machinelearning

package
v0.13.0 Latest Latest
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 Go to latest Published: Oct 2, 2019 License: Apache-2.0 Imports: 8 Imported by: 22

Documentation

Overview

Package machinelearning provides the client and types for making API requests to Amazon Machine Learning.

Definition of the public APIs exposed by Amazon Machine Learning

See machinelearning package documentation for more information. https://docs.aws.amazon.com/sdk-for-go/api/service/machinelearning/

Using the Client

To use Amazon Machine Learning with the SDK use the New function to create a new service client. With that client you can make API requests to the service. These clients are safe to use concurrently.

See the SDK's documentation for more information on how to use the SDK. https://docs.aws.amazon.com/sdk-for-go/api/

See aws.Config documentation for more information on configuring SDK clients. https://docs.aws.amazon.com/sdk-for-go/api/aws/#Config

See the Amazon Machine Learning client for more information on creating client for this service. https://docs.aws.amazon.com/sdk-for-go/api/service/machinelearning/#New

Index

Constants

View Source 
const (
	ServiceName = "Amazon Machine Learning" // Service's name
	ServiceID   = "MachineLearning"         // Service's identifier
	EndpointsID = "machinelearning"         // Service's Endpoint identifier
)
View Source 
const (

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

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

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

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

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

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

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

	// ErrCodeTagLimitExceededException for service response error code
	// "TagLimitExceededException".
	ErrCodeTagLimitExceededException = "TagLimitExceededException"
)

Variables

This section is empty.

Functions

This section is empty.

Types

type AddTagsInput 

type AddTagsInput struct {

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

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

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

func (AddTagsInput) String 

func (s AddTagsInput) String() string

String returns the string representation

func (*AddTagsInput) Validate 

func (s *AddTagsInput) Validate() error

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

type AddTagsOutput 

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 TaggableResourceType `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

Amazon ML returns the following elements.

func (AddTagsOutput) String 

func (s AddTagsOutput) String() string

String returns the string representation

type AddTagsRequest 

type AddTagsRequest struct {
	*aws.Request
	Input *AddTagsInput
	Copy  func(*AddTagsInput) AddTagsRequest
}

AddTagsRequest is the request type for the AddTags API operation.

func (AddTagsRequest) Send 

func (r AddTagsRequest) Send(ctx context.Context) (*AddTagsResponse, error)

Send marshals and sends the AddTags API request.

type AddTagsResponse added in v0.9.0 

type AddTagsResponse struct {
	*AddTagsOutput
	// contains filtered or unexported fields
}

AddTagsResponse is the response type for the AddTags API operation.

func (*AddTagsResponse) SDKResponseMetdata added in v0.9.0 

func (r *AddTagsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the AddTags request.

type Algorithm 

type Algorithm string

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. 

const (
	AlgorithmSgd Algorithm = "sgd"
)

Enum values for Algorithm

func (Algorithm) MarshalValue added in v0.3.0 

func (enum Algorithm) MarshalValue() (string, error)

func (Algorithm) MarshalValueBuf added in v0.3.0 

func (enum Algorithm) MarshalValueBuf(b []byte) ([]byte, error)

type BatchPrediction 

type BatchPrediction struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Represents the output of a GetBatchPrediction operation.

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

func (BatchPrediction) String 

func (s BatchPrediction) String() string

String returns the string representation

type BatchPredictionFilterVariable 

type BatchPredictionFilterVariable string

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. 

const (
	BatchPredictionFilterVariableCreatedAt     BatchPredictionFilterVariable = "CreatedAt"
	BatchPredictionFilterVariableLastUpdatedAt BatchPredictionFilterVariable = "LastUpdatedAt"
	BatchPredictionFilterVariableStatus        BatchPredictionFilterVariable = "Status"
	BatchPredictionFilterVariableName          BatchPredictionFilterVariable = "Name"
	BatchPredictionFilterVariableIamuser       BatchPredictionFilterVariable = "IAMUser"
	BatchPredictionFilterVariableMlmodelId     BatchPredictionFilterVariable = "MLModelId"
	BatchPredictionFilterVariableDataSourceId  BatchPredictionFilterVariable = "DataSourceId"
	BatchPredictionFilterVariableDataUri       BatchPredictionFilterVariable = "DataURI"
)

Enum values for BatchPredictionFilterVariable

func (BatchPredictionFilterVariable) MarshalValue added in v0.3.0 

func (enum BatchPredictionFilterVariable) MarshalValue() (string, error)

func (BatchPredictionFilterVariable) MarshalValueBuf added in v0.3.0 

func (enum BatchPredictionFilterVariable) MarshalValueBuf(b []byte) ([]byte, error)

type Client added in v0.9.0 

type Client struct {
	*aws.Client
}

Client provides the API operation methods for making requests to Amazon Machine Learning. See this package's package overview docs for details on the service.

The client's methods are safe to use concurrently. It is not safe to modify mutate any of the struct's properties though.

func New 

func New(config aws.Config) *Client

New creates a new instance of the client from the provided Config.

Example: 

// Create a client from just a config.
svc := machinelearning.New(myConfig)

func (*Client) AddTagsRequest added in v0.9.0 

func (c *Client) AddTagsRequest(input *AddTagsInput) AddTagsRequest

AddTagsRequest returns a request value for making API operation for Amazon Machine Learning.

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

// Example sending a request using AddTagsRequest.
req := client.AddTagsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateBatchPredictionRequest added in v0.9.0 

func (c *Client) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) CreateBatchPredictionRequest

CreateBatchPredictionRequest returns a request value for making API operation for Amazon Machine Learning.

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

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

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

// Example sending a request using CreateBatchPredictionRequest.
req := client.CreateBatchPredictionRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateDataSourceFromRDSRequest added in v0.9.0 

func (c *Client) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) CreateDataSourceFromRDSRequest

CreateDataSourceFromRDSRequest returns a request value for making API operation for Amazon Machine Learning.

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

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

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

// Example sending a request using CreateDataSourceFromRDSRequest.
req := client.CreateDataSourceFromRDSRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateDataSourceFromRedshiftRequest added in v0.9.0 

func (c *Client) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) CreateDataSourceFromRedshiftRequest

CreateDataSourceFromRedshiftRequest returns a request value for making API operation for Amazon Machine Learning.

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

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

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

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

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

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

// Example sending a request using CreateDataSourceFromRedshiftRequest.
req := client.CreateDataSourceFromRedshiftRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateDataSourceFromS3Request added in v0.9.0 

func (c *Client) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) CreateDataSourceFromS3Request

CreateDataSourceFromS3Request returns a request value for making API operation for Amazon Machine Learning.

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

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

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

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

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

// Example sending a request using CreateDataSourceFromS3Request.
req := client.CreateDataSourceFromS3Request(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateEvaluationRequest added in v0.9.0 

func (c *Client) CreateEvaluationRequest(input *CreateEvaluationInput) CreateEvaluationRequest

CreateEvaluationRequest returns a request value for making API operation for Amazon Machine Learning.

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

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

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

// Example sending a request using CreateEvaluationRequest.
req := client.CreateEvaluationRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateMLModelRequest added in v0.9.0 

func (c *Client) CreateMLModelRequest(input *CreateMLModelInput) CreateMLModelRequest

CreateMLModelRequest returns a request value for making API operation for Amazon Machine Learning.

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

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

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations. 

// Example sending a request using CreateMLModelRequest.
req := client.CreateMLModelRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) CreateRealtimeEndpointRequest added in v0.9.0 

func (c *Client) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) CreateRealtimeEndpointRequest

CreateRealtimeEndpointRequest returns a request value for making API operation for Amazon Machine Learning.

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

// Example sending a request using CreateRealtimeEndpointRequest.
req := client.CreateRealtimeEndpointRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteBatchPredictionRequest added in v0.9.0 

func (c *Client) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) DeleteBatchPredictionRequest

DeleteBatchPredictionRequest returns a request value for making API operation for Amazon Machine Learning.

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible. 

// Example sending a request using DeleteBatchPredictionRequest.
req := client.DeleteBatchPredictionRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteDataSourceRequest added in v0.9.0 

func (c *Client) DeleteDataSourceRequest(input *DeleteDataSourceInput) DeleteDataSourceRequest

DeleteDataSourceRequest returns a request value for making API operation for Amazon Machine Learning.

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible. 

// Example sending a request using DeleteDataSourceRequest.
req := client.DeleteDataSourceRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteEvaluationRequest added in v0.9.0 

func (c *Client) DeleteEvaluationRequest(input *DeleteEvaluationInput) DeleteEvaluationRequest

DeleteEvaluationRequest returns a request value for making API operation for Amazon Machine Learning.

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED. 

Caution

The results of the DeleteEvaluation operation are irreversible. 

// Example sending a request using DeleteEvaluationRequest.
req := client.DeleteEvaluationRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteMLModelRequest added in v0.9.0 

func (c *Client) DeleteMLModelRequest(input *DeleteMLModelInput) DeleteMLModelRequest

DeleteMLModelRequest returns a request value for making API operation for Amazon Machine Learning.

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible. 

// Example sending a request using DeleteMLModelRequest.
req := client.DeleteMLModelRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteRealtimeEndpointRequest added in v0.9.0 

func (c *Client) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) DeleteRealtimeEndpointRequest

DeleteRealtimeEndpointRequest returns a request value for making API operation for Amazon Machine Learning.

Deletes a real time endpoint of an MLModel. 

// Example sending a request using DeleteRealtimeEndpointRequest.
req := client.DeleteRealtimeEndpointRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DeleteTagsRequest added in v0.9.0 

func (c *Client) DeleteTagsRequest(input *DeleteTagsInput) DeleteTagsRequest

DeleteTagsRequest returns a request value for making API operation for Amazon Machine Learning.

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.

If you specify a tag that doesn't exist, Amazon ML ignores it. 

