriented API
=item 0.5
Use perl signatures (L<https://metacpan.org/pod/distribution/perl/pod/perlexperiment.pod#Subroutine-signatures>)
=back
=head1 SEE ALSO
=over 4
=item L<AI::MXNet>
=item L<FFI::Platypus>
lib/AI/XGBoost/Booster.pm view on Meta::CPAN
my %args = @_;
my ( $dtrain, $grad, $hess ) = @args{qw(dtrain grad hess)};
XGBoosterBoostOneIter( $self->_handle, $dtrain, $grad, $hess );
return $self;
}
sub predict {
my $self = shift;
my %args = @_;
my $data = $args{'data'};
my $result = XGBoosterPredict( $self->_handle, $data->handle );
my $result_size = scalar @$result;
my $matrix_rows = $data->num_row;
if ( $result_size != $matrix_rows && $result_size % $matrix_rows == 0 ) {
my $col_size = $result_size / $matrix_rows;
return [ map { [ @$result[ $_ * $col_size .. $_ * $col_size + $col_size - 1 ] ] } 0 .. $matrix_rows - 1 ];
}
return $result;
}
sub set_param {
my $self = shift;
my ( $name, $value ) = @_;
XGBoosterSetParam( $self->_handle, $name, $value );
return $self;
}
sub set_attr {
lib/AI/XGBoost/Booster.pm view on Meta::CPAN
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
lib/AI/XGBoost/CAPI.pm view on Meta::CPAN
XGBoosterSetParam
XGBoosterSetAttr
XGBoosterGetAttr
XGBoosterGetAttrNames
XGBoosterUpdateOneIter
XGBoosterBoostOneIter
XGBoosterPredict
XGBoosterFree
XGBoosterDumpModel
XGBoosterDumpModelEx
XGBoosterDumpModelWithFeatures
XGBoosterDumpModelExWithFeatures
)
]
]
);
use AI::XGBoost::CAPI::RAW;
use FFI::Platypus;
use Exception::Class ( 'XGBoostException' );
our $VERSION = '0.11'; # VERSION
lib/AI/XGBoost/CAPI.pm view on Meta::CPAN
}
sub XGDMatrixSetFloatInfo {
my ( $matrix, $info, $data ) = @_;
_CheckCall( AI::XGBoost::CAPI::RAW::XGDMatrixSetFloatInfo( $matrix, $info, $data, scalar @$data ) );
}
sub XGDMatrixGetFloatInfo {
my ( $matrix, $info ) = @_;
my $out_len = 0;
my $out_result = 0;
_CheckCall( AI::XGBoost::CAPI::RAW::XGDMatrixGetFloatInfo( $matrix, $info, \$out_len, \$out_result ) );
my $ffi = FFI::Platypus->new();
return $ffi->cast( opaque => "float[$out_len]", $out_result );
}
sub XGDMatrixSetUintInfo {
my ( $matrix, $info, $data ) = @_;
_CheckCall( AI::XGBoost::CAPI::RAW::XGDMatrixSetUintInfo( $matrix, $info, $data, scalar @$data ) );
}
sub XGDMatrixGetUintInfo {
my ( $matrix, $info ) = @_;
my $out_len = 0;
my $out_result = 0;
_CheckCall( AI::XGBoost::CAPI::RAW::XGDMatrixGetUintInfo( $matrix, $info, \$out_len, \$out_result ) );
my $ffi = FFI::Platypus->new();
return $ffi->cast( opaque => "uint32[$out_len]", $out_result );
}
sub XGDMatrixSaveBinary {
my ( $matrix, $filename, $silent ) = @_;
$silent //= 1;
_CheckCall( AI::XGBoost::CAPI::RAW::XGDMatrixSaveBinary( $matrix, $filename, $silent ) );
}
sub XGDMatrixSliceDMatrix {
my ( $matrix, $list_of_indices ) = @_;
lib/AI/XGBoost/CAPI.pm view on Meta::CPAN
if ($success) {
my $ffi = FFI::Platypus->new();
return $ffi->cast( opaque => "string", $value );
}
return ();
}
sub XGBoosterGetAttrNames {
my ($booster) = @_;
my $out_len = 0;
my $out_result = 0;
_CheckCall( AI::XGBoost::CAPI::RAW::XGBoosterGetAttrNames( $booster, \$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 XGBoosterUpdateOneIter {
