Documentation
¶
Overview ¶
Package machinelearning provides a client for Amazon Machine Learning.
Index ¶
- Constants
- type AddTagsInput
- type AddTagsOutput
- type BatchPrediction
- type CreateBatchPredictionInput
- type CreateBatchPredictionOutput
- type CreateDataSourceFromRDSInput
- type CreateDataSourceFromRDSOutput
- type CreateDataSourceFromRedshiftInput
- type CreateDataSourceFromRedshiftOutput
- type CreateDataSourceFromS3Input
- type CreateDataSourceFromS3Output
- type CreateEvaluationInput
- type CreateEvaluationOutput
- type CreateMLModelInput
- type CreateMLModelOutput
- type CreateRealtimeEndpointInput
- type CreateRealtimeEndpointOutput
- type DataSource
- type DeleteBatchPredictionInput
- type DeleteBatchPredictionOutput
- type DeleteDataSourceInput
- type DeleteDataSourceOutput
- type DeleteEvaluationInput
- type DeleteEvaluationOutput
- type DeleteMLModelInput
- type DeleteMLModelOutput
- type DeleteRealtimeEndpointInput
- type DeleteRealtimeEndpointOutput
- type DeleteTagsInput
- type DeleteTagsOutput
- type DescribeBatchPredictionsInput
- type DescribeBatchPredictionsOutput
- type DescribeDataSourcesInput
- type DescribeDataSourcesOutput
- type DescribeEvaluationsInput
- type DescribeEvaluationsOutput
- type DescribeMLModelsInput
- type DescribeMLModelsOutput
- type DescribeTagsInput
- type DescribeTagsOutput
- type Evaluation
- type GetBatchPredictionInput
- type GetBatchPredictionOutput
- type GetDataSourceInput
- type GetDataSourceOutput
- type GetEvaluationInput
- type GetEvaluationOutput
- type GetMLModelInput
- type GetMLModelOutput
- type MLModel
- type MachineLearning
- func (c *MachineLearning) AddTags(input *AddTagsInput) (*AddTagsOutput, error)
- func (c *MachineLearning) AddTagsRequest(input *AddTagsInput) (req *request.Request, output *AddTagsOutput)
- func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)
- func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput)
- func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput)
- func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)
- func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput)
- func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)
- func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output)
- func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)
- func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput)
- func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)
- func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput)
- func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)
- func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)
- func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)
- func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)
- func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)
- func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)
- func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)
- func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)
- func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)
- func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)
- func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)
- func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)
- func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)
- func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)
- func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)
- func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, ...) error
- func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)
- func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)
- func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, ...) error
- func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)
- func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)
- func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, ...) error
- func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)
- func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)
- func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, ...) error
- func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)
- func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)
- func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)
- func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)
- func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)
- func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)
- func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)
- func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)
- func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)
- func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)
- func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)
- func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)
- func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)
- func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)
- func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)
- func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)
- func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)
- func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)
- func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)
- func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)
- func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)
- func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error
- func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error
- func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error
- func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error
- type PerformanceMetrics
- type PredictInput
- type PredictOutput
- type Prediction
- type RDSDataSpec
- type RDSDatabase
- type RDSDatabaseCredentials
- type RDSMetadata
- type RealtimeEndpointInfo
- type RedshiftDataSpec
- type RedshiftDatabase
- type RedshiftDatabaseCredentials
- type RedshiftMetadata
- type S3DataSpec
- type Tag
- type UpdateBatchPredictionInput
- type UpdateBatchPredictionOutput
- type UpdateDataSourceInput
- type UpdateDataSourceOutput
- type UpdateEvaluationInput
- type UpdateEvaluationOutput
- type UpdateMLModelInput
- 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 ( // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableCreatedAt = "CreatedAt" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableLastUpdatedAt = "LastUpdatedAt" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableStatus = "Status" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableName = "Name" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableIamuser = "IAMUser" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableMlmodelId = "MLModelId" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableDataSourceId = "DataSourceId" // @enum BatchPredictionFilterVariable BatchPredictionFilterVariableDataUri = "DataURI" )
A list of the variables to use in searching or filtering BatchPrediction.
CreatedAt - Sets the search criteria to BatchPrediction creation date. Status - Sets the search criteria to BatchPrediction status. Name - Sets
the search criteria to the contents of BatchPrediction Name. IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction. DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction. DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
const ( // @enum DataSourceFilterVariable DataSourceFilterVariableCreatedAt = "CreatedAt" // @enum DataSourceFilterVariable DataSourceFilterVariableLastUpdatedAt = "LastUpdatedAt" // @enum DataSourceFilterVariable DataSourceFilterVariableStatus = "Status" // @enum DataSourceFilterVariable DataSourceFilterVariableName = "Name" // @enum DataSourceFilterVariable DataSourceFilterVariableDataLocationS3 = "DataLocationS3" // @enum DataSourceFilterVariable DataSourceFilterVariableIamuser = "IAMUser" )
A list of the variables to use in searching or filtering DataSource.
CreatedAt - Sets the search criteria to DataSource creation date. Status
- Sets the search criteria to DataSource status. Name - Sets the search criteria to the contents of DataSource Name. DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. IAMUser - Sets the search criteria to the user account that invoked the DataSource creation. Note The variable names should match the variable names in the DataSource.
const ( // @enum DetailsAttributes DetailsAttributesPredictiveModelType = "PredictiveModelType" // @enum DetailsAttributes DetailsAttributesAlgorithm = "Algorithm" )
Contains the key values of DetailsMap: PredictiveModelType - Indicates the type of the MLModel. Algorithm - Indicates the algorithm that was used for the MLModel.
const ( // @enum EntityStatus EntityStatusPending = "PENDING" // @enum EntityStatus EntityStatusInprogress = "INPROGRESS" // @enum EntityStatus EntityStatusFailed = "FAILED" // @enum EntityStatus EntityStatusCompleted = "COMPLETED" // @enum EntityStatus EntityStatusDeleted = "DELETED" )
Object status with the following possible values:
PENDING INPROGRESS FAILED COMPLETED DELETED
const ( // @enum EvaluationFilterVariable EvaluationFilterVariableCreatedAt = "CreatedAt" // @enum EvaluationFilterVariable EvaluationFilterVariableLastUpdatedAt = "LastUpdatedAt" // @enum EvaluationFilterVariable EvaluationFilterVariableStatus = "Status" // @enum EvaluationFilterVariable EvaluationFilterVariableName = "Name" // @enum EvaluationFilterVariable EvaluationFilterVariableIamuser = "IAMUser" // @enum EvaluationFilterVariable EvaluationFilterVariableMlmodelId = "MLModelId" // @enum EvaluationFilterVariable EvaluationFilterVariableDataSourceId = "DataSourceId" // @enum EvaluationFilterVariable EvaluationFilterVariableDataUri = "DataURI" )
A list of the variables to use in searching or filtering Evaluation.
CreatedAt - Sets the search criteria to Evaluation creation date. Status
- Sets the search criteria to Evaluation status. Name - Sets the search criteria to the contents of Evaluation Name. IAMUser - Sets the search criteria to the user account that invoked an evaluation. MLModelId - Sets the search criteria to the Predictor that was evaluated. DataSourceId - Sets the search criteria to the DataSource used in evaluation. DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
const ( // @enum MLModelFilterVariable MLModelFilterVariableCreatedAt = "CreatedAt" // @enum MLModelFilterVariable MLModelFilterVariableLastUpdatedAt = "LastUpdatedAt" // @enum MLModelFilterVariable MLModelFilterVariableStatus = "Status" // @enum MLModelFilterVariable MLModelFilterVariableName = "Name" // @enum MLModelFilterVariable MLModelFilterVariableIamuser = "IAMUser" // @enum MLModelFilterVariable MLModelFilterVariableTrainingDataSourceId = "TrainingDataSourceId" // @enum MLModelFilterVariable MLModelFilterVariableRealtimeEndpointStatus = "RealtimeEndpointStatus" // @enum MLModelFilterVariable MLModelFilterVariableMlmodelType = "MLModelType" // @enum MLModelFilterVariable MLModelFilterVariableAlgorithm = "Algorithm" // @enum MLModelFilterVariable MLModelFilterVariableTrainingDataUri = "TrainingDataURI" )
const ( // @enum MLModelType MLModelTypeRegression = "REGRESSION" // @enum MLModelType MLModelTypeBinary = "BINARY" // @enum MLModelType MLModelTypeMulticlass = "MULTICLASS" )
const ( // @enum RealtimeEndpointStatus RealtimeEndpointStatusNone = "NONE" // @enum RealtimeEndpointStatus RealtimeEndpointStatusReady = "READY" // @enum RealtimeEndpointStatus RealtimeEndpointStatusUpdating = "UPDATING" // @enum RealtimeEndpointStatus RealtimeEndpointStatusFailed = "FAILED" )
const ( // @enum SortOrder SortOrderAsc = "asc" // @enum SortOrder SortOrderDsc = "dsc" )
The sort order specified in a listing condition. Possible values include the following:
asc - Present the information in ascending order (from A-Z). dsc - Present
the information in descending order (from Z-A).
const ( // @enum TaggableResourceType TaggableResourceTypeBatchPrediction = "BatchPrediction" // @enum TaggableResourceType TaggableResourceTypeDataSource = "DataSource" // @enum TaggableResourceType TaggableResourceTypeEvaluation = "Evaluation" // @enum TaggableResourceType TaggableResourceTypeMlmodel = "MLModel" )
const (
// @enum Algorithm
AlgorithmSgd = "sgd"
)
The function used to train an MLModel. Training choices supported by Amazon ML include the following:
SGD - Stochastic Gradient Descent. RandomForest - Random forest of decision
trees.
const ServiceName = "machinelearning"
A ServiceName is the name of the service the client will make API calls to.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type AddTagsInput ¶
type AddTagsInput struct { // The ID of the ML object to tag. For example, exampleModelId. ResourceId *string `min:"1" type:"string" required:"true"` // The type of the ML object to tag. ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // The key-value pairs to use to create tags. If you specify a key without specifying // a value, Amazon ML creates a tag with the specified key and a value of null. Tags []*Tag `type:"list" required:"true"` // contains filtered or unexported fields }
func (AddTagsInput) GoString ¶
func (s AddTagsInput) GoString() string
GoString returns the string representation
func (AddTagsInput) String ¶
func (s AddTagsInput) String() string
String returns the string representation
func (*AddTagsInput) Validate ¶
func (s *AddTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type AddTagsOutput ¶
type AddTagsOutput struct { // The ID of the ML object that was tagged. ResourceId *string `min:"1" type:"string"` // The type of the ML object that was tagged. ResourceType *string `type:"string" enum:"TaggableResourceType"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (AddTagsOutput) GoString ¶
func (s AddTagsOutput) GoString() string
GoString returns the string representation
func (AddTagsOutput) String ¶
func (s AddTagsOutput) String() string
String returns the string representation
type BatchPrediction ¶
type BatchPrediction struct { // The ID of the DataSource that points to the group of observations to predict. BatchPredictionDataSourceId *string `min:"1" type:"string"` // The ID assigned to the BatchPrediction at creation. This value should be // identical to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // The time that the BatchPrediction was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the BatchPrediction. The account type can // be either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The ID of the MLModel that generated predictions for the BatchPrediction // request. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about processing the batch prediction // request. Message *string `type:"string"` // A user-supplied name or description of the BatchPrediction. Name *string `type:"string"` // The location of an Amazon S3 bucket or directory to receive the operation // results. The following substrings are not allowed in the s3 key portion of // the outputURI field: ':', '//', '/./', '/../'. OutputUri *string `type:"string"` // The status of the BatchPrediction. This element can have one of the following // values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate // predictions for a batch of observations. INPROGRESS - The process is underway. // FAILED - The request to perform a batch prediction did not run to completion. // It is not usable. COMPLETED - The batch prediction process completed successfully. // DELETED - The BatchPrediction is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetBatchPrediction operation.
