machinelearning

package
v1.2.5 Latest Latest
Warning

This package is not in the latest version of its module.

 Go to latest Published: Jul 13, 2016 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 (
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableCreatedAt = "CreatedAt"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableLastUpdatedAt = "LastUpdatedAt"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableStatus = "Status"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableName = "Name"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableIamuser = "IAMUser"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableMlmodelId = "MLModelId"
	// @enum BatchPredictionFilterVariable
	BatchPredictionFilterVariableDataSourceId = "DataSourceId"
	// @enum BatchPredictionFilterVariable
	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 BatchPrediction Name. 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 (
	// @enum DataSourceFilterVariable
	DataSourceFilterVariableCreatedAt = "CreatedAt"
	// @enum DataSourceFilterVariable
	DataSourceFilterVariableLastUpdatedAt = "LastUpdatedAt"
	// @enum DataSourceFilterVariable
	DataSourceFilterVariableStatus = "Status"
	// @enum DataSourceFilterVariable
	DataSourceFilterVariableName = "Name"
	// @enum DataSourceFilterVariable
	DataSourceFilterVariableDataLocationS3 = "DataLocationS3"
	// @enum DataSourceFilterVariable
	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 DataSource Name. 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. Note The variable names should match the variable names in the DataSource.

View Source 
const (
	// @enum DetailsAttributes
	DetailsAttributesPredictiveModelType = "PredictiveModelType"
	// @enum DetailsAttributes
	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 (
	// @enum EntityStatus
	EntityStatusPending = "PENDING"
	// @enum EntityStatus
	EntityStatusInprogress = "INPROGRESS"
	// @enum EntityStatus
	EntityStatusFailed = "FAILED"
	// @enum EntityStatus
	EntityStatusCompleted = "COMPLETED"
	// @enum EntityStatus
	EntityStatusDeleted = "DELETED"
)

Object status with the following possible values: 

PENDING INPROGRESS FAILED COMPLETED DELETED
View Source 
const (
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableCreatedAt = "CreatedAt"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableLastUpdatedAt = "LastUpdatedAt"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableStatus = "Status"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableName = "Name"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableIamuser = "IAMUser"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableMlmodelId = "MLModelId"
	// @enum EvaluationFilterVariable
	EvaluationFilterVariableDataSourceId = "DataSourceId"
	// @enum EvaluationFilterVariable
	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 Evaluation Name. 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 (
	// @enum MLModelFilterVariable
	MLModelFilterVariableCreatedAt = "CreatedAt"
	// @enum MLModelFilterVariable
	MLModelFilterVariableLastUpdatedAt = "LastUpdatedAt"
	// @enum MLModelFilterVariable
	MLModelFilterVariableStatus = "Status"
	// @enum MLModelFilterVariable
	MLModelFilterVariableName = "Name"
	// @enum MLModelFilterVariable
	MLModelFilterVariableIamuser = "IAMUser"
	// @enum MLModelFilterVariable
	MLModelFilterVariableTrainingDataSourceId = "TrainingDataSourceId"
	// @enum MLModelFilterVariable
	MLModelFilterVariableRealtimeEndpointStatus = "RealtimeEndpointStatus"
	// @enum MLModelFilterVariable
	MLModelFilterVariableMlmodelType = "MLModelType"
	// @enum MLModelFilterVariable
	MLModelFilterVariableAlgorithm = "Algorithm"
	// @enum MLModelFilterVariable
	MLModelFilterVariableTrainingDataUri = "TrainingDataURI"
)
View Source 
const (
	// @enum MLModelType
	MLModelTypeRegression = "REGRESSION"
	// @enum MLModelType
	MLModelTypeBinary = "BINARY"
	// @enum MLModelType
	MLModelTypeMulticlass = "MULTICLASS"
)
View Source 
const (
	// @enum RealtimeEndpointStatus
	RealtimeEndpointStatusNone = "NONE"
	// @enum RealtimeEndpointStatus
	RealtimeEndpointStatusReady = "READY"
	// @enum RealtimeEndpointStatus
	RealtimeEndpointStatusUpdating = "UPDATING"
	// @enum RealtimeEndpointStatus
	RealtimeEndpointStatusFailed = "FAILED"
)
View Source 
const (
	// @enum SortOrder
	SortOrderAsc = "asc"
	// @enum SortOrder
	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 (
	// @enum TaggableResourceType
	TaggableResourceTypeBatchPrediction = "BatchPrediction"
	// @enum TaggableResourceType
	TaggableResourceTypeDataSource = "DataSource"
	// @enum TaggableResourceType
	TaggableResourceTypeEvaluation = "Evaluation"
	// @enum TaggableResourceType
	TaggableResourceTypeMlmodel = "MLModel"
)
View Source 
const (
	// @enum Algorithm
	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.

