AI::XGBoost - Perl wrapper for XGBoost library https://github.com/dmlc/xgboost
version 0.11
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);
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
train
DMatrix
This is a work in progress, feedback, comments, issues, suggestion and pull requests are welcome!!
XGBoost library is used via Alien::XGBoost. That means downloading, compiling and installing if it's not available in your system.
Performs gradient boosting using the data and parameters passed
Returns a trained AI::XGBoost::Booster used
Parameters for the booster object.
Full list available: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md
AI::XGBoost::DMatrix object used for training
Number of boosting iterations
The goal is to make a full wrapper for XGBoost.
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
Alien package for libxgboost.so/xgboost.dll
Object oriented API Moose based with DMatrix and Booster classes
Complete object oriented API
Use perl signatures (https://metacpan.org/pod/distribution/perl/pod/perlexperiment.pod#Subroutine-signatures)
Pablo Rodríguez González <pablo.rodriguez.gonzalez@gmail.com>
Copyright (c) 2017 by Pablo Rodríguez González.
Ruben <me@ruben.tech>
To install AI::XGBoost, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::XGBoost
CPAN shell
perl -MCPAN -e shell install AI::XGBoost
For more information on module installation, please visit the detailed CPAN module installation guide.