// Code generated by smithy-go-codegen DO NOT EDIT.

package machinelearning

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/machinelearning/types"
	"github.com/aws/smithy-go/middleware"
	smithyhttp "github.com/aws/smithy-go/transport/http"
	"time"
)

// Returns an MLModel that includes detailed metadata, data source information,
// and the current status of the MLModel . GetMLModel provides results in normal
// or verbose format.
func (c *Client) GetMLModel(ctx context.Context, params *GetMLModelInput, optFns ...func(*Options)) (*GetMLModelOutput, error) {
	if params == nil {
		params = &GetMLModelInput{}
	}

	result, metadata, err := c.invokeOperation(ctx, "GetMLModel", params, optFns, c.addOperationGetMLModelMiddlewares)
	if err != nil {
		return nil, err
	}

	out := result.(*GetMLModelOutput)
	out.ResultMetadata = metadata
	return out, nil
}

type GetMLModelInput struct {

	// The ID assigned to the MLModel at creation.
	//
	// This member is required.
	MLModelId *string

	// Specifies whether the GetMLModel operation should return Recipe . If true,
	// Recipe is returned. If false, Recipe is not returned.
	Verbose bool

	noSmithyDocumentSerde
}

// Represents the output of a GetMLModel operation, and provides detailed
// information about a MLModel .
type GetMLModelOutput struct {

	// The approximate CPU time in milliseconds that Amazon Machine Learning spent
	// processing the MLModel , normalized and scaled on computation resources.
	// ComputeTime is only available if the MLModel is in the COMPLETED state.
	ComputeTime *int64

	// The time that the MLModel was created. The time is expressed in epoch time.
	CreatedAt *time.Time

	// The AWS user account from which the MLModel was created. The account type can
	// be either an AWS root account or an AWS Identity and Access Management (IAM)
	// user account.
	CreatedByIamUser *string

	// The current endpoint of the MLModel
	EndpointInfo *types.RealtimeEndpointInfo

	// The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or
	// FAILED . FinishedAt is only available when the MLModel is in the COMPLETED or
	// FAILED state.
	FinishedAt *time.Time

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	InputDataLocationS3 *string

	// The time of the most recent edit to the MLModel . The time is expressed in epoch
	// time.
	LastUpdatedAt *time.Time

	// A link to the file that contains logs of the CreateMLModel operation.
	LogUri *string

	// The MLModel ID, which is same as the MLModelId in the request.
	MLModelId *string

	// Identifies the MLModel category. The following are the available types:
	//   - REGRESSION -- Produces a numeric result. For example, "What price should a
	//   house be listed at?"
	//   - BINARY -- Produces one of two possible results. For example, "Is this an
	//   e-commerce website?"
	//   - MULTICLASS -- Produces one of several possible results. For example, "Is
	//   this a HIGH, LOW or MEDIUM risk trade?"
	MLModelType types.MLModelType

	// A description of the most recent details about accessing the MLModel .
	Message *string

	// A user-supplied name or description of the MLModel .
	Name *string

	// The recipe to use when training the MLModel . The Recipe provides detailed
	// information about the observation data to use during training, and manipulations
	// to perform on the observation data during training. Note: This parameter is
	// provided as part of the verbose format.
	Recipe *string

	// The schema used by all of the data files referenced by the DataSource . Note:
	// This parameter is provided as part of the verbose format.
	Schema *string

	// The scoring threshold is used in binary classification MLModel models. It marks
	// the boundary between a positive prediction and a negative prediction. Output
	// values greater than or equal to the threshold receive a positive result from the
	// MLModel, such as true . Output values less than the threshold receive a negative
	// response from the MLModel, such as false .
	ScoreThreshold *float32

	// The time of the most recent edit to the ScoreThreshold . The time is expressed
	// in epoch time.
	ScoreThresholdLastUpdatedAt *time.Time

	// Long integer type that is a 64-bit signed number.
	SizeInBytes *int64

	// The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS .
	// StartedAt isn't available if the MLModel is in the PENDING state.
	StartedAt *time.Time

	// The current status of the MLModel . This element can have one of the following
	// values:
	//   - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//   describe a MLModel .
	//   - INPROGRESS - The request is processing.
	//   - FAILED - The request did not run to completion. The ML model isn't usable.
	//   - COMPLETED - The request completed successfully.
	//   - DELETED - The MLModel is marked as deleted. It isn't usable.
	Status types.EntityStatus

	// The ID of the training DataSource .
	TrainingDataSourceId *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
	//   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.
	TrainingParameters map[string]string

	// Metadata pertaining to the operation's result.
	ResultMetadata middleware.Metadata

	noSmithyDocumentSerde
}

func (c *Client) addOperationGetMLModelMiddlewares(stack *middleware.Stack, options Options) (err error) {
	err = stack.Serialize.Add(&awsAwsjson11_serializeOpGetMLModel{}, middleware.After)
	if err != nil {
		return err
	}
	err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpGetMLModel{}, 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, 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 = addOpGetMLModelValidationMiddleware(stack); err != nil {
		return err
	}
	if err = stack.Initialize.Add(newServiceMetadataMiddleware_opGetMLModel(options.Region), middleware.Before); err != nil {
		return err
	}
	if err = awsmiddleware.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
	}
	return nil
}

func newServiceMetadataMiddleware_opGetMLModel(region string) *awsmiddleware.RegisterServiceMetadata {
	return &awsmiddleware.RegisterServiceMetadata{
		Region:        region,
		ServiceID:     ServiceID,
		SigningName:   "machinelearning",
		OperationName: "GetMLModel",
	}
}