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AWS SDK for Go API Reference
import "github.com/aws/aws-sdk-go/service/machinelearning"
Overview
Constants

Overview ▾

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

Definition of the public APIs exposed by Amazon Machine Learning

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

Using the Client

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

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

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

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

The stub package, machinelearningiface, can be used to provide alternative implementations of service clients, such as mocking the client for testing.

Operations ▾

func New(p client.ConfigProvider, cfgs ...*aws.Config) *MachineLearning
    AddTags
    AddTagsRequest
    AddTagsWithContext
    CreateBatchPrediction
    CreateBatchPredictionRequest
    CreateBatchPredictionWithContext
    CreateDataSourceFromRDS
    CreateDataSourceFromRDSRequest
    CreateDataSourceFromRDSWithContext
    CreateDataSourceFromRedshift
    CreateDataSourceFromRedshiftRequest
    CreateDataSourceFromRedshiftWithContext
    CreateDataSourceFromS3
    CreateDataSourceFromS3Request
    CreateDataSourceFromS3WithContext
    CreateEvaluation
    CreateEvaluationRequest
    CreateEvaluationWithContext
    CreateMLModel
    CreateMLModelRequest
    CreateMLModelWithContext
    CreateRealtimeEndpoint
    CreateRealtimeEndpointRequest
    CreateRealtimeEndpointWithContext
    DeleteBatchPrediction
    DeleteBatchPredictionRequest
    DeleteBatchPredictionWithContext
    DeleteDataSource
    DeleteDataSourceRequest
    DeleteDataSourceWithContext
    DeleteEvaluation
    DeleteEvaluationRequest
    DeleteEvaluationWithContext
    DeleteMLModel
    DeleteMLModelRequest
    DeleteMLModelWithContext
    DeleteRealtimeEndpoint
    DeleteRealtimeEndpointRequest
    DeleteRealtimeEndpointWithContext
    DeleteTags
    DeleteTagsRequest
    DeleteTagsWithContext
    DescribeBatchPredictions
    DescribeBatchPredictionsPagesWithContext
    DescribeBatchPredictionsRequest
    DescribeBatchPredictionsWithContext
    DescribeDataSources
    DescribeDataSourcesPagesWithContext
    DescribeDataSourcesRequest
    DescribeDataSourcesWithContext
    DescribeEvaluations
    DescribeEvaluationsPagesWithContext
    DescribeEvaluationsRequest
    DescribeEvaluationsWithContext
    DescribeMLModels
    DescribeMLModelsPagesWithContext
    DescribeMLModelsRequest
    DescribeMLModelsWithContext
    DescribeTags
    DescribeTagsRequest
    DescribeTagsWithContext
    GetBatchPrediction
    GetBatchPredictionRequest
    GetBatchPredictionWithContext
    GetDataSource
    GetDataSourceRequest
    GetDataSourceWithContext
    GetEvaluation
    GetEvaluationRequest
    GetEvaluationWithContext
    GetMLModel
    GetMLModelRequest
    GetMLModelWithContext
    Predict
    PredictRequest
    PredictWithContext
    UpdateBatchPrediction
    UpdateBatchPredictionRequest
    UpdateBatchPredictionWithContext
    UpdateDataSource
    UpdateDataSourceRequest
    UpdateDataSourceWithContext
    UpdateEvaluation
    UpdateEvaluationRequest
    UpdateEvaluationWithContext
    UpdateMLModel
    UpdateMLModelRequest
    UpdateMLModelWithContext
    WaitUntilBatchPredictionAvailable
    WaitUntilBatchPredictionAvailableWithContext
    WaitUntilDataSourceAvailable
    WaitUntilDataSourceAvailableWithContext
    WaitUntilEvaluationAvailable
    WaitUntilEvaluationAvailableWithContext
    WaitUntilMLModelAvailable
    WaitUntilMLModelAvailableWithContext

