Documentation ¶
Overview ¶
Package machinelearning provides the client and types for making API requests to Amazon Machine Learning.
Definition of the public APIs exposed by Amazon Machine Learning ¶
See machinelearning package documentation for more information. https://docs.aws.amazon.com/sdk-for-go/api/service/machinelearning/
Using the Client ¶
To use the client for Amazon Machine Learning you will first need to create a new instance of it.
When creating a client for an AWS service you'll first need to have a Session already created. The Session provides configuration that can be shared between multiple service clients. Additional configuration can be applied to the Session and service's client when they are constructed. The aws package's Config type contains several fields such as Region for the AWS Region the client should make API requests too. The optional Config value can be provided as the variadic argument for Sessions and client creation.
Once the service's client is created you can use it to make API requests the AWS service. These clients are safe to use concurrently.
// Create a session to share configuration, and load external configuration. sess := session.Must(session.NewSession()) // Create the service's client with the session. svc := machinelearning.New(sess)
See the SDK's documentation for more information on how to use service clients. https://docs.aws.amazon.com/sdk-for-go/api/
See aws package's Config type for more information on configuration options. https://docs.aws.amazon.com/sdk-for-go/api/aws/#Config
See the Amazon Machine Learning client MachineLearning for more information on creating the service's client. https://docs.aws.amazon.com/sdk-for-go/api/service/machinelearning/#New
Once the client is created you can make an API request to the service. Each API method takes a input parameter, and returns the service response and an error.
The API method will document which error codes the service can be returned by the operation if the service models the API operation's errors. These errors will also be available as const strings prefixed with "ErrCode".
result, err := svc.AddTags(params) if err != nil { // Cast err to awserr.Error to handle specific error codes. aerr, ok := err.(awserr.Error) if ok && aerr.Code() == <error code to check for> { // Specific error code handling } return err } fmt.Println("AddTags result:") fmt.Println(result)
Using the Client with Context ¶
The service's client also provides methods to make API requests with a Context value. This allows you to control the timeout, and cancellation of pending requests. These methods also take request Option as variadic parameter to apply additional configuration to the API request.
ctx := context.Background() result, err := svc.AddTagsWithContext(ctx, params)
See the request package documentation for more information on using Context pattern with the SDK. https://docs.aws.amazon.com/sdk-for-go/api/aws/request/
Index ¶
- Constants
- type AddTagsInput
- type AddTagsOutput
- type BatchPrediction
- func (s BatchPrediction) GoString() string
- 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
- func (s BatchPrediction) String() string
- type CreateBatchPredictionInput
- func (s CreateBatchPredictionInput) GoString() string
- 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
- func (s CreateBatchPredictionInput) String() string
- func (s *CreateBatchPredictionInput) Validate() error
- type CreateBatchPredictionOutput
- type CreateDataSourceFromRDSInput
- func (s CreateDataSourceFromRDSInput) GoString() string
- 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
- func (s CreateDataSourceFromRDSInput) String() string
- func (s *CreateDataSourceFromRDSInput) Validate() error
- type CreateDataSourceFromRDSOutput
- type CreateDataSourceFromRedshiftInput
- func (s CreateDataSourceFromRedshiftInput) GoString() string
- 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
- func (s CreateDataSourceFromRedshiftInput) String() string
- func (s *CreateDataSourceFromRedshiftInput) Validate() error
- type CreateDataSourceFromRedshiftOutput
- type CreateDataSourceFromS3Input
- func (s CreateDataSourceFromS3Input) GoString() string
- 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
- func (s CreateDataSourceFromS3Input) String() string
- func (s *CreateDataSourceFromS3Input) Validate() error
- type CreateDataSourceFromS3Output
- type CreateEvaluationInput
- func (s CreateEvaluationInput) GoString() string
- 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
- func (s CreateEvaluationInput) String() string
- func (s *CreateEvaluationInput) Validate() error
- type CreateEvaluationOutput
- type CreateMLModelInput
- func (s CreateMLModelInput) GoString() string
- 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
- func (s CreateMLModelInput) String() string
- func (s *CreateMLModelInput) Validate() error
- type CreateMLModelOutput
- type CreateRealtimeEndpointInput
- type CreateRealtimeEndpointOutput
- func (s CreateRealtimeEndpointOutput) GoString() string
- func (s *CreateRealtimeEndpointOutput) SetMLModelId(v string) *CreateRealtimeEndpointOutput
- func (s *CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *CreateRealtimeEndpointOutput
- func (s CreateRealtimeEndpointOutput) String() string
- type DataSource
- func (s DataSource) GoString() string
- 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
- func (s DataSource) String() string
- type DeleteBatchPredictionInput
- type DeleteBatchPredictionOutput
- type DeleteDataSourceInput
- type DeleteDataSourceOutput
- type DeleteEvaluationInput
- type DeleteEvaluationOutput
- type DeleteMLModelInput
- type DeleteMLModelOutput
- type DeleteRealtimeEndpointInput
- type DeleteRealtimeEndpointOutput
- func (s DeleteRealtimeEndpointOutput) GoString() string
- func (s *DeleteRealtimeEndpointOutput) SetMLModelId(v string) *DeleteRealtimeEndpointOutput
- func (s *DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *DeleteRealtimeEndpointOutput
- func (s DeleteRealtimeEndpointOutput) String() string
- type DeleteTagsInput
- func (s DeleteTagsInput) GoString() string
- func (s *DeleteTagsInput) SetResourceId(v string) *DeleteTagsInput
- func (s *DeleteTagsInput) SetResourceType(v string) *DeleteTagsInput
- func (s *DeleteTagsInput) SetTagKeys(v []*string) *DeleteTagsInput
- func (s DeleteTagsInput) String() string
- func (s *DeleteTagsInput) Validate() error
- type DeleteTagsOutput
- type DescribeBatchPredictionsInput
- func (s DescribeBatchPredictionsInput) GoString() string
- 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
- func (s DescribeBatchPredictionsInput) String() string
- func (s *DescribeBatchPredictionsInput) Validate() error
- type DescribeBatchPredictionsOutput
- func (s DescribeBatchPredictionsOutput) GoString() string
- func (s *DescribeBatchPredictionsOutput) SetNextToken(v string) *DescribeBatchPredictionsOutput
- func (s *DescribeBatchPredictionsOutput) SetResults(v []*BatchPrediction) *DescribeBatchPredictionsOutput
- func (s DescribeBatchPredictionsOutput) String() string
- type DescribeDataSourcesInput
- func (s DescribeDataSourcesInput) GoString() string
- 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
- func (s DescribeDataSourcesInput) String() string
- func (s *DescribeDataSourcesInput) Validate() error
- type DescribeDataSourcesOutput
- type DescribeEvaluationsInput
- func (s DescribeEvaluationsInput) GoString() string
- 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
- func (s DescribeEvaluationsInput) String() string
- func (s *DescribeEvaluationsInput) Validate() error
- type DescribeEvaluationsOutput
- type DescribeMLModelsInput
- func (s DescribeMLModelsInput) GoString() string
- 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
- func (s DescribeMLModelsInput) String() string
- func (s *DescribeMLModelsInput) Validate() error
- type DescribeMLModelsOutput
- type DescribeTagsInput
- type DescribeTagsOutput
- func (s DescribeTagsOutput) GoString() string
- func (s *DescribeTagsOutput) SetResourceId(v string) *DescribeTagsOutput
- func (s *DescribeTagsOutput) SetResourceType(v string) *DescribeTagsOutput
- func (s *DescribeTagsOutput) SetTags(v []*Tag) *DescribeTagsOutput
- func (s DescribeTagsOutput) String() string
- type Evaluation
- func (s Evaluation) GoString() string
- 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
- func (s Evaluation) String() string
- type GetBatchPredictionInput
- type GetBatchPredictionOutput
- func (s GetBatchPredictionOutput) GoString() string
- 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
- func (s GetBatchPredictionOutput) String() string
- type GetDataSourceInput
- type GetDataSourceOutput
- func (s GetDataSourceOutput) GoString() string
- 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
- func (s GetDataSourceOutput) String() string
- type GetEvaluationInput
- type GetEvaluationOutput
- func (s GetEvaluationOutput) GoString() string
- 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
- func (s GetEvaluationOutput) String() string
- type GetMLModelInput
- type GetMLModelOutput
- func (s GetMLModelOutput) GoString() string
- 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
- func (s GetMLModelOutput) String() string
- type MLModel
- func (s MLModel) GoString() string
- 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
- func (s MLModel) String() string
- type MachineLearning
- func (c *MachineLearning) AddTags(input *AddTagsInput) (*AddTagsOutput, error)
- func (c *MachineLearning) AddTagsRequest(input *AddTagsInput) (req *request.Request, output *AddTagsOutput)
- func (c *MachineLearning) AddTagsWithContext(ctx aws.Context, input *AddTagsInput, opts ...request.Option) (*AddTagsOutput, error)
- func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)
- func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput)
- func (c *MachineLearning) CreateBatchPredictionWithContext(ctx aws.Context, input *CreateBatchPredictionInput, opts ...request.Option) (*CreateBatchPredictionOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput)
- func (c *MachineLearning) CreateDataSourceFromRDSWithContext(ctx aws.Context, input *CreateDataSourceFromRDSInput, opts ...request.Option) (*CreateDataSourceFromRDSOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput)
- func (c *MachineLearning) CreateDataSourceFromRedshiftWithContext(ctx aws.Context, input *CreateDataSourceFromRedshiftInput, ...) (*CreateDataSourceFromRedshiftOutput, error)
- func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)
- func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output)
- func (c *MachineLearning) CreateDataSourceFromS3WithContext(ctx aws.Context, input *CreateDataSourceFromS3Input, opts ...request.Option) (*CreateDataSourceFromS3Output, error)
- func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)
- func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput)
- func (c *MachineLearning) CreateEvaluationWithContext(ctx aws.Context, input *CreateEvaluationInput, opts ...request.Option) (*CreateEvaluationOutput, error)
- func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)
- func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput)
- func (c *MachineLearning) CreateMLModelWithContext(ctx aws.Context, input *CreateMLModelInput, opts ...request.Option) (*CreateMLModelOutput, error)
- func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)
- func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)
- func (c *MachineLearning) CreateRealtimeEndpointWithContext(ctx aws.Context, input *CreateRealtimeEndpointInput, opts ...request.Option) (*CreateRealtimeEndpointOutput, error)
- func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)
- func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)
- func (c *MachineLearning) DeleteBatchPredictionWithContext(ctx aws.Context, input *DeleteBatchPredictionInput, opts ...request.Option) (*DeleteBatchPredictionOutput, error)
- func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)
- func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)
- func (c *MachineLearning) DeleteDataSourceWithContext(ctx aws.Context, input *DeleteDataSourceInput, opts ...request.Option) (*DeleteDataSourceOutput, error)
- func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)
- func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)
- func (c *MachineLearning) DeleteEvaluationWithContext(ctx aws.Context, input *DeleteEvaluationInput, opts ...request.Option) (*DeleteEvaluationOutput, error)
- func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)
- func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)
- func (c *MachineLearning) DeleteMLModelWithContext(ctx aws.Context, input *DeleteMLModelInput, opts ...request.Option) (*DeleteMLModelOutput, error)
- func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)
- func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)
- func (c *MachineLearning) DeleteRealtimeEndpointWithContext(ctx aws.Context, input *DeleteRealtimeEndpointInput, opts ...request.Option) (*DeleteRealtimeEndpointOutput, error)
- func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)
- func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)
- func (c *MachineLearning) DeleteTagsWithContext(ctx aws.Context, input *DeleteTagsInput, opts ...request.Option) (*DeleteTagsOutput, error)
- func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)
- func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, ...) error
- func (c *MachineLearning) DescribeBatchPredictionsPagesWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, ...) error
- func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)
- func (c *MachineLearning) DescribeBatchPredictionsWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, opts ...request.Option) (*DescribeBatchPredictionsOutput, error)
- func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)
- func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, ...) error
- func (c *MachineLearning) DescribeDataSourcesPagesWithContext(ctx aws.Context, input *DescribeDataSourcesInput, ...) error
- func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)
- func (c *MachineLearning) DescribeDataSourcesWithContext(ctx aws.Context, input *DescribeDataSourcesInput, opts ...request.Option) (*DescribeDataSourcesOutput, error)
- func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)
- func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, ...) error
- func (c *MachineLearning) DescribeEvaluationsPagesWithContext(ctx aws.Context, input *DescribeEvaluationsInput, ...) error
- func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)
- func (c *MachineLearning) DescribeEvaluationsWithContext(ctx aws.Context, input *DescribeEvaluationsInput, opts ...request.Option) (*DescribeEvaluationsOutput, error)
- func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)
- func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(*DescribeMLModelsOutput, bool) bool) error
- func (c *MachineLearning) DescribeMLModelsPagesWithContext(ctx aws.Context, input *DescribeMLModelsInput, ...) error
- func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)
- func (c *MachineLearning) DescribeMLModelsWithContext(ctx aws.Context, input *DescribeMLModelsInput, opts ...request.Option) (*DescribeMLModelsOutput, error)
- func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)
- func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)
- func (c *MachineLearning) DescribeTagsWithContext(ctx aws.Context, input *DescribeTagsInput, opts ...request.Option) (*DescribeTagsOutput, error)
- func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)
- func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)
- func (c *MachineLearning) GetBatchPredictionWithContext(ctx aws.Context, input *GetBatchPredictionInput, opts ...request.Option) (*GetBatchPredictionOutput, error)
- func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)
- func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)
- func (c *MachineLearning) GetDataSourceWithContext(ctx aws.Context, input *GetDataSourceInput, opts ...request.Option) (*GetDataSourceOutput, error)
- func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)
- func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)
- func (c *MachineLearning) GetEvaluationWithContext(ctx aws.Context, input *GetEvaluationInput, opts ...request.Option) (*GetEvaluationOutput, error)
- func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)
- func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)
- func (c *MachineLearning) GetMLModelWithContext(ctx aws.Context, input *GetMLModelInput, opts ...request.Option) (*GetMLModelOutput, error)
- func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)
- func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)
- func (c *MachineLearning) PredictWithContext(ctx aws.Context, input *PredictInput, opts ...request.Option) (*PredictOutput, error)
- func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)
- func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)
- func (c *MachineLearning) UpdateBatchPredictionWithContext(ctx aws.Context, input *UpdateBatchPredictionInput, opts ...request.Option) (*UpdateBatchPredictionOutput, error)
- func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)
- func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)
- func (c *MachineLearning) UpdateDataSourceWithContext(ctx aws.Context, input *UpdateDataSourceInput, opts ...request.Option) (*UpdateDataSourceOutput, error)
- func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)
- func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)
- func (c *MachineLearning) UpdateEvaluationWithContext(ctx aws.Context, input *UpdateEvaluationInput, opts ...request.Option) (*UpdateEvaluationOutput, error)
- func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)
- func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)
- func (c *MachineLearning) UpdateMLModelWithContext(ctx aws.Context, input *UpdateMLModelInput, opts ...request.Option) (*UpdateMLModelOutput, error)
- func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error
- func (c *MachineLearning) WaitUntilBatchPredictionAvailableWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, ...) error
- func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error
- func (c *MachineLearning) WaitUntilDataSourceAvailableWithContext(ctx aws.Context, input *DescribeDataSourcesInput, opts ...request.WaiterOption) error
- func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error
- func (c *MachineLearning) WaitUntilEvaluationAvailableWithContext(ctx aws.Context, input *DescribeEvaluationsInput, opts ...request.WaiterOption) error
- func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error
- func (c *MachineLearning) WaitUntilMLModelAvailableWithContext(ctx aws.Context, input *DescribeMLModelsInput, opts ...request.WaiterOption) error
- type PerformanceMetrics
- type PredictInput
- func (s PredictInput) GoString() string
- func (s *PredictInput) SetMLModelId(v string) *PredictInput
- func (s *PredictInput) SetPredictEndpoint(v string) *PredictInput
- func (s *PredictInput) SetRecord(v map[string]*string) *PredictInput
- func (s PredictInput) String() string
- func (s *PredictInput) Validate() error
- type PredictOutput
- type Prediction
- func (s Prediction) GoString() string
- 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
- func (s Prediction) String() string
- type RDSDataSpec
- func (s RDSDataSpec) GoString() string
- 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
- func (s RDSDataSpec) String() string
- func (s *RDSDataSpec) Validate() error
- type RDSDatabase
- type RDSDatabaseCredentials
- func (s RDSDatabaseCredentials) GoString() string
- func (s *RDSDatabaseCredentials) SetPassword(v string) *RDSDatabaseCredentials
- func (s *RDSDatabaseCredentials) SetUsername(v string) *RDSDatabaseCredentials
- func (s RDSDatabaseCredentials) String() string
- func (s *RDSDatabaseCredentials) Validate() error
- type RDSMetadata
- func (s RDSMetadata) GoString() string
- 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
- func (s RDSMetadata) String() string
- type RealtimeEndpointInfo
- func (s RealtimeEndpointInfo) GoString() string
- 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
- func (s RealtimeEndpointInfo) String() string
- type RedshiftDataSpec
- func (s RedshiftDataSpec) GoString() string
- 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
- func (s RedshiftDataSpec) String() string
- func (s *RedshiftDataSpec) Validate() error
- type RedshiftDatabase
- type RedshiftDatabaseCredentials
- func (s RedshiftDatabaseCredentials) GoString() string
- func (s *RedshiftDatabaseCredentials) SetPassword(v string) *RedshiftDatabaseCredentials
- func (s *RedshiftDatabaseCredentials) SetUsername(v string) *RedshiftDatabaseCredentials
- func (s RedshiftDatabaseCredentials) String() string
- func (s *RedshiftDatabaseCredentials) Validate() error
- type RedshiftMetadata
- func (s RedshiftMetadata) GoString() string
- func (s *RedshiftMetadata) SetDatabaseUserName(v string) *RedshiftMetadata
- func (s *RedshiftMetadata) SetRedshiftDatabase(v *RedshiftDatabase) *RedshiftMetadata
- func (s *RedshiftMetadata) SetSelectSqlQuery(v string) *RedshiftMetadata
- func (s RedshiftMetadata) String() string
- type S3DataSpec
- func (s S3DataSpec) GoString() string
- 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
- func (s S3DataSpec) String() string
- func (s *S3DataSpec) Validate() error
- type Tag
- type UpdateBatchPredictionInput
- func (s UpdateBatchPredictionInput) GoString() string
- func (s *UpdateBatchPredictionInput) SetBatchPredictionId(v string) *UpdateBatchPredictionInput
- func (s *UpdateBatchPredictionInput) SetBatchPredictionName(v string) *UpdateBatchPredictionInput
- func (s UpdateBatchPredictionInput) String() string
- func (s *UpdateBatchPredictionInput) Validate() error
- type UpdateBatchPredictionOutput
- type UpdateDataSourceInput
- func (s UpdateDataSourceInput) GoString() string
- func (s *UpdateDataSourceInput) SetDataSourceId(v string) *UpdateDataSourceInput
- func (s *UpdateDataSourceInput) SetDataSourceName(v string) *UpdateDataSourceInput
- func (s UpdateDataSourceInput) String() string
- func (s *UpdateDataSourceInput) Validate() error
- type UpdateDataSourceOutput
- type UpdateEvaluationInput
- func (s UpdateEvaluationInput) GoString() string
- func (s *UpdateEvaluationInput) SetEvaluationId(v string) *UpdateEvaluationInput
- func (s *UpdateEvaluationInput) SetEvaluationName(v string) *UpdateEvaluationInput
- func (s UpdateEvaluationInput) String() string
- func (s *UpdateEvaluationInput) Validate() error
- type UpdateEvaluationOutput
- type UpdateMLModelInput
- func (s UpdateMLModelInput) GoString() string
- func (s *UpdateMLModelInput) SetMLModelId(v string) *UpdateMLModelInput
- func (s *UpdateMLModelInput) SetMLModelName(v string) *UpdateMLModelInput
- func (s *UpdateMLModelInput) SetScoreThreshold(v float64) *UpdateMLModelInput
- func (s UpdateMLModelInput) String() string
- func (s *UpdateMLModelInput) Validate() error
- type UpdateMLModelOutput
Examples ¶
- MachineLearning.AddTags
- MachineLearning.CreateBatchPrediction
- MachineLearning.CreateDataSourceFromRDS
- MachineLearning.CreateDataSourceFromRedshift
- MachineLearning.CreateDataSourceFromS3
- MachineLearning.CreateEvaluation
- MachineLearning.CreateMLModel
- MachineLearning.CreateRealtimeEndpoint
- MachineLearning.DeleteBatchPrediction
- MachineLearning.DeleteDataSource
- MachineLearning.DeleteEvaluation
- MachineLearning.DeleteMLModel
- MachineLearning.DeleteRealtimeEndpoint
- MachineLearning.DeleteTags
- MachineLearning.DescribeBatchPredictions
- MachineLearning.DescribeDataSources
- MachineLearning.DescribeEvaluations
- MachineLearning.DescribeMLModels
- MachineLearning.DescribeTags
- MachineLearning.GetBatchPrediction
- MachineLearning.GetDataSource
- MachineLearning.GetEvaluation
- MachineLearning.GetMLModel
- MachineLearning.Predict
- MachineLearning.UpdateBatchPrediction
- MachineLearning.UpdateDataSource
- MachineLearning.UpdateEvaluation
- MachineLearning.UpdateMLModel
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.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type AddTagsInput ¶ added in v1.1.33
type AddTagsInput struct { // The ID of the ML object to tag. For example, exampleModelId. // // ResourceId is a required field ResourceId *string `min:"1" type:"string" required:"true"` // The type of the ML object to tag. // // ResourceType is a required field ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // The key-value pairs to use to create tags. If you specify a key without specifying // a value, Amazon ML creates a tag with the specified key and a value of null. // // Tags is a required field Tags []*Tag `type:"list" required:"true"` // contains filtered or unexported fields }
func (AddTagsInput) GoString ¶ added in v1.1.33
func (s AddTagsInput) GoString() string
GoString returns the string representation
func (*AddTagsInput) SetResourceId ¶ added in v1.5.0
func (s *AddTagsInput) SetResourceId(v string) *AddTagsInput
SetResourceId sets the ResourceId field's value.
func (*AddTagsInput) SetResourceType ¶ added in v1.5.0
func (s *AddTagsInput) SetResourceType(v string) *AddTagsInput
SetResourceType sets the ResourceType field's value.
func (*AddTagsInput) SetTags ¶ added in v1.5.0
func (s *AddTagsInput) SetTags(v []*Tag) *AddTagsInput
SetTags sets the Tags field's value.
func (AddTagsInput) String ¶ added in v1.1.33
func (s AddTagsInput) String() string
String returns the string representation
func (*AddTagsInput) Validate ¶ added in v1.1.33
func (s *AddTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type AddTagsOutput ¶ added in v1.1.33
type AddTagsOutput struct { // The ID of the ML object that was tagged. ResourceId *string `min:"1" type:"string"` // The type of the ML object that was tagged. ResourceType *string `type:"string" enum:"TaggableResourceType"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (AddTagsOutput) GoString ¶ added in v1.1.33
func (s AddTagsOutput) GoString() string
GoString returns the string representation
func (*AddTagsOutput) SetResourceId ¶ added in v1.5.0
func (s *AddTagsOutput) SetResourceId(v string) *AddTagsOutput
SetResourceId sets the ResourceId field's value.
func (*AddTagsOutput) SetResourceType ¶ added in v1.5.0
func (s *AddTagsOutput) SetResourceType(v string) *AddTagsOutput
SetResourceType sets the ResourceType field's value.
func (AddTagsOutput) String ¶ added in v1.1.33
func (s AddTagsOutput) String() string
String returns the string representation
type BatchPrediction ¶
type BatchPrediction struct { // The ID of the DataSource that points to the group of observations to predict. BatchPredictionDataSourceId *string `min:"1" type:"string"` // The ID assigned to the BatchPrediction at creation. This value should be // identical to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // Long integer type that is a 64-bit signed number. ComputeTime *int64 `type:"long"` // The time that the BatchPrediction was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the BatchPrediction. The account type can // be either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // A timestamp represented in epoch time. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // Long integer type that is a 64-bit signed number. InvalidRecordCount *int64 `type:"long"` // The time of the most recent edit to the BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The ID of the MLModel that generated predictions for the BatchPrediction // request. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about processing the batch prediction // request. Message *string `type:"string"` // A user-supplied name or description of the BatchPrediction. Name *string `type:"string"` // The location of an Amazon S3 bucket or directory to receive the operation // results. The following substrings are not allowed in the s3 key portion of // the outputURI field: ':', '//', '/./', '/../'. OutputUri *string `type:"string"` // A timestamp represented in epoch time. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The status of the BatchPrediction. This element can have one of the following // values: // // * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to // generate predictions for a batch of observations. // * INPROGRESS - The process is underway. // * FAILED - The request to perform a batch prediction did not run to completion. // It is not usable. // * COMPLETED - The batch prediction process completed successfully. // * DELETED - The BatchPrediction is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // Long integer type that is a 64-bit signed number. TotalRecordCount *int64 `type:"long"` // contains filtered or unexported fields }
Represents the output of a GetBatchPrediction operation.
