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api_op_CreatePredictor.go
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api_op_CreatePredictor.go
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// Code generated by smithy-go-codegen DO NOT EDIT.
package forecast
import (
"context"
awsmiddleware "github.com/aws/aws-sdk-go-v2/aws/middleware"
"github.com/aws/aws-sdk-go-v2/aws/signer/v4"
"github.com/aws/aws-sdk-go-v2/service/forecast/types"
"github.com/aws/smithy-go/middleware"
smithyhttp "github.com/aws/smithy-go/transport/http"
)
// Creates an Amazon Forecast predictor. In the request, provide a dataset group
// and either specify an algorithm or let Amazon Forecast choose an algorithm for
// you using AutoML. If you specify an algorithm, you also can override
// algorithm-specific hyperparameters. Amazon Forecast uses the algorithm to train
// a predictor using the latest version of the datasets in the specified dataset
// group. You can then generate a forecast using the CreateForecast operation. To
// see the evaluation metrics, use the GetAccuracyMetrics operation. You can
// specify a featurization configuration to fill and aggregate the data fields in
// the TARGET_TIME_SERIES dataset to improve model training. For more information,
// see FeaturizationConfig. For RELATED_TIME_SERIES datasets, CreatePredictor
// verifies that the DataFrequency specified when the dataset was created matches
// the ForecastFrequency. TARGET_TIME_SERIES datasets don't have this restriction.
// Amazon Forecast also verifies the delimiter and timestamp format. For more
// information, see howitworks-datasets-groups. By default, predictors are trained
// and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can
// choose custom forecast types to train and evaluate your predictor by setting the
// ForecastTypes. AutoML If you want Amazon Forecast to evaluate each algorithm and
// choose the one that minimizes the objective function, set PerformAutoML to true.
// The objective function is defined as the mean of the weighted losses over the
// forecast types. By default, these are the p10, p50, and p90 quantile losses. For
// more information, see EvaluationResult. When AutoML is enabled, the following
// properties are disallowed:
//
// * AlgorithmArn
//
// * HPOConfig
//
// * PerformHPO
//
// *
// TrainingParameters
//
// To get a list of all of your predictors, use the
// ListPredictors operation. Before you can use the predictor to create a forecast,
// the Status of the predictor must be ACTIVE, signifying that training has
// completed. To get the status, use the DescribePredictor operation.
func (c *Client) CreatePredictor(ctx context.Context, params *CreatePredictorInput, optFns ...func(*Options)) (*CreatePredictorOutput, error) {
if params == nil {
params = &CreatePredictorInput{}
}
result, metadata, err := c.invokeOperation(ctx, "CreatePredictor", params, optFns, c.addOperationCreatePredictorMiddlewares)
if err != nil {
return nil, err
}
out := result.(*CreatePredictorOutput)
out.ResultMetadata = metadata
return out, nil
}
type CreatePredictorInput struct {
// The featurization configuration.
//
// This member is required.
FeaturizationConfig *types.FeaturizationConfig
// 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 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.
//
// This member is required.
ForecastHorizon *int32
// Describes the dataset group that contains the data to use to train the
// predictor.
//
// This member is required.
InputDataConfig *types.InputDataConfig
// A name for the predictor.
//
// This member is required.
PredictorName *string
// The Amazon Resource Name (ARN) of the algorithm to use for model training.
// Required if PerformAutoML is not set to true. Supported algorithms:
//
// *
// arn:aws:forecast:::algorithm/ARIMA
//
// * arn:aws:forecast:::algorithm/CNN-QR
//
// *
// arn:aws:forecast:::algorithm/Deep_AR_Plus
//
// * arn:aws:forecast:::algorithm/ETS
//
// *
// arn:aws:forecast:::algorithm/NPTS
//
// * arn:aws:forecast:::algorithm/Prophet
AlgorithmArn *string
// The LatencyOptimized AutoML override strategy is only available in private beta.
