Package machinelearning

// Create a session to share configuration, and load external configuration.
sess := session.Must(session.NewSession())

// Create the service's client with the session.
svc := machinelearning.New(sess)

result, err := svc.AddTags(params)
if err != nil {
    // Cast err to awserr.Error to handle specific error codes.
    aerr, ok := err.(awserr.Error)
    if ok && aerr.Code() == <error code to check for> {
        // Specific error code handling
    }
    return err
}

fmt.Println("AddTags result:")
fmt.Println(result)

ctx := context.Background()

result, err := svc.AddTagsWithContext(ctx, params)

Constants

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

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

* Status - Sets the search criteria to BatchPrediction status.
* Name - Sets the search criteria to the contents of BatchPredictionName.

* IAMUser - Sets the search criteria to the user account that invoked
the BatchPrediction creation.
* MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 is a DetailsAttributes enum value
    DetailsAttributesPredictiveModelType = "PredictiveModelType"

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

Object status with the following possible values:

* PENDING
* INPROGRESS
* FAILED
* COMPLETED
* DELETED

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

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

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

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

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

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

* CreatedAt - Sets the search criteria to Evaluation creation date.
* Status - Sets the search criteria to Evaluation status.
* Name - Sets the search criteria to the contents of EvaluationName.
* IAMUser - Sets the search criteria to the user account that invoked
an evaluation.
* MLModelId - Sets the search criteria to the Predictor that was evaluated.

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

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

const (
    // EvaluationFilterVariableCreatedAt is a EvaluationFilterVariable enum value
    EvaluationFilterVariableCreatedAt = "CreatedAt"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    // MLModelFilterVariableTrainingDataUri is a MLModelFilterVariable enum value
    MLModelFilterVariableTrainingDataUri = "TrainingDataURI"
)

const (
    // MLModelTypeRegression is a MLModelType enum value
    MLModelTypeRegression = "REGRESSION"

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

    // MLModelTypeMulticlass is a MLModelType enum value
    MLModelTypeMulticlass = "MULTICLASS"
)

const (
    // RealtimeEndpointStatusNone is a RealtimeEndpointStatus enum value
    RealtimeEndpointStatusNone = "NONE"

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

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

    // RealtimeEndpointStatusFailed is a RealtimeEndpointStatus enum value
    RealtimeEndpointStatusFailed = "FAILED"
)

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 is a SortOrder enum value
    SortOrderAsc = "asc"

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

const (
    // TaggableResourceTypeBatchPrediction is a TaggableResourceType enum value
    TaggableResourceTypeBatchPrediction = "BatchPrediction"

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

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

    // TaggableResourceTypeMlmodel is a TaggableResourceType enum value
    TaggableResourceTypeMlmodel = "MLModel"
)

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"
)

Service information constants

const (
    ServiceName = "machinelearning" // Service endpoint prefix API calls made to.
    EndpointsID = ServiceName       // Service ID for Regions and Endpoints metadata.
)

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 is a Algorithm enum value
    AlgorithmSgd = "sgd"
)

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 *string `type:"string" required:"true" enum:"TaggableResourceType"`

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

func (AddTagsInput) GoString ¶

func (s AddTagsInput) GoString() string

GoString returns the string representation

func (*AddTagsInput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*AddTagsInput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

func (*AddTagsInput) SetTags ¶

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

SetTags sets the Tags field's value.

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 ¶

Amazon ML returns the following elements.

type AddTagsOutput struct {

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

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

func (AddTagsOutput) GoString ¶

func (s AddTagsOutput) GoString() string

GoString returns the string representation

func (*AddTagsOutput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*AddTagsOutput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

func (AddTagsOutput) String ¶

func (s AddTagsOutput) String() string

String returns the string representation

type BatchPrediction ¶

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.

type BatchPrediction struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (BatchPrediction) GoString ¶

func (s BatchPrediction) GoString() string

GoString returns the string representation

func (*BatchPrediction) SetBatchPredictionDataSourceId ¶

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

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*BatchPrediction) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*BatchPrediction) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*BatchPrediction) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*BatchPrediction) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*BatchPrediction) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*BatchPrediction) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*BatchPrediction) SetInvalidRecordCount ¶

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

SetInvalidRecordCount sets the InvalidRecordCount field's value.

func (*BatchPrediction) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*BatchPrediction) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*BatchPrediction) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*BatchPrediction) SetName ¶

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

SetName sets the Name field's value.

func (*BatchPrediction) SetOutputUri ¶

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

SetOutputUri sets the OutputUri field's value.

func (*BatchPrediction) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*BatchPrediction) SetStatus ¶

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

SetStatus sets the Status field's value.

func (*BatchPrediction) SetTotalRecordCount ¶

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

SetTotalRecordCount sets the TotalRecordCount field's value.

func (BatchPrediction) String ¶

func (s BatchPrediction) String() string

String returns the string representation

type CreateBatchPredictionInput ¶

type CreateBatchPredictionInput struct {

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

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

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

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

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

func (CreateBatchPredictionInput) GoString ¶

func (s CreateBatchPredictionInput) GoString() string

GoString returns the string representation

func (*CreateBatchPredictionInput) SetBatchPredictionDataSourceId ¶

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

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*CreateBatchPredictionInput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*CreateBatchPredictionInput) SetBatchPredictionName ¶

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

SetBatchPredictionName sets the BatchPredictionName field's value.

func (*CreateBatchPredictionInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*CreateBatchPredictionInput) SetOutputUri ¶

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

SetOutputUri sets the OutputUri field's value.

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 ¶

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.

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
}

func (CreateBatchPredictionOutput) GoString ¶

func (s CreateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*CreateBatchPredictionOutput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

func (CreateBatchPredictionOutput) String ¶

func (s CreateBatchPredictionOutput) String() string

String returns the string representation

type CreateDataSourceFromRDSInput ¶

type CreateDataSourceFromRDSInput struct {

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

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

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

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

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

func (CreateDataSourceFromRDSInput) GoString ¶

func (s CreateDataSourceFromRDSInput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRDSInput) SetComputeStatistics ¶

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

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromRDSInput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromRDSInput) SetDataSourceName ¶

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

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromRDSInput) SetRDSData ¶

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

SetRDSData sets the RDSData field's value.

func (*CreateDataSourceFromRDSInput) SetRoleARN ¶

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

SetRoleARN sets the RoleARN field's value.

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 ¶

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.

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
}

func (CreateDataSourceFromRDSOutput) GoString ¶

func (s CreateDataSourceFromRDSOutput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRDSOutput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromRDSOutput) String ¶

func (s CreateDataSourceFromRDSOutput) String() string

String returns the string representation

type CreateDataSourceFromRedshiftInput ¶

type CreateDataSourceFromRedshiftInput struct {

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

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

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

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

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

func (CreateDataSourceFromRedshiftInput) GoString ¶

func (s CreateDataSourceFromRedshiftInput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRedshiftInput) SetComputeStatistics ¶

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

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSourceName ¶

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

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromRedshiftInput) SetDataSpec ¶

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

SetDataSpec sets the DataSpec field's value.

func (*CreateDataSourceFromRedshiftInput) SetRoleARN ¶

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

SetRoleARN sets the RoleARN field's value.

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 ¶

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.

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
}

func (CreateDataSourceFromRedshiftOutput) GoString ¶

func (s CreateDataSourceFromRedshiftOutput) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromRedshiftOutput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromRedshiftOutput) String ¶

func (s CreateDataSourceFromRedshiftOutput) String() string

String returns the string representation

type CreateDataSourceFromS3Input ¶

type CreateDataSourceFromS3Input struct {

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

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

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

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

func (CreateDataSourceFromS3Input) GoString ¶

func (s CreateDataSourceFromS3Input) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromS3Input) SetComputeStatistics ¶

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

SetComputeStatistics sets the ComputeStatistics field's value.

func (*CreateDataSourceFromS3Input) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*CreateDataSourceFromS3Input) SetDataSourceName ¶

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

SetDataSourceName sets the DataSourceName field's value.

func (*CreateDataSourceFromS3Input) SetDataSpec ¶

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

SetDataSpec sets the DataSpec field's value.

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 ¶

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.

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
}

func (CreateDataSourceFromS3Output) GoString ¶

func (s CreateDataSourceFromS3Output) GoString() string

GoString returns the string representation

func (*CreateDataSourceFromS3Output) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (CreateDataSourceFromS3Output) String ¶

func (s CreateDataSourceFromS3Output) String() string

String returns the string representation

type CreateEvaluationInput ¶

type CreateEvaluationInput struct {

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

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

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

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

func (CreateEvaluationInput) GoString ¶

func (s CreateEvaluationInput) GoString() string

GoString returns the string representation

func (*CreateEvaluationInput) SetEvaluationDataSourceId ¶

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

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*CreateEvaluationInput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

func (*CreateEvaluationInput) SetEvaluationName ¶

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

SetEvaluationName sets the EvaluationName field's value.

func (*CreateEvaluationInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

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 ¶

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.

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
}

func (CreateEvaluationOutput) GoString ¶

func (s CreateEvaluationOutput) GoString() string

GoString returns the string representation

func (*CreateEvaluationOutput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

func (CreateEvaluationOutput) String ¶

func (s CreateEvaluationOutput) String() string

String returns the string representation

type CreateMLModelInput ¶

type CreateMLModelInput struct {

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

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

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

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

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

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

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

func (CreateMLModelInput) GoString ¶

func (s CreateMLModelInput) GoString() string

GoString returns the string representation

func (*CreateMLModelInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*CreateMLModelInput) SetMLModelName ¶

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

SetMLModelName sets the MLModelName field's value.

func (*CreateMLModelInput) SetMLModelType ¶

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

SetMLModelType sets the MLModelType field's value.

func (*CreateMLModelInput) SetParameters ¶

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

SetParameters sets the Parameters field's value.

func (*CreateMLModelInput) SetRecipe ¶

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

SetRecipe sets the Recipe field's value.

func (*CreateMLModelInput) SetRecipeUri ¶

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

SetRecipeUri sets the RecipeUri field's value.

func (*CreateMLModelInput) SetTrainingDataSourceId ¶

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

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

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 ¶

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

The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

type CreateMLModelOutput struct {

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

func (CreateMLModelOutput) GoString ¶

func (s CreateMLModelOutput) GoString() string

GoString returns the string representation

func (*CreateMLModelOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (CreateMLModelOutput) String ¶

func (s CreateMLModelOutput) String() string

String returns the string representation

type CreateRealtimeEndpointInput ¶

type CreateRealtimeEndpointInput struct {

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

func (CreateRealtimeEndpointInput) GoString ¶

func (s CreateRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (*CreateRealtimeEndpointInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

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 ¶

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.

