NAME

AI::MXNet::Metric - Evaluation Metric API.
=head1 DESCRIPTION

This module hosts all the evaluation metrics available to evaluate the performance of a learned model.
L<Python Docs|http://mxnet.incubator.apache.org/api/python/metric/metric.html>

get_config

Save configurations of metric. Can be recreated
    from configs with mx->metric->create(%{ $config })

NAME

AI::MXNet::Accuracy - Computes accuracy classification score.

DESCRIPTION

The accuracy score is defined as

accuracy(y, y^) = (1/n) * sum(i=0..n-1) { y^(i)==y(i) }

Parameters:
axis (Int, default=1) – The axis that represents classes.
name (Str, default='accuracy') – Name of this metric instance for display.

pdl> use AI::MXNet qw(mx)
pdl> $predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
pdl> $labels   = [mx->nd->array([[0, 1, 1]])]
pdl> $acc = mx->metric->Accuracy()
pdl> $acc->update($labels, $predicts)
pdl> use Data::Dumper
pdl> print Dumper([$acc->get])
$VAR1 = [
      'accuracy',
      '0.666666666666667'
];

NAME

AI::MXNet::TopKAccuracy - Computes top k predictions accuracy.

DESCRIPTION

TopKAccuracy differs from Accuracy in that it considers the prediction
to be True as long as the ground truth label is in the top K predicated labels.

If top_k = 1, then TopKAccuracy is identical to Accuracy.

Parameters:	
top_k(Int, default 1) – Whether targets are in top k predictions.
name (Str, default 'top_k_accuracy') – Name of this metric instance for display.

use AI::MXNet qw(mx);
$top_k = 3;
$predicts = [mx->nd->array(
  [[0.80342804, 0.5275223 , 0.11911147, 0.63968144, 0.09092526,
    0.33222568, 0.42738095, 0.55438581, 0.62812652, 0.69739294],
   [0.78994969, 0.13189035, 0.34277045, 0.20155961, 0.70732423,
    0.03339926, 0.90925004, 0.40516066, 0.76043547, 0.47375838],
   [0.28671892, 0.75129249, 0.09708994, 0.41235779, 0.28163896,
    0.39027778, 0.87110921, 0.08124512, 0.55793117, 0.54753428],
   [0.33220307, 0.97326881, 0.2862761 , 0.5082575 , 0.14795074,
    0.19643398, 0.84082001, 0.0037532 , 0.78262101, 0.83347772],
   [0.93790734, 0.97260166, 0.83282304, 0.06581761, 0.40379256,
    0.37479349, 0.50750135, 0.97787696, 0.81899021, 0.18754124],
   [0.69804812, 0.68261077, 0.99909815, 0.48263116, 0.73059268,
    0.79518236, 0.26139168, 0.16107376, 0.69850315, 0.89950917],
   [0.91515562, 0.31244902, 0.95412616, 0.7242641 , 0.02091039,
    0.72554552, 0.58165923, 0.9545687 , 0.74233195, 0.19750339],
   [0.94900651, 0.85836332, 0.44904621, 0.82365038, 0.99726878,
    0.56413064, 0.5890016 , 0.42402702, 0.89548786, 0.44437266],
   [0.57723744, 0.66019353, 0.30244304, 0.02295771, 0.83766937,
    0.31953292, 0.37552193, 0.18172362, 0.83135182, 0.18487429],
   [0.96968683, 0.69644561, 0.60566253, 0.49600661, 0.70888438,
    0.26044186, 0.65267488, 0.62297362, 0.83609334, 0.3572364 ]]
)];
$labels = [mx->nd->array([2, 6, 9, 2, 3, 4, 7, 8, 9, 6])];
$acc = mx->metric->TopKAccuracy(top_k=>$top_k);
$acc->update($labels, $predicts);
use Data::Dumper;
print Dumper([$acc->get]);
$VAR1 = [
      'top_k_accuracy_3',
      '0.3'
];

NAME

AI::MXNet::F1 - Calculate the F1 score of a binary classification problem.