Types ▾

type AddTagsInput
func (s *AddTagsInput) SetResourceId(v string) *AddTagsInput
func (s *AddTagsInput) SetResourceType(v string) *AddTagsInput
func (s *AddTagsInput) SetTags(v []*Tag) *AddTagsInput
type AddTagsOutput
func (s *AddTagsOutput) SetResourceId(v string) *AddTagsOutput
func (s *AddTagsOutput) SetResourceType(v string) *AddTagsOutput
type BatchPrediction
func (s *BatchPrediction) SetBatchPredictionDataSourceId(v string) *BatchPrediction
func (s *BatchPrediction) SetBatchPredictionId(v string) *BatchPrediction
func (s *BatchPrediction) SetComputeTime(v int64) *BatchPrediction
func (s *BatchPrediction) SetCreatedAt(v time.Time) *BatchPrediction
func (s *BatchPrediction) SetCreatedByIamUser(v string) *BatchPrediction
func (s *BatchPrediction) SetFinishedAt(v time.Time) *BatchPrediction
func (s *BatchPrediction) SetInputDataLocationS3(v string) *BatchPrediction
func (s *BatchPrediction) SetInvalidRecordCount(v int64) *BatchPrediction
func (s *BatchPrediction) SetLastUpdatedAt(v time.Time) *BatchPrediction
func (s *BatchPrediction) SetMLModelId(v string) *BatchPrediction
func (s *BatchPrediction) SetMessage(v string) *BatchPrediction
func (s *BatchPrediction) SetName(v string) *BatchPrediction
func (s *BatchPrediction) SetOutputUri(v string) *BatchPrediction
func (s *BatchPrediction) SetStartedAt(v time.Time) *BatchPrediction
func (s *BatchPrediction) SetStatus(v string) *BatchPrediction
func (s *BatchPrediction) SetTotalRecordCount(v int64) *BatchPrediction
type CreateBatchPredictionInput
func (s *CreateBatchPredictionInput) SetBatchPredictionDataSourceId(v string) *CreateBatchPredictionInput
func (s *CreateBatchPredictionInput) SetBatchPredictionId(v string) *CreateBatchPredictionInput
func (s *CreateBatchPredictionInput) SetBatchPredictionName(v string) *CreateBatchPredictionInput
func (s *CreateBatchPredictionInput) SetMLModelId(v string) *CreateBatchPredictionInput
func (s *CreateBatchPredictionInput) SetOutputUri(v string) *CreateBatchPredictionInput
type CreateBatchPredictionOutput
func (s *CreateBatchPredictionOutput) SetBatchPredictionId(v string) *CreateBatchPredictionOutput
type CreateDataSourceFromRDSInput
func (s *CreateDataSourceFromRDSInput) SetComputeStatistics(v bool) *CreateDataSourceFromRDSInput
func (s *CreateDataSourceFromRDSInput) SetDataSourceId(v string) *CreateDataSourceFromRDSInput
func (s *CreateDataSourceFromRDSInput) SetDataSourceName(v string) *CreateDataSourceFromRDSInput
func (s *CreateDataSourceFromRDSInput) SetRDSData(v *RDSDataSpec) *CreateDataSourceFromRDSInput
func (s *CreateDataSourceFromRDSInput) SetRoleARN(v string) *CreateDataSourceFromRDSInput
type CreateDataSourceFromRDSOutput
func (s *CreateDataSourceFromRDSOutput) SetDataSourceId(v string) *CreateDataSourceFromRDSOutput
type CreateDataSourceFromRedshiftInput
func (s *CreateDataSourceFromRedshiftInput) SetComputeStatistics(v bool) *CreateDataSourceFromRedshiftInput
func (s *CreateDataSourceFromRedshiftInput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftInput
func (s *CreateDataSourceFromRedshiftInput) SetDataSourceName(v string) *CreateDataSourceFromRedshiftInput
func (s *CreateDataSourceFromRedshiftInput) SetDataSpec(v *RedshiftDataSpec) *CreateDataSourceFromRedshiftInput
func (s *CreateDataSourceFromRedshiftInput) SetRoleARN(v string) *CreateDataSourceFromRedshiftInput
type CreateDataSourceFromRedshiftOutput
func (s *CreateDataSourceFromRedshiftOutput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftOutput
type CreateDataSourceFromS3Input
func (s *CreateDataSourceFromS3Input) SetComputeStatistics(v bool) *CreateDataSourceFromS3Input
func (s *CreateDataSourceFromS3Input) SetDataSourceId(v string) *CreateDataSourceFromS3Input
func (s *CreateDataSourceFromS3Input) SetDataSourceName(v string) *CreateDataSourceFromS3Input
func (s *CreateDataSourceFromS3Input) SetDataSpec(v *S3DataSpec) *CreateDataSourceFromS3Input
type CreateDataSourceFromS3Output
func (s *CreateDataSourceFromS3Output) SetDataSourceId(v string) *CreateDataSourceFromS3Output
type CreateEvaluationInput
func (s *CreateEvaluationInput) SetEvaluationDataSourceId(v string) *CreateEvaluationInput
func (s *CreateEvaluationInput) SetEvaluationId(v string) *CreateEvaluationInput
func (s *CreateEvaluationInput) SetEvaluationName(v string) *CreateEvaluationInput
func (s *CreateEvaluationInput) SetMLModelId(v string) *CreateEvaluationInput
type CreateEvaluationOutput
func (s *CreateEvaluationOutput) SetEvaluationId(v string) *CreateEvaluationOutput
type CreateMLModelInput
func (s *CreateMLModelInput) SetMLModelId(v string) *CreateMLModelInput
func (s *CreateMLModelInput) SetMLModelName(v string) *CreateMLModelInput
func (s *CreateMLModelInput) SetMLModelType(v string) *CreateMLModelInput
func (s *CreateMLModelInput) SetParameters(v map[string]*string) *CreateMLModelInput
func (s *CreateMLModelInput) SetRecipe(v string) *CreateMLModelInput
func (s *CreateMLModelInput) SetRecipeUri(v string) *CreateMLModelInput
func (s *CreateMLModelInput) SetTrainingDataSourceId(v string) *CreateMLModelInput
type CreateMLModelOutput
func (s *CreateMLModelOutput) SetMLModelId(v string) *CreateMLModelOutput
type CreateRealtimeEndpointInput
func (s *CreateRealtimeEndpointInput) SetMLModelId(v string) *CreateRealtimeEndpointInput
type CreateRealtimeEndpointOutput
func (s *CreateRealtimeEndpointOutput) SetMLModelId(v string) *CreateRealtimeEndpointOutput
func (s *CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *CreateRealtimeEndpointOutput
type DataSource
func (s *DataSource) SetComputeStatistics(v bool) *DataSource
func (s *DataSource) SetComputeTime(v int64) *DataSource
func (s *DataSource) SetCreatedAt(v time.Time) *DataSource
func (s *DataSource) SetCreatedByIamUser(v string) *DataSource
func (s *DataSource) SetDataLocationS3(v string) *DataSource
func (s *DataSource) SetDataRearrangement(v string) *DataSource
func (s *DataSource) SetDataSizeInBytes(v int64) *DataSource
func (s *DataSource) SetDataSourceId(v string) *DataSource
func (s *DataSource) SetFinishedAt(v time.Time) *DataSource
func (s *DataSource) SetLastUpdatedAt(v time.Time) *DataSource
func (s *DataSource) SetMessage(v string) *DataSource
func (s *DataSource) SetName(v string) *DataSource
func (s *DataSource) SetNumberOfFiles(v int64) *DataSource
func (s *DataSource) SetRDSMetadata(v *RDSMetadata) *DataSource
func (s *DataSource) SetRedshiftMetadata(v *RedshiftMetadata) *DataSource