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
}

func (CreateRealtimeEndpointOutput) GoString ¶

func (s CreateRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (*CreateRealtimeEndpointOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo ¶

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

SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.

func (CreateRealtimeEndpointOutput) String ¶

func (s CreateRealtimeEndpointOutput) String() string

String returns the string representation

type DataSource ¶

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.

type DataSource struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (DataSource) GoString ¶

func (s DataSource) GoString() string

GoString returns the string representation

func (*DataSource) SetComputeStatistics ¶

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

SetComputeStatistics sets the ComputeStatistics field's value.

func (*DataSource) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*DataSource) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*DataSource) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*DataSource) SetDataLocationS3 ¶

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

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*DataSource) SetDataRearrangement ¶

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

SetDataRearrangement sets the DataRearrangement field's value.

func (*DataSource) SetDataSizeInBytes ¶

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

SetDataSizeInBytes sets the DataSizeInBytes field's value.

func (*DataSource) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*DataSource) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*DataSource) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*DataSource) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*DataSource) SetName ¶

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

SetName sets the Name field's value.

func (*DataSource) SetNumberOfFiles ¶

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

SetNumberOfFiles sets the NumberOfFiles field's value.

func (*DataSource) SetRDSMetadata ¶

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

SetRDSMetadata sets the RDSMetadata field's value.

func (*DataSource) SetRedshiftMetadata ¶

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

SetRedshiftMetadata sets the RedshiftMetadata field's value.

func (*DataSource) SetRoleARN ¶

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

SetRoleARN sets the RoleARN field's value.

func (*DataSource) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*DataSource) SetStatus ¶

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

SetStatus sets the Status field's value.

func (DataSource) String ¶

func (s DataSource) String() string

String returns the string representation

type DeleteBatchPredictionInput ¶

type DeleteBatchPredictionInput struct {

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

func (DeleteBatchPredictionInput) GoString ¶

func (s DeleteBatchPredictionInput) GoString() string

GoString returns the string representation

func (*DeleteBatchPredictionInput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

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 ¶

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.

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
}

func (DeleteBatchPredictionOutput) GoString ¶

func (s DeleteBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*DeleteBatchPredictionOutput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

func (DeleteBatchPredictionOutput) String ¶

func (s DeleteBatchPredictionOutput) String() string

String returns the string representation

type DeleteDataSourceInput ¶

type DeleteDataSourceInput struct {

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

func (DeleteDataSourceInput) GoString ¶

func (s DeleteDataSourceInput) GoString() string

GoString returns the string representation

func (*DeleteDataSourceInput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

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 ¶

Represents the output of a DeleteDataSource operation.

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
}

func (DeleteDataSourceOutput) GoString ¶

func (s DeleteDataSourceOutput) GoString() string

GoString returns the string representation

func (*DeleteDataSourceOutput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (DeleteDataSourceOutput) String ¶

func (s DeleteDataSourceOutput) String() string

String returns the string representation

type DeleteEvaluationInput ¶

type DeleteEvaluationInput struct {

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

func (DeleteEvaluationInput) GoString ¶

func (s DeleteEvaluationInput) GoString() string

GoString returns the string representation

func (*DeleteEvaluationInput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

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 ¶

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.

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
}

func (DeleteEvaluationOutput) GoString ¶

func (s DeleteEvaluationOutput) GoString() string

GoString returns the string representation

func (*DeleteEvaluationOutput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

func (DeleteEvaluationOutput) String ¶

func (s DeleteEvaluationOutput) String() string

String returns the string representation

type DeleteMLModelInput ¶

type DeleteMLModelInput struct {

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

func (DeleteMLModelInput) GoString ¶

func (s DeleteMLModelInput) GoString() string

GoString returns the string representation

func (*DeleteMLModelInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

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 ¶

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.

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
}

func (DeleteMLModelOutput) GoString ¶

func (s DeleteMLModelOutput) GoString() string

GoString returns the string representation

func (*DeleteMLModelOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (DeleteMLModelOutput) String ¶

func (s DeleteMLModelOutput) String() string

String returns the string representation

type DeleteRealtimeEndpointInput ¶

type DeleteRealtimeEndpointInput struct {

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

func (DeleteRealtimeEndpointInput) GoString ¶

func (s DeleteRealtimeEndpointInput) GoString() string

GoString returns the string representation

func (*DeleteRealtimeEndpointInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

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 ¶

Represents the output of an DeleteRealtimeEndpoint operation.

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

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
}

func (DeleteRealtimeEndpointOutput) GoString ¶

func (s DeleteRealtimeEndpointOutput) GoString() string

GoString returns the string representation

func (*DeleteRealtimeEndpointOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo ¶

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

SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.

func (DeleteRealtimeEndpointOutput) String ¶

func (s DeleteRealtimeEndpointOutput) String() string

String returns the string representation

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 *string `type:"string" required:"true" enum:"TaggableResourceType"`

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

func (DeleteTagsInput) GoString ¶

func (s DeleteTagsInput) GoString() string

GoString returns the string representation

func (*DeleteTagsInput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*DeleteTagsInput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

func (*DeleteTagsInput) SetTagKeys ¶

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

SetTagKeys sets the TagKeys field's value.

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 ¶

Amazon ML returns the following elements.

type DeleteTagsOutput struct {

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

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

func (DeleteTagsOutput) GoString ¶

func (s DeleteTagsOutput) GoString() string

GoString returns the string representation

func (*DeleteTagsOutput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*DeleteTagsOutput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

func (DeleteTagsOutput) String ¶

func (s DeleteTagsOutput) String() string

String returns the string representation

type DescribeBatchPredictionsInput ¶

type DescribeBatchPredictionsInput struct {

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

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

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

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

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

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

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

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

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

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

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

func (DescribeBatchPredictionsInput) GoString ¶

func (s DescribeBatchPredictionsInput) GoString() string

GoString returns the string representation

func (*DescribeBatchPredictionsInput) SetEQ ¶

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

SetEQ sets the EQ field's value.

func (*DescribeBatchPredictionsInput) SetFilterVariable ¶

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

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeBatchPredictionsInput) SetGE ¶

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

SetGE sets the GE field's value.

func (*DescribeBatchPredictionsInput) SetGT ¶

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

SetGT sets the GT field's value.

func (*DescribeBatchPredictionsInput) SetLE ¶

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

SetLE sets the LE field's value.

func (*DescribeBatchPredictionsInput) SetLT ¶

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

SetLT sets the LT field's value.

func (*DescribeBatchPredictionsInput) SetLimit ¶

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

SetLimit sets the Limit field's value.

func (*DescribeBatchPredictionsInput) SetNE ¶

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

SetNE sets the NE field's value.

func (*DescribeBatchPredictionsInput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeBatchPredictionsInput) SetPrefix ¶

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

SetPrefix sets the Prefix field's value.

func (*DescribeBatchPredictionsInput) SetSortOrder ¶

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

SetSortOrder sets the SortOrder field's value.

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 ¶

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

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
}

func (DescribeBatchPredictionsOutput) GoString ¶

func (s DescribeBatchPredictionsOutput) GoString() string

GoString returns the string representation

func (*DescribeBatchPredictionsOutput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeBatchPredictionsOutput) SetResults ¶

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

SetResults sets the Results field's value.

func (DescribeBatchPredictionsOutput) String ¶

func (s DescribeBatchPredictionsOutput) String() string

String returns the string representation

type DescribeDataSourcesInput ¶

type DescribeDataSourcesInput struct {

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

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

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

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

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

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

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

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

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

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

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

func (DescribeDataSourcesInput) GoString ¶

func (s DescribeDataSourcesInput) GoString() string

GoString returns the string representation

func (*DescribeDataSourcesInput) SetEQ ¶

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

SetEQ sets the EQ field's value.

func (*DescribeDataSourcesInput) SetFilterVariable ¶

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

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeDataSourcesInput) SetGE ¶

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

SetGE sets the GE field's value.

func (*DescribeDataSourcesInput) SetGT ¶

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

SetGT sets the GT field's value.

func (*DescribeDataSourcesInput) SetLE ¶

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

SetLE sets the LE field's value.

func (*DescribeDataSourcesInput) SetLT ¶

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

SetLT sets the LT field's value.

func (*DescribeDataSourcesInput) SetLimit ¶

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

SetLimit sets the Limit field's value.

func (*DescribeDataSourcesInput) SetNE ¶

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

SetNE sets the NE field's value.

func (*DescribeDataSourcesInput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeDataSourcesInput) SetPrefix ¶

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

SetPrefix sets the Prefix field's value.

func (*DescribeDataSourcesInput) SetSortOrder ¶

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

SetSortOrder sets the SortOrder field's value.

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 ¶

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

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
}

func (DescribeDataSourcesOutput) GoString ¶

func (s DescribeDataSourcesOutput) GoString() string

GoString returns the string representation

func (*DescribeDataSourcesOutput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeDataSourcesOutput) SetResults ¶

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

SetResults sets the Results field's value.

func (DescribeDataSourcesOutput) String ¶

func (s DescribeDataSourcesOutput) String() string

String returns the string representation

type DescribeEvaluationsInput ¶

type DescribeEvaluationsInput struct {

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

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

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

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

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

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

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

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

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

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

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

func (DescribeEvaluationsInput) GoString ¶

func (s DescribeEvaluationsInput) GoString() string

GoString returns the string representation

func (*DescribeEvaluationsInput) SetEQ ¶

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

SetEQ sets the EQ field's value.

func (*DescribeEvaluationsInput) SetFilterVariable ¶

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

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeEvaluationsInput) SetGE ¶

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

SetGE sets the GE field's value.

func (*DescribeEvaluationsInput) SetGT ¶

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

SetGT sets the GT field's value.

func (*DescribeEvaluationsInput) SetLE ¶

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

SetLE sets the LE field's value.

func (*DescribeEvaluationsInput) SetLT ¶

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

SetLT sets the LT field's value.

func (*DescribeEvaluationsInput) SetLimit ¶

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

SetLimit sets the Limit field's value.

func (*DescribeEvaluationsInput) SetNE ¶

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

SetNE sets the NE field's value.

func (*DescribeEvaluationsInput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeEvaluationsInput) SetPrefix ¶

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

SetPrefix sets the Prefix field's value.

func (*DescribeEvaluationsInput) SetSortOrder ¶

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

SetSortOrder sets the SortOrder field's value.