// Contact AWS Support or your account manager to learn more about access
// privileges. Used to overide the default AutoML strategy, which is to optimize
// predictor accuracy. To apply an AutoML strategy that minimizes training time,
// use LatencyOptimized. This parameter is only valid for predictors trained using
// AutoML.
AutoMLOverrideStrategy types.AutoMLOverrideStrategy
// 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.
EncryptionConfig *types.EncryptionConfig
// 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.
EvaluationParameters *types.EvaluationParameters
// 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 mean.
// The default value is ["0.10", "0.50", "0.9"].
ForecastTypes []string
// 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
// HPOConfig object, you must set PerformHPO to true.
HPOConfig *types.HyperParameterTuningJobConfig
// The accuracy metric used to optimize the predictor.
OptimizationMetric types.OptimizationMetric
// 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 false. In this case, you are
// required to specify an algorithm. Set PerformAutoML to 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, PerformHPO must be
// false.
PerformAutoML *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 false. In
// this case, Amazon Forecast uses default hyperparameter values from the chosen
// algorithm. To override the default values, set PerformHPO to 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 PerformAutoML must be false. The following
// algorithms support HPO:
//
// * DeepAR+
//
// * CNN-QR
PerformHPO *bool
// 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:
//
// * Maximum number of
// tags per resource - 50.
//
// * For each resource, each tag key must be unique, and
// each tag key can have only one value.
//
// * Maximum key length - 128 Unicode
// characters in UTF-8.
//
// * Maximum value length - 256 Unicode characters in
// UTF-8.
//
// * 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: + - = . _ : / @.
//
// * Tag keys and values are
// case sensitive.
//
// * Do not use aws:, 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 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 aws do not count against your tags per resource limit.
Tags []types.Tag
// 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.
TrainingParameters map[string]string
noSmithyDocumentSerde
}
type CreatePredictorOutput struct {
// The Amazon Resource Name (ARN) of the predictor.
PredictorArn *string
// Metadata pertaining to the operation's result.
ResultMetadata middleware.Metadata
noSmithyDocumentSerde
}
func (c *Client) addOperationCreatePredictorMiddlewares(stack *middleware.Stack, options Options) (err error) {
err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreatePredictor{}, middleware.After)
if err != nil {
return err
}
err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreatePredictor{}, middleware.After)
if err != nil {
return err
}
if err = addSetLoggerMiddleware(stack, options); err != nil {
return err
}
if err = awsmiddleware.AddClientRequestIDMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddComputeContentLengthMiddleware(stack); err != nil {
return err
}
if err = addResolveEndpointMiddleware(stack, options); err != nil {
return err
}
if err = v4.AddComputePayloadSHA256Middleware(stack); err != nil {
return err
}
if err = addRetryMiddlewares(stack, options); err != nil {
return err
}
if err = addHTTPSignerV4Middleware(stack, options); err != nil {
return err
}
if err = awsmiddleware.AddRawResponseToMetadata(stack); err != nil {
return err
}
if err = awsmiddleware.AddRecordResponseTiming(stack); err != nil {
return err
}
if err = addClientUserAgent(stack); err != nil {
return err
}
if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = addOpCreatePredictorValidationMiddleware(stack); err != nil {
return err
}
if err = stack.Initialize.Add(newServiceMetadataMiddleware_opCreatePredictor(options.Region), middleware.Before); err != nil {
return err
}
if err = addRequestIDRetrieverMiddleware(stack); err != nil {
return err
}
if err = addResponseErrorMiddleware(stack); err != nil {
return err
}
if err = addRequestResponseLogging(stack, options); err != nil {
return err
}
return nil
}
func newServiceMetadataMiddleware_opCreatePredictor(region string) *awsmiddleware.RegisterServiceMetadata {
return &awsmiddleware.RegisterServiceMetadata{
Region: region,
ServiceID: ServiceID,
SigningName: "forecast",
OperationName: "CreatePredictor",
}
}