/
api_op_CreateSolution.go
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/
api_op_CreateSolution.go
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// Code generated by smithy-go-codegen DO NOT EDIT.
package personalize
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/personalize/types"
"github.com/awslabs/smithy-go/middleware"
smithyhttp "github.com/awslabs/smithy-go/transport/http"
)
// Creates the configuration for training a model. A trained model is known as a
// solution. After the configuration is created, you train the model (create a
// solution) by calling the CreateSolutionVersion operation. Every time you call
// CreateSolutionVersion, a new version of the solution is created. After creating
// a solution version, you check its accuracy by calling GetSolutionMetrics. When
// you are satisfied with the version, you deploy it using CreateCampaign. The
// campaign provides recommendations to a client through the GetRecommendations
// (https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html)
// API. To train a model, Amazon Personalize requires training data and a recipe.
// The training data comes from the dataset group that you provide in the request.
// A recipe specifies the training algorithm and a feature transformation. You can
// specify one of the predefined recipes provided by Amazon Personalize.
// Alternatively, you can specify performAutoML and Amazon Personalize will analyze
// your data and select the optimum USER_PERSONALIZATION recipe for you. Status A
// solution can be in one of the following states:
//
// * CREATE PENDING > CREATE
// IN_PROGRESS > ACTIVE -or- CREATE FAILED
//
// * DELETE PENDING > DELETE
// IN_PROGRESS
//
// To get the status of the solution, call DescribeSolution. Wait
// until the status shows as ACTIVE before calling CreateSolutionVersion. Related
// APIs
//
// * ListSolutions
//
// * CreateSolutionVersion
//
// * DescribeSolution
//
//
// * DeleteSolution
//
// * ListSolutionVersions
//
// * DescribeSolutionVersion
func (c *Client) CreateSolution(ctx context.Context, params *CreateSolutionInput, optFns ...func(*Options)) (*CreateSolutionOutput, error) {
if params == nil {
params = &CreateSolutionInput{}
}
result, metadata, err := c.invokeOperation(ctx, "CreateSolution", params, optFns, addOperationCreateSolutionMiddlewares)
if err != nil {
return nil, err
}
out := result.(*CreateSolutionOutput)
out.ResultMetadata = metadata
return out, nil
}
type CreateSolutionInput struct {
// The Amazon Resource Name (ARN) of the dataset group that provides the training
// data.
//
// This member is required.
DatasetGroupArn *string
// The name for the solution.
//
// This member is required.
Name *string
// When your have multiple event types (using an EVENT_TYPE schema field), this
// parameter specifies which event type (for example, 'click' or 'like') is used
// for training the model.
EventType *string
// Whether to perform automated machine learning (AutoML). The default is false.
// For this case, you must specify recipeArn. When set to true, Amazon Personalize
// analyzes your training data and selects the optimal USER_PERSONALIZATION recipe
// and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize
// determines the optimal recipe by running tests with different values for the
// hyperparameters. AutoML lengthens the training process as compared to selecting
// a specific recipe.
PerformAutoML *bool
// Whether to perform hyperparameter optimization (HPO) on the specified or
// selected recipe. The default is false. When performing AutoML, this parameter is
// always true and you should not set it to false.
PerformHPO *bool
// The ARN of the recipe to use for model training. Only specified when
// performAutoML is false.
RecipeArn *string
// The configuration to use with the solution. When performAutoML is set to true,
// Amazon Personalize only evaluates the autoMLConfig section of the solution
// configuration.
SolutionConfig *types.SolutionConfig
}
type CreateSolutionOutput struct {
// The ARN of the solution.
SolutionArn *string
// Metadata pertaining to the operation's result.
ResultMetadata middleware.Metadata
}
func addOperationCreateSolutionMiddlewares(stack *middleware.Stack, options Options) (err error) {
err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreateSolution{}, middleware.After)
if err != nil {
return err
}
err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreateSolution{}, middleware.After)
if err != nil {
return err
}
awsmiddleware.AddRequestInvocationIDMiddleware(stack)
smithyhttp.AddContentLengthMiddleware(stack)
addResolveEndpointMiddleware(stack, options)
v4.AddComputePayloadSHA256Middleware(stack)
addRetryMiddlewares(stack, options)
addHTTPSignerV4Middleware(stack, options)
awsmiddleware.AddAttemptClockSkewMiddleware(stack)
addClientUserAgent(stack)
smithyhttp.AddErrorCloseResponseBodyMiddleware(stack)
smithyhttp.AddCloseResponseBodyMiddleware(stack)
addOpCreateSolutionValidationMiddleware(stack)
stack.Initialize.Add(newServiceMetadataMiddleware_opCreateSolution(options.Region), middleware.Before)
addRequestIDRetrieverMiddleware(stack)
addResponseErrorMiddleware(stack)
return nil
}
func newServiceMetadataMiddleware_opCreateSolution(region string) awsmiddleware.RegisterServiceMetadata {
return awsmiddleware.RegisterServiceMetadata{
Region: region,
ServiceID: ServiceID,
SigningName: "personalize",
OperationName: "CreateSolution",
}
}