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 ¶

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

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
}

func (DescribeEvaluationsOutput) GoString ¶

func (s DescribeEvaluationsOutput) GoString() string

GoString returns the string representation

func (*DescribeEvaluationsOutput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeEvaluationsOutput) SetResults ¶

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

SetResults sets the Results field's value.

func (DescribeEvaluationsOutput) String ¶

func (s DescribeEvaluationsOutput) String() string

String returns the string representation

type DescribeMLModelsInput ¶

type DescribeMLModelsInput struct {

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

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

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

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

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

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

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

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

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

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

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

func (DescribeMLModelsInput) GoString ¶

func (s DescribeMLModelsInput) GoString() string

GoString returns the string representation

func (*DescribeMLModelsInput) SetEQ ¶

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

SetEQ sets the EQ field's value.

func (*DescribeMLModelsInput) SetFilterVariable ¶

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

SetFilterVariable sets the FilterVariable field's value.

func (*DescribeMLModelsInput) SetGE ¶

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

SetGE sets the GE field's value.

func (*DescribeMLModelsInput) SetGT ¶

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

SetGT sets the GT field's value.

func (*DescribeMLModelsInput) SetLE ¶

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

SetLE sets the LE field's value.

func (*DescribeMLModelsInput) SetLT ¶

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

SetLT sets the LT field's value.

func (*DescribeMLModelsInput) SetLimit ¶

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

SetLimit sets the Limit field's value.

func (*DescribeMLModelsInput) SetNE ¶

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

SetNE sets the NE field's value.

func (*DescribeMLModelsInput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeMLModelsInput) SetPrefix ¶

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

SetPrefix sets the Prefix field's value.

func (*DescribeMLModelsInput) SetSortOrder ¶

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

SetSortOrder sets the SortOrder field's value.

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 ¶

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

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
}

func (DescribeMLModelsOutput) GoString ¶

func (s DescribeMLModelsOutput) GoString() string

GoString returns the string representation

func (*DescribeMLModelsOutput) SetNextToken ¶

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

SetNextToken sets the NextToken field's value.

func (*DescribeMLModelsOutput) SetResults ¶

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

SetResults sets the Results field's value.

func (DescribeMLModelsOutput) String ¶

func (s DescribeMLModelsOutput) String() string

String returns the string representation

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

func (DescribeTagsInput) GoString ¶

func (s DescribeTagsInput) GoString() string

GoString returns the string representation

func (*DescribeTagsInput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*DescribeTagsInput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

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 ¶

Amazon ML returns the following elements.

type DescribeTagsOutput struct {

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

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

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

func (DescribeTagsOutput) GoString ¶

func (s DescribeTagsOutput) GoString() string

GoString returns the string representation

func (*DescribeTagsOutput) SetResourceId ¶

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

SetResourceId sets the ResourceId field's value.

func (*DescribeTagsOutput) SetResourceType ¶

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

SetResourceType sets the ResourceType field's value.

func (*DescribeTagsOutput) SetTags ¶

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

SetTags sets the Tags field's value.

func (DescribeTagsOutput) String ¶

func (s DescribeTagsOutput) String() string

String returns the string representation

type Evaluation ¶

Represents the output of GetEvaluation operation.

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

type Evaluation struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (Evaluation) GoString ¶

func (s Evaluation) GoString() string

GoString returns the string representation

func (*Evaluation) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*Evaluation) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*Evaluation) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*Evaluation) SetEvaluationDataSourceId ¶

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

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*Evaluation) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

func (*Evaluation) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*Evaluation) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*Evaluation) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*Evaluation) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*Evaluation) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*Evaluation) SetName ¶

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

SetName sets the Name field's value.

func (*Evaluation) SetPerformanceMetrics ¶

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

SetPerformanceMetrics sets the PerformanceMetrics field's value.

func (*Evaluation) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*Evaluation) SetStatus ¶

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

SetStatus sets the Status field's value.

func (Evaluation) String ¶

func (s Evaluation) String() string

String returns the string representation

type GetBatchPredictionInput ¶

type GetBatchPredictionInput struct {

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

func (GetBatchPredictionInput) GoString ¶

func (s GetBatchPredictionInput) GoString() string

GoString returns the string representation

func (*GetBatchPredictionInput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

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 ¶

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

type GetBatchPredictionOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetBatchPredictionOutput) GoString ¶

func (s GetBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*GetBatchPredictionOutput) SetBatchPredictionDataSourceId ¶

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

SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.

func (*GetBatchPredictionOutput) SetBatchPredictionId ¶

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

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*GetBatchPredictionOutput) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*GetBatchPredictionOutput) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*GetBatchPredictionOutput) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetBatchPredictionOutput) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*GetBatchPredictionOutput) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetBatchPredictionOutput) SetInvalidRecordCount ¶

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

SetInvalidRecordCount sets the InvalidRecordCount field's value.

func (*GetBatchPredictionOutput) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetBatchPredictionOutput) SetLogUri ¶

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

SetLogUri sets the LogUri field's value.

func (*GetBatchPredictionOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*GetBatchPredictionOutput) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*GetBatchPredictionOutput) SetName ¶

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

SetName sets the Name field's value.

func (*GetBatchPredictionOutput) SetOutputUri ¶

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

SetOutputUri sets the OutputUri field's value.

func (*GetBatchPredictionOutput) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*GetBatchPredictionOutput) SetStatus ¶

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

SetStatus sets the Status field's value.

func (*GetBatchPredictionOutput) SetTotalRecordCount ¶

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

SetTotalRecordCount sets the TotalRecordCount field's value.

func (GetBatchPredictionOutput) String ¶

func (s GetBatchPredictionOutput) String() string

String returns the string representation

type GetDataSourceInput ¶

type GetDataSourceInput struct {

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

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

func (GetDataSourceInput) GoString ¶

func (s GetDataSourceInput) GoString() string

GoString returns the string representation

func (*GetDataSourceInput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*GetDataSourceInput) SetVerbose ¶

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

SetVerbose sets the Verbose field's value.

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 ¶

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

type GetDataSourceOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetDataSourceOutput) GoString ¶

func (s GetDataSourceOutput) GoString() string

GoString returns the string representation

func (*GetDataSourceOutput) SetComputeStatistics ¶

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

SetComputeStatistics sets the ComputeStatistics field's value.

func (*GetDataSourceOutput) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*GetDataSourceOutput) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*GetDataSourceOutput) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetDataSourceOutput) SetDataLocationS3 ¶

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

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*GetDataSourceOutput) SetDataRearrangement ¶

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

SetDataRearrangement sets the DataRearrangement field's value.

func (*GetDataSourceOutput) SetDataSizeInBytes ¶

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

SetDataSizeInBytes sets the DataSizeInBytes field's value.

func (*GetDataSourceOutput) SetDataSourceId ¶

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

SetDataSourceId sets the DataSourceId field's value.

func (*GetDataSourceOutput) SetDataSourceSchema ¶

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

SetDataSourceSchema sets the DataSourceSchema field's value.

func (*GetDataSourceOutput) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*GetDataSourceOutput) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetDataSourceOutput) SetLogUri ¶

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

SetLogUri sets the LogUri field's value.

func (*GetDataSourceOutput) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*GetDataSourceOutput) SetName ¶

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

SetName sets the Name field's value.

func (*GetDataSourceOutput) SetNumberOfFiles ¶

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

SetNumberOfFiles sets the NumberOfFiles field's value.

func (*GetDataSourceOutput) SetRDSMetadata ¶

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

SetRDSMetadata sets the RDSMetadata field's value.

func (*GetDataSourceOutput) SetRedshiftMetadata ¶

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

SetRedshiftMetadata sets the RedshiftMetadata field's value.

func (*GetDataSourceOutput) SetRoleARN ¶

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

SetRoleARN sets the RoleARN field's value.

func (*GetDataSourceOutput) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*GetDataSourceOutput) SetStatus ¶

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

SetStatus sets the Status field's value.

func (GetDataSourceOutput) String ¶

func (s GetDataSourceOutput) String() string

String returns the string representation

type GetEvaluationInput ¶

type GetEvaluationInput struct {

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

func (GetEvaluationInput) GoString ¶

func (s GetEvaluationInput) GoString() string

GoString returns the string representation

func (*GetEvaluationInput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

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 ¶

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

type GetEvaluationOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetEvaluationOutput) GoString ¶

func (s GetEvaluationOutput) GoString() string

GoString returns the string representation

func (*GetEvaluationOutput) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*GetEvaluationOutput) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*GetEvaluationOutput) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetEvaluationOutput) SetEvaluationDataSourceId ¶

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

SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.

func (*GetEvaluationOutput) SetEvaluationId ¶

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

SetEvaluationId sets the EvaluationId field's value.

func (*GetEvaluationOutput) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*GetEvaluationOutput) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetEvaluationOutput) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetEvaluationOutput) SetLogUri ¶

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

SetLogUri sets the LogUri field's value.

func (*GetEvaluationOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*GetEvaluationOutput) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*GetEvaluationOutput) SetName ¶

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

SetName sets the Name field's value.

func (*GetEvaluationOutput) SetPerformanceMetrics ¶

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

SetPerformanceMetrics sets the PerformanceMetrics field's value.

func (*GetEvaluationOutput) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*GetEvaluationOutput) SetStatus ¶

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

SetStatus sets the Status field's value.

func (GetEvaluationOutput) String ¶

func (s GetEvaluationOutput) String() string

String returns the string representation

type GetMLModelInput ¶

type GetMLModelInput struct {

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

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

func (GetMLModelInput) GoString ¶

func (s GetMLModelInput) GoString() string

GoString returns the string representation

func (*GetMLModelInput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*GetMLModelInput) SetVerbose ¶

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

SetVerbose sets the Verbose field's value.

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 ¶

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

type GetMLModelOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    // The scoring threshold is used in binary classification MLModelmodels. It
    // marks the boundary between a positive prediction and a negative prediction.
    //
    // Output values greater than or equal to the threshold receive a positive result
    // from the MLModel, such as true. Output values less than the threshold receive
    // a negative response from the MLModel, such as false.
    ScoreThreshold *float64 `type:"float"`

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

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

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

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

    // The ID of the training DataSource.
    TrainingDataSourceId *string `min:"1" type:"string"`

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

func (GetMLModelOutput) GoString ¶

func (s GetMLModelOutput) GoString() string

GoString returns the string representation

func (*GetMLModelOutput) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*GetMLModelOutput) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*GetMLModelOutput) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*GetMLModelOutput) SetEndpointInfo ¶

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

SetEndpointInfo sets the EndpointInfo field's value.

func (*GetMLModelOutput) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*GetMLModelOutput) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*GetMLModelOutput) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*GetMLModelOutput) SetLogUri ¶

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

SetLogUri sets the LogUri field's value.

func (*GetMLModelOutput) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*GetMLModelOutput) SetMLModelType ¶

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

SetMLModelType sets the MLModelType field's value.

func (*GetMLModelOutput) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*GetMLModelOutput) SetName ¶

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

SetName sets the Name field's value.

func (*GetMLModelOutput) SetRecipe ¶

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

SetRecipe sets the Recipe field's value.

func (*GetMLModelOutput) SetSchema ¶

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

SetSchema sets the Schema field's value.

func (*GetMLModelOutput) SetScoreThreshold ¶

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

SetScoreThreshold sets the ScoreThreshold field's value.

func (*GetMLModelOutput) SetScoreThresholdLastUpdatedAt ¶

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

SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.

func (*GetMLModelOutput) SetSizeInBytes ¶

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

SetSizeInBytes sets the SizeInBytes field's value.

func (*GetMLModelOutput) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*GetMLModelOutput) SetStatus ¶

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

SetStatus sets the Status field's value.

func (*GetMLModelOutput) SetTrainingDataSourceId ¶

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

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

func (*GetMLModelOutput) SetTrainingParameters ¶

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

SetTrainingParameters sets the TrainingParameters field's value.

func (GetMLModelOutput) String ¶

func (s GetMLModelOutput) String() string

String returns the string representation

type MLModel ¶

Represents the output of a GetMLModel operation.