The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.
func (BatchPrediction) GoString ¶ added in v0.6.5
func (s BatchPrediction) GoString() string
GoString returns the string representation
func (*BatchPrediction) SetBatchPredictionDataSourceId ¶ added in v1.5.0
func (s *BatchPrediction) SetBatchPredictionDataSourceId(v string) *BatchPrediction
SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.
func (*BatchPrediction) SetBatchPredictionId ¶ added in v1.5.0
func (s *BatchPrediction) SetBatchPredictionId(v string) *BatchPrediction
SetBatchPredictionId sets the BatchPredictionId field's value.
func (*BatchPrediction) SetComputeTime ¶ added in v1.5.0
func (s *BatchPrediction) SetComputeTime(v int64) *BatchPrediction
SetComputeTime sets the ComputeTime field's value.
func (*BatchPrediction) SetCreatedAt ¶ added in v1.5.0
func (s *BatchPrediction) SetCreatedAt(v time.Time) *BatchPrediction
SetCreatedAt sets the CreatedAt field's value.
func (*BatchPrediction) SetCreatedByIamUser ¶ added in v1.5.0
func (s *BatchPrediction) SetCreatedByIamUser(v string) *BatchPrediction
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*BatchPrediction) SetFinishedAt ¶ added in v1.5.0
func (s *BatchPrediction) SetFinishedAt(v time.Time) *BatchPrediction
SetFinishedAt sets the FinishedAt field's value.
func (*BatchPrediction) SetInputDataLocationS3 ¶ added in v1.5.0
func (s *BatchPrediction) SetInputDataLocationS3(v string) *BatchPrediction
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*BatchPrediction) SetInvalidRecordCount ¶ added in v1.5.0
func (s *BatchPrediction) SetInvalidRecordCount(v int64) *BatchPrediction
SetInvalidRecordCount sets the InvalidRecordCount field's value.
func (*BatchPrediction) SetLastUpdatedAt ¶ added in v1.5.0
func (s *BatchPrediction) SetLastUpdatedAt(v time.Time) *BatchPrediction
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*BatchPrediction) SetMLModelId ¶ added in v1.5.0
func (s *BatchPrediction) SetMLModelId(v string) *BatchPrediction
SetMLModelId sets the MLModelId field's value.
func (*BatchPrediction) SetMessage ¶ added in v1.5.0
func (s *BatchPrediction) SetMessage(v string) *BatchPrediction
SetMessage sets the Message field's value.
func (*BatchPrediction) SetName ¶ added in v1.5.0
func (s *BatchPrediction) SetName(v string) *BatchPrediction
SetName sets the Name field's value.
func (*BatchPrediction) SetOutputUri ¶ added in v1.5.0
func (s *BatchPrediction) SetOutputUri(v string) *BatchPrediction
SetOutputUri sets the OutputUri field's value.
func (*BatchPrediction) SetStartedAt ¶ added in v1.5.0
func (s *BatchPrediction) SetStartedAt(v time.Time) *BatchPrediction
SetStartedAt sets the StartedAt field's value.
func (*BatchPrediction) SetStatus ¶ added in v1.5.0
func (s *BatchPrediction) SetStatus(v string) *BatchPrediction
SetStatus sets the Status field's value.
func (*BatchPrediction) SetTotalRecordCount ¶ added in v1.5.0
func (s *BatchPrediction) SetTotalRecordCount(v int64) *BatchPrediction
SetTotalRecordCount sets the TotalRecordCount field's value.
func (BatchPrediction) String ¶ added in v0.6.5
func (s BatchPrediction) String() string
String returns the string representation
type CreateBatchPredictionInput ¶
type CreateBatchPredictionInput struct { // The ID of the DataSource that points to the group of observations to predict. // // BatchPredictionDataSourceId is a required field BatchPredictionDataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied ID that uniquely identifies the BatchPrediction. // // BatchPredictionId is a required field BatchPredictionId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the BatchPrediction. BatchPredictionName // can only use the UTF-8 character set. BatchPredictionName *string `type:"string"` // The ID of the MLModel that will generate predictions for the group of observations. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory // to store the batch prediction results. The following substrings are not allowed // in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'. // // Amazon ML needs permissions to store and retrieve the logs on your behalf. // For information about how to set permissions, see the Amazon Machine Learning // Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). // // OutputUri is a required field OutputUri *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateBatchPredictionInput) GoString ¶ added in v0.6.5
func (s CreateBatchPredictionInput) GoString() string
GoString returns the string representation
func (*CreateBatchPredictionInput) SetBatchPredictionDataSourceId ¶ added in v1.5.0
func (s *CreateBatchPredictionInput) SetBatchPredictionDataSourceId(v string) *CreateBatchPredictionInput
SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.
func (*CreateBatchPredictionInput) SetBatchPredictionId ¶ added in v1.5.0
func (s *CreateBatchPredictionInput) SetBatchPredictionId(v string) *CreateBatchPredictionInput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (*CreateBatchPredictionInput) SetBatchPredictionName ¶ added in v1.5.0
func (s *CreateBatchPredictionInput) SetBatchPredictionName(v string) *CreateBatchPredictionInput
SetBatchPredictionName sets the BatchPredictionName field's value.
func (*CreateBatchPredictionInput) SetMLModelId ¶ added in v1.5.0
func (s *CreateBatchPredictionInput) SetMLModelId(v string) *CreateBatchPredictionInput
SetMLModelId sets the MLModelId field's value.
func (*CreateBatchPredictionInput) SetOutputUri ¶ added in v1.5.0
func (s *CreateBatchPredictionInput) SetOutputUri(v string) *CreateBatchPredictionInput
SetOutputUri sets the OutputUri field's value.
func (CreateBatchPredictionInput) String ¶ added in v0.6.5
func (s CreateBatchPredictionInput) String() string
String returns the string representation
func (*CreateBatchPredictionInput) Validate ¶ added in v1.1.21
func (s *CreateBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateBatchPredictionOutput ¶
type CreateBatchPredictionOutput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. This value // is identical to the value of the BatchPredictionId in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.
func (CreateBatchPredictionOutput) GoString ¶ added in v0.6.5
func (s CreateBatchPredictionOutput) GoString() string
GoString returns the string representation
func (*CreateBatchPredictionOutput) SetBatchPredictionId ¶ added in v1.5.0
func (s *CreateBatchPredictionOutput) SetBatchPredictionId(v string) *CreateBatchPredictionOutput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (CreateBatchPredictionOutput) String ¶ added in v0.6.5
func (s CreateBatchPredictionOutput) String() string
String returns the string representation
type CreateDataSourceFromRDSInput ¶
type CreateDataSourceFromRDSInput struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied ID that uniquely identifies the DataSource. Typically, an // Amazon Resource Number (ARN) becomes the ID for a DataSource. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of an Amazon RDS DataSource: // // RDSData is a required field RDSData *RDSDataSpec `type:"structure" required:"true"` // The role that Amazon ML assumes on behalf of the user to create and activate // a data pipeline in the user's account and copy data using the SelectSqlQuery // query from Amazon RDS to Amazon S3. // // RoleARN is a required field RoleARN *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromRDSInput) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromRDSInput) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromRDSInput) SetComputeStatistics ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSInput) SetComputeStatistics(v bool) *CreateDataSourceFromRDSInput
SetComputeStatistics sets the ComputeStatistics field's value.
func (*CreateDataSourceFromRDSInput) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSInput) SetDataSourceId(v string) *CreateDataSourceFromRDSInput
SetDataSourceId sets the DataSourceId field's value.
func (*CreateDataSourceFromRDSInput) SetDataSourceName ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSInput) SetDataSourceName(v string) *CreateDataSourceFromRDSInput
SetDataSourceName sets the DataSourceName field's value.
func (*CreateDataSourceFromRDSInput) SetRDSData ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSInput) SetRDSData(v *RDSDataSpec) *CreateDataSourceFromRDSInput
SetRDSData sets the RDSData field's value.
func (*CreateDataSourceFromRDSInput) SetRoleARN ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSInput) SetRoleARN(v string) *CreateDataSourceFromRDSInput
SetRoleARN sets the RoleARN field's value.
func (CreateDataSourceFromRDSInput) String ¶ added in v0.6.5
func (s CreateDataSourceFromRDSInput) String() string
String returns the string representation
func (*CreateDataSourceFromRDSInput) Validate ¶ added in v1.1.21
func (s *CreateDataSourceFromRDSInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromRDSOutput ¶
type CreateDataSourceFromRDSOutput struct { // A user-supplied ID that uniquely identifies the datasource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.
func (CreateDataSourceFromRDSOutput) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromRDSOutput) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromRDSOutput) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromRDSOutput) SetDataSourceId(v string) *CreateDataSourceFromRDSOutput
SetDataSourceId sets the DataSourceId field's value.
func (CreateDataSourceFromRDSOutput) String ¶ added in v0.6.5
func (s CreateDataSourceFromRDSOutput) String() string
String returns the string representation
type CreateDataSourceFromRedshiftInput ¶
type CreateDataSourceFromRedshiftInput struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied ID that uniquely identifies the DataSource. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of an Amazon Redshift DataSource: // // * DatabaseInformation - DatabaseName - The name of the Amazon Redshift // database. // ClusterIdentifier - The unique ID for the Amazon Redshift cluster. // // * DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials // that are used to connect to the Amazon Redshift database. // // * SelectSqlQuery - The query that is used to retrieve the observation // data for the Datasource. // // * S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location // for staging Amazon Redshift data. The data retrieved from Amazon Redshift // using the SelectSqlQuery query is stored in this location. // // * DataSchemaUri - The Amazon S3 location of the DataSchema. // // * DataSchema - A JSON string representing the schema. This is not required // if DataSchemaUri is specified. // // * DataRearrangement - A JSON string that represents the splitting and // rearrangement requirements for the DataSource. // // Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" // // DataSpec is a required field DataSpec *RedshiftDataSpec `type:"structure" required:"true"` // A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the // role on behalf of the user to create the following: // // A security group to allow Amazon ML to execute the SelectSqlQuery query on // an Amazon Redshift cluster // // An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the // S3StagingLocation // // RoleARN is a required field RoleARN *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromRedshiftInput) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromRedshiftInput) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromRedshiftInput) SetComputeStatistics ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftInput) SetComputeStatistics(v bool) *CreateDataSourceFromRedshiftInput
SetComputeStatistics sets the ComputeStatistics field's value.
func (*CreateDataSourceFromRedshiftInput) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftInput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftInput
SetDataSourceId sets the DataSourceId field's value.
func (*CreateDataSourceFromRedshiftInput) SetDataSourceName ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftInput) SetDataSourceName(v string) *CreateDataSourceFromRedshiftInput
SetDataSourceName sets the DataSourceName field's value.
func (*CreateDataSourceFromRedshiftInput) SetDataSpec ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftInput) SetDataSpec(v *RedshiftDataSpec) *CreateDataSourceFromRedshiftInput
SetDataSpec sets the DataSpec field's value.
func (*CreateDataSourceFromRedshiftInput) SetRoleARN ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftInput) SetRoleARN(v string) *CreateDataSourceFromRedshiftInput
SetRoleARN sets the RoleARN field's value.
func (CreateDataSourceFromRedshiftInput) String ¶ added in v0.6.5
func (s CreateDataSourceFromRedshiftInput) String() string
String returns the string representation
func (*CreateDataSourceFromRedshiftInput) Validate ¶ added in v1.1.21
func (s *CreateDataSourceFromRedshiftInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromRedshiftOutput ¶
type CreateDataSourceFromRedshiftOutput struct { // A user-supplied ID that uniquely identifies the datasource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
func (CreateDataSourceFromRedshiftOutput) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromRedshiftOutput) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromRedshiftOutput) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromRedshiftOutput) SetDataSourceId(v string) *CreateDataSourceFromRedshiftOutput
SetDataSourceId sets the DataSourceId field's value.
func (CreateDataSourceFromRedshiftOutput) String ¶ added in v0.6.5
func (s CreateDataSourceFromRedshiftOutput) String() string
String returns the string representation
type CreateDataSourceFromS3Input ¶
type CreateDataSourceFromS3Input struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied identifier that uniquely identifies the DataSource. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of a DataSource: // // * DataLocationS3 - The Amazon S3 location of the observation data. // // * DataSchemaLocationS3 - The Amazon S3 location of the DataSchema. // // * DataSchema - A JSON string representing the schema. This is not required // if DataSchemaUri is specified. // // * DataRearrangement - A JSON string that represents the splitting and // rearrangement requirements for the Datasource. // // Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" // // DataSpec is a required field DataSpec *S3DataSpec `type:"structure" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromS3Input) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromS3Input) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromS3Input) SetComputeStatistics ¶ added in v1.5.0
func (s *CreateDataSourceFromS3Input) SetComputeStatistics(v bool) *CreateDataSourceFromS3Input
SetComputeStatistics sets the ComputeStatistics field's value.
func (*CreateDataSourceFromS3Input) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromS3Input) SetDataSourceId(v string) *CreateDataSourceFromS3Input
SetDataSourceId sets the DataSourceId field's value.
func (*CreateDataSourceFromS3Input) SetDataSourceName ¶ added in v1.5.0
func (s *CreateDataSourceFromS3Input) SetDataSourceName(v string) *CreateDataSourceFromS3Input
SetDataSourceName sets the DataSourceName field's value.
func (*CreateDataSourceFromS3Input) SetDataSpec ¶ added in v1.5.0
func (s *CreateDataSourceFromS3Input) SetDataSpec(v *S3DataSpec) *CreateDataSourceFromS3Input
SetDataSpec sets the DataSpec field's value.
func (CreateDataSourceFromS3Input) String ¶ added in v0.6.5
func (s CreateDataSourceFromS3Input) String() string
String returns the string representation
func (*CreateDataSourceFromS3Input) Validate ¶ added in v1.1.21
func (s *CreateDataSourceFromS3Input) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromS3Output ¶
type CreateDataSourceFromS3Output struct { // A user-supplied ID that uniquely identifies the DataSource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
func (CreateDataSourceFromS3Output) GoString ¶ added in v0.6.5
func (s CreateDataSourceFromS3Output) GoString() string
GoString returns the string representation
func (*CreateDataSourceFromS3Output) SetDataSourceId ¶ added in v1.5.0
func (s *CreateDataSourceFromS3Output) SetDataSourceId(v string) *CreateDataSourceFromS3Output
SetDataSourceId sets the DataSourceId field's value.
func (CreateDataSourceFromS3Output) String ¶ added in v0.6.5
func (s CreateDataSourceFromS3Output) String() string
String returns the string representation
type CreateEvaluationInput ¶
type CreateEvaluationInput struct { // The ID of the DataSource for the evaluation. The schema of the DataSource // must match the schema used to create the MLModel. // // EvaluationDataSourceId is a required field EvaluationDataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied ID that uniquely identifies the Evaluation. // // EvaluationId is a required field EvaluationId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the Evaluation. EvaluationName *string `type:"string"` // The ID of the MLModel to evaluate. // // The schema used in creating the MLModel must match the schema of the DataSource // used in the Evaluation. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateEvaluationInput) GoString ¶ added in v0.6.5
func (s CreateEvaluationInput) GoString() string
GoString returns the string representation
func (*CreateEvaluationInput) SetEvaluationDataSourceId ¶ added in v1.5.0
func (s *CreateEvaluationInput) SetEvaluationDataSourceId(v string) *CreateEvaluationInput
SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.
func (*CreateEvaluationInput) SetEvaluationId ¶ added in v1.5.0
func (s *CreateEvaluationInput) SetEvaluationId(v string) *CreateEvaluationInput
SetEvaluationId sets the EvaluationId field's value.
func (*CreateEvaluationInput) SetEvaluationName ¶ added in v1.5.0
func (s *CreateEvaluationInput) SetEvaluationName(v string) *CreateEvaluationInput
SetEvaluationName sets the EvaluationName field's value.
func (*CreateEvaluationInput) SetMLModelId ¶ added in v1.5.0
func (s *CreateEvaluationInput) SetMLModelId(v string) *CreateEvaluationInput
SetMLModelId sets the MLModelId field's value.
func (CreateEvaluationInput) String ¶ added in v0.6.5
func (s CreateEvaluationInput) String() string
String returns the string representation
func (*CreateEvaluationInput) Validate ¶ added in v1.1.21
func (s *CreateEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateEvaluationOutput ¶
type CreateEvaluationOutput struct { // The user-supplied ID that uniquely identifies the Evaluation. This value // should be identical to the value of the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.
func (CreateEvaluationOutput) GoString ¶ added in v0.6.5
func (s CreateEvaluationOutput) GoString() string
GoString returns the string representation
func (*CreateEvaluationOutput) SetEvaluationId ¶ added in v1.5.0
func (s *CreateEvaluationOutput) SetEvaluationId(v string) *CreateEvaluationOutput
SetEvaluationId sets the EvaluationId field's value.
func (CreateEvaluationOutput) String ¶ added in v0.6.5
func (s CreateEvaluationOutput) String() string
String returns the string representation
type CreateMLModelInput ¶
type CreateMLModelInput struct { // A user-supplied ID that uniquely identifies the MLModel. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the MLModel. MLModelName *string `type:"string"` // The category of supervised learning that this MLModel will address. Choose // from the following types: // // * Choose REGRESSION if the MLModel will be used to predict a numeric value. // // * Choose BINARY if the MLModel result has two possible values. // * Choose MULTICLASS if the MLModel result has a limited number of values. // // For more information, see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). // // MLModelType is a required field MLModelType *string `type:"string" required:"true" enum:"MLModelType"` // A list of the training parameters in the MLModel. The list is implemented // as a map of key-value pairs. // // The following is the current set of training parameters: // // * sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending // on the input data, the size of the model might affect its performance. // // The value is an integer that ranges from 100000 to 2147483648. The default // value is 33554432. // // * sgd.maxPasses - The number of times that the training process traverses // the observations to build the MLModel. The value is an integer that ranges // from 1 to 10000. The default value is 10. // // * sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling // the data improves a model's ability to find the optimal solution for a // variety of data types. The valid values are auto and none. The default // value is none. We strongly recommend that you shuffle your data. // // * sgd.l1RegularizationAmount - The coefficient regularization L1 norm. // It controls overfitting the data by penalizing large coefficients. This // tends to drive coefficients to zero, resulting in a sparse feature set. // If you use this parameter, start by specifying a small value, such as // 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L1 normalization. This parameter can't be used when L2 is specified. // Use this parameter sparingly. // // * sgd.l2RegularizationAmount - The coefficient regularization L2 norm. // It controls overfitting the data by penalizing large coefficients. This // tends to drive coefficients to small, nonzero values. If you use this // parameter, start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L2 normalization. This parameter can't be used when L1 is specified. // Use this parameter sparingly. Parameters map[string]*string `type:"map"` // The data recipe for creating the MLModel. You must specify either the recipe // or its URI. If you don't specify a recipe or its URI, Amazon ML creates a // default. Recipe *string `type:"string"` // The Amazon Simple Storage Service (Amazon S3) location and file name that // contains the MLModel recipe. You must specify either the recipe or its URI. // If you don't specify a recipe or its URI, Amazon ML creates a default. RecipeUri *string `type:"string"` // The DataSource that points to the training data. // // TrainingDataSourceId is a required field TrainingDataSourceId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateMLModelInput) GoString ¶ added in v0.6.5
func (s CreateMLModelInput) GoString() string
GoString returns the string representation
func (*CreateMLModelInput) SetMLModelId ¶ added in v1.5.0
func (s *CreateMLModelInput) SetMLModelId(v string) *CreateMLModelInput
SetMLModelId sets the MLModelId field's value.
