machinelearning

package
v1.7.9 Latest Latest
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 Go to latest Published: Mar 14, 2017 License: Apache-2.0 Imports: 10 Imported by: 0

Documentation

Overview

Package machinelearning provides a client for Amazon Machine Learning.

Index

Examples

Constants

View Source 
const (
	// BatchPredictionFilterVariableCreatedAt is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableCreatedAt = "CreatedAt"

	// BatchPredictionFilterVariableLastUpdatedAt is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableLastUpdatedAt = "LastUpdatedAt"

	// BatchPredictionFilterVariableStatus is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableStatus = "Status"

	// BatchPredictionFilterVariableName is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableName = "Name"

	// BatchPredictionFilterVariableIamuser is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableIamuser = "IAMUser"

	// BatchPredictionFilterVariableMlmodelId is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableMlmodelId = "MLModelId"

	// BatchPredictionFilterVariableDataSourceId is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableDataSourceId = "DataSourceId"

	// BatchPredictionFilterVariableDataUri is a BatchPredictionFilterVariable enum value
	BatchPredictionFilterVariableDataUri = "DataURI"
)

A list of the variables to use in searching or filtering BatchPrediction.

  • CreatedAt - Sets the search criteria to BatchPrediction creation date.

  • Status - Sets the search criteria to BatchPrediction status.

  • Name - Sets the search criteria to the contents of BatchPredictionName.

  • IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.

  • MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.

  • DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.

  • DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

View Source 
const (
	// DataSourceFilterVariableCreatedAt is a DataSourceFilterVariable enum value
	DataSourceFilterVariableCreatedAt = "CreatedAt"

	// DataSourceFilterVariableLastUpdatedAt is a DataSourceFilterVariable enum value
	DataSourceFilterVariableLastUpdatedAt = "LastUpdatedAt"

	// DataSourceFilterVariableStatus is a DataSourceFilterVariable enum value
	DataSourceFilterVariableStatus = "Status"

	// DataSourceFilterVariableName is a DataSourceFilterVariable enum value
	DataSourceFilterVariableName = "Name"

	// DataSourceFilterVariableDataLocationS3 is a DataSourceFilterVariable enum value
	DataSourceFilterVariableDataLocationS3 = "DataLocationS3"

	// DataSourceFilterVariableIamuser is a DataSourceFilterVariable enum value
	DataSourceFilterVariableIamuser = "IAMUser"
)

A list of the variables to use in searching or filtering DataSource.

  • CreatedAt - Sets the search criteria to DataSource creation date.
  • Status - Sets the search criteria to DataSource status.
  • Name - Sets the search criteria to the contents of DataSourceName.
  • DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
  • IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.

NoteThe variable names should match the variable names in the DataSource.

View Source 
const (
	// DetailsAttributesPredictiveModelType is a DetailsAttributes enum value
	DetailsAttributesPredictiveModelType = "PredictiveModelType"

	// DetailsAttributesAlgorithm is a DetailsAttributes enum value
	DetailsAttributesAlgorithm = "Algorithm"
)

Contains the key values of DetailsMap: PredictiveModelType- Indicates the type of the MLModel. Algorithm- Indicates the algorithm that was used for the MLModel

View Source 
const (
	// EntityStatusPending is a EntityStatus enum value
	EntityStatusPending = "PENDING"

	// EntityStatusInprogress is a EntityStatus enum value
	EntityStatusInprogress = "INPROGRESS"

	// EntityStatusFailed is a EntityStatus enum value
	EntityStatusFailed = "FAILED"

	// EntityStatusCompleted is a EntityStatus enum value
	EntityStatusCompleted = "COMPLETED"

	// EntityStatusDeleted is a EntityStatus enum value
	EntityStatusDeleted = "DELETED"
)

Object status with the following possible values:

  • PENDING
  • INPROGRESS
  • FAILED
  • COMPLETED
  • DELETED
View Source 
const (
	// EvaluationFilterVariableCreatedAt is a EvaluationFilterVariable enum value
	EvaluationFilterVariableCreatedAt = "CreatedAt"

	// EvaluationFilterVariableLastUpdatedAt is a EvaluationFilterVariable enum value
	EvaluationFilterVariableLastUpdatedAt = "LastUpdatedAt"

	// EvaluationFilterVariableStatus is a EvaluationFilterVariable enum value
	EvaluationFilterVariableStatus = "Status"

	// EvaluationFilterVariableName is a EvaluationFilterVariable enum value
	EvaluationFilterVariableName = "Name"

	// EvaluationFilterVariableIamuser is a EvaluationFilterVariable enum value
	EvaluationFilterVariableIamuser = "IAMUser"

	// EvaluationFilterVariableMlmodelId is a EvaluationFilterVariable enum value
	EvaluationFilterVariableMlmodelId = "MLModelId"

	// EvaluationFilterVariableDataSourceId is a EvaluationFilterVariable enum value
	EvaluationFilterVariableDataSourceId = "DataSourceId"

	// EvaluationFilterVariableDataUri is a EvaluationFilterVariable enum value
	EvaluationFilterVariableDataUri = "DataURI"
)

A list of the variables to use in searching or filtering Evaluation.

  • CreatedAt - Sets the search criteria to Evaluation creation date.

  • Status - Sets the search criteria to Evaluation status.

  • Name - Sets the search criteria to the contents of EvaluationName.

  • IAMUser - Sets the search criteria to the user account that invoked an evaluation.

  • MLModelId - Sets the search criteria to the Predictor that was evaluated.

  • DataSourceId - Sets the search criteria to the DataSource used in evaluation.

  • DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

View Source 
const (
	// MLModelFilterVariableCreatedAt is a MLModelFilterVariable enum value
	MLModelFilterVariableCreatedAt = "CreatedAt"

	// MLModelFilterVariableLastUpdatedAt is a MLModelFilterVariable enum value
	MLModelFilterVariableLastUpdatedAt = "LastUpdatedAt"

	// MLModelFilterVariableStatus is a MLModelFilterVariable enum value
	MLModelFilterVariableStatus = "Status"

	// MLModelFilterVariableName is a MLModelFilterVariable enum value
	MLModelFilterVariableName = "Name"

	// MLModelFilterVariableIamuser is a MLModelFilterVariable enum value
	MLModelFilterVariableIamuser = "IAMUser"

	// MLModelFilterVariableTrainingDataSourceId is a MLModelFilterVariable enum value
	MLModelFilterVariableTrainingDataSourceId = "TrainingDataSourceId"

	// MLModelFilterVariableRealtimeEndpointStatus is a MLModelFilterVariable enum value
	MLModelFilterVariableRealtimeEndpointStatus = "RealtimeEndpointStatus"

	// MLModelFilterVariableMlmodelType is a MLModelFilterVariable enum value
	MLModelFilterVariableMlmodelType = "MLModelType"

	// MLModelFilterVariableAlgorithm is a MLModelFilterVariable enum value
	MLModelFilterVariableAlgorithm = "Algorithm"

	// MLModelFilterVariableTrainingDataUri is a MLModelFilterVariable enum value
	MLModelFilterVariableTrainingDataUri = "TrainingDataURI"
)
View Source 
const (
	// MLModelTypeRegression is a MLModelType enum value
	MLModelTypeRegression = "REGRESSION"

	// MLModelTypeBinary is a MLModelType enum value
	MLModelTypeBinary = "BINARY"

	// MLModelTypeMulticlass is a MLModelType enum value
	MLModelTypeMulticlass = "MULTICLASS"
)
View Source 
const (
	// RealtimeEndpointStatusNone is a RealtimeEndpointStatus enum value
	RealtimeEndpointStatusNone = "NONE"

	// RealtimeEndpointStatusReady is a RealtimeEndpointStatus enum value
	RealtimeEndpointStatusReady = "READY"

	// RealtimeEndpointStatusUpdating is a RealtimeEndpointStatus enum value
	RealtimeEndpointStatusUpdating = "UPDATING"

	// RealtimeEndpointStatusFailed is a RealtimeEndpointStatus enum value
	RealtimeEndpointStatusFailed = "FAILED"
)
View Source 
const (
	// SortOrderAsc is a SortOrder enum value
	SortOrderAsc = "asc"

	// SortOrderDsc is a SortOrder enum value
	SortOrderDsc = "dsc"
)

The sort order specified in a listing condition. Possible values include the following:

  • asc - Present the information in ascending order (from A-Z).
  • dsc - Present the information in descending order (from Z-A).
View Source 
const (
	// TaggableResourceTypeBatchPrediction is a TaggableResourceType enum value
	TaggableResourceTypeBatchPrediction = "BatchPrediction"

	// TaggableResourceTypeDataSource is a TaggableResourceType enum value
	TaggableResourceTypeDataSource = "DataSource"

	// TaggableResourceTypeEvaluation is a TaggableResourceType enum value
	TaggableResourceTypeEvaluation = "Evaluation"

	// TaggableResourceTypeMlmodel is a TaggableResourceType enum value
	TaggableResourceTypeMlmodel = "MLModel"
)
View Source 
const (

	// ErrCodeIdempotentParameterMismatchException for service response error code
	// "IdempotentParameterMismatchException".
	//
	// A second request to use or change an object was not allowed. This can result
	// from retrying a request using a parameter that was not present in the original
	// request.
	ErrCodeIdempotentParameterMismatchException = "IdempotentParameterMismatchException"

	// ErrCodeInternalServerException for service response error code
	// "InternalServerException".
	//
	// An error on the server occurred when trying to process a request.
	ErrCodeInternalServerException = "InternalServerException"

	// ErrCodeInvalidInputException for service response error code
	// "InvalidInputException".
	//
	// An error on the client occurred. Typically, the cause is an invalid input
	// value.
	ErrCodeInvalidInputException = "InvalidInputException"

	// ErrCodeInvalidTagException for service response error code
	// "InvalidTagException".
	ErrCodeInvalidTagException = "InvalidTagException"

	// ErrCodeLimitExceededException for service response error code
	// "LimitExceededException".
	//
	// The subscriber exceeded the maximum number of operations. This exception
	// can occur when listing objects such as DataSource.
	ErrCodeLimitExceededException = "LimitExceededException"

	// ErrCodePredictorNotMountedException for service response error code
	// "PredictorNotMountedException".
	//
	// The exception is thrown when a predict request is made to an unmounted MLModel.
	ErrCodePredictorNotMountedException = "PredictorNotMountedException"

	// ErrCodeResourceNotFoundException for service response error code
	// "ResourceNotFoundException".
	//
	// A specified resource cannot be located.
	ErrCodeResourceNotFoundException = "ResourceNotFoundException"

	// ErrCodeTagLimitExceededException for service response error code
	// "TagLimitExceededException".
	ErrCodeTagLimitExceededException = "TagLimitExceededException"
)
View Source 
const (
	ServiceName = "machinelearning" // Service endpoint prefix API calls made to.
	EndpointsID = ServiceName       // Service ID for Regions and Endpoints metadata.
)

Service information constants

View Source 
const (
	// AlgorithmSgd is a Algorithm enum value
	AlgorithmSgd = "sgd"
)

The function used to train an MLModel. Training choices supported by Amazon ML include the following:

  • SGD - Stochastic Gradient Descent.
  • RandomForest - Random forest of decision trees.

Variables

This section is empty.

Functions

This section is empty.

Types

type AddTagsInput added in v1.1.33 

type AddTagsInput struct {

	// The ID of the ML object to tag. For example, exampleModelId.
	//
	// ResourceId is a required field
	ResourceId *string `min:"1" type:"string" required:"true"`

	// The type of the ML object to tag.
	//
	// ResourceType is a required field
	ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"`

	// The key-value pairs to use to create tags. If you specify a key without specifying
	// a value, Amazon ML creates a tag with the specified key and a value of null.
	//
	// Tags is a required field
	Tags []*Tag `type:"list" required:"true"`
	// contains filtered or unexported fields
}

func (AddTagsInput) GoString added in v1.1.33 

func (s AddTagsInput) GoString() string

GoString returns the string representation

func (*AddTagsInput) SetResourceId added in v1.5.1 

func (s *AddTagsInput) SetResourceId(v string) *AddTagsInput

SetResourceId sets the ResourceId field's value.

func (*AddTagsInput) SetResourceType added in v1.5.1 

func (s *AddTagsInput) SetResourceType(v string) *AddTagsInput

SetResourceType sets the ResourceType field's value.

func (*AddTagsInput) SetTags added in v1.5.1 

func (s *AddTagsInput) SetTags(v []*Tag) *AddTagsInput

SetTags sets the Tags field's value.

func (AddTagsInput) String added in v1.1.33 

func (s AddTagsInput) String() string

String returns the string representation

func (*AddTagsInput) Validate added in v1.1.33 

func (s *AddTagsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type AddTagsOutput added in v1.1.33 

type AddTagsOutput struct {

	// The ID of the ML object that was tagged.
	ResourceId *string `min:"1" type:"string"`

	// The type of the ML object that was tagged.
	ResourceType *string `type:"string" enum:"TaggableResourceType"`
	// contains filtered or unexported fields
}

Amazon ML returns the following elements.

func (AddTagsOutput) GoString added in v1.1.33 

func (s AddTagsOutput) GoString() string

GoString returns the string representation

func (*AddTagsOutput) SetResourceId added in v1.5.1 

func (s *AddTagsOutput) SetResourceId(v string) *AddTagsOutput

SetResourceId sets the ResourceId field's value.

func (*AddTagsOutput) SetResourceType added in v1.5.1 

func (s *AddTagsOutput) SetResourceType(v string) *AddTagsOutput

SetResourceType sets the ResourceType field's value.

func (AddTagsOutput) String added in v1.1.33 

func (s AddTagsOutput) String() string

String returns the string representation

type BatchPrediction 

type BatchPrediction struct {

	// The ID of the DataSource that points to the group of observations to predict.
	BatchPredictionDataSourceId *string `min:"1" type:"string"`

	// The ID assigned to the BatchPrediction at creation. This value should be
	// identical to the value of the BatchPredictionID in the request.
	BatchPredictionId *string `min:"1" type:"string"`

	// Long integer type that is a 64-bit signed number.
	ComputeTime *int64 `type:"long"`

	// The time that the BatchPrediction was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The AWS user account that invoked the BatchPrediction. The account type can
	// be either an AWS root account or an AWS Identity and Access Management (IAM)
	// user account.
	CreatedByIamUser *string `type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// Long integer type that is a 64-bit signed number.
	InvalidRecordCount *int64 `type:"long"`

	// The time of the most recent edit to the BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID of the MLModel that generated predictions for the BatchPrediction
	// request.
	MLModelId *string `min:"1" type:"string"`

	// A description of the most recent details about processing the batch prediction
	// request.
	Message *string `type:"string"`

	// A user-supplied name or description of the BatchPrediction.
	Name *string `type:"string"`

	// The location of an Amazon S3 bucket or directory to receive the operation
	// results. The following substrings are not allowed in the s3 key portion of
	// the outputURI field: ':', '//', '/./', '/../'.
	OutputUri *string `type:"string"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The status of the BatchPrediction. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    generate predictions for a batch of observations.
	//    * INPROGRESS - The process is underway.
	//    * FAILED - The request to perform a batch prediction did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The batch prediction process completed successfully.
	//    * DELETED - The BatchPrediction is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`

	// Long integer type that is a 64-bit signed number.
	TotalRecordCount *int64 `type:"long"`
	// contains filtered or unexported fields
}

Represents the output of a GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

func (BatchPrediction) GoString added in v0.6.6 

func (s BatchPrediction) GoString() string

GoString returns the string representation

func (*BatchPrediction) SetBatchPredictionDataSourceId added in v1.5.1 

func (s *BatchPrediction) SetBatchPredictionDataSourceId(v string) *BatchPrediction

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*BatchPrediction) SetBatchPredictionId added in v1.5.1 

func (s *BatchPrediction) SetBatchPredictionId(v string) *BatchPrediction

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*BatchPrediction) SetComputeTime added in v1.5.1 

func (s *BatchPrediction) SetComputeTime(v int64) *BatchPrediction

SetComputeTime sets the ComputeTime field's value.

func (*BatchPrediction) SetCreatedAt added in v1.5.1 

func (s *BatchPrediction) SetCreatedAt(v time.Time) *BatchPrediction

SetCreatedAt sets the CreatedAt field's value.

func (*BatchPrediction) SetCreatedByIamUser added in v1.5.1 

func (s *BatchPrediction) SetCreatedByIamUser(v string) *BatchPrediction

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*BatchPrediction) SetFinishedAt added in v1.5.1 

func (s *BatchPrediction) SetFinishedAt(v time.Time) *BatchPrediction

SetFinishedAt sets the FinishedAt field's value.

func (*BatchPrediction) SetInputDataLocationS3 added in v1.5.1 

func (s *BatchPrediction) SetInputDataLocationS3(v string) *BatchPrediction

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*BatchPrediction) SetInvalidRecordCount added in v1.5.1 

func (s *BatchPrediction) SetInvalidRecordCount(v int64) *BatchPrediction

SetInvalidRecordCount sets the InvalidRecordCount field's value.

func (*BatchPrediction) SetLastUpdatedAt added in v1.5.1 

func (s *BatchPrediction) SetLastUpdatedAt(v time.Time) *BatchPrediction

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*BatchPrediction) SetMLModelId added in v1.5.1 

func (s *BatchPrediction) SetMLModelId(v string) *BatchPrediction

SetMLModelId sets the MLModelId field's value.

func (*BatchPrediction) SetMessage added in v1.5.1 

func (s *BatchPrediction) SetMessage(v string) *BatchPrediction

SetMessage sets the Message field's value.

func (*BatchPrediction) SetName added in v1.5.1 

func (s *BatchPrediction) SetName(v string) *BatchPrediction

SetName sets the Name field's value.

func (*BatchPrediction) SetOutputUri added in v1.5.1 

func (s *BatchPrediction) SetOutputUri(v string) *BatchPrediction

SetOutputUri sets the OutputUri field's value.

func (*BatchPrediction) SetStartedAt added in v1.5.1 

func (s *BatchPrediction) SetStartedAt(v time.Time) *BatchPrediction

SetStartedAt sets the StartedAt field's value.

func (*BatchPrediction) SetStatus added in v1.5.1 

func (s *BatchPrediction) SetStatus(v string) *BatchPrediction

SetStatus sets the Status field's value.

func (*BatchPrediction) SetTotalRecordCount added in v1.5.1 

func (s *BatchPrediction) SetTotalRecordCount(v int64) *BatchPrediction

SetTotalRecordCount sets the TotalRecordCount field's value.

func (BatchPrediction) String added in v0.6.6 

func (s BatchPrediction) String() string

String returns the string representation

type CreateBatchPredictionInput 

type CreateBatchPredictionInput struct {

	// The ID of the DataSource that points to the group of observations to predict.
	//
	// BatchPredictionDataSourceId is a required field
	BatchPredictionDataSourceId *string `min:"1" type:"string" required:"true"`

	// A user-supplied ID that uniquely identifies the BatchPrediction.
	//
	// BatchPredictionId is a required field
	BatchPredictionId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the BatchPrediction. BatchPredictionName
	// can only use the UTF-8 character set.
	BatchPredictionName *string `type:"string"`

	// The ID of the MLModel that will generate predictions for the group of observations.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`

	// The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory
	// to store the batch prediction results. The following substrings are not allowed
	// in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.
	//
	// Amazon ML needs permissions to store and retrieve the logs on your behalf.
	// For information about how to set permissions, see the Amazon Machine Learning
	// Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	//
	// OutputUri is a required field
	OutputUri *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateBatchPredictionInput) GoString added in v0.6.6 

func (s CreateBatchPredictionInput) GoString() string

GoString returns the string representation

func (*CreateBatchPredictionInput) SetBatchPredictionDataSourceId added in v1.5.1 

func (s *CreateBatchPredictionInput) SetBatchPredictionDataSourceId(v string) *CreateBatchPredictionInput

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*CreateBatchPredictionInput) SetBatchPredictionId added in v1.5.1 

func (s *CreateBatchPredictionInput) SetBatchPredictionId(v string) *CreateBatchPredictionInput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*CreateBatchPredictionInput) SetBatchPredictionName added in v1.5.1 

func (s *CreateBatchPredictionInput) SetBatchPredictionName(v string) *CreateBatchPredictionInput

SetBatchPredictionName sets the BatchPredictionName field's value.

func (*CreateBatchPredictionInput) SetMLModelId added in v1.5.1 

func (s *CreateBatchPredictionInput) SetMLModelId(v string) *CreateBatchPredictionInput

SetMLModelId sets the MLModelId field's value.

func (*CreateBatchPredictionInput) SetOutputUri added in v1.5.1 

func (s *CreateBatchPredictionInput) SetOutputUri(v string) *CreateBatchPredictionInput

SetOutputUri sets the OutputUri field's value.

func (CreateBatchPredictionInput) String added in v0.6.6 

func (s CreateBatchPredictionInput) String() string

String returns the string representation

func (*CreateBatchPredictionInput) Validate added in v1.1.24 

func (s *CreateBatchPredictionInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateBatchPredictionOutput 

type CreateBatchPredictionOutput struct {

	// A user-supplied ID that uniquely identifies the BatchPrediction. This value
	// is identical to the value of the BatchPredictionId in the request.
	BatchPredictionId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.