DESCRIPTION

The F1 score is equivalent to harmonic mean of the precision and recall,
where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is:

F1 = 2 * (precision * recall) / (precision + recall)
The formula for precision and recall is:

precision = true_positives / (true_positives + false_positives)
recall    = true_positives / (true_positives + false_negatives)
Note:

This F1 score only supports binary classification.

Parameters:
name (Str, default 'f1') – Name of this metric instance for display.
average (Str, default 'macro') –
Strategy to be used for aggregating across mini-batches.
“macro”: average the F1 scores for each batch. “micro”: compute a single F1 score across all batches.


$predicts = [mx.nd.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])];
$labels   = [mx.nd.array([0., 1., 1.])];
$f1 = mx->metric->F1();
$f1->update($labels, $predicts);
print $f1->get;
f1 0.8

NAME

AI::MXNet::MCC - Computes the Matthews Correlation Coefficient of a binary classification problem.

DESCRIPTION

While slower to compute than F1 the MCC can give insight that F1 or Accuracy cannot.
For instance, if the network always predicts the same result
then the MCC will immeadiately show this. The MCC is also symetric with respect
to positive and negative categorization, however, there needs to be both
positive and negative examples in the labels or it will always return 0.
MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively correlated.

    MCC = (TP * TN - FP * FN)/sqrt( (TP + FP)*( TP + FN )*( TN + FP )*( TN + FN ) )

where 0 terms in the denominator are replaced by 1.

This version of MCC only supports binary classification.

Parameters
----------
name : str, 'mcc'
    Name of this metric instance for display.
average : str, default 'macro'
    Strategy to be used for aggregating across mini-batches.
        "macro": average the MCC for each batch.
        "micro": compute a single MCC across all batches.

Examples
--------
In this example the network almost always predicts positive
>>> $false_positives = 1000
>>> $false_negatives = 1
>>> $true_positives = 10000
>>> $true_negatives = 1
>>> $predicts = [mx->nd->array(
    [
        ([.3, .7])x$false_positives,
        ([.7, .3])x$true_negatives,
        ([.7, .3])x$false_negatives,
        ([.3, .7])xtrue_positives
    ]
)];
>>> $labels  = [mx->nd->array(
    [
        (0)x($false_positives + $true_negatives),
        (1)x($false_negatives + $true_positives)
    ]
)];
>>> $f1 = mx->metric->F1();
>>> $f1->update($labels, $predicts);
>>> $mcc = mx->metric->MCC()
>>> $mcc->update($labels, $predicts)
>>> print $f1->get();
f1 0.95233560306652054
>>> print $mcc->get();
mcc 0.01917751877733392

NAME

AI::MXNet::Perplexity - Calculate perplexity.

DESCRIPTION

Perplexity is a measurement of how well a probability distribution or model predicts a sample.
A low perplexity indicates the model is good at predicting the sample.

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.

$predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])];
$labels   = [mx->nd->array([0, 1, 1])];
$perp = mx->metric->Perplexity(ignore_label=>undef);
$perp->update($labels, $predicts);
print $perp->get()
Perplexity 1.77109762851559

NAME

AI::MXNet::MAE - Calculate Mean Absolute Error loss
=head1 DESCRIPTION

>>> $predicts = [mx->nd->array([3, -0.5, 2, 7])->reshape([4,1])]
>>> $labels = [mx->nd->array([2.5, 0.0, 2, 8])->reshape([4,1])]
>>> $mean_absolute_error = mx->metric->MAE()
>>> $mean_absolute_error->update($labels, $predicts)
>>> print $mean_absolute_error->get()
('mae', 0.5)