// Example sending a request using DeleteTagsRequest.
req := client.DeleteTagsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DescribeBatchPredictionsRequest added in v0.9.0 

func (c *Client) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) DescribeBatchPredictionsRequest

DescribeBatchPredictionsRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a list of BatchPrediction operations that match the search criteria in the request. 

// Example sending a request using DescribeBatchPredictionsRequest.
req := client.DescribeBatchPredictionsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DescribeDataSourcesRequest added in v0.9.0 

func (c *Client) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) DescribeDataSourcesRequest

DescribeDataSourcesRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a list of DataSource that match the search criteria in the request. 

// Example sending a request using DescribeDataSourcesRequest.
req := client.DescribeDataSourcesRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DescribeEvaluationsRequest added in v0.9.0 

func (c *Client) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) DescribeEvaluationsRequest

DescribeEvaluationsRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a list of DescribeEvaluations that match the search criteria in the request. 

// Example sending a request using DescribeEvaluationsRequest.
req := client.DescribeEvaluationsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DescribeMLModelsRequest added in v0.9.0 

func (c *Client) DescribeMLModelsRequest(input *DescribeMLModelsInput) DescribeMLModelsRequest

DescribeMLModelsRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a list of MLModel that match the search criteria in the request. 

// Example sending a request using DescribeMLModelsRequest.
req := client.DescribeMLModelsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) DescribeTagsRequest added in v0.9.0 

func (c *Client) DescribeTagsRequest(input *DescribeTagsInput) DescribeTagsRequest

DescribeTagsRequest returns a request value for making API operation for Amazon Machine Learning.

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

// Example sending a request using DescribeTagsRequest.
req := client.DescribeTagsRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) GetBatchPredictionRequest added in v0.9.0 

func (c *Client) GetBatchPredictionRequest(input *GetBatchPredictionInput) GetBatchPredictionRequest

GetBatchPredictionRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request. 

// Example sending a request using GetBatchPredictionRequest.
req := client.GetBatchPredictionRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) GetDataSourceRequest added in v0.9.0 

func (c *Client) GetDataSourceRequest(input *GetDataSourceInput) GetDataSourceRequest

GetDataSourceRequest returns a request value for making API operation for Amazon Machine Learning.

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format. 

// Example sending a request using GetDataSourceRequest.
req := client.GetDataSourceRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) GetEvaluationRequest added in v0.9.0 

func (c *Client) GetEvaluationRequest(input *GetEvaluationInput) GetEvaluationRequest

GetEvaluationRequest returns a request value for making API operation for Amazon Machine Learning.

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

// Example sending a request using GetEvaluationRequest.
req := client.GetEvaluationRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) GetMLModelRequest added in v0.9.0 

func (c *Client) GetMLModelRequest(input *GetMLModelInput) GetMLModelRequest

GetMLModelRequest returns a request value for making API operation for Amazon Machine Learning.

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format. 

// Example sending a request using GetMLModelRequest.
req := client.GetMLModelRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) PredictRequest added in v0.9.0 

func (c *Client) PredictRequest(input *PredictInput) PredictRequest

PredictRequest returns a request value for making API operation for Amazon Machine Learning.

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 sending a request using PredictRequest.
req := client.PredictRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) UpdateBatchPredictionRequest added in v0.9.0 

func (c *Client) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) UpdateBatchPredictionRequest

UpdateBatchPredictionRequest returns a request value for making API operation for Amazon Machine Learning.

Updates the BatchPredictionName of a BatchPrediction.

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

// Example sending a request using UpdateBatchPredictionRequest.
req := client.UpdateBatchPredictionRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) UpdateDataSourceRequest added in v0.9.0 

func (c *Client) UpdateDataSourceRequest(input *UpdateDataSourceInput) UpdateDataSourceRequest

UpdateDataSourceRequest returns a request value for making API operation for Amazon Machine Learning.

Updates the DataSourceName of a DataSource.

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

// Example sending a request using UpdateDataSourceRequest.
req := client.UpdateDataSourceRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) UpdateEvaluationRequest added in v0.9.0 

func (c *Client) UpdateEvaluationRequest(input *UpdateEvaluationInput) UpdateEvaluationRequest

UpdateEvaluationRequest returns a request value for making API operation for Amazon Machine Learning.

Updates the EvaluationName of an Evaluation.

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

// Example sending a request using UpdateEvaluationRequest.
req := client.UpdateEvaluationRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) UpdateMLModelRequest added in v0.9.0 

func (c *Client) UpdateMLModelRequest(input *UpdateMLModelInput) UpdateMLModelRequest

UpdateMLModelRequest returns a request value for making API operation for Amazon Machine Learning.

Updates the MLModelName and the ScoreThreshold of an MLModel.

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

// Example sending a request using UpdateMLModelRequest.
req := client.UpdateMLModelRequest(params)
resp, err := req.Send(context.TODO())
if err == nil {
    fmt.Println(resp)
}

func (*Client) WaitUntilBatchPredictionAvailable added in v0.9.0 

func (c *Client) WaitUntilBatchPredictionAvailable(ctx context.Context, input *DescribeBatchPredictionsInput, opts ...aws.WaiterOption) error

WaitUntilBatchPredictionAvailable uses the Amazon Machine Learning API operation DescribeBatchPredictions to wait for a condition to be met before returning. If the condition is not met within the max attempt window, an error will be returned.

The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.

func (*Client) WaitUntilDataSourceAvailable added in v0.9.0 

func (c *Client) WaitUntilDataSourceAvailable(ctx context.Context, input *DescribeDataSourcesInput, opts ...aws.WaiterOption) error

WaitUntilDataSourceAvailable uses the Amazon Machine Learning API operation DescribeDataSources to wait for a condition to be met before returning. If the condition is not met within the max attempt window, an error will be returned.

The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.

func (*Client) WaitUntilEvaluationAvailable added in v0.9.0 

func (c *Client) WaitUntilEvaluationAvailable(ctx context.Context, input *DescribeEvaluationsInput, opts ...aws.WaiterOption) error

WaitUntilEvaluationAvailable uses the Amazon Machine Learning API operation DescribeEvaluations to wait for a condition to be met before returning. If the condition is not met within the max attempt window, an error will be returned.

The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.

func (*Client) WaitUntilMLModelAvailable added in v0.9.0 

func (c *Client) WaitUntilMLModelAvailable(ctx context.Context, input *DescribeMLModelsInput, opts ...aws.WaiterOption) error

WaitUntilMLModelAvailable uses the Amazon Machine Learning API operation DescribeMLModels to wait for a condition to be met before returning. If the condition is not met within the max attempt window, an error will be returned.

The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.

type CreateBatchPredictionInput 

type CreateBatchPredictionInput struct {

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

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

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

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

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

func (CreateBatchPredictionInput) String 

func (s CreateBatchPredictionInput) String() string

String returns the string representation

func (*CreateBatchPredictionInput) Validate 

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) String 

func (s CreateBatchPredictionOutput) String() string

String returns the string representation

type CreateBatchPredictionRequest 

type CreateBatchPredictionRequest struct {
	*aws.Request
	Input *CreateBatchPredictionInput
	Copy  func(*CreateBatchPredictionInput) CreateBatchPredictionRequest
}

CreateBatchPredictionRequest is the request type for the CreateBatchPrediction API operation.

func (CreateBatchPredictionRequest) Send 

func (r CreateBatchPredictionRequest) Send(ctx context.Context) (*CreateBatchPredictionResponse, error)

Send marshals and sends the CreateBatchPrediction API request.

type CreateBatchPredictionResponse added in v0.9.0 

type CreateBatchPredictionResponse struct {
	*CreateBatchPredictionOutput
	// contains filtered or unexported fields
}

CreateBatchPredictionResponse is the response type for the CreateBatchPrediction API operation.

func (*CreateBatchPredictionResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateBatchPredictionResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateBatchPrediction request.

type CreateDataSourceFromRDSInput 

type CreateDataSourceFromRDSInput struct {

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

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

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

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

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

func (CreateDataSourceFromRDSInput) String 

func (s CreateDataSourceFromRDSInput) String() string

String returns the string representation

func (*CreateDataSourceFromRDSInput) Validate 

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) String 

func (s CreateDataSourceFromRDSOutput) String() string

String returns the string representation

type CreateDataSourceFromRDSRequest 

type CreateDataSourceFromRDSRequest struct {
	*aws.Request
	Input *CreateDataSourceFromRDSInput
	Copy  func(*CreateDataSourceFromRDSInput) CreateDataSourceFromRDSRequest
}

CreateDataSourceFromRDSRequest is the request type for the CreateDataSourceFromRDS API operation.

func (CreateDataSourceFromRDSRequest) Send 

func (r CreateDataSourceFromRDSRequest) Send(ctx context.Context) (*CreateDataSourceFromRDSResponse, error)

Send marshals and sends the CreateDataSourceFromRDS API request.

type CreateDataSourceFromRDSResponse added in v0.9.0 

type CreateDataSourceFromRDSResponse struct {
	*CreateDataSourceFromRDSOutput
	// contains filtered or unexported fields
}

CreateDataSourceFromRDSResponse is the response type for the CreateDataSourceFromRDS API operation.

func (*CreateDataSourceFromRDSResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateDataSourceFromRDSResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateDataSourceFromRDS request.

type CreateDataSourceFromRedshiftInput 

type CreateDataSourceFromRedshiftInput struct {

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

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

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

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

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

func (CreateDataSourceFromRedshiftInput) String 

func (s CreateDataSourceFromRedshiftInput) String() string

String returns the string representation

func (*CreateDataSourceFromRedshiftInput) Validate 

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) String 

func (s CreateDataSourceFromRedshiftOutput) String() string

String returns the string representation

type CreateDataSourceFromRedshiftRequest 

type CreateDataSourceFromRedshiftRequest struct {
	*aws.Request
	Input *CreateDataSourceFromRedshiftInput
	Copy  func(*CreateDataSourceFromRedshiftInput) CreateDataSourceFromRedshiftRequest
}