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

type MLModel struct {

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

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

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

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

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

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

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

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

    // The ID assigned to the MLModel at creation.
    MLModelId *string `min:"1" type:"string"`

    // Identifies the MLModel category. The following are the available types:
    //
    //    * REGRESSION - Produces a numeric result. For example, "What price should
    //    a house be listed at?"
    //    * BINARY - Produces one of two possible results. For example, "Is this
    //    a child-friendly web site?".
    //    * MULTICLASS - Produces one of several possible results. For example,
    //    "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    MLModelType *string `type:"string" enum:"MLModelType"`

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

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

    ScoreThreshold *float64 `type:"float"`

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

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

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

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

    // The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    TrainingDataSourceId *string `min:"1" type:"string"`

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

func (MLModel) GoString ¶

func (s MLModel) GoString() string

GoString returns the string representation

func (*MLModel) SetAlgorithm ¶

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

SetAlgorithm sets the Algorithm field's value.

func (*MLModel) SetComputeTime ¶

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

SetComputeTime sets the ComputeTime field's value.

func (*MLModel) SetCreatedAt ¶

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

SetCreatedAt sets the CreatedAt field's value.

func (*MLModel) SetCreatedByIamUser ¶

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

SetCreatedByIamUser sets the CreatedByIamUser field's value.

func (*MLModel) SetEndpointInfo ¶

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

SetEndpointInfo sets the EndpointInfo field's value.

func (*MLModel) SetFinishedAt ¶

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

SetFinishedAt sets the FinishedAt field's value.

func (*MLModel) SetInputDataLocationS3 ¶

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

SetInputDataLocationS3 sets the InputDataLocationS3 field's value.

func (*MLModel) SetLastUpdatedAt ¶

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

SetLastUpdatedAt sets the LastUpdatedAt field's value.

func (*MLModel) SetMLModelId ¶

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

SetMLModelId sets the MLModelId field's value.

func (*MLModel) SetMLModelType ¶

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

SetMLModelType sets the MLModelType field's value.

func (*MLModel) SetMessage ¶

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

SetMessage sets the Message field's value.

func (*MLModel) SetName ¶

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

SetName sets the Name field's value.

func (*MLModel) SetScoreThreshold ¶

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

SetScoreThreshold sets the ScoreThreshold field's value.

func (*MLModel) SetScoreThresholdLastUpdatedAt ¶

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

SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.

func (*MLModel) SetSizeInBytes ¶

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

SetSizeInBytes sets the SizeInBytes field's value.

func (*MLModel) SetStartedAt ¶

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

SetStartedAt sets the StartedAt field's value.

func (*MLModel) SetStatus ¶

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

SetStatus sets the Status field's value.

func (*MLModel) SetTrainingDataSourceId ¶

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

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

func (*MLModel) SetTrainingParameters ¶

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

SetTrainingParameters sets the TrainingParameters field's value.

func (MLModel) String ¶

func (s MLModel) String() string

String returns the string representation

type MachineLearning ¶

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

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

type MachineLearning struct {
    *client.Client
}

func New ¶

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

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

Example:

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

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

func (*MachineLearning) AddTags ¶

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

AddTags API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

* ErrCodeInvalidTagException "InvalidTagException"

* ErrCodeTagLimitExceededException "TagLimitExceededException"

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

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

func (*MachineLearning) AddTagsRequest ¶

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

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

See AddTags for usage and error information.

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

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

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

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

func (*MachineLearning) AddTagsWithContext ¶

func (c *MachineLearning) AddTagsWithContext(ctx aws.Context, input *AddTagsInput, opts ...request.Option) (*AddTagsOutput, error)

AddTagsWithContext is the same as AddTags with the addition of the ability to pass a context and additional request options.

See AddTags for details on how to use this API operation.

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 (*MachineLearning) CreateBatchPrediction ¶

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

CreateBatchPrediction API operation for Amazon Machine Learning.

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateBatchPredictionRequest ¶

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

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

See CreateBatchPrediction for usage and error information.

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

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

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

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

func (*MachineLearning) CreateBatchPredictionWithContext ¶

func (c *MachineLearning) CreateBatchPredictionWithContext(ctx aws.Context, input *CreateBatchPredictionInput, opts ...request.Option) (*CreateBatchPredictionOutput, error)

CreateBatchPredictionWithContext is the same as CreateBatchPrediction with the addition of the ability to pass a context and additional request options.

See CreateBatchPrediction for details on how to use this API operation.

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 (*MachineLearning) CreateDataSourceFromRDS ¶

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

CreateDataSourceFromRDS API operation for Amazon Machine Learning.

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateDataSourceFromRDSRequest ¶

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

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

See CreateDataSourceFromRDS for usage and error information.

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

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

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

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

func (*MachineLearning) CreateDataSourceFromRDSWithContext ¶

func (c *MachineLearning) CreateDataSourceFromRDSWithContext(ctx aws.Context, input *CreateDataSourceFromRDSInput, opts ...request.Option) (*CreateDataSourceFromRDSOutput, error)

CreateDataSourceFromRDSWithContext is the same as CreateDataSourceFromRDS with the addition of the ability to pass a context and additional request options.

See CreateDataSourceFromRDS for details on how to use this API operation.

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 (*MachineLearning) CreateDataSourceFromRedshift ¶

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

CreateDataSourceFromRedshift API operation for Amazon Machine Learning.

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

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

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

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateDataSourceFromRedshiftRequest ¶

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

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

See CreateDataSourceFromRedshift for usage and error information.

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

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

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

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

func (*MachineLearning) CreateDataSourceFromRedshiftWithContext ¶

func (c *MachineLearning) CreateDataSourceFromRedshiftWithContext(ctx aws.Context, input *CreateDataSourceFromRedshiftInput, opts ...request.Option) (*CreateDataSourceFromRedshiftOutput, error)

CreateDataSourceFromRedshiftWithContext is the same as CreateDataSourceFromRedshift with the addition of the ability to pass a context and additional request options.

See CreateDataSourceFromRedshift for details on how to use this API operation.

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 (*MachineLearning) CreateDataSourceFromS3 ¶

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

CreateDataSourceFromS3 API operation for Amazon Machine Learning.

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

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

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateDataSourceFromS3Request ¶

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

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

See CreateDataSourceFromS3 for usage and error information.

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

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

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

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

func (*MachineLearning) CreateDataSourceFromS3WithContext ¶

func (c *MachineLearning) CreateDataSourceFromS3WithContext(ctx aws.Context, input *CreateDataSourceFromS3Input, opts ...request.Option) (*CreateDataSourceFromS3Output, error)

CreateDataSourceFromS3WithContext is the same as CreateDataSourceFromS3 with the addition of the ability to pass a context and additional request options.

See CreateDataSourceFromS3 for details on how to use this API operation.

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 (*MachineLearning) CreateEvaluation ¶

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

CreateEvaluation API operation for Amazon Machine Learning.

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateEvaluationRequest ¶

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

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

See CreateEvaluation for usage and error information.

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

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

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

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

func (*MachineLearning) CreateEvaluationWithContext ¶

func (c *MachineLearning) CreateEvaluationWithContext(ctx aws.Context, input *CreateEvaluationInput, opts ...request.Option) (*CreateEvaluationOutput, error)

CreateEvaluationWithContext is the same as CreateEvaluation with the addition of the ability to pass a context and additional request options.

See CreateEvaluation for details on how to use this API operation.

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 (*MachineLearning) CreateMLModel ¶

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

CreateMLModel API operation for Amazon Machine Learning.

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

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

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

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

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateMLModelRequest ¶

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

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

See CreateMLModel for usage and error information.

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

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

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

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

func (*MachineLearning) CreateMLModelWithContext ¶

func (c *MachineLearning) CreateMLModelWithContext(ctx aws.Context, input *CreateMLModelInput, opts ...request.Option) (*CreateMLModelOutput, error)

CreateMLModelWithContext is the same as CreateMLModel with the addition of the ability to pass a context and additional request options.

See CreateMLModel for details on how to use this API operation.

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 (*MachineLearning) CreateRealtimeEndpoint ¶

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

CreateRealtimeEndpoint API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) CreateRealtimeEndpointRequest ¶

func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)

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

See CreateRealtimeEndpoint for usage and error information.

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

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

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

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

func (*MachineLearning) CreateRealtimeEndpointWithContext ¶

func (c *MachineLearning) CreateRealtimeEndpointWithContext(ctx aws.Context, input *CreateRealtimeEndpointInput, opts ...request.Option) (*CreateRealtimeEndpointOutput, error)

CreateRealtimeEndpointWithContext is the same as CreateRealtimeEndpoint with the addition of the ability to pass a context and additional request options.

See CreateRealtimeEndpoint for details on how to use this API operation.

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 (*MachineLearning) DeleteBatchPrediction ¶

func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)

DeleteBatchPrediction API operation for Amazon Machine Learning.

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

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

Caution: The result of the DeleteBatchPrediction operation is irreversible.

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) DeleteBatchPredictionRequest ¶

func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)

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

See DeleteBatchPrediction for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteBatchPredictionWithContext ¶

func (c *MachineLearning) DeleteBatchPredictionWithContext(ctx aws.Context, input *DeleteBatchPredictionInput, opts ...request.Option) (*DeleteBatchPredictionOutput, error)

DeleteBatchPredictionWithContext is the same as DeleteBatchPrediction with the addition of the ability to pass a context and additional request options.

See DeleteBatchPrediction for details on how to use this API operation.

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 (*MachineLearning) DeleteDataSource ¶

func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)

DeleteDataSource API operation for Amazon Machine Learning.

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

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

Caution: The results of the DeleteDataSource operation are irreversible.

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) DeleteDataSourceRequest ¶

func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)

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

See DeleteDataSource for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteDataSourceWithContext ¶

func (c *MachineLearning) DeleteDataSourceWithContext(ctx aws.Context, input *DeleteDataSourceInput, opts ...request.Option) (*DeleteDataSourceOutput, error)

DeleteDataSourceWithContext is the same as DeleteDataSource with the addition of the ability to pass a context and additional request options.

