

package Paws::Forecast::CreatePredictor;
  use Moose;
  has AlgorithmArn => (is => 'ro', isa => 'Str');
  has AutoMLOverrideStrategy => (is => 'ro', isa => 'Str');
  has EncryptionConfig => (is => 'ro', isa => 'Paws::Forecast::EncryptionConfig');
  has EvaluationParameters => (is => 'ro', isa => 'Paws::Forecast::EvaluationParameters');
  has FeaturizationConfig => (is => 'ro', isa => 'Paws::Forecast::FeaturizationConfig', required => 1);
  has ForecastHorizon => (is => 'ro', isa => 'Int', required => 1);
  has ForecastTypes => (is => 'ro', isa => 'ArrayRef[Str|Undef]');
  has HPOConfig => (is => 'ro', isa => 'Paws::Forecast::HyperParameterTuningJobConfig');
  has InputDataConfig => (is => 'ro', isa => 'Paws::Forecast::InputDataConfig', required => 1);
  has PerformAutoML => (is => 'ro', isa => 'Bool');
  has PerformHPO => (is => 'ro', isa => 'Bool');
  has PredictorName => (is => 'ro', isa => 'Str', required => 1);
  has Tags => (is => 'ro', isa => 'ArrayRef[Paws::Forecast::Tag]');
  has TrainingParameters => (is => 'ro', isa => 'Paws::Forecast::TrainingParameters');

  use MooseX::ClassAttribute;

  class_has _api_call => (isa => 'Str', is => 'ro', default => 'CreatePredictor');
  class_has _returns => (isa => 'Str', is => 'ro', default => 'Paws::Forecast::CreatePredictorResponse');
  class_has _result_key => (isa => 'Str', is => 'ro');
1;

### main pod documentation begin ###

=head1 NAME

Paws::Forecast::CreatePredictor - Arguments for method CreatePredictor on L<Paws::Forecast>

=head1 DESCRIPTION

This class represents the parameters used for calling the method CreatePredictor on the
L<Amazon Forecast Service|Paws::Forecast> service. Use the attributes of this class
as arguments to method CreatePredictor.

You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreatePredictor.

=head1 SYNOPSIS

    my $forecast = Paws->service('Forecast');
    my $CreatePredictorResponse = $forecast->CreatePredictor(
      FeaturizationConfig => {
        ForecastFrequency => 'MyFrequency',
        Featurizations    => [
          {
            AttributeName         => 'MyName',    # min: 1, max: 63
            FeaturizationPipeline => [
              {
                FeaturizationMethodName       => 'filling',    # values: filling
                FeaturizationMethodParameters => {
                  'MyParameterKey' =>
                    'MyParameterValue',    # key: max: 256, value: max: 256
                },    # min: 1, max: 20; OPTIONAL
              },
              ...
            ],    # min: 1, max: 1; OPTIONAL
          },
          ...
        ],    # min: 1, max: 50; OPTIONAL
        ForecastDimensions => [
          'MyName', ...    # min: 1, max: 63
        ],    # min: 1, max: 5; OPTIONAL
      },
      ForecastHorizon => 1,
      InputDataConfig => {
        DatasetGroupArn       => 'MyArn',    # max: 256
        SupplementaryFeatures => [
          {
            Name  => 'MyName',     # min: 1, max: 63
            Value => 'MyValue',    # max: 256

          },
          ...
        ],    # min: 1, max: 2; OPTIONAL
      },
      PredictorName          => 'MyName',
      AlgorithmArn           => 'MyArn',               # OPTIONAL
      AutoMLOverrideStrategy => 'LatencyOptimized',    # OPTIONAL
      EncryptionConfig       => {
        KMSKeyArn => 'MyKMSKeyArn',                    # max: 256
        RoleArn   => 'MyArn',                          # max: 256