CreateDataSourceFromRedshiftRequest is the request type for the CreateDataSourceFromRedshift API operation.

func (CreateDataSourceFromRedshiftRequest) Send 

func (r CreateDataSourceFromRedshiftRequest) Send(ctx context.Context) (*CreateDataSourceFromRedshiftResponse, error)

Send marshals and sends the CreateDataSourceFromRedshift API request.

type CreateDataSourceFromRedshiftResponse added in v0.9.0 

type CreateDataSourceFromRedshiftResponse struct {
	*CreateDataSourceFromRedshiftOutput
	// contains filtered or unexported fields
}

CreateDataSourceFromRedshiftResponse is the response type for the CreateDataSourceFromRedshift API operation.

func (*CreateDataSourceFromRedshiftResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateDataSourceFromRedshiftResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateDataSourceFromRedshift request.

type CreateDataSourceFromS3Input 

type CreateDataSourceFromS3Input struct {

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

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

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

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

func (CreateDataSourceFromS3Input) String 

func (s CreateDataSourceFromS3Input) String() string

String returns the string representation

func (*CreateDataSourceFromS3Input) Validate 

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) String 

func (s CreateDataSourceFromS3Output) String() string

String returns the string representation

type CreateDataSourceFromS3Request 

type CreateDataSourceFromS3Request struct {
	*aws.Request
	Input *CreateDataSourceFromS3Input
	Copy  func(*CreateDataSourceFromS3Input) CreateDataSourceFromS3Request
}

CreateDataSourceFromS3Request is the request type for the CreateDataSourceFromS3 API operation.

func (CreateDataSourceFromS3Request) Send 

func (r CreateDataSourceFromS3Request) Send(ctx context.Context) (*CreateDataSourceFromS3Response, error)

Send marshals and sends the CreateDataSourceFromS3 API request.

type CreateDataSourceFromS3Response added in v0.9.0 

type CreateDataSourceFromS3Response struct {
	*CreateDataSourceFromS3Output
	// contains filtered or unexported fields
}

CreateDataSourceFromS3Response is the response type for the CreateDataSourceFromS3 API operation.

func (*CreateDataSourceFromS3Response) SDKResponseMetdata added in v0.9.0 

func (r *CreateDataSourceFromS3Response) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateDataSourceFromS3 request.

type CreateEvaluationInput 

type CreateEvaluationInput struct {

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

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

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

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

func (CreateEvaluationInput) String 

func (s CreateEvaluationInput) String() string

String returns the string representation

func (*CreateEvaluationInput) Validate 

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) String 

func (s CreateEvaluationOutput) String() string

String returns the string representation

type CreateEvaluationRequest 

type CreateEvaluationRequest struct {
	*aws.Request
	Input *CreateEvaluationInput
	Copy  func(*CreateEvaluationInput) CreateEvaluationRequest
}

CreateEvaluationRequest is the request type for the CreateEvaluation API operation.

func (CreateEvaluationRequest) Send 

func (r CreateEvaluationRequest) Send(ctx context.Context) (*CreateEvaluationResponse, error)

Send marshals and sends the CreateEvaluation API request.

type CreateEvaluationResponse added in v0.9.0 

type CreateEvaluationResponse struct {
	*CreateEvaluationOutput
	// contains filtered or unexported fields
}

CreateEvaluationResponse is the response type for the CreateEvaluation API operation.

func (*CreateEvaluationResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateEvaluationResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateEvaluation request.

type CreateMLModelInput 

type CreateMLModelInput struct {

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

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

	// The category of supervised learning that this MLModel will address. Choose
	// from the following types:
	//
	//    * Choose REGRESSION if the MLModel will be used to predict a numeric value.
	//
	//    * Choose BINARY if the MLModel result has two possible values.
	//
	//    * Choose MULTICLASS if the MLModel result has a limited number of values.
	//
	// For more information, see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
	//
	// MLModelType is a required field
	MLModelType MLModelType `type:"string" required:"true" enum:"true"`

	// A list of the training parameters in the MLModel. The list is implemented
	// as a map of key-value pairs.
	//
	// The following is the current set of training parameters:
	//
	//    * sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending
	//    on the input data, the size of the model might affect its performance.
	//    The value is an integer that ranges from 100000 to 2147483648. The default
	//    value is 33554432.
	//
	//    * sgd.maxPasses - The number of times that the training process traverses
	//    the observations to build the MLModel. The value is an integer that ranges
	//    from 1 to 10000. The default value is 10.
	//
	//    * sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling
	//    the data improves a model's ability to find the optimal solution for a
	//    variety of data types. The valid values are auto and none. The default
	//    value is none. We strongly recommend that you shuffle your data.
	//
	//    * sgd.l1RegularizationAmount - The coefficient regularization L1 norm.
	//    It controls overfitting the data by penalizing large coefficients. This
	//    tends to drive coefficients to zero, resulting in a sparse feature set.
	//    If you use this parameter, start by specifying a small value, such as
	//    1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default
	//    is to not use L1 normalization. This parameter can't be used when L2 is
	//    specified. Use this parameter sparingly.
	//
	//    * sgd.l2RegularizationAmount - The coefficient regularization L2 norm.
	//    It controls overfitting the data by penalizing large coefficients. This
	//    tends to drive coefficients to small, nonzero values. If you use this
	//    parameter, start by specifying a small value, such as 1.0E-08. The value
	//    is a double that ranges from 0 to MAX_DOUBLE. The default is to not use
	//    L2 normalization. This parameter can't be used when L1 is specified. Use
	//    this parameter sparingly.
	Parameters map[string]string `type:"map"`

	// The data recipe for creating the MLModel. You must specify either the recipe
	// or its URI. If you don't specify a recipe or its URI, Amazon ML creates a
	// default.
	Recipe *string `type:"string"`

	// The Amazon Simple Storage Service (Amazon S3) location and file name that
	// contains the MLModel recipe. You must specify either the recipe or its URI.
	// If you don't specify a recipe or its URI, Amazon ML creates a default.
	RecipeUri *string `type:"string"`

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

func (CreateMLModelInput) String 

func (s CreateMLModelInput) String() string

String returns the string representation

func (*CreateMLModelInput) Validate 

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) String 

func (s CreateMLModelOutput) String() string

String returns the string representation

type CreateMLModelRequest 

type CreateMLModelRequest struct {
	*aws.Request
	Input *CreateMLModelInput
	Copy  func(*CreateMLModelInput) CreateMLModelRequest
}

CreateMLModelRequest is the request type for the CreateMLModel API operation.

func (CreateMLModelRequest) Send 

func (r CreateMLModelRequest) Send(ctx context.Context) (*CreateMLModelResponse, error)

Send marshals and sends the CreateMLModel API request.

type CreateMLModelResponse added in v0.9.0 

type CreateMLModelResponse struct {
	*CreateMLModelOutput
	// contains filtered or unexported fields
}

CreateMLModelResponse is the response type for the CreateMLModel API operation.

func (*CreateMLModelResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateMLModelResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateMLModel request.

type CreateRealtimeEndpointInput 

type CreateRealtimeEndpointInput struct {

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

func (CreateRealtimeEndpointInput) String 

func (s CreateRealtimeEndpointInput) String() string

String returns the string representation

func (*CreateRealtimeEndpointInput) Validate 

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) String 

func (s CreateRealtimeEndpointOutput) String() string

String returns the string representation

type CreateRealtimeEndpointRequest 

type CreateRealtimeEndpointRequest struct {
	*aws.Request
	Input *CreateRealtimeEndpointInput
	Copy  func(*CreateRealtimeEndpointInput) CreateRealtimeEndpointRequest
}

CreateRealtimeEndpointRequest is the request type for the CreateRealtimeEndpoint API operation.

func (CreateRealtimeEndpointRequest) Send 

func (r CreateRealtimeEndpointRequest) Send(ctx context.Context) (*CreateRealtimeEndpointResponse, error)

Send marshals and sends the CreateRealtimeEndpoint API request.

type CreateRealtimeEndpointResponse added in v0.9.0 

type CreateRealtimeEndpointResponse struct {
	*CreateRealtimeEndpointOutput
	// contains filtered or unexported fields
}

CreateRealtimeEndpointResponse is the response type for the CreateRealtimeEndpoint API operation.

func (*CreateRealtimeEndpointResponse) SDKResponseMetdata added in v0.9.0 

func (r *CreateRealtimeEndpointResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the CreateRealtimeEndpoint request.

type DataSource 

type DataSource struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

	// 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 EntityStatus `type:"string" enum:"true"`
	// 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) String 

func (s DataSource) String() string

String returns the string representation

type DataSourceFilterVariable 

type DataSourceFilterVariable string

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. 

const (
	DataSourceFilterVariableCreatedAt      DataSourceFilterVariable = "CreatedAt"
	DataSourceFilterVariableLastUpdatedAt  DataSourceFilterVariable = "LastUpdatedAt"
	DataSourceFilterVariableStatus         DataSourceFilterVariable = "Status"
	DataSourceFilterVariableName           DataSourceFilterVariable = "Name"
	DataSourceFilterVariableDataLocationS3 DataSourceFilterVariable = "DataLocationS3"
	DataSourceFilterVariableIamuser        DataSourceFilterVariable = "IAMUser"
)