View Source 
const ServiceName = "machinelearning"

A ServiceName is the name of the service the client will make API calls to.

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 *string `min:"1" type:"string" required:"true"`

	// The type of the ML object to tag.
	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 []*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) 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) 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"`

	// 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"`

	// 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 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"`

	// 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"`
	// 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 v0.6.5 

func (s BatchPrediction) GoString() string

GoString returns the string representation

func (BatchPrediction) String added in v0.6.5 

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 *string `min:"1" type:"string" required:"true"`

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

func (CreateBatchPredictionInput) GoString added in v0.6.5 

func (s CreateBatchPredictionInput) GoString() string

GoString returns the string representation

func (CreateBatchPredictionInput) String added in v0.6.5 

func (s CreateBatchPredictionInput) String() string

String returns the string representation

func (*CreateBatchPredictionInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (CreateBatchPredictionOutput) String added in v0.6.5 

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 *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:
	//
	//  DatabaseInformation -   DatabaseName - The name of the Amazon RDS database.
	//  InstanceIdentifier  - A unique identifier for the Amazon RDS database instance.
	//
	//
	// DatabaseCredentials - AWS Identity and Access Management (IAM) credentials
	// that are used to connect to the Amazon RDS database.
	//
	// ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2
	// instance to carry out the copy task from Amazon RDS to Amazon Simple Storage
	// Service (Amazon S3). For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
	//
	// SecurityInfo - The security information to use to access an RDS DB instance.
	// You need to set up appropriate ingress rules for the security entity IDs
	// provided to allow access to the Amazon RDS instance. Specify a [SubnetId,
	// SecurityGroupIds] pair for a VPC-based RDS DB instance.
	//
	// SelectSqlQuery - A query that is used to retrieve the observation data for
	// the Datasource.
	//
	// S3StagingLocation - The Amazon S3 location for staging Amazon RDS data.
	// The data retrieved from Amazon RDS using SelectSqlQuery 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}}"
	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 *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (CreateDataSourceFromRDSInput) GoString added in v0.6.5 

func (s CreateDataSourceFromRDSInput) GoString() string

GoString returns the string representation

func (CreateDataSourceFromRDSInput) String added in v0.6.5 

func (s CreateDataSourceFromRDSInput) String() string

String returns the string representation

func (*CreateDataSourceFromRDSInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateDataSourceFromRDSOutput) GoString() string

GoString returns the string representation

func (CreateDataSourceFromRDSOutput) String added in v0.6.5 

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

func (CreateDataSourceFromRedshiftInput) GoString added in v0.6.5 

func (s CreateDataSourceFromRedshiftInput) GoString() string

GoString returns the string representation

func (CreateDataSourceFromRedshiftInput) String added in v0.6.5 

func (s CreateDataSourceFromRedshiftInput) String() string

String returns the string representation

func (*CreateDataSourceFromRedshiftInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateDataSourceFromRedshiftOutput) GoString() string

