view release on metacpan or  search on metacpan

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

        $self->sum_metric($self->sum_metric + $f1_score);
        $self->num_inst($self->num_inst + 1);
    }, $labels, $preds);
}

package AI::MXNet::Perplexity;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'        => (default => 'Perplexity');
has 'ignore_label' => (is => 'ro', isa => 'Maybe[Int]');
has 'axis'         => (is => 'ro', isa => 'Int', default => -1);
around BUILDARGS => sub {
    my $orig  = shift;
    my $class = shift;
    return $class->$orig(ignore_label => $_[0]) if @_ == 1;
    return $class->$orig(@_);
};

=head1 NAME

    AI::MXNet::Perplexity
=cut

=head1 DESCRIPTION

    Calculate perplexity.

    Parameters
    ----------
    ignore_label : int or undef
        index of invalid label to ignore when
        counting. usually should be -1. Include
        all entries if undef.
    axis : int (default -1)
        The axis from prediction that was used to
        compute softmax. By default uses the last
        axis.
=cut

method update(ArrayRef[AI::MXNet::NDArray] $labels, ArrayRef[AI::MXNet::NDArray] $preds)
{
    AI::MXNet::Metric::check_label_shapes($labels, $preds);
    my ($loss, $num) = (0, 0);
    zip(sub {
        my ($label, $pred) = @_;
        my $label_shape = $label->shape;
        my $pred_shape  = $pred->shape;
        assert(
            (product(@{ $label_shape }) == product(@{ $pred_shape })/$pred_shape->[-1]),
            "shape mismatch: (@$label_shape) vs. (@$pred_shape)"
        );
        $label = $label->as_in_context($pred->context)->reshape([$label->size]);
        $pred = AI::MXNet::NDArray->pick($pred, $label->astype('int32'), { axis => $self->axis });
        if(defined $self->ignore_label)
        {
            my $ignore = ($label == $self->ignore_label);
            $num -= $ignore->sum->asscalar;
            $pred = $pred*(1-$ignore) + $ignore;
        }
        $loss -= $pred->maximum(1e-10)->log->sum->asscalar;
        $num  += $pred->size;
    }, $labels, $preds);
    $self->sum_metric($self->sum_metric + $loss);
    $self->num_inst($self->num_inst + $num);
}

method get()
{
    return ($self->name, exp($self->sum_metric / $self->num_inst));
}

####################
# REGRESSION METRICS
####################

# Calculate Mean Absolute Error loss
package AI::MXNet::MAE;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'   => (default => 'mae');

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) = @_;
        $label = $label->aspdl;
        $pred =  $pred->aspdl;
        if($label->ndims == 1)
        {
            $label = $label->reshape(1, $label->shape->at(0));
        }
        $self->sum_metric($self->sum_metric + ($label - $pred)->abs->avg);
        $self->num_inst($self->num_inst + 1);
    }, $labels, $preds);
}

# Calculate Mean Squared Error loss
package AI::MXNet::MSE;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'   => (default => 'mse');

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) = @_;
        $label = $label->aspdl;
        $pred =  $pred->aspdl;
        if($label->ndims == 1)
        {
            $label = $label->reshape(1, $label->shape->at(0));
        }
        $self->sum_metric($self->sum_metric + (($label - $pred)**2)->avg);
        $self->num_inst($self->num_inst + 1);
    }, $labels, $preds);
}

# Calculate Root Mean Squred Error loss
package AI::MXNet::RMSE;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'   => (default => 'rmse');

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) = @_;
        $label = $label->aspdl;
        $pred =  $pred->aspdl;
        if($label->ndims == 1)
        {
            $label = $label->reshape(1, $label->shape->at(0));
        }
        $self->sum_metric($self->sum_metric + sqrt((($label - $pred)**2)->avg));
        $self->num_inst($self->num_inst + 1);
    }, $labels, $preds);
}

# Calculate Cross Entropy loss
package AI::MXNet::CrossEntropy;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::EvalMetric';
has '+name'   => (default => 'cross-entropy');
has 'eps'     => (is => 'ro', isa => 'Num', default => 1e-8);
around BUILDARGS => sub {
    my $orig  = shift;
    my $class = shift;
    return $class->$orig(eps => $_[0]) if @_ == 1;
    return $class->$orig(@_);
};

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) = @_;
        $label = $label->aspdl->flat;
        $pred =  $pred->aspdl;
        my $label_shape = $label->shape->at(0);
        my $pred_shape  = $pred->shape->at(-1);
        confess(
            "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