Enum values for DataSourceFilterVariable

func (DataSourceFilterVariable) MarshalValue added in v0.3.0 

func (enum DataSourceFilterVariable) MarshalValue() (string, error)

func (DataSourceFilterVariable) MarshalValueBuf added in v0.3.0 

func (enum DataSourceFilterVariable) MarshalValueBuf(b []byte) ([]byte, error)

type DeleteBatchPredictionInput 

type DeleteBatchPredictionInput struct {

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

func (DeleteBatchPredictionInput) String 

func (s DeleteBatchPredictionInput) String() string

String returns the string representation

func (*DeleteBatchPredictionInput) Validate 

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) String 

func (s DeleteBatchPredictionOutput) String() string

String returns the string representation

type DeleteBatchPredictionRequest 

type DeleteBatchPredictionRequest struct {
	*aws.Request
	Input *DeleteBatchPredictionInput
	Copy  func(*DeleteBatchPredictionInput) DeleteBatchPredictionRequest
}

DeleteBatchPredictionRequest is the request type for the DeleteBatchPrediction API operation.

func (DeleteBatchPredictionRequest) Send 

func (r DeleteBatchPredictionRequest) Send(ctx context.Context) (*DeleteBatchPredictionResponse, error)

Send marshals and sends the DeleteBatchPrediction API request.

type DeleteBatchPredictionResponse added in v0.9.0 

type DeleteBatchPredictionResponse struct {
	*DeleteBatchPredictionOutput
	// contains filtered or unexported fields
}

DeleteBatchPredictionResponse is the response type for the DeleteBatchPrediction API operation.

func (*DeleteBatchPredictionResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteBatchPredictionResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteBatchPrediction request.

type DeleteDataSourceInput 

type DeleteDataSourceInput struct {

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

func (DeleteDataSourceInput) String 

func (s DeleteDataSourceInput) String() string

String returns the string representation

func (*DeleteDataSourceInput) Validate 

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) String 

func (s DeleteDataSourceOutput) String() string

String returns the string representation

type DeleteDataSourceRequest 

type DeleteDataSourceRequest struct {
	*aws.Request
	Input *DeleteDataSourceInput
	Copy  func(*DeleteDataSourceInput) DeleteDataSourceRequest
}

DeleteDataSourceRequest is the request type for the DeleteDataSource API operation.

func (DeleteDataSourceRequest) Send 

func (r DeleteDataSourceRequest) Send(ctx context.Context) (*DeleteDataSourceResponse, error)

Send marshals and sends the DeleteDataSource API request.

type DeleteDataSourceResponse added in v0.9.0 

type DeleteDataSourceResponse struct {
	*DeleteDataSourceOutput
	// contains filtered or unexported fields
}

DeleteDataSourceResponse is the response type for the DeleteDataSource API operation.

func (*DeleteDataSourceResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteDataSourceResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteDataSource request.

type DeleteEvaluationInput 

type DeleteEvaluationInput struct {

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

func (DeleteEvaluationInput) String 

func (s DeleteEvaluationInput) String() string

String returns the string representation

func (*DeleteEvaluationInput) Validate 

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) String 

func (s DeleteEvaluationOutput) String() string

String returns the string representation

type DeleteEvaluationRequest 

type DeleteEvaluationRequest struct {
	*aws.Request
	Input *DeleteEvaluationInput
	Copy  func(*DeleteEvaluationInput) DeleteEvaluationRequest
}

DeleteEvaluationRequest is the request type for the DeleteEvaluation API operation.

func (DeleteEvaluationRequest) Send 

func (r DeleteEvaluationRequest) Send(ctx context.Context) (*DeleteEvaluationResponse, error)

Send marshals and sends the DeleteEvaluation API request.

type DeleteEvaluationResponse added in v0.9.0 

type DeleteEvaluationResponse struct {
	*DeleteEvaluationOutput
	// contains filtered or unexported fields
}

DeleteEvaluationResponse is the response type for the DeleteEvaluation API operation.

func (*DeleteEvaluationResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteEvaluationResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteEvaluation request.

type DeleteMLModelInput 

type DeleteMLModelInput struct {

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

func (DeleteMLModelInput) String 

func (s DeleteMLModelInput) String() string

String returns the string representation

func (*DeleteMLModelInput) Validate 

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) String 

func (s DeleteMLModelOutput) String() string

String returns the string representation

type DeleteMLModelRequest 

type DeleteMLModelRequest struct {
	*aws.Request
	Input *DeleteMLModelInput
	Copy  func(*DeleteMLModelInput) DeleteMLModelRequest
}

DeleteMLModelRequest is the request type for the DeleteMLModel API operation.

func (DeleteMLModelRequest) Send 

func (r DeleteMLModelRequest) Send(ctx context.Context) (*DeleteMLModelResponse, error)

Send marshals and sends the DeleteMLModel API request.

type DeleteMLModelResponse added in v0.9.0 

type DeleteMLModelResponse struct {
	*DeleteMLModelOutput
	// contains filtered or unexported fields
}

DeleteMLModelResponse is the response type for the DeleteMLModel API operation.

func (*DeleteMLModelResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteMLModelResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteMLModel request.

type DeleteRealtimeEndpointInput 

type DeleteRealtimeEndpointInput struct {

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

func (DeleteRealtimeEndpointInput) String 

func (s DeleteRealtimeEndpointInput) String() string

String returns the string representation

func (*DeleteRealtimeEndpointInput) Validate 

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) String 

func (s DeleteRealtimeEndpointOutput) String() string

String returns the string representation

type DeleteRealtimeEndpointRequest 

type DeleteRealtimeEndpointRequest struct {
	*aws.Request
	Input *DeleteRealtimeEndpointInput
	Copy  func(*DeleteRealtimeEndpointInput) DeleteRealtimeEndpointRequest
}

DeleteRealtimeEndpointRequest is the request type for the DeleteRealtimeEndpoint API operation.

func (DeleteRealtimeEndpointRequest) Send 

func (r DeleteRealtimeEndpointRequest) Send(ctx context.Context) (*DeleteRealtimeEndpointResponse, error)

Send marshals and sends the DeleteRealtimeEndpoint API request.

type DeleteRealtimeEndpointResponse added in v0.9.0 

type DeleteRealtimeEndpointResponse struct {
	*DeleteRealtimeEndpointOutput
	// contains filtered or unexported fields
}

DeleteRealtimeEndpointResponse is the response type for the DeleteRealtimeEndpoint API operation.

func (*DeleteRealtimeEndpointResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteRealtimeEndpointResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteRealtimeEndpoint request.

type DeleteTagsInput 

type DeleteTagsInput struct {

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

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

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

func (DeleteTagsInput) String 

func (s DeleteTagsInput) String() string

String returns the string representation

func (*DeleteTagsInput) Validate 

func (s *DeleteTagsInput) Validate() error

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

type DeleteTagsOutput 

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 TaggableResourceType `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

Amazon ML returns the following elements.

func (DeleteTagsOutput) String 

func (s DeleteTagsOutput) String() string

String returns the string representation

type DeleteTagsRequest 

type DeleteTagsRequest struct {
	*aws.Request
	Input *DeleteTagsInput
	Copy  func(*DeleteTagsInput) DeleteTagsRequest
}

DeleteTagsRequest is the request type for the DeleteTags API operation.

func (DeleteTagsRequest) Send 

func (r DeleteTagsRequest) Send(ctx context.Context) (*DeleteTagsResponse, error)

Send marshals and sends the DeleteTags API request.

type DeleteTagsResponse added in v0.9.0 

type DeleteTagsResponse struct {
	*DeleteTagsOutput
	// contains filtered or unexported fields
}

DeleteTagsResponse is the response type for the DeleteTags API operation.

func (*DeleteTagsResponse) SDKResponseMetdata added in v0.9.0 

func (r *DeleteTagsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DeleteTags request.

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 BatchPredictionFilterVariable `type:"string" enum:"true"`

	// 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 SortOrder `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

func (DescribeBatchPredictionsInput) String 

func (s DescribeBatchPredictionsInput) String() string

String returns the string representation

func (*DescribeBatchPredictionsInput) Validate 

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) String 

func (s DescribeBatchPredictionsOutput) String() string

String returns the string representation

type DescribeBatchPredictionsPaginator added in v0.9.0 

type DescribeBatchPredictionsPaginator struct {
	aws.Pager
}

DescribeBatchPredictionsPaginator is used to paginate the request. This can be done by calling Next and CurrentPage.

func NewDescribeBatchPredictionsPaginator added in v0.9.0 

func NewDescribeBatchPredictionsPaginator(req DescribeBatchPredictionsRequest) DescribeBatchPredictionsPaginator

NewDescribeBatchPredictionsRequestPaginator returns a paginator for DescribeBatchPredictions. Use Next method to get the next page, and CurrentPage to get the current response page from the paginator. Next will return false, if there are no more pages, or an error was encountered.

Note: This operation can generate multiple requests to a service. 

// Example iterating over pages.
req := client.DescribeBatchPredictionsRequest(input)
p := machinelearning.NewDescribeBatchPredictionsRequestPaginator(req)

for p.Next(context.TODO()) {
    page := p.CurrentPage()
}

if err := p.Err(); err != nil {
    return err
}

func (*DescribeBatchPredictionsPaginator) CurrentPage added in v0.9.0 

func (p *DescribeBatchPredictionsPaginator) CurrentPage() *DescribeBatchPredictionsOutput

type DescribeBatchPredictionsRequest 

type DescribeBatchPredictionsRequest struct {
	*aws.Request
	Input *DescribeBatchPredictionsInput
	Copy  func(*DescribeBatchPredictionsInput) DescribeBatchPredictionsRequest
}

DescribeBatchPredictionsRequest is the request type for the DescribeBatchPredictions API operation.

func (DescribeBatchPredictionsRequest) Send 

func (r DescribeBatchPredictionsRequest) Send(ctx context.Context) (*DescribeBatchPredictionsResponse, error)

Send marshals and sends the DescribeBatchPredictions API request.

type DescribeBatchPredictionsResponse added in v0.9.0 

type DescribeBatchPredictionsResponse struct {
	*DescribeBatchPredictionsOutput
	// contains filtered or unexported fields
}

DescribeBatchPredictionsResponse is the response type for the DescribeBatchPredictions API operation.

func (*DescribeBatchPredictionsResponse) SDKResponseMetdata added in v0.9.0 

func (r *DescribeBatchPredictionsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DescribeBatchPredictions request.