The content consists of the detailed metadata, the status, and the data
file information of a Batch Prediction.
func (BatchPrediction) GoString ¶
func (s BatchPrediction) GoString() string
GoString returns the string representation
func (BatchPrediction) String ¶
func (s BatchPrediction) String() string
String returns the string representation
type CreateBatchPredictionInput ¶
type CreateBatchPredictionInput struct { // The ID of the DataSource that points to the group of observations to predict. BatchPredictionDataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied ID that uniquely identifies the BatchPrediction. BatchPredictionId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the BatchPrediction. BatchPredictionName // can only use the UTF-8 character set. BatchPredictionName *string `type:"string"` // The ID of the MLModel that will generate predictions for the group of observations. MLModelId *string `min:"1" type:"string" required:"true"` // The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory // to store the batch prediction results. The following substrings are not allowed // in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'. // // Amazon ML needs permissions to store and retrieve the logs on your behalf. // For information about how to set permissions, see the Amazon Machine Learning // Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). OutputUri *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateBatchPredictionInput) GoString ¶
func (s CreateBatchPredictionInput) GoString() string
GoString returns the string representation
func (CreateBatchPredictionInput) String ¶
func (s CreateBatchPredictionInput) String() string
String returns the string representation
func (*CreateBatchPredictionInput) Validate ¶
func (s *CreateBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateBatchPredictionOutput ¶
type CreateBatchPredictionOutput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. This value // is identical to the value of the BatchPredictionId in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.
func (CreateBatchPredictionOutput) GoString ¶
func (s CreateBatchPredictionOutput) GoString() string
GoString returns the string representation
func (CreateBatchPredictionOutput) String ¶
func (s CreateBatchPredictionOutput) String() string
String returns the string representation
type CreateDataSourceFromRDSInput ¶
type CreateDataSourceFromRDSInput struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied ID that uniquely identifies the DataSource. Typically, an // Amazon Resource Number (ARN) becomes the ID for a DataSource. DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of an Amazon RDS DataSource: // // DatabaseInformation - DatabaseName - The name of the Amazon RDS database. // InstanceIdentifier - A unique identifier for the Amazon RDS database instance. // // // DatabaseCredentials - AWS Identity and Access Management (IAM) credentials // that are used to connect to the Amazon RDS database. // // ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 // instance to carry out the copy task from Amazon RDS to Amazon Simple Storage // Service (Amazon S3). For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. // // ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline // service to monitor the progress of the copy task from Amazon RDS to Amazon // S3. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. // // SecurityInfo - The security information to use to access an RDS DB instance. // You need to set up appropriate ingress rules for the security entity IDs // provided to allow access to the Amazon RDS instance. Specify a [SubnetId, // SecurityGroupIds] pair for a VPC-based RDS DB instance. // // SelectSqlQuery - A query that is used to retrieve the observation data for // the Datasource. // // S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. // The data retrieved from Amazon RDS using SelectSqlQuery is stored in this // location. // // DataSchemaUri - The Amazon S3 location of the DataSchema. // // DataSchema - A JSON string representing the schema. This is not required // if DataSchemaUri is specified. // // DataRearrangement - A JSON string that represents the splitting and rearrangement // requirements for the Datasource. // // Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" RDSData *RDSDataSpec `type:"structure" required:"true"` // The role that Amazon ML assumes on behalf of the user to create and activate // a data pipeline in the user's account and copy data using the SelectSqlQuery // query from Amazon RDS to Amazon S3. RoleARN *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromRDSInput) GoString ¶
func (s CreateDataSourceFromRDSInput) GoString() string
GoString returns the string representation
func (CreateDataSourceFromRDSInput) String ¶
func (s CreateDataSourceFromRDSInput) String() string
String returns the string representation
func (*CreateDataSourceFromRDSInput) Validate ¶
func (s *CreateDataSourceFromRDSInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromRDSOutput ¶
type CreateDataSourceFromRDSOutput struct { // A user-supplied ID that uniquely identifies the datasource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.
func (CreateDataSourceFromRDSOutput) GoString ¶
func (s CreateDataSourceFromRDSOutput) GoString() string
GoString returns the string representation
func (CreateDataSourceFromRDSOutput) String ¶
func (s CreateDataSourceFromRDSOutput) String() string
String returns the string representation
type CreateDataSourceFromRedshiftInput ¶
type CreateDataSourceFromRedshiftInput struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied ID that uniquely identifies the DataSource. DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of an Amazon Redshift DataSource: // // DatabaseInformation - DatabaseName - The name of the Amazon Redshift // database. ClusterIdentifier - The unique ID for the Amazon Redshift cluster. // // DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials // that are used to connect to the Amazon Redshift database. // // SelectSqlQuery - The query that is used to retrieve the observation data // for the Datasource. // // S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location // for staging Amazon Redshift data. The data retrieved from Amazon Redshift // using the SelectSqlQuery query is stored in this location. // // DataSchemaUri - The Amazon S3 location of the DataSchema. // // DataSchema - A JSON string representing the schema. This is not required // if DataSchemaUri is specified. // // DataRearrangement - A JSON string that represents the splitting and rearrangement // requirements for the DataSource. // // Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" DataSpec *RedshiftDataSpec `type:"structure" required:"true"` // A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the // role on behalf of the user to create the following: // // A security group to allow Amazon ML to execute the SelectSqlQuery query // on an Amazon Redshift cluster // // An Amazon S3 bucket policy to grant Amazon ML read/write permissions on // the S3StagingLocation RoleARN *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromRedshiftInput) GoString ¶
func (s CreateDataSourceFromRedshiftInput) GoString() string
GoString returns the string representation
func (CreateDataSourceFromRedshiftInput) String ¶
func (s CreateDataSourceFromRedshiftInput) String() string
String returns the string representation
func (*CreateDataSourceFromRedshiftInput) Validate ¶
func (s *CreateDataSourceFromRedshiftInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromRedshiftOutput ¶
type CreateDataSourceFromRedshiftOutput struct { // A user-supplied ID that uniquely identifies the datasource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
func (CreateDataSourceFromRedshiftOutput) GoString ¶
func (s CreateDataSourceFromRedshiftOutput) GoString() string
GoString returns the string representation
func (CreateDataSourceFromRedshiftOutput) String ¶
func (s CreateDataSourceFromRedshiftOutput) String() string
String returns the string representation
type CreateDataSourceFromS3Input ¶
type CreateDataSourceFromS3Input struct { // The compute statistics for a DataSource. The statistics are generated from // the observation data referenced by a DataSource. Amazon ML uses the statistics // internally during MLModel training. This parameter must be set to true if // the DataSource needs to be used for MLModel training. ComputeStatistics *bool `type:"boolean"` // A user-supplied identifier that uniquely identifies the DataSource. DataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the DataSource. DataSourceName *string `type:"string"` // The data specification of a DataSource: // // DataLocationS3 - The Amazon S3 location of the observation data. // // DataSchemaLocationS3 - The Amazon S3 location of the DataSchema. // // DataSchema - A JSON string representing the schema. This is not required // if DataSchemaUri is specified. // // DataRearrangement - A JSON string that represents the splitting and rearrangement // requirements for the Datasource. // // Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" DataSpec *S3DataSpec `type:"structure" required:"true"` // contains filtered or unexported fields }
func (CreateDataSourceFromS3Input) GoString ¶
func (s CreateDataSourceFromS3Input) GoString() string
GoString returns the string representation
func (CreateDataSourceFromS3Input) String ¶
func (s CreateDataSourceFromS3Input) String() string
String returns the string representation
func (*CreateDataSourceFromS3Input) Validate ¶
func (s *CreateDataSourceFromS3Input) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateDataSourceFromS3Output ¶
type CreateDataSourceFromS3Output struct { // A user-supplied ID that uniquely identifies the DataSource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.
func (CreateDataSourceFromS3Output) GoString ¶
func (s CreateDataSourceFromS3Output) GoString() string
GoString returns the string representation
func (CreateDataSourceFromS3Output) String ¶
func (s CreateDataSourceFromS3Output) String() string
String returns the string representation
type CreateEvaluationInput ¶
type CreateEvaluationInput struct { // The ID of the DataSource for the evaluation. The schema of the DataSource // must match the schema used to create the MLModel. EvaluationDataSourceId *string `min:"1" type:"string" required:"true"` // A user-supplied ID that uniquely identifies the Evaluation. EvaluationId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the Evaluation. EvaluationName *string `type:"string"` // The ID of the MLModel to evaluate. // // The schema used in creating the MLModel must match the schema of the DataSource // used in the Evaluation. MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateEvaluationInput) GoString ¶
func (s CreateEvaluationInput) GoString() string
GoString returns the string representation
func (CreateEvaluationInput) String ¶
func (s CreateEvaluationInput) String() string
String returns the string representation
func (*CreateEvaluationInput) Validate ¶
func (s *CreateEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateEvaluationOutput ¶
type CreateEvaluationOutput struct { // The user-supplied ID that uniquely identifies the Evaluation. This value // should be identical to the value of the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.