GoString returns the string representation

func (CreateDataSourceFromRedshiftOutput) String added in v0.6.5 

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 *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 *S3DataSpec `type:"structure" required:"true"`
	// contains filtered or unexported fields
}

func (CreateDataSourceFromS3Input) GoString added in v0.6.5 

func (s CreateDataSourceFromS3Input) GoString() string

GoString returns the string representation

func (CreateDataSourceFromS3Input) String added in v0.6.5 

func (s CreateDataSourceFromS3Input) String() string

String returns the string representation

func (*CreateDataSourceFromS3Input) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateDataSourceFromS3Output) GoString() string

GoString returns the string representation

func (CreateDataSourceFromS3Output) String added in v0.6.5 

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 *string `min:"1" type:"string" required:"true"`

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

func (CreateEvaluationInput) GoString added in v0.6.5 

func (s CreateEvaluationInput) GoString() string

GoString returns the string representation

func (CreateEvaluationInput) String added in v0.6.5 

func (s CreateEvaluationInput) String() string

String returns the string representation

func (*CreateEvaluationInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateEvaluationOutput) GoString() string

GoString returns the string representation

func (CreateEvaluationOutput) String added in v0.6.5 

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

func (CreateMLModelInput) GoString added in v0.6.5 

func (s CreateMLModelInput) GoString() string

GoString returns the string representation

func (CreateMLModelInput) String added in v0.6.5 

func (s CreateMLModelInput) String() string

String returns the string representation

func (*CreateMLModelInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateMLModelOutput) GoString() string

GoString returns the string representation

func (CreateMLModelOutput) String added in v0.6.5 

func (s CreateMLModelOutput) String() string

String returns the string representation

type CreateRealtimeEndpointInput 

type CreateRealtimeEndpointInput struct {

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

func (CreateRealtimeEndpointInput) GoString added in v0.6.5 

func (s CreateRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (CreateRealtimeEndpointInput) String added in v0.6.5 

func (s CreateRealtimeEndpointInput) String() string

String returns the string representation

func (*CreateRealtimeEndpointInput) Validate added in v1.1.21 

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 v0.6.5 

func (s CreateRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (CreateRealtimeEndpointOutput) String added in v0.6.5 

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"`

	// 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"`

	// 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"`

	// 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 v0.6.5 

func (s DataSource) GoString() string

GoString returns the string representation

func (DataSource) String added in v0.6.5 

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

func (DeleteBatchPredictionInput) GoString added in v0.6.5 

func (s DeleteBatchPredictionInput) GoString() string

GoString returns the string representation

func (DeleteBatchPredictionInput) String added in v0.6.5 

func (s DeleteBatchPredictionInput) String() string

String returns the string representation

func (*DeleteBatchPredictionInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DeleteBatchPredictionOutput) GoString() string

GoString returns the string representation

func (DeleteBatchPredictionOutput) String added in v0.6.5 

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

func (DeleteDataSourceInput) GoString added in v0.6.5 

func (s DeleteDataSourceInput) GoString() string

GoString returns the string representation

func (DeleteDataSourceInput) String added in v0.6.5 

func (s DeleteDataSourceInput) String() string

String returns the string representation

func (*DeleteDataSourceInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DeleteDataSourceOutput) GoString() string

GoString returns the string representation

func (DeleteDataSourceOutput) String added in v0.6.5 

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

func (DeleteEvaluationInput) GoString added in v0.6.5 

func (s DeleteEvaluationInput) GoString() string

GoString returns the string representation

func (DeleteEvaluationInput) String added in v0.6.5 

func (s DeleteEvaluationInput) String() string

String returns the string representation

func (*DeleteEvaluationInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DeleteEvaluationOutput) GoString() string

GoString returns the string representation

func (DeleteEvaluationOutput) String added in v0.6.5 

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

func (DeleteMLModelInput) GoString added in v0.6.5 

func (s DeleteMLModelInput) GoString() string

GoString returns the string representation

func (DeleteMLModelInput) String added in v0.6.5 

func (s DeleteMLModelInput) String() string

String returns the string representation

func (*DeleteMLModelInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DeleteMLModelOutput) GoString() string