my ( $booster, $iter, $train_matrix ) = @_;
_CheckCall( AI::XGBoost::CAPI::RAW::XGBoosterUpdateOneIter( $booster, $iter, $train_matrix ) );
return ();
}
sub XGBoosterBoostOneIter {
my ( $booster, $train_matrix, $gradient, $hessian ) = @_;
my $out_result = 0;
_CheckCall(
AI::XGBoost::CAPI::RAW::XGBoosterBoostOneIter(
$booster, $train_matrix, $gradient, $hessian, scalar(@$gradient)
)
);
return ();
}
sub XGBoosterEvalOneIter {
my ( $booster, $iter, $matrices, $matrices_names ) = @_;
my $out_result = 0;
my $number_of_matrices = scalar @$matrices;
my $ffi = FFI::Platypus->new();
my $array_of_opaque_matrices_names = [ map { $ffi->cast( string => "opaque", $_ ) } @$matrices_names ];
_CheckCall(
AI::XGBoost::CAPI::RAW::XGBoosterEvalOneIter(
$booster, $iter, $matrices, $array_of_opaque_matrices_names,
$number_of_matrices, \$out_result
)
);
$out_result = $ffi->cast( opaque => "opaque[$number_of_matrices]", $out_result );
return [ map { $ffi->cast( opaque => "string", $_ ) } @$out_result ];
}
sub XGBoosterPredict {
my ( $booster, $data_matrix, $option_mask, $ntree_limit ) = @_;
$option_mask //= 0;
$ntree_limit //= 0;
my $out_len = 0;
my $out_result = 0;
_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) );
return ();
}
# _CheckCall
#
lib/AI/XGBoost/CAPI.pm view on Meta::CPAN
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>
=head1 FUNCTIONS
=head2 XGDMatrixCreateFromFile
Load a data matrix
Parameters:
lib/AI/XGBoost/CAPI.pm view on Meta::CPAN
=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
Returns an arrayref with the predictions corresponding to the rows of data matrix
=head2 XGBoosterDumpModel
=head2 XGBoosterDumpModelEx
=head2 XGBoosterDumpModelWithFeatures
=head2 XGBoosterDumpModelExWithFeatures
=head2 XGBoosterFree
Free booster object
Parameters:
=over 4
=item booster
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
$ffi->attach( XGBoosterSaveModel => [qw(opaque string)] => 'int' );
$ffi->attach( XGBoosterLoadModelFromBuffer => [qw(opaque opaque uint64)] => 'int' );
$ffi->attach( XGBoosterGetModelRaw => [qw(opaque uint64* opaque*)] => 'int' );
$ffi->attach( XGBoosterDumpModel => [qw(opaque string int uint64* opaque*)] => 'int' );
$ffi->attach( XGBoosterDumpModelEx => [qw(opaque string int string uint64* opaque*)] => 'int' );
$ffi->attach( XGBoosterDumpModelWithFeatures => [qw(opaque int opaque[] opaque[] int uint64* opaque*)] => 'int' );
$ffi->attach(
XGBoosterDumpModelExWithFeatures => [qw(opaque int opaque[] opaque[] int string uint64* opaque*)] => 'int' );
$ffi->attach( XGBoosterSetAttr => [qw(opaque string string)] => 'int' );
$ffi->attach( XGBoosterGetAttr => [qw(opaque string opaque* int*)] => 'int' );
$ffi->attach( XGBoosterGetAttrNames => [qw(opaque uint64* opaque*)] => 'int' );
$ffi->attach( XGBoosterLoadRabitCheckpoint => [qw(opaque int)] => 'int' );
$ffi->attach( XGBoosterSaveRabitCheckpoint => [qw(opaque)] => 'int' );
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
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.