func (s *DataSource) SetRoleARN(v string) *DataSource
func (s *DataSource) SetStartedAt(v time.Time) *DataSource
func (s *DataSource) SetStatus(v string) *DataSource
type DeleteBatchPredictionInput
func (s *DeleteBatchPredictionInput) SetBatchPredictionId(v string) *DeleteBatchPredictionInput
type DeleteBatchPredictionOutput
func (s *DeleteBatchPredictionOutput) SetBatchPredictionId(v string) *DeleteBatchPredictionOutput
type DeleteDataSourceInput
func (s *DeleteDataSourceInput) SetDataSourceId(v string) *DeleteDataSourceInput
type DeleteDataSourceOutput
func (s *DeleteDataSourceOutput) SetDataSourceId(v string) *DeleteDataSourceOutput
type DeleteEvaluationInput
func (s *DeleteEvaluationInput) SetEvaluationId(v string) *DeleteEvaluationInput
type DeleteEvaluationOutput
func (s *DeleteEvaluationOutput) SetEvaluationId(v string) *DeleteEvaluationOutput
type DeleteMLModelInput
func (s *DeleteMLModelInput) SetMLModelId(v string) *DeleteMLModelInput
type DeleteMLModelOutput
func (s *DeleteMLModelOutput) SetMLModelId(v string) *DeleteMLModelOutput
type DeleteRealtimeEndpointInput
func (s *DeleteRealtimeEndpointInput) SetMLModelId(v string) *DeleteRealtimeEndpointInput
type DeleteRealtimeEndpointOutput
func (s *DeleteRealtimeEndpointOutput) SetMLModelId(v string) *DeleteRealtimeEndpointOutput
func (s *DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *DeleteRealtimeEndpointOutput
type DeleteTagsInput
func (s *DeleteTagsInput) SetResourceId(v string) *DeleteTagsInput
func (s *DeleteTagsInput) SetResourceType(v string) *DeleteTagsInput
func (s *DeleteTagsInput) SetTagKeys(v []*string) *DeleteTagsInput
type DeleteTagsOutput
func (s *DeleteTagsOutput) SetResourceId(v string) *DeleteTagsOutput
func (s *DeleteTagsOutput) SetResourceType(v string) *DeleteTagsOutput
type DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetEQ(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetFilterVariable(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetGE(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetGT(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetLE(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetLT(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetLimit(v int64) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetNE(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetNextToken(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetPrefix(v string) *DescribeBatchPredictionsInput
func (s *DescribeBatchPredictionsInput) SetSortOrder(v string) *DescribeBatchPredictionsInput
type DescribeBatchPredictionsOutput
func (s *DescribeBatchPredictionsOutput) SetNextToken(v string) *DescribeBatchPredictionsOutput
func (s *DescribeBatchPredictionsOutput) SetResults(v []*BatchPrediction) *DescribeBatchPredictionsOutput
type DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetEQ(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetFilterVariable(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetGE(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetGT(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetLE(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetLT(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetLimit(v int64) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetNE(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetNextToken(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetPrefix(v string) *DescribeDataSourcesInput
func (s *DescribeDataSourcesInput) SetSortOrder(v string) *DescribeDataSourcesInput
type DescribeDataSourcesOutput
func (s *DescribeDataSourcesOutput) SetNextToken(v string) *DescribeDataSourcesOutput
func (s *DescribeDataSourcesOutput) SetResults(v []*DataSource) *DescribeDataSourcesOutput
type DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetEQ(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetFilterVariable(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetGE(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetGT(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetLE(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetLT(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetLimit(v int64) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetNE(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetNextToken(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetPrefix(v string) *DescribeEvaluationsInput
func (s *DescribeEvaluationsInput) SetSortOrder(v string) *DescribeEvaluationsInput
type DescribeEvaluationsOutput
func (s *DescribeEvaluationsOutput) SetNextToken(v string) *DescribeEvaluationsOutput
func (s *DescribeEvaluationsOutput) SetResults(v []*Evaluation) *DescribeEvaluationsOutput
type DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetEQ(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetFilterVariable(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetGE(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetGT(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetLE(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetLT(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetLimit(v int64) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetNE(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetNextToken(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetPrefix(v string) *DescribeMLModelsInput
func (s *DescribeMLModelsInput) SetSortOrder(v string) *DescribeMLModelsInput
type DescribeMLModelsOutput
func (s *DescribeMLModelsOutput) SetNextToken(v string) *DescribeMLModelsOutput
func (s *DescribeMLModelsOutput) SetResults(v []*MLModel) *DescribeMLModelsOutput
type DescribeTagsInput
func (s *DescribeTagsInput) SetResourceId(v string) *DescribeTagsInput
func (s *DescribeTagsInput) SetResourceType(v string) *DescribeTagsInput
type DescribeTagsOutput
func (s *DescribeTagsOutput) SetResourceId(v string) *DescribeTagsOutput
func (s *DescribeTagsOutput) SetResourceType(v string) *DescribeTagsOutput
func (s *DescribeTagsOutput) SetTags(v []*Tag) *DescribeTagsOutput
type Evaluation
func (s *Evaluation) SetComputeTime(v int64) *Evaluation
func (s *Evaluation) SetCreatedAt(v time.Time) *Evaluation
func (s *Evaluation) SetCreatedByIamUser(v string) *Evaluation
func (s *Evaluation) SetEvaluationDataSourceId(v string) *Evaluation
func (s *Evaluation) SetEvaluationId(v string) *Evaluation
func (s *Evaluation) SetFinishedAt(v time.Time) *Evaluation
func (s *Evaluation) SetInputDataLocationS3(v string) *Evaluation