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 DataSourceFilterVariable `type:"string" enum:"true"`

	// 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 SortOrder `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

func (DescribeDataSourcesInput) String 

func (s DescribeDataSourcesInput) String() string

String returns the string representation

func (*DescribeDataSourcesInput) Validate 

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) String 

func (s DescribeDataSourcesOutput) String() string

String returns the string representation

type DescribeDataSourcesPaginator added in v0.9.0 

type DescribeDataSourcesPaginator struct {
	aws.Pager
}

DescribeDataSourcesPaginator is used to paginate the request. This can be done by calling Next and CurrentPage.

func NewDescribeDataSourcesPaginator added in v0.9.0 

func NewDescribeDataSourcesPaginator(req DescribeDataSourcesRequest) DescribeDataSourcesPaginator

NewDescribeDataSourcesRequestPaginator returns a paginator for DescribeDataSources. Use Next method to get the next page, and CurrentPage to get the current response page from the paginator. Next will return false, if there are no more pages, or an error was encountered.

Note: This operation can generate multiple requests to a service. 

// Example iterating over pages.
req := client.DescribeDataSourcesRequest(input)
p := machinelearning.NewDescribeDataSourcesRequestPaginator(req)

for p.Next(context.TODO()) {
    page := p.CurrentPage()
}

if err := p.Err(); err != nil {
    return err
}

func (*DescribeDataSourcesPaginator) CurrentPage added in v0.9.0 

func (p *DescribeDataSourcesPaginator) CurrentPage() *DescribeDataSourcesOutput

type DescribeDataSourcesRequest 

type DescribeDataSourcesRequest struct {
	*aws.Request
	Input *DescribeDataSourcesInput
	Copy  func(*DescribeDataSourcesInput) DescribeDataSourcesRequest
}

DescribeDataSourcesRequest is the request type for the DescribeDataSources API operation.

func (DescribeDataSourcesRequest) Send 

func (r DescribeDataSourcesRequest) Send(ctx context.Context) (*DescribeDataSourcesResponse, error)

Send marshals and sends the DescribeDataSources API request.

type DescribeDataSourcesResponse added in v0.9.0 

type DescribeDataSourcesResponse struct {
	*DescribeDataSourcesOutput
	// contains filtered or unexported fields
}

DescribeDataSourcesResponse is the response type for the DescribeDataSources API operation.

func (*DescribeDataSourcesResponse) SDKResponseMetdata added in v0.9.0 

func (r *DescribeDataSourcesResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DescribeDataSources request.

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 EvaluationFilterVariable `type:"string" enum:"true"`

	// 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 SortOrder `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

func (DescribeEvaluationsInput) String 

func (s DescribeEvaluationsInput) String() string

String returns the string representation

func (*DescribeEvaluationsInput) Validate 

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) String 

func (s DescribeEvaluationsOutput) String() string

String returns the string representation

type DescribeEvaluationsPaginator added in v0.9.0 

type DescribeEvaluationsPaginator struct {
	aws.Pager
}

DescribeEvaluationsPaginator is used to paginate the request. This can be done by calling Next and CurrentPage.

func NewDescribeEvaluationsPaginator added in v0.9.0 

func NewDescribeEvaluationsPaginator(req DescribeEvaluationsRequest) DescribeEvaluationsPaginator

NewDescribeEvaluationsRequestPaginator returns a paginator for DescribeEvaluations. Use Next method to get the next page, and CurrentPage to get the current response page from the paginator. Next will return false, if there are no more pages, or an error was encountered.

Note: This operation can generate multiple requests to a service. 

// Example iterating over pages.
req := client.DescribeEvaluationsRequest(input)
p := machinelearning.NewDescribeEvaluationsRequestPaginator(req)

for p.Next(context.TODO()) {
    page := p.CurrentPage()
}

if err := p.Err(); err != nil {
    return err
}

func (*DescribeEvaluationsPaginator) CurrentPage added in v0.9.0 

func (p *DescribeEvaluationsPaginator) CurrentPage() *DescribeEvaluationsOutput

type DescribeEvaluationsRequest 

type DescribeEvaluationsRequest struct {
	*aws.Request
	Input *DescribeEvaluationsInput
	Copy  func(*DescribeEvaluationsInput) DescribeEvaluationsRequest
}

DescribeEvaluationsRequest is the request type for the DescribeEvaluations API operation.

func (DescribeEvaluationsRequest) Send 

func (r DescribeEvaluationsRequest) Send(ctx context.Context) (*DescribeEvaluationsResponse, error)

Send marshals and sends the DescribeEvaluations API request.

type DescribeEvaluationsResponse added in v0.9.0 

type DescribeEvaluationsResponse struct {
	*DescribeEvaluationsOutput
	// contains filtered or unexported fields
}

DescribeEvaluationsResponse is the response type for the DescribeEvaluations API operation.

func (*DescribeEvaluationsResponse) SDKResponseMetdata added in v0.9.0 

func (r *DescribeEvaluationsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DescribeEvaluations request.

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 MLModelFilterVariable `type:"string" enum:"true"`

	// 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 SortOrder `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

func (DescribeMLModelsInput) String 

func (s DescribeMLModelsInput) String() string

String returns the string representation

func (*DescribeMLModelsInput) Validate 

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) String 

func (s DescribeMLModelsOutput) String() string

String returns the string representation

type DescribeMLModelsPaginator added in v0.9.0 

type DescribeMLModelsPaginator struct {
	aws.Pager
}

DescribeMLModelsPaginator is used to paginate the request. This can be done by calling Next and CurrentPage.

func NewDescribeMLModelsPaginator added in v0.9.0 

func NewDescribeMLModelsPaginator(req DescribeMLModelsRequest) DescribeMLModelsPaginator

NewDescribeMLModelsRequestPaginator returns a paginator for DescribeMLModels. Use Next method to get the next page, and CurrentPage to get the current response page from the paginator. Next will return false, if there are no more pages, or an error was encountered.

Note: This operation can generate multiple requests to a service. 

// Example iterating over pages.
req := client.DescribeMLModelsRequest(input)
p := machinelearning.NewDescribeMLModelsRequestPaginator(req)

for p.Next(context.TODO()) {
    page := p.CurrentPage()
}

if err := p.Err(); err != nil {
    return err
}

func (*DescribeMLModelsPaginator) CurrentPage added in v0.9.0 

func (p *DescribeMLModelsPaginator) CurrentPage() *DescribeMLModelsOutput

type DescribeMLModelsRequest 

type DescribeMLModelsRequest struct {
	*aws.Request
	Input *DescribeMLModelsInput
	Copy  func(*DescribeMLModelsInput) DescribeMLModelsRequest
}

DescribeMLModelsRequest is the request type for the DescribeMLModels API operation.

func (DescribeMLModelsRequest) Send 

func (r DescribeMLModelsRequest) Send(ctx context.Context) (*DescribeMLModelsResponse, error)

Send marshals and sends the DescribeMLModels API request.

type DescribeMLModelsResponse added in v0.9.0 

type DescribeMLModelsResponse struct {
	*DescribeMLModelsOutput
	// contains filtered or unexported fields
}

DescribeMLModelsResponse is the response type for the DescribeMLModels API operation.

func (*DescribeMLModelsResponse) SDKResponseMetdata added in v0.9.0 

func (r *DescribeMLModelsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DescribeMLModels request.

type DescribeTagsInput 

type DescribeTagsInput struct {

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

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

func (DescribeTagsInput) String 

func (s DescribeTagsInput) String() string

String returns the string representation

func (*DescribeTagsInput) Validate 

func (s *DescribeTagsInput) Validate() error

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

type DescribeTagsOutput 

type DescribeTagsOutput struct {

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

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

	// 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) String 

func (s DescribeTagsOutput) String() string

String returns the string representation

type DescribeTagsRequest 

type DescribeTagsRequest struct {
	*aws.Request
	Input *DescribeTagsInput
	Copy  func(*DescribeTagsInput) DescribeTagsRequest
}

DescribeTagsRequest is the request type for the DescribeTags API operation.

func (DescribeTagsRequest) Send 

func (r DescribeTagsRequest) Send(ctx context.Context) (*DescribeTagsResponse, error)

Send marshals and sends the DescribeTags API request.

type DescribeTagsResponse added in v0.9.0 

type DescribeTagsResponse struct {
	*DescribeTagsOutput
	// contains filtered or unexported fields
}

DescribeTagsResponse is the response type for the DescribeTags API operation.

func (*DescribeTagsResponse) SDKResponseMetdata added in v0.9.0 

func (r *DescribeTagsResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the DescribeTags request.

type DetailsAttributes 

type DetailsAttributes string

Contains the key values of 

DetailsMap

: 

  PredictiveModelType
- Indicates the type of the
  MLModel

. 