func (CreateEvaluationOutput) GoString ¶
func (s CreateEvaluationOutput) GoString() string
GoString returns the string representation
func (CreateEvaluationOutput) String ¶
func (s CreateEvaluationOutput) String() string
String returns the string representation
type CreateMLModelInput ¶
type CreateMLModelInput struct { // A user-supplied ID that uniquely identifies the MLModel. MLModelId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the MLModel. MLModelName *string `type:"string"` // The category of supervised learning that this MLModel will address. Choose // from the following types: // // Choose REGRESSION if the MLModel will be used to predict a numeric value. // Choose BINARY if the MLModel result has two possible values. Choose MULTICLASS // if the MLModel result has a limited number of values. For more information, // see the Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). MLModelType *string `type:"string" required:"true" enum:"MLModelType"` // A list of the training parameters in the MLModel. The list is implemented // as a map of key-value pairs. // // The following is the current set of training parameters: // // sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending // on the input data, the size of the model might affect its performance. // // The value is an integer that ranges from 100000 to 2147483648. The default // value is 33554432. // // sgd.maxPasses - The number of times that the training process traverses // the observations to build the MLModel. The value is an integer that ranges // from 1 to 10000. The default value is 10. // // sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling // the data improves a model's ability to find the optimal solution for a variety // of data types. The valid values are auto and none. The default value is none. // We strongly recommend that you shuffle your data. // // sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It // controls overfitting the data by penalizing large coefficients. This tends // to drive coefficients to zero, resulting in a sparse feature set. If you // use this parameter, start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L1 normalization. This parameter can't be used when L2 is specified. // Use this parameter sparingly. // // sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It // controls overfitting the data by penalizing large coefficients. This tends // to drive coefficients to small, nonzero values. If you use this parameter, // start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L2 normalization. This parameter can't be used when L1 is specified. // Use this parameter sparingly. Parameters map[string]*string `type:"map"` // The data recipe for creating the MLModel. You must specify either the recipe // or its URI. If you don't specify a recipe or its URI, Amazon ML creates a // default. Recipe *string `type:"string"` // The Amazon Simple Storage Service (Amazon S3) location and file name that // contains the MLModel recipe. You must specify either the recipe or its URI. // If you don't specify a recipe or its URI, Amazon ML creates a default. RecipeUri *string `type:"string"` // The DataSource that points to the training data. TrainingDataSourceId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateMLModelInput) GoString ¶
func (s CreateMLModelInput) GoString() string
GoString returns the string representation
func (CreateMLModelInput) String ¶
func (s CreateMLModelInput) String() string
String returns the string representation
func (*CreateMLModelInput) Validate ¶
func (s *CreateMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateMLModelOutput ¶
type CreateMLModelOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.
func (CreateMLModelOutput) GoString ¶
func (s CreateMLModelOutput) GoString() string
GoString returns the string representation
func (CreateMLModelOutput) String ¶
func (s CreateMLModelOutput) String() string
String returns the string representation
type CreateRealtimeEndpointInput ¶
type CreateRealtimeEndpointInput struct { // The ID assigned to the MLModel during creation. MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (CreateRealtimeEndpointInput) GoString ¶
func (s CreateRealtimeEndpointInput) GoString() string
GoString returns the string representation
func (CreateRealtimeEndpointInput) String ¶
func (s CreateRealtimeEndpointInput) String() string
String returns the string representation
func (*CreateRealtimeEndpointInput) Validate ¶
func (s *CreateRealtimeEndpointInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type CreateRealtimeEndpointOutput ¶
type CreateRealtimeEndpointOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // The endpoint information of the MLModel RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"` // contains filtered or unexported fields }
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
The endpoint information includes the URI of the MLModel; that is, the
location to send online prediction requests for the specified MLModel.
func (CreateRealtimeEndpointOutput) GoString ¶
func (s CreateRealtimeEndpointOutput) GoString() string
GoString returns the string representation
func (CreateRealtimeEndpointOutput) String ¶
func (s CreateRealtimeEndpointOutput) String() string
String returns the string representation
type DataSource ¶
type DataSource struct { // The parameter is true if statistics need to be generated from the observation // data. ComputeStatistics *bool `type:"boolean"` // The time that the DataSource was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the DataSource was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The location and name of the data in Amazon Simple Storage Service (Amazon // S3) that is used by a DataSource. DataLocationS3 *string `type:"string"` // A JSON string that represents the splitting and rearrangement requirement // used when this DataSource was created. DataRearrangement *string `type:"string"` // The total number of observations contained in the data files that the DataSource // references. DataSizeInBytes *int64 `type:"long"` // The ID that is assigned to the DataSource during creation. DataSourceId *string `min:"1" type:"string"` // The time of the most recent edit to the BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A description of the most recent details about creating the DataSource. Message *string `type:"string"` // A user-supplied name or description of the DataSource. Name *string `type:"string"` // The number of data files referenced by the DataSource. NumberOfFiles *int64 `type:"long"` // The datasource details that are specific to Amazon RDS. RDSMetadata *RDSMetadata `type:"structure"` // Describes the DataSource details specific to Amazon Redshift. RedshiftMetadata *RedshiftMetadata `type:"structure"` // The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts), // such as the following: arn:aws:iam::account:role/rolename. RoleARN *string `min:"1" type:"string"` // The current status of the DataSource. This element can have one of the following // values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create // a DataSource. INPROGRESS - The creation process is underway. FAILED - The // request to create a DataSource did not run to completion. It is not usable. // COMPLETED - The creation process completed successfully. DELETED - The DataSource // is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of the GetDataSource operation.
The content consists of the detailed metadata and data file information
and the current status of the DataSource.
func (DataSource) GoString ¶
func (s DataSource) GoString() string
GoString returns the string representation
func (DataSource) String ¶
func (s DataSource) String() string
String returns the string representation
type DeleteBatchPredictionInput ¶
type DeleteBatchPredictionInput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. BatchPredictionId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteBatchPredictionInput) GoString ¶
func (s DeleteBatchPredictionInput) GoString() string
GoString returns the string representation
func (DeleteBatchPredictionInput) String ¶
func (s DeleteBatchPredictionInput) String() string
String returns the string representation
func (*DeleteBatchPredictionInput) Validate ¶
func (s *DeleteBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteBatchPredictionOutput ¶
type DeleteBatchPredictionOutput struct { // A user-supplied ID that uniquely identifies the BatchPrediction. This value // should be identical to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteBatchPrediction operation.
You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.
func (DeleteBatchPredictionOutput) GoString ¶
func (s DeleteBatchPredictionOutput) GoString() string
GoString returns the string representation
func (DeleteBatchPredictionOutput) String ¶
func (s DeleteBatchPredictionOutput) String() string
String returns the string representation
type DeleteDataSourceInput ¶
type DeleteDataSourceInput struct { // A user-supplied ID that uniquely identifies the DataSource. DataSourceId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteDataSourceInput) GoString ¶
func (s DeleteDataSourceInput) GoString() string
GoString returns the string representation
func (DeleteDataSourceInput) String ¶
func (s DeleteDataSourceInput) String() string
String returns the string representation
func (*DeleteDataSourceInput) Validate ¶
func (s *DeleteDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteDataSourceOutput ¶
type DeleteDataSourceOutput struct { // A user-supplied ID that uniquely identifies the DataSource. This value should // be identical to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteDataSource operation.
func (DeleteDataSourceOutput) GoString ¶
func (s DeleteDataSourceOutput) GoString() string
GoString returns the string representation
func (DeleteDataSourceOutput) String ¶
func (s DeleteDataSourceOutput) String() string
String returns the string representation
type DeleteEvaluationInput ¶
type DeleteEvaluationInput struct { // A user-supplied ID that uniquely identifies the Evaluation to delete. EvaluationId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteEvaluationInput) GoString ¶
func (s DeleteEvaluationInput) GoString() string
GoString returns the string representation
func (DeleteEvaluationInput) String ¶
func (s DeleteEvaluationInput) String() string
String returns the string representation
func (*DeleteEvaluationInput) Validate ¶
func (s *DeleteEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteEvaluationOutput ¶
type DeleteEvaluationOutput struct { // A user-supplied ID that uniquely identifies the Evaluation. This value should // be identical to the value of the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.
func (DeleteEvaluationOutput) GoString ¶
func (s DeleteEvaluationOutput) GoString() string
GoString returns the string representation
func (DeleteEvaluationOutput) String ¶
func (s DeleteEvaluationOutput) String() string
String returns the string representation
type DeleteMLModelInput ¶
type DeleteMLModelInput struct { // A user-supplied ID that uniquely identifies the MLModel. MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteMLModelInput) GoString ¶
func (s DeleteMLModelInput) GoString() string
GoString returns the string representation
func (DeleteMLModelInput) String ¶
func (s DeleteMLModelInput) String() string
String returns the string representation
func (*DeleteMLModelInput) Validate ¶
func (s *DeleteMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteMLModelOutput ¶
type DeleteMLModelOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelID in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of a DeleteMLModel operation.
You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.
func (DeleteMLModelOutput) GoString ¶
func (s DeleteMLModelOutput) GoString() string
GoString returns the string representation
func (DeleteMLModelOutput) String ¶
func (s DeleteMLModelOutput) String() string
String returns the string representation
type DeleteRealtimeEndpointInput ¶
type DeleteRealtimeEndpointInput struct { // The ID assigned to the MLModel during creation. MLModelId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (DeleteRealtimeEndpointInput) GoString ¶
func (s DeleteRealtimeEndpointInput) GoString() string
GoString returns the string representation
func (DeleteRealtimeEndpointInput) String ¶
func (s DeleteRealtimeEndpointInput) String() string
String returns the string representation
func (*DeleteRealtimeEndpointInput) Validate ¶
func (s *DeleteRealtimeEndpointInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteRealtimeEndpointOutput ¶
type DeleteRealtimeEndpointOutput struct { // A user-supplied ID that uniquely identifies the MLModel. This value should // be identical to the value of the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // The endpoint information of the MLModel RealtimeEndpointInfo *RealtimeEndpointInfo `type:"structure"` // contains filtered or unexported fields }
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
func (DeleteRealtimeEndpointOutput) GoString ¶
func (s DeleteRealtimeEndpointOutput) GoString() string
GoString returns the string representation
func (DeleteRealtimeEndpointOutput) String ¶
func (s DeleteRealtimeEndpointOutput) String() string
String returns the string representation
type DeleteTagsInput ¶
type DeleteTagsInput struct { // The ID of the tagged ML object. For example, exampleModelId. ResourceId *string `min:"1" type:"string" required:"true"` // The type of the tagged ML object. ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // One or more tags to delete. TagKeys []*string `type:"list" required:"true"` // contains filtered or unexported fields }
func (DeleteTagsInput) GoString ¶
func (s DeleteTagsInput) GoString() string
GoString returns the string representation
func (DeleteTagsInput) String ¶
func (s DeleteTagsInput) String() string
String returns the string representation
func (*DeleteTagsInput) Validate ¶
func (s *DeleteTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DeleteTagsOutput ¶
type DeleteTagsOutput struct { // The ID of the ML object from which tags were deleted. ResourceId *string `min:"1" type:"string"` // The type of the ML object from which tags were deleted. ResourceType *string `type:"string" enum:"TaggableResourceType"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (DeleteTagsOutput) GoString ¶
func (s DeleteTagsOutput) GoString() string
GoString returns the string representation
func (DeleteTagsOutput) String ¶
func (s DeleteTagsOutput) String() string
String returns the string representation
type DescribeBatchPredictionsInput ¶
type DescribeBatchPredictionsInput struct { // The equal to operator. The BatchPrediction results will have FilterVariable // values that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of BatchPrediction: // // CreatedAt - Sets the search criteria to the BatchPrediction creation date. // Status - Sets the search criteria to the BatchPrediction status. Name - // Sets the search criteria to the contents of the BatchPrediction Name. IAMUser // - Sets the search criteria to the user account that invoked the BatchPrediction // creation. MLModelId - Sets the search criteria to the MLModel used in the // BatchPrediction. DataSourceId - Sets the search criteria to the DataSource // used in the BatchPrediction. DataURI - Sets the search criteria to the data // file(s) used in the BatchPrediction. The URL can identify either a file or // an Amazon Simple Storage Solution (Amazon S3) bucket or directory. FilterVariable *string `type:"string" enum:"BatchPredictionFilterVariable"` // The greater than or equal to operator. The BatchPrediction results will have // FilterVariable values that are greater than or equal to the value specified // with GE. GE *string `type:"string"` // The greater than operator. The BatchPrediction results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The BatchPrediction results will have // FilterVariable values that are less than or equal to the value specified // with LE. LE *string `type:"string"` // The less than operator. The BatchPrediction results will have FilterVariable // values that are less than the value specified with LT. LT *string `type:"string"` // The number of pages of information to include in the result. The range of // acceptable values is 1 through 100. The default value is 100. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The BatchPrediction results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // An ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. // To search for this BatchPrediction, select Name for the FilterVariable and // any of the following strings for the Prefix: // // 2014-09 // // 2014-09-09 // // 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of MLModels. // // asc - Arranges the list in ascending order (A-Z, 0-9). dsc - Arranges // the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeBatchPredictionsInput) GoString ¶
func (s DescribeBatchPredictionsInput) GoString() string
GoString returns the string representation
func (DescribeBatchPredictionsInput) String ¶
func (s DescribeBatchPredictionsInput) String() string
String returns the string representation
func (*DescribeBatchPredictionsInput) Validate ¶
func (s *DescribeBatchPredictionsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeBatchPredictionsOutput ¶
type DescribeBatchPredictionsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of BatchPrediction objects that meet the search criteria. Results []*BatchPrediction `type:"list"` // contains filtered or unexported fields }
Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.