GoString returns the string representation

func (DeleteMLModelOutput) String added in v0.6.5 

func (s DeleteMLModelOutput) String() string

String returns the string representation

type DeleteRealtimeEndpointInput 

type DeleteRealtimeEndpointInput struct {

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

func (DeleteRealtimeEndpointInput) GoString added in v0.6.5 

func (s DeleteRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (DeleteRealtimeEndpointInput) String added in v0.6.5 

func (s DeleteRealtimeEndpointInput) String() string

String returns the string representation

func (*DeleteRealtimeEndpointInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DeleteRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (DeleteRealtimeEndpointOutput) String added in v0.6.5 

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 *string `min:"1" type:"string" required:"true"`

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

	// One or more tags to delete.
	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) 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) 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 BatchPrediction  Name.  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 Name 2014-09-09-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 v0.6.5 

func (s DescribeBatchPredictionsInput) GoString() string

GoString returns the string representation

func (DescribeBatchPredictionsInput) String added in v0.6.5 

func (s DescribeBatchPredictionsInput) String() string

String returns the string representation

func (*DescribeBatchPredictionsInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DescribeBatchPredictionsOutput) GoString() string

GoString returns the string representation

func (DescribeBatchPredictionsOutput) String added in v0.6.5 

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 DataSource   Name.  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 Name 2014-09-09-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 v0.6.5 

func (s DescribeDataSourcesInput) GoString() string

GoString returns the string representation

func (DescribeDataSourcesInput) String added in v0.6.5 

func (s DescribeDataSourcesInput) String() string

String returns the string representation

func (*DescribeDataSourcesInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DescribeDataSourcesOutput) GoString() string

GoString returns the string representation

func (DescribeDataSourcesOutput) String added in v0.6.5 

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 Evaluation   Name.  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 Name 2014-09-09-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 v0.6.5 

func (s DescribeEvaluationsInput) GoString() string

GoString returns the string representation

func (DescribeEvaluationsInput) String added in v0.6.5 

func (s DescribeEvaluationsInput) String() string

String returns the string representation

func (*DescribeEvaluationsInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DescribeEvaluationsOutput) GoString() string

GoString returns the string representation

func (DescribeEvaluationsOutput) String added in v0.6.5 

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 MLModel  Name.  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 Name 2014-09-09-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 v0.6.5 

func (s DescribeMLModelsInput) GoString() string

GoString returns the string representation

func (DescribeMLModelsInput) String added in v0.6.5 

func (s DescribeMLModelsInput) String() string

String returns the string representation

func (*DescribeMLModelsInput) Validate added in v1.1.21 

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 v0.6.5 

func (s DescribeMLModelsOutput) GoString() string

GoString returns the string representation

func (DescribeMLModelsOutput) String added in v0.6.5 

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 *string `min:"1" type:"string" required:"true"`

	// The type of the ML object.
	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) 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) String added in v1.1.33 

func (s DescribeTagsOutput) String() string

String returns the string representation

type Evaluation 

type Evaluation struct {

	// 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"`

	// 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"`

	// 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 v0.6.5 

func (s Evaluation) GoString() string

GoString returns the string representation

func (Evaluation) String added in v0.6.5 

func (s Evaluation) String() string

String returns the string representation

type GetBatchPredictionInput 

type GetBatchPredictionInput struct {

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

func (GetBatchPredictionInput) GoString added in v0.6.5 

func (s GetBatchPredictionInput) GoString() string

GoString returns the string representation

func (GetBatchPredictionInput) String added in v0.6.5 

func (s GetBatchPredictionInput) String() string

String returns the string representation

func (*GetBatchPredictionInput) Validate added in v1.1.21 

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 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 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 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 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"`
	// contains filtered or unexported fields
}