func (*CreateMLModelInput) SetMLModelName ¶ added in v1.5.0
func (s *CreateMLModelInput) SetMLModelName(v string) *CreateMLModelInput
SetMLModelName sets the MLModelName field's value.
func (*CreateMLModelInput) SetMLModelType ¶ added in v1.5.0
func (s *CreateMLModelInput) SetMLModelType(v string) *CreateMLModelInput
SetMLModelType sets the MLModelType field's value.
func (*CreateMLModelInput) SetParameters ¶ added in v1.5.0
func (s *CreateMLModelInput) SetParameters(v map[string]*string) *CreateMLModelInput
SetParameters sets the Parameters field's value.
func (*CreateMLModelInput) SetRecipe ¶ added in v1.5.0
func (s *CreateMLModelInput) SetRecipe(v string) *CreateMLModelInput
SetRecipe sets the Recipe field's value.
func (*CreateMLModelInput) SetRecipeUri ¶ added in v1.5.0
func (s *CreateMLModelInput) SetRecipeUri(v string) *CreateMLModelInput
SetRecipeUri sets the RecipeUri field's value.
func (*CreateMLModelInput) SetTrainingDataSourceId ¶ added in v1.5.0
func (s *CreateMLModelInput) SetTrainingDataSourceId(v string) *CreateMLModelInput
SetTrainingDataSourceId sets the TrainingDataSourceId field's value.
func (CreateMLModelInput) String ¶ added in v0.6.5
func (s CreateMLModelInput) String() string
String returns the string representation
func (*CreateMLModelInput) Validate ¶ added in v1.1.21
func (s *CreateMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateMLModelOutput ¶
type CreateMLModelOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.
func (CreateMLModelOutput) GoString ¶ added in v0.6.5
func (s CreateMLModelOutput) GoString() string
GoString returns the string representation
func (*CreateMLModelOutput) SetMLModelId ¶ added in v1.5.0
func (s *CreateMLModelOutput) SetMLModelId(v string) *CreateMLModelOutput
SetMLModelId sets the MLModelId field's value.
func (CreateMLModelOutput) String ¶ added in v0.6.5
func (s CreateMLModelOutput) String() string
String returns the string representation
type CreateRealtimeEndpointInput ¶
type CreateRealtimeEndpointInput struct { // The ID assigned to the MLModel during creation. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateRealtimeEndpointInput) GoString ¶ added in v0.6.5
func (s CreateRealtimeEndpointInput) GoString() string
GoString returns the string representation
func (*CreateRealtimeEndpointInput) SetMLModelId ¶ added in v1.5.0
func (s *CreateRealtimeEndpointInput) SetMLModelId(v string) *CreateRealtimeEndpointInput
SetMLModelId sets the MLModelId field's value.
func (CreateRealtimeEndpointInput) String ¶ added in v0.6.5
func (s CreateRealtimeEndpointInput) String() string
String returns the string representation
func (*CreateRealtimeEndpointInput) Validate ¶ added in v1.1.21
func (s *CreateRealtimeEndpointInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateRealtimeEndpointOutput ¶
type CreateRealtimeEndpointOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // The endpoint information of the MLModel RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"` // contains filtered or unexported fields }
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.
func (CreateRealtimeEndpointOutput) GoString ¶ added in v0.6.5
func (s CreateRealtimeEndpointOutput) GoString() string
GoString returns the string representation
func (*CreateRealtimeEndpointOutput) SetMLModelId ¶ added in v1.5.0
func (s *CreateRealtimeEndpointOutput) SetMLModelId(v string) *CreateRealtimeEndpointOutput
SetMLModelId sets the MLModelId field's value.
func (*CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo ¶ added in v1.5.0
func (s *CreateRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *CreateRealtimeEndpointOutput
SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.
func (CreateRealtimeEndpointOutput) String ¶ added in v0.6.5
func (s CreateRealtimeEndpointOutput) String() string
String returns the string representation
type DataSource ¶
type DataSource struct { // The parameter is true if statistics need to be generated from the observation // data. ComputeStatistics *bool `type:"boolean"` // Long integer type that is a 64-bit signed number. ComputeTime *int64 `type:"long"` // The time that the DataSource was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the DataSource was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The location and name of the data in Amazon Simple Storage Service (Amazon // S3) that is used by a DataSource. DataLocationS3 *string `type:"string"` // A JSON string that represents the splitting and rearrangement requirement // used when this DataSource was created. DataRearrangement *string `type:"string"` // The total number of observations contained in the data files that the DataSource // references. DataSizeInBytes *int64 `type:"long"` // The ID that is assigned to the DataSource during creation. DataSourceId *string `min:"1" type:"string"` // A timestamp represented in epoch time. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The time of the most recent edit to the BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A description of the most recent details about creating the DataSource. Message *string `type:"string"` // A user-supplied name or description of the DataSource. Name *string `type:"string"` // The number of data files referenced by the DataSource. NumberOfFiles *int64 `type:"long"` // The datasource details that are specific to Amazon RDS. RDSMetadata *RDSMetadata `type:"structure"` // Describes the DataSource details specific to Amazon Redshift. RedshiftMetadata *RedshiftMetadata `type:"structure"` // The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts), // such as the following: arn:aws:iam::account:role/rolename. RoleARN *string `min:"1" type:"string"` // A timestamp represented in epoch time. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The current status of the DataSource. This element can have one of the following // values: // // * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to // create a DataSource. // * INPROGRESS - The creation process is underway. // * FAILED - The request to create a DataSource did not run to completion. // It is not usable. // * COMPLETED - The creation process completed successfully. // * DELETED - The DataSource is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of the GetDataSource operation.
The content consists of the detailed metadata and data file information and the current status of the DataSource.
func (DataSource) GoString ¶ added in v0.6.5
func (s DataSource) GoString() string
GoString returns the string representation
func (*DataSource) SetComputeStatistics ¶ added in v1.5.0
func (s *DataSource) SetComputeStatistics(v bool) *DataSource
SetComputeStatistics sets the ComputeStatistics field's value.
func (*DataSource) SetComputeTime ¶ added in v1.5.0
func (s *DataSource) SetComputeTime(v int64) *DataSource
SetComputeTime sets the ComputeTime field's value.
func (*DataSource) SetCreatedAt ¶ added in v1.5.0
func (s *DataSource) SetCreatedAt(v time.Time) *DataSource
SetCreatedAt sets the CreatedAt field's value.
func (*DataSource) SetCreatedByIamUser ¶ added in v1.5.0
func (s *DataSource) SetCreatedByIamUser(v string) *DataSource
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*DataSource) SetDataLocationS3 ¶ added in v1.5.0
func (s *DataSource) SetDataLocationS3(v string) *DataSource
SetDataLocationS3 sets the DataLocationS3 field's value.
func (*DataSource) SetDataRearrangement ¶ added in v1.5.0
func (s *DataSource) SetDataRearrangement(v string) *DataSource
SetDataRearrangement sets the DataRearrangement field's value.
func (*DataSource) SetDataSizeInBytes ¶ added in v1.5.0
func (s *DataSource) SetDataSizeInBytes(v int64) *DataSource
SetDataSizeInBytes sets the DataSizeInBytes field's value.
func (*DataSource) SetDataSourceId ¶ added in v1.5.0
func (s *DataSource) SetDataSourceId(v string) *DataSource
SetDataSourceId sets the DataSourceId field's value.
func (*DataSource) SetFinishedAt ¶ added in v1.5.0
func (s *DataSource) SetFinishedAt(v time.Time) *DataSource
SetFinishedAt sets the FinishedAt field's value.
func (*DataSource) SetLastUpdatedAt ¶ added in v1.5.0
func (s *DataSource) SetLastUpdatedAt(v time.Time) *DataSource
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*DataSource) SetMessage ¶ added in v1.5.0
func (s *DataSource) SetMessage(v string) *DataSource
SetMessage sets the Message field's value.
func (*DataSource) SetName ¶ added in v1.5.0
func (s *DataSource) SetName(v string) *DataSource
SetName sets the Name field's value.
func (*DataSource) SetNumberOfFiles ¶ added in v1.5.0
func (s *DataSource) SetNumberOfFiles(v int64) *DataSource
SetNumberOfFiles sets the NumberOfFiles field's value.
func (*DataSource) SetRDSMetadata ¶ added in v1.5.0
func (s *DataSource) SetRDSMetadata(v *RDSMetadata) *DataSource
SetRDSMetadata sets the RDSMetadata field's value.
func (*DataSource) SetRedshiftMetadata ¶ added in v1.5.0
func (s *DataSource) SetRedshiftMetadata(v *RedshiftMetadata) *DataSource
SetRedshiftMetadata sets the RedshiftMetadata field's value.
func (*DataSource) SetRoleARN ¶ added in v1.5.0
func (s *DataSource) SetRoleARN(v string) *DataSource
SetRoleARN sets the RoleARN field's value.
func (*DataSource) SetStartedAt ¶ added in v1.5.0
func (s *DataSource) SetStartedAt(v time.Time) *DataSource
SetStartedAt sets the StartedAt field's value.
func (*DataSource) SetStatus ¶ added in v1.5.0
func (s *DataSource) SetStatus(v string) *DataSource
SetStatus sets the Status field's value.
func (DataSource) String ¶ added in v0.6.5
func (s DataSource) String() string
String returns the string representation
type DeleteBatchPredictionInput ¶
type DeleteBatchPredictionInput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. // // BatchPredictionId is a required field BatchPredictionId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteBatchPredictionInput) GoString ¶ added in v0.6.5
func (s DeleteBatchPredictionInput) GoString() string
GoString returns the string representation
func (*DeleteBatchPredictionInput) SetBatchPredictionId ¶ added in v1.5.0
func (s *DeleteBatchPredictionInput) SetBatchPredictionId(v string) *DeleteBatchPredictionInput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (DeleteBatchPredictionInput) String ¶ added in v0.6.5
func (s DeleteBatchPredictionInput) String() string
String returns the string representation
func (*DeleteBatchPredictionInput) Validate ¶ added in v1.1.21
func (s *DeleteBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteBatchPredictionOutput ¶
type DeleteBatchPredictionOutput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. This value // should be identical to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteBatchPrediction operation.
You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.
func (DeleteBatchPredictionOutput) GoString ¶ added in v0.6.5
func (s DeleteBatchPredictionOutput) GoString() string
GoString returns the string representation
func (*DeleteBatchPredictionOutput) SetBatchPredictionId ¶ added in v1.5.0
func (s *DeleteBatchPredictionOutput) SetBatchPredictionId(v string) *DeleteBatchPredictionOutput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (DeleteBatchPredictionOutput) String ¶ added in v0.6.5
func (s DeleteBatchPredictionOutput) String() string
String returns the string representation
type DeleteDataSourceInput ¶
type DeleteDataSourceInput struct { // A user-supplied ID that uniquely identifies the DataSource. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteDataSourceInput) GoString ¶ added in v0.6.5
func (s DeleteDataSourceInput) GoString() string
GoString returns the string representation
func (*DeleteDataSourceInput) SetDataSourceId ¶ added in v1.5.0
func (s *DeleteDataSourceInput) SetDataSourceId(v string) *DeleteDataSourceInput
SetDataSourceId sets the DataSourceId field's value.
func (DeleteDataSourceInput) String ¶ added in v0.6.5
func (s DeleteDataSourceInput) String() string
String returns the string representation
func (*DeleteDataSourceInput) Validate ¶ added in v1.1.21
func (s *DeleteDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteDataSourceOutput ¶
type DeleteDataSourceOutput struct { // A user-supplied ID that uniquely identifies the DataSource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteDataSource operation.
func (DeleteDataSourceOutput) GoString ¶ added in v0.6.5
func (s DeleteDataSourceOutput) GoString() string
GoString returns the string representation
func (*DeleteDataSourceOutput) SetDataSourceId ¶ added in v1.5.0
func (s *DeleteDataSourceOutput) SetDataSourceId(v string) *DeleteDataSourceOutput
SetDataSourceId sets the DataSourceId field's value.
func (DeleteDataSourceOutput) String ¶ added in v0.6.5
func (s DeleteDataSourceOutput) String() string
String returns the string representation
type DeleteEvaluationInput ¶
type DeleteEvaluationInput struct { // A user-supplied ID that uniquely identifies the Evaluation to delete. // // EvaluationId is a required field EvaluationId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteEvaluationInput) GoString ¶ added in v0.6.5
func (s DeleteEvaluationInput) GoString() string
GoString returns the string representation
func (*DeleteEvaluationInput) SetEvaluationId ¶ added in v1.5.0
func (s *DeleteEvaluationInput) SetEvaluationId(v string) *DeleteEvaluationInput
SetEvaluationId sets the EvaluationId field's value.
func (DeleteEvaluationInput) String ¶ added in v0.6.5
func (s DeleteEvaluationInput) String() string
String returns the string representation
func (*DeleteEvaluationInput) Validate ¶ added in v1.1.21
func (s *DeleteEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteEvaluationOutput ¶
type DeleteEvaluationOutput struct { // A user-supplied ID that uniquely identifies the Evaluation. This value should // be identical to the value of the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.
func (DeleteEvaluationOutput) GoString ¶ added in v0.6.5
func (s DeleteEvaluationOutput) GoString() string
GoString returns the string representation
func (*DeleteEvaluationOutput) SetEvaluationId ¶ added in v1.5.0
func (s *DeleteEvaluationOutput) SetEvaluationId(v string) *DeleteEvaluationOutput
SetEvaluationId sets the EvaluationId field's value.
func (DeleteEvaluationOutput) String ¶ added in v0.6.5
func (s DeleteEvaluationOutput) String() string
String returns the string representation
type DeleteMLModelInput ¶
type DeleteMLModelInput struct { // A user-supplied ID that uniquely identifies the MLModel. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteMLModelInput) GoString ¶ added in v0.6.5
func (s DeleteMLModelInput) GoString() string
GoString returns the string representation
func (*DeleteMLModelInput) SetMLModelId ¶ added in v1.5.0
func (s *DeleteMLModelInput) SetMLModelId(v string) *DeleteMLModelInput
SetMLModelId sets the MLModelId field's value.
func (DeleteMLModelInput) String ¶ added in v0.6.5
func (s DeleteMLModelInput) String() string
String returns the string representation
func (*DeleteMLModelInput) Validate ¶ added in v1.1.21
func (s *DeleteMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteMLModelOutput ¶
type DeleteMLModelOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelID in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteMLModel operation.
You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.
func (DeleteMLModelOutput) GoString ¶ added in v0.6.5
func (s DeleteMLModelOutput) GoString() string
GoString returns the string representation
func (*DeleteMLModelOutput) SetMLModelId ¶ added in v1.5.0
func (s *DeleteMLModelOutput) SetMLModelId(v string) *DeleteMLModelOutput
SetMLModelId sets the MLModelId field's value.
func (DeleteMLModelOutput) String ¶ added in v0.6.5
func (s DeleteMLModelOutput) String() string
String returns the string representation
type DeleteRealtimeEndpointInput ¶
type DeleteRealtimeEndpointInput struct { // The ID assigned to the MLModel during creation. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteRealtimeEndpointInput) GoString ¶ added in v0.6.5
func (s DeleteRealtimeEndpointInput) GoString() string
GoString returns the string representation
func (*DeleteRealtimeEndpointInput) SetMLModelId ¶ added in v1.5.0
func (s *DeleteRealtimeEndpointInput) SetMLModelId(v string) *DeleteRealtimeEndpointInput
SetMLModelId sets the MLModelId field's value.
func (DeleteRealtimeEndpointInput) String ¶ added in v0.6.5
func (s DeleteRealtimeEndpointInput) String() string
String returns the string representation
func (*DeleteRealtimeEndpointInput) Validate ¶ added in v1.1.21
func (s *DeleteRealtimeEndpointInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteRealtimeEndpointOutput ¶
type DeleteRealtimeEndpointOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // The endpoint information of the MLModel RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"` // contains filtered or unexported fields }
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
func (DeleteRealtimeEndpointOutput) GoString ¶ added in v0.6.5
func (s DeleteRealtimeEndpointOutput) GoString() string
GoString returns the string representation
func (*DeleteRealtimeEndpointOutput) SetMLModelId ¶ added in v1.5.0
func (s *DeleteRealtimeEndpointOutput) SetMLModelId(v string) *DeleteRealtimeEndpointOutput
SetMLModelId sets the MLModelId field's value.
func (*DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo ¶ added in v1.5.0
func (s *DeleteRealtimeEndpointOutput) SetRealtimeEndpointInfo(v *RealtimeEndpointInfo) *DeleteRealtimeEndpointOutput
SetRealtimeEndpointInfo sets the RealtimeEndpointInfo field's value.
func (DeleteRealtimeEndpointOutput) String ¶ added in v0.6.5
func (s DeleteRealtimeEndpointOutput) String() string
String returns the string representation
type DeleteTagsInput ¶ added in v1.1.33
type DeleteTagsInput struct { // The ID of the tagged ML object. For example, exampleModelId. // // ResourceId is a required field ResourceId *string `min:"1" type:"string" required:"true"` // The type of the tagged ML object. // // ResourceType is a required field ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // One or more tags to delete. // // TagKeys is a required field TagKeys []*string `type:"list" required:"true"` // contains filtered or unexported fields }
func (DeleteTagsInput) GoString ¶ added in v1.1.33
func (s DeleteTagsInput) GoString() string
GoString returns the string representation
func (*DeleteTagsInput) SetResourceId ¶ added in v1.5.0
func (s *DeleteTagsInput) SetResourceId(v string) *DeleteTagsInput
SetResourceId sets the ResourceId field's value.
func (*DeleteTagsInput) SetResourceType ¶ added in v1.5.0
func (s *DeleteTagsInput) SetResourceType(v string) *DeleteTagsInput
SetResourceType sets the ResourceType field's value.
func (*DeleteTagsInput) SetTagKeys ¶ added in v1.5.0
func (s *DeleteTagsInput) SetTagKeys(v []*string) *DeleteTagsInput
SetTagKeys sets the TagKeys field's value.
func (DeleteTagsInput) String ¶ added in v1.1.33
func (s DeleteTagsInput) String() string
String returns the string representation
func (*DeleteTagsInput) Validate ¶ added in v1.1.33
func (s *DeleteTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteTagsOutput ¶ added in v1.1.33
type DeleteTagsOutput struct { // The ID of the ML object from which tags were deleted. ResourceId *string `min:"1" type:"string"` // The type of the ML object from which tags were deleted. ResourceType *string `type:"string" enum:"TaggableResourceType"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (DeleteTagsOutput) GoString ¶ added in v1.1.33
func (s DeleteTagsOutput) GoString() string
GoString returns the string representation
func (*DeleteTagsOutput) SetResourceId ¶ added in v1.5.0
func (s *DeleteTagsOutput) SetResourceId(v string) *DeleteTagsOutput
SetResourceId sets the ResourceId field's value.
func (*DeleteTagsOutput) SetResourceType ¶ added in v1.5.0
func (s *DeleteTagsOutput) SetResourceType(v string) *DeleteTagsOutput
SetResourceType sets the ResourceType field's value.
func (DeleteTagsOutput) String ¶ added in v1.1.33
func (s DeleteTagsOutput) String() string
String returns the string representation
type DescribeBatchPredictionsInput ¶
type DescribeBatchPredictionsInput struct { // The equal to operator. The BatchPrediction results will have FilterVariable // values that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of BatchPrediction: // // * CreatedAt - Sets the search criteria to the BatchPrediction creation // date. // * Status - Sets the search criteria to the BatchPrediction status. // * Name - Sets the search criteria to the contents of the BatchPredictionName. // // * IAMUser - Sets the search criteria to the user account that invoked // the BatchPrediction creation. // * MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction. // // * DataSourceId - Sets the search criteria to the DataSource used in the // BatchPrediction. // * DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. // The URL can identify either a file or an Amazon Simple Storage Solution // (Amazon S3) bucket or directory. FilterVariable *string `type:"string" enum:"BatchPredictionFilterVariable"` // The greater than or equal to operator. The BatchPrediction results will have // FilterVariable values that are greater than or equal to the value specified // with GE. GE *string `type:"string"` // The greater than operator. The BatchPrediction results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The BatchPrediction results will have // FilterVariable values that are less than or equal to the value specified // with LE. LE *string `type:"string"` // The less than operator. The BatchPrediction results will have FilterVariable // values that are less than the value specified with LT. LT *string `type:"string"` // The number of pages of information to include in the result. The range of // acceptable values is 1 through 100. The default value is 100. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The BatchPrediction results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // An ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, a Batch Prediction operation could have the Name2014-09-09-HolidayGiftMailer. // To search for this BatchPrediction, select Name for the FilterVariable and // any of the following strings for the Prefix: // // * 2014-09 // // * 2014-09-09 // // * 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of MLModels. // // * asc - Arranges the list in ascending order (A-Z, 0-9). // * dsc - Arranges the list in descending order (Z-A, 9-0). // Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeBatchPredictionsInput) GoString ¶ added in v0.6.5
func (s DescribeBatchPredictionsInput) GoString() string
GoString returns the string representation
func (*DescribeBatchPredictionsInput) SetEQ ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetEQ(v string) *DescribeBatchPredictionsInput
SetEQ sets the EQ field's value.
func (*DescribeBatchPredictionsInput) SetFilterVariable ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetFilterVariable(v string) *DescribeBatchPredictionsInput
SetFilterVariable sets the FilterVariable field's value.
func (*DescribeBatchPredictionsInput) SetGE ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetGE(v string) *DescribeBatchPredictionsInput
SetGE sets the GE field's value.
func (*DescribeBatchPredictionsInput) SetGT ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetGT(v string) *DescribeBatchPredictionsInput
SetGT sets the GT field's value.
func (*DescribeBatchPredictionsInput) SetLE ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetLE(v string) *DescribeBatchPredictionsInput
SetLE sets the LE field's value.
func (*DescribeBatchPredictionsInput) SetLT ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetLT(v string) *DescribeBatchPredictionsInput
SetLT sets the LT field's value.
func (*DescribeBatchPredictionsInput) SetLimit ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetLimit(v int64) *DescribeBatchPredictionsInput
SetLimit sets the Limit field's value.
func (*DescribeBatchPredictionsInput) SetNE ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetNE(v string) *DescribeBatchPredictionsInput
SetNE sets the NE field's value.
func (*DescribeBatchPredictionsInput) SetNextToken ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetNextToken(v string) *DescribeBatchPredictionsInput
SetNextToken sets the NextToken field's value.