func (DescribeBatchPredictionsOutput) GoString ¶
func (s DescribeBatchPredictionsOutput) GoString() string
GoString returns the string representation
func (DescribeBatchPredictionsOutput) String ¶
func (s DescribeBatchPredictionsOutput) String() string
String returns the string representation
type DescribeDataSourcesInput ¶
type DescribeDataSourcesInput struct { // The equal to operator. The DataSource results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of DataSource: // // CreatedAt - Sets the search criteria to DataSource creation dates. Status // - Sets the search criteria to DataSource statuses. Name - Sets the search // criteria to the contents of DataSource Name. DataUri - Sets the search // criteria to the URI of data files used to create the DataSource. The URI // can identify either a file or an Amazon Simple Storage Service (Amazon S3) // bucket or directory. IAMUser - Sets the search criteria to the user account // that invoked the DataSource creation. FilterVariable *string `type:"string" enum:"DataSourceFilterVariable"` // The greater than or equal to operator. The DataSource results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The DataSource results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The DataSource results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The DataSource results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The maximum number of DataSource to include in the result. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The DataSource results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, a DataSource could have the Name 2014-09-09-HolidayGiftMailer. // To search for this DataSource, select Name for the FilterVariable and any // of the following strings for the Prefix: // // 2014-09 // // 2014-09-09 // // 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of DataSource. // // asc - Arranges the list in ascending order (A-Z, 0-9). dsc - Arranges // the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeDataSourcesInput) GoString ¶
func (s DescribeDataSourcesInput) GoString() string
GoString returns the string representation
func (DescribeDataSourcesInput) String ¶
func (s DescribeDataSourcesInput) String() string
String returns the string representation
func (*DescribeDataSourcesInput) Validate ¶
func (s *DescribeDataSourcesInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeDataSourcesOutput ¶
type DescribeDataSourcesOutput struct { // An ID of the next page in the paginated results that indicates at least one // more page follows. NextToken *string `type:"string"` // A list of DataSource that meet the search criteria. Results []*DataSource `type:"list"` // contains filtered or unexported fields }
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.
func (DescribeDataSourcesOutput) GoString ¶
func (s DescribeDataSourcesOutput) GoString() string
GoString returns the string representation
func (DescribeDataSourcesOutput) String ¶
func (s DescribeDataSourcesOutput) String() string
String returns the string representation
type DescribeEvaluationsInput ¶
type DescribeEvaluationsInput struct { // The equal to operator. The Evaluation results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variable to filter a list of Evaluation objects: // // CreatedAt - Sets the search criteria to the Evaluation creation date. // Status - Sets the search criteria to the Evaluation status. Name - Sets // the search criteria to the contents of Evaluation Name. IAMUser - Sets // the search criteria to the user account that invoked an Evaluation. MLModelId // - Sets the search criteria to the MLModel that was evaluated. DataSourceId // - Sets the search criteria to the DataSource used in Evaluation. DataUri // - Sets the search criteria to the data file(s) used in Evaluation. The URL // can identify either a file or an Amazon Simple Storage Solution (Amazon S3) // bucket or directory. FilterVariable *string `type:"string" enum:"EvaluationFilterVariable"` // The greater than or equal to operator. The Evaluation results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The Evaluation results will have FilterVariable // values that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The Evaluation results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The Evaluation results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The maximum number of Evaluation to include in the result. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The Evaluation results will have FilterVariable // values not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer. // To search for this Evaluation, select Name for the FilterVariable and any // of the following strings for the Prefix: // // 2014-09 // // 2014-09-09 // // 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of Evaluation. // // asc - Arranges the list in ascending order (A-Z, 0-9). dsc - Arranges // the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeEvaluationsInput) GoString ¶
func (s DescribeEvaluationsInput) GoString() string
GoString returns the string representation
func (DescribeEvaluationsInput) String ¶
func (s DescribeEvaluationsInput) String() string
String returns the string representation
func (*DescribeEvaluationsInput) Validate ¶
func (s *DescribeEvaluationsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeEvaluationsOutput ¶
type DescribeEvaluationsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of Evaluation that meet the search criteria. Results []*Evaluation `type:"list"` // contains filtered or unexported fields }
Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.
func (DescribeEvaluationsOutput) GoString ¶
func (s DescribeEvaluationsOutput) GoString() string
GoString returns the string representation
func (DescribeEvaluationsOutput) String ¶
func (s DescribeEvaluationsOutput) String() string
String returns the string representation
type DescribeMLModelsInput ¶
type DescribeMLModelsInput struct { // The equal to operator. The MLModel results will have FilterVariable values // that exactly match the value specified with EQ. EQ *string `type:"string"` // Use one of the following variables to filter a list of MLModel: // // CreatedAt - Sets the search criteria to MLModel creation date. Status // - Sets the search criteria to MLModel status. Name - Sets the search criteria // to the contents of MLModel Name. IAMUser - Sets the search criteria to // the user account that invoked the MLModel creation. TrainingDataSourceId // - Sets the search criteria to the DataSource used to train one or more MLModel. // RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time // endpoint status. MLModelType - Sets the search criteria to MLModel type: // binary, regression, or multi-class. Algorithm - Sets the search criteria // to the algorithm that the MLModel uses. TrainingDataURI - Sets the search // criteria to the data file(s) used in training a MLModel. The URL can identify // either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. FilterVariable *string `type:"string" enum:"MLModelFilterVariable"` // The greater than or equal to operator. The MLModel results will have FilterVariable // values that are greater than or equal to the value specified with GE. GE *string `type:"string"` // The greater than operator. The MLModel results will have FilterVariable values // that are greater than the value specified with GT. GT *string `type:"string"` // The less than or equal to operator. The MLModel results will have FilterVariable // values that are less than or equal to the value specified with LE. LE *string `type:"string"` // The less than operator. The MLModel results will have FilterVariable values // that are less than the value specified with LT. LT *string `type:"string"` // The number of pages of information to include in the result. The range of // acceptable values is 1 through 100. The default value is 100. Limit *int64 `min:"1" type:"integer"` // The not equal to operator. The MLModel results will have FilterVariable values // not equal to the value specified with NE. NE *string `type:"string"` // The ID of the page in the paginated results. NextToken *string `type:"string"` // A string that is found at the beginning of a variable, such as Name or Id. // // For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. // To search for this MLModel, select Name for the FilterVariable and any of // the following strings for the Prefix: // // 2014-09 // // 2014-09-09 // // 2014-09-09-Holiday Prefix *string `type:"string"` // A two-value parameter that determines the sequence of the resulting list // of MLModel. // // asc - Arranges the list in ascending order (A-Z, 0-9). dsc - Arranges // the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. SortOrder *string `type:"string" enum:"SortOrder"` // contains filtered or unexported fields }
func (DescribeMLModelsInput) GoString ¶
func (s DescribeMLModelsInput) GoString() string
GoString returns the string representation
func (DescribeMLModelsInput) String ¶
func (s DescribeMLModelsInput) String() string
String returns the string representation
func (*DescribeMLModelsInput) Validate ¶
func (s *DescribeMLModelsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeMLModelsOutput ¶
type DescribeMLModelsOutput struct { // The ID of the next page in the paginated results that indicates at least // one more page follows. NextToken *string `type:"string"` // A list of MLModel that meet the search criteria. Results []*MLModel `type:"list"` // contains filtered or unexported fields }
Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.
func (DescribeMLModelsOutput) GoString ¶
func (s DescribeMLModelsOutput) GoString() string
GoString returns the string representation
func (DescribeMLModelsOutput) String ¶
func (s DescribeMLModelsOutput) String() string
String returns the string representation
type DescribeTagsInput ¶
type DescribeTagsInput struct { // The ID of the ML object. For example, exampleModelId. ResourceId *string `min:"1" type:"string" required:"true"` // The type of the ML object. ResourceType *string `type:"string" required:"true" enum:"TaggableResourceType"` // contains filtered or unexported fields }
func (DescribeTagsInput) GoString ¶
func (s DescribeTagsInput) GoString() string
GoString returns the string representation
func (DescribeTagsInput) String ¶
func (s DescribeTagsInput) String() string
String returns the string representation
func (*DescribeTagsInput) Validate ¶
func (s *DescribeTagsInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type DescribeTagsOutput ¶
type DescribeTagsOutput struct { // The ID of the tagged ML object. ResourceId *string `min:"1" type:"string"` // The type of the tagged ML object. ResourceType *string `type:"string" enum:"TaggableResourceType"` // A list of tags associated with the ML object. Tags []*Tag `type:"list"` // contains filtered or unexported fields }
Amazon ML returns the following elements.