  Algorithm
- Indicates the algorithm that was used for the
  MLModel

. 

const (
	DetailsAttributesPredictiveModelType DetailsAttributes = "PredictiveModelType"
	DetailsAttributesAlgorithm           DetailsAttributes = "Algorithm"
)

Enum values for DetailsAttributes

func (DetailsAttributes) MarshalValue added in v0.3.0 

func (enum DetailsAttributes) MarshalValue() (string, error)

func (DetailsAttributes) MarshalValueBuf added in v0.3.0 

func (enum DetailsAttributes) MarshalValueBuf(b []byte) ([]byte, error)

type EntityStatus 

type EntityStatus string

Object status with the following possible values:

  • PENDING

  • INPROGRESS

  • FAILED

  • COMPLETED

  • DELETED 

const (
	EntityStatusPending    EntityStatus = "PENDING"
	EntityStatusInprogress EntityStatus = "INPROGRESS"
	EntityStatusFailed     EntityStatus = "FAILED"
	EntityStatusCompleted  EntityStatus = "COMPLETED"
	EntityStatusDeleted    EntityStatus = "DELETED"
)

Enum values for EntityStatus

func (EntityStatus) MarshalValue added in v0.3.0 

func (enum EntityStatus) MarshalValue() (string, error)

func (EntityStatus) MarshalValueBuf added in v0.3.0 

func (enum EntityStatus) MarshalValueBuf(b []byte) ([]byte, error)

type Evaluation 

type Evaluation struct {

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

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

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

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

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

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

	// 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"`

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

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

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

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

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

	// 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 EntityStatus `type:"string" enum:"true"`
	// 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) String 

func (s Evaluation) String() string

String returns the string representation

type EvaluationFilterVariable 

type EvaluationFilterVariable string

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. 

const (
	EvaluationFilterVariableCreatedAt     EvaluationFilterVariable = "CreatedAt"
	EvaluationFilterVariableLastUpdatedAt EvaluationFilterVariable = "LastUpdatedAt"
	EvaluationFilterVariableStatus        EvaluationFilterVariable = "Status"
	EvaluationFilterVariableName          EvaluationFilterVariable = "Name"
	EvaluationFilterVariableIamuser       EvaluationFilterVariable = "IAMUser"
	EvaluationFilterVariableMlmodelId     EvaluationFilterVariable = "MLModelId"
	EvaluationFilterVariableDataSourceId  EvaluationFilterVariable = "DataSourceId"
	EvaluationFilterVariableDataUri       EvaluationFilterVariable = "DataURI"
)

Enum values for EvaluationFilterVariable

func (EvaluationFilterVariable) MarshalValue added in v0.3.0 

func (enum EvaluationFilterVariable) MarshalValue() (string, error)

func (EvaluationFilterVariable) MarshalValueBuf added in v0.3.0 

func (enum EvaluationFilterVariable) MarshalValueBuf(b []byte) ([]byte, error)

type GetBatchPredictionInput 

type GetBatchPredictionInput struct {

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

func (GetBatchPredictionInput) String 

func (s GetBatchPredictionInput) String() string

String returns the string representation

func (*GetBatchPredictionInput) Validate 

func (s *GetBatchPredictionInput) Validate() error

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

type GetBatchPredictionOutput 

type GetBatchPredictionOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetBatchPredictionOutput) String 

func (s GetBatchPredictionOutput) String() string

String returns the string representation

type GetBatchPredictionRequest 

type GetBatchPredictionRequest struct {
	*aws.Request
	Input *GetBatchPredictionInput
	Copy  func(*GetBatchPredictionInput) GetBatchPredictionRequest
}

GetBatchPredictionRequest is the request type for the GetBatchPrediction API operation.

func (GetBatchPredictionRequest) Send 

func (r GetBatchPredictionRequest) Send(ctx context.Context) (*GetBatchPredictionResponse, error)

Send marshals and sends the GetBatchPrediction API request.

type GetBatchPredictionResponse added in v0.9.0 

type GetBatchPredictionResponse struct {
	*GetBatchPredictionOutput
	// contains filtered or unexported fields
}

GetBatchPredictionResponse is the response type for the GetBatchPrediction API operation.

func (*GetBatchPredictionResponse) SDKResponseMetdata added in v0.9.0 

func (r *GetBatchPredictionResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the GetBatchPrediction request.

type GetDataSourceInput 

type GetDataSourceInput struct {

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

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

func (GetDataSourceInput) String 

func (s GetDataSourceInput) String() string

String returns the string representation

func (*GetDataSourceInput) Validate 

func (s *GetDataSourceInput) Validate() error

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

type GetDataSourceOutput 

type GetDataSourceOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

	// 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 EntityStatus `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

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

func (GetDataSourceOutput) String 

func (s GetDataSourceOutput) String() string

String returns the string representation

type GetDataSourceRequest 

type GetDataSourceRequest struct {
	*aws.Request
	Input *GetDataSourceInput
	Copy  func(*GetDataSourceInput) GetDataSourceRequest
}

GetDataSourceRequest is the request type for the GetDataSource API operation.

func (GetDataSourceRequest) Send 

func (r GetDataSourceRequest) Send(ctx context.Context) (*GetDataSourceResponse, error)

Send marshals and sends the GetDataSource API request.

type GetDataSourceResponse added in v0.9.0 

type GetDataSourceResponse struct {
	*GetDataSourceOutput
	// contains filtered or unexported fields
}

GetDataSourceResponse is the response type for the GetDataSource API operation.

func (*GetDataSourceResponse) SDKResponseMetdata added in v0.9.0 

func (r *GetDataSourceResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the GetDataSource request.

type GetEvaluationInput 

type GetEvaluationInput struct {

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

func (GetEvaluationInput) String 

func (s GetEvaluationInput) String() string

String returns the string representation

func (*GetEvaluationInput) Validate 

func (s *GetEvaluationInput) Validate() error

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

type GetEvaluationOutput 

type GetEvaluationOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

	// 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 EntityStatus `type:"string" enum:"true"`
	// contains filtered or unexported fields
}

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

func (GetEvaluationOutput) String 

func (s GetEvaluationOutput) String() string

String returns the string representation

type GetEvaluationRequest 

type GetEvaluationRequest struct {
	*aws.Request
	Input *GetEvaluationInput
	Copy  func(*GetEvaluationInput) GetEvaluationRequest
}

GetEvaluationRequest is the request type for the GetEvaluation API operation.

func (GetEvaluationRequest) Send 

func (r GetEvaluationRequest) Send(ctx context.Context) (*GetEvaluationResponse, error)

Send marshals and sends the GetEvaluation API request.

type GetEvaluationResponse added in v0.9.0 

type GetEvaluationResponse struct {
	*GetEvaluationOutput
	// contains filtered or unexported fields
}

GetEvaluationResponse is the response type for the GetEvaluation API operation.

func (*GetEvaluationResponse) SDKResponseMetdata added in v0.9.0 

func (r *GetEvaluationResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the GetEvaluation request.

type GetMLModelInput 

type GetMLModelInput struct {

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

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

func (GetMLModelInput) String 

func (s GetMLModelInput) String() string

String returns the string representation

func (*GetMLModelInput) Validate 

func (s *GetMLModelInput) Validate() error

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

type GetMLModelOutput 

type GetMLModelOutput struct {

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

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

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

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

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

	// 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"`

	// 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 MLModelType `type:"string" enum:"true"`

	// 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"`

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

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

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

	// 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) String 

func (s GetMLModelOutput) String() string

String returns the string representation

type GetMLModelRequest 

type GetMLModelRequest struct {
	*aws.Request
	Input *GetMLModelInput
	Copy  func(*GetMLModelInput) GetMLModelRequest
}

GetMLModelRequest is the request type for the GetMLModel API operation.

func (GetMLModelRequest) Send 

func (r GetMLModelRequest) Send(ctx context.Context) (*GetMLModelResponse, error)

Send marshals and sends the GetMLModel API request.

type GetMLModelResponse added in v0.9.0 

type GetMLModelResponse struct {
	*GetMLModelOutput
	// contains filtered or unexported fields
}

GetMLModelResponse is the response type for the GetMLModel API operation.

func (*GetMLModelResponse) SDKResponseMetdata added in v0.9.0 

func (r *GetMLModelResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the GetMLModel request.

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 Algorithm `type:"string" enum:"true"`

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

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

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

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

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

	// 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"`

	// 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 MLModelType `type:"string" enum:"true"`

	// 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"`

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

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

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

	// 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) String 

func (s MLModel) String() string

String returns the string representation

type MLModelFilterVariable 

type MLModelFilterVariable string
const (
	MLModelFilterVariableCreatedAt              MLModelFilterVariable = "CreatedAt"
	MLModelFilterVariableLastUpdatedAt          MLModelFilterVariable = "LastUpdatedAt"
	MLModelFilterVariableStatus                 MLModelFilterVariable = "Status"
	MLModelFilterVariableName                   MLModelFilterVariable = "Name"
	MLModelFilterVariableIamuser                MLModelFilterVariable = "IAMUser"
	MLModelFilterVariableTrainingDataSourceId   MLModelFilterVariable = "TrainingDataSourceId"
	MLModelFilterVariableRealtimeEndpointStatus MLModelFilterVariable = "RealtimeEndpointStatus"
	MLModelFilterVariableMlmodelType            MLModelFilterVariable = "MLModelType"
	MLModelFilterVariableAlgorithm              MLModelFilterVariable = "Algorithm"
	MLModelFilterVariableTrainingDataUri        MLModelFilterVariable = "TrainingDataURI"
)

Enum values for MLModelFilterVariable

func (MLModelFilterVariable) MarshalValue added in v0.3.0 

func (enum MLModelFilterVariable) MarshalValue() (string, error)

func (MLModelFilterVariable) MarshalValueBuf added in v0.3.0 

func (enum MLModelFilterVariable) MarshalValueBuf(b []byte) ([]byte, error)

type MLModelType 

type MLModelType string
const (
	MLModelTypeRegression MLModelType = "REGRESSION"
	MLModelTypeBinary     MLModelType = "BINARY"
	MLModelTypeMulticlass MLModelType = "MULTICLASS"
)

Enum values for MLModelType

func (MLModelType) MarshalValue added in v0.3.0 

func (enum MLModelType) MarshalValue() (string, error)

func (MLModelType) MarshalValueBuf added in v0.3.0 

func (enum MLModelType) MarshalValueBuf(b []byte) ([]byte, 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) String 

func (s PerformanceMetrics) String() string

String returns the string representation

type PredictInput 

type PredictInput struct {

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

	// PredictEndpoint is a required field
	PredictEndpoint *string `type:"string" required:"true"`

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

func (PredictInput) String 

func (s PredictInput) String() string

String returns the string representation

func (*PredictInput) Validate 

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) String 

func (s PredictOutput) String() string

String returns the string representation

type PredictRequest 

type PredictRequest struct {
	*aws.Request
	Input *PredictInput
	Copy  func(*PredictInput) PredictRequest
}

PredictRequest is the request type for the Predict API operation.

func (PredictRequest) Send 

func (r PredictRequest) Send(ctx context.Context) (*PredictResponse, error)

Send marshals and sends the Predict API request.

type PredictResponse added in v0.9.0 

type PredictResponse struct {
	*PredictOutput
	// contains filtered or unexported fields
}

PredictResponse is the response type for the Predict API operation.

func (*PredictResponse) SDKResponseMetdata added in v0.9.0 

func (r *PredictResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the Predict request.