func (*DescribeBatchPredictionsInput) SetPrefix ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetPrefix(v string) *DescribeBatchPredictionsInput
SetPrefix sets the Prefix field's value.
func (*DescribeBatchPredictionsInput) SetSortOrder ¶ added in v1.5.0
func (s *DescribeBatchPredictionsInput) SetSortOrder(v string) *DescribeBatchPredictionsInput
SetSortOrder sets the SortOrder field's value.
func (DescribeBatchPredictionsInput) String ¶ added in v0.6.5
func (s DescribeBatchPredictionsInput) String() string
String returns the string representation
func (*DescribeBatchPredictionsInput) Validate ¶ added in v1.1.21
func (s *DescribeBatchPredictionsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeBatchPredictionsOutput ¶
type DescribeBatchPredictionsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of BatchPrediction objects that meet the search criteria. Results []*BatchPrediction `type:"list"` // contains filtered or unexported fields }
Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.
func (DescribeBatchPredictionsOutput) GoString ¶ added in v0.6.5
func (s DescribeBatchPredictionsOutput) GoString() string
GoString returns the string representation
func (*DescribeBatchPredictionsOutput) SetNextToken ¶ added in v1.5.0
func (s *DescribeBatchPredictionsOutput) SetNextToken(v string) *DescribeBatchPredictionsOutput
SetNextToken sets the NextToken field's value.
func (*DescribeBatchPredictionsOutput) SetResults ¶ added in v1.5.0
func (s *DescribeBatchPredictionsOutput) SetResults(v []*BatchPrediction) *DescribeBatchPredictionsOutput
SetResults sets the Results field's value.
func (DescribeBatchPredictionsOutput) String ¶ added in v0.6.5
func (s DescribeBatchPredictionsOutput) String() string
String returns the string representation
type DescribeDataSourcesInput ¶
type DescribeDataSourcesInput struct { // The equal to operator. The DataSource results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of DataSource: // // * CreatedAt - Sets the search criteria to DataSource creation dates. // * Status - Sets the search criteria to DataSource statuses. // * Name - Sets the search criteria to the contents of DataSourceName. // * DataUri - Sets the search criteria to the URI of data files used to // create the DataSource. The URI can identify either a file or an Amazon // Simple Storage Service (Amazon S3) bucket or directory. // * IAMUser - Sets the search criteria to the user account that invoked // the DataSource creation. FilterVariable *string `type:"string" enum:"DataSourceFilterVariable"` // The greater than or equal to operator. The DataSource results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The DataSource results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The DataSource results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The DataSource results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The maximum number of DataSource to include in the result. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The DataSource results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, a DataSource could have the Name2014-09-09-HolidayGiftMailer. // To search for this DataSource, select Name for the FilterVariable and any // of the following strings for the Prefix: // // * 2014-09 // // * 2014-09-09 // // * 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of DataSource. // // * asc - Arranges the list in ascending order (A-Z, 0-9). // * dsc - Arranges the list in descending order (Z-A, 9-0). // Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeDataSourcesInput) GoString ¶ added in v0.6.5
func (s DescribeDataSourcesInput) GoString() string
GoString returns the string representation
func (*DescribeDataSourcesInput) SetEQ ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetEQ(v string) *DescribeDataSourcesInput
SetEQ sets the EQ field's value.
func (*DescribeDataSourcesInput) SetFilterVariable ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetFilterVariable(v string) *DescribeDataSourcesInput
SetFilterVariable sets the FilterVariable field's value.
func (*DescribeDataSourcesInput) SetGE ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetGE(v string) *DescribeDataSourcesInput
SetGE sets the GE field's value.
func (*DescribeDataSourcesInput) SetGT ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetGT(v string) *DescribeDataSourcesInput
SetGT sets the GT field's value.
func (*DescribeDataSourcesInput) SetLE ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetLE(v string) *DescribeDataSourcesInput
SetLE sets the LE field's value.
func (*DescribeDataSourcesInput) SetLT ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetLT(v string) *DescribeDataSourcesInput
SetLT sets the LT field's value.
func (*DescribeDataSourcesInput) SetLimit ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetLimit(v int64) *DescribeDataSourcesInput
SetLimit sets the Limit field's value.
func (*DescribeDataSourcesInput) SetNE ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetNE(v string) *DescribeDataSourcesInput
SetNE sets the NE field's value.
func (*DescribeDataSourcesInput) SetNextToken ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetNextToken(v string) *DescribeDataSourcesInput
SetNextToken sets the NextToken field's value.
func (*DescribeDataSourcesInput) SetPrefix ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetPrefix(v string) *DescribeDataSourcesInput
SetPrefix sets the Prefix field's value.
func (*DescribeDataSourcesInput) SetSortOrder ¶ added in v1.5.0
func (s *DescribeDataSourcesInput) SetSortOrder(v string) *DescribeDataSourcesInput
SetSortOrder sets the SortOrder field's value.
func (DescribeDataSourcesInput) String ¶ added in v0.6.5
func (s DescribeDataSourcesInput) String() string
String returns the string representation
func (*DescribeDataSourcesInput) Validate ¶ added in v1.1.21
func (s *DescribeDataSourcesInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeDataSourcesOutput ¶
type DescribeDataSourcesOutput struct { // An ID of the next page in the paginated results that indicates at least one // more page follows. NextToken *string `type:"string"` // A list of DataSource that meet the search criteria. Results []*DataSource `type:"list"` // contains filtered or unexported fields }
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.
func (DescribeDataSourcesOutput) GoString ¶ added in v0.6.5
func (s DescribeDataSourcesOutput) GoString() string
GoString returns the string representation
func (*DescribeDataSourcesOutput) SetNextToken ¶ added in v1.5.0
func (s *DescribeDataSourcesOutput) SetNextToken(v string) *DescribeDataSourcesOutput
SetNextToken sets the NextToken field's value.
func (*DescribeDataSourcesOutput) SetResults ¶ added in v1.5.0
func (s *DescribeDataSourcesOutput) SetResults(v []*DataSource) *DescribeDataSourcesOutput
SetResults sets the Results field's value.
func (DescribeDataSourcesOutput) String ¶ added in v0.6.5
func (s DescribeDataSourcesOutput) String() string
String returns the string representation
type DescribeEvaluationsInput ¶
type DescribeEvaluationsInput struct { // The equal to operator. The Evaluation results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variable to filter a list of Evaluation objects: // // * CreatedAt - Sets the search criteria to the Evaluation creation date. // // * Status - Sets the search criteria to the Evaluation status. // * Name - Sets the search criteria to the contents of EvaluationName. // * IAMUser - Sets the search criteria to the user account that invoked // an Evaluation. // * MLModelId - Sets the search criteria to the MLModel that was evaluated. // // * DataSourceId - Sets the search criteria to the DataSource used in Evaluation. // // * DataUri - Sets the search criteria to the data file(s) used in Evaluation. // The URL can identify either a file or an Amazon Simple Storage Solution // (Amazon S3) bucket or directory. FilterVariable *string `type:"string" enum:"EvaluationFilterVariable"` // The greater than or equal to operator. The Evaluation results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The Evaluation results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The Evaluation results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The Evaluation results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The maximum number of Evaluation to include in the result. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The Evaluation results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, an Evaluation could have the Name2014-09-09-HolidayGiftMailer. // To search for this Evaluation, select Name for the FilterVariable and any // of the following strings for the Prefix: // // * 2014-09 // // * 2014-09-09 // // * 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of Evaluation. // // * asc - Arranges the list in ascending order (A-Z, 0-9). // * dsc - Arranges the list in descending order (Z-A, 9-0). // Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeEvaluationsInput) GoString ¶ added in v0.6.5
func (s DescribeEvaluationsInput) GoString() string
GoString returns the string representation
func (*DescribeEvaluationsInput) SetEQ ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetEQ(v string) *DescribeEvaluationsInput
SetEQ sets the EQ field's value.
func (*DescribeEvaluationsInput) SetFilterVariable ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetFilterVariable(v string) *DescribeEvaluationsInput
SetFilterVariable sets the FilterVariable field's value.
func (*DescribeEvaluationsInput) SetGE ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetGE(v string) *DescribeEvaluationsInput
SetGE sets the GE field's value.
func (*DescribeEvaluationsInput) SetGT ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetGT(v string) *DescribeEvaluationsInput
SetGT sets the GT field's value.
func (*DescribeEvaluationsInput) SetLE ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetLE(v string) *DescribeEvaluationsInput
SetLE sets the LE field's value.
func (*DescribeEvaluationsInput) SetLT ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetLT(v string) *DescribeEvaluationsInput
SetLT sets the LT field's value.
func (*DescribeEvaluationsInput) SetLimit ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetLimit(v int64) *DescribeEvaluationsInput
SetLimit sets the Limit field's value.
func (*DescribeEvaluationsInput) SetNE ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetNE(v string) *DescribeEvaluationsInput
SetNE sets the NE field's value.
func (*DescribeEvaluationsInput) SetNextToken ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetNextToken(v string) *DescribeEvaluationsInput
SetNextToken sets the NextToken field's value.
func (*DescribeEvaluationsInput) SetPrefix ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetPrefix(v string) *DescribeEvaluationsInput
SetPrefix sets the Prefix field's value.
func (*DescribeEvaluationsInput) SetSortOrder ¶ added in v1.5.0
func (s *DescribeEvaluationsInput) SetSortOrder(v string) *DescribeEvaluationsInput
SetSortOrder sets the SortOrder field's value.
func (DescribeEvaluationsInput) String ¶ added in v0.6.5
func (s DescribeEvaluationsInput) String() string
String returns the string representation
func (*DescribeEvaluationsInput) Validate ¶ added in v1.1.21
func (s *DescribeEvaluationsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeEvaluationsOutput ¶
type DescribeEvaluationsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of Evaluation that meet the search criteria. Results []*Evaluation `type:"list"` // contains filtered or unexported fields }
Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.
func (DescribeEvaluationsOutput) GoString ¶ added in v0.6.5
func (s DescribeEvaluationsOutput) GoString() string
GoString returns the string representation
func (*DescribeEvaluationsOutput) SetNextToken ¶ added in v1.5.0
func (s *DescribeEvaluationsOutput) SetNextToken(v string) *DescribeEvaluationsOutput
SetNextToken sets the NextToken field's value.
func (*DescribeEvaluationsOutput) SetResults ¶ added in v1.5.0
func (s *DescribeEvaluationsOutput) SetResults(v []*Evaluation) *DescribeEvaluationsOutput
SetResults sets the Results field's value.
func (DescribeEvaluationsOutput) String ¶ added in v0.6.5
func (s DescribeEvaluationsOutput) String() string
String returns the string representation
type DescribeMLModelsInput ¶
type DescribeMLModelsInput struct { // The equal to operator. The MLModel results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of MLModel: // // * CreatedAt - Sets the search criteria to MLModel creation date. // * Status - Sets the search criteria to MLModel status. // * Name - Sets the search criteria to the contents of MLModelName. // * IAMUser - Sets the search criteria to the user account that invoked // the MLModel creation. // * TrainingDataSourceId - Sets the search criteria to the DataSource used // to train one or more MLModel. // * RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time // endpoint status. // * MLModelType - Sets the search criteria to MLModel type: binary, regression, // or multi-class. // * Algorithm - Sets the search criteria to the algorithm that the MLModel // uses. // * TrainingDataURI - Sets the search criteria to the data file(s) used // in training a MLModel. The URL can identify either a file or an Amazon // Simple Storage Service (Amazon S3) bucket or directory. FilterVariable *string `type:"string" enum:"MLModelFilterVariable"` // The greater than or equal to operator. The MLModel results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The MLModel results will have FilterVariable values // that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The MLModel results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The MLModel results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The number of pages of information to include in the result. The range of // acceptable values is 1 through 100. The default value is 100. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The MLModel results will have FilterVariable values // not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, an MLModel could have the Name2014-09-09-HolidayGiftMailer. // To search for this MLModel, select Name for the FilterVariable and any of // the following strings for the Prefix: // // * 2014-09 // // * 2014-09-09 // // * 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of MLModel. // // * asc - Arranges the list in ascending order (A-Z, 0-9). // * dsc - Arranges the list in descending order (Z-A, 9-0). // Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeMLModelsInput) GoString ¶ added in v0.6.5
func (s DescribeMLModelsInput) GoString() string
GoString returns the string representation
func (*DescribeMLModelsInput) SetEQ ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetEQ(v string) *DescribeMLModelsInput
SetEQ sets the EQ field's value.
func (*DescribeMLModelsInput) SetFilterVariable ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetFilterVariable(v string) *DescribeMLModelsInput
SetFilterVariable sets the FilterVariable field's value.
func (*DescribeMLModelsInput) SetGE ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetGE(v string) *DescribeMLModelsInput
SetGE sets the GE field's value.
func (*DescribeMLModelsInput) SetGT ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetGT(v string) *DescribeMLModelsInput
SetGT sets the GT field's value.
func (*DescribeMLModelsInput) SetLE ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetLE(v string) *DescribeMLModelsInput
SetLE sets the LE field's value.
func (*DescribeMLModelsInput) SetLT ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetLT(v string) *DescribeMLModelsInput
SetLT sets the LT field's value.
func (*DescribeMLModelsInput) SetLimit ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetLimit(v int64) *DescribeMLModelsInput
SetLimit sets the Limit field's value.
func (*DescribeMLModelsInput) SetNE ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetNE(v string) *DescribeMLModelsInput
SetNE sets the NE field's value.
func (*DescribeMLModelsInput) SetNextToken ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetNextToken(v string) *DescribeMLModelsInput
SetNextToken sets the NextToken field's value.
func (*DescribeMLModelsInput) SetPrefix ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetPrefix(v string) *DescribeMLModelsInput
SetPrefix sets the Prefix field's value.
func (*DescribeMLModelsInput) SetSortOrder ¶ added in v1.5.0
func (s *DescribeMLModelsInput) SetSortOrder(v string) *DescribeMLModelsInput
SetSortOrder sets the SortOrder field's value.
func (DescribeMLModelsInput) String ¶ added in v0.6.5
func (s DescribeMLModelsInput) String() string
String returns the string representation
func (*DescribeMLModelsInput) Validate ¶ added in v1.1.21
func (s *DescribeMLModelsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeMLModelsOutput ¶
type DescribeMLModelsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of MLModel that meet the search criteria. Results []*MLModel `type:"list"` // contains filtered or unexported fields }
Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.
func (DescribeMLModelsOutput) GoString ¶ added in v0.6.5
func (s DescribeMLModelsOutput) GoString() string
GoString returns the string representation
func (*DescribeMLModelsOutput) SetNextToken ¶ added in v1.5.0
func (s *DescribeMLModelsOutput) SetNextToken(v string) *DescribeMLModelsOutput
SetNextToken sets the NextToken field's value.
func (*DescribeMLModelsOutput) SetResults ¶ added in v1.5.0
func (s *DescribeMLModelsOutput) SetResults(v []*MLModel) *DescribeMLModelsOutput
SetResults sets the Results field's value.
func (DescribeMLModelsOutput) String ¶ added in v0.6.5
func (s DescribeMLModelsOutput) String() string
String returns the string representation
type DescribeTagsInput ¶ added in v1.1.33
type DescribeTagsInput struct { // The ID of the ML object. For example, exampleModelId. // // ResourceId is a required field ResourceId *string `min:"1" type:"string" required:"true"` // The type of the ML object. // // ResourceType is a required field ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // contains filtered or unexported fields }
func (DescribeTagsInput) GoString ¶ added in v1.1.33
func (s DescribeTagsInput) GoString() string
GoString returns the string representation
func (*DescribeTagsInput) SetResourceId ¶ added in v1.5.0
func (s *DescribeTagsInput) SetResourceId(v string) *DescribeTagsInput
SetResourceId sets the ResourceId field's value.
func (*DescribeTagsInput) SetResourceType ¶ added in v1.5.0
func (s *DescribeTagsInput) SetResourceType(v string) *DescribeTagsInput
SetResourceType sets the ResourceType field's value.
func (DescribeTagsInput) String ¶ added in v1.1.33
func (s DescribeTagsInput) String() string
String returns the string representation
func (*DescribeTagsInput) Validate ¶ added in v1.1.33
func (s *DescribeTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeTagsOutput ¶ added in v1.1.33
type DescribeTagsOutput struct { // The ID of the tagged ML object. ResourceId *string `min:"1" type:"string"` // The type of the tagged ML object. ResourceType *string `type:"string" enum:"TaggableResourceType"` // A list of tags associated with the ML object. Tags []*Tag `type:"list"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (DescribeTagsOutput) GoString ¶ added in v1.1.33
func (s DescribeTagsOutput) GoString() string
GoString returns the string representation
func (*DescribeTagsOutput) SetResourceId ¶ added in v1.5.0
func (s *DescribeTagsOutput) SetResourceId(v string) *DescribeTagsOutput
SetResourceId sets the ResourceId field's value.
func (*DescribeTagsOutput) SetResourceType ¶ added in v1.5.0
func (s *DescribeTagsOutput) SetResourceType(v string) *DescribeTagsOutput
SetResourceType sets the ResourceType field's value.
func (*DescribeTagsOutput) SetTags ¶ added in v1.5.0
func (s *DescribeTagsOutput) SetTags(v []*Tag) *DescribeTagsOutput
SetTags sets the Tags field's value.
func (DescribeTagsOutput) String ¶ added in v1.1.33
func (s DescribeTagsOutput) String() string
String returns the string representation
type Evaluation ¶
type Evaluation struct { // Long integer type that is a 64-bit signed number. ComputeTime *int64 `type:"long"` // The time that the Evaluation was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the evaluation. The account type can be // either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The ID of the DataSource that is used to evaluate the MLModel. EvaluationDataSourceId *string `min:"1" type:"string"` // The ID that is assigned to the Evaluation at creation. EvaluationId *string `min:"1" type:"string"` // A timestamp represented in epoch time. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The location and name of the data in Amazon Simple Storage Server (Amazon // S3) that is used in the evaluation. InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the Evaluation. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The ID of the MLModel that is the focus of the evaluation. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about evaluating the MLModel. Message *string `type:"string"` // A user-supplied name or description of the Evaluation. Name *string `type:"string"` // Measurements of how well the MLModel performed, using observations referenced // by the DataSource. One of the following metrics is returned, based on the // type of the MLModel: // // * BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique // to measure performance. // // * RegressionRMSE: A regression MLModel uses the Root Mean Square Error // (RMSE) technique to measure performance. RMSE measures the difference // between predicted and actual values for a single variable. // // * MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique // to measure performance. // // For more information about performance metrics, please see the Amazon Machine // Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). PerformanceMetrics *PerformanceMetrics `type:"structure"` // A timestamp represented in epoch time. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The status of the evaluation. This element can have one of the following // values: // // * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to // evaluate an MLModel. // * INPROGRESS - The evaluation is underway. // * FAILED - The request to evaluate an MLModel did not run to completion. // It is not usable. // * COMPLETED - The evaluation process completed successfully. // * DELETED - The Evaluation is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation.
func (Evaluation) GoString ¶ added in v0.6.5
func (s Evaluation) GoString() string
GoString returns the string representation
func (*Evaluation) SetComputeTime ¶ added in v1.5.0
func (s *Evaluation) SetComputeTime(v int64) *Evaluation
SetComputeTime sets the ComputeTime field's value.
func (*Evaluation) SetCreatedAt ¶ added in v1.5.0
func (s *Evaluation) SetCreatedAt(v time.Time) *Evaluation
SetCreatedAt sets the CreatedAt field's value.
func (*Evaluation) SetCreatedByIamUser ¶ added in v1.5.0
func (s *Evaluation) SetCreatedByIamUser(v string) *Evaluation
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*Evaluation) SetEvaluationDataSourceId ¶ added in v1.5.0
func (s *Evaluation) SetEvaluationDataSourceId(v string) *Evaluation
SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.
func (*Evaluation) SetEvaluationId ¶ added in v1.5.0
func (s *Evaluation) SetEvaluationId(v string) *Evaluation
SetEvaluationId sets the EvaluationId field's value.
func (*Evaluation) SetFinishedAt ¶ added in v1.5.0
func (s *Evaluation) SetFinishedAt(v time.Time) *Evaluation
SetFinishedAt sets the FinishedAt field's value.
func (*Evaluation) SetInputDataLocationS3 ¶ added in v1.5.0
func (s *Evaluation) SetInputDataLocationS3(v string) *Evaluation
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*Evaluation) SetLastUpdatedAt ¶ added in v1.5.0
func (s *Evaluation) SetLastUpdatedAt(v time.Time) *Evaluation
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*Evaluation) SetMLModelId ¶ added in v1.5.0
func (s *Evaluation) SetMLModelId(v string) *Evaluation
SetMLModelId sets the MLModelId field's value.
func (*Evaluation) SetMessage ¶ added in v1.5.0
func (s *Evaluation) SetMessage(v string) *Evaluation
SetMessage sets the Message field's value.
func (*Evaluation) SetName ¶ added in v1.5.0
func (s *Evaluation) SetName(v string) *Evaluation
SetName sets the Name field's value.
func (*Evaluation) SetPerformanceMetrics ¶ added in v1.5.0
func (s *Evaluation) SetPerformanceMetrics(v *PerformanceMetrics) *Evaluation
SetPerformanceMetrics sets the PerformanceMetrics field's value.
func (*Evaluation) SetStartedAt ¶ added in v1.5.0
func (s *Evaluation) SetStartedAt(v time.Time) *Evaluation
SetStartedAt sets the StartedAt field's value.
func (*Evaluation) SetStatus ¶ added in v1.5.0
func (s *Evaluation) SetStatus(v string) *Evaluation
SetStatus sets the Status field's value.