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

func (GetBatchPredictionOutput) GoString added in v0.6.5 

func (s GetBatchPredictionOutput) GoString() string

GoString returns the string representation

func (GetBatchPredictionOutput) String added in v0.6.5 

func (s GetBatchPredictionOutput) String() string

String returns the string representation

type GetDataSourceInput 

type GetDataSourceInput struct {

	// The ID assigned to the DataSource at creation.
	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 v0.6.5 

func (s GetDataSourceInput) GoString() string

GoString returns the string representation

func (GetDataSourceInput) String added in v0.6.5 

func (s GetDataSourceInput) String() string

String returns the string representation

func (*GetDataSourceInput) Validate added in v1.1.21 

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 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.
	//
	// Note This parameter is provided as part of the verbose format.
	DataSourceSchema *string `type:"string"`

	// 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 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 v0.6.5 

func (s GetDataSourceOutput) GoString() string

GoString returns the string representation

func (GetDataSourceOutput) String added in v0.6.5 

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

func (GetEvaluationInput) GoString added in v0.6.5 

func (s GetEvaluationInput) GoString() string

GoString returns the string representation

func (GetEvaluationInput) String added in v0.6.5 

func (s GetEvaluationInput) String() string

String returns the string representation

func (*GetEvaluationInput) Validate added in v1.1.21 

func (s *GetEvaluationInput) Validate() error

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

type GetEvaluationOutput 

type GetEvaluationOutput struct {

	// 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 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 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 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 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 v0.6.5 

func (s GetEvaluationOutput) GoString() string

GoString returns the string representation

func (GetEvaluationOutput) String added in v0.6.5 

func (s GetEvaluationOutput) String() string

String returns the string representation

type GetMLModelInput 

type GetMLModelInput struct {

	// The ID assigned to the MLModel at creation.
	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 v0.6.5 

func (s GetMLModelInput) GoString() string

GoString returns the string representation

func (GetMLModelInput) String added in v0.6.5 

func (s GetMLModelInput) String() string

String returns the string representation

func (*GetMLModelInput) Validate added in v1.1.21 

func (s *GetMLModelInput) Validate() error

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

type GetMLModelOutput 

type GetMLModelOutput struct {

	// 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 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.
	//
	// Note This 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.
	//
	// Note This parameter is provided as part of the verbose format.
	Schema *string `type:"string"`

	// The scoring threshold is used in binary classification MLModel models. It
	// marks the boundary between a positive prediction and a negative prediction.
	//
	// Output values greater than or equal to the threshold receive a positive
	// result from the MLModel, such as true. Output values less than the threshold
	// receive a negative response from the MLModel, such as false.
	ScoreThreshold *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 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 v0.6.5 

func (s GetMLModelOutput) GoString() string

GoString returns the string representation

func (GetMLModelOutput) String added in v0.6.5 

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"`

	// 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 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"`

	// 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 v0.6.5 

func (s MLModel) GoString() string

GoString returns the string representation

func (MLModel) String added in v0.6.5 

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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 CreateDataSourcceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

Caution The results of the DeleteEvaluation operation are irreversible.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Deletes a real time endpoint of an 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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns a list of BatchPrediction operations that match the search criteria in the 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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns a list of DataSource that match the search criteria in the 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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns a list of DescribeEvaluations that match the search criteria in the 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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns a list of MLModel that match the search criteria in the 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() {
	svc := machinelearning.New(session.New())

	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.

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)

Describes one or more of the tags for your Amazon ML object.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction 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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

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.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Generates a prediction for the observation using the specified ML Model.

Note Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

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() {
	svc := machinelearning.New(session.New())

	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.

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)

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

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() {
	svc := machinelearning.New(session.New())

	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.