See DeleteDataSource for details on how to use this API operation.

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 (*MachineLearning) DeleteEvaluation ¶

func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)

DeleteEvaluation API operation for Amazon Machine Learning.

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

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

CautionThe results of the DeleteEvaluation operation are irreversible.

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) DeleteEvaluationRequest ¶

func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)

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

See DeleteEvaluation for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteEvaluationWithContext ¶

func (c *MachineLearning) DeleteEvaluationWithContext(ctx aws.Context, input *DeleteEvaluationInput, opts ...request.Option) (*DeleteEvaluationOutput, error)

DeleteEvaluationWithContext is the same as DeleteEvaluation with the addition of the ability to pass a context and additional request options.

See DeleteEvaluation for details on how to use this API operation.

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 (*MachineLearning) DeleteMLModel ¶

func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)

DeleteMLModel API operation for Amazon Machine Learning.

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

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

Caution: The result of the DeleteMLModel operation is irreversible.

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) DeleteMLModelRequest ¶

func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)

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

See DeleteMLModel for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteMLModelWithContext ¶

func (c *MachineLearning) DeleteMLModelWithContext(ctx aws.Context, input *DeleteMLModelInput, opts ...request.Option) (*DeleteMLModelOutput, error)

DeleteMLModelWithContext is the same as DeleteMLModel with the addition of the ability to pass a context and additional request options.

See DeleteMLModel for details on how to use this API operation.

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 (*MachineLearning) DeleteRealtimeEndpoint ¶

func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)

DeleteRealtimeEndpoint API operation for Amazon Machine Learning.

Deletes a real time endpoint of an MLModel.

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

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

Returned Error Codes:

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

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

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

func (*MachineLearning) DeleteRealtimeEndpointRequest ¶

func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)

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

See DeleteRealtimeEndpoint for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteRealtimeEndpointWithContext ¶

func (c *MachineLearning) DeleteRealtimeEndpointWithContext(ctx aws.Context, input *DeleteRealtimeEndpointInput, opts ...request.Option) (*DeleteRealtimeEndpointOutput, error)

DeleteRealtimeEndpointWithContext is the same as DeleteRealtimeEndpoint with the addition of the ability to pass a context and additional request options.

See DeleteRealtimeEndpoint for details on how to use this API operation.

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 (*MachineLearning) DeleteTags ¶

func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)

DeleteTags API operation for Amazon Machine Learning.

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

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

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

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

Returned Error Codes:

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

* ErrCodeInvalidTagException "InvalidTagException"

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

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

func (*MachineLearning) DeleteTagsRequest ¶

func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)

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

See DeleteTags for usage and error information.

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

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

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

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

func (*MachineLearning) DeleteTagsWithContext ¶

func (c *MachineLearning) DeleteTagsWithContext(ctx aws.Context, input *DeleteTagsInput, opts ...request.Option) (*DeleteTagsOutput, error)

DeleteTagsWithContext is the same as DeleteTags with the addition of the ability to pass a context and additional request options.

See DeleteTags for details on how to use this API operation.

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 (*MachineLearning) DescribeBatchPredictions ¶

func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)

DescribeBatchPredictions API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

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

func (*MachineLearning) DescribeBatchPredictionsPages ¶

func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, fn func(*DescribeBatchPredictionsOutput, bool) bool) error

DescribeBatchPredictionsPages iterates over the pages of a DescribeBatchPredictions operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeBatchPredictions method for more information on how to use this operation.

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

// Example iterating over at most 3 pages of a DescribeBatchPredictions operation.
pageNum := 0
err := client.DescribeBatchPredictionsPages(params,
    func(page *DescribeBatchPredictionsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeBatchPredictionsPagesWithContext ¶

func (c *MachineLearning) DescribeBatchPredictionsPagesWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, fn func(*DescribeBatchPredictionsOutput, bool) bool, opts ...request.Option) error

DescribeBatchPredictionsPagesWithContext same as DescribeBatchPredictionsPages except it takes a Context and allows setting request options on the pages.

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 (*MachineLearning) DescribeBatchPredictionsRequest ¶

func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)

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

See DescribeBatchPredictions for usage and error information.

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

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

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

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

func (*MachineLearning) DescribeBatchPredictionsWithContext ¶

func (c *MachineLearning) DescribeBatchPredictionsWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, opts ...request.Option) (*DescribeBatchPredictionsOutput, error)

DescribeBatchPredictionsWithContext is the same as DescribeBatchPredictions with the addition of the ability to pass a context and additional request options.

See DescribeBatchPredictions for details on how to use this API operation.

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 (*MachineLearning) DescribeDataSources ¶

func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)

DescribeDataSources API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

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

func (*MachineLearning) DescribeDataSourcesPages ¶

func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, fn func(*DescribeDataSourcesOutput, bool) bool) error

DescribeDataSourcesPages iterates over the pages of a DescribeDataSources operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeDataSources method for more information on how to use this operation.

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

// Example iterating over at most 3 pages of a DescribeDataSources operation.
pageNum := 0
err := client.DescribeDataSourcesPages(params,
    func(page *DescribeDataSourcesOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeDataSourcesPagesWithContext ¶

func (c *MachineLearning) DescribeDataSourcesPagesWithContext(ctx aws.Context, input *DescribeDataSourcesInput, fn func(*DescribeDataSourcesOutput, bool) bool, opts ...request.Option) error

DescribeDataSourcesPagesWithContext same as DescribeDataSourcesPages except it takes a Context and allows setting request options on the pages.

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 (*MachineLearning) DescribeDataSourcesRequest ¶

func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)

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

See DescribeDataSources for usage and error information.

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

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

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

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

func (*MachineLearning) DescribeDataSourcesWithContext ¶

func (c *MachineLearning) DescribeDataSourcesWithContext(ctx aws.Context, input *DescribeDataSourcesInput, opts ...request.Option) (*DescribeDataSourcesOutput, error)

DescribeDataSourcesWithContext is the same as DescribeDataSources with the addition of the ability to pass a context and additional request options.

See DescribeDataSources for details on how to use this API operation.

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 (*MachineLearning) DescribeEvaluations ¶

func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)

DescribeEvaluations API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

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

func (*MachineLearning) DescribeEvaluationsPages ¶

func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, fn func(*DescribeEvaluationsOutput, bool) bool) error

DescribeEvaluationsPages iterates over the pages of a DescribeEvaluations operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeEvaluations method for more information on how to use this operation.

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

// Example iterating over at most 3 pages of a DescribeEvaluations operation.
pageNum := 0
err := client.DescribeEvaluationsPages(params,
    func(page *DescribeEvaluationsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeEvaluationsPagesWithContext ¶

func (c *MachineLearning) DescribeEvaluationsPagesWithContext(ctx aws.Context, input *DescribeEvaluationsInput, fn func(*DescribeEvaluationsOutput, bool) bool, opts ...request.Option) error

DescribeEvaluationsPagesWithContext same as DescribeEvaluationsPages except it takes a Context and allows setting request options on the pages.

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 (*MachineLearning) DescribeEvaluationsRequest ¶

func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)

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

See DescribeEvaluations for usage and error information.

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

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

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

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

func (*MachineLearning) DescribeEvaluationsWithContext ¶

func (c *MachineLearning) DescribeEvaluationsWithContext(ctx aws.Context, input *DescribeEvaluationsInput, opts ...request.Option) (*DescribeEvaluationsOutput, error)

DescribeEvaluationsWithContext is the same as DescribeEvaluations with the addition of the ability to pass a context and additional request options.

See DescribeEvaluations for details on how to use this API operation.

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 (*MachineLearning) DescribeMLModels ¶

func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)

DescribeMLModels API operation for Amazon Machine Learning.

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

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

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

Returned Error Codes:

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

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

func (*MachineLearning) DescribeMLModelsPages ¶

func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(*DescribeMLModelsOutput, bool) bool) error

DescribeMLModelsPages iterates over the pages of a DescribeMLModels operation, calling the "fn" function with the response data for each page. To stop iterating, return false from the fn function.

See DescribeMLModels method for more information on how to use this operation.

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

// Example iterating over at most 3 pages of a DescribeMLModels operation.
pageNum := 0
err := client.DescribeMLModelsPages(params,
    func(page *DescribeMLModelsOutput, lastPage bool) bool {
        pageNum++
        fmt.Println(page)
        return pageNum <= 3
    })

func (*MachineLearning) DescribeMLModelsPagesWithContext ¶

func (c *MachineLearning) DescribeMLModelsPagesWithContext(ctx aws.Context, input *DescribeMLModelsInput, fn func(*DescribeMLModelsOutput, bool) bool, opts ...request.Option) error

DescribeMLModelsPagesWithContext same as DescribeMLModelsPages except it takes a Context and allows setting request options on the pages.

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 (*MachineLearning) DescribeMLModelsRequest ¶

func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)

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

See DescribeMLModels for usage and error information.

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

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

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

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

func (*MachineLearning) DescribeMLModelsWithContext ¶

func (c *MachineLearning) DescribeMLModelsWithContext(ctx aws.Context, input *DescribeMLModelsInput, opts ...request.Option) (*DescribeMLModelsOutput, error)

DescribeMLModelsWithContext is the same as DescribeMLModels with the addition of the ability to pass a context and additional request options.

See DescribeMLModels for details on how to use this API operation.

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 (*MachineLearning) DescribeTags ¶

func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)

DescribeTags API operation for Amazon Machine Learning.

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

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation DescribeTags for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) DescribeTagsRequest ¶

func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)

DescribeTagsRequest generates a "aws/request.Request" representing the client's request for the DescribeTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See DescribeTags for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeTags method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the DescribeTagsRequest method.
req, resp := client.DescribeTagsRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) DescribeTagsWithContext ¶

func (c *MachineLearning) DescribeTagsWithContext(ctx aws.Context, input *DescribeTagsInput, opts ...request.Option) (*DescribeTagsOutput, error)

DescribeTagsWithContext is the same as DescribeTags with the addition of the ability to pass a context and additional request options.

See DescribeTags for details on how to use this API operation.

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 (*MachineLearning) GetBatchPrediction ¶

func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)

GetBatchPrediction API operation for Amazon Machine Learning.

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetBatchPrediction for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) GetBatchPredictionRequest ¶

func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)

GetBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the GetBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the GetBatchPredictionRequest method.
req, resp := client.GetBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetBatchPredictionWithContext ¶

func (c *MachineLearning) GetBatchPredictionWithContext(ctx aws.Context, input *GetBatchPredictionInput, opts ...request.Option) (*GetBatchPredictionOutput, error)

GetBatchPredictionWithContext is the same as GetBatchPrediction with the addition of the ability to pass a context and additional request options.