The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.

func (CreateBatchPredictionOutput) GoString added in v0.6.6 

func (s CreateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*CreateBatchPredictionOutput) SetBatchPredictionId added in v1.5.1 

func (s *CreateBatchPredictionOutput) SetBatchPredictionId(v string) *CreateBatchPredictionOutput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (CreateBatchPredictionOutput) String added in v0.6.6 

func (s CreateBatchPredictionOutput) String() string

String returns the string representation

type CreateDataSourceFromRDSInput 

type CreateDataSourceFromRDSInput struct {

	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during MLModel training. This parameter must be set to true if
	// the DataSource needs to be used for MLModel training.
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied ID that uniquely identifies the DataSource. Typically, an
	// Amazon Resource Number (ARN) becomes the ID for a DataSource.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of an Amazon RDS DataSource:
	//
	// RDSData is a required field
	RDSData *RDSDataSpec `type:"structure" required:"true"`

	// The role that Amazon ML assumes on behalf of the user to create and activate
	// a data pipeline in the user's account and copy data using the SelectSqlQuery
	// query from Amazon RDS to Amazon S3.
	//
	// RoleARN is a required field
	RoleARN *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateDataSourceFromRDSInput) GoString added in v0.6.6 

func (s CreateDataSourceFromRDSInput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRDSInput) SetComputeStatistics added in v1.5.1 

func (s *CreateDataSourceFromRDSInput) SetComputeStatistics(v bool) *CreateDataSourceFromRDSInput

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromRDSInput) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromRDSInput) SetDataSourceId(v string) *CreateDataSourceFromRDSInput

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromRDSInput) SetDataSourceName added in v1.5.1 

func (s *CreateDataSourceFromRDSInput) SetDataSourceName(v string) *CreateDataSourceFromRDSInput

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromRDSInput) SetRDSData added in v1.5.1 

func (s *CreateDataSourceFromRDSInput) SetRDSData(v *RDSDataSpec) *CreateDataSourceFromRDSInput

SetRDSData sets the RDSData field's value.

func (*CreateDataSourceFromRDSInput) SetRoleARN added in v1.5.1 

func (s *CreateDataSourceFromRDSInput) SetRoleARN(v string) *CreateDataSourceFromRDSInput

SetRoleARN sets the RoleARN field's value.

func (CreateDataSourceFromRDSInput) String added in v0.6.6 

func (s CreateDataSourceFromRDSInput) String() string

String returns the string representation

func (*CreateDataSourceFromRDSInput) Validate added in v1.1.24 

func (s *CreateDataSourceFromRDSInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateDataSourceFromRDSOutput 

type CreateDataSourceFromRDSOutput struct {

	// A user-supplied ID that uniquely identifies the datasource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

func (CreateDataSourceFromRDSOutput) GoString added in v0.6.6 

func (s CreateDataSourceFromRDSOutput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRDSOutput) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromRDSOutput) SetDataSourceId(v string) *CreateDataSourceFromRDSOutput

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromRDSOutput) String added in v0.6.6 

func (s CreateDataSourceFromRDSOutput) String() string

String returns the string representation

type CreateDataSourceFromRedshiftInput 

type CreateDataSourceFromRedshiftInput struct {

	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during MLModel training. This parameter must be set to true if
	// the DataSource needs to be used for MLModel training.
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied ID that uniquely identifies the DataSource.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of an Amazon Redshift DataSource:
	//
	//    * DatabaseInformation - DatabaseName - The name of the Amazon Redshift
	//    database.
	//  ClusterIdentifier - The unique ID for the Amazon Redshift cluster.
	//
	//    * DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials
	//    that are used to connect to the Amazon Redshift database.
	//
	//    * SelectSqlQuery - The query that is used to retrieve the observation
	//    data for the Datasource.
	//
	//    * S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location
	//    for staging Amazon Redshift data. The data retrieved from Amazon Redshift
	//    using the SelectSqlQuery query is stored in this location.
	//
	//    * DataSchemaUri - The Amazon S3 location of the DataSchema.
	//
	//    * DataSchema - A JSON string representing the schema. This is not required
	//    if DataSchemaUri is specified.
	//
	//    * DataRearrangement - A JSON string that represents the splitting and
	//    rearrangement requirements for the DataSource.
	//
	//  Sample -  "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	//
	// DataSpec is a required field
	DataSpec *RedshiftDataSpec `type:"structure" required:"true"`

	// A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the
	// role on behalf of the user to create the following:
	//
	// A security group to allow Amazon ML to execute the SelectSqlQuery query on
	// an Amazon Redshift cluster
	//
	// An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the
	// S3StagingLocation
	//
	// RoleARN is a required field
	RoleARN *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateDataSourceFromRedshiftInput) GoString added in v0.6.6 

func (s CreateDataSourceFromRedshiftInput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRedshiftInput) SetComputeStatistics added in v1.5.1 

func (s *CreateDataSourceFromRedshiftInput) SetComputeStatistics(v bool) *CreateDataSourceFromRedshiftInput

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromRedshiftInput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftInput

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSourceName added in v1.5.1 

func (s *CreateDataSourceFromRedshiftInput) SetDataSourceName(v string) *CreateDataSourceFromRedshiftInput

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSpec added in v1.5.1 

func (s *CreateDataSourceFromRedshiftInput) SetDataSpec(v *RedshiftDataSpec) *CreateDataSourceFromRedshiftInput

SetDataSpec sets the DataSpec field's value.

func (*CreateDataSourceFromRedshiftInput) SetRoleARN added in v1.5.1 

func (s *CreateDataSourceFromRedshiftInput) SetRoleARN(v string) *CreateDataSourceFromRedshiftInput

SetRoleARN sets the RoleARN field's value.

func (CreateDataSourceFromRedshiftInput) String added in v0.6.6 

func (s CreateDataSourceFromRedshiftInput) String() string

String returns the string representation

func (*CreateDataSourceFromRedshiftInput) Validate added in v1.1.24 

func (s *CreateDataSourceFromRedshiftInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateDataSourceFromRedshiftOutput 

type CreateDataSourceFromRedshiftOutput struct {

	// A user-supplied ID that uniquely identifies the datasource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

func (CreateDataSourceFromRedshiftOutput) GoString added in v0.6.6 

func (s CreateDataSourceFromRedshiftOutput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRedshiftOutput) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromRedshiftOutput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftOutput

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromRedshiftOutput) String added in v0.6.6 

func (s CreateDataSourceFromRedshiftOutput) String() string

String returns the string representation

type CreateDataSourceFromS3Input 

type CreateDataSourceFromS3Input struct {

	// The compute statistics for a DataSource. The statistics are generated from
	// the observation data referenced by a DataSource. Amazon ML uses the statistics
	// internally during MLModel training. This parameter must be set to true if
	// the DataSource needs to be used for MLModel training.
	ComputeStatistics *bool `type:"boolean"`

	// A user-supplied identifier that uniquely identifies the DataSource.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the DataSource.
	DataSourceName *string `type:"string"`

	// The data specification of a DataSource:
	//
	//    * DataLocationS3 - The Amazon S3 location of the observation data.
	//
	//    * DataSchemaLocationS3 - The Amazon S3 location of the DataSchema.
	//
	//    * DataSchema - A JSON string representing the schema. This is not required
	//    if DataSchemaUri is specified.
	//
	//    * DataRearrangement - A JSON string that represents the splitting and
	//    rearrangement requirements for the Datasource.
	//
	//  Sample -  "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
	//
	// DataSpec is a required field
	DataSpec *S3DataSpec `type:"structure" required:"true"`
	// contains filtered or unexported fields
}

func (CreateDataSourceFromS3Input) GoString added in v0.6.6 

func (s CreateDataSourceFromS3Input) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromS3Input) SetComputeStatistics added in v1.5.1 

func (s *CreateDataSourceFromS3Input) SetComputeStatistics(v bool) *CreateDataSourceFromS3Input

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromS3Input) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromS3Input) SetDataSourceId(v string) *CreateDataSourceFromS3Input

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromS3Input) SetDataSourceName added in v1.5.1 

func (s *CreateDataSourceFromS3Input) SetDataSourceName(v string) *CreateDataSourceFromS3Input

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromS3Input) SetDataSpec added in v1.5.1 

func (s *CreateDataSourceFromS3Input) SetDataSpec(v *S3DataSpec) *CreateDataSourceFromS3Input

SetDataSpec sets the DataSpec field's value.

func (CreateDataSourceFromS3Input) String added in v0.6.6 

func (s CreateDataSourceFromS3Input) String() string

String returns the string representation

func (*CreateDataSourceFromS3Input) Validate added in v1.1.24 

func (s *CreateDataSourceFromS3Input) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateDataSourceFromS3Output 

type CreateDataSourceFromS3Output struct {

	// A user-supplied ID that uniquely identifies the DataSource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

func (CreateDataSourceFromS3Output) GoString added in v0.6.6 

func (s CreateDataSourceFromS3Output) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromS3Output) SetDataSourceId added in v1.5.1 

func (s *CreateDataSourceFromS3Output) SetDataSourceId(v string) *CreateDataSourceFromS3Output

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromS3Output) String added in v0.6.6 

func (s CreateDataSourceFromS3Output) String() string

String returns the string representation

type CreateEvaluationInput 

type CreateEvaluationInput struct {

	// The ID of the DataSource for the evaluation. The schema of the DataSource
	// must match the schema used to create the MLModel.
	//
	// EvaluationDataSourceId is a required field
	EvaluationDataSourceId *string `min:"1" type:"string" required:"true"`

	// A user-supplied ID that uniquely identifies the Evaluation.
	//
	// EvaluationId is a required field
	EvaluationId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the Evaluation.
	EvaluationName *string `type:"string"`

	// The ID of the MLModel to evaluate.
	//
	// The schema used in creating the MLModel must match the schema of the DataSource
	// used in the Evaluation.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateEvaluationInput) GoString added in v0.6.6 

func (s CreateEvaluationInput) GoString() string

GoString returns the string representation

func (*CreateEvaluationInput) SetEvaluationDataSourceId added in v1.5.1 

func (s *CreateEvaluationInput) SetEvaluationDataSourceId(v string) *CreateEvaluationInput

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*CreateEvaluationInput) SetEvaluationId added in v1.5.1 

func (s *CreateEvaluationInput) SetEvaluationId(v string) *CreateEvaluationInput

SetEvaluationId sets the EvaluationId field's value.

func (*CreateEvaluationInput) SetEvaluationName added in v1.5.1 

func (s *CreateEvaluationInput) SetEvaluationName(v string) *CreateEvaluationInput

SetEvaluationName sets the EvaluationName field's value.

func (*CreateEvaluationInput) SetMLModelId added in v1.5.1 

func (s *CreateEvaluationInput) SetMLModelId(v string) *CreateEvaluationInput

SetMLModelId sets the MLModelId field's value.

func (CreateEvaluationInput) String added in v0.6.6 

func (s CreateEvaluationInput) String() string

String returns the string representation

func (*CreateEvaluationInput) Validate added in v1.1.24 

func (s *CreateEvaluationInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateEvaluationOutput 

type CreateEvaluationOutput struct {

	// The user-supplied ID that uniquely identifies the Evaluation. This value
	// should be identical to the value of the EvaluationId in the request.
	EvaluationId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

func (CreateEvaluationOutput) GoString added in v0.6.6 

func (s CreateEvaluationOutput) GoString() string

GoString returns the string representation

func (*CreateEvaluationOutput) SetEvaluationId added in v1.5.1 

func (s *CreateEvaluationOutput) SetEvaluationId(v string) *CreateEvaluationOutput

SetEvaluationId sets the EvaluationId field's value.

func (CreateEvaluationOutput) String added in v0.6.6 

func (s CreateEvaluationOutput) String() string

String returns the string representation

type CreateMLModelInput 

type CreateMLModelInput struct {

	// A user-supplied ID that uniquely identifies the MLModel.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the MLModel.
	MLModelName *string `type:"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 (http://docs.aws.amazon.com/machine-learning/latest/dg).
	//
	// MLModelType is a required field
	MLModelType *string `type:"string" required:"true" enum:"MLModelType"`

	// 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 `type:"map"`

	// 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 `type:"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 `type:"string"`

	// The DataSource that points to the training data.
	//
	// TrainingDataSourceId is a required field
	TrainingDataSourceId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateMLModelInput) GoString added in v0.6.6 

func (s CreateMLModelInput) GoString() string

GoString returns the string representation

func (*CreateMLModelInput) SetMLModelId added in v1.5.1 

func (s *CreateMLModelInput) SetMLModelId(v string) *CreateMLModelInput

SetMLModelId sets the MLModelId field's value.

func (*CreateMLModelInput) SetMLModelName added in v1.5.1 

func (s *CreateMLModelInput) SetMLModelName(v string) *CreateMLModelInput

SetMLModelName sets the MLModelName field's value.

func (*CreateMLModelInput) SetMLModelType added in v1.5.1 

func (s *CreateMLModelInput) SetMLModelType(v string) *CreateMLModelInput

SetMLModelType sets the MLModelType field's value.

func (*CreateMLModelInput) SetParameters added in v1.5.1 

func (s *CreateMLModelInput) SetParameters(v map[string]*string) *CreateMLModelInput

SetParameters sets the Parameters field's value.

func (*CreateMLModelInput) SetRecipe added in v1.5.1 

func (s *CreateMLModelInput) SetRecipe(v string) *CreateMLModelInput

SetRecipe sets the Recipe field's value.

func (*CreateMLModelInput) SetRecipeUri added in v1.5.1 

func (s *CreateMLModelInput) SetRecipeUri(v string) *CreateMLModelInput

SetRecipeUri sets the RecipeUri field's value.

func (*CreateMLModelInput) SetTrainingDataSourceId added in v1.5.1 

func (s *CreateMLModelInput) SetTrainingDataSourceId(v string) *CreateMLModelInput

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

func (CreateMLModelInput) String added in v0.6.6 

func (s CreateMLModelInput) String() string

String returns the string representation

func (*CreateMLModelInput) Validate added in v1.1.24 

func (s *CreateMLModelInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateMLModelOutput 

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 `min:"1" type:"string"`
	// contains filtered or unexported fields
}

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.

func (CreateMLModelOutput) GoString added in v0.6.6 

func (s CreateMLModelOutput) GoString() string

GoString returns the string representation

func (*CreateMLModelOutput) SetMLModelId added in v1.5.1 

func (s *CreateMLModelOutput) SetMLModelId(v string) *CreateMLModelOutput

SetMLModelId sets the MLModelId field's value.

func (CreateMLModelOutput) String added in v0.6.6 

func (s CreateMLModelOutput) String() string

String returns the string representation

type CreateRealtimeEndpointInput 

type CreateRealtimeEndpointInput struct {

	// The ID assigned to the MLModel during creation.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateRealtimeEndpointInput) GoString added in v0.6.6 

func (s CreateRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (*CreateRealtimeEndpointInput) SetMLModelId added in v1.5.1 

func (s *CreateRealtimeEndpointInput) SetMLModelId(v string) *CreateRealtimeEndpointInput

SetMLModelId sets the MLModelId field's value.

func (CreateRealtimeEndpointInput) String added in v0.6.6 

func (s CreateRealtimeEndpointInput) String() string

String returns the string representation

func (*CreateRealtimeEndpointInput) Validate added in v1.1.24 

func (s *CreateRealtimeEndpointInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type CreateRealtimeEndpointOutput 

type CreateRealtimeEndpointOutput 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 `min:"1" type:"string"`

	// The endpoint information of the MLModel
	RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"`
	// contains filtered or unexported fields
}

Represents the output of an CreateRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.

func (CreateRealtimeEndpointOutput) GoString added in v0.6.6 

func (s CreateRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (*CreateRealtimeEndpointOutput) SetMLModelId added in v1.5.1 

func (s *CreateRealtimeEndpointOutput) SetMLModelId(v string) *CreateRealtimeEndpointOutput

SetMLModelId sets the MLModelId field's value.

func (*CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo added in v1.5.1 

func (s *CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *CreateRealtimeEndpointOutput

SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.

func (CreateRealtimeEndpointOutput) String added in v0.6.6 

func (s CreateRealtimeEndpointOutput) String() string

String returns the string representation

type DataSource 

type DataSource struct {

	// The parameter is true if statistics need to be generated from the observation
	// data.
	ComputeStatistics *bool `type:"boolean"`

	// Long integer type that is a 64-bit signed number.
	ComputeTime *int64 `type:"long"`

	// The time that the DataSource was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The location and name of the data in Amazon Simple Storage Service (Amazon
	// S3) that is used by a DataSource.
	DataLocationS3 *string `type:"string"`

	// A JSON string that represents the splitting and rearrangement requirement
	// used when this DataSource was created.
	DataRearrangement *string `type:"string"`

	// The total number of observations contained in the data files that the DataSource
	// references.
	DataSizeInBytes *int64 `type:"long"`

	// The ID that is assigned to the DataSource during creation.
	DataSourceId *string `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The time of the most recent edit to the BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A description of the most recent details about creating the DataSource.
	Message *string `type:"string"`

	// A user-supplied name or description of the DataSource.
	Name *string `type:"string"`

	// The number of data files referenced by the DataSource.
	NumberOfFiles *int64 `type:"long"`

	// The datasource details that are specific to Amazon RDS.
	RDSMetadata *RDSMetadata `type:"structure"`

	// Describes the DataSource details specific to Amazon Redshift.
	RedshiftMetadata *RedshiftMetadata `type:"structure"`

	// The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts),
	// such as the following: arn:aws:iam::account:role/rolename.
	RoleARN *string `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The current status of the DataSource. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    create a DataSource.
	//    * INPROGRESS - The creation process is underway.
	//    * FAILED - The request to create a DataSource did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The creation process completed successfully.
	//    * DELETED - The DataSource is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`
	// contains filtered or unexported fields
}

Represents the output of the GetDataSource operation.

The content consists of the detailed metadata and data file information and the current status of the DataSource.

func (DataSource) GoString added in v0.6.6 

func (s DataSource) GoString() string

GoString returns the string representation

func (*DataSource) SetComputeStatistics added in v1.5.1 

func (s *DataSource) SetComputeStatistics(v bool) *DataSource

SetComputeStatistics sets the ComputeStatistics field's value.

func (*DataSource) SetComputeTime added in v1.5.1 

func (s *DataSource) SetComputeTime(v int64) *DataSource

SetComputeTime sets the ComputeTime field's value.

func (*DataSource) SetCreatedAt added in v1.5.1 

func (s *DataSource) SetCreatedAt(v time.Time) *DataSource

SetCreatedAt sets the CreatedAt field's value.

func (*DataSource) SetCreatedByIamUser added in v1.5.1 

func (s *DataSource) SetCreatedByIamUser(v string) *DataSource

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*DataSource) SetDataLocationS3 added in v1.5.1 

func (s *DataSource) SetDataLocationS3(v string) *DataSource

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*DataSource) SetDataRearrangement added in v1.5.1 

func (s *DataSource) SetDataRearrangement(v string) *DataSource

SetDataRearrangement sets the DataRearrangement field's value.

func (*DataSource) SetDataSizeInBytes added in v1.5.1 

func (s *DataSource) SetDataSizeInBytes(v int64) *DataSource

SetDataSizeInBytes sets the DataSizeInBytes field's value.

func (*DataSource) SetDataSourceId added in v1.5.1 

func (s *DataSource) SetDataSourceId(v string) *DataSource

SetDataSourceId sets the DataSourceId field's value.

func (*DataSource) SetFinishedAt added in v1.5.1 

func (s *DataSource) SetFinishedAt(v time.Time) *DataSource

SetFinishedAt sets the FinishedAt field's value.

func (*DataSource) SetLastUpdatedAt added in v1.5.1 

func (s *DataSource) SetLastUpdatedAt(v time.Time) *DataSource

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*DataSource) SetMessage added in v1.5.1 

func (s *DataSource) SetMessage(v string) *DataSource

SetMessage sets the Message field's value.

func (*DataSource) SetName added in v1.5.1 

func (s *DataSource) SetName(v string) *DataSource

SetName sets the Name field's value.

func (*DataSource) SetNumberOfFiles added in v1.5.1 

func (s *DataSource) SetNumberOfFiles(v int64) *DataSource

SetNumberOfFiles sets the NumberOfFiles field's value.

func (*DataSource) SetRDSMetadata added in v1.5.1 

func (s *DataSource) SetRDSMetadata(v *RDSMetadata) *DataSource

SetRDSMetadata sets the RDSMetadata field's value.

func (*DataSource) SetRedshiftMetadata added in v1.5.1 

func (s *DataSource) SetRedshiftMetadata(v *RedshiftMetadata) *DataSource

SetRedshiftMetadata sets the RedshiftMetadata field's value.

func (*DataSource) SetRoleARN added in v1.5.1 

func (s *DataSource) SetRoleARN(v string) *DataSource

SetRoleARN sets the RoleARN field's value.

func (*DataSource) SetStartedAt added in v1.5.1 

func (s *DataSource) SetStartedAt(v time.Time) *DataSource

SetStartedAt sets the StartedAt field's value.

func (*DataSource) SetStatus added in v1.5.1 

func (s *DataSource) SetStatus(v string) *DataSource

SetStatus sets the Status field's value.

func (DataSource) String added in v0.6.6 

func (s DataSource) String() string

String returns the string representation

type DeleteBatchPredictionInput 

type DeleteBatchPredictionInput struct {

	// A user-supplied ID that uniquely identifies the BatchPrediction.
	//
	// BatchPredictionId is a required field
	BatchPredictionId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteBatchPredictionInput) GoString added in v0.6.6 

func (s DeleteBatchPredictionInput) GoString() string

GoString returns the string representation

func (*DeleteBatchPredictionInput) SetBatchPredictionId added in v1.5.1 

func (s *DeleteBatchPredictionInput) SetBatchPredictionId(v string) *DeleteBatchPredictionInput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (DeleteBatchPredictionInput) String added in v0.6.6 

func (s DeleteBatchPredictionInput) String() string

String returns the string representation

func (*DeleteBatchPredictionInput) Validate added in v1.1.24 

func (s *DeleteBatchPredictionInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteBatchPredictionOutput 

type DeleteBatchPredictionOutput struct {

	// A user-supplied ID that uniquely identifies the BatchPrediction. This value
	// should be identical to the value of the BatchPredictionID in the request.
	BatchPredictionId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteBatchPrediction operation.