func (Evaluation) String ¶ added in v0.6.5
func (s Evaluation) String() string
String returns the string representation
type GetBatchPredictionInput ¶
type GetBatchPredictionInput struct { // An ID assigned to the BatchPrediction at creation. // // BatchPredictionId is a required field BatchPredictionId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (GetBatchPredictionInput) GoString ¶ added in v0.6.5
func (s GetBatchPredictionInput) GoString() string
GoString returns the string representation
func (*GetBatchPredictionInput) SetBatchPredictionId ¶ added in v1.5.0
func (s *GetBatchPredictionInput) SetBatchPredictionId(v string) *GetBatchPredictionInput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (GetBatchPredictionInput) String ¶ added in v0.6.5
func (s GetBatchPredictionInput) String() string
String returns the string representation
func (*GetBatchPredictionInput) Validate ¶ added in v1.1.21
func (s *GetBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetBatchPredictionOutput ¶
type GetBatchPredictionOutput struct { // The ID of the DataSource that was used to create the BatchPrediction. BatchPredictionDataSourceId *string `min:"1" type:"string"` // An ID assigned to the BatchPrediction at creation. This value should be identical // to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // The approximate CPU time in milliseconds that Amazon Machine Learning spent // processing the BatchPrediction, normalized and scaled on computation resources. // ComputeTime is only available if the BatchPrediction is in the COMPLETED // state. ComputeTime *int64 `type:"long"` // The time when the BatchPrediction was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the BatchPrediction. The account type can // be either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The epoch time when Amazon Machine Learning marked the BatchPrediction as // COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction // is in the COMPLETED or FAILED state. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The number of invalid records that Amazon Machine Learning saw while processing // the BatchPrediction. InvalidRecordCount *int64 `type:"long"` // The time of the most recent edit to BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateBatchPrediction operation. LogUri *string `type:"string"` // The ID of the MLModel that generated predictions for the BatchPrediction // request. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about processing the batch prediction // request. Message *string `type:"string"` // A user-supplied name or description of the BatchPrediction. Name *string `type:"string"` // The location of an Amazon S3 bucket or directory to receive the operation // results. OutputUri *string `type:"string"` // The epoch time when Amazon Machine Learning marked the BatchPrediction as // INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING // state. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The status of the BatchPrediction, which can be one of the following values: // // * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to // generate batch predictions. // * INPROGRESS - The batch predictions are in progress. // * FAILED - The request to perform a batch prediction did not run to completion. // It is not usable. // * COMPLETED - The batch prediction process completed successfully. // * DELETED - The BatchPrediction is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // The number of total records that Amazon Machine Learning saw while processing // the BatchPrediction. TotalRecordCount *int64 `type:"long"` // contains filtered or unexported fields }
Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.
func (GetBatchPredictionOutput) GoString ¶ added in v0.6.5
func (s GetBatchPredictionOutput) GoString() string
GoString returns the string representation
func (*GetBatchPredictionOutput) SetBatchPredictionDataSourceId ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetBatchPredictionDataSourceId(v string) *GetBatchPredictionOutput
SetBatchPredictionDataSourceId sets the BatchPredictionDataSourceId field's value.
func (*GetBatchPredictionOutput) SetBatchPredictionId ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetBatchPredictionId(v string) *GetBatchPredictionOutput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (*GetBatchPredictionOutput) SetComputeTime ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetComputeTime(v int64) *GetBatchPredictionOutput
SetComputeTime sets the ComputeTime field's value.
func (*GetBatchPredictionOutput) SetCreatedAt ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetCreatedAt(v time.Time) *GetBatchPredictionOutput
SetCreatedAt sets the CreatedAt field's value.
func (*GetBatchPredictionOutput) SetCreatedByIamUser ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetCreatedByIamUser(v string) *GetBatchPredictionOutput
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*GetBatchPredictionOutput) SetFinishedAt ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetFinishedAt(v time.Time) *GetBatchPredictionOutput
SetFinishedAt sets the FinishedAt field's value.
func (*GetBatchPredictionOutput) SetInputDataLocationS3 ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetInputDataLocationS3(v string) *GetBatchPredictionOutput
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*GetBatchPredictionOutput) SetInvalidRecordCount ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetInvalidRecordCount(v int64) *GetBatchPredictionOutput
SetInvalidRecordCount sets the InvalidRecordCount field's value.
func (*GetBatchPredictionOutput) SetLastUpdatedAt ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetLastUpdatedAt(v time.Time) *GetBatchPredictionOutput
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*GetBatchPredictionOutput) SetLogUri ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetLogUri(v string) *GetBatchPredictionOutput
SetLogUri sets the LogUri field's value.
func (*GetBatchPredictionOutput) SetMLModelId ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetMLModelId(v string) *GetBatchPredictionOutput
SetMLModelId sets the MLModelId field's value.
func (*GetBatchPredictionOutput) SetMessage ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetMessage(v string) *GetBatchPredictionOutput
SetMessage sets the Message field's value.
func (*GetBatchPredictionOutput) SetName ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetName(v string) *GetBatchPredictionOutput
SetName sets the Name field's value.
func (*GetBatchPredictionOutput) SetOutputUri ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetOutputUri(v string) *GetBatchPredictionOutput
SetOutputUri sets the OutputUri field's value.
func (*GetBatchPredictionOutput) SetStartedAt ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetStartedAt(v time.Time) *GetBatchPredictionOutput
SetStartedAt sets the StartedAt field's value.
func (*GetBatchPredictionOutput) SetStatus ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetStatus(v string) *GetBatchPredictionOutput
SetStatus sets the Status field's value.
func (*GetBatchPredictionOutput) SetTotalRecordCount ¶ added in v1.5.0
func (s *GetBatchPredictionOutput) SetTotalRecordCount(v int64) *GetBatchPredictionOutput
SetTotalRecordCount sets the TotalRecordCount field's value.
func (GetBatchPredictionOutput) String ¶ added in v0.6.5
func (s GetBatchPredictionOutput) String() string
String returns the string representation
type GetDataSourceInput ¶
type GetDataSourceInput struct { // The ID assigned to the DataSource at creation. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // Specifies whether the GetDataSource operation should return DataSourceSchema. // // If true, DataSourceSchema is returned. // // If false, DataSourceSchema is not returned. Verbose *bool `type:"boolean"` // contains filtered or unexported fields }
func (GetDataSourceInput) GoString ¶ added in v0.6.5
func (s GetDataSourceInput) GoString() string
GoString returns the string representation
func (*GetDataSourceInput) SetDataSourceId ¶ added in v1.5.0
func (s *GetDataSourceInput) SetDataSourceId(v string) *GetDataSourceInput
SetDataSourceId sets the DataSourceId field's value.
func (*GetDataSourceInput) SetVerbose ¶ added in v1.5.0
func (s *GetDataSourceInput) SetVerbose(v bool) *GetDataSourceInput
SetVerbose sets the Verbose field's value.
func (GetDataSourceInput) String ¶ added in v0.6.5
func (s GetDataSourceInput) String() string
String returns the string representation
func (*GetDataSourceInput) Validate ¶ added in v1.1.21
func (s *GetDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetDataSourceOutput ¶
type GetDataSourceOutput struct { // The parameter is true if statistics need to be generated from the observation // data. ComputeStatistics *bool `type:"boolean"` // The approximate CPU time in milliseconds that Amazon Machine Learning spent // processing the DataSource, normalized and scaled on computation resources. // ComputeTime is only available if the DataSource is in the COMPLETED state // and the ComputeStatistics is set to true. ComputeTime *int64 `type:"long"` // The time that the DataSource was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the DataSource was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). DataLocationS3 *string `type:"string"` // A JSON string that represents the splitting and rearrangement requirement // used when this DataSource was created. DataRearrangement *string `type:"string"` // The total size of observations in the data files. DataSizeInBytes *int64 `type:"long"` // The ID assigned to the DataSource at creation. This value should be identical // to the value of the DataSourceId in the request. DataSourceId *string `min:"1" type:"string"` // The schema used by all of the data files of this DataSource. // // NoteThis parameter is provided as part of the verbose format. DataSourceSchema *string `type:"string"` // The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED // or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED // or FAILED state. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The time of the most recent edit to the DataSource. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file containing logs of CreateDataSourceFrom* operations. LogUri *string `type:"string"` // The user-supplied description of the most recent details about creating the // DataSource. Message *string `type:"string"` // A user-supplied name or description of the DataSource. Name *string `type:"string"` // The number of data files referenced by the DataSource. NumberOfFiles *int64 `type:"long"` // The datasource details that are specific to Amazon RDS. RDSMetadata *RDSMetadata `type:"structure"` // Describes the DataSource details specific to Amazon Redshift. RedshiftMetadata *RedshiftMetadata `type:"structure"` // The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts), // such as the following: arn:aws:iam::account:role/rolename. RoleARN *string `min:"1" type:"string"` // The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. // StartedAt isn't available if the DataSource is in the PENDING state. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The current status of the DataSource. This element can have one of the following // values: // // * PENDING - Amazon ML submitted a request to create a DataSource. // * INPROGRESS - The creation process is underway. // * FAILED - The request to create a DataSource did not run to completion. // It is not usable. // * COMPLETED - The creation process completed successfully. // * DELETED - The DataSource is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetDataSource operation and describes a DataSource.
func (GetDataSourceOutput) GoString ¶ added in v0.6.5
func (s GetDataSourceOutput) GoString() string
GoString returns the string representation
func (*GetDataSourceOutput) SetComputeStatistics ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetComputeStatistics(v bool) *GetDataSourceOutput
SetComputeStatistics sets the ComputeStatistics field's value.
func (*GetDataSourceOutput) SetComputeTime ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetComputeTime(v int64) *GetDataSourceOutput
SetComputeTime sets the ComputeTime field's value.
func (*GetDataSourceOutput) SetCreatedAt ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetCreatedAt(v time.Time) *GetDataSourceOutput
SetCreatedAt sets the CreatedAt field's value.
func (*GetDataSourceOutput) SetCreatedByIamUser ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetCreatedByIamUser(v string) *GetDataSourceOutput
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*GetDataSourceOutput) SetDataLocationS3 ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetDataLocationS3(v string) *GetDataSourceOutput
SetDataLocationS3 sets the DataLocationS3 field's value.
func (*GetDataSourceOutput) SetDataRearrangement ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetDataRearrangement(v string) *GetDataSourceOutput
SetDataRearrangement sets the DataRearrangement field's value.
func (*GetDataSourceOutput) SetDataSizeInBytes ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetDataSizeInBytes(v int64) *GetDataSourceOutput
SetDataSizeInBytes sets the DataSizeInBytes field's value.
func (*GetDataSourceOutput) SetDataSourceId ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetDataSourceId(v string) *GetDataSourceOutput
SetDataSourceId sets the DataSourceId field's value.
func (*GetDataSourceOutput) SetDataSourceSchema ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetDataSourceSchema(v string) *GetDataSourceOutput
SetDataSourceSchema sets the DataSourceSchema field's value.
func (*GetDataSourceOutput) SetFinishedAt ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetFinishedAt(v time.Time) *GetDataSourceOutput
SetFinishedAt sets the FinishedAt field's value.
func (*GetDataSourceOutput) SetLastUpdatedAt ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetLastUpdatedAt(v time.Time) *GetDataSourceOutput
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*GetDataSourceOutput) SetLogUri ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetLogUri(v string) *GetDataSourceOutput
SetLogUri sets the LogUri field's value.
func (*GetDataSourceOutput) SetMessage ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetMessage(v string) *GetDataSourceOutput
SetMessage sets the Message field's value.
func (*GetDataSourceOutput) SetName ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetName(v string) *GetDataSourceOutput
SetName sets the Name field's value.
func (*GetDataSourceOutput) SetNumberOfFiles ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetNumberOfFiles(v int64) *GetDataSourceOutput
SetNumberOfFiles sets the NumberOfFiles field's value.
func (*GetDataSourceOutput) SetRDSMetadata ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetRDSMetadata(v *RDSMetadata) *GetDataSourceOutput
SetRDSMetadata sets the RDSMetadata field's value.
func (*GetDataSourceOutput) SetRedshiftMetadata ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetRedshiftMetadata(v *RedshiftMetadata) *GetDataSourceOutput
SetRedshiftMetadata sets the RedshiftMetadata field's value.
func (*GetDataSourceOutput) SetRoleARN ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetRoleARN(v string) *GetDataSourceOutput
SetRoleARN sets the RoleARN field's value.
func (*GetDataSourceOutput) SetStartedAt ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetStartedAt(v time.Time) *GetDataSourceOutput
SetStartedAt sets the StartedAt field's value.
func (*GetDataSourceOutput) SetStatus ¶ added in v1.5.0
func (s *GetDataSourceOutput) SetStatus(v string) *GetDataSourceOutput
SetStatus sets the Status field's value.
func (GetDataSourceOutput) String ¶ added in v0.6.5
func (s GetDataSourceOutput) String() string
String returns the string representation
type GetEvaluationInput ¶
type GetEvaluationInput struct { // The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded // and cataloged. The ID provides the means to access the information. // // EvaluationId is a required field EvaluationId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (GetEvaluationInput) GoString ¶ added in v0.6.5
func (s GetEvaluationInput) GoString() string
GoString returns the string representation
func (*GetEvaluationInput) SetEvaluationId ¶ added in v1.5.0
func (s *GetEvaluationInput) SetEvaluationId(v string) *GetEvaluationInput
SetEvaluationId sets the EvaluationId field's value.
func (GetEvaluationInput) String ¶ added in v0.6.5
func (s GetEvaluationInput) String() string
String returns the string representation
func (*GetEvaluationInput) Validate ¶ added in v1.1.21
func (s *GetEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetEvaluationOutput ¶
type GetEvaluationOutput struct { // The approximate CPU time in milliseconds that Amazon Machine Learning spent // processing the Evaluation, normalized and scaled on computation resources. // ComputeTime is only available if the Evaluation is in the COMPLETED state. ComputeTime *int64 `type:"long"` // The time that the Evaluation was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the evaluation. The account type can be // either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The DataSource used for this evaluation. EvaluationDataSourceId *string `min:"1" type:"string"` // The evaluation ID which is same as the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED // or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED // or FAILED state. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the Evaluation. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateEvaluation operation. LogUri *string `type:"string"` // The ID of the MLModel that was the focus of the evaluation. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about evaluating the MLModel. Message *string `type:"string"` // A user-supplied name or description of the Evaluation. Name *string `type:"string"` // Measurements of how well the MLModel performed using observations referenced // by the DataSource. One of the following metric is returned based on the type // of the MLModel: // // * BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique // to measure performance. // // * RegressionRMSE: A regression MLModel uses the Root Mean Square Error // (RMSE) technique to measure performance. RMSE measures the difference // between predicted and actual values for a single variable. // // * MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique // to measure performance. // // For more information about performance metrics, please see the Amazon Machine // Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). PerformanceMetrics *PerformanceMetrics `type:"structure"` // The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. // StartedAt isn't available if the Evaluation is in the PENDING state. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The status of the evaluation. This element can have one of the following // values: // // * PENDING - Amazon Machine Language (Amazon ML) submitted a request to // evaluate an MLModel. // * INPROGRESS - The evaluation is underway. // * FAILED - The request to evaluate an MLModel did not run to completion. // It is not usable. // * COMPLETED - The evaluation process completed successfully. // * DELETED - The Evaluation is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetEvaluation operation and describes an Evaluation.
func (GetEvaluationOutput) GoString ¶ added in v0.6.5
func (s GetEvaluationOutput) GoString() string
GoString returns the string representation
func (*GetEvaluationOutput) SetComputeTime ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetComputeTime(v int64) *GetEvaluationOutput
SetComputeTime sets the ComputeTime field's value.
func (*GetEvaluationOutput) SetCreatedAt ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetCreatedAt(v time.Time) *GetEvaluationOutput
SetCreatedAt sets the CreatedAt field's value.
func (*GetEvaluationOutput) SetCreatedByIamUser ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetCreatedByIamUser(v string) *GetEvaluationOutput
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*GetEvaluationOutput) SetEvaluationDataSourceId ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetEvaluationDataSourceId(v string) *GetEvaluationOutput
SetEvaluationDataSourceId sets the EvaluationDataSourceId field's value.
func (*GetEvaluationOutput) SetEvaluationId ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetEvaluationId(v string) *GetEvaluationOutput
SetEvaluationId sets the EvaluationId field's value.
func (*GetEvaluationOutput) SetFinishedAt ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetFinishedAt(v time.Time) *GetEvaluationOutput
SetFinishedAt sets the FinishedAt field's value.
func (*GetEvaluationOutput) SetInputDataLocationS3 ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetInputDataLocationS3(v string) *GetEvaluationOutput
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*GetEvaluationOutput) SetLastUpdatedAt ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetLastUpdatedAt(v time.Time) *GetEvaluationOutput
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*GetEvaluationOutput) SetLogUri ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetLogUri(v string) *GetEvaluationOutput
SetLogUri sets the LogUri field's value.
func (*GetEvaluationOutput) SetMLModelId ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetMLModelId(v string) *GetEvaluationOutput
SetMLModelId sets the MLModelId field's value.
func (*GetEvaluationOutput) SetMessage ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetMessage(v string) *GetEvaluationOutput
SetMessage sets the Message field's value.
func (*GetEvaluationOutput) SetName ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetName(v string) *GetEvaluationOutput
SetName sets the Name field's value.
func (*GetEvaluationOutput) SetPerformanceMetrics ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetPerformanceMetrics(v *PerformanceMetrics) *GetEvaluationOutput
SetPerformanceMetrics sets the PerformanceMetrics field's value.
func (*GetEvaluationOutput) SetStartedAt ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetStartedAt(v time.Time) *GetEvaluationOutput
SetStartedAt sets the StartedAt field's value.
func (*GetEvaluationOutput) SetStatus ¶ added in v1.5.0
func (s *GetEvaluationOutput) SetStatus(v string) *GetEvaluationOutput
SetStatus sets the Status field's value.
func (GetEvaluationOutput) String ¶ added in v0.6.5
func (s GetEvaluationOutput) String() string
String returns the string representation
type GetMLModelInput ¶
type GetMLModelInput struct { // The ID assigned to the MLModel at creation. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // Specifies whether the GetMLModel operation should return Recipe. // // If true, Recipe is returned. // // If false, Recipe is not returned. Verbose *bool `type:"boolean"` // contains filtered or unexported fields }
func (GetMLModelInput) GoString ¶ added in v0.6.5
func (s GetMLModelInput) GoString() string
GoString returns the string representation
func (*GetMLModelInput) SetMLModelId ¶ added in v1.5.0
func (s *GetMLModelInput) SetMLModelId(v string) *GetMLModelInput
SetMLModelId sets the MLModelId field's value.
func (*GetMLModelInput) SetVerbose ¶ added in v1.5.0
func (s *GetMLModelInput) SetVerbose(v bool) *GetMLModelInput
SetVerbose sets the Verbose field's value.
func (GetMLModelInput) String ¶ added in v0.6.5
func (s GetMLModelInput) String() string
String returns the string representation
func (*GetMLModelInput) Validate ¶ added in v1.1.21
func (s *GetMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetMLModelOutput ¶
type GetMLModelOutput struct { // The approximate CPU time in milliseconds that Amazon Machine Learning spent // processing the MLModel, normalized and scaled on computation resources. ComputeTime // is only available if the MLModel is in the COMPLETED state. ComputeTime *int64 `type:"long"` // The time that the MLModel was created. The time is expressed in epoch time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the MLModel was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The current endpoint of the MLModel EndpointInfo *RealtimeEndpointInfo `type:"structure"` // The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED // or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED // or FAILED state. FinishedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the MLModel. The time is expressed in // epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateMLModel operation. LogUri *string `type:"string"` // The MLModel ID, which is same as the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // Identifies the MLModel category. The following are the available types: // // * REGRESSION -- Produces a numeric result. For example, "What price should // a house be listed at?" // * BINARY -- Produces one of two possible results. For example, "Is this // an e-commerce website?" // * MULTICLASS -- Produces one of several possible results. For example, // "Is this a HIGH, LOW or MEDIUM risk trade?" MLModelType *string `type:"string" enum:"MLModelType"` // A description of the most recent details about accessing the MLModel. Message *string `type:"string"` // A user-supplied name or description of the MLModel. Name *string `type:"string"` // The recipe to use when training the MLModel. The Recipe provides detailed // information about the observation data to use during training, and manipulations // to perform on the observation data during training. // // NoteThis parameter is provided as part of the verbose format. Recipe *string `type:"string"` // The schema used by all of the data files referenced by the DataSource. // // NoteThis parameter is provided as part of the verbose format. Schema *string `type:"string"` // The scoring threshold is used in binary classification MLModelmodels. It // marks the boundary between a positive prediction and a negative prediction. // // Output values greater than or equal to the threshold receive a positive result // from the MLModel, such as true. Output values less than the threshold receive // a negative response from the MLModel, such as false. ScoreThreshold *float64 `type:"float"` // The time of the most recent edit to the ScoreThreshold. The time is expressed // in epoch time. ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // Long integer type that is a 64-bit signed number. SizeInBytes *int64 `type:"long"` // The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. // StartedAt isn't available if the MLModel is in the PENDING state. StartedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The current status of the MLModel. This element can have one of the following // values: // // * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to // describe a MLModel. // * INPROGRESS - The request is processing. // * FAILED - The request did not run to completion. The ML model isn't usable. // // * COMPLETED - The request completed successfully. // * DELETED - The MLModel is marked as deleted. It isn't usable. Status *string `type:"string" enum:"EntityStatus"` // The ID of the training DataSource. TrainingDataSourceId *string `min:"1" type:"string"` // A list of the training parameters in the MLModel. The list is implemented // as a map of key-value pairs. // // The following is the current set of training parameters: // // * sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending // on the input data, the size of the model might affect its performance. // // The value is an integer that ranges from 100000 to 2147483648. The default // value is 33554432. // // * sgd.maxPasses - The number of times that the training process traverses // the observations to build the MLModel. The value is an integer that ranges // from 1 to 10000. The default value is 10. // // * sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling // data improves a model's ability to find the optimal solution for a variety // of data types. The valid values are auto and none. The default value is // none. We strongly recommend that you shuffle your data. // // * sgd.l1RegularizationAmount - The coefficient regularization L1 norm. // It controls overfitting the data by penalizing large coefficients. This // tends to drive coefficients to zero, resulting in a sparse feature set. // If you use this parameter, start by specifying a small value, such as // 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L1 normalization. This parameter can't be used when L2 is specified. // Use this parameter sparingly. // // * sgd.l2RegularizationAmount - The coefficient regularization L2 norm. // It controls overfitting the data by penalizing large coefficients. This // tends to drive coefficients to small, nonzero values. If you use this // parameter, start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L2 normalization. This parameter can't be used when L1 is specified. // Use this parameter sparingly. TrainingParameters map[string]*string `type:"map"` // contains filtered or unexported fields }
Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.
func (GetMLModelOutput) GoString ¶ added in v0.6.5
func (s GetMLModelOutput) GoString() string
GoString returns the string representation
func (*GetMLModelOutput) SetComputeTime ¶ added in v1.5.0
func (s *GetMLModelOutput) SetComputeTime(v int64) *GetMLModelOutput
SetComputeTime sets the ComputeTime field's value.