func (s *Evaluation) SetLastUpdatedAt(v time.Time) *Evaluation
func (s *Evaluation) SetMLModelId(v string) *Evaluation
func (s *Evaluation) SetMessage(v string) *Evaluation
func (s *Evaluation) SetName(v string) *Evaluation
func (s *Evaluation) SetPerformanceMetrics(v *PerformanceMetrics) *Evaluation
func (s *Evaluation) SetStartedAt(v time.Time) *Evaluation
func (s *Evaluation) SetStatus(v string) *Evaluation
type GetBatchPredictionInput
func (s *GetBatchPredictionInput) SetBatchPredictionId(v string) *GetBatchPredictionInput
type GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetBatchPredictionDataSourceId(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetBatchPredictionId(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetComputeTime(v int64) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetCreatedAt(v time.Time) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetCreatedByIamUser(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetFinishedAt(v time.Time) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetInputDataLocationS3(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetInvalidRecordCount(v int64) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetLastUpdatedAt(v time.Time) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetLogUri(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetMLModelId(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetMessage(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetName(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetOutputUri(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetStartedAt(v time.Time) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetStatus(v string) *GetBatchPredictionOutput
func (s *GetBatchPredictionOutput) SetTotalRecordCount(v int64) *GetBatchPredictionOutput
type GetDataSourceInput
func (s *GetDataSourceInput) SetDataSourceId(v string) *GetDataSourceInput
func (s *GetDataSourceInput) SetVerbose(v bool) *GetDataSourceInput
type GetDataSourceOutput
func (s *GetDataSourceOutput) SetComputeStatistics(v bool) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetComputeTime(v int64) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetCreatedAt(v time.Time) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetCreatedByIamUser(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetDataLocationS3(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetDataRearrangement(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetDataSizeInBytes(v int64) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetDataSourceId(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetDataSourceSchema(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetFinishedAt(v time.Time) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetLastUpdatedAt(v time.Time) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetLogUri(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetMessage(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetName(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetNumberOfFiles(v int64) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetRDSMetadata(v *RDSMetadata) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetRedshiftMetadata(v *RedshiftMetadata) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetRoleARN(v string) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetStartedAt(v time.Time) *GetDataSourceOutput
func (s *GetDataSourceOutput) SetStatus(v string) *GetDataSourceOutput
type GetEvaluationInput
func (s *GetEvaluationInput) SetEvaluationId(v string) *GetEvaluationInput
type GetEvaluationOutput
func (s *GetEvaluationOutput) SetComputeTime(v int64) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetCreatedAt(v time.Time) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetCreatedByIamUser(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetEvaluationDataSourceId(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetEvaluationId(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetFinishedAt(v time.Time) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetInputDataLocationS3(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetLastUpdatedAt(v time.Time) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetLogUri(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetMLModelId(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetMessage(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetName(v string) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetPerformanceMetrics(v *PerformanceMetrics) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetStartedAt(v time.Time) *GetEvaluationOutput
func (s *GetEvaluationOutput) SetStatus(v string) *GetEvaluationOutput
type GetMLModelInput
func (s *GetMLModelInput) SetMLModelId(v string) *GetMLModelInput
func (s *GetMLModelInput) SetVerbose(v bool) *GetMLModelInput
type GetMLModelOutput
func (s *GetMLModelOutput) SetComputeTime(v int64) *GetMLModelOutput
func (s *GetMLModelOutput) SetCreatedAt(v time.Time) *GetMLModelOutput
func (s *GetMLModelOutput) SetCreatedByIamUser(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetEndpointInfo(v *RealtimeEndpointInfo) *GetMLModelOutput
func (s *GetMLModelOutput) SetFinishedAt(v time.Time) *GetMLModelOutput
func (s *GetMLModelOutput) SetInputDataLocationS3(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetLastUpdatedAt(v time.Time) *GetMLModelOutput
func (s *GetMLModelOutput) SetLogUri(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetMLModelId(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetMLModelType(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetMessage(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetName(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetRecipe(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetSchema(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetScoreThreshold(v float64) *GetMLModelOutput
func (s *GetMLModelOutput) SetScoreThresholdLastUpdatedAt(v time.Time) *GetMLModelOutput
func (s *GetMLModelOutput) SetSizeInBytes(v int64) *GetMLModelOutput
func (s *GetMLModelOutput) SetStartedAt(v time.Time) *GetMLModelOutput
func (s *GetMLModelOutput) SetStatus(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetTrainingDataSourceId(v string) *GetMLModelOutput
func (s *GetMLModelOutput) SetTrainingParameters(v map[string]*string) *GetMLModelOutput
type MLModel
func (s *MLModel) SetAlgorithm(v string) *MLModel