You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

func (DeleteBatchPredictionOutput) GoString added in v0.6.6 

func (s DeleteBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*DeleteBatchPredictionOutput) SetBatchPredictionId added in v1.5.1 

func (s *DeleteBatchPredictionOutput) SetBatchPredictionId(v string) *DeleteBatchPredictionOutput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (DeleteBatchPredictionOutput) String added in v0.6.6 

func (s DeleteBatchPredictionOutput) String() string

String returns the string representation

type DeleteDataSourceInput 

type DeleteDataSourceInput struct {

	// A user-supplied ID that uniquely identifies the DataSource.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteDataSourceInput) GoString added in v0.6.6 

func (s DeleteDataSourceInput) GoString() string

GoString returns the string representation

func (*DeleteDataSourceInput) SetDataSourceId added in v1.5.1 

func (s *DeleteDataSourceInput) SetDataSourceId(v string) *DeleteDataSourceInput

SetDataSourceId sets the DataSourceId field's value.

func (DeleteDataSourceInput) String added in v0.6.6 

func (s DeleteDataSourceInput) String() string

String returns the string representation

func (*DeleteDataSourceInput) Validate added in v1.1.24 

func (s *DeleteDataSourceInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteDataSourceOutput 

type DeleteDataSourceOutput struct {

	// A user-supplied ID that uniquely identifies the DataSource. This value should
	// be identical to the value of the DataSourceID in the request.
	DataSourceId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteDataSource operation.

func (DeleteDataSourceOutput) GoString added in v0.6.6 

func (s DeleteDataSourceOutput) GoString() string

GoString returns the string representation

func (*DeleteDataSourceOutput) SetDataSourceId added in v1.5.1 

func (s *DeleteDataSourceOutput) SetDataSourceId(v string) *DeleteDataSourceOutput

SetDataSourceId sets the DataSourceId field's value.

func (DeleteDataSourceOutput) String added in v0.6.6 

func (s DeleteDataSourceOutput) String() string

String returns the string representation

type DeleteEvaluationInput 

type DeleteEvaluationInput struct {

	// A user-supplied ID that uniquely identifies the Evaluation to delete.
	//
	// EvaluationId is a required field
	EvaluationId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteEvaluationInput) GoString added in v0.6.6 

func (s DeleteEvaluationInput) GoString() string

GoString returns the string representation

func (*DeleteEvaluationInput) SetEvaluationId added in v1.5.1 

func (s *DeleteEvaluationInput) SetEvaluationId(v string) *DeleteEvaluationInput

SetEvaluationId sets the EvaluationId field's value.

func (DeleteEvaluationInput) String added in v0.6.6 

func (s DeleteEvaluationInput) String() string

String returns the string representation

func (*DeleteEvaluationInput) Validate added in v1.1.24 

func (s *DeleteEvaluationInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteEvaluationOutput 

type DeleteEvaluationOutput struct {

	// A user-supplied ID that uniquely identifies the Evaluation. This value should
	// be identical to the value of the EvaluationId in the request.
	EvaluationId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.

You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

func (DeleteEvaluationOutput) GoString added in v0.6.6 

func (s DeleteEvaluationOutput) GoString() string

GoString returns the string representation

func (*DeleteEvaluationOutput) SetEvaluationId added in v1.5.1 

func (s *DeleteEvaluationOutput) SetEvaluationId(v string) *DeleteEvaluationOutput

SetEvaluationId sets the EvaluationId field's value.

func (DeleteEvaluationOutput) String added in v0.6.6 

func (s DeleteEvaluationOutput) String() string

String returns the string representation

type DeleteMLModelInput 

type DeleteMLModelInput struct {

	// A user-supplied ID that uniquely identifies the MLModel.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteMLModelInput) GoString added in v0.6.6 

func (s DeleteMLModelInput) GoString() string

GoString returns the string representation

func (*DeleteMLModelInput) SetMLModelId added in v1.5.1 

func (s *DeleteMLModelInput) SetMLModelId(v string) *DeleteMLModelInput

SetMLModelId sets the MLModelId field's value.

func (DeleteMLModelInput) String added in v0.6.6 

func (s DeleteMLModelInput) String() string

String returns the string representation

func (*DeleteMLModelInput) Validate added in v1.1.24 

func (s *DeleteMLModelInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteMLModelOutput 

type DeleteMLModelOutput 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 `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of a DeleteMLModel operation.

You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

func (DeleteMLModelOutput) GoString added in v0.6.6 

func (s DeleteMLModelOutput) GoString() string

GoString returns the string representation

func (*DeleteMLModelOutput) SetMLModelId added in v1.5.1 

func (s *DeleteMLModelOutput) SetMLModelId(v string) *DeleteMLModelOutput

SetMLModelId sets the MLModelId field's value.

func (DeleteMLModelOutput) String added in v0.6.6 

func (s DeleteMLModelOutput) String() string

String returns the string representation

type DeleteRealtimeEndpointInput 

type DeleteRealtimeEndpointInput struct {

	// The ID assigned to the MLModel during creation.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteRealtimeEndpointInput) GoString added in v0.6.6 

func (s DeleteRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (*DeleteRealtimeEndpointInput) SetMLModelId added in v1.5.1 

func (s *DeleteRealtimeEndpointInput) SetMLModelId(v string) *DeleteRealtimeEndpointInput

SetMLModelId sets the MLModelId field's value.

func (DeleteRealtimeEndpointInput) String added in v0.6.6 

func (s DeleteRealtimeEndpointInput) String() string

String returns the string representation

func (*DeleteRealtimeEndpointInput) Validate added in v1.1.24 

func (s *DeleteRealtimeEndpointInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteRealtimeEndpointOutput 

type DeleteRealtimeEndpointOutput 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 `min:"1" type:"string"`

	// The endpoint information of the MLModel
	RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"`
	// contains filtered or unexported fields
}

Represents the output of an DeleteRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

func (DeleteRealtimeEndpointOutput) GoString added in v0.6.6 

func (s DeleteRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (*DeleteRealtimeEndpointOutput) SetMLModelId added in v1.5.1 

func (s *DeleteRealtimeEndpointOutput) SetMLModelId(v string) *DeleteRealtimeEndpointOutput

SetMLModelId sets the MLModelId field's value.

func (*DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo added in v1.5.1 

func (s *DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *DeleteRealtimeEndpointOutput

SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.

func (DeleteRealtimeEndpointOutput) String added in v0.6.6 

func (s DeleteRealtimeEndpointOutput) String() string

String returns the string representation

type DeleteTagsInput added in v1.1.33 

type DeleteTagsInput struct {

	// The ID of the tagged ML object. For example, exampleModelId.
	//
	// ResourceId is a required field
	ResourceId *string `min:"1" type:"string" required:"true"`

	// The type of the tagged ML object.
	//
	// ResourceType is a required field
	ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"`

	// One or more tags to delete.
	//
	// TagKeys is a required field
	TagKeys []*string `type:"list" required:"true"`
	// contains filtered or unexported fields
}

func (DeleteTagsInput) GoString added in v1.1.33 

func (s DeleteTagsInput) GoString() string

GoString returns the string representation

func (*DeleteTagsInput) SetResourceId added in v1.5.1 

func (s *DeleteTagsInput) SetResourceId(v string) *DeleteTagsInput

SetResourceId sets the ResourceId field's value.

func (*DeleteTagsInput) SetResourceType added in v1.5.1 

func (s *DeleteTagsInput) SetResourceType(v string) *DeleteTagsInput

SetResourceType sets the ResourceType field's value.

func (*DeleteTagsInput) SetTagKeys added in v1.5.1 

func (s *DeleteTagsInput) SetTagKeys(v []*string) *DeleteTagsInput

SetTagKeys sets the TagKeys field's value.

func (DeleteTagsInput) String added in v1.1.33 

func (s DeleteTagsInput) String() string

String returns the string representation

func (*DeleteTagsInput) Validate added in v1.1.33 

func (s *DeleteTagsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DeleteTagsOutput added in v1.1.33 

type DeleteTagsOutput struct {

	// The ID of the ML object from which tags were deleted.
	ResourceId *string `min:"1" type:"string"`

	// The type of the ML object from which tags were deleted.
	ResourceType *string `type:"string" enum:"TaggableResourceType"`
	// contains filtered or unexported fields
}

Amazon ML returns the following elements.

func (DeleteTagsOutput) GoString added in v1.1.33 

func (s DeleteTagsOutput) GoString() string

GoString returns the string representation

func (*DeleteTagsOutput) SetResourceId added in v1.5.1 

func (s *DeleteTagsOutput) SetResourceId(v string) *DeleteTagsOutput

SetResourceId sets the ResourceId field's value.

func (*DeleteTagsOutput) SetResourceType added in v1.5.1 

func (s *DeleteTagsOutput) SetResourceType(v string) *DeleteTagsOutput

SetResourceType sets the ResourceType field's value.

func (DeleteTagsOutput) String added in v1.1.33 

func (s DeleteTagsOutput) String() string

String returns the string representation

type DescribeBatchPredictionsInput 

type DescribeBatchPredictionsInput struct {

	// The equal to operator. The BatchPrediction results will have FilterVariable
	// values that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of BatchPrediction:
	//
	//    * CreatedAt - Sets the search criteria to the BatchPrediction creation
	//    date.
	//    * Status - Sets the search criteria to the BatchPrediction status.
	//    * Name - Sets the search criteria to the contents of the BatchPredictionName.
	//
	//    * IAMUser - Sets the search criteria to the user account that invoked
	//    the BatchPrediction creation.
	//    * MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.
	//
	//    * DataSourceId - Sets the search criteria to the DataSource used in the
	//    BatchPrediction.
	//    * DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction.
	//    The URL can identify either a file or an Amazon Simple Storage Solution
	//    (Amazon S3) bucket or directory.
	FilterVariable *string `type:"string" enum:"BatchPredictionFilterVariable"`

	// The greater than or equal to operator. The BatchPrediction results will have
	// FilterVariable values that are greater than or equal to the value specified
	// with GE.
	GE *string `type:"string"`

	// The greater than operator. The BatchPrediction results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The BatchPrediction results will have
	// FilterVariable values that are less than or equal to the value specified
	// with LE.
	LE *string `type:"string"`

	// The less than operator. The BatchPrediction results will have FilterVariable
	// values that are less than the value specified with LT.
	LT *string `type:"string"`

	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit *int64 `min:"1" type:"integer"`

	// The not equal to operator. The BatchPrediction results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// An ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, a Batch Prediction operation could have the Name2014-09-09-HolidayGiftMailer.
	// To search for this BatchPrediction, select Name for the FilterVariable and
	// any of the following strings for the Prefix:
	//
	//    * 2014-09
	//
	//    * 2014-09-09
	//
	//    * 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of MLModels.
	//
	//    * asc - Arranges the list in ascending order (A-Z, 0-9).
	//    * dsc - Arranges the list in descending order (Z-A, 9-0).
	// Results are sorted by FilterVariable.
	SortOrder *string `type:"string" enum:"SortOrder"`
	// contains filtered or unexported fields
}

func (DescribeBatchPredictionsInput) GoString added in v0.6.6 

func (s DescribeBatchPredictionsInput) GoString() string

GoString returns the string representation

func (*DescribeBatchPredictionsInput) SetEQ added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetEQ(v string) *DescribeBatchPredictionsInput

SetEQ sets the EQ field's value.

func (*DescribeBatchPredictionsInput) SetFilterVariable added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetFilterVariable(v string) *DescribeBatchPredictionsInput

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeBatchPredictionsInput) SetGE added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetGE(v string) *DescribeBatchPredictionsInput

SetGE sets the GE field's value.

func (*DescribeBatchPredictionsInput) SetGT added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetGT(v string) *DescribeBatchPredictionsInput

SetGT sets the GT field's value.

func (*DescribeBatchPredictionsInput) SetLE added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetLE(v string) *DescribeBatchPredictionsInput

SetLE sets the LE field's value.

func (*DescribeBatchPredictionsInput) SetLT added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetLT(v string) *DescribeBatchPredictionsInput

SetLT sets the LT field's value.

func (*DescribeBatchPredictionsInput) SetLimit added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetLimit(v int64) *DescribeBatchPredictionsInput

SetLimit sets the Limit field's value.

func (*DescribeBatchPredictionsInput) SetNE added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetNE(v string) *DescribeBatchPredictionsInput

SetNE sets the NE field's value.

func (*DescribeBatchPredictionsInput) SetNextToken added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetNextToken(v string) *DescribeBatchPredictionsInput

SetNextToken sets the NextToken field's value.

func (*DescribeBatchPredictionsInput) SetPrefix added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetPrefix(v string) *DescribeBatchPredictionsInput

SetPrefix sets the Prefix field's value.

func (*DescribeBatchPredictionsInput) SetSortOrder added in v1.5.1 

func (s *DescribeBatchPredictionsInput) SetSortOrder(v string) *DescribeBatchPredictionsInput

SetSortOrder sets the SortOrder field's value.

func (DescribeBatchPredictionsInput) String added in v0.6.6 

func (s DescribeBatchPredictionsInput) String() string

String returns the string representation

func (*DescribeBatchPredictionsInput) Validate added in v1.1.24 

func (s *DescribeBatchPredictionsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DescribeBatchPredictionsOutput 

type DescribeBatchPredictionsOutput struct {

	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of BatchPrediction objects that meet the search criteria.
	Results []*BatchPrediction `type:"list"`
	// contains filtered or unexported fields
}

Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

func (DescribeBatchPredictionsOutput) GoString added in v0.6.6 

func (s DescribeBatchPredictionsOutput) GoString() string

GoString returns the string representation

func (*DescribeBatchPredictionsOutput) SetNextToken added in v1.5.1 

func (s *DescribeBatchPredictionsOutput) SetNextToken(v string) *DescribeBatchPredictionsOutput

SetNextToken sets the NextToken field's value.

func (*DescribeBatchPredictionsOutput) SetResults added in v1.5.1 

func (s *DescribeBatchPredictionsOutput) SetResults(v []*BatchPrediction) *DescribeBatchPredictionsOutput

SetResults sets the Results field's value.

func (DescribeBatchPredictionsOutput) String added in v0.6.6 

func (s DescribeBatchPredictionsOutput) String() string

String returns the string representation

type DescribeDataSourcesInput 

type DescribeDataSourcesInput struct {

	// The equal to operator. The DataSource results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of DataSource:
	//
	//    * CreatedAt - Sets the search criteria to DataSource creation dates.
	//    * Status - Sets the search criteria to DataSource statuses.
	//    * Name - Sets the search criteria to the contents of DataSourceName.
	//    * DataUri - Sets the search criteria to the URI of data files used to
	//    create the DataSource. The URI can identify either a file or an Amazon
	//    Simple Storage Service (Amazon S3) bucket or directory.
	//    * IAMUser - Sets the search criteria to the user account that invoked
	//    the DataSource creation.
	FilterVariable *string `type:"string" enum:"DataSourceFilterVariable"`

	// The greater than or equal to operator. The DataSource results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The DataSource results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The DataSource results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The DataSource results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The maximum number of DataSource to include in the result.
	Limit *int64 `min:"1" type:"integer"`

	// The not equal to operator. The DataSource results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, a DataSource could have the Name2014-09-09-HolidayGiftMailer.
	// To search for this DataSource, select Name for the FilterVariable and any
	// of the following strings for the Prefix:
	//
	//    * 2014-09
	//
	//    * 2014-09-09
	//
	//    * 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of DataSource.
	//
	//    * asc - Arranges the list in ascending order (A-Z, 0-9).
	//    * dsc - Arranges the list in descending order (Z-A, 9-0).
	// Results are sorted by FilterVariable.
	SortOrder *string `type:"string" enum:"SortOrder"`
	// contains filtered or unexported fields
}

func (DescribeDataSourcesInput) GoString added in v0.6.6 

func (s DescribeDataSourcesInput) GoString() string

GoString returns the string representation

func (*DescribeDataSourcesInput) SetEQ added in v1.5.1 

func (s *DescribeDataSourcesInput) SetEQ(v string) *DescribeDataSourcesInput

SetEQ sets the EQ field's value.

func (*DescribeDataSourcesInput) SetFilterVariable added in v1.5.1 

func (s *DescribeDataSourcesInput) SetFilterVariable(v string) *DescribeDataSourcesInput

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeDataSourcesInput) SetGE added in v1.5.1 

func (s *DescribeDataSourcesInput) SetGE(v string) *DescribeDataSourcesInput

SetGE sets the GE field's value.

func (*DescribeDataSourcesInput) SetGT added in v1.5.1 

func (s *DescribeDataSourcesInput) SetGT(v string) *DescribeDataSourcesInput

SetGT sets the GT field's value.

func (*DescribeDataSourcesInput) SetLE added in v1.5.1 

func (s *DescribeDataSourcesInput) SetLE(v string) *DescribeDataSourcesInput

SetLE sets the LE field's value.

func (*DescribeDataSourcesInput) SetLT added in v1.5.1 

func (s *DescribeDataSourcesInput) SetLT(v string) *DescribeDataSourcesInput

SetLT sets the LT field's value.

func (*DescribeDataSourcesInput) SetLimit added in v1.5.1 

func (s *DescribeDataSourcesInput) SetLimit(v int64) *DescribeDataSourcesInput

SetLimit sets the Limit field's value.

func (*DescribeDataSourcesInput) SetNE added in v1.5.1 

func (s *DescribeDataSourcesInput) SetNE(v string) *DescribeDataSourcesInput

SetNE sets the NE field's value.

func (*DescribeDataSourcesInput) SetNextToken added in v1.5.1 

func (s *DescribeDataSourcesInput) SetNextToken(v string) *DescribeDataSourcesInput

SetNextToken sets the NextToken field's value.

func (*DescribeDataSourcesInput) SetPrefix added in v1.5.1 

func (s *DescribeDataSourcesInput) SetPrefix(v string) *DescribeDataSourcesInput

SetPrefix sets the Prefix field's value.

func (*DescribeDataSourcesInput) SetSortOrder added in v1.5.1 

func (s *DescribeDataSourcesInput) SetSortOrder(v string) *DescribeDataSourcesInput

SetSortOrder sets the SortOrder field's value.

func (DescribeDataSourcesInput) String added in v0.6.6 

func (s DescribeDataSourcesInput) String() string

String returns the string representation

func (*DescribeDataSourcesInput) Validate added in v1.1.24 

func (s *DescribeDataSourcesInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DescribeDataSourcesOutput 

type DescribeDataSourcesOutput struct {

	// An ID of the next page in the paginated results that indicates at least one
	// more page follows.
	NextToken *string `type:"string"`

	// A list of DataSource that meet the search criteria.
	Results []*DataSource `type:"list"`
	// contains filtered or unexported fields
}

Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.

func (DescribeDataSourcesOutput) GoString added in v0.6.6 

func (s DescribeDataSourcesOutput) GoString() string

GoString returns the string representation

func (*DescribeDataSourcesOutput) SetNextToken added in v1.5.1 

func (s *DescribeDataSourcesOutput) SetNextToken(v string) *DescribeDataSourcesOutput

SetNextToken sets the NextToken field's value.

func (*DescribeDataSourcesOutput) SetResults added in v1.5.1 

func (s *DescribeDataSourcesOutput) SetResults(v []*DataSource) *DescribeDataSourcesOutput

SetResults sets the Results field's value.

func (DescribeDataSourcesOutput) String added in v0.6.6 

func (s DescribeDataSourcesOutput) String() string

String returns the string representation

type DescribeEvaluationsInput 

type DescribeEvaluationsInput struct {

	// The equal to operator. The Evaluation results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variable to filter a list of Evaluation objects:
	//
	//    * CreatedAt - Sets the search criteria to the Evaluation creation date.
	//
	//    * Status - Sets the search criteria to the Evaluation status.
	//    * Name - Sets the search criteria to the contents of EvaluationName.
	//    * IAMUser - Sets the search criteria to the user account that invoked
	//    an Evaluation.
	//    * MLModelId - Sets the search criteria to the MLModel that was evaluated.
	//
	//    * DataSourceId - Sets the search criteria to the DataSource used in Evaluation.
	//
	//    * DataUri - Sets the search criteria to the data file(s) used in Evaluation.
	//    The URL can identify either a file or an Amazon Simple Storage Solution
	//    (Amazon S3) bucket or directory.
	FilterVariable *string `type:"string" enum:"EvaluationFilterVariable"`

	// The greater than or equal to operator. The Evaluation results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The Evaluation results will have FilterVariable
	// values that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The Evaluation results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The Evaluation results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The maximum number of Evaluation to include in the result.
	Limit *int64 `min:"1" type:"integer"`

	// The not equal to operator. The Evaluation results will have FilterVariable
	// values not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, an Evaluation could have the Name2014-09-09-HolidayGiftMailer.
	// To search for this Evaluation, select Name for the FilterVariable and any
	// of the following strings for the Prefix:
	//
	//    * 2014-09
	//
	//    * 2014-09-09
	//
	//    * 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of Evaluation.
	//
	//    * asc - Arranges the list in ascending order (A-Z, 0-9).
	//    * dsc - Arranges the list in descending order (Z-A, 9-0).
	// Results are sorted by FilterVariable.
	SortOrder *string `type:"string" enum:"SortOrder"`
	// contains filtered or unexported fields
}

func (DescribeEvaluationsInput) GoString added in v0.6.6 

func (s DescribeEvaluationsInput) GoString() string

GoString returns the string representation

func (*DescribeEvaluationsInput) SetEQ added in v1.5.1 

func (s *DescribeEvaluationsInput) SetEQ(v string) *DescribeEvaluationsInput

SetEQ sets the EQ field's value.

func (*DescribeEvaluationsInput) SetFilterVariable added in v1.5.1 

func (s *DescribeEvaluationsInput) SetFilterVariable(v string) *DescribeEvaluationsInput

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeEvaluationsInput) SetGE added in v1.5.1 

func (s *DescribeEvaluationsInput) SetGE(v string) *DescribeEvaluationsInput

SetGE sets the GE field's value.

func (*DescribeEvaluationsInput) SetGT added in v1.5.1 

func (s *DescribeEvaluationsInput) SetGT(v string) *DescribeEvaluationsInput

SetGT sets the GT field's value.

func (*DescribeEvaluationsInput) SetLE added in v1.5.1 

func (s *DescribeEvaluationsInput) SetLE(v string) *DescribeEvaluationsInput

SetLE sets the LE field's value.

func (*DescribeEvaluationsInput) SetLT added in v1.5.1 

func (s *DescribeEvaluationsInput) SetLT(v string) *DescribeEvaluationsInput

SetLT sets the LT field's value.

func (*DescribeEvaluationsInput) SetLimit added in v1.5.1 

func (s *DescribeEvaluationsInput) SetLimit(v int64) *DescribeEvaluationsInput

SetLimit sets the Limit field's value.