func (*GetMLModelOutput) SetCreatedAt ¶ added in v1.5.0
func (s *GetMLModelOutput) SetCreatedAt(v time.Time) *GetMLModelOutput
SetCreatedAt sets the CreatedAt field's value.
func (*GetMLModelOutput) SetCreatedByIamUser ¶ added in v1.5.0
func (s *GetMLModelOutput) SetCreatedByIamUser(v string) *GetMLModelOutput
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*GetMLModelOutput) SetEndpointInfo ¶ added in v1.5.0
func (s *GetMLModelOutput) SetEndpointInfo(v *RealtimeEndpointInfo) *GetMLModelOutput
SetEndpointInfo sets the EndpointInfo field's value.
func (*GetMLModelOutput) SetFinishedAt ¶ added in v1.5.0
func (s *GetMLModelOutput) SetFinishedAt(v time.Time) *GetMLModelOutput
SetFinishedAt sets the FinishedAt field's value.
func (*GetMLModelOutput) SetInputDataLocationS3 ¶ added in v1.5.0
func (s *GetMLModelOutput) SetInputDataLocationS3(v string) *GetMLModelOutput
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*GetMLModelOutput) SetLastUpdatedAt ¶ added in v1.5.0
func (s *GetMLModelOutput) SetLastUpdatedAt(v time.Time) *GetMLModelOutput
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*GetMLModelOutput) SetLogUri ¶ added in v1.5.0
func (s *GetMLModelOutput) SetLogUri(v string) *GetMLModelOutput
SetLogUri sets the LogUri field's value.
func (*GetMLModelOutput) SetMLModelId ¶ added in v1.5.0
func (s *GetMLModelOutput) SetMLModelId(v string) *GetMLModelOutput
SetMLModelId sets the MLModelId field's value.
func (*GetMLModelOutput) SetMLModelType ¶ added in v1.5.0
func (s *GetMLModelOutput) SetMLModelType(v string) *GetMLModelOutput
SetMLModelType sets the MLModelType field's value.
func (*GetMLModelOutput) SetMessage ¶ added in v1.5.0
func (s *GetMLModelOutput) SetMessage(v string) *GetMLModelOutput
SetMessage sets the Message field's value.
func (*GetMLModelOutput) SetName ¶ added in v1.5.0
func (s *GetMLModelOutput) SetName(v string) *GetMLModelOutput
SetName sets the Name field's value.
func (*GetMLModelOutput) SetRecipe ¶ added in v1.5.0
func (s *GetMLModelOutput) SetRecipe(v string) *GetMLModelOutput
SetRecipe sets the Recipe field's value.
func (*GetMLModelOutput) SetSchema ¶ added in v1.5.0
func (s *GetMLModelOutput) SetSchema(v string) *GetMLModelOutput
SetSchema sets the Schema field's value.
func (*GetMLModelOutput) SetScoreThreshold ¶ added in v1.5.0
func (s *GetMLModelOutput) SetScoreThreshold(v float64) *GetMLModelOutput
SetScoreThreshold sets the ScoreThreshold field's value.
func (*GetMLModelOutput) SetScoreThresholdLastUpdatedAt ¶ added in v1.5.0
func (s *GetMLModelOutput) SetScoreThresholdLastUpdatedAt(v time.Time) *GetMLModelOutput
SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.
func (*GetMLModelOutput) SetSizeInBytes ¶ added in v1.5.0
func (s *GetMLModelOutput) SetSizeInBytes(v int64) *GetMLModelOutput
SetSizeInBytes sets the SizeInBytes field's value.
func (*GetMLModelOutput) SetStartedAt ¶ added in v1.5.0
func (s *GetMLModelOutput) SetStartedAt(v time.Time) *GetMLModelOutput
SetStartedAt sets the StartedAt field's value.
func (*GetMLModelOutput) SetStatus ¶ added in v1.5.0
func (s *GetMLModelOutput) SetStatus(v string) *GetMLModelOutput
SetStatus sets the Status field's value.
func (*GetMLModelOutput) SetTrainingDataSourceId ¶ added in v1.5.0
func (s *GetMLModelOutput) SetTrainingDataSourceId(v string) *GetMLModelOutput
SetTrainingDataSourceId sets the TrainingDataSourceId field's value.
func (*GetMLModelOutput) SetTrainingParameters ¶ added in v1.5.0
func (s *GetMLModelOutput) SetTrainingParameters(v map[string]*string) *GetMLModelOutput
SetTrainingParameters sets the TrainingParameters field's value.
func (GetMLModelOutput) String ¶ added in v0.6.5
func (s GetMLModelOutput) String() string
String returns the string representation
type MLModel ¶
type MLModel struct { // The algorithm used to train the MLModel. The following algorithm is supported: // // * SGD -- Stochastic gradient descent. The goal of SGD is to minimize the // gradient of the loss function. Algorithm *string `type:"string" enum:"Algorithm"` // 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) SetAlgorithm ¶ added in v1.5.0
SetAlgorithm sets the Algorithm field's value.
func (*MLModel) SetComputeTime ¶ added in v1.5.0
SetComputeTime sets the ComputeTime field's value.
func (*MLModel) SetCreatedAt ¶ added in v1.5.0
SetCreatedAt sets the CreatedAt field's value.
func (*MLModel) SetCreatedByIamUser ¶ added in v1.5.0
SetCreatedByIamUser sets the CreatedByIamUser field's value.
func (*MLModel) SetEndpointInfo ¶ added in v1.5.0
func (s *MLModel) SetEndpointInfo(v *RealtimeEndpointInfo) *MLModel
SetEndpointInfo sets the EndpointInfo field's value.
func (*MLModel) SetFinishedAt ¶ added in v1.5.0
SetFinishedAt sets the FinishedAt field's value.
func (*MLModel) SetInputDataLocationS3 ¶ added in v1.5.0
SetInputDataLocationS3 sets the InputDataLocationS3 field's value.
func (*MLModel) SetLastUpdatedAt ¶ added in v1.5.0
SetLastUpdatedAt sets the LastUpdatedAt field's value.
func (*MLModel) SetMLModelId ¶ added in v1.5.0
SetMLModelId sets the MLModelId field's value.
func (*MLModel) SetMLModelType ¶ added in v1.5.0
SetMLModelType sets the MLModelType field's value.
func (*MLModel) SetMessage ¶ added in v1.5.0
SetMessage sets the Message field's value.
func (*MLModel) SetScoreThreshold ¶ added in v1.5.0
SetScoreThreshold sets the ScoreThreshold field's value.
func (*MLModel) SetScoreThresholdLastUpdatedAt ¶ added in v1.5.0
SetScoreThresholdLastUpdatedAt sets the ScoreThresholdLastUpdatedAt field's value.
func (*MLModel) SetSizeInBytes ¶ added in v1.5.0
SetSizeInBytes sets the SizeInBytes field's value.
func (*MLModel) SetStartedAt ¶ added in v1.5.0
SetStartedAt sets the StartedAt field's value.
func (*MLModel) SetTrainingDataSourceId ¶ added in v1.5.0
SetTrainingDataSourceId sets the TrainingDataSourceId field's value.
func (*MLModel) SetTrainingParameters ¶ added in v1.5.0
SetTrainingParameters sets the TrainingParameters field's value.
type MachineLearning ¶
MachineLearning provides the API operation methods for making requests to Amazon Machine Learning. See this package's package overview docs for details on the service.
MachineLearning methods are safe to use concurrently. It is not safe to modify mutate any of the struct's properties though.
func New ¶
func New(p client.ConfigProvider, cfgs ...*aws.Config) *MachineLearning
New creates a new instance of the MachineLearning client with a session. If additional configuration is needed for the client instance use the optional aws.Config parameter to add your extra config.
Example:
// Create a MachineLearning client from just a session. svc := machinelearning.New(mySession) // Create a MachineLearning client with additional configuration svc := machinelearning.New(mySession, aws.NewConfig().WithRegion("us-west-2"))
func (*MachineLearning) AddTags ¶ added in v1.1.33
func (c *MachineLearning) AddTags(input *AddTagsInput) (*AddTagsOutput, error)
AddTags API operation for Amazon Machine Learning.
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation AddTags for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInvalidTagException "InvalidTagException"
ErrCodeTagLimitExceededException "TagLimitExceededException"
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) AddTagsRequest ¶ added in v1.1.33
func (c *MachineLearning) AddTagsRequest(input *AddTagsInput) (req *request.Request, output *AddTagsOutput)
AddTagsRequest generates a "aws/request.Request" representing the client's request for the AddTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See AddTags for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the AddTags method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the AddTagsRequest method. req, resp := client.AddTagsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) AddTagsWithContext ¶ added in v1.8.0
func (c *MachineLearning) AddTagsWithContext(ctx aws.Context, input *AddTagsInput, opts ...request.Option) (*AddTagsOutput, error)
AddTagsWithContext is the same as AddTags with the addition of the ability to pass a context and additional request options.
See AddTags for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateBatchPrediction ¶
func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)
CreateBatchPrediction API operation for Amazon Machine Learning.
Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.
CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.
You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateBatchPrediction for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateBatchPredictionRequest ¶
func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput)
CreateBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the CreateBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateBatchPrediction for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateBatchPrediction method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateBatchPredictionRequest method. req, resp := client.CreateBatchPredictionRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateBatchPredictionWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateBatchPredictionWithContext(ctx aws.Context, input *CreateBatchPredictionInput, opts ...request.Option) (*CreateBatchPredictionOutput, error)
CreateBatchPredictionWithContext is the same as CreateBatchPrediction with the addition of the ability to pass a context and additional request options.
See CreateBatchPrediction for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateDataSourceFromRDS ¶
func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)
CreateDataSourceFromRDS API operation for Amazon Machine Learning.
Creates a DataSource object from an Amazon Relational Database Service (http://aws.amazon.com/rds/) (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromRDS for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateDataSourceFromRDSRequest ¶
func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput)
CreateDataSourceFromRDSRequest generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromRDS operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateDataSourceFromRDS for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromRDS method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateDataSourceFromRDSRequest method. req, resp := client.CreateDataSourceFromRDSRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateDataSourceFromRDSWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateDataSourceFromRDSWithContext(ctx aws.Context, input *CreateDataSourceFromRDSInput, opts ...request.Option) (*CreateDataSourceFromRDSOutput, error)
CreateDataSourceFromRDSWithContext is the same as CreateDataSourceFromRDS with the addition of the ability to pass a context and additional request options.
See CreateDataSourceFromRDS for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateDataSourceFromRedshift ¶
func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)
CreateDataSourceFromRedshift API operation for Amazon Machine Learning.
Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.
After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromRedshift for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateDataSourceFromRedshiftRequest ¶
func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput)
CreateDataSourceFromRedshiftRequest generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromRedshift operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateDataSourceFromRedshift for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromRedshift method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateDataSourceFromRedshiftRequest method. req, resp := client.CreateDataSourceFromRedshiftRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateDataSourceFromRedshiftWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateDataSourceFromRedshiftWithContext(ctx aws.Context, input *CreateDataSourceFromRedshiftInput, opts ...request.Option) (*CreateDataSourceFromRedshiftOutput, error)
CreateDataSourceFromRedshiftWithContext is the same as CreateDataSourceFromRedshift with the addition of the ability to pass a context and additional request options.
See CreateDataSourceFromRedshift for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateDataSourceFromS3 ¶
func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)
CreateDataSourceFromS3 API operation for Amazon Machine Learning.
Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.
After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateDataSourceFromS3 for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateDataSourceFromS3Request ¶
func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output)
CreateDataSourceFromS3Request generates a "aws/request.Request" representing the client's request for the CreateDataSourceFromS3 operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateDataSourceFromS3 for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateDataSourceFromS3 method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateDataSourceFromS3Request method. req, resp := client.CreateDataSourceFromS3Request(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateDataSourceFromS3WithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateDataSourceFromS3WithContext(ctx aws.Context, input *CreateDataSourceFromS3Input, opts ...request.Option) (*CreateDataSourceFromS3Output, error)
CreateDataSourceFromS3WithContext is the same as CreateDataSourceFromS3 with the addition of the ability to pass a context and additional request options.
See CreateDataSourceFromS3 for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateEvaluation ¶
func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)
CreateEvaluation API operation for Amazon Machine Learning.
Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.
CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.
You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateEvaluation for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateEvaluationRequest ¶
func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput)
CreateEvaluationRequest generates a "aws/request.Request" representing the client's request for the CreateEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateEvaluation for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateEvaluation method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateEvaluationRequest method. req, resp := client.CreateEvaluationRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateEvaluationWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateEvaluationWithContext(ctx aws.Context, input *CreateEvaluationInput, opts ...request.Option) (*CreateEvaluationOutput, error)
CreateEvaluationWithContext is the same as CreateEvaluation with the addition of the ability to pass a context and additional request options.
See CreateEvaluation for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateMLModel ¶
func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)
CreateMLModel API operation for Amazon Machine Learning.
Creates a new MLModel using the DataSource and the recipe as information sources.
An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.
CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateMLModel for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodeIdempotentParameterMismatchException "IdempotentParameterMismatchException" A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
func (*MachineLearning) CreateMLModelRequest ¶
func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput)
CreateMLModelRequest generates a "aws/request.Request" representing the client's request for the CreateMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateMLModel for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateMLModel method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateMLModelRequest method. req, resp := client.CreateMLModelRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateMLModelWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateMLModelWithContext(ctx aws.Context, input *CreateMLModelInput, opts ...request.Option) (*CreateMLModelOutput, error)
CreateMLModelWithContext is the same as CreateMLModel with the addition of the ability to pass a context and additional request options.
See CreateMLModel for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) CreateRealtimeEndpoint ¶
func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)
CreateRealtimeEndpoint API operation for Amazon Machine Learning.
Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation CreateRealtimeEndpoint for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) CreateRealtimeEndpointRequest ¶
func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)
CreateRealtimeEndpointRequest generates a "aws/request.Request" representing the client's request for the CreateRealtimeEndpoint operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See CreateRealtimeEndpoint for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the CreateRealtimeEndpoint method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the CreateRealtimeEndpointRequest method. req, resp := client.CreateRealtimeEndpointRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) CreateRealtimeEndpointWithContext ¶ added in v1.8.0
func (c *MachineLearning) CreateRealtimeEndpointWithContext(ctx aws.Context, input *CreateRealtimeEndpointInput, opts ...request.Option) (*CreateRealtimeEndpointOutput, error)
CreateRealtimeEndpointWithContext is the same as CreateRealtimeEndpoint with the addition of the ability to pass a context and additional request options.
See CreateRealtimeEndpoint for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DeleteBatchPrediction ¶
func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)
DeleteBatchPrediction API operation for Amazon Machine Learning.
Assigns the DELETED status to a BatchPrediction, rendering it unusable.
After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction operation is irreversible.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteBatchPrediction for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteBatchPredictionRequest ¶
func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)
DeleteBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the DeleteBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteBatchPrediction for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteBatchPrediction method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteBatchPredictionRequest method. req, resp := client.DeleteBatchPredictionRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteBatchPredictionWithContext ¶ added in v1.8.0
func (c *MachineLearning) DeleteBatchPredictionWithContext(ctx aws.Context, input *DeleteBatchPredictionInput, opts ...request.Option) (*DeleteBatchPredictionOutput, error)
DeleteBatchPredictionWithContext is the same as DeleteBatchPrediction with the addition of the ability to pass a context and additional request options.
See DeleteBatchPrediction for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DeleteDataSource ¶
func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)
DeleteDataSource API operation for Amazon Machine Learning.
Assigns the DELETED status to a DataSource, rendering it unusable.
After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.
Caution: The results of the DeleteDataSource operation are irreversible.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteDataSource for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteDataSourceRequest ¶
func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)
DeleteDataSourceRequest generates a "aws/request.Request" representing the client's request for the DeleteDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteDataSource for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteDataSource method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteDataSourceRequest method. req, resp := client.DeleteDataSourceRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteDataSourceWithContext ¶ added in v1.8.0
func (c *MachineLearning) DeleteDataSourceWithContext(ctx aws.Context, input *DeleteDataSourceInput, opts ...request.Option) (*DeleteDataSourceOutput, error)
DeleteDataSourceWithContext is the same as DeleteDataSource with the addition of the ability to pass a context and additional request options.
See DeleteDataSource for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DeleteEvaluation ¶
func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)
DeleteEvaluation API operation for Amazon Machine Learning.
Assigns the DELETED status to an Evaluation, rendering it unusable.
After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.
CautionThe results of the DeleteEvaluation operation are irreversible.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteEvaluation for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteEvaluationRequest ¶
func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)
DeleteEvaluationRequest generates a "aws/request.Request" representing the client's request for the DeleteEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteEvaluation for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteEvaluation method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteEvaluationRequest method. req, resp := client.DeleteEvaluationRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteEvaluationWithContext ¶ added in v1.8.0
func (c *MachineLearning) DeleteEvaluationWithContext(ctx aws.Context, input *DeleteEvaluationInput, opts ...request.Option) (*DeleteEvaluationOutput, error)
DeleteEvaluationWithContext is the same as DeleteEvaluation with the addition of the ability to pass a context and additional request options.
See DeleteEvaluation for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DeleteMLModel ¶
func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)
DeleteMLModel API operation for Amazon Machine Learning.
Assigns the DELETED status to an MLModel, rendering it unusable.
After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is irreversible.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteMLModel for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteMLModelRequest ¶
func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)
DeleteMLModelRequest generates a "aws/request.Request" representing the client's request for the DeleteMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteMLModel for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteMLModel method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteMLModelRequest method. req, resp := client.DeleteMLModelRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteMLModelWithContext ¶ added in v1.8.0
func (c *MachineLearning) DeleteMLModelWithContext(ctx aws.Context, input *DeleteMLModelInput, opts ...request.Option) (*DeleteMLModelOutput, error)
DeleteMLModelWithContext is the same as DeleteMLModel with the addition of the ability to pass a context and additional request options.
See DeleteMLModel for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DeleteRealtimeEndpoint ¶
func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)
DeleteRealtimeEndpoint API operation for Amazon Machine Learning.
Deletes a real time endpoint of an MLModel.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteRealtimeEndpoint for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteRealtimeEndpointRequest ¶
func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)
DeleteRealtimeEndpointRequest generates a "aws/request.Request" representing the client's request for the DeleteRealtimeEndpoint operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteRealtimeEndpoint for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteRealtimeEndpoint method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteRealtimeEndpointRequest method. req, resp := client.DeleteRealtimeEndpointRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteRealtimeEndpointWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.33
func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)
DeleteTags API operation for Amazon Machine Learning.
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DeleteTags for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeInvalidTagException "InvalidTagException"
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DeleteTagsRequest ¶ added in v1.1.33
func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)
DeleteTagsRequest generates a "aws/request.Request" representing the client's request for the DeleteTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DeleteTags for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DeleteTags method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DeleteTagsRequest method. req, resp := client.DeleteTagsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DeleteTagsWithContext ¶ added in v1.8.0
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 ¶ added in v1.8.0
func (c *MachineLearning) DescribeBatchPredictionsPagesWithContext(ctx aws.Context, input *DescribeBatchPredictionsInput, fn func(*DescribeBatchPredictionsOutput, bool) bool, opts ...request.Option) error
DescribeBatchPredictionsPagesWithContext same as DescribeBatchPredictionsPages except it takes a Context and allows setting request options on the pages.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DescribeBatchPredictionsRequest ¶
func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)
DescribeBatchPredictionsRequest generates a "aws/request.Request" representing the client's request for the DescribeBatchPredictions operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DescribeBatchPredictions for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeBatchPredictions method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DescribeBatchPredictionsRequest method. req, resp := client.DescribeBatchPredictionsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DescribeBatchPredictionsWithContext ¶ added in v1.8.0
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 ¶ added in v1.8.0
func (c *MachineLearning) DescribeDataSourcesPagesWithContext(ctx aws.Context, input *DescribeDataSourcesInput, fn func(*DescribeDataSourcesOutput, bool) bool, opts ...request.Option) error
DescribeDataSourcesPagesWithContext same as DescribeDataSourcesPages except it takes a Context and allows setting request options on the pages.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DescribeDataSourcesRequest ¶
func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)
DescribeDataSourcesRequest generates a "aws/request.Request" representing the client's request for the DescribeDataSources operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DescribeDataSources for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeDataSources method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DescribeDataSourcesRequest method. req, resp := client.DescribeDataSourcesRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DescribeDataSourcesWithContext ¶ added in v1.8.0
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 ¶ added in v1.8.0
func (c *MachineLearning) DescribeEvaluationsPagesWithContext(ctx aws.Context, input *DescribeEvaluationsInput, fn func(*DescribeEvaluationsOutput, bool) bool, opts ...request.Option) error
DescribeEvaluationsPagesWithContext same as DescribeEvaluationsPages except it takes a Context and allows setting request options on the pages.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DescribeEvaluationsRequest ¶
func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)
DescribeEvaluationsRequest generates a "aws/request.Request" representing the client's request for the DescribeEvaluations operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DescribeEvaluations for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeEvaluations method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DescribeEvaluationsRequest method. req, resp := client.DescribeEvaluationsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DescribeEvaluationsWithContext ¶ added in v1.8.0
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 ¶ added in v1.8.0
func (c *MachineLearning) DescribeMLModelsPagesWithContext(ctx aws.Context, input *DescribeMLModelsInput, fn func(*DescribeMLModelsOutput, bool) bool, opts ...request.Option) error
DescribeMLModelsPagesWithContext same as DescribeMLModelsPages except it takes a Context and allows setting request options on the pages.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) DescribeMLModelsRequest ¶
func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)
DescribeMLModelsRequest generates a "aws/request.Request" representing the client's request for the DescribeMLModels operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DescribeMLModels for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeMLModels method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DescribeMLModelsRequest method. req, resp := client.DescribeMLModelsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DescribeMLModelsWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.33
func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)
DescribeTags API operation for Amazon Machine Learning.
Describes one or more of the tags for your Amazon ML object.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation DescribeTags for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) DescribeTagsRequest ¶ added in v1.1.33
func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)
DescribeTagsRequest generates a "aws/request.Request" representing the client's request for the DescribeTags operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See DescribeTags for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the DescribeTags method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the DescribeTagsRequest method. req, resp := client.DescribeTagsRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) DescribeTagsWithContext ¶ added in v1.8.0
func (c *MachineLearning) DescribeTagsWithContext(ctx aws.Context, input *DescribeTagsInput, opts ...request.Option) (*DescribeTagsOutput, error)
DescribeTagsWithContext is the same as DescribeTags with the addition of the ability to pass a context and additional request options.