      },    # OPTIONAL
      EvaluationParameters => {
        BackTestWindowOffset    => 1,
        NumberOfBacktestWindows => 1,
      },    # OPTIONAL
      ForecastTypes => [ 'MyForecastType', ... ],    # OPTIONAL
      HPOConfig     => {
        ParameterRanges => {
          CategoricalParameterRanges => [
            {
              Name   => 'MyName',    # min: 1, max: 63
              Values => [
                'MyValue', ...       # max: 256
              ],    # min: 1, max: 20

            },
            ...
          ],    # min: 1, max: 20; OPTIONAL
          ContinuousParameterRanges => [
            {
              MaxValue    => 1,
              MinValue    => 1,
              Name        => 'MyName',    # min: 1, max: 63
              ScalingType => 'Auto'
              , # values: Auto, Linear, Logarithmic, ReverseLogarithmic; OPTIONAL
            },
            ...
          ],    # min: 1, max: 20; OPTIONAL
          IntegerParameterRanges => [
            {
              MaxValue    => 1,
              MinValue    => 1,
              Name        => 'MyName',    # min: 1, max: 63
              ScalingType => 'Auto'
              , # values: Auto, Linear, Logarithmic, ReverseLogarithmic; OPTIONAL
            },
            ...
          ],    # min: 1, max: 20; OPTIONAL
        },    # OPTIONAL
      },    # OPTIONAL
      PerformAutoML => 1,    # OPTIONAL
      PerformHPO    => 1,    # OPTIONAL
      Tags          => [
        {
          Key   => 'MyTagKey',      # min: 1, max: 128
          Value => 'MyTagValue',    # max: 256

        },
        ...
      ],    # OPTIONAL
      TrainingParameters => {
        'MyParameterKey' => 'MyParameterValue', # key: max: 256, value: max: 256
      },    # OPTIONAL
    );

    # Results:
    my $PredictorArn = $CreatePredictorResponse->PredictorArn;

    # Returns a L<Paws::Forecast::CreatePredictorResponse> object.

Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object.
For the AWS API documentation, see L<https://docs.aws.amazon.com/goto/WebAPI/forecast/CreatePredictor>

=head1 ATTRIBUTES


=head2 AlgorithmArn => Str

The Amazon Resource Name (ARN) of the algorithm to use for model
training. Required if C<PerformAutoML> is not set to C<true>.

B<Supported algorithms:>

=over

=item *

C<arn:aws:forecast:::algorithm/ARIMA>

=item *

C<arn:aws:forecast:::algorithm/CNN-QR>

=item *

C<arn:aws:forecast:::algorithm/Deep_AR_Plus>

=item *

C<arn:aws:forecast:::algorithm/ETS>

=item *

C<arn:aws:forecast:::algorithm/NPTS>

=item *

C<arn:aws:forecast:::algorithm/Prophet>

=back


=head2 AutoMLOverrideStrategy => Str

Used to overide the default AutoML strategy, which is to optimize
predictor accuracy. To apply an AutoML strategy that minimizes training
time, use C<LatencyOptimized>.

This parameter is only valid for predictors trained using AutoML.

Valid values are: C<"LatencyOptimized">

=head2 EncryptionConfig => L<Paws::Forecast::EncryptionConfig>

An AWS Key Management Service (KMS) key and the AWS Identity and Access
Management (IAM) role that Amazon Forecast can assume to access the
key.


=head2 EvaluationParameters => L<Paws::Forecast::EvaluationParameters>

Used to override the default evaluation parameters of the specified
algorithm. Amazon Forecast evaluates a predictor by splitting a dataset
into training data and testing data. The evaluation parameters define
how to perform the split and the number of iterations.


=head2 B<REQUIRED> FeaturizationConfig => L<Paws::Forecast::FeaturizationConfig>

The featurization configuration.


=head2 B<REQUIRED> ForecastHorizon => Int

Specifies the number of time-steps that the model is trained to
predict. The forecast horizon is also called the prediction length.

For example, if you configure a dataset for daily data collection
(using the C<DataFrequency> parameter of the CreateDataset operation)
and set the forecast horizon to 10, the model returns predictions for
10 days.

The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of
the TARGET_TIME_SERIES dataset length.