func (s *MLModel) SetComputeTime(v int64) *MLModel
func (s *MLModel) SetCreatedAt(v time.Time) *MLModel
func (s *MLModel) SetCreatedByIamUser(v string) *MLModel
func (s *MLModel) SetEndpointInfo(v *RealtimeEndpointInfo) *MLModel
func (s *MLModel) SetFinishedAt(v time.Time) *MLModel
func (s *MLModel) SetInputDataLocationS3(v string) *MLModel
func (s *MLModel) SetLastUpdatedAt(v time.Time) *MLModel
func (s *MLModel) SetMLModelId(v string) *MLModel
func (s *MLModel) SetMLModelType(v string) *MLModel
func (s *MLModel) SetMessage(v string) *MLModel
func (s *MLModel) SetName(v string) *MLModel
func (s *MLModel) SetScoreThreshold(v float64) *MLModel
func (s *MLModel) SetScoreThresholdLastUpdatedAt(v time.Time) *MLModel
func (s *MLModel) SetSizeInBytes(v int64) *MLModel
func (s *MLModel) SetStartedAt(v time.Time) *MLModel
func (s *MLModel) SetStatus(v string) *MLModel
func (s *MLModel) SetTrainingDataSourceId(v string) *MLModel
func (s *MLModel) SetTrainingParameters(v map[string]*string) *MLModel
type MachineLearning
type PerformanceMetrics
func (s *PerformanceMetrics) SetProperties(v map[string]*string) *PerformanceMetrics
type PredictInput
func (s *PredictInput) SetMLModelId(v string) *PredictInput
func (s *PredictInput) SetPredictEndpoint(v string) *PredictInput
func (s *PredictInput) SetRecord(v map[string]*string) *PredictInput
type PredictOutput
func (s *PredictOutput) SetPrediction(v *Prediction) *PredictOutput
type Prediction
func (s *Prediction) SetDetails(v map[string]*string) *Prediction
func (s *Prediction) SetPredictedLabel(v string) *Prediction
func (s *Prediction) SetPredictedScores(v map[string]*float64) *Prediction
func (s *Prediction) SetPredictedValue(v float64) *Prediction
type RDSDataSpec
func (s *RDSDataSpec) SetDataRearrangement(v string) *RDSDataSpec
func (s *RDSDataSpec) SetDataSchema(v string) *RDSDataSpec
func (s *RDSDataSpec) SetDataSchemaUri(v string) *RDSDataSpec
func (s *RDSDataSpec) SetDatabaseCredentials(v *RDSDatabaseCredentials) *RDSDataSpec
func (s *RDSDataSpec) SetDatabaseInformation(v *RDSDatabase) *RDSDataSpec
func (s *RDSDataSpec) SetResourceRole(v string) *RDSDataSpec
func (s *RDSDataSpec) SetS3StagingLocation(v string) *RDSDataSpec
func (s *RDSDataSpec) SetSecurityGroupIds(v []*string) *RDSDataSpec
func (s *RDSDataSpec) SetSelectSqlQuery(v string) *RDSDataSpec
func (s *RDSDataSpec) SetServiceRole(v string) *RDSDataSpec
func (s *RDSDataSpec) SetSubnetId(v string) *RDSDataSpec
type RDSDatabase
func (s *RDSDatabase) SetDatabaseName(v string) *RDSDatabase
func (s *RDSDatabase) SetInstanceIdentifier(v string) *RDSDatabase
type RDSDatabaseCredentials
func (s *RDSDatabaseCredentials) SetPassword(v string) *RDSDatabaseCredentials
func (s *RDSDatabaseCredentials) SetUsername(v string) *RDSDatabaseCredentials
type RDSMetadata
func (s *RDSMetadata) SetDataPipelineId(v string) *RDSMetadata
func (s *RDSMetadata) SetDatabase(v *RDSDatabase) *RDSMetadata
func (s *RDSMetadata) SetDatabaseUserName(v string) *RDSMetadata
func (s *RDSMetadata) SetResourceRole(v string) *RDSMetadata
func (s *RDSMetadata) SetSelectSqlQuery(v string) *RDSMetadata
func (s *RDSMetadata) SetServiceRole(v string) *RDSMetadata
type RealtimeEndpointInfo
func (s *RealtimeEndpointInfo) SetCreatedAt(v time.Time) *RealtimeEndpointInfo
func (s *RealtimeEndpointInfo) SetEndpointStatus(v string) *RealtimeEndpointInfo
func (s *RealtimeEndpointInfo) SetEndpointUrl(v string) *RealtimeEndpointInfo
func (s *RealtimeEndpointInfo) SetPeakRequestsPerSecond(v int64) *RealtimeEndpointInfo
type RedshiftDataSpec
func (s *RedshiftDataSpec) SetDataRearrangement(v string) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetDataSchema(v string) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetDataSchemaUri(v string) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetDatabaseCredentials(v *RedshiftDatabaseCredentials) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetDatabaseInformation(v *RedshiftDatabase) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetS3StagingLocation(v string) *RedshiftDataSpec
func (s *RedshiftDataSpec) SetSelectSqlQuery(v string) *RedshiftDataSpec
type RedshiftDatabase
func (s *RedshiftDatabase) SetClusterIdentifier(v string) *RedshiftDatabase
func (s *RedshiftDatabase) SetDatabaseName(v string) *RedshiftDatabase
type RedshiftDatabaseCredentials
func (s *RedshiftDatabaseCredentials) SetPassword(v string) *RedshiftDatabaseCredentials
func (s *RedshiftDatabaseCredentials) SetUsername(v string) *RedshiftDatabaseCredentials
type RedshiftMetadata
func (s *RedshiftMetadata) SetDatabaseUserName(v string) *RedshiftMetadata
func (s *RedshiftMetadata) SetRedshiftDatabase(v *RedshiftDatabase) *RedshiftMetadata
func (s *RedshiftMetadata) SetSelectSqlQuery(v string) *RedshiftMetadata
type S3DataSpec
func (s *S3DataSpec) SetDataLocationS3(v string) *S3DataSpec
func (s *S3DataSpec) SetDataRearrangement(v string) *S3DataSpec
func (s *S3DataSpec) SetDataSchema(v string) *S3DataSpec
func (s *S3DataSpec) SetDataSchemaLocationS3(v string) *S3DataSpec
type Tag
func (s *Tag) SetKey(v string) *Tag
func (s *Tag) SetValue(v string) *Tag
type UpdateBatchPredictionInput
func (s *UpdateBatchPredictionInput) SetBatchPredictionId(v string) *UpdateBatchPredictionInput
func (s *UpdateBatchPredictionInput) SetBatchPredictionName(v string) *UpdateBatchPredictionInput
type UpdateBatchPredictionOutput
func (s *UpdateBatchPredictionOutput) SetBatchPredictionId(v string) *UpdateBatchPredictionOutput
type UpdateDataSourceInput
func (s *UpdateDataSourceInput) SetDataSourceId(v string) *UpdateDataSourceInput
func (s *UpdateDataSourceInput) SetDataSourceName(v string) *UpdateDataSourceInput
type UpdateDataSourceOutput
func (s *UpdateDataSourceOutput) SetDataSourceId(v string) *UpdateDataSourceOutput
type UpdateEvaluationInput
func (s *UpdateEvaluationInput) SetEvaluationId(v string) *UpdateEvaluationInput
func (s *UpdateEvaluationInput) SetEvaluationName(v string) *UpdateEvaluationInput
type UpdateEvaluationOutput
func (s *UpdateEvaluationOutput) SetEvaluationId(v string) *UpdateEvaluationOutput
type UpdateMLModelInput
func (s *UpdateMLModelInput) SetMLModelId(v string) *UpdateMLModelInput
func (s *UpdateMLModelInput) SetMLModelName(v string) *UpdateMLModelInput
func (s *UpdateMLModelInput) SetScoreThreshold(v float64) *UpdateMLModelInput
type UpdateMLModelOutput
func (s *UpdateMLModelOutput) SetMLModelId(v string) *UpdateMLModelOutput