func (*DescribeEvaluationsInput) SetNE added in v1.5.1 

func (s *DescribeEvaluationsInput) SetNE(v string) *DescribeEvaluationsInput

SetNE sets the NE field's value.

func (*DescribeEvaluationsInput) SetNextToken added in v1.5.1 

func (s *DescribeEvaluationsInput) SetNextToken(v string) *DescribeEvaluationsInput

SetNextToken sets the NextToken field's value.

func (*DescribeEvaluationsInput) SetPrefix added in v1.5.1 

func (s *DescribeEvaluationsInput) SetPrefix(v string) *DescribeEvaluationsInput

SetPrefix sets the Prefix field's value.

func (*DescribeEvaluationsInput) SetSortOrder added in v1.5.1 

func (s *DescribeEvaluationsInput) SetSortOrder(v string) *DescribeEvaluationsInput

SetSortOrder sets the SortOrder field's value.

func (DescribeEvaluationsInput) String added in v0.6.6 

func (s DescribeEvaluationsInput) String() string

String returns the string representation

func (*DescribeEvaluationsInput) Validate added in v1.1.24 

func (s *DescribeEvaluationsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DescribeEvaluationsOutput 

type DescribeEvaluationsOutput struct {

	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of Evaluation that meet the search criteria.
	Results []*Evaluation `type:"list"`
	// contains filtered or unexported fields
}

Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.

func (DescribeEvaluationsOutput) GoString added in v0.6.6 

func (s DescribeEvaluationsOutput) GoString() string

GoString returns the string representation

func (*DescribeEvaluationsOutput) SetNextToken added in v1.5.1 

func (s *DescribeEvaluationsOutput) SetNextToken(v string) *DescribeEvaluationsOutput

SetNextToken sets the NextToken field's value.

func (*DescribeEvaluationsOutput) SetResults added in v1.5.1 

func (s *DescribeEvaluationsOutput) SetResults(v []*Evaluation) *DescribeEvaluationsOutput

SetResults sets the Results field's value.

func (DescribeEvaluationsOutput) String added in v0.6.6 

func (s DescribeEvaluationsOutput) String() string

String returns the string representation

type DescribeMLModelsInput 

type DescribeMLModelsInput struct {

	// The equal to operator. The MLModel results will have FilterVariable values
	// that exactly match the value specified with EQ.
	EQ *string `type:"string"`

	// Use one of the following variables to filter a list of MLModel:
	//
	//    * CreatedAt - Sets the search criteria to MLModel creation date.
	//    * Status - Sets the search criteria to MLModel status.
	//    * Name - Sets the search criteria to the contents of MLModelName.
	//    * IAMUser - Sets the search criteria to the user account that invoked
	//    the MLModel creation.
	//    * TrainingDataSourceId - Sets the search criteria to the DataSource used
	//    to train one or more MLModel.
	//    * RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time
	//    endpoint status.
	//    * MLModelType - Sets the search criteria to MLModel type: binary, regression,
	//    or multi-class.
	//    * Algorithm - Sets the search criteria to the algorithm that the MLModel
	//    uses.
	//    * TrainingDataURI - Sets the search criteria to the data file(s) used
	//    in training a MLModel. The URL can identify either a file or an Amazon
	//    Simple Storage Service (Amazon S3) bucket or directory.
	FilterVariable *string `type:"string" enum:"MLModelFilterVariable"`

	// The greater than or equal to operator. The MLModel results will have FilterVariable
	// values that are greater than or equal to the value specified with GE.
	GE *string `type:"string"`

	// The greater than operator. The MLModel results will have FilterVariable values
	// that are greater than the value specified with GT.
	GT *string `type:"string"`

	// The less than or equal to operator. The MLModel results will have FilterVariable
	// values that are less than or equal to the value specified with LE.
	LE *string `type:"string"`

	// The less than operator. The MLModel results will have FilterVariable values
	// that are less than the value specified with LT.
	LT *string `type:"string"`

	// The number of pages of information to include in the result. The range of
	// acceptable values is 1 through 100. The default value is 100.
	Limit *int64 `min:"1" type:"integer"`

	// The not equal to operator. The MLModel results will have FilterVariable values
	// not equal to the value specified with NE.
	NE *string `type:"string"`

	// The ID of the page in the paginated results.
	NextToken *string `type:"string"`

	// A string that is found at the beginning of a variable, such as Name or Id.
	//
	// For example, an MLModel could have the Name2014-09-09-HolidayGiftMailer.
	// To search for this MLModel, select Name for the FilterVariable and any of
	// the following strings for the Prefix:
	//
	//    * 2014-09
	//
	//    * 2014-09-09
	//
	//    * 2014-09-09-Holiday
	Prefix *string `type:"string"`

	// A two-value parameter that determines the sequence of the resulting list
	// of MLModel.
	//
	//    * asc - Arranges the list in ascending order (A-Z, 0-9).
	//    * dsc - Arranges the list in descending order (Z-A, 9-0).
	// Results are sorted by FilterVariable.
	SortOrder *string `type:"string" enum:"SortOrder"`
	// contains filtered or unexported fields
}

func (DescribeMLModelsInput) GoString added in v0.6.6 

func (s DescribeMLModelsInput) GoString() string

GoString returns the string representation

func (*DescribeMLModelsInput) SetEQ added in v1.5.1 

func (s *DescribeMLModelsInput) SetEQ(v string) *DescribeMLModelsInput

SetEQ sets the EQ field's value.

func (*DescribeMLModelsInput) SetFilterVariable added in v1.5.1 

func (s *DescribeMLModelsInput) SetFilterVariable(v string) *DescribeMLModelsInput

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeMLModelsInput) SetGE added in v1.5.1 

func (s *DescribeMLModelsInput) SetGE(v string) *DescribeMLModelsInput

SetGE sets the GE field's value.

func (*DescribeMLModelsInput) SetGT added in v1.5.1 

func (s *DescribeMLModelsInput) SetGT(v string) *DescribeMLModelsInput

SetGT sets the GT field's value.

func (*DescribeMLModelsInput) SetLE added in v1.5.1 

func (s *DescribeMLModelsInput) SetLE(v string) *DescribeMLModelsInput

SetLE sets the LE field's value.

func (*DescribeMLModelsInput) SetLT added in v1.5.1 

func (s *DescribeMLModelsInput) SetLT(v string) *DescribeMLModelsInput

SetLT sets the LT field's value.

func (*DescribeMLModelsInput) SetLimit added in v1.5.1 

func (s *DescribeMLModelsInput) SetLimit(v int64) *DescribeMLModelsInput

SetLimit sets the Limit field's value.

func (*DescribeMLModelsInput) SetNE added in v1.5.1 

func (s *DescribeMLModelsInput) SetNE(v string) *DescribeMLModelsInput

SetNE sets the NE field's value.

func (*DescribeMLModelsInput) SetNextToken added in v1.5.1 

func (s *DescribeMLModelsInput) SetNextToken(v string) *DescribeMLModelsInput

SetNextToken sets the NextToken field's value.

func (*DescribeMLModelsInput) SetPrefix added in v1.5.1 

func (s *DescribeMLModelsInput) SetPrefix(v string) *DescribeMLModelsInput

SetPrefix sets the Prefix field's value.

func (*DescribeMLModelsInput) SetSortOrder added in v1.5.1 

func (s *DescribeMLModelsInput) SetSortOrder(v string) *DescribeMLModelsInput

SetSortOrder sets the SortOrder field's value.

func (DescribeMLModelsInput) String added in v0.6.6 

func (s DescribeMLModelsInput) String() string

String returns the string representation

func (*DescribeMLModelsInput) Validate added in v1.1.24 

func (s *DescribeMLModelsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DescribeMLModelsOutput 

type DescribeMLModelsOutput struct {

	// The ID of the next page in the paginated results that indicates at least
	// one more page follows.
	NextToken *string `type:"string"`

	// A list of MLModel that meet the search criteria.
	Results []*MLModel `type:"list"`
	// contains filtered or unexported fields
}

Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

func (DescribeMLModelsOutput) GoString added in v0.6.6 

func (s DescribeMLModelsOutput) GoString() string

GoString returns the string representation

func (*DescribeMLModelsOutput) SetNextToken added in v1.5.1 

func (s *DescribeMLModelsOutput) SetNextToken(v string) *DescribeMLModelsOutput

SetNextToken sets the NextToken field's value.

func (*DescribeMLModelsOutput) SetResults added in v1.5.1 

func (s *DescribeMLModelsOutput) SetResults(v []*MLModel) *DescribeMLModelsOutput

SetResults sets the Results field's value.

func (DescribeMLModelsOutput) String added in v0.6.6 

func (s DescribeMLModelsOutput) String() string

String returns the string representation

type DescribeTagsInput added in v1.1.33 

type DescribeTagsInput struct {

	// The ID of the ML object. For example, exampleModelId.
	//
	// ResourceId is a required field
	ResourceId *string `min:"1" type:"string" required:"true"`

	// The type of the ML object.
	//
	// ResourceType is a required field
	ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"`
	// contains filtered or unexported fields
}

func (DescribeTagsInput) GoString added in v1.1.33 

func (s DescribeTagsInput) GoString() string

GoString returns the string representation

func (*DescribeTagsInput) SetResourceId added in v1.5.1 

func (s *DescribeTagsInput) SetResourceId(v string) *DescribeTagsInput

SetResourceId sets the ResourceId field's value.

func (*DescribeTagsInput) SetResourceType added in v1.5.1 

func (s *DescribeTagsInput) SetResourceType(v string) *DescribeTagsInput

SetResourceType sets the ResourceType field's value.

func (DescribeTagsInput) String added in v1.1.33 

func (s DescribeTagsInput) String() string

String returns the string representation

func (*DescribeTagsInput) Validate added in v1.1.33 

func (s *DescribeTagsInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type DescribeTagsOutput added in v1.1.33 

type DescribeTagsOutput struct {

	// The ID of the tagged ML object.
	ResourceId *string `min:"1" type:"string"`

	// The type of the tagged ML object.
	ResourceType *string `type:"string" enum:"TaggableResourceType"`

	// A list of tags associated with the ML object.
	Tags []*Tag `type:"list"`
	// contains filtered or unexported fields
}

Amazon ML returns the following elements.

func (DescribeTagsOutput) GoString added in v1.1.33 

func (s DescribeTagsOutput) GoString() string

GoString returns the string representation

func (*DescribeTagsOutput) SetResourceId added in v1.5.1 

func (s *DescribeTagsOutput) SetResourceId(v string) *DescribeTagsOutput

SetResourceId sets the ResourceId field's value.

func (*DescribeTagsOutput) SetResourceType added in v1.5.1 

func (s *DescribeTagsOutput) SetResourceType(v string) *DescribeTagsOutput

SetResourceType sets the ResourceType field's value.

func (*DescribeTagsOutput) SetTags added in v1.5.1 

func (s *DescribeTagsOutput) SetTags(v []*Tag) *DescribeTagsOutput

SetTags sets the Tags field's value.

func (DescribeTagsOutput) String added in v1.1.33 

func (s DescribeTagsOutput) String() string

String returns the string representation

type Evaluation 

type Evaluation struct {

	// Long integer type that is a 64-bit signed number.
	ComputeTime *int64 `type:"long"`

	// The time that the Evaluation was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The AWS user account that invoked the evaluation. The account type can be
	// either an AWS root account or an AWS Identity and Access Management (IAM)
	// user account.
	CreatedByIamUser *string `type:"string"`

	// The ID of the DataSource that is used to evaluate the MLModel.
	EvaluationDataSourceId *string `min:"1" type:"string"`

	// The ID that is assigned to the Evaluation at creation.
	EvaluationId *string `min:"1" type:"string"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The location and name of the data in Amazon Simple Storage Server (Amazon
	// S3) that is used in the evaluation.
	InputDataLocationS3 *string `type:"string"`

	// The time of the most recent edit to the Evaluation. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID of the MLModel that is the focus of the evaluation.
	MLModelId *string `min:"1" type:"string"`

	// A description of the most recent details about evaluating the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the Evaluation.
	Name *string `type:"string"`

	// Measurements of how well the MLModel performed, using observations referenced
	// by the DataSource. One of the following metrics is returned, based on the
	// type of the MLModel:
	//
	//    * BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique
	//    to measure performance.
	//
	//    * RegressionRMSE: A regression MLModel uses the Root Mean Square Error
	//    (RMSE) technique to measure performance. RMSE measures the difference
	//    between predicted and actual values for a single variable.
	//
	//    * MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique
	//    to measure performance.
	//
	// For more information about performance metrics, please see the Amazon Machine
	// Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	PerformanceMetrics *PerformanceMetrics `type:"structure"`

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The status of the evaluation. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    evaluate an MLModel.
	//    * INPROGRESS - The evaluation is underway.
	//    * FAILED - The request to evaluate an MLModel did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The evaluation process completed successfully.
	//    * DELETED - The Evaluation is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`
	// contains filtered or unexported fields
}

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

func (Evaluation) GoString added in v0.6.6 

func (s Evaluation) GoString() string

GoString returns the string representation

func (*Evaluation) SetComputeTime added in v1.5.1 

func (s *Evaluation) SetComputeTime(v int64) *Evaluation

SetComputeTime sets the ComputeTime field's value.

func (*Evaluation) SetCreatedAt added in v1.5.1 

func (s *Evaluation) SetCreatedAt(v time.Time) *Evaluation

SetCreatedAt sets the CreatedAt field's value.

func (*Evaluation) SetCreatedByIamUser added in v1.5.1 

func (s *Evaluation) SetCreatedByIamUser(v string) *Evaluation

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*Evaluation) SetEvaluationDataSourceId added in v1.5.1 

func (s *Evaluation) SetEvaluationDataSourceId(v string) *Evaluation

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*Evaluation) SetEvaluationId added in v1.5.1 

func (s *Evaluation) SetEvaluationId(v string) *Evaluation

SetEvaluationId sets the EvaluationId field's value.

func (*Evaluation) SetFinishedAt added in v1.5.1 

func (s *Evaluation) SetFinishedAt(v time.Time) *Evaluation

SetFinishedAt sets the FinishedAt field's value.

func (*Evaluation) SetInputDataLocationS3 added in v1.5.1 

func (s *Evaluation) SetInputDataLocationS3(v string) *Evaluation

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*Evaluation) SetLastUpdatedAt added in v1.5.1 

func (s *Evaluation) SetLastUpdatedAt(v time.Time) *Evaluation

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*Evaluation) SetMLModelId added in v1.5.1 

func (s *Evaluation) SetMLModelId(v string) *Evaluation

SetMLModelId sets the MLModelId field's value.

func (*Evaluation) SetMessage added in v1.5.1 

func (s *Evaluation) SetMessage(v string) *Evaluation

SetMessage sets the Message field's value.

func (*Evaluation) SetName added in v1.5.1 

func (s *Evaluation) SetName(v string) *Evaluation

SetName sets the Name field's value.

func (*Evaluation) SetPerformanceMetrics added in v1.5.1 

func (s *Evaluation) SetPerformanceMetrics(v *PerformanceMetrics) *Evaluation

SetPerformanceMetrics sets the PerformanceMetrics field's value.

func (*Evaluation) SetStartedAt added in v1.5.1 

func (s *Evaluation) SetStartedAt(v time.Time) *Evaluation

SetStartedAt sets the StartedAt field's value.

func (*Evaluation) SetStatus added in v1.5.1 

func (s *Evaluation) SetStatus(v string) *Evaluation

SetStatus sets the Status field's value.

func (Evaluation) String added in v0.6.6 

func (s Evaluation) String() string

String returns the string representation

type GetBatchPredictionInput 

type GetBatchPredictionInput struct {

	// An ID assigned to the BatchPrediction at creation.
	//
	// BatchPredictionId is a required field
	BatchPredictionId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (GetBatchPredictionInput) GoString added in v0.6.6 

func (s GetBatchPredictionInput) GoString() string

GoString returns the string representation

func (*GetBatchPredictionInput) SetBatchPredictionId added in v1.5.1 

func (s *GetBatchPredictionInput) SetBatchPredictionId(v string) *GetBatchPredictionInput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (GetBatchPredictionInput) String added in v0.6.6 

func (s GetBatchPredictionInput) String() string

String returns the string representation

func (*GetBatchPredictionInput) Validate added in v1.1.24 

func (s *GetBatchPredictionInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type GetBatchPredictionOutput 

type GetBatchPredictionOutput struct {

	// The ID of the DataSource that was used to create the BatchPrediction.
	BatchPredictionDataSourceId *string `min:"1" type:"string"`

	// An ID assigned to the BatchPrediction at creation. This value should be identical
	// to the value of the BatchPredictionID in the request.
	BatchPredictionId *string `min:"1" type:"string"`

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

	// The time when the BatchPrediction was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The AWS user account that invoked the BatchPrediction. The account type can
	// be either an AWS root account or an AWS Identity and Access Management (IAM)
	// user account.
	CreatedByIamUser *string `type:"string"`

	// The epoch time when Amazon Machine Learning marked the BatchPrediction as
	// COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction
	// is in the COMPLETED or FAILED state.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The number of invalid records that Amazon Machine Learning saw while processing
	// the BatchPrediction.
	InvalidRecordCount *int64 `type:"long"`

	// The time of the most recent edit to BatchPrediction. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file that contains logs of the CreateBatchPrediction operation.
	LogUri *string `type:"string"`

	// The ID of the MLModel that generated predictions for the BatchPrediction
	// request.
	MLModelId *string `min:"1" type:"string"`

	// A description of the most recent details about processing the batch prediction
	// request.
	Message *string `type:"string"`

	// A user-supplied name or description of the BatchPrediction.
	Name *string `type:"string"`

	// The location of an Amazon S3 bucket or directory to receive the operation
	// results.
	OutputUri *string `type:"string"`

	// The epoch time when Amazon Machine Learning marked the BatchPrediction as
	// INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING
	// state.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The status of the BatchPrediction, which can be one of the following values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    generate batch predictions.
	//    * INPROGRESS - The batch predictions are in progress.
	//    * FAILED - The request to perform a batch prediction did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The batch prediction process completed successfully.
	//    * DELETED - The BatchPrediction is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`

	// The number of total records that Amazon Machine Learning saw while processing
	// the BatchPrediction.
	TotalRecordCount *int64 `type:"long"`
	// contains filtered or unexported fields
}

Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.

func (GetBatchPredictionOutput) GoString added in v0.6.6 

func (s GetBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*GetBatchPredictionOutput) SetBatchPredictionDataSourceId added in v1.5.1 

func (s *GetBatchPredictionOutput) SetBatchPredictionDataSourceId(v string) *GetBatchPredictionOutput

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*GetBatchPredictionOutput) SetBatchPredictionId added in v1.5.1 

func (s *GetBatchPredictionOutput) SetBatchPredictionId(v string) *GetBatchPredictionOutput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*GetBatchPredictionOutput) SetComputeTime added in v1.5.1 

func (s *GetBatchPredictionOutput) SetComputeTime(v int64) *GetBatchPredictionOutput

SetComputeTime sets the ComputeTime field's value.

func (*GetBatchPredictionOutput) SetCreatedAt added in v1.5.1 

func (s *GetBatchPredictionOutput) SetCreatedAt(v time.Time) *GetBatchPredictionOutput

SetCreatedAt sets the CreatedAt field's value.

func (*GetBatchPredictionOutput) SetCreatedByIamUser added in v1.5.1 

func (s *GetBatchPredictionOutput) SetCreatedByIamUser(v string) *GetBatchPredictionOutput

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetBatchPredictionOutput) SetFinishedAt added in v1.5.1 

func (s *GetBatchPredictionOutput) SetFinishedAt(v time.Time) *GetBatchPredictionOutput

SetFinishedAt sets the FinishedAt field's value.

func (*GetBatchPredictionOutput) SetInputDataLocationS3 added in v1.5.1 

func (s *GetBatchPredictionOutput) SetInputDataLocationS3(v string) *GetBatchPredictionOutput

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetBatchPredictionOutput) SetInvalidRecordCount added in v1.5.1 

func (s *GetBatchPredictionOutput) SetInvalidRecordCount(v int64) *GetBatchPredictionOutput

SetInvalidRecordCount sets the InvalidRecordCount field's value.

func (*GetBatchPredictionOutput) SetLastUpdatedAt added in v1.5.1 

func (s *GetBatchPredictionOutput) SetLastUpdatedAt(v time.Time) *GetBatchPredictionOutput

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetBatchPredictionOutput) SetLogUri added in v1.5.1 

func (s *GetBatchPredictionOutput) SetLogUri(v string) *GetBatchPredictionOutput

SetLogUri sets the LogUri field's value.

func (*GetBatchPredictionOutput) SetMLModelId added in v1.5.1 

func (s *GetBatchPredictionOutput) SetMLModelId(v string) *GetBatchPredictionOutput

SetMLModelId sets the MLModelId field's value.

func (*GetBatchPredictionOutput) SetMessage added in v1.5.1 

func (s *GetBatchPredictionOutput) SetMessage(v string) *GetBatchPredictionOutput

SetMessage sets the Message field's value.

func (*GetBatchPredictionOutput) SetName added in v1.5.1 

func (s *GetBatchPredictionOutput) SetName(v string) *GetBatchPredictionOutput

SetName sets the Name field's value.

func (*GetBatchPredictionOutput) SetOutputUri added in v1.5.1 

func (s *GetBatchPredictionOutput) SetOutputUri(v string) *GetBatchPredictionOutput

SetOutputUri sets the OutputUri field's value.

func (*GetBatchPredictionOutput) SetStartedAt added in v1.5.1 

func (s *GetBatchPredictionOutput) SetStartedAt(v time.Time) *GetBatchPredictionOutput

SetStartedAt sets the StartedAt field's value.

func (*GetBatchPredictionOutput) SetStatus added in v1.5.1 

func (s *GetBatchPredictionOutput) SetStatus(v string) *GetBatchPredictionOutput

SetStatus sets the Status field's value.

func (*GetBatchPredictionOutput) SetTotalRecordCount added in v1.5.1 

func (s *GetBatchPredictionOutput) SetTotalRecordCount(v int64) *GetBatchPredictionOutput

SetTotalRecordCount sets the TotalRecordCount field's value.