See GetBatchPrediction for details on how to use this API operation.

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 (*MachineLearning) GetDataSource ¶

func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)

GetDataSource API operation for Amazon Machine Learning.

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetDataSource for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) GetDataSourceRequest ¶

func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)

GetDataSourceRequest generates a "aws/request.Request" representing the client's request for the GetDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetDataSource for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetDataSource method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the GetDataSourceRequest method.
req, resp := client.GetDataSourceRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetDataSourceWithContext ¶

func (c *MachineLearning) GetDataSourceWithContext(ctx aws.Context, input *GetDataSourceInput, opts ...request.Option) (*GetDataSourceOutput, error)

GetDataSourceWithContext is the same as GetDataSource with the addition of the ability to pass a context and additional request options.

See GetDataSource for details on how to use this API operation.

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 (*MachineLearning) GetEvaluation ¶

func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)

GetEvaluation API operation for Amazon Machine Learning.

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetEvaluation for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) GetEvaluationRequest ¶

func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)

GetEvaluationRequest generates a "aws/request.Request" representing the client's request for the GetEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the GetEvaluationRequest method.
req, resp := client.GetEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetEvaluationWithContext ¶

func (c *MachineLearning) GetEvaluationWithContext(ctx aws.Context, input *GetEvaluationInput, opts ...request.Option) (*GetEvaluationOutput, error)

GetEvaluationWithContext is the same as GetEvaluation with the addition of the ability to pass a context and additional request options.

See GetEvaluation for details on how to use this API operation.

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 (*MachineLearning) GetMLModel ¶

func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)

GetMLModel API operation for Amazon Machine Learning.

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation GetMLModel for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) GetMLModelRequest ¶

func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)

GetMLModelRequest generates a "aws/request.Request" representing the client's request for the GetMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See GetMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the GetMLModelRequest method.
req, resp := client.GetMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) GetMLModelWithContext ¶

func (c *MachineLearning) GetMLModelWithContext(ctx aws.Context, input *GetMLModelInput, opts ...request.Option) (*GetMLModelOutput, error)

GetMLModelWithContext is the same as GetMLModel with the addition of the ability to pass a context and additional request options.

See GetMLModel for details on how to use this API operation.

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 (*MachineLearning) Predict ¶

func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)

Predict API operation for Amazon Machine Learning.

Generates a prediction for the observation using the specified ML Model.

NoteNot all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation Predict for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeLimitExceededException "LimitExceededException"
The subscriber exceeded the maximum number of operations. This exception
can occur when listing objects such as DataSource.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

* ErrCodePredictorNotMountedException "PredictorNotMountedException"
The exception is thrown when a predict request is made to an unmounted MLModel.

func (*MachineLearning) PredictRequest ¶

func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)

PredictRequest generates a "aws/request.Request" representing the client's request for the Predict operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See Predict for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the Predict method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the PredictRequest method.
req, resp := client.PredictRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) PredictWithContext ¶

func (c *MachineLearning) PredictWithContext(ctx aws.Context, input *PredictInput, opts ...request.Option) (*PredictOutput, error)

PredictWithContext is the same as Predict with the addition of the ability to pass a context and additional request options.

See Predict for details on how to use this API operation.

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 (*MachineLearning) UpdateBatchPrediction ¶

func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)

UpdateBatchPrediction API operation for Amazon Machine Learning.

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateBatchPrediction for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) UpdateBatchPredictionRequest ¶

func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)

UpdateBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the UpdateBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateBatchPrediction for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateBatchPrediction method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the UpdateBatchPredictionRequest method.
req, resp := client.UpdateBatchPredictionRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateBatchPredictionWithContext ¶

func (c *MachineLearning) UpdateBatchPredictionWithContext(ctx aws.Context, input *UpdateBatchPredictionInput, opts ...request.Option) (*UpdateBatchPredictionOutput, error)

UpdateBatchPredictionWithContext is the same as UpdateBatchPrediction with the addition of the ability to pass a context and additional request options.

See UpdateBatchPrediction for details on how to use this API operation.

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 (*MachineLearning) UpdateDataSource ¶

func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)

UpdateDataSource API operation for Amazon Machine Learning.

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateDataSource for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) UpdateDataSourceRequest ¶

func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)

UpdateDataSourceRequest generates a "aws/request.Request" representing the client's request for the UpdateDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateDataSource for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateDataSource method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the UpdateDataSourceRequest method.
req, resp := client.UpdateDataSourceRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateDataSourceWithContext ¶

func (c *MachineLearning) UpdateDataSourceWithContext(ctx aws.Context, input *UpdateDataSourceInput, opts ...request.Option) (*UpdateDataSourceOutput, error)

UpdateDataSourceWithContext is the same as UpdateDataSource with the addition of the ability to pass a context and additional request options.

See UpdateDataSource for details on how to use this API operation.

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 (*MachineLearning) UpdateEvaluation ¶

func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)

UpdateEvaluation API operation for Amazon Machine Learning.

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateEvaluation for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) UpdateEvaluationRequest ¶

func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)

UpdateEvaluationRequest generates a "aws/request.Request" representing the client's request for the UpdateEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateEvaluation for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateEvaluation method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the UpdateEvaluationRequest method.
req, resp := client.UpdateEvaluationRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateEvaluationWithContext ¶

func (c *MachineLearning) UpdateEvaluationWithContext(ctx aws.Context, input *UpdateEvaluationInput, opts ...request.Option) (*UpdateEvaluationOutput, error)

UpdateEvaluationWithContext is the same as UpdateEvaluation with the addition of the ability to pass a context and additional request options.

See UpdateEvaluation for details on how to use this API operation.

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 (*MachineLearning) UpdateMLModel ¶

func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)

UpdateMLModel API operation for Amazon Machine Learning.

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.

See the AWS API reference guide for Amazon Machine Learning's API operation UpdateMLModel for usage and error information.

Returned Error Codes:

* ErrCodeInvalidInputException "InvalidInputException"
An error on the client occurred. Typically, the cause is an invalid input
value.

* ErrCodeResourceNotFoundException "ResourceNotFoundException"
A specified resource cannot be located.

* ErrCodeInternalServerException "InternalServerException"
An error on the server occurred when trying to process a request.

func (*MachineLearning) UpdateMLModelRequest ¶

func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)

UpdateMLModelRequest generates a "aws/request.Request" representing the client's request for the UpdateMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.

See UpdateMLModel for usage and error information.

Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateMLModel method directly instead.

Note: You must call the "Send" method on the returned request object in order to execute the request.

// Example sending a request using the UpdateMLModelRequest method.
req, resp := client.UpdateMLModelRequest(params)

err := req.Send()
if err == nil { // resp is now filled
    fmt.Println(resp)
}

func (*MachineLearning) UpdateMLModelWithContext ¶

func (c *MachineLearning) UpdateMLModelWithContext(ctx aws.Context, input *UpdateMLModelInput, opts ...request.Option) (*UpdateMLModelOutput, error)

UpdateMLModelWithContext is the same as UpdateMLModel with the addition of the ability to pass a context and additional request options.

See UpdateMLModel for details on how to use this API operation.

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 (*MachineLearning) WaitUntilBatchPredictionAvailable ¶

func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error

WaitUntilBatchPredictionAvailable uses the Amazon Machine Learning API operation DescribeBatchPredictions to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilBatchPredictionAvailableWithContext ¶

func (c *MachineLearning) WaitUntilBatchPredictionAvailableWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, opts ...request.WaiterOption) error

WaitUntilBatchPredictionAvailableWithContext is an extended version of WaitUntilBatchPredictionAvailable. With the support for passing in a context and options to configure the Waiter and the underlying request options.

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 (*MachineLearning) WaitUntilDataSourceAvailable ¶

func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error

WaitUntilDataSourceAvailable uses the Amazon Machine Learning API operation DescribeDataSources to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilDataSourceAvailableWithContext ¶

func (c *MachineLearning) WaitUntilDataSourceAvailableWithContext(ctx aws.Context, input *DescribeDataSourcesInput, opts ...request.WaiterOption) error

WaitUntilDataSourceAvailableWithContext is an extended version of WaitUntilDataSourceAvailable. With the support for passing in a context and options to configure the Waiter and the underlying request options.

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 (*MachineLearning) WaitUntilEvaluationAvailable ¶

func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error

WaitUntilEvaluationAvailable uses the Amazon Machine Learning API operation DescribeEvaluations to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilEvaluationAvailableWithContext ¶

func (c *MachineLearning) WaitUntilEvaluationAvailableWithContext(ctx aws.Context, input *DescribeEvaluationsInput, opts ...request.WaiterOption) error

WaitUntilEvaluationAvailableWithContext is an extended version of WaitUntilEvaluationAvailable. With the support for passing in a context and options to configure the Waiter and the underlying request options.

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 (*MachineLearning) WaitUntilMLModelAvailable ¶

func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error

WaitUntilMLModelAvailable uses the Amazon Machine Learning API operation DescribeMLModels to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.

func (*MachineLearning) WaitUntilMLModelAvailableWithContext ¶

func (c *MachineLearning) WaitUntilMLModelAvailableWithContext(ctx aws.Context, input *DescribeMLModelsInput, opts ...request.WaiterOption) error

WaitUntilMLModelAvailableWithContext is an extended version of WaitUntilMLModelAvailable. With the support for passing in a context and options to configure the Waiter and the underlying request options.

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 PerformanceMetrics ¶

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).

type PerformanceMetrics struct {
    Properties map[string]*string `type:"map"`
    // contains filtered or unexported fields
}

func (PerformanceMetrics) GoString ¶

func (s PerformanceMetrics) GoString() string

GoString returns the string representation

func (*PerformanceMetrics) SetProperties ¶

func (s *PerformanceMetrics) SetProperties(v map[string]*string) *PerformanceMetrics

SetProperties sets the Properties field's value.

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) GoString ¶

func (s PredictInput) GoString() string

GoString returns the string representation

func (*PredictInput) SetMLModelId ¶

func (s *PredictInput) SetMLModelId(v string) *PredictInput

SetMLModelId sets the MLModelId field's value.

func (*PredictInput) SetPredictEndpoint ¶

func (s *PredictInput) SetPredictEndpoint(v string) *PredictInput

SetPredictEndpoint sets the PredictEndpoint field's value.

func (*PredictInput) SetRecord ¶

func (s *PredictInput) SetRecord(v map[string]*string) *PredictInput

SetRecord sets the Record field's value.