Constants ▾

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

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

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

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

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

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

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

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

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

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

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

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

* DataSourceId - Sets the search criteria to the DataSource used in the
BatchPrediction.
* DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction.
The URL can identify either a file or an Amazon Simple Storage Service
(Amazon S3) bucket or directory.
const (
    // DataSourceFilterVariableCreatedAt is a DataSourceFilterVariable enum value
    DataSourceFilterVariableCreatedAt = "CreatedAt"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Object status with the following possible values:

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

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

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

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

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

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

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

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

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

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

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

* DataUri - Sets the search criteria to the data file(s) used in evaluation.
The URL can identify either a file or an Amazon Simple Storage Service
(Amazon S3) bucket or directory.
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"
)
const (
    // SortOrderAsc is a SortOrder enum value
    SortOrderAsc = "asc"

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

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

* asc - Present the information in ascending order (from A-Z).
* dsc - Present the information in descending order (from Z-A).
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"
)
const (
    ServiceName = "machinelearning" // Service endpoint prefix API calls made to.
    EndpointsID = ServiceName       // Service ID for Regions and Endpoints metadata.
)

Service information constants

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

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

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

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

type AddTagsOutput struct {

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

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

Amazon ML returns the following elements.

func (AddTagsOutput) GoString

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

type BatchPrediction struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Represents the output of a GetBatchPrediction operation.

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

func (BatchPrediction) GoString

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

type CreateBatchPredictionOutput struct {

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

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

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

func (CreateBatchPredictionOutput) GoString

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

type CreateDataSourceFromRDSOutput struct {

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

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

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

func (CreateDataSourceFromRDSOutput) GoString

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

type CreateDataSourceFromRedshiftOutput struct {

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

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

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

func (CreateDataSourceFromRedshiftOutput) GoString

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

type CreateDataSourceFromS3Output struct {

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

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

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

func (CreateDataSourceFromS3Output) GoString

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

type CreateEvaluationOutput struct {

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

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

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

func (CreateEvaluationOutput) GoString

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

type CreateMLModelOutput struct {

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

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

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

func (CreateMLModelOutput) GoString

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

type CreateRealtimeEndpointOutput struct {

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

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

Represents the output of an CreateRealtimeEndpoint operation.

