/ 
AI-XGBoost-0.008
River stage zero No dependents
/ AI::XGBoost

NAME

AI::XGBoost - Perl wrapper for XGBoost library https://github.com/dmlc/xgboost

VERSION

version 0.008

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];
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!!

Currently this module need the xgboost binary available in your system. I'm going to make an Alien module for xgboost but meanwhile you need to compile yourself xgboost: https://github.com/dmlc/xgboost

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

0.1

Full raw C API available as AI::XGBoost::CAPI::RAW

0.2

Full C API "easy" to use, with PDL support as AI::XGBoost::CAPI

Easy means clients don't have to use FFI::Platypus or modules dealing with C structures

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)

SEE ALSO

AI::MXNet
FFI::Platypus
NativeCall

AUTHOR

Pablo Rodríguez González <pablo.rodriguez.gonzalez@gmail.com>

COPYRIGHT AND LICENSE

This software is Copyright (c) 2017 by Pablo Rodríguez González.

This is free software, licensed under:

The Apache License, Version 2.0, January 2004

CONTRIBUTOR

Ruben <me@ruben.tech>