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lib/AI/MXNet/Metric.pm  view on Meta::CPAN

package AI::MXNet::Metric;
use strict;
use warnings;
use AI::MXNet::Function::Parameters;
use Scalar::Util qw/blessed/;

=head1 NAME

    AI::MXNet::Metric - Online evaluation metric module.
=cut

# Check to see if the two arrays are the same size.
sub _calculate_shape
{
    my $input = shift;
    my ($shape);
    if(blessed($input))
    {
        if($input->isa('PDL'))
        {
            $shape = $input->shape->at(-1);
        }
        else
        {
            $shape = $input->shape->[0];
        }
    }
    else
    {
        $shape = @{ $input };
    }
    return $shape;
}
func check_label_shapes(
    ArrayRef|AI::MXNet::NDArray|PDL $labels,
    ArrayRef|AI::MXNet::NDArray|PDL $preds
)
{
    my ($label_shape, $pred_shape) = (_calculate_shape($labels), _calculate_shape($preds));
    Carp::confess(
        "Shape of labels $label_shape does not "
        ."match shape of predictions $pred_shape"
    ) unless $pred_shape == $label_shape;
}

=head1 DESCRIPTION

    Base class of all evaluation metrics.
=cut

package AI::MXNet::EvalMetric;
use Mouse;
use overload '""' => sub {
    return "EvalMetric: "
            .Data::Dumper->new(
                [shift->get_name_value()]
            )->Purity(1)->Deepcopy(1)->Terse(1)->Dump
},  fallback => 1;
has 'name'       => (is => 'rw', isa => 'Str');
has 'num'        => (is => 'rw', isa => 'Int');
has 'num_inst'   => (is => 'rw', isa => 'Maybe[Int|ArrayRef[Int]]');
has 'sum_metric' => (is => 'rw', isa => 'Maybe[Num|ArrayRef[Num]]');

sub BUILD
{
    shift->reset;
}

method update($label, $pred)
{
    confess('NotImplemented');
}

method reset()
{
    if(not defined $self->num)
    {
        $self->num_inst(0);
        $self->sum_metric(0);
    }
    else
    {
        $self->num_inst([(0) x $self->num]);
        $self->sum_metric([(0) x $self->num]);
    }
}

method get()
{
    if(not defined $self->num)
    {
        if($self->num_inst == 0)
        {
            return ($self->name, 'nan');
        }
        else
        {
            return ($self->name, $self->sum_metric / $self->num_inst);
        }
    }
    else
    {
        my $names = [map { sprintf('%s_%d', $self->name, $_) } 0..$self->num-1];
        my $values = [];
        for (my $i = 0; $i < @{ $self->sum_metric }; $i++)
        {
            my ($x, $y) = ($self->sum_metric->[$i], $self->num_inst->[$i]);
            if($y != 0)

lib/AI/MXNet/Metric.pm  view on Meta::CPAN

            "Size of label  $label_shape and 
            .first dimension of pred $pred_shape do not match"
        ) unless $label_shape == $pred_shape;
        my $prob = $pred->index($label);
        $self->sum_metric($self->sum_metric + (-($prob + $self->eps)->log)->sum);
        $self->num_inst($self->num_inst + $label_shape);
    }, $labels, $preds);
}

package AI::MXNet::PearsonCorrelation;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'   => (default => 'pearson-correlation');

=head1 NAME

    AI::MXNet::PearsonCorrelation
=cut

=head1 DESCRIPTION

    Computes Pearson correlation.

    Parameters
    ----------
    name : str
        Name of this metric instance for display.