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

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

func (CreateRealtimeEndpointOutput) GoString

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

type DataSource struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Represents the output of the GetDataSource operation.

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

func (DataSource) GoString

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

type DeleteBatchPredictionOutput struct {

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

Represents the output of a DeleteBatchPrediction operation.

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

func (DeleteBatchPredictionOutput) GoString

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

type DeleteDataSourceOutput struct {

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

Represents the output of a DeleteDataSource operation.

func (DeleteDataSourceOutput) GoString

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

type DeleteEvaluationOutput struct {

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

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

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

func (DeleteEvaluationOutput) GoString

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

type DeleteMLModelOutput struct {

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

Represents the output of a DeleteMLModel operation.

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

func (DeleteMLModelOutput) GoString

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

type DeleteRealtimeEndpointOutput struct {

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

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

Represents the output of an DeleteRealtimeEndpoint operation.

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

func (DeleteRealtimeEndpointOutput) GoString

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

type DeleteTagsOutput struct {

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

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

Amazon ML returns the following elements.

func (DeleteTagsOutput) GoString

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

type DescribeBatchPredictionsOutput struct {

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

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

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

func (DescribeBatchPredictionsOutput) GoString

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

type DescribeDataSourcesOutput struct {

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

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

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

func (DescribeDataSourcesOutput) GoString

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

type DescribeEvaluationsOutput struct {

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

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

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

func (DescribeEvaluationsOutput) GoString

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

type DescribeMLModelsOutput struct {

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

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

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

func (DescribeMLModelsOutput) GoString

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

type DescribeTagsOutput struct {

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

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

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

Amazon ML returns the following elements.

func (DescribeTagsOutput) GoString

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

type Evaluation struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Represents the output of GetEvaluation operation.

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

func (Evaluation) GoString

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

type GetBatchPredictionOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetBatchPredictionOutput) GoString

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

type GetDataSourceOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetDataSourceOutput) GoString

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

type GetEvaluationOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetEvaluationOutput) GoString

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

type GetMLModelOutput struct {

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

func (GetMLModelOutput) GoString

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

type MLModel struct {

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

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

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

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

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

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

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

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

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

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

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

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

    ScoreThreshold *float64 `type:"float"`

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

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

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

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

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

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

Represents the output of a GetMLModel operation.

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

func (MLModel) GoString

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

type MachineLearning struct {
    *client.Client
}

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.

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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See AddTags for more information on using the AddTags API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateBatchPrediction for more information on using the CreateBatchPrediction API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateDataSourceFromRDS for more information on using the CreateDataSourceFromRDS API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateDataSourceFromRedshift for more information on using the CreateDataSourceFromRedshift API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateDataSourceFromS3 for more information on using the CreateDataSourceFromS3 API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateEvaluation for more information on using the CreateEvaluation API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateMLModel for more information on using the CreateMLModel API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See CreateRealtimeEndpoint for more information on using the CreateRealtimeEndpoint API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteBatchPrediction for more information on using the DeleteBatchPrediction API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteDataSource for more information on using the DeleteDataSource API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteEvaluation for more information on using the DeleteEvaluation API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteMLModel for more information on using the DeleteMLModel API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteRealtimeEndpoint for more information on using the DeleteRealtimeEndpoint API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DeleteTags for more information on using the DeleteTags API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DescribeBatchPredictions for more information on using the DescribeBatchPredictions API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DescribeDataSources for more information on using the DescribeDataSources API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DescribeEvaluations for more information on using the DescribeEvaluations API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DescribeMLModels for more information on using the DescribeMLModels API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See DescribeTags for more information on using the DescribeTags API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See GetBatchPrediction for more information on using the GetBatchPrediction API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See GetDataSource for more information on using the GetDataSource API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See GetEvaluation for more information on using the GetEvaluation API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See GetMLModel for more information on using the GetMLModel API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See Predict for more information on using the Predict API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See UpdateBatchPrediction for more information on using the UpdateBatchPrediction API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See UpdateDataSource for more information on using the UpdateDataSource API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See UpdateEvaluation for more information on using the UpdateEvaluation API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 will be populated with the request's response once the request complets successfuly.

Use "Send" method on the returned Request to send the API call to the service. the "output" return value is not valid until after Send returns without error.

See UpdateMLModel for more information on using the UpdateMLModel API call, and error handling.

This method is useful when you want to inject custom logic or configuration into the SDK's request lifecycle. Such as custom headers, or retry logic.

// 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 met 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 met 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 met 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 met 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

type PerformanceMetrics struct {
    Properties map[string]*string `type:"map"`
    // contains filtered or unexported fields
}

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

* BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique
to measure performance.

* RegressionRMSE: The regression MLModel uses the Root Mean Square Error
(RMSE) technique to measure performance. RMSE measures the difference
between predicted and actual values for a single variable.

* MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique
to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).

func (PerformanceMetrics) GoString

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

type Prediction struct {

    // Provides any additional details regarding the prediction.
    Details map[string]*string `locationName:"details" type:"map"`

    // The prediction label for either a BINARY or MULTICLASSMLModel.
    PredictedLabel *string `locationName:"predictedLabel" min:"1" type:"string"`

    // Provides the raw classification score corresponding to each label.
    PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"`

    // The prediction value for REGRESSIONMLModel
    PredictedValue *float64 `locationName:"predictedValue" type:"float"`
    // contains filtered or unexported fields
}

The output from a Predict operation:

* Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
- REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD

* PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.