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 v1.1.15 

func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error

func (*MachineLearning) WaitUntilDataSourceAvailable added in v1.1.15 

func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error

func (*MachineLearning) WaitUntilEvaluationAvailable added in v1.1.15 

func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error

func (*MachineLearning) WaitUntilMLModelAvailable added in v1.1.15 

func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error

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 v0.6.5 

func (s PerformanceMetrics) GoString() string

GoString returns the string representation

func (PerformanceMetrics) String added in v0.6.5 

func (s PerformanceMetrics) String() string

String returns the string representation

type PredictInput 

type PredictInput struct {

	// A unique identifier of the MLModel.
	MLModelId *string `min:"1" type:"string" required:"true"`

	PredictEndpoint *string `type:"string" required:"true"`

	// A map of variable name-value pairs that represent an observation.
	Record map[string]*string `type:"map" required:"true"`
	// contains filtered or unexported fields
}

func (PredictInput) GoString added in v0.6.5 

func (s PredictInput) GoString() string

GoString returns the string representation

func (PredictInput) String added in v0.6.5 

func (s PredictInput) String() string

String returns the string representation

func (*PredictInput) Validate added in v1.1.21 

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 | MULTICLASS DetailsAttributes.ALGORITHM - SGD
	//
	//    PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
	//
	//    PredictedScores - Contains the raw classification score corresponding
	// to each label.
	//
	//    PredictedValue - Present for a REGRESSION MLModel request.
	Prediction *Prediction `type:"structure"`
	// contains filtered or unexported fields
}

func (PredictOutput) GoString added in v0.6.5 

func (s PredictOutput) GoString() string

GoString returns the string representation

func (PredictOutput) String added in v0.6.5 

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 MULTICLASS MLModel.
	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 REGRESSION MLModel.
	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 | MULTICLASS DetailsAttributes.ALGORITHM - SGD 

PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.

PredictedScores - Contains the raw classification score corresponding

to each label. 

PredictedValue - Present for a REGRESSION MLModel request.

func (Prediction) GoString added in v0.6.5 

func (s Prediction) GoString() string

GoString returns the string representation

func (Prediction) String added in v0.6.5 

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 *RDSDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
	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 *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 *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 []*string `type:"list" required:"true"`

	// The query that is used to retrieve the observation data for the DataSource.
	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 *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 *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 v0.6.5 

func (s RDSDataSpec) GoString() string

GoString returns the string representation

func (RDSDataSpec) String added in v0.6.5 

func (s RDSDataSpec) String() string

String returns the string representation

func (*RDSDataSpec) Validate added in v1.1.21 

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 *string `min:"1" type:"string" required:"true"`

	// The ID of an RDS DB instance.
	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 v0.6.5 

func (s RDSDatabase) GoString() string

GoString returns the string representation

func (RDSDatabase) String added in v0.6.5 

func (s RDSDatabase) String() string

String returns the string representation

func (*RDSDatabase) Validate added in v1.1.21 

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 *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 *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 v0.6.5 

func (s RDSDatabaseCredentials) GoString() string

GoString returns the string representation

func (RDSDatabaseCredentials) String added in v0.6.5 

func (s RDSDatabaseCredentials) String() string

String returns the string representation

func (*RDSDatabaseCredentials) Validate added in v1.1.21 

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 v0.6.5 

func (s RDSMetadata) GoString() string

GoString returns the string representation

func (RDSMetadata) String added in v0.6.5 

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.
	//
	// Note The 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 v0.6.5 

func (s RealtimeEndpointInfo) GoString() string

GoString returns the string representation

func (RealtimeEndpointInfo) String added in v0.6.5 

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 *RedshiftDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
	DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"`

	// Describes an Amazon S3 location to store the result set of the SelectSqlQuery
	// query.
	S3StagingLocation *string `type:"string" required:"true"`

	// Describes the SQL Query to execute on an Amazon Redshift database for an
	// Amazon Redshift DataSource.
	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 v0.6.5 

func (s RedshiftDataSpec) GoString() string

GoString returns the string representation

func (RedshiftDataSpec) String added in v0.6.5 

func (s RedshiftDataSpec) String() string

String returns the string representation

func (*RedshiftDataSpec) Validate added in v1.1.21 

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 *string `min:"1" type:"string" required:"true"`