func (DescribeTagsOutput) GoString ¶
func (s DescribeTagsOutput) GoString() string
GoString returns the string representation
func (DescribeTagsOutput) String ¶
func (s DescribeTagsOutput) String() string
String returns the string representation
type Evaluation ¶
type Evaluation struct { // The time that the Evaluation was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the evaluation. The account type can be // either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The ID of the DataSource that is used to evaluate the MLModel. EvaluationDataSourceId *string `min:"1" type:"string"` // The ID that is assigned to the Evaluation at creation. EvaluationId *string `min:"1" type:"string"` // The location and name of the data in Amazon Simple Storage Server (Amazon // S3) that is used in the evaluation. InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the Evaluation. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The ID of the MLModel that is the focus of the evaluation. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about evaluating the MLModel. Message *string `type:"string"` // A user-supplied name or description of the Evaluation. Name *string `type:"string"` // Measurements of how well the MLModel performed, using observations referenced // by the DataSource. One of the following metrics is returned, based on the // type of the MLModel: // // BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique // to measure performance. // // RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) // technique to measure performance. RMSE measures the difference between predicted // and actual values for a single variable. // // MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique // to measure performance. // // For more information about performance metrics, please see the Amazon // Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). PerformanceMetrics *PerformanceMetrics `type:"structure"` // The status of the evaluation. This element can have one of the following // values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate // an MLModel. INPROGRESS - The evaluation is underway. FAILED - The request // to evaluate an MLModel did not run to completion. It is not usable. COMPLETED // - The evaluation process completed successfully. DELETED - The Evaluation // is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation.
func (Evaluation) GoString ¶
func (s Evaluation) GoString() string
GoString returns the string representation
func (Evaluation) String ¶
func (s Evaluation) String() string
String returns the string representation
type GetBatchPredictionInput ¶
type GetBatchPredictionInput struct { // An ID assigned to the BatchPrediction at creation. BatchPredictionId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (GetBatchPredictionInput) GoString ¶
func (s GetBatchPredictionInput) GoString() string
GoString returns the string representation
func (GetBatchPredictionInput) String ¶
func (s GetBatchPredictionInput) String() string
String returns the string representation
func (*GetBatchPredictionInput) Validate ¶
func (s *GetBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetBatchPredictionOutput ¶
type GetBatchPredictionOutput struct { // The ID of the DataSource that was used to create the BatchPrediction. BatchPredictionDataSourceId *string `min:"1" type:"string"` // An ID assigned to the BatchPrediction at creation. This value should be identical // to the value of the BatchPredictionID in the request. BatchPredictionId *string `min:"1" type:"string"` // The time when the BatchPrediction was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the BatchPrediction. The account type can // be either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateBatchPrediction operation. LogUri *string `type:"string"` // The ID of the MLModel that generated predictions for the BatchPrediction // request. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about processing the batch prediction // request. Message *string `type:"string"` // A user-supplied name or description of the BatchPrediction. Name *string `type:"string"` // The location of an Amazon S3 bucket or directory to receive the operation // results. OutputUri *string `type:"string"` // The status of the BatchPrediction, which can be one of the following values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate // batch predictions. INPROGRESS - The batch predictions are in progress. // FAILED - The request to perform a batch prediction did not run to completion. // It is not usable. COMPLETED - The batch prediction process completed successfully. // DELETED - The BatchPrediction is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.
func (GetBatchPredictionOutput) GoString ¶
func (s GetBatchPredictionOutput) GoString() string
GoString returns the string representation
func (GetBatchPredictionOutput) String ¶
func (s GetBatchPredictionOutput) String() string
String returns the string representation
type GetDataSourceInput ¶
type GetDataSourceInput struct { // The ID assigned to the DataSource at creation. DataSourceId *string `min:"1" type:"string" required:"true"` // Specifies whether the GetDataSource operation should return DataSourceSchema. // // If true, DataSourceSchema is returned. // // If false, DataSourceSchema is not returned. Verbose *bool `type:"boolean"` // contains filtered or unexported fields }
func (GetDataSourceInput) GoString ¶
func (s GetDataSourceInput) GoString() string
GoString returns the string representation
func (GetDataSourceInput) String ¶
func (s GetDataSourceInput) String() string
String returns the string representation
func (*GetDataSourceInput) Validate ¶
func (s *GetDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetDataSourceOutput ¶
type GetDataSourceOutput struct { // The parameter is true if statistics need to be generated from the observation // data. ComputeStatistics *bool `type:"boolean"` // The time that the DataSource was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the DataSource was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). DataLocationS3 *string `type:"string"` // A JSON string that represents the splitting and rearrangement requirement // used when this DataSource was created. DataRearrangement *string `type:"string"` // The total size of observations in the data files. DataSizeInBytes *int64 `type:"long"` // The ID assigned to the DataSource at creation. This value should be identical // to the value of the DataSourceId in the request. DataSourceId *string `min:"1" type:"string"` // The schema used by all of the data files of this DataSource. // // Note This parameter is provided as part of the verbose format. DataSourceSchema *string `type:"string"` // The time of the most recent edit to the DataSource. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file containing logs of CreateDataSourceFrom* operations. LogUri *string `type:"string"` // The user-supplied description of the most recent details about creating the // DataSource. Message *string `type:"string"` // A user-supplied name or description of the DataSource. Name *string `type:"string"` // The number of data files referenced by the DataSource. NumberOfFiles *int64 `type:"long"` // The datasource details that are specific to Amazon RDS. RDSMetadata *RDSMetadata `type:"structure"` // Describes the DataSource details specific to Amazon Redshift. RedshiftMetadata *RedshiftMetadata `type:"structure"` // The Amazon Resource Name (ARN) of an AWS IAM Role (http://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts), // such as the following: arn:aws:iam::account:role/rolename. RoleARN *string `min:"1" type:"string"` // The current status of the DataSource. This element can have one of the following // values: // // PENDING - Amazon ML submitted a request to create a DataSource. INPROGRESS // - The creation process is underway. FAILED - The request to create a DataSource // did not run to completion. It is not usable. COMPLETED - The creation process // completed successfully. DELETED - The DataSource is marked as deleted. It // is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetDataSource operation and describes a DataSource.
func (GetDataSourceOutput) GoString ¶
func (s GetDataSourceOutput) GoString() string
GoString returns the string representation
func (GetDataSourceOutput) String ¶
func (s GetDataSourceOutput) String() string
String returns the string representation
type GetEvaluationInput ¶
type GetEvaluationInput struct { // The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded // and cataloged. The ID provides the means to access the information. EvaluationId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
func (GetEvaluationInput) GoString ¶
func (s GetEvaluationInput) GoString() string
GoString returns the string representation
func (GetEvaluationInput) String ¶
func (s GetEvaluationInput) String() string
String returns the string representation
func (*GetEvaluationInput) Validate ¶
func (s *GetEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetEvaluationOutput ¶
type GetEvaluationOutput struct { // The time that the Evaluation was created. The time is expressed in epoch // time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account that invoked the evaluation. The account type can be // either an AWS root account or an AWS Identity and Access Management (IAM) // user account. CreatedByIamUser *string `type:"string"` // The DataSource used for this evaluation. EvaluationDataSourceId *string `min:"1" type:"string"` // The evaluation ID which is same as the EvaluationId in the request. EvaluationId *string `min:"1" type:"string"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the BatchPrediction. The time is expressed // in epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateEvaluation operation. LogUri *string `type:"string"` // The ID of the MLModel that was the focus of the evaluation. MLModelId *string `min:"1" type:"string"` // A description of the most recent details about evaluating the MLModel. Message *string `type:"string"` // A user-supplied name or description of the Evaluation. Name *string `type:"string"` // Measurements of how well the MLModel performed using observations referenced // by the DataSource. One of the following metric is returned based on the type // of the MLModel: // // BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique // to measure performance. // // RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) // technique to measure performance. RMSE measures the difference between predicted // and actual values for a single variable. // // MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique // to measure performance. // // For more information about performance metrics, please see the Amazon // Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg). PerformanceMetrics *PerformanceMetrics `type:"structure"` // The status of the evaluation. This element can have one of the following // values: // // PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate // an MLModel. INPROGRESS - The evaluation is underway. FAILED - The request // to evaluate an MLModel did not run to completion. It is not usable. COMPLETED // - The evaluation process completed successfully. DELETED - The Evaluation // is marked as deleted. It is not usable. Status *string `type:"string" enum:"EntityStatus"` // contains filtered or unexported fields }
Represents the output of a GetEvaluation operation and describes an Evaluation.
func (GetEvaluationOutput) GoString ¶
func (s GetEvaluationOutput) GoString() string
GoString returns the string representation
func (GetEvaluationOutput) String ¶
func (s GetEvaluationOutput) String() string
String returns the string representation
type GetMLModelInput ¶
type GetMLModelInput struct { // The ID assigned to the MLModel at creation. MLModelId *string `min:"1" type:"string" required:"true"` // Specifies whether the GetMLModel operation should return Recipe. // // If true, Recipe is returned. // // If false, Recipe is not returned. Verbose *bool `type:"boolean"` // contains filtered or unexported fields }
func (GetMLModelInput) GoString ¶
func (s GetMLModelInput) GoString() string
GoString returns the string representation
func (GetMLModelInput) String ¶
func (s GetMLModelInput) String() string
String returns the string representation
func (*GetMLModelInput) Validate ¶
func (s *GetMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type GetMLModelOutput ¶
type GetMLModelOutput struct { // The time that the MLModel was created. The time is expressed in epoch time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the MLModel was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The current endpoint of the MLModel EndpointInfo *RealtimeEndpointInfo `type:"structure"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the MLModel. The time is expressed in // epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // A link to the file that contains logs of the CreateMLModel operation. LogUri *string `type:"string"` // The MLModel ID, which is same as the MLModelId in the request. MLModelId *string `min:"1" type:"string"` // Identifies the MLModel category. The following are the available types: // // REGRESSION -- Produces a numeric result. For example, "What price should // a house be listed at?" BINARY -- Produces one of two possible results. For // example, "Is this an e-commerce website?" MULTICLASS -- Produces one of several // possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" MLModelType *string `type:"string" enum:"MLModelType"` // A description of the most recent details about accessing the MLModel. Message *string `type:"string"` // A user-supplied name or description of the MLModel. Name *string `type:"string"` // The recipe to use when training the MLModel. The Recipe provides detailed // information about the observation data to use during training, and manipulations // to perform on the observation data during training. // // Note This parameter is provided as part of the verbose format. Recipe *string `type:"string"` // The schema used by all of the data files referenced by the DataSource. // // Note This parameter is provided as part of the verbose format. Schema *string `type:"string"` // The scoring threshold is used in binary classification MLModel models. It // marks the boundary between a positive prediction and a negative prediction. // // Output values greater than or equal to the threshold receive a positive // result from the MLModel, such as true. Output values less than the threshold // receive a negative response from the MLModel, such as false. ScoreThreshold *float64 `type:"float"` // The time of the most recent edit to the ScoreThreshold. The time is expressed // in epoch time. ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // Long integer type that is a 64-bit signed number. SizeInBytes *int64 `type:"long"` // The current status of the MLModel. This element can have one of the following // values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe // a MLModel. INPROGRESS - The request is processing. FAILED - The request // did not run to completion. The ML model isn't usable. COMPLETED - The request // completed successfully. DELETED - The MLModel is marked as deleted. It isn't // usable. Status *string `type:"string" enum:"EntityStatus"` // The ID of the training DataSource. TrainingDataSourceId *string `min:"1" type:"string"` // A list of the training parameters in the MLModel. The list is implemented // as a map of key-value pairs. // // The following is the current set of training parameters: // // sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending // on the input data, the size of the model might affect its performance. // // The value is an integer that ranges from 100000 to 2147483648. The default // value is 33554432. // // sgd.maxPasses - The number of times that the training process traverses // the observations to build the MLModel. The value is an integer that ranges // from 1 to 10000. The default value is 10. // // sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling // data improves a model's ability to find the optimal solution for a variety // of data types. The valid values are auto and none. The default value is none. // We strongly recommend that you shuffle your data. // // sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It // controls overfitting the data by penalizing large coefficients. This tends // to drive coefficients to zero, resulting in a sparse feature set. If you // use this parameter, start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L1 normalization. This parameter can't be used when L2 is specified. // Use this parameter sparingly. // // sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It // controls overfitting the data by penalizing large coefficients. This tends // to drive coefficients to small, nonzero values. If you use this parameter, // start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L2 normalization. This parameter can't be used when L1 is specified. // Use this parameter sparingly. TrainingParameters map[string]*string `type:"map"` // contains filtered or unexported fields }
Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.