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 | MULTICLASSDetailsAttributes.ALGORITHM - SGD
    //
    //    * PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.
    //
    //
    //    * PredictedScores - Contains the raw classification score corresponding
    //    to each label.
    //
    //    * PredictedValue - Present for a REGRESSIONMLModel request.
    Prediction *Prediction `type:"structure"`
    // contains filtered or unexported fields
}

func (PredictOutput) GoString ¶

func (s PredictOutput) GoString() string

GoString returns the string representation

func (*PredictOutput) SetPrediction ¶

func (s *PredictOutput) SetPrediction(v *Prediction) *PredictOutput

SetPrediction sets the Prediction field's value.

func (PredictOutput) String ¶

func (s PredictOutput) String() string

String returns the string representation

type Prediction ¶

The output from a Predict operation:

* Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
- REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

* PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

* PredictedScores - Contains the raw classification score corresponding
to each label.

* PredictedValue - Present for a REGRESSIONMLModel request.

type Prediction struct {

    // Provides any additional details regarding the prediction.
    Details map[string]*string `locationName:"details" type:"map"`

    // The prediction label for either a BINARY or MULTICLASSMLModel.
    PredictedLabel *string `locationName:"predictedLabel" min:"1" type:"string"`

    // Provides the raw classification score corresponding to each label.
    PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"`

    // The prediction value for REGRESSIONMLModel
    PredictedValue *float64 `locationName:"predictedValue" type:"float"`
    // contains filtered or unexported fields
}

func (Prediction) GoString ¶

func (s Prediction) GoString() string

GoString returns the string representation

func (*Prediction) SetDetails ¶

func (s *Prediction) SetDetails(v map[string]*string) *Prediction

SetDetails sets the Details field's value.

func (*Prediction) SetPredictedLabel ¶

func (s *Prediction) SetPredictedLabel(v string) *Prediction

SetPredictedLabel sets the PredictedLabel field's value.

func (*Prediction) SetPredictedScores ¶

func (s *Prediction) SetPredictedScores(v map[string]*float64) *Prediction

SetPredictedScores sets the PredictedScores field's value.

func (*Prediction) SetPredictedValue ¶

func (s *Prediction) SetPredictedValue(v float64) *Prediction

SetPredictedValue sets the PredictedValue field's value.

func (Prediction) String ¶

func (s Prediction) String() string

String returns the string representation

type RDSDataSpec ¶

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

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
}

func (RDSDataSpec) GoString ¶

func (s RDSDataSpec) GoString() string

GoString returns the string representation

func (*RDSDataSpec) SetDataRearrangement ¶

func (s *RDSDataSpec) SetDataRearrangement(v string) *RDSDataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*RDSDataSpec) SetDataSchema ¶

func (s *RDSDataSpec) SetDataSchema(v string) *RDSDataSpec

SetDataSchema sets the DataSchema field's value.

func (*RDSDataSpec) SetDataSchemaUri ¶

func (s *RDSDataSpec) SetDataSchemaUri(v string) *RDSDataSpec

SetDataSchemaUri sets the DataSchemaUri field's value.

func (*RDSDataSpec) SetDatabaseCredentials ¶

func (s *RDSDataSpec) SetDatabaseCredentials(v *RDSDatabaseCredentials) *RDSDataSpec

SetDatabaseCredentials sets the DatabaseCredentials field's value.

func (*RDSDataSpec) SetDatabaseInformation ¶

func (s *RDSDataSpec) SetDatabaseInformation(v *RDSDatabase) *RDSDataSpec

SetDatabaseInformation sets the DatabaseInformation field's value.

func (*RDSDataSpec) SetResourceRole ¶

func (s *RDSDataSpec) SetResourceRole(v string) *RDSDataSpec

SetResourceRole sets the ResourceRole field's value.

func (*RDSDataSpec) SetS3StagingLocation ¶

func (s *RDSDataSpec) SetS3StagingLocation(v string) *RDSDataSpec

SetS3StagingLocation sets the S3StagingLocation field's value.

func (*RDSDataSpec) SetSecurityGroupIds ¶

func (s *RDSDataSpec) SetSecurityGroupIds(v []*string) *RDSDataSpec

SetSecurityGroupIds sets the SecurityGroupIds field's value.

func (*RDSDataSpec) SetSelectSqlQuery ¶

func (s *RDSDataSpec) SetSelectSqlQuery(v string) *RDSDataSpec

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (*RDSDataSpec) SetServiceRole ¶

func (s *RDSDataSpec) SetServiceRole(v string) *RDSDataSpec

SetServiceRole sets the ServiceRole field's value.

func (*RDSDataSpec) SetSubnetId ¶

func (s *RDSDataSpec) SetSubnetId(v string) *RDSDataSpec

SetSubnetId sets the SubnetId field's value.

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 ¶

The database details of an Amazon RDS database.

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
}

func (RDSDatabase) GoString ¶

func (s RDSDatabase) GoString() string

GoString returns the string representation

func (*RDSDatabase) SetDatabaseName ¶

func (s *RDSDatabase) SetDatabaseName(v string) *RDSDatabase

SetDatabaseName sets the DatabaseName field's value.

func (*RDSDatabase) SetInstanceIdentifier ¶

func (s *RDSDatabase) SetInstanceIdentifier(v string) *RDSDatabase

SetInstanceIdentifier sets the InstanceIdentifier field's value.

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 ¶

The database credentials to connect to a database on an RDS DB instance.

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
}

func (RDSDatabaseCredentials) GoString ¶

func (s RDSDatabaseCredentials) GoString() string

GoString returns the string representation

func (*RDSDatabaseCredentials) SetPassword ¶

func (s *RDSDatabaseCredentials) SetPassword(v string) *RDSDatabaseCredentials

SetPassword sets the Password field's value.

func (*RDSDatabaseCredentials) SetUsername ¶

func (s *RDSDatabaseCredentials) SetUsername(v string) *RDSDatabaseCredentials

SetUsername sets the Username field's value.

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 ¶

The datasource details that are specific to Amazon RDS.

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
}

func (RDSMetadata) GoString ¶

func (s RDSMetadata) GoString() string

GoString returns the string representation

func (*RDSMetadata) SetDataPipelineId ¶

func (s *RDSMetadata) SetDataPipelineId(v string) *RDSMetadata

SetDataPipelineId sets the DataPipelineId field's value.

func (*RDSMetadata) SetDatabase ¶

func (s *RDSMetadata) SetDatabase(v *RDSDatabase) *RDSMetadata

SetDatabase sets the Database field's value.

func (*RDSMetadata) SetDatabaseUserName ¶

func (s *RDSMetadata) SetDatabaseUserName(v string) *RDSMetadata

SetDatabaseUserName sets the DatabaseUserName field's value.

func (*RDSMetadata) SetResourceRole ¶

func (s *RDSMetadata) SetResourceRole(v string) *RDSMetadata

SetResourceRole sets the ResourceRole field's value.

func (*RDSMetadata) SetSelectSqlQuery ¶

func (s *RDSMetadata) SetSelectSqlQuery(v string) *RDSMetadata

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (*RDSMetadata) SetServiceRole ¶

func (s *RDSMetadata) SetServiceRole(v string) *RDSMetadata

SetServiceRole sets the ServiceRole field's value.

func (RDSMetadata) String ¶

func (s RDSMetadata) String() string

String returns the string representation

type RealtimeEndpointInfo ¶

Describes the real-time endpoint information for an MLModel.

type RealtimeEndpointInfo struct {

    // The time that the request to create the real-time endpoint for the MLModel
    // was received. The time is expressed in epoch time.
    CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

    // The current status of the real-time endpoint for the MLModel. This element
    // can have one of the following values:
    //
    //    * NONE - Endpoint does not exist or was previously deleted.
    //    * READY - Endpoint is ready to be used for real-time predictions.
    //    * UPDATING - Updating/creating the endpoint.
    EndpointStatus *string `type:"string" enum:"RealtimeEndpointStatus"`

    // The URI that specifies where to send real-time prediction requests for the
    // MLModel.
    //
    // NoteThe application must wait until the real-time endpoint is ready before
    // using this URI.
    EndpointUrl *string `type:"string"`

    // The maximum processing rate for the real-time endpoint for MLModel, measured
    // in incoming requests per second.
    PeakRequestsPerSecond *int64 `type:"integer"`
    // contains filtered or unexported fields
}

func (RealtimeEndpointInfo) GoString ¶

func (s RealtimeEndpointInfo) GoString() string

GoString returns the string representation

func (*RealtimeEndpointInfo) SetCreatedAt ¶

func (s *RealtimeEndpointInfo) SetCreatedAt(v time.Time) *RealtimeEndpointInfo

SetCreatedAt sets the CreatedAt field's value.

func (*RealtimeEndpointInfo) SetEndpointStatus ¶

func (s *RealtimeEndpointInfo) SetEndpointStatus(v string) *RealtimeEndpointInfo

SetEndpointStatus sets the EndpointStatus field's value.

func (*RealtimeEndpointInfo) SetEndpointUrl ¶

func (s *RealtimeEndpointInfo) SetEndpointUrl(v string) *RealtimeEndpointInfo

SetEndpointUrl sets the EndpointUrl field's value.

func (*RealtimeEndpointInfo) SetPeakRequestsPerSecond ¶

func (s *RealtimeEndpointInfo) SetPeakRequestsPerSecond(v int64) *RealtimeEndpointInfo

SetPeakRequestsPerSecond sets the PeakRequestsPerSecond field's value.

func (RealtimeEndpointInfo) String ¶

func (s RealtimeEndpointInfo) String() string

String returns the string representation

type RedshiftDataSpec ¶

Describes the data specification of an Amazon Redshift DataSource.

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
}

func (RedshiftDataSpec) GoString ¶

func (s RedshiftDataSpec) GoString() string

GoString returns the string representation

func (*RedshiftDataSpec) SetDataRearrangement ¶

func (s *RedshiftDataSpec) SetDataRearrangement(v string) *RedshiftDataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*RedshiftDataSpec) SetDataSchema ¶

func (s *RedshiftDataSpec) SetDataSchema(v string) *RedshiftDataSpec

SetDataSchema sets the DataSchema field's value.

func (*RedshiftDataSpec) SetDataSchemaUri ¶

func (s *RedshiftDataSpec) SetDataSchemaUri(v string) *RedshiftDataSpec

SetDataSchemaUri sets the DataSchemaUri field's value.

func (*RedshiftDataSpec) SetDatabaseCredentials ¶

func (s *RedshiftDataSpec) SetDatabaseCredentials(v *RedshiftDatabaseCredentials) *RedshiftDataSpec

SetDatabaseCredentials sets the DatabaseCredentials field's value.

func (*RedshiftDataSpec) SetDatabaseInformation ¶

func (s *RedshiftDataSpec) SetDatabaseInformation(v *RedshiftDatabase) *RedshiftDataSpec

SetDatabaseInformation sets the DatabaseInformation field's value.

func (*RedshiftDataSpec) SetS3StagingLocation ¶

func (s *RedshiftDataSpec) SetS3StagingLocation(v string) *RedshiftDataSpec

SetS3StagingLocation sets the S3StagingLocation field's value.

func (*RedshiftDataSpec) SetSelectSqlQuery ¶

func (s *RedshiftDataSpec) SetSelectSqlQuery(v string) *RedshiftDataSpec

SetSelectSqlQuery sets the SelectSqlQuery field's value.