	// The name of a database hosted on an Amazon Redshift cluster.
	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 v0.6.5 

func (s RedshiftDatabase) GoString() string

GoString returns the string representation

func (RedshiftDatabase) String added in v0.6.5 

func (s RedshiftDatabase) String() string

String returns the string representation

func (*RedshiftDatabase) Validate added in v1.1.21 

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 *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 *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 v0.6.5 

func (s RedshiftDatabaseCredentials) GoString() string

GoString returns the string representation

func (RedshiftDatabaseCredentials) String added in v0.6.5 

func (s RedshiftDatabaseCredentials) String() string

String returns the string representation

func (*RedshiftDatabaseCredentials) Validate added in v1.1.21 

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 v0.6.5 

func (s RedshiftMetadata) GoString() string

GoString returns the string representation

func (RedshiftMetadata) String added in v0.6.5 

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 *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 v0.6.5 

func (s S3DataSpec) GoString() string

GoString returns the string representation

func (S3DataSpec) String added in v0.6.5 

func (s S3DataSpec) String() string

String returns the string representation

func (*S3DataSpec) Validate added in v1.1.21 

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) 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 *string `min:"1" type:"string" required:"true"`

	// A new user-supplied name or description of the BatchPrediction.
	BatchPredictionName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateBatchPredictionInput) GoString added in v0.6.5 

func (s UpdateBatchPredictionInput) GoString() string

GoString returns the string representation

func (UpdateBatchPredictionInput) String added in v0.6.5 

func (s UpdateBatchPredictionInput) String() string

String returns the string representation

func (*UpdateBatchPredictionInput) Validate added in v1.1.21 

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 v0.6.5 

func (s UpdateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (UpdateBatchPredictionOutput) String added in v0.6.5 

func (s UpdateBatchPredictionOutput) String() string

String returns the string representation

type UpdateDataSourceInput 

type UpdateDataSourceInput struct {

	// The ID assigned to the DataSource during creation.
	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 *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateDataSourceInput) GoString added in v0.6.5 

func (s UpdateDataSourceInput) GoString() string

GoString returns the string representation

func (UpdateDataSourceInput) String added in v0.6.5 

func (s UpdateDataSourceInput) String() string

String returns the string representation

func (*UpdateDataSourceInput) Validate added in v1.1.21 

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 v0.6.5 

func (s UpdateDataSourceOutput) GoString() string

GoString returns the string representation

func (UpdateDataSourceOutput) String added in v0.6.5 

func (s UpdateDataSourceOutput) String() string

String returns the string representation

type UpdateEvaluationInput 

type UpdateEvaluationInput struct {

	// The ID assigned to the Evaluation during creation.
	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 *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateEvaluationInput) GoString added in v0.6.5 

func (s UpdateEvaluationInput) GoString() string

GoString returns the string representation

func (UpdateEvaluationInput) String added in v0.6.5 

func (s UpdateEvaluationInput) String() string

String returns the string representation

func (*UpdateEvaluationInput) Validate added in v1.1.21 

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 v0.6.5 

func (s UpdateEvaluationOutput) GoString() string

GoString returns the string representation

func (UpdateEvaluationOutput) String added in v0.6.5 

func (s UpdateEvaluationOutput) String() string

String returns the string representation

type UpdateMLModelInput 

type UpdateMLModelInput struct {

	// The ID assigned to the MLModel during creation.
	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 v0.6.5 

func (s UpdateMLModelInput) GoString() string

GoString returns the string representation

func (UpdateMLModelInput) String added in v0.6.5 

func (s UpdateMLModelInput) String() string

String returns the string representation

func (*UpdateMLModelInput) Validate added in v1.1.21 

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 v0.6.5 

func (s UpdateMLModelOutput) GoString() string

GoString returns the string representation

func (UpdateMLModelOutput) String added in v0.6.5 

func (s UpdateMLModelOutput) String() string

String returns the string representation

Source Files

View all Source files

Directories

Path Synopsis
Package machinelearningiface provides an interface for the Amazon Machine Learning.
 Package machinelearningiface provides an interface for the Amazon Machine Learning.

Jump to

Keyboard shortcuts

? : This menu
/ : Search site
f or F : Jump to
y or Y : Canonical URL
go.dev uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more.