* PredictedScores - Contains the raw classification score corresponding
to each label.

* PredictedValue - Present for a REGRESSIONMLModel request.

func (Prediction) GoString

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

type RDSDataSpec struct {

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

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

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

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

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

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

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

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

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

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

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

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

func (RDSDataSpec) GoString

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

type RDSDatabase struct {

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

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

The database details of an Amazon RDS database.

func (RDSDatabase) GoString

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

type RDSDatabaseCredentials struct {

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

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

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

func (RDSDatabaseCredentials) GoString

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

type RDSMetadata struct {

    // The ID of the Data Pipeline instance that is used to carry to copy data from
    // Amazon RDS to Amazon S3. You can use the ID to find details about the instance
    // in the Data Pipeline console.
    DataPipelineId *string `min:"1" type:"string"`

    // The database details required to connect to an Amazon RDS.
    Database *RDSDatabase `type:"structure"`

    // The username to be used by Amazon ML to connect to database on an Amazon
    // RDS instance. The username should have sufficient permissions to execute
    // an RDSSelectSqlQuery query.
    DatabaseUserName *string `min:"1" type:"string"`

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

    // The SQL query that is supplied during CreateDataSourceFromRDS. Returns only
    // if Verbose is true in GetDataSourceInput.
    SelectSqlQuery *string `min:"1" type:"string"`

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

The datasource details that are specific to Amazon RDS.

func (RDSMetadata) GoString

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

type RealtimeEndpointInfo struct {

    // The time that the request to create the real-time endpoint for the MLModel
    // was received. The time is expressed in epoch time.
    CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`

    // The current status of the real-time endpoint for the MLModel. This element
    // can have one of the following values:
    //
    //    * NONE - Endpoint does not exist or was previously deleted.
    //    * READY - Endpoint is ready to be used for real-time predictions.
    //    * UPDATING - Updating/creating the endpoint.
    EndpointStatus *string `type:"string" enum:"RealtimeEndpointStatus"`

    // The URI that specifies where to send real-time prediction requests for the
    // MLModel.
    //
    // NoteThe application must wait until the real-time endpoint is ready before
    // using this URI.
    EndpointUrl *string `type:"string"`

    // The maximum processing rate for the real-time endpoint for MLModel, measured
    // in incoming requests per second.
    PeakRequestsPerSecond *int64 `type:"integer"`
    // contains filtered or unexported fields
}

Describes the real-time endpoint information for an MLModel.

func (RealtimeEndpointInfo) GoString

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

type RedshiftDataSpec struct {

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

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

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

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

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

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

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

Describes the data specification of an Amazon Redshift DataSource.

func (RedshiftDataSpec) GoString

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

type RedshiftDatabase struct {

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

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

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

func (RedshiftDatabase) GoString

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

type RedshiftDatabaseCredentials struct {

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

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

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

func (RedshiftDatabaseCredentials) GoString

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

type RedshiftMetadata struct {

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

    // Describes the database details required to connect to an Amazon Redshift
    // database.
    RedshiftDatabase *RedshiftDatabase `type:"structure"`

    // The SQL query that is specified during CreateDataSourceFromRedshift. Returns
    // only if Verbose is true in GetDataSourceInput.
    SelectSqlQuery *string `min:"1" type:"string"`
    // contains filtered or unexported fields
}

Describes the DataSource details specific to Amazon Redshift.

func (RedshiftMetadata) GoString

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

type S3DataSpec struct {

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

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

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

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

Describes the data specification of a DataSource.

func (S3DataSpec) GoString

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

type Tag struct {

    // A unique identifier for the tag. Valid characters include Unicode letters,
    // digits, white space, _, ., /, =, +, -, %, and @.
    Key *string `min:"1" type:"string"`

    // An optional string, typically used to describe or define the tag. Valid characters
    // include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
    Value *string `type:"string"`
    // contains filtered or unexported fields
}

A custom key-value pair associated with an ML object, such as an ML model.

func (Tag) GoString

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

type UpdateBatchPredictionOutput struct {

    // The ID assigned to the BatchPrediction during creation. This value should
    // be identical to the value of the BatchPredictionId in the request.
    BatchPredictionId *string `min:"1" type:"string"`
    // contains filtered or unexported fields
}

Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

func (UpdateBatchPredictionOutput) GoString

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

type UpdateDataSourceOutput struct {

    // The ID assigned to the DataSource during creation. This value should be identical
    // to the value of the DataSourceID in the request.
    DataSourceId *string `min:"1" type:"string"`
    // contains filtered or unexported fields
}

Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

func (UpdateDataSourceOutput) GoString

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

type UpdateEvaluationOutput struct {

    // The ID assigned to the Evaluation during creation. This value should be identical
    // to the value of the Evaluation in the request.
    EvaluationId *string `min:"1" type:"string"`
    // contains filtered or unexported fields
}

Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

func (UpdateEvaluationOutput) GoString

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

type UpdateMLModelOutput struct {

    // The ID assigned to the MLModel during creation. This value should be identical
    // to the value of the MLModelID in the request.
    MLModelId *string `min:"1" type:"string"`
    // contains filtered or unexported fields
}

Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.

func (UpdateMLModelOutput) GoString

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