=head2 ForecastTypes => ArrayRef[Str|Undef]

Specifies the forecast types used to train a predictor. You can specify
up to five forecast types. Forecast types can be quantiles from 0.01 to
0.99, by increments of 0.01 or higher. You can also specify the mean
forecast with C<mean>.

The default value is C<["0.10", "0.50", "0.9"]>.


=head2 HPOConfig => L<Paws::Forecast::HyperParameterTuningJobConfig>

Provides hyperparameter override values for the algorithm. If you don't
provide this parameter, Amazon Forecast uses default values. The
individual algorithms specify which hyperparameters support
hyperparameter optimization (HPO). For more information, see
aws-forecast-choosing-recipes.

If you included the C<HPOConfig> object, you must set C<PerformHPO> to
true.


=head2 B<REQUIRED> InputDataConfig => L<Paws::Forecast::InputDataConfig>

Describes the dataset group that contains the data to use to train the
predictor.


=head2 PerformAutoML => Bool

Whether to perform AutoML. When Amazon Forecast performs AutoML, it
evaluates the algorithms it provides and chooses the best algorithm and
configuration for your training dataset.

The default value is C<false>. In this case, you are required to
specify an algorithm.

Set C<PerformAutoML> to C<true> to have Amazon Forecast perform AutoML.
This is a good option if you aren't sure which algorithm is suitable
for your training data. In this case, C<PerformHPO> must be false.


=head2 PerformHPO => Bool

Whether to perform hyperparameter optimization (HPO). HPO finds optimal
hyperparameter values for your training data. The process of performing
HPO is known as running a hyperparameter tuning job.

The default value is C<false>. In this case, Amazon Forecast uses
default hyperparameter values from the chosen algorithm.

To override the default values, set C<PerformHPO> to C<true> and,
optionally, supply the HyperParameterTuningJobConfig object. The tuning
job specifies a metric to optimize, which hyperparameters participate
in tuning, and the valid range for each tunable hyperparameter. In this
case, you are required to specify an algorithm and C<PerformAutoML>
must be false.

The following algorithms support HPO:

=over

=item *

DeepAR+

=item *

CNN-QR

=back


=head2 B<REQUIRED> PredictorName => Str

A name for the predictor.


=head2 Tags => ArrayRef[L<Paws::Forecast::Tag>]

The optional metadata that you apply to the predictor to help you
categorize and organize them. Each tag consists of a key and an
optional value, both of which you define.

The following basic restrictions apply to tags:

=over

=item *

Maximum number of tags per resource - 50.

=item *

For each resource, each tag key must be unique, and each tag key can
have only one value.

=item *

Maximum key length - 128 Unicode characters in UTF-8.

=item *

Maximum value length - 256 Unicode characters in UTF-8.

=item *

If your tagging schema is used across multiple services and resources,
remember that other services may have restrictions on allowed
characters. Generally allowed characters are: letters, numbers, and
spaces representable in UTF-8, and the following characters: + - = . _
: / @.

=item *

Tag keys and values are case sensitive.

=item *

Do not use C<aws:>, C<AWS:>, or any upper or lowercase combination of
such as a prefix for keys as it is reserved for AWS use. You cannot
edit or delete tag keys with this prefix. Values can have this prefix.
If a tag value has C<aws> as its prefix but the key does not, then
Forecast considers it to be a user tag and will count against the limit
of 50 tags. Tags with only the key prefix of C<aws> do not count
against your tags per resource limit.

=back


=head2 TrainingParameters => L<Paws::Forecast::TrainingParameters>

The hyperparameters to override for model training. The hyperparameters
that you can override are listed in the individual algorithms. For the
list of supported algorithms, see aws-forecast-choosing-recipes.


=head1 SEE ALSO

This class forms part of L<Paws>, documenting arguments for method CreatePredictor in L<Paws::Forecast>

=head1 BUGS and CONTRIBUTIONS

The source code is located here: L<https://github.com/pplu/aws-sdk-perl>

Please report bugs to: L<https://github.com/pplu/aws-sdk-perl/issues>

=cut