文件列表:

.perltidyrc (38, 2017-08-21)
Changes (157, 2017-08-21)
dist.ini (887, 2017-08-21)
examples (0, 2017-08-21)
examples\agaricus.txt.test (183611, 2017-08-21)
examples\agaricus.txt.train (742257, 2017-08-21)
examples\basic.pl (1039, 2017-08-21)
examples\capi.pl (564, 2017-08-21)
examples\capi_dump_model.pl (496, 2017-08-21)
examples\capi_raw.pl (1070, 2017-08-21)
examples\featmap.txt (3203, 2017-08-21)
examples\iris.pl (1729, 2017-08-21)
lib (0, 2017-08-21)
lib\AI (0, 2017-08-21)
lib\AI\XGBoost.pm (2331, 2017-08-21)
lib\AI\XGBoost (0, 2017-08-21)
lib\AI\XGBoost\Booster.pm (4433, 2017-08-21)
lib\AI\XGBoost\CAPI.pm (12382, 2017-08-21)
lib\AI\XGBoost\CAPI (0, 2017-08-21)
lib\AI\XGBoost\CAPI\RAW.pm (10096, 2017-08-21)
lib\AI\XGBoost\DMatrix.pm (6996, 2017-08-21)
misc (0, 2017-08-21)
misc\using_capi.c (859, 2017-08-21)
t (0, 2017-08-21)
t\00-load.t (126, 2017-08-21)
t\10-cast_arguments.t (239, 2017-08-21)
t\20-dmatrix.t (893, 2017-08-21)
weaver.ini (27, 2017-08-21)

# 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 Rodriguez Gonzalez # COPYRIGHT AND LICENSE Copyright (c) 2017 by Pablo Rodriguez Gonzalez. # CONTRIBUTOR Ruben

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