func (GetBatchPredictionOutput) String added in v0.6.6 

func (s GetBatchPredictionOutput) String() string

String returns the string representation

type GetDataSourceInput 

type GetDataSourceInput struct {

	// The ID assigned to the DataSource at creation.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`

	// Specifies whether the GetDataSource operation should return DataSourceSchema.
	//
	// If true, DataSourceSchema is returned.
	//
	// If false, DataSourceSchema is not returned.
	Verbose *bool `type:"boolean"`
	// contains filtered or unexported fields
}

func (GetDataSourceInput) GoString added in v0.6.6 

func (s GetDataSourceInput) GoString() string

GoString returns the string representation

func (*GetDataSourceInput) SetDataSourceId added in v1.5.1 

func (s *GetDataSourceInput) SetDataSourceId(v string) *GetDataSourceInput

SetDataSourceId sets the DataSourceId field's value.

func (*GetDataSourceInput) SetVerbose added in v1.5.1 

func (s *GetDataSourceInput) SetVerbose(v bool) *GetDataSourceInput

SetVerbose sets the Verbose field's value.

func (GetDataSourceInput) String added in v0.6.6 

func (s GetDataSourceInput) String() string

String returns the string representation

func (*GetDataSourceInput) Validate added in v1.1.24 

func (s *GetDataSourceInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type GetDataSourceOutput 

type GetDataSourceOutput struct {

	// The parameter is true if statistics need to be generated from the observation
	// data.
	ComputeStatistics *bool `type:"boolean"`

	// The approximate CPU time in milliseconds that Amazon Machine Learning spent
	// processing the DataSource, normalized and scaled on computation resources.
	// ComputeTime is only available if the DataSource is in the COMPLETED state
	// and the ComputeStatistics is set to true.
	ComputeTime *int64 `type:"long"`

	// The time that the DataSource was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The location of the data file or directory in Amazon Simple Storage Service
	// (Amazon S3).
	DataLocationS3 *string `type:"string"`

	// A JSON string that represents the splitting and rearrangement requirement
	// used when this DataSource was created.
	DataRearrangement *string `type:"string"`

	// The total size of observations in the data files.
	DataSizeInBytes *int64 `type:"long"`

	// The ID assigned to the DataSource at creation. This value should be identical
	// to the value of the DataSourceId in the request.
	DataSourceId *string `min:"1" type:"string"`

	// The schema used by all of the data files of this DataSource.
	//
	// NoteThis parameter is provided as part of the verbose format.
	DataSourceSchema *string `type:"string"`

	// The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED
	// or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED
	// or FAILED state.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The time of the most recent edit to the DataSource. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file containing logs of CreateDataSourceFrom* operations.
	LogUri *string `type:"string"`

	// The user-supplied description of the most recent details about creating the
	// DataSource.
	Message *string `type:"string"`

	// A user-supplied name or description of the DataSource.
	Name *string `type:"string"`

	// The number of data files referenced by the DataSource.
	NumberOfFiles *int64 `type:"long"`

	// The datasource details that are specific to Amazon RDS.
	RDSMetadata *RDSMetadata `type:"structure"`

	// Describes the DataSource details specific to Amazon Redshift.
	RedshiftMetadata *RedshiftMetadata `type:"structure"`

	// The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts),
	// such as the following: arn:aws:iam::account:role/rolename.
	RoleARN *string `min:"1" type:"string"`

	// The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS.
	// StartedAt isn't available if the DataSource is in the PENDING state.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The current status of the DataSource. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon ML submitted a request to create a DataSource.
	//    * INPROGRESS - The creation process is underway.
	//    * FAILED - The request to create a DataSource did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The creation process completed successfully.
	//    * DELETED - The DataSource is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`
	// contains filtered or unexported fields
}

Represents the output of a GetDataSource operation and describes a DataSource.

func (GetDataSourceOutput) GoString added in v0.6.6 

func (s GetDataSourceOutput) GoString() string

GoString returns the string representation

func (*GetDataSourceOutput) SetComputeStatistics added in v1.5.1 

func (s *GetDataSourceOutput) SetComputeStatistics(v bool) *GetDataSourceOutput

SetComputeStatistics sets the ComputeStatistics field's value.

func (*GetDataSourceOutput) SetComputeTime added in v1.5.1 

func (s *GetDataSourceOutput) SetComputeTime(v int64) *GetDataSourceOutput

SetComputeTime sets the ComputeTime field's value.

func (*GetDataSourceOutput) SetCreatedAt added in v1.5.1 

func (s *GetDataSourceOutput) SetCreatedAt(v time.Time) *GetDataSourceOutput

SetCreatedAt sets the CreatedAt field's value.

func (*GetDataSourceOutput) SetCreatedByIamUser added in v1.5.1 

func (s *GetDataSourceOutput) SetCreatedByIamUser(v string) *GetDataSourceOutput

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetDataSourceOutput) SetDataLocationS3 added in v1.5.1 

func (s *GetDataSourceOutput) SetDataLocationS3(v string) *GetDataSourceOutput

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*GetDataSourceOutput) SetDataRearrangement added in v1.5.1 

func (s *GetDataSourceOutput) SetDataRearrangement(v string) *GetDataSourceOutput

SetDataRearrangement sets the DataRearrangement field's value.

func (*GetDataSourceOutput) SetDataSizeInBytes added in v1.5.1 

func (s *GetDataSourceOutput) SetDataSizeInBytes(v int64) *GetDataSourceOutput

SetDataSizeInBytes sets the DataSizeInBytes field's value.

func (*GetDataSourceOutput) SetDataSourceId added in v1.5.1 

func (s *GetDataSourceOutput) SetDataSourceId(v string) *GetDataSourceOutput

SetDataSourceId sets the DataSourceId field's value.

func (*GetDataSourceOutput) SetDataSourceSchema added in v1.5.1 

func (s *GetDataSourceOutput) SetDataSourceSchema(v string) *GetDataSourceOutput

SetDataSourceSchema sets the DataSourceSchema field's value.

func (*GetDataSourceOutput) SetFinishedAt added in v1.5.1 

func (s *GetDataSourceOutput) SetFinishedAt(v time.Time) *GetDataSourceOutput

SetFinishedAt sets the FinishedAt field's value.

func (*GetDataSourceOutput) SetLastUpdatedAt added in v1.5.1 

func (s *GetDataSourceOutput) SetLastUpdatedAt(v time.Time) *GetDataSourceOutput

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetDataSourceOutput) SetLogUri added in v1.5.1 

func (s *GetDataSourceOutput) SetLogUri(v string) *GetDataSourceOutput

SetLogUri sets the LogUri field's value.

func (*GetDataSourceOutput) SetMessage added in v1.5.1 

func (s *GetDataSourceOutput) SetMessage(v string) *GetDataSourceOutput

SetMessage sets the Message field's value.

func (*GetDataSourceOutput) SetName added in v1.5.1 

func (s *GetDataSourceOutput) SetName(v string) *GetDataSourceOutput

SetName sets the Name field's value.

func (*GetDataSourceOutput) SetNumberOfFiles added in v1.5.1 

func (s *GetDataSourceOutput) SetNumberOfFiles(v int64) *GetDataSourceOutput

SetNumberOfFiles sets the NumberOfFiles field's value.

func (*GetDataSourceOutput) SetRDSMetadata added in v1.5.1 

func (s *GetDataSourceOutput) SetRDSMetadata(v *RDSMetadata) *GetDataSourceOutput

SetRDSMetadata sets the RDSMetadata field's value.

func (*GetDataSourceOutput) SetRedshiftMetadata added in v1.5.1 

func (s *GetDataSourceOutput) SetRedshiftMetadata(v *RedshiftMetadata) *GetDataSourceOutput

SetRedshiftMetadata sets the RedshiftMetadata field's value.

func (*GetDataSourceOutput) SetRoleARN added in v1.5.1 

func (s *GetDataSourceOutput) SetRoleARN(v string) *GetDataSourceOutput

SetRoleARN sets the RoleARN field's value.

func (*GetDataSourceOutput) SetStartedAt added in v1.5.1 

func (s *GetDataSourceOutput) SetStartedAt(v time.Time) *GetDataSourceOutput

SetStartedAt sets the StartedAt field's value.

func (*GetDataSourceOutput) SetStatus added in v1.5.1 

func (s *GetDataSourceOutput) SetStatus(v string) *GetDataSourceOutput

SetStatus sets the Status field's value.

func (GetDataSourceOutput) String added in v0.6.6 

func (s GetDataSourceOutput) String() string

String returns the string representation

type GetEvaluationInput 

type GetEvaluationInput struct {

	// The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded
	// and cataloged. The ID provides the means to access the information.
	//
	// EvaluationId is a required field
	EvaluationId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (GetEvaluationInput) GoString added in v0.6.6 

func (s GetEvaluationInput) GoString() string

GoString returns the string representation

func (*GetEvaluationInput) SetEvaluationId added in v1.5.1 

func (s *GetEvaluationInput) SetEvaluationId(v string) *GetEvaluationInput

SetEvaluationId sets the EvaluationId field's value.

func (GetEvaluationInput) String added in v0.6.6 

func (s GetEvaluationInput) String() string

String returns the string representation

func (*GetEvaluationInput) Validate added in v1.1.24 

func (s *GetEvaluationInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type GetEvaluationOutput 

type GetEvaluationOutput struct {

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

	// The time that the Evaluation was created. The time is expressed in epoch
	// time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The AWS user account that invoked the evaluation. The account type can be
	// either an AWS root account or an AWS Identity and Access Management (IAM)
	// user account.
	CreatedByIamUser *string `type:"string"`

	// The DataSource used for this evaluation.
	EvaluationDataSourceId *string `min:"1" type:"string"`

	// The evaluation ID which is same as the EvaluationId in the request.
	EvaluationId *string `min:"1" type:"string"`

	// The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED
	// or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED
	// or FAILED state.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The time of the most recent edit to the Evaluation. The time is expressed
	// in epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// A link to the file that contains logs of the CreateEvaluation operation.
	LogUri *string `type:"string"`

	// The ID of the MLModel that was the focus of the evaluation.
	MLModelId *string `min:"1" type:"string"`

	// A description of the most recent details about evaluating the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the Evaluation.
	Name *string `type:"string"`

	// Measurements of how well the MLModel performed using observations referenced
	// by the DataSource. One of the following metric is returned based on the type
	// of the MLModel:
	//
	//    * BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique
	//    to measure performance.
	//
	//    * RegressionRMSE: A regression MLModel uses the Root Mean Square Error
	//    (RMSE) technique to measure performance. RMSE measures the difference
	//    between predicted and actual values for a single variable.
	//
	//    * MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique
	//    to measure performance.
	//
	// For more information about performance metrics, please see the Amazon Machine
	// Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	PerformanceMetrics *PerformanceMetrics `type:"structure"`

	// The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS.
	// StartedAt isn't available if the Evaluation is in the PENDING state.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The status of the evaluation. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Language (Amazon ML) submitted a request to
	//    evaluate an MLModel.
	//    * INPROGRESS - The evaluation is underway.
	//    * FAILED - The request to evaluate an MLModel did not run to completion.
	//    It is not usable.
	//    * COMPLETED - The evaluation process completed successfully.
	//    * DELETED - The Evaluation is marked as deleted. It is not usable.
	Status *string `type:"string" enum:"EntityStatus"`
	// contains filtered or unexported fields
}

Represents the output of a GetEvaluation operation and describes an Evaluation.

func (GetEvaluationOutput) GoString added in v0.6.6 

func (s GetEvaluationOutput) GoString() string

GoString returns the string representation

func (*GetEvaluationOutput) SetComputeTime added in v1.5.1 

func (s *GetEvaluationOutput) SetComputeTime(v int64) *GetEvaluationOutput

SetComputeTime sets the ComputeTime field's value.

func (*GetEvaluationOutput) SetCreatedAt added in v1.5.1 

func (s *GetEvaluationOutput) SetCreatedAt(v time.Time) *GetEvaluationOutput

SetCreatedAt sets the CreatedAt field's value.

func (*GetEvaluationOutput) SetCreatedByIamUser added in v1.5.1 

func (s *GetEvaluationOutput) SetCreatedByIamUser(v string) *GetEvaluationOutput

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetEvaluationOutput) SetEvaluationDataSourceId added in v1.5.1 

func (s *GetEvaluationOutput) SetEvaluationDataSourceId(v string) *GetEvaluationOutput

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*GetEvaluationOutput) SetEvaluationId added in v1.5.1 

func (s *GetEvaluationOutput) SetEvaluationId(v string) *GetEvaluationOutput

SetEvaluationId sets the EvaluationId field's value.

func (*GetEvaluationOutput) SetFinishedAt added in v1.5.1 

func (s *GetEvaluationOutput) SetFinishedAt(v time.Time) *GetEvaluationOutput

SetFinishedAt sets the FinishedAt field's value.

func (*GetEvaluationOutput) SetInputDataLocationS3 added in v1.5.1 

func (s *GetEvaluationOutput) SetInputDataLocationS3(v string) *GetEvaluationOutput

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetEvaluationOutput) SetLastUpdatedAt added in v1.5.1 

func (s *GetEvaluationOutput) SetLastUpdatedAt(v time.Time) *GetEvaluationOutput

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetEvaluationOutput) SetLogUri added in v1.5.1 

func (s *GetEvaluationOutput) SetLogUri(v string) *GetEvaluationOutput

SetLogUri sets the LogUri field's value.

func (*GetEvaluationOutput) SetMLModelId added in v1.5.1 

func (s *GetEvaluationOutput) SetMLModelId(v string) *GetEvaluationOutput

SetMLModelId sets the MLModelId field's value.

func (*GetEvaluationOutput) SetMessage added in v1.5.1 

func (s *GetEvaluationOutput) SetMessage(v string) *GetEvaluationOutput

SetMessage sets the Message field's value.

func (*GetEvaluationOutput) SetName added in v1.5.1 

func (s *GetEvaluationOutput) SetName(v string) *GetEvaluationOutput

SetName sets the Name field's value.

func (*GetEvaluationOutput) SetPerformanceMetrics added in v1.5.1 

func (s *GetEvaluationOutput) SetPerformanceMetrics(v *PerformanceMetrics) *GetEvaluationOutput

SetPerformanceMetrics sets the PerformanceMetrics field's value.

func (*GetEvaluationOutput) SetStartedAt added in v1.5.1 

func (s *GetEvaluationOutput) SetStartedAt(v time.Time) *GetEvaluationOutput

SetStartedAt sets the StartedAt field's value.

func (*GetEvaluationOutput) SetStatus added in v1.5.1 

func (s *GetEvaluationOutput) SetStatus(v string) *GetEvaluationOutput

SetStatus sets the Status field's value.

func (GetEvaluationOutput) String added in v0.6.6 

func (s GetEvaluationOutput) String() string

String returns the string representation

type GetMLModelInput 

type GetMLModelInput struct {

	// The ID assigned to the MLModel at creation.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`

	// Specifies whether the GetMLModel operation should return Recipe.
	//
	// If true, Recipe is returned.
	//
	// If false, Recipe is not returned.
	Verbose *bool `type:"boolean"`
	// contains filtered or unexported fields
}

func (GetMLModelInput) GoString added in v0.6.6 

func (s GetMLModelInput) GoString() string

GoString returns the string representation

func (*GetMLModelInput) SetMLModelId added in v1.5.1 

func (s *GetMLModelInput) SetMLModelId(v string) *GetMLModelInput

SetMLModelId sets the MLModelId field's value.

func (*GetMLModelInput) SetVerbose added in v1.5.1 

func (s *GetMLModelInput) SetVerbose(v bool) *GetMLModelInput

SetVerbose sets the Verbose field's value.

func (GetMLModelInput) String added in v0.6.6 

func (s GetMLModelInput) String() string

String returns the string representation

func (*GetMLModelInput) Validate added in v1.1.24 

func (s *GetMLModelInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type GetMLModelOutput 

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 `type:"long"`

	// The time that the MLModel was created. The time is expressed in epoch time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `type:"string"`

	// The current endpoint of the MLModel
	EndpointInfo *RealtimeEndpointInfo `type:"structure"`

	// 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 `type:"timestamp" timestampFormat:"unix"`

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

	// The time of the most recent edit to the MLModel. The time is expressed in
	// epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The MLModel ID, which is same as the MLModelId in the request.
	MLModelId *string `min:"1" type:"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 *string `type:"string" enum:"MLModelType"`

	// A description of the most recent details about accessing the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the MLModel.
	Name *string `type:"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.
	//
	// NoteThis parameter is provided as part of the verbose format.
	Recipe *string `type:"string"`

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

	// The scoring threshold is used in binary classification MLModelmodels. 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 *float64 `type:"float"`

	// The time of the most recent edit to the ScoreThreshold. The time is expressed
	// in epoch time.
	ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// 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 `type:"timestamp" timestampFormat:"unix"`

	// 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 *string `type:"string" enum:"EntityStatus"`

	// The ID of the training DataSource.
	TrainingDataSourceId *string `min:"1" type:"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 `type:"map"`
	// contains filtered or unexported fields
}

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

func (GetMLModelOutput) GoString added in v0.6.6 

func (s GetMLModelOutput) GoString() string

GoString returns the string representation

func (*GetMLModelOutput) SetComputeTime added in v1.5.1 

func (s *GetMLModelOutput) SetComputeTime(v int64) *GetMLModelOutput

SetComputeTime sets the ComputeTime field's value.

func (*GetMLModelOutput) SetCreatedAt added in v1.5.1 

func (s *GetMLModelOutput) SetCreatedAt(v time.Time) *GetMLModelOutput

SetCreatedAt sets the CreatedAt field's value.

func (*GetMLModelOutput) SetCreatedByIamUser added in v1.5.1 

func (s *GetMLModelOutput) SetCreatedByIamUser(v string) *GetMLModelOutput

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetMLModelOutput) SetEndpointInfo added in v1.5.1 

func (s *GetMLModelOutput) SetEndpointInfo(v *RealtimeEndpointInfo) *GetMLModelOutput

SetEndpointInfo sets the EndpointInfo field's value.

func (*GetMLModelOutput) SetFinishedAt added in v1.5.1 

func (s *GetMLModelOutput) SetFinishedAt(v time.Time) *GetMLModelOutput

SetFinishedAt sets the FinishedAt field's value.

func (*GetMLModelOutput) SetInputDataLocationS3 added in v1.5.1 

func (s *GetMLModelOutput) SetInputDataLocationS3(v string) *GetMLModelOutput

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetMLModelOutput) SetLastUpdatedAt added in v1.5.1 

func (s *GetMLModelOutput) SetLastUpdatedAt(v time.Time) *GetMLModelOutput

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetMLModelOutput) SetLogUri added in v1.5.1 

func (s *GetMLModelOutput) SetLogUri(v string) *GetMLModelOutput

SetLogUri sets the LogUri field's value.

func (*GetMLModelOutput) SetMLModelId added in v1.5.1 

func (s *GetMLModelOutput) SetMLModelId(v string) *GetMLModelOutput

SetMLModelId sets the MLModelId field's value.

func (*GetMLModelOutput) SetMLModelType added in v1.5.1 

func (s *GetMLModelOutput) SetMLModelType(v string) *GetMLModelOutput

SetMLModelType sets the MLModelType field's value.

func (*GetMLModelOutput) SetMessage added in v1.5.1 

func (s *GetMLModelOutput) SetMessage(v string) *GetMLModelOutput

SetMessage sets the Message field's value.

func (*GetMLModelOutput) SetName added in v1.5.1 

func (s *GetMLModelOutput) SetName(v string) *GetMLModelOutput

SetName sets the Name field's value.

func (*GetMLModelOutput) SetRecipe added in v1.5.1 

func (s *GetMLModelOutput) SetRecipe(v string) *GetMLModelOutput

SetRecipe sets the Recipe field's value.

func (*GetMLModelOutput) SetSchema added in v1.5.1 

func (s *GetMLModelOutput) SetSchema(v string) *GetMLModelOutput

SetSchema sets the Schema field's value.

func (*GetMLModelOutput) SetScoreThreshold added in v1.5.1 

func (s *GetMLModelOutput) SetScoreThreshold(v float64) *GetMLModelOutput

SetScoreThreshold sets the ScoreThreshold field's value.

func (*GetMLModelOutput) SetScoreThresholdLastUpdatedAt added in v1.5.1 

func (s *GetMLModelOutput) SetScoreThresholdLastUpdatedAt(v time.Time) *GetMLModelOutput

SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.

func (*GetMLModelOutput) SetSizeInBytes added in v1.5.1 

func (s *GetMLModelOutput) SetSizeInBytes(v int64) *GetMLModelOutput

SetSizeInBytes sets the SizeInBytes field's value.

func (*GetMLModelOutput) SetStartedAt added in v1.5.1 

func (s *GetMLModelOutput) SetStartedAt(v time.Time) *GetMLModelOutput

SetStartedAt sets the StartedAt field's value.

func (*GetMLModelOutput) SetStatus added in v1.5.1 

func (s *GetMLModelOutput) SetStatus(v string) *GetMLModelOutput

SetStatus sets the Status field's value.

func (*GetMLModelOutput) SetTrainingDataSourceId added in v1.5.1 

func (s *GetMLModelOutput) SetTrainingDataSourceId(v string) *GetMLModelOutput

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

func (*GetMLModelOutput) SetTrainingParameters added in v1.5.1 

func (s *GetMLModelOutput) SetTrainingParameters(v map[string]*string) *GetMLModelOutput

SetTrainingParameters sets the TrainingParameters field's value.

func (GetMLModelOutput) String added in v0.6.6 

func (s GetMLModelOutput) String() string

String returns the string representation

type MLModel 

type MLModel struct {

	// The algorithm used to train the MLModel. The following algorithm is supported:
	//
	//    * SGD -- Stochastic gradient descent. The goal of SGD is to minimize the
	//    gradient of the loss function.
	Algorithm *string `type:"string" enum:"Algorithm"`

	// Long integer type that is a 64-bit signed number.
	ComputeTime *int64 `type:"long"`

	// The time that the MLModel was created. The time is expressed in epoch time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// 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 `type:"string"`

	// The current endpoint of the MLModel.
	EndpointInfo *RealtimeEndpointInfo `type:"structure"`