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) String 

func (s Prediction) String() string

String returns the string representation

type RDSDataSpec 

type RDSDataSpec struct {

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon RDS DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// The Amazon S3 location of the DataSchema.
	DataSchemaUri *string `type:"string"`

	// The AWS Identity and Access Management (IAM) credentials that are used connect
	// to the Amazon RDS database.
	//
	// DatabaseCredentials is a required field
	DatabaseCredentials *RDSDatabaseCredentials `type:"structure" required:"true"`

	// Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
	//
	// DatabaseInformation is a required field
	DatabaseInformation *RDSDatabase `type:"structure" required:"true"`

	// The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
	// Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS
	// to an Amazon S3 task. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ResourceRole is a required field
	ResourceRole *string `min:"1" type:"string" required:"true"`

	// The Amazon S3 location for staging Amazon RDS data. The data retrieved from
	// Amazon RDS using SelectSqlQuery is stored in this location.
	//
	// S3StagingLocation is a required field
	S3StagingLocation *string `type:"string" required:"true"`

	// The security group IDs to be used to access a VPC-based RDS DB instance.
	// Ensure that there are appropriate ingress rules set up to allow access to
	// the RDS DB instance. This attribute is used by Data Pipeline to carry out
	// the copy operation from Amazon RDS to an Amazon S3 task.
	//
	// SecurityGroupIds is a required field
	SecurityGroupIds []string `type:"list" required:"true"`

	// The query that is used to retrieve the observation data for the DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" type:"string" required:"true"`

	// The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to
	// monitor the progress of the copy task from Amazon RDS to Amazon S3. For more
	// information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html)
	// for data pipelines.
	//
	// ServiceRole is a required field
	ServiceRole *string `min:"1" type:"string" required:"true"`

	// The subnet ID to be used to access a VPC-based RDS DB instance. This attribute
	// is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon
	// S3.
	//
	// SubnetId is a required field
	SubnetId *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

func (RDSDataSpec) String 

func (s RDSDataSpec) String() string

String returns the string representation

func (*RDSDataSpec) Validate 

func (s *RDSDataSpec) Validate() error

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

type RDSDatabase 

type RDSDatabase struct {

	// The name of a database hosted on an RDS DB instance.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`

	// The ID of an RDS DB instance.
	//
	// InstanceIdentifier is a required field
	InstanceIdentifier *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The database details of an Amazon RDS database.

func (RDSDatabase) String 

func (s RDSDatabase) String() string

String returns the string representation

func (*RDSDatabase) Validate 

func (s *RDSDatabase) Validate() error

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

type RDSDatabaseCredentials 

type RDSDatabaseCredentials struct {

	// The password to be used by Amazon ML to connect to a database on an RDS DB
	// instance. The password should have sufficient permissions to execute the
	// RDSSelectQuery query.
	//
	// Password is a required field
	Password *string `min:"8" type:"string" required:"true"`

	// The username to be used by Amazon ML to connect to database on an Amazon
	// RDS instance. The username should have sufficient permissions to execute
	// an RDSSelectSqlQuery query.
	//
	// Username is a required field
	Username *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

The database credentials to connect to a database on an RDS DB instance.

func (RDSDatabaseCredentials) String 

func (s RDSDatabaseCredentials) String() string

String returns the string representation

func (*RDSDatabaseCredentials) Validate 

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) String 

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"`

	// 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 RealtimeEndpointStatus `type:"string" enum:"true"`

	// 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) String 

func (s RealtimeEndpointInfo) String() string

String returns the string representation

type RealtimeEndpointStatus 

type RealtimeEndpointStatus string
const (
	RealtimeEndpointStatusNone     RealtimeEndpointStatus = "NONE"
	RealtimeEndpointStatusReady    RealtimeEndpointStatus = "READY"
	RealtimeEndpointStatusUpdating RealtimeEndpointStatus = "UPDATING"
	RealtimeEndpointStatusFailed   RealtimeEndpointStatus = "FAILED"
)

Enum values for RealtimeEndpointStatus

func (RealtimeEndpointStatus) MarshalValue added in v0.3.0 

func (enum RealtimeEndpointStatus) MarshalValue() (string, error)

func (RealtimeEndpointStatus) MarshalValueBuf added in v0.3.0 

func (enum RealtimeEndpointStatus) MarshalValueBuf(b []byte) ([]byte, error)

type RedshiftDataSpec 

type RedshiftDataSpec struct {

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon Redshift DataSource.
	// The DataSchema defines the structure of the observation data in the data
	// file(s) referenced in the DataSource.
	//
	// A DataSchema is not required if you specify a DataSchemaUri.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// Describes the schema location for an Amazon Redshift DataSource.
	DataSchemaUri *string `type:"string"`

	// Describes AWS Identity and Access Management (IAM) credentials that are used
	// connect to the Amazon Redshift database.
	//
	// DatabaseCredentials is a required field
	DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"`

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

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

	// Describes the SQL Query to execute on an Amazon Redshift database for an
	// Amazon Redshift DataSource.
	//
	// SelectSqlQuery is a required field
	SelectSqlQuery *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the data specification of an Amazon Redshift DataSource.

func (RedshiftDataSpec) String 

func (s RedshiftDataSpec) String() string

String returns the string representation

func (*RedshiftDataSpec) Validate 

func (s *RedshiftDataSpec) Validate() error

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

type RedshiftDatabase 

type RedshiftDatabase struct {

	// The ID of an Amazon Redshift cluster.
	//
	// ClusterIdentifier is a required field
	ClusterIdentifier *string `min:"1" type:"string" required:"true"`

	// The name of a database hosted on an Amazon Redshift cluster.
	//
	// DatabaseName is a required field
	DatabaseName *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the database details required to connect to an Amazon Redshift database.

func (RedshiftDatabase) String 

func (s RedshiftDatabase) String() string

String returns the string representation

func (*RedshiftDatabase) Validate 

func (s *RedshiftDatabase) Validate() error

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

type RedshiftDatabaseCredentials 

type RedshiftDatabaseCredentials struct {

	// A password to be used by Amazon ML to connect to a database on an Amazon
	// Redshift cluster. The password should have sufficient permissions to execute
	// a RedshiftSelectSqlQuery query. The password should be valid for an Amazon
	// Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	//
	// Password is a required field
	Password *string `min:"8" type:"string" required:"true"`

	// A username to be used by Amazon Machine Learning (Amazon ML)to connect to
	// a database on an Amazon Redshift cluster. The username should have sufficient
	// permissions to execute the RedshiftSelectSqlQuery query. The username should
	// be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html).
	//
	// Username is a required field
	Username *string `min:"1" type:"string" required:"true"`
	// contains filtered or unexported fields
}

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

func (RedshiftDatabaseCredentials) String 

func (s RedshiftDatabaseCredentials) String() string

String returns the string representation

func (*RedshiftDatabaseCredentials) Validate 

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) String 

func (s RedshiftMetadata) String() string

String returns the string representation

type S3DataSpec 

type S3DataSpec struct {

	// The location of the data file(s) used by a DataSource. The URI specifies
	// a data file or an Amazon Simple Storage Service (Amazon S3) directory or
	// bucket containing data files.
	//
	// DataLocationS3 is a required field
	DataLocationS3 *string `type:"string" required:"true"`

	// A JSON string that represents the splitting and rearrangement processing
	// to be applied to a DataSource. If the DataRearrangement parameter is not
	// provided, all of the input data is used to create the Datasource.
	//
	// There are multiple parameters that control what data is used to create a
	// datasource:
	//
	//    * percentBegin Use percentBegin to indicate the beginning of the range
	//    of the data used to create the Datasource. If you do not include percentBegin
	//    and percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * percentEnd Use percentEnd to indicate the end of the range of the data
	//    used to create the Datasource. If you do not include percentBegin and
	//    percentEnd, Amazon ML includes all of the data when creating the datasource.
	//
	//    * complement The complement parameter instructs Amazon ML to use the data
	//    that is not included in the range of percentBegin to percentEnd to create
	//    a datasource. The complement parameter is useful if you need to create
	//    complementary datasources for training and evaluation. To create a complementary
	//    datasource, use the same values for percentBegin and percentEnd, along
	//    with the complement parameter. For example, the following two datasources
	//    do not share any data, and can be used to train and evaluate a model.
	//    The first datasource has 25 percent of the data, and the second one has
	//    75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0,
	//    "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0,
	//    "percentEnd":25, "complement":"true"}}
	//
	//    * strategy To change how Amazon ML splits the data for a datasource, use
	//    the strategy parameter. The default value for the strategy parameter is
	//    sequential, meaning that Amazon ML takes all of the data records between
	//    the percentBegin and percentEnd parameters for the datasource, in the
	//    order that the records appear in the input data. The following two DataRearrangement
	//    lines are examples of sequentially ordered training and evaluation datasources:
	//    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly
	//    split the input data into the proportions indicated by the percentBegin
	//    and percentEnd parameters, set the strategy parameter to random and provide
	//    a string that is used as the seed value for the random data splitting
	//    (for example, you can use the S3 path to your data as the random seed
	//    string). If you choose the random split strategy, Amazon ML assigns each
	//    row of data a pseudo-random number between 0 and 100, and then selects
	//    the rows that have an assigned number between percentBegin and percentEnd.
	//    Pseudo-random numbers are assigned using both the input seed string value
	//    and the byte offset as a seed, so changing the data results in a different
	//    split. Any existing ordering is preserved. The random splitting strategy
	//    ensures that variables in the training and evaluation data are distributed
	//    similarly. It is useful in the cases where the input data may have an
	//    implicit sort order, which would otherwise result in training and evaluation
	//    datasources containing non-similar data records. The following two DataRearrangement
	//    lines are examples of non-sequentially ordered training and evaluation
	//    datasources: Datasource for evaluation: {"splitting":{"percentBegin":70,
	//    "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
	//    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,
	//    "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
	DataRearrangement *string `type:"string"`