NAME

AI::MXNet::MSE - Calculate Mean Squared Error loss
=head1 DESCRIPTION

>>> $predicts = [mx->nd->array([3, -0.5, 2, 7])->reshape([4,1])]
>>> $labels = [mx->nd->array([2.5, 0.0, 2, 8])->reshape([4,1])]
>>> $mean_squared_error = mx->metric->MSE()
>>> $mean_squared_error->update($labels, $predicts)
>>> print $mean_squared_error->get()
('mse', 0.375)

NAME

AI::MXNet::RMSE - Calculate Root Mean Squred Error loss
=head1 DESCRIPTION

>>> $predicts = [mx->nd->array([3, -0.5, 2, 7])->reshape([4,1])]
>>> $labels = [mx->nd->array([2.5, 0.0, 2, 8])->reshape([4,1])]
>>> $root_mean_squared_error = mx->metric->RMSE()
>>> $root_mean_squared_error->update($labels, $predicts)
>>> print $root_mean_squared_error->get()
'rmse', 0.612372457981

NAME

AI::MXNet::CrossEntropy - Calculate Cross Entropy loss
=head1 DESCRIPTION

>>> $predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> $labels   = [mx->nd->array([0, 1, 1])]
>>> $ce = mx->metric->CrossEntropy()
>>> $ce->update($labels, $predicts)
>>> print $ce->get()
('cross-entropy', 0.57159948348999023)

NAME

AI::MXNet::NegativeLogLikelihood - Computes the negative log-likelihood loss.
=head1 DESCRIPTION

>>> $predicts = [mx->nd->array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> $labels   = [mx->nd->array([0, 1, 1])]
>>> $nll_loss = mx->metric->NegativeLogLikelihood
>>> $nll_loss->update($labels, $predicts)
>>> print $nll_loss->get()
('cross-entropy', 0.57159948348999023)

NAME

AI::MXNet::PearsonCorrelation - Computes Pearson correlation.

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')

NAME

AI::MXNet::Loss - Dummy metric for directly printing loss.

DESCRIPTION

Dummy metric for directly printing loss.

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

NAME

AI::MXNet::Confidence - Accuracy by confidence buckets.

DESCRIPTION

Accuracy by confidence buckets.

Parameters
----------
name : str
    Name of this metric instance for display.
num_classes: Int
    number of classes
confidence_thresholds: ArrayRef[Num]
    confidence buckets
For example
my $composite_metric  = AI::MXNet::CompositeEvalMetric->new;
$composite_metric->add(mx->metric->create('acc'));
$composite_metric->add(
    AI::MXNet::Confidence->new(
        num_classes => 2,
        confidence_thresholds => [ 0.5, 0.7, 0.8, 0.9 ],
    )
);

NAME

AI::MXNet::CustomMetric - Custom evaluation metric that takes a sub ref.

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.

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.

1 POD Error

The following errors were encountered while parsing the POD:

Around line 246:: Non-ASCII character seen before =encoding in '–'. Assuming UTF-8

To install AI::MXNet, copy and paste the appropriate command in to your terminal.

cpanm

cpanm AI::MXNet

CPAN shell

perl -MCPAN -e shell
install AI::MXNet

For more information on module installation, please visit the detailed CPAN module installation guide.

	Global
`s`	Focus search bar
`?`	Bring up this help dialog

	GitHub
`g` `p`	Go to pull requests
`g` `i`	go to github issues (only if github is preferred repository)

	POD
`g` `a`	Go to author
`g` `c`	Go to changes
`g` `i`	Go to issues
`g` `d`	Go to dist
`g` `r`	Go to repository/SCM
`g` `s`	Go to source
`g` `b`	Go to file browse

	Search terms
module: (e.g. module:Plugin)
distribution: (e.g. distribution:Dancer auth)
author: (e.g. author:SONGMU Redis)
version: (e.g. version:1.00)

NAME

get_config

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

NAME

NAME

NAME

NAME

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

DESCRIPTION

NAME

DESCRIPTION

create

Module Install Instructions