func (GetMLModelOutput) GoString ¶
func (s GetMLModelOutput) GoString() string
GoString returns the string representation
func (GetMLModelOutput) String ¶
func (s GetMLModelOutput) String() string
String returns the string representation
type MLModel ¶
type MLModel struct { // The algorithm used to train the MLModel. The following algorithm is supported: // // SGD -- Stochastic gradient descent. The goal of SGD is to minimize the // gradient of the loss function. Algorithm *string `type:"string" enum:"Algorithm"` // The time that the MLModel was created. The time is expressed in epoch time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The AWS user account from which the MLModel was created. The account type // can be either an AWS root account or an AWS Identity and Access Management // (IAM) user account. CreatedByIamUser *string `type:"string"` // The current endpoint of the MLModel. EndpointInfo *RealtimeEndpointInfo `type:"structure"` // The location of the data file or directory in Amazon Simple Storage Service // (Amazon S3). InputDataLocationS3 *string `type:"string"` // The time of the most recent edit to the MLModel. The time is expressed in // epoch time. LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The ID assigned to the MLModel at creation. MLModelId *string `min:"1" type:"string"` // Identifies the MLModel category. The following are the available types: // // REGRESSION - Produces a numeric result. For example, "What price should // a house be listed at?" BINARY - Produces one of two possible results. For // example, "Is this a child-friendly web site?". MULTICLASS - Produces one // of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk // trade?". MLModelType *string `type:"string" enum:"MLModelType"` // A description of the most recent details about accessing the MLModel. Message *string `type:"string"` // A user-supplied name or description of the MLModel. Name *string `type:"string"` ScoreThreshold *float64 `type:"float"` // The time of the most recent edit to the ScoreThreshold. The time is expressed // in epoch time. ScoreThresholdLastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // Long integer type that is a 64-bit signed number. SizeInBytes *int64 `type:"long"` // The current status of an MLModel. This element can have one of the following // values: // // PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create // an MLModel. INPROGRESS - The creation process is underway. FAILED - The // request to create an MLModel didn't run to completion. The model isn't usable. // COMPLETED - The creation process completed successfully. DELETED - The // MLModel is marked as deleted. It isn't usable. Status *string `type:"string" enum:"EntityStatus"` // The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId. TrainingDataSourceId *string `min:"1" type:"string"` // A list of the training parameters in the MLModel. The list is implemented // as a map of key-value pairs. // // The following is the current set of training parameters: // // sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending // on the input data, the size of the model might affect its performance. // // The value is an integer that ranges from 100000 to 2147483648. The default // value is 33554432. // // sgd.maxPasses - The number of times that the training process traverses // the observations to build the MLModel. The value is an integer that ranges // from 1 to 10000. The default value is 10. // // sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling // the data improves a model's ability to find the optimal solution for a variety // of data types. The valid values are auto and none. The default value is none. // // sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which // controls overfitting the data by penalizing large coefficients. This parameter // tends to drive coefficients to zero, resulting in sparse feature set. If // you use this parameter, start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L1 normalization. This parameter can't be used when L2 is specified. // Use this parameter sparingly. // // sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which // controls overfitting the data by penalizing large coefficients. This tends // to drive coefficients to small, nonzero values. If you use this parameter, // start by specifying a small value, such as 1.0E-08. // // The value is a double that ranges from 0 to MAX_DOUBLE. The default is to // not use L2 normalization. This parameter can't be used when L1 is specified. // Use this parameter sparingly. TrainingParameters map[string]*string `type:"map"` // contains filtered or unexported fields }
Represents the output of a GetMLModel operation.
The content consists of the detailed metadata and the current status of the MLModel.
type MachineLearning ¶
Definition of the public APIs exposed by Amazon Machine Learning The service client's operations are safe to be used concurrently. It is not safe to mutate any of the client's properties though.
func New ¶
func New(p client.ConfigProvider, cfgs ...*aws.Config) *MachineLearning
New creates a new instance of the MachineLearning client with a session. If additional configuration is needed for the client instance use the optional aws.Config parameter to add your extra config.
Example:
// Create a MachineLearning client from just a session. svc := machinelearning.New(mySession) // Create a MachineLearning client with additional configuration svc := machinelearning.New(mySession, aws.NewConfig().WithRegion("us-west-2"))
func (*MachineLearning) AddTags ¶
func (c *MachineLearning) AddTags(input *AddTagsInput) (*AddTagsOutput, error)
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.AddTagsInput{ ResourceId: aws.String("EntityId"), // Required ResourceType: aws.String("TaggableResourceType"), // Required Tags: []*machinelearning.Tag{ // Required { // Required Key: aws.String("TagKey"), Value: aws.String("TagValue"), }, // More values... }, } resp, err := svc.AddTags(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) AddTagsRequest ¶
func (c *MachineLearning) AddTagsRequest(input *AddTagsInput) (req *request.Request, output *AddTagsOutput)
AddTagsRequest generates a request for the AddTags operation.
func (*MachineLearning) CreateBatchPrediction ¶
func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error)
Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.
CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.
You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateBatchPredictionInput{ BatchPredictionDataSourceId: aws.String("EntityId"), // Required BatchPredictionId: aws.String("EntityId"), // Required MLModelId: aws.String("EntityId"), // Required OutputUri: aws.String("S3Url"), // Required BatchPredictionName: aws.String("EntityName"), } resp, err := svc.CreateBatchPrediction(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateBatchPredictionRequest ¶
func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput)
CreateBatchPredictionRequest generates a request for the CreateBatchPrediction operation.
func (*MachineLearning) CreateDataSourceFromRDS ¶
func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error)
Creates a DataSource object from an Amazon Relational Database Service (http://aws.amazon.com/rds/) (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the Status parameter
to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateDataSourceFromRDSInput{ DataSourceId: aws.String("EntityId"), // Required RDSData: &machinelearning.RDSDataSpec{ // Required DatabaseCredentials: &machinelearning.RDSDatabaseCredentials{ // Required Password: aws.String("RDSDatabasePassword"), // Required Username: aws.String("RDSDatabaseUsername"), // Required }, DatabaseInformation: &machinelearning.RDSDatabase{ // Required DatabaseName: aws.String("RDSDatabaseName"), // Required InstanceIdentifier: aws.String("RDSInstanceIdentifier"), // Required }, ResourceRole: aws.String("EDPResourceRole"), // Required S3StagingLocation: aws.String("S3Url"), // Required SecurityGroupIds: []*string{ // Required aws.String("EDPSecurityGroupId"), // Required // More values... }, SelectSqlQuery: aws.String("RDSSelectSqlQuery"), // Required ServiceRole: aws.String("EDPServiceRole"), // Required SubnetId: aws.String("EDPSubnetId"), // Required DataRearrangement: aws.String("DataRearrangement"), DataSchema: aws.String("DataSchema"), DataSchemaUri: aws.String("S3Url"), }, RoleARN: aws.String("RoleARN"), // Required ComputeStatistics: aws.Bool(true), DataSourceName: aws.String("EntityName"), } resp, err := svc.CreateDataSourceFromRDS(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateDataSourceFromRDSRequest ¶
func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput)
CreateDataSourceFromRDSRequest generates a request for the CreateDataSourceFromRDS operation.
func (*MachineLearning) CreateDataSourceFromRedshift ¶
func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error)
Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the Status parameter
to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.
After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateDataSourceFromRedshiftInput{ DataSourceId: aws.String("EntityId"), // Required DataSpec: &machinelearning.RedshiftDataSpec{ // Required DatabaseCredentials: &machinelearning.RedshiftDatabaseCredentials{ // Required Password: aws.String("RedshiftDatabasePassword"), // Required Username: aws.String("RedshiftDatabaseUsername"), // Required }, DatabaseInformation: &machinelearning.RedshiftDatabase{ // Required ClusterIdentifier: aws.String("RedshiftClusterIdentifier"), // Required DatabaseName: aws.String("RedshiftDatabaseName"), // Required }, S3StagingLocation: aws.String("S3Url"), // Required SelectSqlQuery: aws.String("RedshiftSelectSqlQuery"), // Required DataRearrangement: aws.String("DataRearrangement"), DataSchema: aws.String("DataSchema"), DataSchemaUri: aws.String("S3Url"), }, RoleARN: aws.String("RoleARN"), // Required ComputeStatistics: aws.Bool(true), DataSourceName: aws.String("EntityName"), } resp, err := svc.CreateDataSourceFromRedshift(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateDataSourceFromRedshiftRequest ¶
func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput)
CreateDataSourceFromRedshiftRequest generates a request for the CreateDataSourceFromRedshift operation.
func (*MachineLearning) CreateDataSourceFromS3 ¶
func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error)
Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
If Amazon ML can't accept the input source, it sets the Status parameter
to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.
The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.