	// A timestamp represented in epoch time.
	FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// The time of the most recent edit to the MLModel. The time is expressed in
	// epoch time.
	LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The ID assigned to the MLModel at creation.
	MLModelId *string `min:"1" type:"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
	//    a child-friendly web site?".
	//    * MULTICLASS - Produces one of several possible results. For example,
	//    "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
	MLModelType *string `type:"string" enum:"MLModelType"`

	// A description of the most recent details about accessing the MLModel.
	Message *string `type:"string"`

	// A user-supplied name or description of the MLModel.
	Name *string `type:"string"`

	ScoreThreshold *float64 `type:"float"`

	// The time of the most recent edit to the ScoreThreshold. The time is expressed
	// in epoch time.
	ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

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

	// A timestamp represented in epoch time.
	StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The current status of an MLModel. This element can have one of the following
	// values:
	//
	//    * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
	//    create an MLModel.
	//    * INPROGRESS - The creation process is underway.
	//    * FAILED - The request to create an MLModel didn't run to completion.
	//    The model isn't usable.
	//    * COMPLETED - The creation process completed successfully.
	//    * DELETED - The MLModel is marked as deleted. It isn't usable.
	Status *string `type:"string" enum:"EntityStatus"`

	// The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
	TrainingDataSourceId *string `min:"1" type:"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.
	//
	//    * sgd.l1RegularizationAmount - The coefficient regularization L1 norm,
	//    which controls overfitting the data by penalizing large coefficients.
	//    This parameter tends to drive coefficients to zero, resulting in 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,
	//    which 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 `type:"map"`
	// contains filtered or unexported fields
}

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

func (MLModel) GoString added in v0.6.6 

func (s MLModel) GoString() string

GoString returns the string representation

func (*MLModel) SetAlgorithm added in v1.5.1 

func (s *MLModel) SetAlgorithm(v string) *MLModel

SetAlgorithm sets the Algorithm field's value.

func (*MLModel) SetComputeTime added in v1.5.1 

func (s *MLModel) SetComputeTime(v int64) *MLModel

SetComputeTime sets the ComputeTime field's value.

func (*MLModel) SetCreatedAt added in v1.5.1 

func (s *MLModel) SetCreatedAt(v time.Time) *MLModel

SetCreatedAt sets the CreatedAt field's value.

func (*MLModel) SetCreatedByIamUser added in v1.5.1 

func (s *MLModel) SetCreatedByIamUser(v string) *MLModel

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*MLModel) SetEndpointInfo added in v1.5.1 

func (s *MLModel) SetEndpointInfo(v *RealtimeEndpointInfo) *MLModel

SetEndpointInfo sets the EndpointInfo field's value.

func (*MLModel) SetFinishedAt added in v1.5.1 

func (s *MLModel) SetFinishedAt(v time.Time) *MLModel

SetFinishedAt sets the FinishedAt field's value.

func (*MLModel) SetInputDataLocationS3 added in v1.5.1 

func (s *MLModel) SetInputDataLocationS3(v string) *MLModel

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*MLModel) SetLastUpdatedAt added in v1.5.1 

func (s *MLModel) SetLastUpdatedAt(v time.Time) *MLModel

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*MLModel) SetMLModelId added in v1.5.1 

func (s *MLModel) SetMLModelId(v string) *MLModel

SetMLModelId sets the MLModelId field's value.

func (*MLModel) SetMLModelType added in v1.5.1 

func (s *MLModel) SetMLModelType(v string) *MLModel

SetMLModelType sets the MLModelType field's value.

func (*MLModel) SetMessage added in v1.5.1 

func (s *MLModel) SetMessage(v string) *MLModel

SetMessage sets the Message field's value.

func (*MLModel) SetName added in v1.5.1 

func (s *MLModel) SetName(v string) *MLModel

SetName sets the Name field's value.

func (*MLModel) SetScoreThreshold added in v1.5.1 

func (s *MLModel) SetScoreThreshold(v float64) *MLModel

SetScoreThreshold sets the ScoreThreshold field's value.

func (*MLModel) SetScoreThresholdLastUpdatedAt added in v1.5.1 

func (s *MLModel) SetScoreThresholdLastUpdatedAt(v time.Time) *MLModel

SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.

func (*MLModel) SetSizeInBytes added in v1.5.1 

func (s *MLModel) SetSizeInBytes(v int64) *MLModel

SetSizeInBytes sets the SizeInBytes field's value.

func (*MLModel) SetStartedAt added in v1.5.1 

func (s *MLModel) SetStartedAt(v time.Time) *MLModel

SetStartedAt sets the StartedAt field's value.

func (*MLModel) SetStatus added in v1.5.1 

func (s *MLModel) SetStatus(v string) *MLModel

SetStatus sets the Status field's value.

func (*MLModel) SetTrainingDataSourceId added in v1.5.1 

func (s *MLModel) SetTrainingDataSourceId(v string) *MLModel

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

func (*MLModel) SetTrainingParameters added in v1.5.1 

func (s *MLModel) SetTrainingParameters(v map[string]*string) *MLModel

SetTrainingParameters sets the TrainingParameters field's value.

func (MLModel) String added in v0.6.6 

func (s MLModel) String() string

String returns the string representation

type MachineLearning 

type MachineLearning struct {
	*client.Client
}

Definition of the public APIs exposed by Amazon Machine Learning The service client's operations are safe to be used concurrently. It is not safe to mutate any of the client's properties though.

func New 

func New(p client.ConfigProvider, cfgs ...*aws.Config) *MachineLearning

New creates a new instance of the MachineLearning client with a session. If additional configuration is needed for the client instance use the optional aws.Config parameter to add your extra config.

Example: 

// Create a MachineLearning client from just a session.
svc := machinelearning.New(mySession)

// Create a MachineLearning client with additional configuration
svc := machinelearning.New(mySession, aws.NewConfig().WithRegion("us-west-2"))

func (*MachineLearning) AddTags added in v1.1.33 

func (c *MachineLearning) AddTags(input *AddTagsInput) (*AddTagsOutput, error)

AddTags API operation for Amazon Machine Learning.

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation AddTags for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInvalidTagException "InvalidTagException"

  • ErrCodeTagLimitExceededException "TagLimitExceededException"

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.AddTagsInput{
		ResourceId:   aws.String("EntityId"),             // Required
		ResourceType: aws.String("TaggableResourceType"), // Required
		Tags: []*machinelearning.Tag{ // Required
			{ // Required
				Key:   aws.String("TagKey"),
				Value: aws.String("TagValue"),
			},
			// More values...
		},
	}
	resp, err := svc.AddTags(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) AddTagsRequest added in v1.1.33 

func (c *MachineLearning) AddTagsRequest(input *AddTagsInput) (req *request.Request, output *AddTagsOutput)

AddTagsRequest generates a "aws/request.Request" representing the client's request for the AddTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See AddTags for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the AddTags method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the AddTagsRequest method.
req, resp := client.AddTagsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateBatchPrediction 

func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)

CreateBatchPrediction API operation for Amazon Machine Learning.

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateBatchPrediction for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateBatchPredictionInput{
		BatchPredictionDataSourceId: aws.String("EntityId"), // Required
		BatchPredictionId:           aws.String("EntityId"), // Required
		MLModelId:                   aws.String("EntityId"), // Required
		OutputUri:                   aws.String("S3Url"),    // Required
		BatchPredictionName:         aws.String("EntityName"),
	}
	resp, err := svc.CreateBatchPrediction(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateBatchPredictionRequest 

func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput)

CreateBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the CreateBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateBatchPredictionRequest method.
req, resp := client.CreateBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateDataSourceFromRDS 

func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)

CreateDataSourceFromRDS API operation for Amazon Machine Learning.

Creates a DataSource object from an Amazon Relational Database Service (http://aws.amazon.com/rds/) (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromRDS for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateDataSourceFromRDSInput{
		DataSourceId: aws.String("EntityId"), // Required
		RDSData: &machinelearning.RDSDataSpec{ // Required
			DatabaseCredentials: &machinelearning.RDSDatabaseCredentials{ // Required
				Password: aws.String("RDSDatabasePassword"), // Required
				Username: aws.String("RDSDatabaseUsername"), // Required
			},
			DatabaseInformation: &machinelearning.RDSDatabase{ // Required
				DatabaseName:       aws.String("RDSDatabaseName"),       // Required
				InstanceIdentifier: aws.String("RDSInstanceIdentifier"), // Required
			},
			ResourceRole:      aws.String("EDPResourceRole"), // Required
			S3StagingLocation: aws.String("S3Url"),           // Required
			SecurityGroupIds: []*string{ // Required
				aws.String("EDPSecurityGroupId"), // Required
				// More values...
			},
			SelectSqlQuery:    aws.String("RDSSelectSqlQuery"), // Required
			ServiceRole:       aws.String("EDPServiceRole"),    // Required
			SubnetId:          aws.String("EDPSubnetId"),       // Required
			DataRearrangement: aws.String("DataRearrangement"),
			DataSchema:        aws.String("DataSchema"),
			DataSchemaUri:     aws.String("S3Url"),
		},
		RoleARN:           aws.String("RoleARN"), // Required
		ComputeStatistics: aws.Bool(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromRDS(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateDataSourceFromRDSRequest 

func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput)

CreateDataSourceFromRDSRequest generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromRDS operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateDataSourceFromRDS for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromRDS method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateDataSourceFromRDSRequest method.
req, resp := client.CreateDataSourceFromRDSRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateDataSourceFromRedshift 

func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)

CreateDataSourceFromRedshift API operation for Amazon Machine Learning.

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromRedshift for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateDataSourceFromRedshiftInput{
		DataSourceId: aws.String("EntityId"), // Required
		DataSpec: &machinelearning.RedshiftDataSpec{ // Required
			DatabaseCredentials: &machinelearning.RedshiftDatabaseCredentials{ // Required
				Password: aws.String("RedshiftDatabasePassword"), // Required
				Username: aws.String("RedshiftDatabaseUsername"), // Required
			},
			DatabaseInformation: &machinelearning.RedshiftDatabase{ // Required
				ClusterIdentifier: aws.String("RedshiftClusterIdentifier"), // Required
				DatabaseName:      aws.String("RedshiftDatabaseName"),      // Required
			},
			S3StagingLocation: aws.String("S3Url"),                  // Required
			SelectSqlQuery:    aws.String("RedshiftSelectSqlQuery"), // Required
			DataRearrangement: aws.String("DataRearrangement"),
			DataSchema:        aws.String("DataSchema"),
			DataSchemaUri:     aws.String("S3Url"),
		},
		RoleARN:           aws.String("RoleARN"), // Required
		ComputeStatistics: aws.Bool(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromRedshift(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateDataSourceFromRedshiftRequest 

func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput)

CreateDataSourceFromRedshiftRequest generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromRedshift operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateDataSourceFromRedshift for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromRedshift method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateDataSourceFromRedshiftRequest method.
req, resp := client.CreateDataSourceFromRedshiftRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateDataSourceFromS3 

func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)

CreateDataSourceFromS3 API operation for Amazon Machine Learning.

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromS3 for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateDataSourceFromS3Input{
		DataSourceId: aws.String("EntityId"), // Required
		DataSpec: &machinelearning.S3DataSpec{ // Required
			DataLocationS3:       aws.String("S3Url"), // Required
			DataRearrangement:    aws.String("DataRearrangement"),
			DataSchema:           aws.String("DataSchema"),
			DataSchemaLocationS3: aws.String("S3Url"),
		},
		ComputeStatistics: aws.Bool(true),
		DataSourceName:    aws.String("EntityName"),
	}
	resp, err := svc.CreateDataSourceFromS3(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateDataSourceFromS3Request 

func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output)

CreateDataSourceFromS3Request generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromS3 operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateDataSourceFromS3 for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromS3 method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateDataSourceFromS3Request method.
req, resp := client.CreateDataSourceFromS3Request(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateEvaluation 

func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)

CreateEvaluation API operation for Amazon Machine Learning.

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateEvaluation for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateEvaluationInput{
		EvaluationDataSourceId: aws.String("EntityId"), // Required
		EvaluationId:           aws.String("EntityId"), // Required
		MLModelId:              aws.String("EntityId"), // Required
		EvaluationName:         aws.String("EntityName"),
	}
	resp, err := svc.CreateEvaluation(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateEvaluationRequest 

func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput)

CreateEvaluationRequest generates a "aws/request.Request" representing the client's request for the CreateEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateEvaluationRequest method.
req, resp := client.CreateEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateMLModel 

func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)

CreateMLModel API operation for Amazon Machine Learning.

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.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateMLModel for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateMLModelInput{
		MLModelId:            aws.String("EntityId"),    // Required
		MLModelType:          aws.String("MLModelType"), // Required
		TrainingDataSourceId: aws.String("EntityId"),    // Required
		MLModelName:          aws.String("EntityName"),
		Parameters: map[string]*string{
			"Key": aws.String("StringType"), // Required
			// More values...
		},
		Recipe:    aws.String("Recipe"),
		RecipeUri: aws.String("S3Url"),
	}
	resp, err := svc.CreateMLModel(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateMLModelRequest 

func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput)

CreateMLModelRequest generates a "aws/request.Request" representing the client's request for the CreateMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateMLModelRequest method.
req, resp := client.CreateMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) CreateRealtimeEndpoint 

func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)

CreateRealtimeEndpoint API operation for Amazon Machine Learning.

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation CreateRealtimeEndpoint for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.CreateRealtimeEndpointInput{
		MLModelId: aws.String("EntityId"), // Required
	}
	resp, err := svc.CreateRealtimeEndpoint(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) CreateRealtimeEndpointRequest 

func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)

CreateRealtimeEndpointRequest generates a "aws/request.Request" representing the client's request for the CreateRealtimeEndpoint operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See CreateRealtimeEndpoint for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateRealtimeEndpoint method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the CreateRealtimeEndpointRequest method.
req, resp := client.CreateRealtimeEndpointRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteBatchPrediction 

func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)

DeleteBatchPrediction API operation for Amazon Machine Learning.

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteBatchPrediction for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteBatchPredictionInput{
		BatchPredictionId: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteBatchPrediction(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteBatchPredictionRequest 

func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)

DeleteBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the DeleteBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteBatchPredictionRequest method.
req, resp := client.DeleteBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteDataSource 

func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)

DeleteDataSource API operation for Amazon Machine Learning.

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteDataSource for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteDataSourceInput{
		DataSourceId: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteDataSource(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteDataSourceRequest 

func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)

DeleteDataSourceRequest generates a "aws/request.Request" representing the client's request for the DeleteDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteDataSource for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteDataSource method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteDataSourceRequest method.
req, resp := client.DeleteDataSourceRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteEvaluation 

func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)

DeleteEvaluation API operation for Amazon Machine Learning.

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

CautionThe results of the DeleteEvaluation operation are irreversible.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteEvaluation for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteEvaluationInput{
		EvaluationId: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteEvaluation(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteEvaluationRequest 

func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)

DeleteEvaluationRequest generates a "aws/request.Request" representing the client's request for the DeleteEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteEvaluationRequest method.
req, resp := client.DeleteEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteMLModel 

func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)

DeleteMLModel API operation for Amazon Machine Learning.

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteMLModel for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteMLModelInput{
		MLModelId: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteMLModel(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteMLModelRequest 

func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)

DeleteMLModelRequest generates a "aws/request.Request" representing the client's request for the DeleteMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteMLModelRequest method.
req, resp := client.DeleteMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteRealtimeEndpoint 

func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)

DeleteRealtimeEndpoint API operation for Amazon Machine Learning.

Deletes a real time endpoint of an MLModel.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteRealtimeEndpoint for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteRealtimeEndpointInput{
		MLModelId: aws.String("EntityId"), // Required
	}
	resp, err := svc.DeleteRealtimeEndpoint(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteRealtimeEndpointRequest 

func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)

DeleteRealtimeEndpointRequest generates a "aws/request.Request" representing the client's request for the DeleteRealtimeEndpoint operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteRealtimeEndpoint for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteRealtimeEndpoint method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteRealtimeEndpointRequest method.
req, resp := client.DeleteRealtimeEndpointRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DeleteTags added in v1.1.33 

func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)

DeleteTags API operation for Amazon Machine Learning.

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.

If you specify a tag that doesn't exist, Amazon ML ignores it.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DeleteTags for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInvalidTagException "InvalidTagException"

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DeleteTagsInput{
		ResourceId:   aws.String("EntityId"),             // Required
		ResourceType: aws.String("TaggableResourceType"), // Required
		TagKeys: []*string{ // Required
			aws.String("TagKey"), // Required
			// More values...
		},
	}
	resp, err := svc.DeleteTags(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DeleteTagsRequest added in v1.1.33 

func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)

DeleteTagsRequest generates a "aws/request.Request" representing the client's request for the DeleteTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DeleteTags for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteTags method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DeleteTagsRequest method.
req, resp := client.DeleteTagsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeBatchPredictions 

func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)

DescribeBatchPredictions API operation for Amazon Machine Learning.

Returns a list of BatchPrediction operations that match the search criteria in the request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeBatchPredictions for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DescribeBatchPredictionsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("BatchPredictionFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Int64(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeBatchPredictions(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DescribeBatchPredictionsPages 

func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, fn func(p *DescribeBatchPredictionsOutput, lastPage bool) (shouldContinue bool)) error

DescribeBatchPredictionsPages iterates over the pages of a DescribeBatchPredictions operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeBatchPredictions method for more information on how to use this operation.

Note: This operation can generate multiple requests to a service. 

// Example iterating over at most 3 pages of a DescribeBatchPredictions operation.
pageNum := 0
err := client.DescribeBatchPredictionsPages(params,
    func(page *DescribeBatchPredictionsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeBatchPredictionsRequest 

func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)

DescribeBatchPredictionsRequest generates a "aws/request.Request" representing the client's request for the DescribeBatchPredictions operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeBatchPredictions for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeBatchPredictions method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DescribeBatchPredictionsRequest method.
req, resp := client.DescribeBatchPredictionsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeDataSources 

func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)

DescribeDataSources API operation for Amazon Machine Learning.

Returns a list of DataSource that match the search criteria in the request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeDataSources for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DescribeDataSourcesInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("DataSourceFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Int64(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeDataSources(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DescribeDataSourcesPages 

func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, fn func(p *DescribeDataSourcesOutput, lastPage bool) (shouldContinue bool)) error

DescribeDataSourcesPages iterates over the pages of a DescribeDataSources operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeDataSources method for more information on how to use this operation.

Note: This operation can generate multiple requests to a service. 

// Example iterating over at most 3 pages of a DescribeDataSources operation.
pageNum := 0
err := client.DescribeDataSourcesPages(params,
    func(page *DescribeDataSourcesOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeDataSourcesRequest 

func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)

DescribeDataSourcesRequest generates a "aws/request.Request" representing the client's request for the DescribeDataSources operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeDataSources for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeDataSources method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DescribeDataSourcesRequest method.
req, resp := client.DescribeDataSourcesRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeEvaluations 

func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)

DescribeEvaluations API operation for Amazon Machine Learning.

Returns a list of DescribeEvaluations that match the search criteria in the request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeEvaluations for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DescribeEvaluationsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("EvaluationFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Int64(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeEvaluations(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DescribeEvaluationsPages 

func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, fn func(p *DescribeEvaluationsOutput, lastPage bool) (shouldContinue bool)) error

DescribeEvaluationsPages iterates over the pages of a DescribeEvaluations operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeEvaluations method for more information on how to use this operation.

Note: This operation can generate multiple requests to a service. 

// Example iterating over at most 3 pages of a DescribeEvaluations operation.
pageNum := 0
err := client.DescribeEvaluationsPages(params,
    func(page *DescribeEvaluationsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeEvaluationsRequest 

func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)

DescribeEvaluationsRequest generates a "aws/request.Request" representing the client's request for the DescribeEvaluations operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeEvaluations for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeEvaluations method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DescribeEvaluationsRequest method.
req, resp := client.DescribeEvaluationsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeMLModels 

func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)

DescribeMLModels API operation for Amazon Machine Learning.

Returns a list of MLModel that match the search criteria in the request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeMLModels for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DescribeMLModelsInput{
		EQ:             aws.String("ComparatorValue"),
		FilterVariable: aws.String("MLModelFilterVariable"),
		GE:             aws.String("ComparatorValue"),
		GT:             aws.String("ComparatorValue"),
		LE:             aws.String("ComparatorValue"),
		LT:             aws.String("ComparatorValue"),
		Limit:          aws.Int64(1),
		NE:             aws.String("ComparatorValue"),
		NextToken:      aws.String("StringType"),
		Prefix:         aws.String("ComparatorValue"),
		SortOrder:      aws.String("SortOrder"),
	}
	resp, err := svc.DescribeMLModels(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DescribeMLModelsPages 

func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(p *DescribeMLModelsOutput, lastPage bool) (shouldContinue bool)) error

DescribeMLModelsPages iterates over the pages of a DescribeMLModels operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeMLModels method for more information on how to use this operation.

Note: This operation can generate multiple requests to a service. 

// Example iterating over at most 3 pages of a DescribeMLModels operation.
pageNum := 0
err := client.DescribeMLModelsPages(params,
    func(page *DescribeMLModelsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeMLModelsRequest 

func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)

DescribeMLModelsRequest generates a "aws/request.Request" representing the client's request for the DescribeMLModels operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeMLModels for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeMLModels method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DescribeMLModelsRequest method.
req, resp := client.DescribeMLModelsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeTags added in v1.1.33 

func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)

DescribeTags API operation for Amazon Machine Learning.

Describes one or more of the tags for your Amazon ML object.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeTags for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.DescribeTagsInput{
		ResourceId:   aws.String("EntityId"),             // Required
		ResourceType: aws.String("TaggableResourceType"), // Required
	}
	resp, err := svc.DescribeTags(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) DescribeTagsRequest added in v1.1.33 

func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)

DescribeTagsRequest generates a "aws/request.Request" representing the client's request for the DescribeTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeTags for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeTags method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the DescribeTagsRequest method.
req, resp := client.DescribeTagsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetBatchPrediction 

func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)

GetBatchPrediction API operation for Amazon Machine Learning.