    Examples
    --------
    >>> $predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
    >>> $labels   = [mx->nd->array([[1, 0], [0, 1], [0, 1]])]
    >>> $pr = mx->metric->PearsonCorrelation()
    >>> $pr->update($labels, $predicts)
    >>> print pr->get()
    ('pearson-correlation', '0.421637061887229')
=cut

method update(ArrayRef[AI::MXNet::NDArray] $labels, ArrayRef[AI::MXNet::NDArray] $preds)
{
    AI::MXNet::Metric::check_label_shapes($labels, $preds);
    zip(sub {
        my ($label, $pred) = @_;
        AI::MXNet::Metric::check_label_shapes($label, $pred);
        $label = $label->aspdl->flat;
        $pred  = $pred->aspdl->flat;
        my ($label_mean, $label_stdv) = ($label->stats)[0, 6];
        my ($pred_mean, $pred_stdv) = ($pred->stats)[0, 6];
        $self->sum_metric(
            $self->sum_metric
                +
            ((($label-$label_mean)*($pred-$pred_mean))->sum/$label->nelem)/(($label_stdv*$pred_stdv)->at(0))
        );
        $self->num_inst($self->num_inst + 1);
    }, $labels, $preds);
}

=head1 DESCRIPTION

    Custom evaluation metric that takes a sub ref.

    Parameters
    ----------
    eval_function : subref
        Customized evaluation function.
    name : str, optional
        The name of the metric
    allow_extra_outputs : bool
        If true, the prediction outputs can have extra outputs.
        This is useful in RNN, where the states are also produced
        in outputs for forwarding.
=cut


package AI::MXNet::CustomMetric;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has 'eval_function'       => (is => 'ro', isa => 'CodeRef');
has 'allow_extra_outputs' => (is => 'ro', isa => 'Int', default => 0);

method update(ArrayRef[AI::MXNet::NDArray] $labels, ArrayRef[AI::MXNet::NDArray] $preds)
{
    AI::MXNet::Metric::check_label_shapes($labels, $preds)
        unless $self->allow_extra_outputs;
    zip(sub {
        my ($label, $pred) = @_;
        $label = $label->aspdl;
        $pred =  $pred->aspdl;
        my $value = $self->eval_function->($label, $pred);
        my $sum_metric = ref $value ? $value->[0] : $value;
        my $num_inst   = ref $value ? $value->[1] : 1;
        $self->sum_metric($self->sum_metric + $sum_metric);
        $self->num_inst($self->num_inst + $num_inst);
    }, $labels, $preds);
}

package AI::MXNet::Metric;

=head2 create

    Create an evaluation metric.

    Parameters
    ----------
    metric : str or sub ref
        The name of the metric, or a function
        providing statistics given pred, label NDArray.
=cut

my %metrics = qw/
    acc            AI::MXNet::Accuracy
    accuracy       AI::MXNet::Accuracy
    ce             AI::MXNet::CrossEntropy
    f1             AI::MXNet::F1
    mae            AI::MXNet::MAE
    mse            AI::MXNet::MSE
    rmse           AI::MXNet::RMSE
    top_k_accuracy AI::MXNet::TopKAccuracy
    Perplexity     AI::MXNet::Perplexity
    perplexity     AI::MXNet::Perplexity
    pearsonr       AI::MXNet::PearsonCorrelation
/;

method create(Metric|ArrayRef[Metric] $metric, %kwargs)
{
    Carp::confess("metric must be defined") unless defined $metric;
    if(my $ref = ref $metric)
    {
        if($ref eq 'ARRAY')
        {
            my $composite_metric = AI::MXNet::CompositeEvalMetric->new();
            for my $child_metric (@{ $metric })
            {
                $composite_metric->add(__PACKAGE__->create($child_metric, %kwargs))
            }
            return $composite_metric;
        }
        else
        {
            return AI::MXNet::CustomMetric->new(eval_function => $metric, %kwargs);
        }
    }
    else
    {
        if(not exists $metrics{ lc($metric) })
        {
            my @metrics = keys %metrics;
            Carp::confess("Metric must be either subref or one of [@metrics]");
        }
        return $metrics{ lc($metric) }->new(%kwargs);
    }
}

{
    no strict 'refs';
    no warnings 'redefine';
    for my $metric (values %metrics)
    {
        my ($name) = $metric =~ /(\w+)$/;
        *{__PACKAGE__."::$name"} = sub { shift; $metric->new(@_); };
    }
}

1;