See DescribeTags for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) GetBatchPrediction ¶
func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)
GetBatchPrediction API operation for Amazon Machine Learning.
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation GetBatchPrediction for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) GetBatchPredictionRequest ¶
func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)
GetBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the GetBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See GetBatchPrediction for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetBatchPrediction method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the GetBatchPredictionRequest method. req, resp := client.GetBatchPredictionRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) GetBatchPredictionWithContext ¶ added in v1.8.0
func (c *MachineLearning) GetBatchPredictionWithContext(ctx aws.Context, input *GetBatchPredictionInput, opts ...request.Option) (*GetBatchPredictionOutput, error)
GetBatchPredictionWithContext is the same as GetBatchPrediction with the addition of the ability to pass a context and additional request options.
See GetBatchPrediction for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) GetDataSource ¶
func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)
GetDataSource API operation for Amazon Machine Learning.
Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation GetDataSource for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) GetDataSourceRequest ¶
func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)
GetDataSourceRequest generates a "aws/request.Request" representing the client's request for the GetDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See GetDataSource for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetDataSource method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the GetDataSourceRequest method. req, resp := client.GetDataSourceRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) GetDataSourceWithContext ¶ added in v1.8.0
func (c *MachineLearning) GetDataSourceWithContext(ctx aws.Context, input *GetDataSourceInput, opts ...request.Option) (*GetDataSourceOutput, error)
GetDataSourceWithContext is the same as GetDataSource with the addition of the ability to pass a context and additional request options.
See GetDataSource for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) GetEvaluation ¶
func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)
GetEvaluation API operation for Amazon Machine Learning.
Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation GetEvaluation for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) GetEvaluationRequest ¶
func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)
GetEvaluationRequest generates a "aws/request.Request" representing the client's request for the GetEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See GetEvaluation for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetEvaluation method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the GetEvaluationRequest method. req, resp := client.GetEvaluationRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) GetEvaluationWithContext ¶ added in v1.8.0
func (c *MachineLearning) GetEvaluationWithContext(ctx aws.Context, input *GetEvaluationInput, opts ...request.Option) (*GetEvaluationOutput, error)
GetEvaluationWithContext is the same as GetEvaluation with the addition of the ability to pass a context and additional request options.
See GetEvaluation for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) GetMLModel ¶
func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)
GetMLModel API operation for Amazon Machine Learning.
Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation GetMLModel for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) GetMLModelRequest ¶
func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)
GetMLModelRequest generates a "aws/request.Request" representing the client's request for the GetMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See GetMLModel for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the GetMLModel method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the GetMLModelRequest method. req, resp := client.GetMLModelRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) GetMLModelWithContext ¶ added in v1.8.0
func (c *MachineLearning) GetMLModelWithContext(ctx aws.Context, input *GetMLModelInput, opts ...request.Option) (*GetMLModelOutput, error)
GetMLModelWithContext is the same as GetMLModel with the addition of the ability to pass a context and additional request options.
See GetMLModel for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) Predict ¶
func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)
Predict API operation for Amazon Machine Learning.
Generates a prediction for the observation using the specified ML Model.
NoteNot all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation Predict for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeLimitExceededException "LimitExceededException" The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
ErrCodePredictorNotMountedException "PredictorNotMountedException" The exception is thrown when a predict request is made to an unmounted MLModel.
func (*MachineLearning) PredictRequest ¶
func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)
PredictRequest generates a "aws/request.Request" representing the client's request for the Predict operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See Predict for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the Predict method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the PredictRequest method. req, resp := client.PredictRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) PredictWithContext ¶ added in v1.8.0
func (c *MachineLearning) PredictWithContext(ctx aws.Context, input *PredictInput, opts ...request.Option) (*PredictOutput, error)
PredictWithContext is the same as Predict with the addition of the ability to pass a context and additional request options.
See Predict for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) UpdateBatchPrediction ¶
func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)
UpdateBatchPrediction API operation for Amazon Machine Learning.
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation UpdateBatchPrediction for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) UpdateBatchPredictionRequest ¶
func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)
UpdateBatchPredictionRequest generates a "aws/request.Request" representing the client's request for the UpdateBatchPrediction operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See UpdateBatchPrediction for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateBatchPrediction method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the UpdateBatchPredictionRequest method. req, resp := client.UpdateBatchPredictionRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) UpdateBatchPredictionWithContext ¶ added in v1.8.0
func (c *MachineLearning) UpdateBatchPredictionWithContext(ctx aws.Context, input *UpdateBatchPredictionInput, opts ...request.Option) (*UpdateBatchPredictionOutput, error)
UpdateBatchPredictionWithContext is the same as UpdateBatchPrediction with the addition of the ability to pass a context and additional request options.
See UpdateBatchPrediction for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) UpdateDataSource ¶
func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)
UpdateDataSource API operation for Amazon Machine Learning.
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation UpdateDataSource for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) UpdateDataSourceRequest ¶
func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)
UpdateDataSourceRequest generates a "aws/request.Request" representing the client's request for the UpdateDataSource operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See UpdateDataSource for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateDataSource method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the UpdateDataSourceRequest method. req, resp := client.UpdateDataSourceRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) UpdateDataSourceWithContext ¶ added in v1.8.0
func (c *MachineLearning) UpdateDataSourceWithContext(ctx aws.Context, input *UpdateDataSourceInput, opts ...request.Option) (*UpdateDataSourceOutput, error)
UpdateDataSourceWithContext is the same as UpdateDataSource with the addition of the ability to pass a context and additional request options.
See UpdateDataSource for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) UpdateEvaluation ¶
func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)
UpdateEvaluation API operation for Amazon Machine Learning.
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation UpdateEvaluation for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) UpdateEvaluationRequest ¶
func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)
UpdateEvaluationRequest generates a "aws/request.Request" representing the client's request for the UpdateEvaluation operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See UpdateEvaluation for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateEvaluation method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the UpdateEvaluationRequest method. req, resp := client.UpdateEvaluationRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) UpdateEvaluationWithContext ¶ added in v1.8.0
func (c *MachineLearning) UpdateEvaluationWithContext(ctx aws.Context, input *UpdateEvaluationInput, opts ...request.Option) (*UpdateEvaluationOutput, error)
UpdateEvaluationWithContext is the same as UpdateEvaluation with the addition of the ability to pass a context and additional request options.
See UpdateEvaluation for details on how to use this API operation.
The context must be non-nil and will be used for request cancellation. If the context is nil a panic will occur. In the future the SDK may create sub-contexts for http.Requests. See https://golang.org/pkg/context/ for more information on using Contexts.
func (*MachineLearning) UpdateMLModel ¶
func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)
UpdateMLModel API operation for Amazon Machine Learning.
Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data element.
Returns awserr.Error for service API and SDK errors. Use runtime type assertions with awserr.Error's Code and Message methods to get detailed information about the error.
See the AWS API reference guide for Amazon Machine Learning's API operation UpdateMLModel for usage and error information.
Returned Error Codes:
ErrCodeInvalidInputException "InvalidInputException" An error on the client occurred. Typically, the cause is an invalid input value.
ErrCodeResourceNotFoundException "ResourceNotFoundException" A specified resource cannot be located.
ErrCodeInternalServerException "InternalServerException" An error on the server occurred when trying to process a request.
func (*MachineLearning) UpdateMLModelRequest ¶
func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)
UpdateMLModelRequest generates a "aws/request.Request" representing the client's request for the UpdateMLModel operation. The "output" return value can be used to capture response data after the request's "Send" method is called.
See UpdateMLModel for usage and error information.
Creating a request object using this method should be used when you want to inject custom logic into the request's lifecycle using a custom handler, or if you want to access properties on the request object before or after sending the request. If you just want the service response, call the UpdateMLModel method directly instead.
Note: You must call the "Send" method on the returned request object in order to execute the request.
// Example sending a request using the UpdateMLModelRequest method. req, resp := client.UpdateMLModelRequest(params) err := req.Send() if err == nil { // resp is now filled fmt.Println(resp) }
func (*MachineLearning) UpdateMLModelWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.15
func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error
WaitUntilBatchPredictionAvailable uses the Amazon Machine Learning API operation DescribeBatchPredictions to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.
func (*MachineLearning) WaitUntilBatchPredictionAvailableWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.15
func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error
WaitUntilDataSourceAvailable uses the Amazon Machine Learning API operation DescribeDataSources to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.
func (*MachineLearning) WaitUntilDataSourceAvailableWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.15
func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error
WaitUntilEvaluationAvailable uses the Amazon Machine Learning API operation DescribeEvaluations to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.
func (*MachineLearning) WaitUntilEvaluationAvailableWithContext ¶ added in v1.8.0
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 ¶ added in v1.1.15
func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error
WaitUntilMLModelAvailable uses the Amazon Machine Learning API operation DescribeMLModels to wait for a condition to be met before returning. If the condition is not meet within the max attempt window an error will be returned.
func (*MachineLearning) WaitUntilMLModelAvailableWithContext ¶ added in v1.8.0
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 ¶ added in v0.6.5
func (s PerformanceMetrics) GoString() string
GoString returns the string representation
func (*PerformanceMetrics) SetProperties ¶ added in v1.5.0
func (s *PerformanceMetrics) SetProperties(v map[string]*string) *PerformanceMetrics
SetProperties sets the Properties field's value.
func (PerformanceMetrics) String ¶ added in v0.6.5
func (s PerformanceMetrics) String() string
String returns the string representation
type PredictInput ¶
type PredictInput struct { // A unique identifier of the MLModel. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // PredictEndpoint is a required field PredictEndpoint *string `type:"string" required:"true"` // A map of variable name-value pairs that represent an observation. // // Record is a required field Record map[string]*string `type:"map" required:"true"` // contains filtered or unexported fields }
func (PredictInput) GoString ¶ added in v0.6.5
func (s PredictInput) GoString() string
GoString returns the string representation
func (*PredictInput) SetMLModelId ¶ added in v1.5.0
func (s *PredictInput) SetMLModelId(v string) *PredictInput
SetMLModelId sets the MLModelId field's value.
func (*PredictInput) SetPredictEndpoint ¶ added in v1.5.0
func (s *PredictInput) SetPredictEndpoint(v string) *PredictInput
SetPredictEndpoint sets the PredictEndpoint field's value.
func (*PredictInput) SetRecord ¶ added in v1.5.0
func (s *PredictInput) SetRecord(v map[string]*string) *PredictInput
SetRecord sets the Record field's value.
func (PredictInput) String ¶ added in v0.6.5
func (s PredictInput) String() string
String returns the string representation
func (*PredictInput) Validate ¶ added in v1.1.21
func (s *PredictInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type PredictOutput ¶
type PredictOutput struct { // The output from a Predict operation: // // * Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE // - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD // // * PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request. // // // * PredictedScores - Contains the raw classification score corresponding // to each label. // // * PredictedValue - Present for a REGRESSIONMLModel request. Prediction *Prediction `type:"structure"` // contains filtered or unexported fields }
func (PredictOutput) GoString ¶ added in v0.6.5
func (s PredictOutput) GoString() string
GoString returns the string representation
func (*PredictOutput) SetPrediction ¶ added in v1.5.0
func (s *PredictOutput) SetPrediction(v *Prediction) *PredictOutput
SetPrediction sets the Prediction field's value.
func (PredictOutput) String ¶ added in v0.6.5
func (s PredictOutput) String() string
String returns the string representation
type Prediction ¶
type Prediction struct { // Provides any additional details regarding the prediction. Details map[string]*string `locationName:"details" type:"map"` // The prediction label for either a BINARY or MULTICLASSMLModel. PredictedLabel *string `locationName:"predictedLabel" min:"1" type:"string"` // Provides the raw classification score corresponding to each label. PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"` // The prediction value for REGRESSIONMLModel PredictedValue *float64 `locationName:"predictedValue" type:"float"` // contains filtered or unexported fields }
The output from a Predict operation:
Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGD
PredictedLabel - Present for either a BINARY or MULTICLASSMLModel request.
PredictedScores - Contains the raw classification score corresponding to each label.
PredictedValue - Present for a REGRESSIONMLModel request.
func (Prediction) GoString ¶ added in v0.6.5
func (s Prediction) GoString() string
GoString returns the string representation
func (*Prediction) SetDetails ¶ added in v1.5.0
func (s *Prediction) SetDetails(v map[string]*string) *Prediction
SetDetails sets the Details field's value.
func (*Prediction) SetPredictedLabel ¶ added in v1.5.0
func (s *Prediction) SetPredictedLabel(v string) *Prediction
SetPredictedLabel sets the PredictedLabel field's value.
func (*Prediction) SetPredictedScores ¶ added in v1.5.0
func (s *Prediction) SetPredictedScores(v map[string]*float64) *Prediction
SetPredictedScores sets the PredictedScores field's value.
func (*Prediction) SetPredictedValue ¶ added in v1.5.0
func (s *Prediction) SetPredictedValue(v float64) *Prediction
SetPredictedValue sets the PredictedValue field's value.
func (Prediction) String ¶ added in v0.6.5
func (s Prediction) String() string
String returns the string representation
type RDSDataSpec ¶
type RDSDataSpec struct { // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // * percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // * percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // * complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary // datasources for training and evaluation. To create a complementary datasource, // use the same values for percentBegin and percentEnd, along with the complement // parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // * strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in // the input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting // (for example, you can use the S3 path to your data as the random seed // string). If you choose the random split strategy, Amazon ML assigns each // row of data a pseudo-random number between 0 and 100, and then selects // the rows that have an assigned number between percentBegin and percentEnd. // Pseudo-random numbers are assigned using both the input seed string value // and the byte offset as a seed, so changing the data results in a different // split. Any existing ordering is preserved. The random splitting strategy // ensures that variables in the training and evaluation data are distributed // similarly. It is useful in the cases where the input data may have an // implicit sort order, which would otherwise result in training and evaluation // datasources containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon RDS DataSource. The // DataSchema defines the structure of the observation data in the data file(s) // referenced in the DataSource. // // A DataSchema is not required if you specify a DataSchemaUri // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // The Amazon S3 location of the DataSchema. DataSchemaUri *string `type:"string"` // The AWS Identity and Access Management (IAM) credentials that are used connect // to the Amazon RDS database. // // DatabaseCredentials is a required field DatabaseCredentials *RDSDatabaseCredentials `type:"structure" required:"true"` // Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database. // // DatabaseInformation is a required field DatabaseInformation *RDSDatabase `type:"structure" required:"true"` // The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute // Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS // to an Amazon S3 task. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. // // ResourceRole is a required field ResourceRole *string `min:"1" type:"string" required:"true"` // The Amazon S3 location for staging Amazon RDS data. The data retrieved from // Amazon RDS using SelectSqlQuery is stored in this location. // // S3StagingLocation is a required field S3StagingLocation *string `type:"string" required:"true"` // The security group IDs to be used to access a VPC-based RDS DB instance. // Ensure that there are appropriate ingress rules set up to allow access to // the RDS DB instance. This attribute is used by Data Pipeline to carry out // the copy operation from Amazon RDS to an Amazon S3 task. // // SecurityGroupIds is a required field SecurityGroupIds []*string `type:"list" required:"true"` // The query that is used to retrieve the observation data for the DataSource. // // SelectSqlQuery is a required field SelectSqlQuery *string `min:"1" type:"string" required:"true"` // The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to // monitor the progress of the copy task from Amazon RDS to Amazon S3. For more // information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. // // ServiceRole is a required field ServiceRole *string `min:"1" type:"string" required:"true"` // The subnet ID to be used to access a VPC-based RDS DB instance. This attribute // is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon // S3. // // SubnetId is a required field SubnetId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
func (RDSDataSpec) GoString ¶ added in v0.6.5
func (s RDSDataSpec) GoString() string
GoString returns the string representation
func (*RDSDataSpec) SetDataRearrangement ¶ added in v1.5.0
func (s *RDSDataSpec) SetDataRearrangement(v string) *RDSDataSpec
SetDataRearrangement sets the DataRearrangement field's value.
func (*RDSDataSpec) SetDataSchema ¶ added in v1.5.0
func (s *RDSDataSpec) SetDataSchema(v string) *RDSDataSpec
SetDataSchema sets the DataSchema field's value.
func (*RDSDataSpec) SetDataSchemaUri ¶ added in v1.5.0
func (s *RDSDataSpec) SetDataSchemaUri(v string) *RDSDataSpec
SetDataSchemaUri sets the DataSchemaUri field's value.
func (*RDSDataSpec) SetDatabaseCredentials ¶ added in v1.5.0
func (s *RDSDataSpec) SetDatabaseCredentials(v *RDSDatabaseCredentials) *RDSDataSpec
SetDatabaseCredentials sets the DatabaseCredentials field's value.
func (*RDSDataSpec) SetDatabaseInformation ¶ added in v1.5.0
func (s *RDSDataSpec) SetDatabaseInformation(v *RDSDatabase) *RDSDataSpec
SetDatabaseInformation sets the DatabaseInformation field's value.
func (*RDSDataSpec) SetResourceRole ¶ added in v1.5.0
func (s *RDSDataSpec) SetResourceRole(v string) *RDSDataSpec
SetResourceRole sets the ResourceRole field's value.
func (*RDSDataSpec) SetS3StagingLocation ¶ added in v1.5.0
func (s *RDSDataSpec) SetS3StagingLocation(v string) *RDSDataSpec
SetS3StagingLocation sets the S3StagingLocation field's value.
func (*RDSDataSpec) SetSecurityGroupIds ¶ added in v1.5.0
func (s *RDSDataSpec) SetSecurityGroupIds(v []*string) *RDSDataSpec
SetSecurityGroupIds sets the SecurityGroupIds field's value.
func (*RDSDataSpec) SetSelectSqlQuery ¶ added in v1.5.0
func (s *RDSDataSpec) SetSelectSqlQuery(v string) *RDSDataSpec
SetSelectSqlQuery sets the SelectSqlQuery field's value.
func (*RDSDataSpec) SetServiceRole ¶ added in v1.5.0
func (s *RDSDataSpec) SetServiceRole(v string) *RDSDataSpec
SetServiceRole sets the ServiceRole field's value.
func (*RDSDataSpec) SetSubnetId ¶ added in v1.5.0
func (s *RDSDataSpec) SetSubnetId(v string) *RDSDataSpec
SetSubnetId sets the SubnetId field's value.
func (RDSDataSpec) String ¶ added in v0.6.5
func (s RDSDataSpec) String() string
String returns the string representation
func (*RDSDataSpec) Validate ¶ added in v1.1.21
func (s *RDSDataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSDatabase ¶
type RDSDatabase struct { // The name of a database hosted on an RDS DB instance. // // DatabaseName is a required field DatabaseName *string `min:"1" type:"string" required:"true"` // The ID of an RDS DB instance. // // InstanceIdentifier is a required field InstanceIdentifier *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The database details of an Amazon RDS database.
func (RDSDatabase) GoString ¶ added in v0.6.5
func (s RDSDatabase) GoString() string
GoString returns the string representation
func (*RDSDatabase) SetDatabaseName ¶ added in v1.5.0
func (s *RDSDatabase) SetDatabaseName(v string) *RDSDatabase
SetDatabaseName sets the DatabaseName field's value.
func (*RDSDatabase) SetInstanceIdentifier ¶ added in v1.5.0
func (s *RDSDatabase) SetInstanceIdentifier(v string) *RDSDatabase
SetInstanceIdentifier sets the InstanceIdentifier field's value.
func (RDSDatabase) String ¶ added in v0.6.5
func (s RDSDatabase) String() string
String returns the string representation
func (*RDSDatabase) Validate ¶ added in v1.1.21
func (s *RDSDatabase) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSDatabaseCredentials ¶
type RDSDatabaseCredentials struct { // The password to be used by Amazon ML to connect to a database on an RDS DB // instance. The password should have sufficient permissions to execute the // RDSSelectQuery query. // // Password is a required field Password *string `min:"8" type:"string" required:"true"` // The username to be used by Amazon ML to connect to database on an Amazon // RDS instance. The username should have sufficient permissions to execute // an RDSSelectSqlQuery query. // // Username is a required field Username *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The database credentials to connect to a database on an RDS DB instance.
func (RDSDatabaseCredentials) GoString ¶ added in v0.6.5
func (s RDSDatabaseCredentials) GoString() string
GoString returns the string representation
func (*RDSDatabaseCredentials) SetPassword ¶ added in v1.5.0
func (s *RDSDatabaseCredentials) SetPassword(v string) *RDSDatabaseCredentials
SetPassword sets the Password field's value.
func (*RDSDatabaseCredentials) SetUsername ¶ added in v1.5.0
func (s *RDSDatabaseCredentials) SetUsername(v string) *RDSDatabaseCredentials
SetUsername sets the Username field's value.
func (RDSDatabaseCredentials) String ¶ added in v0.6.5
func (s RDSDatabaseCredentials) String() string
String returns the string representation
func (*RDSDatabaseCredentials) Validate ¶ added in v1.1.21
func (s *RDSDatabaseCredentials) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSMetadata ¶
type RDSMetadata struct { // The ID of the Data Pipeline instance that is used to carry to copy data from // Amazon RDS to Amazon S3. You can use the ID to find details about the instance // in the Data Pipeline console. DataPipelineId *string `min:"1" type:"string"` // The database details required to connect to an Amazon RDS. Database *RDSDatabase `type:"structure"` // The username to be used by Amazon ML to connect to database on an Amazon // RDS instance. The username should have sufficient permissions to execute // an RDSSelectSqlQuery query. DatabaseUserName *string `min:"1" type:"string"` // The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance // to carry out the copy task from Amazon RDS to Amazon S3. For more information, // see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ResourceRole *string `min:"1" type:"string"` // The SQL query that is supplied during CreateDataSourceFromRDS. Returns only // if Verbose is true in GetDataSourceInput. SelectSqlQuery *string `min:"1" type:"string"` // The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to // monitor the progress of the copy task from Amazon RDS to Amazon S3. For more // information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ServiceRole *string `min:"1" type:"string"` // contains filtered or unexported fields }
The datasource details that are specific to Amazon RDS.
func (RDSMetadata) GoString ¶ added in v0.6.5
func (s RDSMetadata) GoString() string
GoString returns the string representation
func (*RDSMetadata) SetDataPipelineId ¶ added in v1.5.0
func (s *RDSMetadata) SetDataPipelineId(v string) *RDSMetadata
SetDataPipelineId sets the DataPipelineId field's value.