After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateDataSourceFromS3Input{ DataSourceId: aws.String("EntityId"), // Required DataSpec: &machinelearning.S3DataSpec{ // Required DataLocationS3: aws.String("S3Url"), // Required DataRearrangement: aws.String("DataRearrangement"), DataSchema: aws.String("DataSchema"), DataSchemaLocationS3: aws.String("S3Url"), }, ComputeStatistics: aws.Bool(true), DataSourceName: aws.String("EntityName"), } resp, err := svc.CreateDataSourceFromS3(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateDataSourceFromS3Request ¶
func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output)
CreateDataSourceFromS3Request generates a request for the CreateDataSourceFromS3 operation.
func (*MachineLearning) CreateEvaluation ¶
func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error)
Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.
CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.
You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateEvaluationInput{ EvaluationDataSourceId: aws.String("EntityId"), // Required EvaluationId: aws.String("EntityId"), // Required MLModelId: aws.String("EntityId"), // Required EvaluationName: aws.String("EntityName"), } resp, err := svc.CreateEvaluation(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateEvaluationRequest ¶
func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput)
CreateEvaluationRequest generates a request for the CreateEvaluation operation.
func (*MachineLearning) CreateMLModel ¶
func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error)
Creates a new MLModel using the DataSource and the recipe as information sources.
An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.
CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.
You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.
CreateMLModel requires a DataSource with computed statistics, which can
be created by setting ComputeStatistics to true in CreateDataSourcceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateMLModelInput{ MLModelId: aws.String("EntityId"), // Required MLModelType: aws.String("MLModelType"), // Required TrainingDataSourceId: aws.String("EntityId"), // Required MLModelName: aws.String("EntityName"), Parameters: map[string]*string{ "Key": aws.String("StringType"), // Required // More values... }, Recipe: aws.String("Recipe"), RecipeUri: aws.String("S3Url"), } resp, err := svc.CreateMLModel(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateMLModelRequest ¶
func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput)
CreateMLModelRequest generates a request for the CreateMLModel operation.
func (*MachineLearning) CreateRealtimeEndpoint ¶
func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error)
Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.CreateRealtimeEndpointInput{ MLModelId: aws.String("EntityId"), // Required } resp, err := svc.CreateRealtimeEndpoint(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) CreateRealtimeEndpointRequest ¶
func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput)
CreateRealtimeEndpointRequest generates a request for the CreateRealtimeEndpoint operation.
func (*MachineLearning) DeleteBatchPrediction ¶
func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error)
Assigns the DELETED status to a BatchPrediction, rendering it unusable.
After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.
Caution: The result of the DeleteBatchPrediction operation is irreversible.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteBatchPredictionInput{ BatchPredictionId: aws.String("EntityId"), // Required } resp, err := svc.DeleteBatchPrediction(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteBatchPredictionRequest ¶
func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput)
DeleteBatchPredictionRequest generates a request for the DeleteBatchPrediction operation.
func (*MachineLearning) DeleteDataSource ¶
func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error)
Assigns the DELETED status to a DataSource, rendering it unusable.
After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.
Caution: The results of the DeleteDataSource operation are irreversible.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteDataSourceInput{ DataSourceId: aws.String("EntityId"), // Required } resp, err := svc.DeleteDataSource(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteDataSourceRequest ¶
func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput)
DeleteDataSourceRequest generates a request for the DeleteDataSource operation.
func (*MachineLearning) DeleteEvaluation ¶
func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error)
Assigns the DELETED status to an Evaluation, rendering it unusable.
After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.
Caution The results of the DeleteEvaluation operation are irreversible.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteEvaluationInput{ EvaluationId: aws.String("EntityId"), // Required } resp, err := svc.DeleteEvaluation(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteEvaluationRequest ¶
func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput)
DeleteEvaluationRequest generates a request for the DeleteEvaluation operation.
func (*MachineLearning) DeleteMLModel ¶
func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error)
Assigns the DELETED status to an MLModel, rendering it unusable.
After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.
Caution: The result of the DeleteMLModel operation is irreversible.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteMLModelInput{ MLModelId: aws.String("EntityId"), // Required } resp, err := svc.DeleteMLModel(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteMLModelRequest ¶
func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput)
DeleteMLModelRequest generates a request for the DeleteMLModel operation.
func (*MachineLearning) DeleteRealtimeEndpoint ¶
func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error)
Deletes a real time endpoint of an MLModel.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteRealtimeEndpointInput{ MLModelId: aws.String("EntityId"), // Required } resp, err := svc.DeleteRealtimeEndpoint(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteRealtimeEndpointRequest ¶
func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput)
DeleteRealtimeEndpointRequest generates a request for the DeleteRealtimeEndpoint operation.
func (*MachineLearning) DeleteTags ¶
func (c *MachineLearning) DeleteTags(input *DeleteTagsInput) (*DeleteTagsOutput, error)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DeleteTagsInput{ ResourceId: aws.String("EntityId"), // Required ResourceType: aws.String("TaggableResourceType"), // Required TagKeys: []*string{ // Required aws.String("TagKey"), // Required // More values... }, } resp, err := svc.DeleteTags(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DeleteTagsRequest ¶
func (c *MachineLearning) DeleteTagsRequest(input *DeleteTagsInput) (req *request.Request, output *DeleteTagsOutput)
DeleteTagsRequest generates a request for the DeleteTags operation.
func (*MachineLearning) DescribeBatchPredictions ¶
func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error)
Returns a list of BatchPrediction operations that match the search criteria in the request.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DescribeBatchPredictionsInput{ EQ: aws.String("ComparatorValue"), FilterVariable: aws.String("BatchPredictionFilterVariable"), GE: aws.String("ComparatorValue"), GT: aws.String("ComparatorValue"), LE: aws.String("ComparatorValue"), LT: aws.String("ComparatorValue"), Limit: aws.Int64(1), NE: aws.String("ComparatorValue"), NextToken: aws.String("StringType"), Prefix: aws.String("ComparatorValue"), SortOrder: aws.String("SortOrder"), } resp, err := svc.DescribeBatchPredictions(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DescribeBatchPredictionsPages ¶
func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, fn func(p *DescribeBatchPredictionsOutput, lastPage bool) (shouldContinue bool)) error
func (*MachineLearning) DescribeBatchPredictionsRequest ¶
func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput)
DescribeBatchPredictionsRequest generates a request for the DescribeBatchPredictions operation.
func (*MachineLearning) DescribeDataSources ¶
func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error)
Returns a list of DataSource that match the search criteria in the request.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DescribeDataSourcesInput{ EQ: aws.String("ComparatorValue"), FilterVariable: aws.String("DataSourceFilterVariable"), GE: aws.String("ComparatorValue"), GT: aws.String("ComparatorValue"), LE: aws.String("ComparatorValue"), LT: aws.String("ComparatorValue"), Limit: aws.Int64(1), NE: aws.String("ComparatorValue"), NextToken: aws.String("StringType"), Prefix: aws.String("ComparatorValue"), SortOrder: aws.String("SortOrder"), } resp, err := svc.DescribeDataSources(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DescribeDataSourcesPages ¶
func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, fn func(p *DescribeDataSourcesOutput, lastPage bool) (shouldContinue bool)) error
func (*MachineLearning) DescribeDataSourcesRequest ¶
func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput)
DescribeDataSourcesRequest generates a request for the DescribeDataSources operation.
func (*MachineLearning) DescribeEvaluations ¶
func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error)
Returns a list of DescribeEvaluations that match the search criteria in the request.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DescribeEvaluationsInput{ EQ: aws.String("ComparatorValue"), FilterVariable: aws.String("EvaluationFilterVariable"), GE: aws.String("ComparatorValue"), GT: aws.String("ComparatorValue"), LE: aws.String("ComparatorValue"), LT: aws.String("ComparatorValue"), Limit: aws.Int64(1), NE: aws.String("ComparatorValue"), NextToken: aws.String("StringType"), Prefix: aws.String("ComparatorValue"), SortOrder: aws.String("SortOrder"), } resp, err := svc.DescribeEvaluations(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DescribeEvaluationsPages ¶
func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, fn func(p *DescribeEvaluationsOutput, lastPage bool) (shouldContinue bool)) error
func (*MachineLearning) DescribeEvaluationsRequest ¶
func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput)
DescribeEvaluationsRequest generates a request for the DescribeEvaluations operation.
func (*MachineLearning) DescribeMLModels ¶
func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error)
Returns a list of MLModel that match the search criteria in the request.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DescribeMLModelsInput{ EQ: aws.String("ComparatorValue"), FilterVariable: aws.String("MLModelFilterVariable"), GE: aws.String("ComparatorValue"), GT: aws.String("ComparatorValue"), LE: aws.String("ComparatorValue"), LT: aws.String("ComparatorValue"), Limit: aws.Int64(1), NE: aws.String("ComparatorValue"), NextToken: aws.String("StringType"), Prefix: aws.String("ComparatorValue"), SortOrder: aws.String("SortOrder"), } resp, err := svc.DescribeMLModels(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DescribeMLModelsPages ¶
func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(p *DescribeMLModelsOutput, lastPage bool) (shouldContinue bool)) error
func (*MachineLearning) DescribeMLModelsRequest ¶
func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput)
DescribeMLModelsRequest generates a request for the DescribeMLModels operation.
func (*MachineLearning) DescribeTags ¶
func (c *MachineLearning) DescribeTags(input *DescribeTagsInput) (*DescribeTagsOutput, error)
Describes one or more of the tags for your Amazon ML object.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.DescribeTagsInput{ ResourceId: aws.String("EntityId"), // Required ResourceType: aws.String("TaggableResourceType"), // Required } resp, err := svc.DescribeTags(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) DescribeTagsRequest ¶
func (c *MachineLearning) DescribeTagsRequest(input *DescribeTagsInput) (req *request.Request, output *DescribeTagsOutput)
DescribeTagsRequest generates a request for the DescribeTags operation.
func (*MachineLearning) GetBatchPrediction ¶
func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error)
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.GetBatchPredictionInput{ BatchPredictionId: aws.String("EntityId"), // Required } resp, err := svc.GetBatchPrediction(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) GetBatchPredictionRequest ¶
func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput)
GetBatchPredictionRequest generates a request for the GetBatchPrediction operation.
func (*MachineLearning) GetDataSource ¶
func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error)
Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.GetDataSourceInput{ DataSourceId: aws.String("EntityId"), // Required Verbose: aws.Bool(true), } resp, err := svc.GetDataSource(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) GetDataSourceRequest ¶
func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput)
GetDataSourceRequest generates a request for the GetDataSource operation.
func (*MachineLearning) GetEvaluation ¶
func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error)
Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.GetEvaluationInput{ EvaluationId: aws.String("EntityId"), // Required } resp, err := svc.GetEvaluation(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) GetEvaluationRequest ¶
func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput)
GetEvaluationRequest generates a request for the GetEvaluation operation.
func (*MachineLearning) GetMLModel ¶
func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error)
Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
GetMLModel provides results in normal or verbose format.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.GetMLModelInput{ MLModelId: aws.String("EntityId"), // Required Verbose: aws.Bool(true), } resp, err := svc.GetMLModel(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) GetMLModelRequest ¶
func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput)
GetMLModelRequest generates a request for the GetMLModel operation.
func (*MachineLearning) Predict ¶
func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error)
Generates a prediction for the observation using the specified ML Model.