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetBatchPrediction for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.GetBatchPredictionInput{
		BatchPredictionId: aws.String("EntityId"), // Required
	}
	resp, err := svc.GetBatchPrediction(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) GetBatchPredictionRequest 

func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)

GetBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the GetBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the GetBatchPredictionRequest method.
req, resp := client.GetBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetDataSource 

func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)

GetDataSource API operation for Amazon Machine Learning.

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetDataSource for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.GetDataSourceInput{
		DataSourceId: aws.String("EntityId"), // Required
		Verbose:      aws.Bool(true),
	}
	resp, err := svc.GetDataSource(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) GetDataSourceRequest 

func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)

GetDataSourceRequest generates a "aws/request.Request" representing the client's request for the GetDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetDataSource for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetDataSource method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the GetDataSourceRequest method.
req, resp := client.GetDataSourceRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetEvaluation 

func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)

GetEvaluation API operation for Amazon Machine Learning.

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetEvaluation for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.GetEvaluationInput{
		EvaluationId: aws.String("EntityId"), // Required
	}
	resp, err := svc.GetEvaluation(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) GetEvaluationRequest 

func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)

GetEvaluationRequest generates a "aws/request.Request" representing the client's request for the GetEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the GetEvaluationRequest method.
req, resp := client.GetEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetMLModel 

func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)

GetMLModel API operation for Amazon Machine Learning.

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.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetMLModel for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.GetMLModelInput{
		MLModelId: aws.String("EntityId"), // Required
		Verbose:   aws.Bool(true),
	}
	resp, err := svc.GetMLModel(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) GetMLModelRequest 

func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)

GetMLModelRequest generates a "aws/request.Request" representing the client's request for the GetMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the GetMLModelRequest method.
req, resp := client.GetMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) Predict 

func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)

Predict API operation for Amazon Machine Learning.

Generates a prediction for the observation using the specified ML Model.

NoteNot all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation Predict for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeLimitExceededException "LimitExceededException" The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

  • ErrCodePredictorNotMountedException "PredictorNotMountedException" The exception is thrown when a predict request is made to an unmounted MLModel.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.PredictInput{
		MLModelId:       aws.String("EntityId"), // Required
		PredictEndpoint: aws.String("VipURL"),   // Required
		Record: map[string]*string{ // Required
			"Key": aws.String("VariableValue"), // Required
			// More values...
		},
	}
	resp, err := svc.Predict(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) PredictRequest 

func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)

PredictRequest generates a "aws/request.Request" representing the client's request for the Predict operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See Predict for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the Predict method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the PredictRequest method.
req, resp := client.PredictRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateBatchPrediction 

func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)

UpdateBatchPrediction API operation for Amazon Machine Learning.

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateBatchPrediction for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.UpdateBatchPredictionInput{
		BatchPredictionId:   aws.String("EntityId"),   // Required
		BatchPredictionName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateBatchPrediction(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) UpdateBatchPredictionRequest 

func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)

UpdateBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the UpdateBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the UpdateBatchPredictionRequest method.
req, resp := client.UpdateBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateDataSource 

func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)

UpdateDataSource API operation for Amazon Machine Learning.

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateDataSource for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.UpdateDataSourceInput{
		DataSourceId:   aws.String("EntityId"),   // Required
		DataSourceName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateDataSource(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) UpdateDataSourceRequest 

func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)

UpdateDataSourceRequest generates a "aws/request.Request" representing the client's request for the UpdateDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateDataSource for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateDataSource method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the UpdateDataSourceRequest method.
req, resp := client.UpdateDataSourceRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateEvaluation 

func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)

UpdateEvaluation API operation for Amazon Machine Learning.

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateEvaluation for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.UpdateEvaluationInput{
		EvaluationId:   aws.String("EntityId"),   // Required
		EvaluationName: aws.String("EntityName"), // Required
	}
	resp, err := svc.UpdateEvaluation(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) UpdateEvaluationRequest 

func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)

UpdateEvaluationRequest generates a "aws/request.Request" representing the client's request for the UpdateEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the UpdateEvaluationRequest method.
req, resp := client.UpdateEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateMLModel 

func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)

UpdateMLModel API operation for Amazon Machine Learning.

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateMLModel for usage and error information.

Returned Error Codes:

  • ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.

  • ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.

  • ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.

Example 
package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/machinelearning"
)

func main() {
	sess := session.Must(session.NewSession())

	svc := machinelearning.New(sess)

	params := &machinelearning.UpdateMLModelInput{
		MLModelId:      aws.String("EntityId"), // Required
		MLModelName:    aws.String("EntityName"),
		ScoreThreshold: aws.Float64(1.0),
	}
	resp, err := svc.UpdateMLModel(params)

	if err != nil {
		// Print the error, cast err to awserr.Error to get the Code and
		// Message from an error.
		fmt.Println(err.Error())
		return
	}

	// Pretty-print the response data.
	fmt.Println(resp)
}

Output:

func (*MachineLearning) UpdateMLModelRequest 

func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)

UpdateMLModelRequest generates a "aws/request.Request" representing the client's request for the UpdateMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request. 

// Example sending a request using the UpdateMLModelRequest method.
req, resp := client.UpdateMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) WaitUntilBatchPredictionAvailable added in v1.1.16 

func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error

WaitUntilBatchPredictionAvailable uses the Amazon Machine Learning API operation DescribeBatchPredictions to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilDataSourceAvailable added in v1.1.16 

func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error

WaitUntilDataSourceAvailable uses the Amazon Machine Learning API operation DescribeDataSources to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilEvaluationAvailable added in v1.1.16 

func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error

WaitUntilEvaluationAvailable uses the Amazon Machine Learning API operation DescribeEvaluations to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilMLModelAvailable added in v1.1.16 

func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error

WaitUntilMLModelAvailable uses the Amazon Machine Learning API operation DescribeMLModels to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

type PerformanceMetrics 

type PerformanceMetrics struct {
	Properties map[string]*string `type:"map"`
	// contains filtered or unexported fields
}

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

  • BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

  • RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

  • MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).

func (PerformanceMetrics) GoString added in v0.6.6 

func (s PerformanceMetrics) GoString() string

GoString returns the string representation

func (*PerformanceMetrics) SetProperties added in v1.5.1 

func (s *PerformanceMetrics) SetProperties(v map[string]*string) *PerformanceMetrics

SetProperties sets the Properties field's value.

func (PerformanceMetrics) String added in v0.6.6 

func (s PerformanceMetrics) String() string

String returns the string representation

type PredictInput 

type PredictInput struct {

	// A unique identifier of the MLModel.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`

	// PredictEndpoint is a required field
	PredictEndpoint *string `type:"string" required:"true"`

	// A map of variable name-value pairs that represent an observation.
	//
	// Record is a required field
	Record map[string]*string `type:"map" required:"true"`
	// contains filtered or unexported fields
}

func (PredictInput) GoString added in v0.6.6 

func (s PredictInput) GoString() string

GoString returns the string representation

func (*PredictInput) SetMLModelId added in v1.5.1 

func (s *PredictInput) SetMLModelId(v string) *PredictInput

SetMLModelId sets the MLModelId field's value.

func (*PredictInput) SetPredictEndpoint added in v1.5.1 

func (s *PredictInput) SetPredictEndpoint(v string) *PredictInput

SetPredictEndpoint sets the PredictEndpoint field's value.

func (*PredictInput) SetRecord added in v1.5.1 

func (s *PredictInput) SetRecord(v map[string]*string) *PredictInput

SetRecord sets the Record field's value.

func (PredictInput) String added in v0.6.6 

func (s PredictInput) String() string

String returns the string representation

func (*PredictInput) Validate added in v1.1.24 

func (s *PredictInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type PredictOutput 

type PredictOutput struct {

	// The output from a Predict operation:
	//
	//    * Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
	//    - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD
	//
	//    * PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.
	//
	//
	//    * PredictedScores - Contains the raw classification score corresponding
	//    to each label.
	//
	//    * PredictedValue - Present for a REGRESSIONMLModel request.
	Prediction *Prediction `type:"structure"`
	// contains filtered or unexported fields
}

func (PredictOutput) GoString added in v0.6.6 

func (s PredictOutput) GoString() string

GoString returns the string representation

func (*PredictOutput) SetPrediction added in v1.5.1 

func (s *PredictOutput) SetPrediction(v *Prediction) *PredictOutput

SetPrediction sets the Prediction field's value.

func (PredictOutput) String added in v0.6.6 

func (s PredictOutput) String() string

String returns the string representation

type Prediction 

type Prediction struct {

	// Provides any additional details regarding the prediction.
	Details map[string]*string `locationName:"details" type:"map"`

	// The prediction label for either a BINARY or MULTICLASSMLModel.
	PredictedLabel *string `locationName:"predictedLabel" min:"1" type:"string"`

	// Provides the raw classification score corresponding to each label.
	PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"`

	// The prediction value for REGRESSIONMLModel
	PredictedValue *float64 `locationName:"predictedValue" type:"float"`
	// contains filtered or unexported fields
}

The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE

  • REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

  • PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

  • PredictedScores - Contains the raw classification score corresponding to each label.

  • PredictedValue - Present for a REGRESSIONMLModel request.

func (Prediction) GoString added in v0.6.6 

func (s Prediction) GoString() string

GoString returns the string representation

func (*Prediction) SetDetails added in v1.5.1 

func (s *Prediction) SetDetails(v map[string]*string) *Prediction

SetDetails sets the Details field's value.

func (*Prediction) SetPredictedLabel added in v1.5.1 

func (s *Prediction) SetPredictedLabel(v string) *Prediction

SetPredictedLabel sets the PredictedLabel field's value.

func (*Prediction) SetPredictedScores added in v1.5.1 

func (s *Prediction) SetPredictedScores(v map[string]*float64) *Prediction

SetPredictedScores sets the PredictedScores field's value.

func (*Prediction) SetPredictedValue added in v1.5.1 

func (s *Prediction) SetPredictedValue(v float64) *Prediction

SetPredictedValue sets the PredictedValue field's value.

func (Prediction) String added in v0.6.6 

func (s Prediction) String() string

String returns the string representation

type RDSDataSpec 

type RDSDataSpec struct {

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin
	//
	// Use percentBegin to indicate the beginning of the range of the data used
	//    to create the Datasource. If you do not include percentBegin and percentEnd,
	//    Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd
	//
	// Use percentEnd to indicate the end of the range of the data used to create
	//    the Datasource. If you do not include percentBegin and percentEnd, Amazon
	//    ML includes all of the data when creating the datasource.
	//
	//    * complement
	//
	// The complement parameter instructs Amazon ML to use the data that is not
	//    included in the range of percentBegin to percentEnd to create a datasource.
	//    The complement parameter is useful if you need to create complementary
	//    datasources for training and evaluation. To create a complementary datasource,
	//    use the same values for percentBegin and percentEnd, along with the complement
	//    parameter.
	//
	// For example, the following two datasources do not share any data, and can
	//    be used to train and evaluate a model. The first datasource has 25 percent
	//    of the data, and the second one has 75 percent of the data.
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
	//
	// Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25,
	//    "complement":"true"}}
	//
	//    * strategy
	//
	// To change how Amazon ML splits the data for a datasource, use the strategy
	//    parameter.
	//
	// The default value for the strategy parameter is sequential, meaning that
	//    Amazon ML takes all of the data records between the percentBegin and percentEnd
	//    parameters for the datasource, in the order that the records appear in
	//    the input data.
	//
	// The following two DataRearrangement lines are examples of sequentially ordered
	//    training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential", "complement":"true"}}
	//
	// To randomly split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records.
	//
	// The following two DataRearrangement lines are examples of non-sequentially
	//    ordered training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon RDS DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// The Amazon S3 location of the DataSchema.
	DataSchemaUri *string `type:"string"`

	// The AWS Identity and Access Management (IAM) credentials that are used connect
	// to the Amazon RDS database.
	//
	// DatabaseCredentials is a required field
	DatabaseCredentials *RDSDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
	//
	// DatabaseInformation is a required field
	DatabaseInformation *RDSDatabase `type:"structure" required:"true"`

	// The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
	// Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS
	// to an Amazon S3 task. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ResourceRole is a required field
	ResourceRole *string `min:"1" type:"string" required:"true"`

	// The Amazon S3 location for staging Amazon RDS data. The data retrieved from
	// Amazon RDS using SelectSqlQuery is stored in this location.
	//
	// S3StagingLocation is a required field
	S3StagingLocation *string `type:"string" required:"true"`

	// The security group IDs to be used to access a VPC-based RDS DB instance.
	// Ensure that there are appropriate ingress rules set up to allow access to
	// the RDS DB instance. This attribute is used by Data Pipeline to carry out
	// the copy operation from Amazon RDS to an Amazon S3 task.
	//
	// SecurityGroupIds is a required field
	SecurityGroupIds []*string `type:"list" required:"true"`

	// The query that is used to retrieve the observation data for the DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" type:"string" required:"true"`

	// The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to
	// monitor the progress of the copy task from Amazon RDS to Amazon S3. For more
	// information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ServiceRole is a required field
	ServiceRole *string `min:"1" type:"string" required:"true"`

	// The subnet ID to be used to access a VPC-based RDS DB instance. This attribute
	// is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon
	// S3.
	//
	// SubnetId is a required field
	SubnetId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

func (RDSDataSpec) GoString added in v0.6.6 

func (s RDSDataSpec) GoString() string

GoString returns the string representation

func (*RDSDataSpec) SetDataRearrangement added in v1.5.1 

func (s *RDSDataSpec) SetDataRearrangement(v string) *RDSDataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*RDSDataSpec) SetDataSchema added in v1.5.1 

func (s *RDSDataSpec) SetDataSchema(v string) *RDSDataSpec

SetDataSchema sets the DataSchema field's value.

func (*RDSDataSpec) SetDataSchemaUri added in v1.5.1 

func (s *RDSDataSpec) SetDataSchemaUri(v string) *RDSDataSpec

SetDataSchemaUri sets the DataSchemaUri field's value.

func (*RDSDataSpec) SetDatabaseCredentials added in v1.5.1 

func (s *RDSDataSpec) SetDatabaseCredentials(v *RDSDatabaseCredentials) *RDSDataSpec

SetDatabaseCredentials sets the DatabaseCredentials field's value.

func (*RDSDataSpec) SetDatabaseInformation added in v1.5.1 

func (s *RDSDataSpec) SetDatabaseInformation(v *RDSDatabase) *RDSDataSpec

SetDatabaseInformation sets the DatabaseInformation field's value.

func (*RDSDataSpec) SetResourceRole added in v1.5.1 

func (s *RDSDataSpec) SetResourceRole(v string) *RDSDataSpec

SetResourceRole sets the ResourceRole field's value.

func (*RDSDataSpec) SetS3StagingLocation added in v1.5.1 

func (s *RDSDataSpec) SetS3StagingLocation(v string) *RDSDataSpec

SetS3StagingLocation sets the S3StagingLocation field's value.

func (*RDSDataSpec) SetSecurityGroupIds added in v1.5.1 

func (s *RDSDataSpec) SetSecurityGroupIds(v []*string) *RDSDataSpec

SetSecurityGroupIds sets the SecurityGroupIds field's value.

func (*RDSDataSpec) SetSelectSqlQuery added in v1.5.1 

func (s *RDSDataSpec) SetSelectSqlQuery(v string) *RDSDataSpec

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (*RDSDataSpec) SetServiceRole added in v1.5.1 

func (s *RDSDataSpec) SetServiceRole(v string) *RDSDataSpec

SetServiceRole sets the ServiceRole field's value.

func (*RDSDataSpec) SetSubnetId added in v1.5.1 

func (s *RDSDataSpec) SetSubnetId(v string) *RDSDataSpec

SetSubnetId sets the SubnetId field's value.

func (RDSDataSpec) String added in v0.6.6 

func (s RDSDataSpec) String() string

String returns the string representation

func (*RDSDataSpec) Validate added in v1.1.24 

func (s *RDSDataSpec) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RDSDatabase 

type RDSDatabase struct {

	// The name of a database hosted on an RDS DB instance.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`

	// The ID of an RDS DB instance.
	//
	// InstanceIdentifier is a required field
	InstanceIdentifier *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The database details of an Amazon RDS database.

func (RDSDatabase) GoString added in v0.6.6 

func (s RDSDatabase) GoString() string

GoString returns the string representation

func (*RDSDatabase) SetDatabaseName added in v1.5.1 

func (s *RDSDatabase) SetDatabaseName(v string) *RDSDatabase

SetDatabaseName sets the DatabaseName field's value.

func (*RDSDatabase) SetInstanceIdentifier added in v1.5.1 

func (s *RDSDatabase) SetInstanceIdentifier(v string) *RDSDatabase

SetInstanceIdentifier sets the InstanceIdentifier field's value.

func (RDSDatabase) String added in v0.6.6 

func (s RDSDatabase) String() string

String returns the string representation

func (*RDSDatabase) Validate added in v1.1.24 

func (s *RDSDatabase) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RDSDatabaseCredentials 

type RDSDatabaseCredentials struct {

	// The password to be used by Amazon ML to connect to a database on an RDS DB
	// instance. The password should have sufficient permissions to execute the
	// RDSSelectQuery query.
	//
	// Password is a required field
	Password *string `min:"8" type:"string" required:"true"`

	// The username to be used by Amazon ML to connect to database on an Amazon
	// RDS instance. The username should have sufficient permissions to execute
	// an RDSSelectSqlQuery query.
	//
	// Username is a required field
	Username *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The database credentials to connect to a database on an RDS DB instance.

func (RDSDatabaseCredentials) GoString added in v0.6.6 

func (s RDSDatabaseCredentials) GoString() string

GoString returns the string representation

func (*RDSDatabaseCredentials) SetPassword added in v1.5.1 

func (s *RDSDatabaseCredentials) SetPassword(v string) *RDSDatabaseCredentials

SetPassword sets the Password field's value.

func (*RDSDatabaseCredentials) SetUsername added in v1.5.1 

func (s *RDSDatabaseCredentials) SetUsername(v string) *RDSDatabaseCredentials

SetUsername sets the Username field's value.

func (RDSDatabaseCredentials) String added in v0.6.6 

func (s RDSDatabaseCredentials) String() string

String returns the string representation

func (*RDSDatabaseCredentials) Validate added in v1.1.24 

func (s *RDSDatabaseCredentials) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RDSMetadata 

type RDSMetadata struct {

	// The ID of the Data Pipeline instance that is used to carry to copy data from
	// Amazon RDS to Amazon S3. You can use the ID to find details about the instance
	// in the Data Pipeline console.
	DataPipelineId *string `min:"1" type:"string"`

	// The database details required to connect to an Amazon RDS.
	Database *RDSDatabase `type:"structure"`

	// The username to be used by Amazon ML to connect to database on an Amazon
	// RDS instance. The username should have sufficient permissions to execute
	// an RDSSelectSqlQuery query.
	DatabaseUserName *string `min:"1" type:"string"`

	// The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance
	// to carry out the copy task from Amazon RDS to Amazon S3. For more information,
	// see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ResourceRole *string `min:"1" type:"string"`

	// The SQL query that is supplied during CreateDataSourceFromRDS. Returns only
	// if Verbose is true in GetDataSourceInput.
	SelectSqlQuery *string `min:"1" type:"string"`

	// The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to
	// monitor the progress of the copy task from Amazon RDS to Amazon S3. For more
	// information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	ServiceRole *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

The datasource details that are specific to Amazon RDS.

func (RDSMetadata) GoString added in v0.6.6 

func (s RDSMetadata) GoString() string

GoString returns the string representation

func (*RDSMetadata) SetDataPipelineId added in v1.5.1 

func (s *RDSMetadata) SetDataPipelineId(v string) *RDSMetadata

SetDataPipelineId sets the DataPipelineId field's value.

func (*RDSMetadata) SetDatabase added in v1.5.1 

func (s *RDSMetadata) SetDatabase(v *RDSDatabase) *RDSMetadata

SetDatabase sets the Database field's value.

func (*RDSMetadata) SetDatabaseUserName added in v1.5.1 

func (s *RDSMetadata) SetDatabaseUserName(v string) *RDSMetadata

SetDatabaseUserName sets the DatabaseUserName field's value.

func (*RDSMetadata) SetResourceRole added in v1.5.1 

func (s *RDSMetadata) SetResourceRole(v string) *RDSMetadata

SetResourceRole sets the ResourceRole field's value.

func (*RDSMetadata) SetSelectSqlQuery added in v1.5.1 

func (s *RDSMetadata) SetSelectSqlQuery(v string) *RDSMetadata

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (*RDSMetadata) SetServiceRole added in v1.5.1 

func (s *RDSMetadata) SetServiceRole(v string) *RDSMetadata

SetServiceRole sets the ServiceRole field's value.

func (RDSMetadata) String added in v0.6.6 

func (s RDSMetadata) String() string

String returns the string representation

type RealtimeEndpointInfo 

type RealtimeEndpointInfo struct {

	// The time that the request to create the real-time endpoint for the MLModel
	// was received. The time is expressed in epoch time.
	CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

	// The current status of the real-time endpoint for the MLModel. This element
	// can have one of the following values:
	//
	//    * NONE - Endpoint does not exist or was previously deleted.
	//    * READY - Endpoint is ready to be used for real-time predictions.
	//    * UPDATING - Updating/creating the endpoint.
	EndpointStatus *string `type:"string" enum:"RealtimeEndpointStatus"`

	// The URI that specifies where to send real-time prediction requests for the
	// MLModel.
	//
	// NoteThe application must wait until the real-time endpoint is ready before
	// using this URI.
	EndpointUrl *string `type:"string"`

	// The maximum processing rate for the real-time endpoint for MLModel, measured
	// in incoming requests per second.
	PeakRequestsPerSecond *int64 `type:"integer"`
	// contains filtered or unexported fields
}

Describes the real-time endpoint information for an MLModel.

func (RealtimeEndpointInfo) GoString added in v0.6.6 

func (s RealtimeEndpointInfo) GoString() string

GoString returns the string representation

func (*RealtimeEndpointInfo) SetCreatedAt added in v1.5.1 

func (s *RealtimeEndpointInfo) SetCreatedAt(v time.Time) *RealtimeEndpointInfo

SetCreatedAt sets the CreatedAt field's value.