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 ¶

Describes the database details required to connect to an Amazon Redshift database.

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
}

func (RedshiftDatabase) GoString ¶

func (s RedshiftDatabase) GoString() string

GoString returns the string representation

func (*RedshiftDatabase) SetClusterIdentifier ¶

func (s *RedshiftDatabase) SetClusterIdentifier(v string) *RedshiftDatabase

SetClusterIdentifier sets the ClusterIdentifier field's value.

func (*RedshiftDatabase) SetDatabaseName ¶

func (s *RedshiftDatabase) SetDatabaseName(v string) *RedshiftDatabase

SetDatabaseName sets the DatabaseName field's value.

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 ¶

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

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
}

func (RedshiftDatabaseCredentials) GoString ¶

func (s RedshiftDatabaseCredentials) GoString() string

GoString returns the string representation

func (*RedshiftDatabaseCredentials) SetPassword ¶

func (s *RedshiftDatabaseCredentials) SetPassword(v string) *RedshiftDatabaseCredentials

SetPassword sets the Password field's value.

func (*RedshiftDatabaseCredentials) SetUsername ¶

func (s *RedshiftDatabaseCredentials) SetUsername(v string) *RedshiftDatabaseCredentials

SetUsername sets the Username field's value.

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 ¶

Describes the DataSource details specific to Amazon Redshift.

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
}

func (RedshiftMetadata) GoString ¶

func (s RedshiftMetadata) GoString() string

GoString returns the string representation

func (*RedshiftMetadata) SetDatabaseUserName ¶

func (s *RedshiftMetadata) SetDatabaseUserName(v string) *RedshiftMetadata

SetDatabaseUserName sets the DatabaseUserName field's value.

func (*RedshiftMetadata) SetRedshiftDatabase ¶

func (s *RedshiftMetadata) SetRedshiftDatabase(v *RedshiftDatabase) *RedshiftMetadata

SetRedshiftDatabase sets the RedshiftDatabase field's value.

func (*RedshiftMetadata) SetSelectSqlQuery ¶

func (s *RedshiftMetadata) SetSelectSqlQuery(v string) *RedshiftMetadata

SetSelectSqlQuery sets the SelectSqlQuery field's value.

func (RedshiftMetadata) String ¶

func (s RedshiftMetadata) String() string

String returns the string representation

type S3DataSpec ¶

Describes the data specification of a DataSource.

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
}

func (S3DataSpec) GoString ¶

func (s S3DataSpec) GoString() string

GoString returns the string representation

func (*S3DataSpec) SetDataLocationS3 ¶

func (s *S3DataSpec) SetDataLocationS3(v string) *S3DataSpec

SetDataLocationS3 sets the DataLocationS3 field's value.

func (*S3DataSpec) SetDataRearrangement ¶

func (s *S3DataSpec) SetDataRearrangement(v string) *S3DataSpec

SetDataRearrangement sets the DataRearrangement field's value.

func (*S3DataSpec) SetDataSchema ¶

func (s *S3DataSpec) SetDataSchema(v string) *S3DataSpec

SetDataSchema sets the DataSchema field's value.

func (*S3DataSpec) SetDataSchemaLocationS3 ¶

func (s *S3DataSpec) SetDataSchemaLocationS3(v string) *S3DataSpec

SetDataSchemaLocationS3 sets the DataSchemaLocationS3 field's value.

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 Tag ¶

A custom key-value pair associated with an ML object, such as an ML model.

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
}

func (Tag) GoString ¶

func (s Tag) GoString() string

GoString returns the string representation

func (*Tag) SetKey ¶

func (s *Tag) SetKey(v string) *Tag

SetKey sets the Key field's value.

func (*Tag) SetValue ¶

func (s *Tag) SetValue(v string) *Tag

SetValue sets the Value field's value.

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 UpdateBatchPredictionInput ¶

type UpdateBatchPredictionInput struct {

    // The ID assigned to the BatchPrediction during creation.
    //
    // BatchPredictionId is a required field
    BatchPredictionId *string `min:"1" type:"string" required:"true"`

    // A new user-supplied name or description of the BatchPrediction.
    //
    // BatchPredictionName is a required field
    BatchPredictionName *string `type:"string" required:"true"`
    // contains filtered or unexported fields
}

func (UpdateBatchPredictionInput) GoString ¶

func (s UpdateBatchPredictionInput) GoString() string

GoString returns the string representation

func (*UpdateBatchPredictionInput) SetBatchPredictionId ¶

func (s *UpdateBatchPredictionInput) SetBatchPredictionId(v string) *UpdateBatchPredictionInput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (*UpdateBatchPredictionInput) SetBatchPredictionName ¶

func (s *UpdateBatchPredictionInput) SetBatchPredictionName(v string) *UpdateBatchPredictionInput

SetBatchPredictionName sets the BatchPredictionName field's value.

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 ¶

Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

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
}

func (UpdateBatchPredictionOutput) GoString ¶

func (s UpdateBatchPredictionOutput) GoString() string

GoString returns the string representation

func (*UpdateBatchPredictionOutput) SetBatchPredictionId ¶

func (s *UpdateBatchPredictionOutput) SetBatchPredictionId(v string) *UpdateBatchPredictionOutput

SetBatchPredictionId sets the BatchPredictionId field's value.

func (UpdateBatchPredictionOutput) String ¶

func (s UpdateBatchPredictionOutput) String() string

String returns the string representation

type UpdateDataSourceInput ¶

type UpdateDataSourceInput struct {

    // The ID assigned to the DataSource during creation.
    //
    // DataSourceId is a required field
    DataSourceId *string `min:"1" type:"string" required:"true"`

    // A new user-supplied name or description of the DataSource that will replace
    // the current description.
    //
    // DataSourceName is a required field
    DataSourceName *string `type:"string" required:"true"`
    // contains filtered or unexported fields
}

func (UpdateDataSourceInput) GoString ¶

func (s UpdateDataSourceInput) GoString() string

GoString returns the string representation

func (*UpdateDataSourceInput) SetDataSourceId ¶

func (s *UpdateDataSourceInput) SetDataSourceId(v string) *UpdateDataSourceInput

SetDataSourceId sets the DataSourceId field's value.

func (*UpdateDataSourceInput) SetDataSourceName ¶

func (s *UpdateDataSourceInput) SetDataSourceName(v string) *UpdateDataSourceInput

SetDataSourceName sets the DataSourceName field's value.

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 ¶

Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

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
}

func (UpdateDataSourceOutput) GoString ¶

func (s UpdateDataSourceOutput) GoString() string

GoString returns the string representation

func (*UpdateDataSourceOutput) SetDataSourceId ¶

func (s *UpdateDataSourceOutput) SetDataSourceId(v string) *UpdateDataSourceOutput

SetDataSourceId sets the DataSourceId field's value.

func (UpdateDataSourceOutput) String ¶

func (s UpdateDataSourceOutput) String() string

String returns the string representation

type UpdateEvaluationInput ¶

type UpdateEvaluationInput struct {

    // The ID assigned to the Evaluation during creation.
    //
    // EvaluationId is a required field
    EvaluationId *string `min:"1" type:"string" required:"true"`

    // A new user-supplied name or description of the Evaluation that will replace
    // the current content.
    //
    // EvaluationName is a required field
    EvaluationName *string `type:"string" required:"true"`
    // contains filtered or unexported fields
}

func (UpdateEvaluationInput) GoString ¶

func (s UpdateEvaluationInput) GoString() string

GoString returns the string representation

func (*UpdateEvaluationInput) SetEvaluationId ¶

func (s *UpdateEvaluationInput) SetEvaluationId(v string) *UpdateEvaluationInput

SetEvaluationId sets the EvaluationId field's value.

func (*UpdateEvaluationInput) SetEvaluationName ¶

func (s *UpdateEvaluationInput) SetEvaluationName(v string) *UpdateEvaluationInput

SetEvaluationName sets the EvaluationName field's value.

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 ¶

Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

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
}

func (UpdateEvaluationOutput) GoString ¶

func (s UpdateEvaluationOutput) GoString() string

GoString returns the string representation

func (*UpdateEvaluationOutput) SetEvaluationId ¶

func (s *UpdateEvaluationOutput) SetEvaluationId(v string) *UpdateEvaluationOutput

SetEvaluationId sets the EvaluationId field's value.

func (UpdateEvaluationOutput) String ¶

func (s UpdateEvaluationOutput) String() string

String returns the string representation

type UpdateMLModelInput ¶

type UpdateMLModelInput struct {

    // The ID assigned to the MLModel during creation.
    //
    // MLModelId is a required field
    MLModelId *string `min:"1" type:"string" required:"true"`

    // A user-supplied name or description of the MLModel.
    MLModelName *string `type:"string"`

    // The ScoreThreshold used in binary classification MLModel that marks the boundary
    // between a positive prediction and a negative prediction.
    //
    // Output values greater than or equal to the ScoreThreshold receive a positive
    // result from the MLModel, such as true. Output values less than the ScoreThreshold
    // receive a negative response from the MLModel, such as false.
    ScoreThreshold *float64 `type:"float"`
    // contains filtered or unexported fields
}

func (UpdateMLModelInput) GoString ¶

func (s UpdateMLModelInput) GoString() string

GoString returns the string representation

func (*UpdateMLModelInput) SetMLModelId ¶

func (s *UpdateMLModelInput) SetMLModelId(v string) *UpdateMLModelInput

SetMLModelId sets the MLModelId field's value.

func (*UpdateMLModelInput) SetMLModelName ¶

func (s *UpdateMLModelInput) SetMLModelName(v string) *UpdateMLModelInput

SetMLModelName sets the MLModelName field's value.

func (*UpdateMLModelInput) SetScoreThreshold ¶

func (s *UpdateMLModelInput) SetScoreThreshold(v float64) *UpdateMLModelInput

SetScoreThreshold sets the ScoreThreshold field's value.

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 ¶

Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.

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
}

func (UpdateMLModelOutput) GoString ¶

func (s UpdateMLModelOutput) GoString() string

GoString returns the string representation

func (*UpdateMLModelOutput) SetMLModelId ¶

func (s *UpdateMLModelOutput) SetMLModelId(v string) *UpdateMLModelOutput

SetMLModelId sets the MLModelId field's value.

func (UpdateMLModelOutput) String ¶

func (s UpdateMLModelOutput) String() string

String returns the string representation

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