func (*RDSMetadata) SetDatabase ¶ added in v1.5.0
func (s *RDSMetadata) SetDatabase(v *RDSDatabase) *RDSMetadata
SetDatabase sets the Database field's value.
func (*RDSMetadata) SetDatabaseUserName ¶ added in v1.5.0
func (s *RDSMetadata) SetDatabaseUserName(v string) *RDSMetadata
SetDatabaseUserName sets the DatabaseUserName field's value.
func (*RDSMetadata) SetResourceRole ¶ added in v1.5.0
func (s *RDSMetadata) SetResourceRole(v string) *RDSMetadata
SetResourceRole sets the ResourceRole field's value.
func (*RDSMetadata) SetSelectSqlQuery ¶ added in v1.5.0
func (s *RDSMetadata) SetSelectSqlQuery(v string) *RDSMetadata
SetSelectSqlQuery sets the SelectSqlQuery field's value.
func (*RDSMetadata) SetServiceRole ¶ added in v1.5.0
func (s *RDSMetadata) SetServiceRole(v string) *RDSMetadata
SetServiceRole sets the ServiceRole field's value.
func (RDSMetadata) String ¶ added in v0.6.5
func (s RDSMetadata) String() string
String returns the string representation
type RealtimeEndpointInfo ¶
type RealtimeEndpointInfo struct { // The time that the request to create the real-time endpoint for the MLModel // was received. The time is expressed in epoch time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The current status of the real-time endpoint for the MLModel. This element // can have one of the following values: // // * NONE - Endpoint does not exist or was previously deleted. // * READY - Endpoint is ready to be used for real-time predictions. // * UPDATING - Updating/creating the endpoint. EndpointStatus *string `type:"string" enum:"RealtimeEndpointStatus"` // The URI that specifies where to send real-time prediction requests for the // MLModel. // // NoteThe application must wait until the real-time endpoint is ready before // using this URI. EndpointUrl *string `type:"string"` // The maximum processing rate for the real-time endpoint for MLModel, measured // in incoming requests per second. PeakRequestsPerSecond *int64 `type:"integer"` // contains filtered or unexported fields }
Describes the real-time endpoint information for an MLModel.
func (RealtimeEndpointInfo) GoString ¶ added in v0.6.5
func (s RealtimeEndpointInfo) GoString() string
GoString returns the string representation
func (*RealtimeEndpointInfo) SetCreatedAt ¶ added in v1.5.0
func (s *RealtimeEndpointInfo) SetCreatedAt(v time.Time) *RealtimeEndpointInfo
SetCreatedAt sets the CreatedAt field's value.
func (*RealtimeEndpointInfo) SetEndpointStatus ¶ added in v1.5.0
func (s *RealtimeEndpointInfo) SetEndpointStatus(v string) *RealtimeEndpointInfo
SetEndpointStatus sets the EndpointStatus field's value.
func (*RealtimeEndpointInfo) SetEndpointUrl ¶ added in v1.5.0
func (s *RealtimeEndpointInfo) SetEndpointUrl(v string) *RealtimeEndpointInfo
SetEndpointUrl sets the EndpointUrl field's value.
func (*RealtimeEndpointInfo) SetPeakRequestsPerSecond ¶ added in v1.5.0
func (s *RealtimeEndpointInfo) SetPeakRequestsPerSecond(v int64) *RealtimeEndpointInfo
SetPeakRequestsPerSecond sets the PeakRequestsPerSecond field's value.
func (RealtimeEndpointInfo) String ¶ added in v0.6.5
func (s RealtimeEndpointInfo) String() string
String returns the string representation
type RedshiftDataSpec ¶
type RedshiftDataSpec struct { // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // * percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // * percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // * complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary // datasources for training and evaluation. To create a complementary datasource, // use the same values for percentBegin and percentEnd, along with the complement // parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // * strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in // the input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting // (for example, you can use the S3 path to your data as the random seed // string). If you choose the random split strategy, Amazon ML assigns each // row of data a pseudo-random number between 0 and 100, and then selects // the rows that have an assigned number between percentBegin and percentEnd. // Pseudo-random numbers are assigned using both the input seed string value // and the byte offset as a seed, so changing the data results in a different // split. Any existing ordering is preserved. The random splitting strategy // ensures that variables in the training and evaluation data are distributed // similarly. It is useful in the cases where the input data may have an // implicit sort order, which would otherwise result in training and evaluation // datasources containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon Redshift DataSource. // The DataSchema defines the structure of the observation data in the data // file(s) referenced in the DataSource. // // A DataSchema is not required if you specify a DataSchemaUri. // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // Describes the schema location for an Amazon Redshift DataSource. DataSchemaUri *string `type:"string"` // Describes AWS Identity and Access Management (IAM) credentials that are used // connect to the Amazon Redshift database. // // DatabaseCredentials is a required field DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"` // Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource. // // DatabaseInformation is a required field DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"` // Describes an Amazon S3 location to store the result set of the SelectSqlQuery // query. // // S3StagingLocation is a required field S3StagingLocation *string `type:"string" required:"true"` // Describes the SQL Query to execute on an Amazon Redshift database for an // Amazon Redshift DataSource. // // SelectSqlQuery is a required field SelectSqlQuery *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the data specification of an Amazon Redshift DataSource.
func (RedshiftDataSpec) GoString ¶ added in v0.6.5
func (s RedshiftDataSpec) GoString() string
GoString returns the string representation
func (*RedshiftDataSpec) SetDataRearrangement ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetDataRearrangement(v string) *RedshiftDataSpec
SetDataRearrangement sets the DataRearrangement field's value.
func (*RedshiftDataSpec) SetDataSchema ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetDataSchema(v string) *RedshiftDataSpec
SetDataSchema sets the DataSchema field's value.
func (*RedshiftDataSpec) SetDataSchemaUri ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetDataSchemaUri(v string) *RedshiftDataSpec
SetDataSchemaUri sets the DataSchemaUri field's value.
func (*RedshiftDataSpec) SetDatabaseCredentials ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetDatabaseCredentials(v *RedshiftDatabaseCredentials) *RedshiftDataSpec
SetDatabaseCredentials sets the DatabaseCredentials field's value.
func (*RedshiftDataSpec) SetDatabaseInformation ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetDatabaseInformation(v *RedshiftDatabase) *RedshiftDataSpec
SetDatabaseInformation sets the DatabaseInformation field's value.
func (*RedshiftDataSpec) SetS3StagingLocation ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetS3StagingLocation(v string) *RedshiftDataSpec
SetS3StagingLocation sets the S3StagingLocation field's value.
func (*RedshiftDataSpec) SetSelectSqlQuery ¶ added in v1.5.0
func (s *RedshiftDataSpec) SetSelectSqlQuery(v string) *RedshiftDataSpec
SetSelectSqlQuery sets the SelectSqlQuery field's value.
func (RedshiftDataSpec) String ¶ added in v0.6.5
func (s RedshiftDataSpec) String() string
String returns the string representation
func (*RedshiftDataSpec) Validate ¶ added in v1.1.21
func (s *RedshiftDataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftDatabase ¶
type RedshiftDatabase struct { // The ID of an Amazon Redshift cluster. // // ClusterIdentifier is a required field ClusterIdentifier *string `min:"1" type:"string" required:"true"` // The name of a database hosted on an Amazon Redshift cluster. // // DatabaseName is a required field DatabaseName *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the database details required to connect to an Amazon Redshift database.
func (RedshiftDatabase) GoString ¶ added in v0.6.5
func (s RedshiftDatabase) GoString() string
GoString returns the string representation
func (*RedshiftDatabase) SetClusterIdentifier ¶ added in v1.5.0
func (s *RedshiftDatabase) SetClusterIdentifier(v string) *RedshiftDatabase
SetClusterIdentifier sets the ClusterIdentifier field's value.
func (*RedshiftDatabase) SetDatabaseName ¶ added in v1.5.0
func (s *RedshiftDatabase) SetDatabaseName(v string) *RedshiftDatabase
SetDatabaseName sets the DatabaseName field's value.
func (RedshiftDatabase) String ¶ added in v0.6.5
func (s RedshiftDatabase) String() string
String returns the string representation
func (*RedshiftDatabase) Validate ¶ added in v1.1.21
func (s *RedshiftDatabase) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftDatabaseCredentials ¶
type RedshiftDatabaseCredentials struct { // A password to be used by Amazon ML to connect to a database on an Amazon // Redshift cluster. The password should have sufficient permissions to execute // a RedshiftSelectSqlQuery query. The password should be valid for an Amazon // Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). // // Password is a required field Password *string `min:"8" type:"string" required:"true"` // A username to be used by Amazon Machine Learning (Amazon ML)to connect to // a database on an Amazon Redshift cluster. The username should have sufficient // permissions to execute the RedshiftSelectSqlQuery query. The username should // be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). // // Username is a required field Username *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
func (RedshiftDatabaseCredentials) GoString ¶ added in v0.6.5
func (s RedshiftDatabaseCredentials) GoString() string
GoString returns the string representation
func (*RedshiftDatabaseCredentials) SetPassword ¶ added in v1.5.0
func (s *RedshiftDatabaseCredentials) SetPassword(v string) *RedshiftDatabaseCredentials
SetPassword sets the Password field's value.
func (*RedshiftDatabaseCredentials) SetUsername ¶ added in v1.5.0
func (s *RedshiftDatabaseCredentials) SetUsername(v string) *RedshiftDatabaseCredentials
SetUsername sets the Username field's value.
func (RedshiftDatabaseCredentials) String ¶ added in v0.6.5
func (s RedshiftDatabaseCredentials) String() string
String returns the string representation
func (*RedshiftDatabaseCredentials) Validate ¶ added in v1.1.21
func (s *RedshiftDatabaseCredentials) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftMetadata ¶
type RedshiftMetadata struct { // A username to be used by Amazon Machine Learning (Amazon ML)to connect to // a database on an Amazon Redshift cluster. The username should have sufficient // permissions to execute the RedshiftSelectSqlQuery query. The username should // be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). DatabaseUserName *string `min:"1" type:"string"` // Describes the database details required to connect to an Amazon Redshift // database. RedshiftDatabase *RedshiftDatabase `type:"structure"` // The SQL query that is specified during CreateDataSourceFromRedshift. Returns // only if Verbose is true in GetDataSourceInput. SelectSqlQuery *string `min:"1" type:"string"` // contains filtered or unexported fields }
Describes the DataSource details specific to Amazon Redshift.
func (RedshiftMetadata) GoString ¶ added in v0.6.5
func (s RedshiftMetadata) GoString() string
GoString returns the string representation
func (*RedshiftMetadata) SetDatabaseUserName ¶ added in v1.5.0
func (s *RedshiftMetadata) SetDatabaseUserName(v string) *RedshiftMetadata
SetDatabaseUserName sets the DatabaseUserName field's value.
func (*RedshiftMetadata) SetRedshiftDatabase ¶ added in v1.5.0
func (s *RedshiftMetadata) SetRedshiftDatabase(v *RedshiftDatabase) *RedshiftMetadata
SetRedshiftDatabase sets the RedshiftDatabase field's value.
func (*RedshiftMetadata) SetSelectSqlQuery ¶ added in v1.5.0
func (s *RedshiftMetadata) SetSelectSqlQuery(v string) *RedshiftMetadata
SetSelectSqlQuery sets the SelectSqlQuery field's value.
func (RedshiftMetadata) String ¶ added in v0.6.5
func (s RedshiftMetadata) String() string
String returns the string representation
type S3DataSpec ¶
type S3DataSpec struct { // The location of the data file(s) used by a DataSource. The URI specifies // a data file or an Amazon Simple Storage Service (Amazon S3) directory or // bucket containing data files. // // DataLocationS3 is a required field DataLocationS3 *string `type:"string" required:"true"` // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // * percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // * percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // * complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary // datasources for training and evaluation. To create a complementary datasource, // use the same values for percentBegin and percentEnd, along with the complement // parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // * strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in // the input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting // (for example, you can use the S3 path to your data as the random seed // string). If you choose the random split strategy, Amazon ML assigns each // row of data a pseudo-random number between 0 and 100, and then selects // the rows that have an assigned number between percentBegin and percentEnd. // Pseudo-random numbers are assigned using both the input seed string value // and the byte offset as a seed, so changing the data results in a different // split. Any existing ordering is preserved. The random splitting strategy // ensures that variables in the training and evaluation data are distributed // similarly. It is useful in the cases where the input data may have an // implicit sort order, which would otherwise result in training and evaluation // datasources containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon S3 DataSource. The // DataSchema defines the structure of the observation data in the data file(s) // referenced in the DataSource. // // You must provide either the DataSchema or the DataSchemaLocationS3. // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // Describes the schema location in Amazon S3. You must provide either the DataSchema // or the DataSchemaLocationS3. DataSchemaLocationS3 *string `type:"string"` // contains filtered or unexported fields }
Describes the data specification of a DataSource.
func (S3DataSpec) GoString ¶ added in v0.6.5
func (s S3DataSpec) GoString() string
GoString returns the string representation
func (*S3DataSpec) SetDataLocationS3 ¶ added in v1.5.0
func (s *S3DataSpec) SetDataLocationS3(v string) *S3DataSpec
SetDataLocationS3 sets the DataLocationS3 field's value.
func (*S3DataSpec) SetDataRearrangement ¶ added in v1.5.0
func (s *S3DataSpec) SetDataRearrangement(v string) *S3DataSpec
SetDataRearrangement sets the DataRearrangement field's value.
func (*S3DataSpec) SetDataSchema ¶ added in v1.5.0
func (s *S3DataSpec) SetDataSchema(v string) *S3DataSpec
SetDataSchema sets the DataSchema field's value.
func (*S3DataSpec) SetDataSchemaLocationS3 ¶ added in v1.5.0
func (s *S3DataSpec) SetDataSchemaLocationS3(v string) *S3DataSpec
SetDataSchemaLocationS3 sets the DataSchemaLocationS3 field's value.
func (S3DataSpec) String ¶ added in v0.6.5
func (s S3DataSpec) String() string
String returns the string representation
func (*S3DataSpec) Validate ¶ added in v1.1.21
func (s *S3DataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type Tag ¶ added in v1.1.33
type Tag struct { // A unique identifier for the tag. Valid characters include Unicode letters, // digits, white space, _, ., /, =, +, -, %, and @. Key *string `min:"1" type:"string"` // An optional string, typically used to describe or define the tag. Valid characters // include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. Value *string `type:"string"` // contains filtered or unexported fields }
A custom key-value pair associated with an ML object, such as an ML model.
type UpdateBatchPredictionInput ¶
type UpdateBatchPredictionInput struct { // The ID assigned to the BatchPrediction during creation. // // BatchPredictionId is a required field BatchPredictionId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the BatchPrediction. // // BatchPredictionName is a required field BatchPredictionName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateBatchPredictionInput) GoString ¶ added in v0.6.5
func (s UpdateBatchPredictionInput) GoString() string
GoString returns the string representation
func (*UpdateBatchPredictionInput) SetBatchPredictionId ¶ added in v1.5.0
func (s *UpdateBatchPredictionInput) SetBatchPredictionId(v string) *UpdateBatchPredictionInput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (*UpdateBatchPredictionInput) SetBatchPredictionName ¶ added in v1.5.0
func (s *UpdateBatchPredictionInput) SetBatchPredictionName(v string) *UpdateBatchPredictionInput
SetBatchPredictionName sets the BatchPredictionName field's value.
func (UpdateBatchPredictionInput) String ¶ added in v0.6.5
func (s UpdateBatchPredictionInput) String() string
String returns the string representation
func (*UpdateBatchPredictionInput) Validate ¶ added in v1.1.21
func (s *UpdateBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateBatchPredictionOutput ¶
type UpdateBatchPredictionOutput struct { // The ID assigned to the BatchPrediction during creation. This value should // be identical to the value of the BatchPredictionId in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
func (UpdateBatchPredictionOutput) GoString ¶ added in v0.6.5
func (s UpdateBatchPredictionOutput) GoString() string
GoString returns the string representation
func (*UpdateBatchPredictionOutput) SetBatchPredictionId ¶ added in v1.5.0
func (s *UpdateBatchPredictionOutput) SetBatchPredictionId(v string) *UpdateBatchPredictionOutput
SetBatchPredictionId sets the BatchPredictionId field's value.
func (UpdateBatchPredictionOutput) String ¶ added in v0.6.5
func (s UpdateBatchPredictionOutput) String() string
String returns the string representation
type UpdateDataSourceInput ¶
type UpdateDataSourceInput struct { // The ID assigned to the DataSource during creation. // // DataSourceId is a required field DataSourceId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the DataSource that will replace // the current description. // // DataSourceName is a required field DataSourceName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateDataSourceInput) GoString ¶ added in v0.6.5
func (s UpdateDataSourceInput) GoString() string
GoString returns the string representation
func (*UpdateDataSourceInput) SetDataSourceId ¶ added in v1.5.0
func (s *UpdateDataSourceInput) SetDataSourceId(v string) *UpdateDataSourceInput
SetDataSourceId sets the DataSourceId field's value.
func (*UpdateDataSourceInput) SetDataSourceName ¶ added in v1.5.0
func (s *UpdateDataSourceInput) SetDataSourceName(v string) *UpdateDataSourceInput
SetDataSourceName sets the DataSourceName field's value.
func (UpdateDataSourceInput) String ¶ added in v0.6.5
func (s UpdateDataSourceInput) String() string
String returns the string representation
func (*UpdateDataSourceInput) Validate ¶ added in v1.1.21
func (s *UpdateDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateDataSourceOutput ¶
type UpdateDataSourceOutput struct { // The ID assigned to the DataSource during creation. This value should be identical // to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
func (UpdateDataSourceOutput) GoString ¶ added in v0.6.5
func (s UpdateDataSourceOutput) GoString() string
GoString returns the string representation
func (*UpdateDataSourceOutput) SetDataSourceId ¶ added in v1.5.0
func (s *UpdateDataSourceOutput) SetDataSourceId(v string) *UpdateDataSourceOutput
SetDataSourceId sets the DataSourceId field's value.
func (UpdateDataSourceOutput) String ¶ added in v0.6.5
func (s UpdateDataSourceOutput) String() string
String returns the string representation
type UpdateEvaluationInput ¶
type UpdateEvaluationInput struct { // The ID assigned to the Evaluation during creation. // // EvaluationId is a required field EvaluationId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the Evaluation that will replace // the current content. // // EvaluationName is a required field EvaluationName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateEvaluationInput) GoString ¶ added in v0.6.5
func (s UpdateEvaluationInput) GoString() string
GoString returns the string representation
func (*UpdateEvaluationInput) SetEvaluationId ¶ added in v1.5.0
func (s *UpdateEvaluationInput) SetEvaluationId(v string) *UpdateEvaluationInput
SetEvaluationId sets the EvaluationId field's value.
func (*UpdateEvaluationInput) SetEvaluationName ¶ added in v1.5.0
func (s *UpdateEvaluationInput) SetEvaluationName(v string) *UpdateEvaluationInput
SetEvaluationName sets the EvaluationName field's value.
func (UpdateEvaluationInput) String ¶ added in v0.6.5
func (s UpdateEvaluationInput) String() string
String returns the string representation
func (*UpdateEvaluationInput) Validate ¶ added in v1.1.21
func (s *UpdateEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateEvaluationOutput ¶
type UpdateEvaluationOutput struct { // The ID assigned to the Evaluation during creation. This value should be identical // to the value of the Evaluation in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
func (UpdateEvaluationOutput) GoString ¶ added in v0.6.5
func (s UpdateEvaluationOutput) GoString() string
GoString returns the string representation
func (*UpdateEvaluationOutput) SetEvaluationId ¶ added in v1.5.0
func (s *UpdateEvaluationOutput) SetEvaluationId(v string) *UpdateEvaluationOutput
SetEvaluationId sets the EvaluationId field's value.
func (UpdateEvaluationOutput) String ¶ added in v0.6.5
func (s UpdateEvaluationOutput) String() string
String returns the string representation
type UpdateMLModelInput ¶
type UpdateMLModelInput struct { // The ID assigned to the MLModel during creation. // // MLModelId is a required field MLModelId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the MLModel. MLModelName *string `type:"string"` // The ScoreThreshold used in binary classification MLModel that marks the boundary // between a positive prediction and a negative prediction. // // Output values greater than or equal to the ScoreThreshold receive a positive // result from the MLModel, such as true. Output values less than the ScoreThreshold // receive a negative response from the MLModel, such as false. ScoreThreshold *float64 `type:"float"` // contains filtered or unexported fields }
func (UpdateMLModelInput) GoString ¶ added in v0.6.5
func (s UpdateMLModelInput) GoString() string
GoString returns the string representation
func (*UpdateMLModelInput) SetMLModelId ¶ added in v1.5.0
func (s *UpdateMLModelInput) SetMLModelId(v string) *UpdateMLModelInput
SetMLModelId sets the MLModelId field's value.
func (*UpdateMLModelInput) SetMLModelName ¶ added in v1.5.0
func (s *UpdateMLModelInput) SetMLModelName(v string) *UpdateMLModelInput
SetMLModelName sets the MLModelName field's value.
func (*UpdateMLModelInput) SetScoreThreshold ¶ added in v1.5.0
func (s *UpdateMLModelInput) SetScoreThreshold(v float64) *UpdateMLModelInput
SetScoreThreshold sets the ScoreThreshold field's value.
func (UpdateMLModelInput) String ¶ added in v0.6.5
func (s UpdateMLModelInput) String() string
String returns the string representation
func (*UpdateMLModelInput) Validate ¶ added in v1.1.21
func (s *UpdateMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateMLModelOutput ¶
type UpdateMLModelOutput struct { // The ID assigned to the MLModel during creation. This value should be identical // to the value of the MLModelID in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
func (UpdateMLModelOutput) GoString ¶ added in v0.6.5
func (s UpdateMLModelOutput) GoString() string
GoString returns the string representation
func (*UpdateMLModelOutput) SetMLModelId ¶ added in v1.5.0
func (s *UpdateMLModelOutput) SetMLModelId(v string) *UpdateMLModelOutput
SetMLModelId sets the MLModelId field's value.
func (UpdateMLModelOutput) String ¶ added in v0.6.5
func (s UpdateMLModelOutput) String() string
String returns the string representation
Source Files ¶
Directories ¶
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Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.
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Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code. |