func (*RealtimeEndpointInfo) SetEndpointStatus added in v1.5.1 

func (s *RealtimeEndpointInfo) SetEndpointStatus(v string) *RealtimeEndpointInfo

SetEndpointStatus sets the EndpointStatus field's value.

func (*RealtimeEndpointInfo) SetEndpointUrl added in v1.5.1 

func (s *RealtimeEndpointInfo) SetEndpointUrl(v string) *RealtimeEndpointInfo

SetEndpointUrl sets the EndpointUrl field's value.

func (*RealtimeEndpointInfo) SetPeakRequestsPerSecond added in v1.5.1 

func (s *RealtimeEndpointInfo) SetPeakRequestsPerSecond(v int64) *RealtimeEndpointInfo

SetPeakRequestsPerSecond sets the PeakRequestsPerSecond field's value.

func (RealtimeEndpointInfo) String added in v0.6.6 

func (s RealtimeEndpointInfo) String() string

String returns the string representation

type RedshiftDataSpec 

type RedshiftDataSpec struct {

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin
	//
	// Use percentBegin to indicate the beginning of the range of the data used
	//    to create the Datasource. If you do not include percentBegin and percentEnd,
	//    Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd
	//
	// Use percentEnd to indicate the end of the range of the data used to create
	//    the Datasource. If you do not include percentBegin and percentEnd, Amazon
	//    ML includes all of the data when creating the datasource.
	//
	//    * complement
	//
	// The complement parameter instructs Amazon ML to use the data that is not
	//    included in the range of percentBegin to percentEnd to create a datasource.
	//    The complement parameter is useful if you need to create complementary
	//    datasources for training and evaluation. To create a complementary datasource,
	//    use the same values for percentBegin and percentEnd, along with the complement
	//    parameter.
	//
	// For example, the following two datasources do not share any data, and can
	//    be used to train and evaluate a model. The first datasource has 25 percent
	//    of the data, and the second one has 75 percent of the data.
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
	//
	// Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25,
	//    "complement":"true"}}
	//
	//    * strategy
	//
	// To change how Amazon ML splits the data for a datasource, use the strategy
	//    parameter.
	//
	// The default value for the strategy parameter is sequential, meaning that
	//    Amazon ML takes all of the data records between the percentBegin and percentEnd
	//    parameters for the datasource, in the order that the records appear in
	//    the input data.
	//
	// The following two DataRearrangement lines are examples of sequentially ordered
	//    training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential", "complement":"true"}}
	//
	// To randomly split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records.
	//
	// The following two DataRearrangement lines are examples of non-sequentially
	//    ordered training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon Redshift DataSource.
	// The DataSchema defines the structure of the observation data in the data
	// file(s) referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// Describes the schema location for an Amazon Redshift DataSource.
	DataSchemaUri *string `type:"string"`

	// Describes AWS Identity and Access Management (IAM) credentials that are used
	// connect to the Amazon Redshift database.
	//
	// DatabaseCredentials is a required field
	DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
	//
	// DatabaseInformation is a required field
	DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"`

	// Describes an Amazon S3 location to store the result set of the SelectSqlQuery
	// query.
	//
	// S3StagingLocation is a required field
	S3StagingLocation *string `type:"string" required:"true"`

	// Describes the SQL Query to execute on an Amazon Redshift database for an
	// Amazon Redshift DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the data specification of an Amazon Redshift DataSource.

func (RedshiftDataSpec) GoString added in v0.6.6 

func (s RedshiftDataSpec) GoString() string

GoString returns the string representation

func (*RedshiftDataSpec) SetDataRearrangement added in v1.5.1 

func (s *RedshiftDataSpec) SetDataRearrangement(v string) *RedshiftDataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*RedshiftDataSpec) SetDataSchema added in v1.5.1 

func (s *RedshiftDataSpec) SetDataSchema(v string) *RedshiftDataSpec

SetDataSchema sets the DataSchema field's value.

func (*RedshiftDataSpec) SetDataSchemaUri added in v1.5.1 

func (s *RedshiftDataSpec) SetDataSchemaUri(v string) *RedshiftDataSpec

SetDataSchemaUri sets the DataSchemaUri field's value.

func (*RedshiftDataSpec) SetDatabaseCredentials added in v1.5.1 

func (s *RedshiftDataSpec) SetDatabaseCredentials(v *RedshiftDatabaseCredentials) *RedshiftDataSpec

SetDatabaseCredentials sets the DatabaseCredentials field's value.

func (*RedshiftDataSpec) SetDatabaseInformation added in v1.5.1 

func (s *RedshiftDataSpec) SetDatabaseInformation(v *RedshiftDatabase) *RedshiftDataSpec

SetDatabaseInformation sets the DatabaseInformation field's value.

func (*RedshiftDataSpec) SetS3StagingLocation added in v1.5.1 

func (s *RedshiftDataSpec) SetS3StagingLocation(v string) *RedshiftDataSpec

SetS3StagingLocation sets the S3StagingLocation field's value.

func (*RedshiftDataSpec) SetSelectSqlQuery added in v1.5.1 

func (s *RedshiftDataSpec) SetSelectSqlQuery(v string) *RedshiftDataSpec

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (RedshiftDataSpec) String added in v0.6.6 

func (s RedshiftDataSpec) String() string

String returns the string representation

func (*RedshiftDataSpec) Validate added in v1.1.24 

func (s *RedshiftDataSpec) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RedshiftDatabase 

type RedshiftDatabase struct {

	// The ID of an Amazon Redshift cluster.
	//
	// ClusterIdentifier is a required field
	ClusterIdentifier *string `min:"1" type:"string" required:"true"`

	// The name of a database hosted on an Amazon Redshift cluster.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the database details required to connect to an Amazon Redshift database.

func (RedshiftDatabase) GoString added in v0.6.6 

func (s RedshiftDatabase) GoString() string

GoString returns the string representation

func (*RedshiftDatabase) SetClusterIdentifier added in v1.5.1 

func (s *RedshiftDatabase) SetClusterIdentifier(v string) *RedshiftDatabase

SetClusterIdentifier sets the ClusterIdentifier field's value.

func (*RedshiftDatabase) SetDatabaseName added in v1.5.1 

func (s *RedshiftDatabase) SetDatabaseName(v string) *RedshiftDatabase

SetDatabaseName sets the DatabaseName field's value.

func (RedshiftDatabase) String added in v0.6.6 

func (s RedshiftDatabase) String() string

String returns the string representation

func (*RedshiftDatabase) Validate added in v1.1.24 

func (s *RedshiftDatabase) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RedshiftDatabaseCredentials 

type RedshiftDatabaseCredentials struct {

	// A password to be used by Amazon ML to connect to a database on an Amazon
	// Redshift cluster. The password should have sufficient permissions to execute
	// a RedshiftSelectSqlQuery query. The password should be valid for an Amazon
	// Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	//
	// Password is a required field
	Password *string `min:"8" type:"string" required:"true"`

	// A username to be used by Amazon Machine Learning (Amazon ML)to connect to
	// a database on an Amazon Redshift cluster. The username should have sufficient
	// permissions to execute the RedshiftSelectSqlQuery query. The username should
	// be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	//
	// Username is a required field
	Username *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

func (RedshiftDatabaseCredentials) GoString added in v0.6.6 

func (s RedshiftDatabaseCredentials) GoString() string

GoString returns the string representation

func (*RedshiftDatabaseCredentials) SetPassword added in v1.5.1 

func (s *RedshiftDatabaseCredentials) SetPassword(v string) *RedshiftDatabaseCredentials

SetPassword sets the Password field's value.

func (*RedshiftDatabaseCredentials) SetUsername added in v1.5.1 

func (s *RedshiftDatabaseCredentials) SetUsername(v string) *RedshiftDatabaseCredentials

SetUsername sets the Username field's value.

func (RedshiftDatabaseCredentials) String added in v0.6.6 

func (s RedshiftDatabaseCredentials) String() string

String returns the string representation

func (*RedshiftDatabaseCredentials) Validate added in v1.1.24 

func (s *RedshiftDatabaseCredentials) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type RedshiftMetadata 

type RedshiftMetadata struct {

	// A username to be used by Amazon Machine Learning (Amazon ML)to connect to
	// a database on an Amazon Redshift cluster. The username should have sufficient
	// permissions to execute the RedshiftSelectSqlQuery query. The username should
	// be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	DatabaseUserName *string `min:"1" type:"string"`

	// Describes the database details required to connect to an Amazon Redshift
	// database.
	RedshiftDatabase *RedshiftDatabase `type:"structure"`

	// The SQL query that is specified during CreateDataSourceFromRedshift. Returns
	// only if Verbose is true in GetDataSourceInput.
	SelectSqlQuery *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Describes the DataSource details specific to Amazon Redshift.

func (RedshiftMetadata) GoString added in v0.6.6 

func (s RedshiftMetadata) GoString() string

GoString returns the string representation

func (*RedshiftMetadata) SetDatabaseUserName added in v1.5.1 

func (s *RedshiftMetadata) SetDatabaseUserName(v string) *RedshiftMetadata

SetDatabaseUserName sets the DatabaseUserName field's value.

func (*RedshiftMetadata) SetRedshiftDatabase added in v1.5.1 

func (s *RedshiftMetadata) SetRedshiftDatabase(v *RedshiftDatabase) *RedshiftMetadata

SetRedshiftDatabase sets the RedshiftDatabase field's value.

func (*RedshiftMetadata) SetSelectSqlQuery added in v1.5.1 

func (s *RedshiftMetadata) SetSelectSqlQuery(v string) *RedshiftMetadata

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (RedshiftMetadata) String added in v0.6.6 

func (s RedshiftMetadata) String() string

String returns the string representation

type S3DataSpec 

type S3DataSpec struct {

	// The location of the data file(s) used by a DataSource. The URI specifies
	// a data file or an Amazon Simple Storage Service (Amazon S3) directory or
	// bucket containing data files.
	//
	// DataLocationS3 is a required field
	DataLocationS3 *string `type:"string" required:"true"`

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin
	//
	// Use percentBegin to indicate the beginning of the range of the data used
	//    to create the Datasource. If you do not include percentBegin and percentEnd,
	//    Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd
	//
	// Use percentEnd to indicate the end of the range of the data used to create
	//    the Datasource. If you do not include percentBegin and percentEnd, Amazon
	//    ML includes all of the data when creating the datasource.
	//
	//    * complement
	//
	// The complement parameter instructs Amazon ML to use the data that is not
	//    included in the range of percentBegin to percentEnd to create a datasource.
	//    The complement parameter is useful if you need to create complementary
	//    datasources for training and evaluation. To create a complementary datasource,
	//    use the same values for percentBegin and percentEnd, along with the complement
	//    parameter.
	//
	// For example, the following two datasources do not share any data, and can
	//    be used to train and evaluate a model. The first datasource has 25 percent
	//    of the data, and the second one has 75 percent of the data.
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
	//
	// Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25,
	//    "complement":"true"}}
	//
	//    * strategy
	//
	// To change how Amazon ML splits the data for a datasource, use the strategy
	//    parameter.
	//
	// The default value for the strategy parameter is sequential, meaning that
	//    Amazon ML takes all of the data records between the percentBegin and percentEnd
	//    parameters for the datasource, in the order that the records appear in
	//    the input data.
	//
	// The following two DataRearrangement lines are examples of sequentially ordered
	//    training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential", "complement":"true"}}
	//
	// To randomly split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records.
	//
	// The following two DataRearrangement lines are examples of non-sequentially
	//    ordered training and evaluation datasources:
	//
	// Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//
	// Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon S3 DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// You must provide either the DataSchema or the DataSchemaLocationS3.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// Describes the schema location in Amazon S3. You must provide either the DataSchema
	// or the DataSchemaLocationS3.
	DataSchemaLocationS3 *string `type:"string"`
	// contains filtered or unexported fields
}

Describes the data specification of a DataSource.

func (S3DataSpec) GoString added in v0.6.6 

func (s S3DataSpec) GoString() string

GoString returns the string representation

func (*S3DataSpec) SetDataLocationS3 added in v1.5.1 

func (s *S3DataSpec) SetDataLocationS3(v string) *S3DataSpec

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*S3DataSpec) SetDataRearrangement added in v1.5.1 

func (s *S3DataSpec) SetDataRearrangement(v string) *S3DataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*S3DataSpec) SetDataSchema added in v1.5.1 

func (s *S3DataSpec) SetDataSchema(v string) *S3DataSpec

SetDataSchema sets the DataSchema field's value.

func (*S3DataSpec) SetDataSchemaLocationS3 added in v1.5.1 

func (s *S3DataSpec) SetDataSchemaLocationS3(v string) *S3DataSpec

SetDataSchemaLocationS3 sets the DataSchemaLocationS3 field's value.

func (S3DataSpec) String added in v0.6.6 

func (s S3DataSpec) String() string

String returns the string representation

func (*S3DataSpec) Validate added in v1.1.24 

func (s *S3DataSpec) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type Tag added in v1.1.33 

type Tag struct {

	// A unique identifier for the tag. Valid characters include Unicode letters,
	// digits, white space, _, ., /, =, +, -, %, and @.
	Key *string `min:"1" type:"string"`

	// An optional string, typically used to describe or define the tag. Valid characters
	// include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
	Value *string `type:"string"`
	// contains filtered or unexported fields
}

A custom key-value pair associated with an ML object, such as an ML model.

func (Tag) GoString added in v1.1.33 

func (s Tag) GoString() string

GoString returns the string representation

func (*Tag) SetKey added in v1.5.1 

func (s *Tag) SetKey(v string) *Tag

SetKey sets the Key field's value.

func (*Tag) SetValue added in v1.5.1 

func (s *Tag) SetValue(v string) *Tag

SetValue sets the Value field's value.

func (Tag) String added in v1.1.33 

func (s Tag) String() string

String returns the string representation

func (*Tag) Validate added in v1.1.33 

func (s *Tag) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type UpdateBatchPredictionInput 

type UpdateBatchPredictionInput struct {

	// The ID assigned to the BatchPrediction during creation.
	//
	// BatchPredictionId is a required field
	BatchPredictionId *string `min:"1" type:"string" required:"true"`

	// A new user-supplied name or description of the BatchPrediction.
	//
	// BatchPredictionName is a required field
	BatchPredictionName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateBatchPredictionInput) GoString added in v0.6.6 

func (s UpdateBatchPredictionInput) GoString() string

GoString returns the string representation

func (*UpdateBatchPredictionInput) SetBatchPredictionId added in v1.5.1 

func (s *UpdateBatchPredictionInput) SetBatchPredictionId(v string) *UpdateBatchPredictionInput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*UpdateBatchPredictionInput) SetBatchPredictionName added in v1.5.1 

func (s *UpdateBatchPredictionInput) SetBatchPredictionName(v string) *UpdateBatchPredictionInput

SetBatchPredictionName sets the BatchPredictionName field's value.

func (UpdateBatchPredictionInput) String added in v0.6.6 

func (s UpdateBatchPredictionInput) String() string

String returns the string representation

func (*UpdateBatchPredictionInput) Validate added in v1.1.24 

func (s *UpdateBatchPredictionInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type UpdateBatchPredictionOutput 

type UpdateBatchPredictionOutput struct {

	// The ID assigned to the BatchPrediction during creation. This value should
	// be identical to the value of the BatchPredictionId in the request.
	BatchPredictionId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

func (UpdateBatchPredictionOutput) GoString added in v0.6.6 

func (s UpdateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*UpdateBatchPredictionOutput) SetBatchPredictionId added in v1.5.1 

func (s *UpdateBatchPredictionOutput) SetBatchPredictionId(v string) *UpdateBatchPredictionOutput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (UpdateBatchPredictionOutput) String added in v0.6.6 

func (s UpdateBatchPredictionOutput) String() string

String returns the string representation

type UpdateDataSourceInput 

type UpdateDataSourceInput struct {

	// The ID assigned to the DataSource during creation.
	//
	// DataSourceId is a required field
	DataSourceId *string `min:"1" type:"string" required:"true"`

	// A new user-supplied name or description of the DataSource that will replace
	// the current description.
	//
	// DataSourceName is a required field
	DataSourceName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateDataSourceInput) GoString added in v0.6.6 

func (s UpdateDataSourceInput) GoString() string

GoString returns the string representation

func (*UpdateDataSourceInput) SetDataSourceId added in v1.5.1 

func (s *UpdateDataSourceInput) SetDataSourceId(v string) *UpdateDataSourceInput

SetDataSourceId sets the DataSourceId field's value.

func (*UpdateDataSourceInput) SetDataSourceName added in v1.5.1 

func (s *UpdateDataSourceInput) SetDataSourceName(v string) *UpdateDataSourceInput

SetDataSourceName sets the DataSourceName field's value.

func (UpdateDataSourceInput) String added in v0.6.6 

func (s UpdateDataSourceInput) String() string

String returns the string representation

func (*UpdateDataSourceInput) Validate added in v1.1.24 

func (s *UpdateDataSourceInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type UpdateDataSourceOutput 

type UpdateDataSourceOutput struct {

	// The ID assigned to the DataSource during creation. This value should be identical
	// to the value of the DataSourceID in the request.
	DataSourceId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

func (UpdateDataSourceOutput) GoString added in v0.6.6 

func (s UpdateDataSourceOutput) GoString() string

GoString returns the string representation

func (*UpdateDataSourceOutput) SetDataSourceId added in v1.5.1 

func (s *UpdateDataSourceOutput) SetDataSourceId(v string) *UpdateDataSourceOutput

SetDataSourceId sets the DataSourceId field's value.

func (UpdateDataSourceOutput) String added in v0.6.6 

func (s UpdateDataSourceOutput) String() string

String returns the string representation

type UpdateEvaluationInput 

type UpdateEvaluationInput struct {

	// The ID assigned to the Evaluation during creation.
	//
	// EvaluationId is a required field
	EvaluationId *string `min:"1" type:"string" required:"true"`

	// A new user-supplied name or description of the Evaluation that will replace
	// the current content.
	//
	// EvaluationName is a required field
	EvaluationName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateEvaluationInput) GoString added in v0.6.6 

func (s UpdateEvaluationInput) GoString() string

GoString returns the string representation

func (*UpdateEvaluationInput) SetEvaluationId added in v1.5.1 

func (s *UpdateEvaluationInput) SetEvaluationId(v string) *UpdateEvaluationInput

SetEvaluationId sets the EvaluationId field's value.

func (*UpdateEvaluationInput) SetEvaluationName added in v1.5.1 

func (s *UpdateEvaluationInput) SetEvaluationName(v string) *UpdateEvaluationInput

SetEvaluationName sets the EvaluationName field's value.

func (UpdateEvaluationInput) String added in v0.6.6 

func (s UpdateEvaluationInput) String() string

String returns the string representation

func (*UpdateEvaluationInput) Validate added in v1.1.24 

func (s *UpdateEvaluationInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type UpdateEvaluationOutput 

type UpdateEvaluationOutput struct {

	// The ID assigned to the Evaluation during creation. This value should be identical
	// to the value of the Evaluation in the request.
	EvaluationId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

func (UpdateEvaluationOutput) GoString added in v0.6.6 

func (s UpdateEvaluationOutput) GoString() string

GoString returns the string representation

func (*UpdateEvaluationOutput) SetEvaluationId added in v1.5.1 

func (s *UpdateEvaluationOutput) SetEvaluationId(v string) *UpdateEvaluationOutput

SetEvaluationId sets the EvaluationId field's value.

func (UpdateEvaluationOutput) String added in v0.6.6 

func (s UpdateEvaluationOutput) String() string

String returns the string representation

type UpdateMLModelInput 

type UpdateMLModelInput struct {

	// The ID assigned to the MLModel during creation.
	//
	// MLModelId is a required field
	MLModelId *string `min:"1" type:"string" required:"true"`

	// A user-supplied name or description of the MLModel.
	MLModelName *string `type:"string"`

	// The ScoreThreshold used in binary classification MLModel that marks the boundary
	// between a positive prediction and a negative prediction.
	//
	// Output values greater than or equal to the ScoreThreshold receive a positive
	// result from the MLModel, such as true. Output values less than the ScoreThreshold
	// receive a negative response from the MLModel, such as false.
	ScoreThreshold *float64 `type:"float"`
	// contains filtered or unexported fields
}

func (UpdateMLModelInput) GoString added in v0.6.6 

func (s UpdateMLModelInput) GoString() string

GoString returns the string representation

func (*UpdateMLModelInput) SetMLModelId added in v1.5.1 

func (s *UpdateMLModelInput) SetMLModelId(v string) *UpdateMLModelInput

SetMLModelId sets the MLModelId field's value.

func (*UpdateMLModelInput) SetMLModelName added in v1.5.1 

func (s *UpdateMLModelInput) SetMLModelName(v string) *UpdateMLModelInput

SetMLModelName sets the MLModelName field's value.

func (*UpdateMLModelInput) SetScoreThreshold added in v1.5.1 

func (s *UpdateMLModelInput) SetScoreThreshold(v float64) *UpdateMLModelInput

SetScoreThreshold sets the ScoreThreshold field's value.

func (UpdateMLModelInput) String added in v0.6.6 

func (s UpdateMLModelInput) String() string

String returns the string representation

func (*UpdateMLModelInput) Validate added in v1.1.24 

func (s *UpdateMLModelInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

type UpdateMLModelOutput 

type UpdateMLModelOutput struct {

	// The ID assigned to the MLModel during creation. This value should be identical
	// to the value of the MLModelID in the request.
	MLModelId *string `min:"1" type:"string"`
	// contains filtered or unexported fields
}

Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.

func (UpdateMLModelOutput) GoString added in v0.6.6 

func (s UpdateMLModelOutput) GoString() string

GoString returns the string representation

func (*UpdateMLModelOutput) SetMLModelId added in v1.5.1 

func (s *UpdateMLModelOutput) SetMLModelId(v string) *UpdateMLModelOutput

SetMLModelId sets the MLModelId field's value.

func (UpdateMLModelOutput) String added in v0.6.6 

func (s UpdateMLModelOutput) String() string

String returns the string representation

Source Files

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Path Synopsis
Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.
 Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.

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