/
api_op_CreateMLModel.go
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/
api_op_CreateMLModel.go
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// Code generated by smithy-go-codegen DO NOT EDIT.
package machinelearning
import (
"context"
"fmt"
awsmiddleware "github.com/aws/aws-sdk-go-v2/aws/middleware"
"github.com/aws/aws-sdk-go-v2/service/machinelearning/types"
"github.com/aws/smithy-go/middleware"
smithyhttp "github.com/aws/smithy-go/transport/http"
)
// Creates a new MLModel using the DataSource and the recipe as information
// sources. An MLModel is nearly immutable. Users can update only the MLModelName
// and the ScoreThreshold in an MLModel without creating a new MLModel .
// CreateMLModel is an asynchronous operation. In response to CreateMLModel ,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel
// status to PENDING . After the MLModel has been created and ready is for use,
// Amazon ML sets the status to COMPLETED . You can use the GetMLModel operation
// to check the progress of the MLModel during the creation operation.
// CreateMLModel requires a DataSource with computed statistics, which can be
// created by setting ComputeStatistics to true in CreateDataSourceFromRDS ,
// CreateDataSourceFromS3 , or CreateDataSourceFromRedshift operations.
func (c *Client) CreateMLModel(ctx context.Context, params *CreateMLModelInput, optFns ...func(*Options)) (*CreateMLModelOutput, error) {
if params == nil {
params = &CreateMLModelInput{}
}
result, metadata, err := c.invokeOperation(ctx, "CreateMLModel", params, optFns, c.addOperationCreateMLModelMiddlewares)
if err != nil {
return nil, err
}
out := result.(*CreateMLModelOutput)
out.ResultMetadata = metadata
return out, nil
}
type CreateMLModelInput struct {
// A user-supplied ID that uniquely identifies the MLModel .
//
// This member is required.
MLModelId *string
// The category of supervised learning that this MLModel will address. Choose from
// the following types:
// - Choose REGRESSION if the MLModel will be used to predict a numeric value.
// - Choose BINARY if the MLModel result has two possible values.
// - Choose MULTICLASS if the MLModel result has a limited number of values.
// For more information, see the Amazon Machine Learning Developer Guide (https://docs.aws.amazon.com/machine-learning/latest/dg)
// .
//
// This member is required.
MLModelType types.MLModelType
// The DataSource that points to the training data.
//
// This member is required.
TrainingDataSourceId *string
// A user-supplied name or description of the MLModel .
MLModelName *string
// A list of the training parameters in the MLModel . The list is implemented as a
// map of key-value pairs. The following is the current set of training parameters:
//
// - sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending
// on the input data, the size of the model might affect its performance. The value
// is an integer that ranges from 100000 to 2147483648 . The default value is
// 33554432 .
// - sgd.maxPasses - The number of times that the training process traverses the
// observations to build the MLModel . The value is an integer that ranges from 1
// to 10000 . The default value is 10 .
// - sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling
// the data improves a model's ability to find the optimal solution for a variety
// of data types. The valid values are auto and none . The default value is none
// . We strongly recommend that you shuffle your data.
// - sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It
// controls overfitting the data by penalizing large coefficients. This tends to
// drive coefficients to zero, resulting in a sparse feature set. If you use this
// parameter, start by specifying a small value, such as 1.0E-08 . The value is a
// double that ranges from 0 to MAX_DOUBLE . The default is to not use L1
// normalization. This parameter can't be used when L2 is specified. Use this
// parameter sparingly.
// - sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It
// controls overfitting the data by penalizing large coefficients. This tends to
// drive coefficients to small, nonzero values. If you use this parameter, start by
// specifying a small value, such as 1.0E-08 . The value is a double that ranges
// from 0 to MAX_DOUBLE . The default is to not use L2 normalization. This
// parameter can't be used when L1 is specified. Use this parameter sparingly.
Parameters map[string]string
// The data recipe for creating the MLModel . You must specify either the recipe or
// its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
Recipe *string
// The Amazon Simple Storage Service (Amazon S3) location and file name that
// contains the MLModel recipe. You must specify either the recipe or its URI. If
// you don't specify a recipe or its URI, Amazon ML creates a default.
RecipeUri *string
noSmithyDocumentSerde
}
// Represents the output of a CreateMLModel operation, and is an acknowledgement
// that Amazon ML received the request. The CreateMLModel operation is
// asynchronous. You can poll for status updates by using the GetMLModel operation
// and checking the Status parameter.
type CreateMLModelOutput struct {
// A user-supplied ID that uniquely identifies the MLModel . This value should be
// identical to the value of the MLModelId in the request.
MLModelId *string
// Metadata pertaining to the operation's result.
ResultMetadata middleware.Metadata
noSmithyDocumentSerde
}
func (c *Client) addOperationCreateMLModelMiddlewares(stack *middleware.Stack, options Options) (err error) {
if err := stack.Serialize.Add(&setOperationInputMiddleware{}, middleware.After); err != nil {
return err
}
err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreateMLModel{}, middleware.After)
if err != nil {
return err
}
err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreateMLModel{}, middleware.After)
if err != nil {
return err
}
if err := addProtocolFinalizerMiddlewares(stack, options, "CreateMLModel"); err != nil {
return fmt.Errorf("add protocol finalizers: %v", err)
}
if err = addlegacyEndpointContextSetter(stack, options); err != nil {
return err
}
if err = addSetLoggerMiddleware(stack, options); err != nil {
return err
}
if err = addClientRequestID(stack); err != nil {
return err
}
if err = addComputeContentLength(stack); err != nil {
return err
}
if err = addResolveEndpointMiddleware(stack, options); err != nil {
return err
}
if err = addComputePayloadSHA256(stack); err != nil {
return err
}
if err = addRetry(stack, options); err != nil {
return err
}
if err = addRawResponseToMetadata(stack); err != nil {
return err
}
if err = addRecordResponseTiming(stack); err != nil {
return err
}
if err = addClientUserAgent(stack, options); err != nil {
return err
}
if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = addSetLegacyContextSigningOptionsMiddleware(stack); err != nil {
return err
}
if err = addOpCreateMLModelValidationMiddleware(stack); err != nil {
return err
}
if err = stack.Initialize.Add(newServiceMetadataMiddleware_opCreateMLModel(options.Region), middleware.Before); err != nil {
return err
}
if err = addRecursionDetection(stack); 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
}
if err = addDisableHTTPSMiddleware(stack, options); err != nil {
return err
}
return nil
}
func newServiceMetadataMiddleware_opCreateMLModel(region string) *awsmiddleware.RegisterServiceMetadata {
return &awsmiddleware.RegisterServiceMetadata{
Region: region,
ServiceID: ServiceID,
OperationName: "CreateMLModel",
}
}