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=item nrow
number of rows
=item ncol
number columns
=item missing
which value to represent missing value
=item out
created dmatrix
=back
=head2 XGDMatrixCreateFromMat_omp
Create matrix content from dense matrix
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=item nrow
number of rows
=item ncol
number columns
=item missing
which value to represent missing value
=item out
created dmatrix
=item nthread
number of threads (up to maximum cores available, if <=0 use all cores)
=back
=head2 XGDMatrixSliceDMatrix
Create a new dmatrix from sliced content of existing matrix
Parameters:
=over 4
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
Parameters:
=over 4
=item handle
the handle to the DMatrix
=item out
The address to hold number of rows.
=back
=head2 XGDMatrixNumCol
Get number of cols.
Parameters:
=over 4
=item handle
the handle to the DMatrix
=item out
The address to hold number of cols.
=back
=head2 XGDMatrixSaveBinary
load a data matrix into binary file
Parameters:
=over 4
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=item handle
a instance of data matrix
=item field
field name
=item out_len
used to set result length
=item out_dptr
pointer to the result
=back
=head2 XGDMatrixGetUIntInfo
Get uint32 info vector from matrix
Parameters:
=over 4
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=item field
field name
=item out_len
The length of the field
=item out_dptr
pointer to the result
=back
=head2 XGDMatrixFree
Free space in data matrix
=head2 XGBoosterCreate
Create xgboost learner
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=item dmats
matrices that are set to be cached
=item len
length of dmats
=item out
handle to the result booster
=back
=head2 XGBoosterFree
Free obj in handle
Parameters:
=over 4
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=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
used to store length of returning result
=item out_result
used to set a pointer to array
=back
=head2 XGBoosterLoadModel
Load model form existing file
Parameters:
lib/AI/XGBoost/CAPI/RAW.pm view on Meta::CPAN
=back
=head2 XGBoosterLoadModelFromBuffer
=head2 XGBoosterGetModelRaw
=head2 XGBoosterDumpModel
=head2 XGBoosterDumpModelEx
=head2 XGBoosterDumpModelWithFeatures
=head2 XGBoosterDumpModelExWithFeatures
=head2 XGBoosterSetAttr
=head2 XGBoosterGetAttr
=head2 XGBoosterGetAttrNames
=head2 XGBoosterLoadRabitCheckpoint
=head2 XGBoosterSaveRabitCheckpoint
lib/AI/XGBoost/DMatrix.pm view on Meta::CPAN
=head1 SYNOPSIS
use aliased 'AI::XGBoost::DMatrix';
my $train_data = DMatrix->FromFile(filename => 'agaricus.txt.train');
=head1 DESCRIPTION
XGBoost DMatrix perl model
Work In Progress, the API may change. Comments and suggestions are welcome!
=head1 METHODS
=head2 From
Construct a DMatrix from diferent sources. Based on parameters
dispatch to the correct From* method
Refer to From* to see what can be done.
lib/AI/XGBoost/DMatrix.pm view on Meta::CPAN
=head3 Parameters
=over 4
=item filename
File to read
=item silent
Supress messages
=back
=head2 FromMat
Construct a DMatrix from a bidimensional array
=head3 Parameters
=over 4
misc/using_capi.c view on Meta::CPAN
XGDMatrixCreateFromFile("agaricus.txt.test", 0, &dtest);
XGDMatrixCreateFromFile("agaricus.txt.train", 0, &dtrain);
DMatrixHandle cache[] = {dtrain};
BoosterHandle booster;
XGBoosterCreate(cache, 1, &booster);
for (int iter = 0; iter < 11; iter++) {
XGBoosterUpdateOneIter(booster, iter, dtrain);
}
bst_ulong out_len;
const float *out_result;
XGBoosterPredict(booster, dtest, 0, 0, &out_len, &out_result);
printf("Length: %ld\n", out_len);
for (int output = 0; output < out_len; output++) {
printf("%f\n", out_result[output]);
}
}
( run in 0.606 second using v1.01-cache-2.11-cpan-49f99fa48dc )