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https://github.com/pablrod/p5-ai-xgboost

Perl wrapper for XGBoost library
https://github.com/pablrod/p5-ai-xgboost

gbm machine-learning perl xgboost

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Perl wrapper for XGBoost library

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README 

        
# NAME



AI::XGBoost - Perl wrapper for XGBoost library [https://github.com/dmlc/xgboost](https://github.com/dmlc/xgboost)



# VERSION



version 0.11



# SYNOPSIS



```perl

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);

```



# DESCRIPTION



Perl wrapper for XGBoost library. 



The easiest way to use the wrapper is using `train`, but beforehand 

you need the data to be used contained in a `DMatrix` object



This is a work in progress, feedback, comments, issues, suggestion and

pull requests are welcome!!



XGBoost library is used via [Alien::XGBoost](https://metacpan.org/pod/Alien::XGBoost). That means downloading,

compiling and installing if it's not available in your system.



# FUNCTIONS



## train



Performs gradient boosting using the data and parameters passed



Returns a trained AI::XGBoost::Booster used



### Parameters



- params



    Parameters for the booster object. 



    Full list available: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md 



- data



    AI::XGBoost::DMatrix object used for training



- number\_of\_rounds



    Number of boosting iterations



# ROADMAP



The goal is to make a full wrapper for XGBoost.



## VERSIONS



- 0.2 



    Full C API "easy" to use, with PDL support as [AI::XGBoost::CAPI](https://metacpan.org/pod/AI::XGBoost::CAPI)



    Easy means clients don't have to use [FFI::Platypus](https://metacpan.org/pod/FFI::Platypus) or modules dealing

    with C structures



- 0.25



    Alien package for libxgboost.so/xgboost.dll



- 0.3



    Object oriented API Moose based with DMatrix and Booster classes



- 0.4



    Complete object oriented API



- 0.5



    Use perl signatures ([https://metacpan.org/pod/distribution/perl/pod/perlexperiment.pod#Subroutine-signatures](https://metacpan.org/pod/distribution/perl/pod/perlexperiment.pod#Subroutine-signatures))



# SEE ALSO



- [AI::MXNet](https://metacpan.org/pod/AI::MXNet)

- [FFI::Platypus](https://metacpan.org/pod/FFI::Platypus)

- [NativeCall](https://metacpan.org/pod/NativeCall)



# AUTHOR



Pablo Rodríguez González 



# COPYRIGHT AND LICENSE



Copyright (c) 2017 by Pablo Rodríguez González.



# CONTRIBUTOR



Ruben