	// A JSON string that represents the schema for an Amazon S3 DataSource. The
	// DataSchema defines the structure of the observation data in the data file(s)
	// referenced in the DataSource.
	//
	// You must provide either the DataSchema or the DataSchemaLocationS3.
	//
	// Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames
	// have an array of key-value pairs for their value. Use the following format
	// to define your DataSchema.
	//
	// { "version": "1.0",
	//
	// "recordAnnotationFieldName": "F1",
	//
	// "recordWeightFieldName": "F2",
	//
	// "targetFieldName": "F3",
	//
	// "dataFormat": "CSV",
	//
	// "dataFileContainsHeader": true,
	//
	// "attributes": [
	//
	// { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType":
	// "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName":
	// "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL"
	// }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType":
	// "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE"
	// } ],
	//
	// "excludedVariableNames": [ "F6" ] }
	DataSchema *string `type:"string"`

	// Describes the schema location in Amazon S3. You must provide either the DataSchema
	// or the DataSchemaLocationS3.
	DataSchemaLocationS3 *string `type:"string"`
	// contains filtered or unexported fields
}

Describes the data specification of a DataSource.

func (S3DataSpec) String 

func (s S3DataSpec) String() string

String returns the string representation

func (*S3DataSpec) Validate 

func (s *S3DataSpec) Validate() error

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

type SortOrder 

type SortOrder string

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). 

const (
	SortOrderAsc SortOrder = "asc"
	SortOrderDsc SortOrder = "dsc"
)

Enum values for SortOrder

func (SortOrder) MarshalValue added in v0.3.0 

func (enum SortOrder) MarshalValue() (string, error)

func (SortOrder) MarshalValueBuf added in v0.3.0 

func (enum SortOrder) MarshalValueBuf(b []byte) ([]byte, error)

type Tag 

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) String 

func (s Tag) String() string

String returns the string representation

func (*Tag) Validate 

func (s *Tag) Validate() error

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

type TaggableResourceType 

type TaggableResourceType string
const (
	TaggableResourceTypeBatchPrediction TaggableResourceType = "BatchPrediction"
	TaggableResourceTypeDataSource      TaggableResourceType = "DataSource"
	TaggableResourceTypeEvaluation      TaggableResourceType = "Evaluation"
	TaggableResourceTypeMlmodel         TaggableResourceType = "MLModel"
)

Enum values for TaggableResourceType

func (TaggableResourceType) MarshalValue added in v0.3.0 

func (enum TaggableResourceType) MarshalValue() (string, error)

func (TaggableResourceType) MarshalValueBuf added in v0.3.0 

func (enum TaggableResourceType) MarshalValueBuf(b []byte) ([]byte, error)

type UpdateBatchPredictionInput 

type UpdateBatchPredictionInput struct {

	// The ID assigned to the BatchPrediction during creation.
	//
	// BatchPredictionId is a required field
	BatchPredictionId *string `min:"1" type:"string" required:"true"`

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

func (UpdateBatchPredictionInput) String 

func (s UpdateBatchPredictionInput) String() string

String returns the string representation

func (*UpdateBatchPredictionInput) Validate 

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) String 

func (s UpdateBatchPredictionOutput) String() string

String returns the string representation

type UpdateBatchPredictionRequest 

type UpdateBatchPredictionRequest struct {
	*aws.Request
	Input *UpdateBatchPredictionInput
	Copy  func(*UpdateBatchPredictionInput) UpdateBatchPredictionRequest
}

UpdateBatchPredictionRequest is the request type for the UpdateBatchPrediction API operation.

func (UpdateBatchPredictionRequest) Send 

func (r UpdateBatchPredictionRequest) Send(ctx context.Context) (*UpdateBatchPredictionResponse, error)

Send marshals and sends the UpdateBatchPrediction API request.

type UpdateBatchPredictionResponse added in v0.9.0 

type UpdateBatchPredictionResponse struct {
	*UpdateBatchPredictionOutput
	// contains filtered or unexported fields
}

UpdateBatchPredictionResponse is the response type for the UpdateBatchPrediction API operation.

func (*UpdateBatchPredictionResponse) SDKResponseMetdata added in v0.9.0 

func (r *UpdateBatchPredictionResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the UpdateBatchPrediction request.

type UpdateDataSourceInput 

type UpdateDataSourceInput struct {

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

	// A new user-supplied name or description of the DataSource that will replace
	// the current description.
	//
	// DataSourceName is a required field
	DataSourceName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateDataSourceInput) String 

func (s UpdateDataSourceInput) String() string

String returns the string representation

func (*UpdateDataSourceInput) Validate 

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) String 

func (s UpdateDataSourceOutput) String() string

String returns the string representation

type UpdateDataSourceRequest 

type UpdateDataSourceRequest struct {
	*aws.Request
	Input *UpdateDataSourceInput
	Copy  func(*UpdateDataSourceInput) UpdateDataSourceRequest
}

UpdateDataSourceRequest is the request type for the UpdateDataSource API operation.

func (UpdateDataSourceRequest) Send 

func (r UpdateDataSourceRequest) Send(ctx context.Context) (*UpdateDataSourceResponse, error)

Send marshals and sends the UpdateDataSource API request.

type UpdateDataSourceResponse added in v0.9.0 

type UpdateDataSourceResponse struct {
	*UpdateDataSourceOutput
	// contains filtered or unexported fields
}

UpdateDataSourceResponse is the response type for the UpdateDataSource API operation.

func (*UpdateDataSourceResponse) SDKResponseMetdata added in v0.9.0 

func (r *UpdateDataSourceResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the UpdateDataSource request.

type UpdateEvaluationInput 

type UpdateEvaluationInput struct {

	// The ID assigned to the Evaluation during creation.
	//
	// EvaluationId is a required field
	EvaluationId *string `min:"1" type:"string" required:"true"`

	// A new user-supplied name or description of the Evaluation that will replace
	// the current content.
	//
	// EvaluationName is a required field
	EvaluationName *string `type:"string" required:"true"`
	// contains filtered or unexported fields
}

func (UpdateEvaluationInput) String 

func (s UpdateEvaluationInput) String() string

String returns the string representation

func (*UpdateEvaluationInput) Validate 

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) String 

func (s UpdateEvaluationOutput) String() string

String returns the string representation

type UpdateEvaluationRequest 

type UpdateEvaluationRequest struct {
	*aws.Request
	Input *UpdateEvaluationInput
	Copy  func(*UpdateEvaluationInput) UpdateEvaluationRequest
}

UpdateEvaluationRequest is the request type for the UpdateEvaluation API operation.

func (UpdateEvaluationRequest) Send 

func (r UpdateEvaluationRequest) Send(ctx context.Context) (*UpdateEvaluationResponse, error)

Send marshals and sends the UpdateEvaluation API request.

type UpdateEvaluationResponse added in v0.9.0 

type UpdateEvaluationResponse struct {
	*UpdateEvaluationOutput
	// contains filtered or unexported fields
}

UpdateEvaluationResponse is the response type for the UpdateEvaluation API operation.

func (*UpdateEvaluationResponse) SDKResponseMetdata added in v0.9.0 

func (r *UpdateEvaluationResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the UpdateEvaluation request.

type UpdateMLModelInput 

type UpdateMLModelInput struct {

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

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

	// The ScoreThreshold used in binary classification MLModel that marks the boundary
	// between a positive prediction and a negative prediction.
	//
	// Output values greater than or equal to the ScoreThreshold receive a positive
	// result from the MLModel, such as true. Output values less than the ScoreThreshold
	// receive a negative response from the MLModel, such as false.
	ScoreThreshold *float64 `type:"float"`
	// contains filtered or unexported fields
}

func (UpdateMLModelInput) String 

func (s UpdateMLModelInput) String() string

String returns the string representation

func (*UpdateMLModelInput) Validate 

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) String 

func (s UpdateMLModelOutput) String() string

String returns the string representation

type UpdateMLModelRequest 

type UpdateMLModelRequest struct {
	*aws.Request
	Input *UpdateMLModelInput
	Copy  func(*UpdateMLModelInput) UpdateMLModelRequest
}

UpdateMLModelRequest is the request type for the UpdateMLModel API operation.

func (UpdateMLModelRequest) Send 

func (r UpdateMLModelRequest) Send(ctx context.Context) (*UpdateMLModelResponse, error)

Send marshals and sends the UpdateMLModel API request.

type UpdateMLModelResponse added in v0.9.0 

type UpdateMLModelResponse struct {
	*UpdateMLModelOutput
	// contains filtered or unexported fields
}

UpdateMLModelResponse is the response type for the UpdateMLModel API operation.

func (*UpdateMLModelResponse) SDKResponseMetdata added in v0.9.0 

func (r *UpdateMLModelResponse) SDKResponseMetdata() *aws.Response

SDKResponseMetdata returns the response metadata for the UpdateMLModel request.

Source Files

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Directories

Path Synopsis
Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.
 Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.

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