Note Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.PredictInput{ MLModelId: aws.String("EntityId"), // Required PredictEndpoint: aws.String("VipURL"), // Required Record: map[string]*string{ // Required "Key": aws.String("VariableValue"), // Required // More values... }, } resp, err := svc.Predict(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) PredictRequest ¶
func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput)
PredictRequest generates a request for the Predict operation.
func (*MachineLearning) UpdateBatchPrediction ¶
func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error)
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.UpdateBatchPredictionInput{ BatchPredictionId: aws.String("EntityId"), // Required BatchPredictionName: aws.String("EntityName"), // Required } resp, err := svc.UpdateBatchPrediction(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) UpdateBatchPredictionRequest ¶
func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput)
UpdateBatchPredictionRequest generates a request for the UpdateBatchPrediction operation.
func (*MachineLearning) UpdateDataSource ¶
func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error)
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.UpdateDataSourceInput{ DataSourceId: aws.String("EntityId"), // Required DataSourceName: aws.String("EntityName"), // Required } resp, err := svc.UpdateDataSource(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) UpdateDataSourceRequest ¶
func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput)
UpdateDataSourceRequest generates a request for the UpdateDataSource operation.
func (*MachineLearning) UpdateEvaluation ¶
func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error)
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.UpdateEvaluationInput{ EvaluationId: aws.String("EntityId"), // Required EvaluationName: aws.String("EntityName"), // Required } resp, err := svc.UpdateEvaluation(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) UpdateEvaluationRequest ¶
func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput)
UpdateEvaluationRequest generates a request for the UpdateEvaluation operation.
func (*MachineLearning) UpdateMLModel ¶
func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error)
Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data element.
Example ¶
package main import ( "fmt" "github.com/djannot/aws-sdk-go/aws" "github.com/djannot/aws-sdk-go/aws/session" "github.com/djannot/aws-sdk-go/service/machinelearning" ) func main() { svc := machinelearning.New(session.New()) params := &machinelearning.UpdateMLModelInput{ MLModelId: aws.String("EntityId"), // Required MLModelName: aws.String("EntityName"), ScoreThreshold: aws.Float64(1.0), } resp, err := svc.UpdateMLModel(params) if err != nil { // Print the error, cast err to awserr.Error to get the Code and // Message from an error. fmt.Println(err.Error()) return } // Pretty-print the response data. fmt.Println(resp) }
Output:
func (*MachineLearning) UpdateMLModelRequest ¶
func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput)
UpdateMLModelRequest generates a request for the UpdateMLModel operation.
func (*MachineLearning) WaitUntilBatchPredictionAvailable ¶
func (c *MachineLearning) WaitUntilBatchPredictionAvailable(input *DescribeBatchPredictionsInput) error
func (*MachineLearning) WaitUntilDataSourceAvailable ¶
func (c *MachineLearning) WaitUntilDataSourceAvailable(input *DescribeDataSourcesInput) error
func (*MachineLearning) WaitUntilEvaluationAvailable ¶
func (c *MachineLearning) WaitUntilEvaluationAvailable(input *DescribeEvaluationsInput) error
func (*MachineLearning) WaitUntilMLModelAvailable ¶
func (c *MachineLearning) WaitUntilMLModelAvailable(input *DescribeMLModelsInput) error
type PerformanceMetrics ¶
type PerformanceMetrics struct { Properties map[string]*string `type:"map"` // contains filtered or unexported fields }
Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:
BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique
to measure performance.
RegressionRMSE: The regression MLModel uses the Root Mean Square Error
(RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique
to measure performance.
For more information about performance metrics, please see the Amazon
Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
func (PerformanceMetrics) GoString ¶
func (s PerformanceMetrics) GoString() string
GoString returns the string representation
func (PerformanceMetrics) String ¶
func (s PerformanceMetrics) String() string
String returns the string representation
type PredictInput ¶
type PredictInput struct { // A unique identifier of the MLModel. MLModelId *string `min:"1" type:"string" required:"true"` PredictEndpoint *string `type:"string" required:"true"` // A map of variable name-value pairs that represent an observation. Record map[string]*string `type:"map" required:"true"` // contains filtered or unexported fields }
func (PredictInput) GoString ¶
func (s PredictInput) GoString() string
GoString returns the string representation
func (PredictInput) String ¶
func (s PredictInput) String() string
String returns the string representation
func (*PredictInput) Validate ¶
func (s *PredictInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type PredictOutput ¶
type PredictOutput struct { // The output from a Predict operation: // // Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE // - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD // // PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request. // // PredictedScores - Contains the raw classification score corresponding // to each label. // // PredictedValue - Present for a REGRESSION MLModel request. Prediction *Prediction `type:"structure"` // contains filtered or unexported fields }
func (PredictOutput) GoString ¶
func (s PredictOutput) GoString() string
GoString returns the string representation
func (PredictOutput) String ¶
func (s PredictOutput) String() string
String returns the string representation
type Prediction ¶
type Prediction struct { // Provides any additional details regarding the prediction. Details map[string]*string `locationName:"details" type:"map"` // The prediction label for either a BINARY or MULTICLASS MLModel. PredictedLabel *string `locationName:"predictedLabel" min:"1" type:"string"` // Provides the raw classification score corresponding to each label. PredictedScores map[string]*float64 `locationName:"predictedScores" type:"map"` // The prediction value for REGRESSION MLModel. PredictedValue *float64 `locationName:"predictedValue" type:"float"` // contains filtered or unexported fields }
The output from a Predict operation:
Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE
- REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request. PredictedScores - Contains the raw classification score corresponding
to each label.
PredictedValue - Present for a REGRESSION MLModel request.
func (Prediction) GoString ¶
func (s Prediction) GoString() string
GoString returns the string representation
func (Prediction) String ¶
func (s Prediction) String() string
String returns the string representation
type RDSDataSpec ¶
type RDSDataSpec struct { // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary datasources // for training and evaluation. To create a complementary datasource, use the // same values for percentBegin and percentEnd, along with the complement parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in the // input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting (for // example, you can use the S3 path to your data as the random seed string). // If you choose the random split strategy, Amazon ML assigns each row of data // a pseudo-random number between 0 and 100, and then selects the rows that // have an assigned number between percentBegin and percentEnd. Pseudo-random // numbers are assigned using both the input seed string value and the byte // offset as a seed, so changing the data results in a different split. Any // existing ordering is preserved. The random splitting strategy ensures that // variables in the training and evaluation data are distributed similarly. // It is useful in the cases where the input data may have an implicit sort // order, which would otherwise result in training and evaluation datasources // containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon RDS DataSource. The // DataSchema defines the structure of the observation data in the data file(s) // referenced in the DataSource. // // A DataSchema is not required if you specify a DataSchemaUri // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // The Amazon S3 location of the DataSchema. DataSchemaUri *string `type:"string"` // The AWS Identity and Access Management (IAM) credentials that are used connect // to the Amazon RDS database. DatabaseCredentials *RDSDatabaseCredentials `type:"structure" required:"true"` // Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database. DatabaseInformation *RDSDatabase `type:"structure" required:"true"` // The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute // Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS // to an Amazon S3 task. For more information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ResourceRole *string `min:"1" type:"string" required:"true"` // The Amazon S3 location for staging Amazon RDS data. The data retrieved from // Amazon RDS using SelectSqlQuery is stored in this location. S3StagingLocation *string `type:"string" required:"true"` // The security group IDs to be used to access a VPC-based RDS DB instance. // Ensure that there are appropriate ingress rules set up to allow access to // the RDS DB instance. This attribute is used by Data Pipeline to carry out // the copy operation from Amazon RDS to an Amazon S3 task. SecurityGroupIds []*string `type:"list" required:"true"` // The query that is used to retrieve the observation data for the DataSource. SelectSqlQuery *string `min:"1" type:"string" required:"true"` // The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to // monitor the progress of the copy task from Amazon RDS to Amazon S3. For more // information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ServiceRole *string `min:"1" type:"string" required:"true"` // The subnet ID to be used to access a VPC-based RDS DB instance. This attribute // is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon // S3. SubnetId *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
func (RDSDataSpec) GoString ¶
func (s RDSDataSpec) GoString() string
GoString returns the string representation
func (RDSDataSpec) String ¶
func (s RDSDataSpec) String() string
String returns the string representation
func (*RDSDataSpec) Validate ¶
func (s *RDSDataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSDatabase ¶
type RDSDatabase struct { // The name of a database hosted on an RDS DB instance. DatabaseName *string `min:"1" type:"string" required:"true"` // The ID of an RDS DB instance. InstanceIdentifier *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The database details of an Amazon RDS database.
func (RDSDatabase) GoString ¶
func (s RDSDatabase) GoString() string
GoString returns the string representation
func (RDSDatabase) String ¶
func (s RDSDatabase) String() string
String returns the string representation
func (*RDSDatabase) Validate ¶
func (s *RDSDatabase) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSDatabaseCredentials ¶
type RDSDatabaseCredentials struct { // The password to be used by Amazon ML to connect to a database on an RDS DB // instance. The password should have sufficient permissions to execute the // RDSSelectQuery query. Password *string `min:"8" type:"string" required:"true"` // The username to be used by Amazon ML to connect to database on an Amazon // RDS instance. The username should have sufficient permissions to execute // an RDSSelectSqlQuery query. Username *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
The database credentials to connect to a database on an RDS DB instance.
func (RDSDatabaseCredentials) GoString ¶
func (s RDSDatabaseCredentials) GoString() string
GoString returns the string representation
func (RDSDatabaseCredentials) String ¶
func (s RDSDatabaseCredentials) String() string
String returns the string representation
func (*RDSDatabaseCredentials) Validate ¶
func (s *RDSDatabaseCredentials) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RDSMetadata ¶
type RDSMetadata struct { // The ID of the Data Pipeline instance that is used to carry to copy data from // Amazon RDS to Amazon S3. You can use the ID to find details about the instance // in the Data Pipeline console. DataPipelineId *string `min:"1" type:"string"` // The database details required to connect to an Amazon RDS. Database *RDSDatabase `type:"structure"` // The username to be used by Amazon ML to connect to database on an Amazon // RDS instance. The username should have sufficient permissions to execute // an RDSSelectSqlQuery query. DatabaseUserName *string `min:"1" type:"string"` // The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance // to carry out the copy task from Amazon RDS to Amazon S3. For more information, // see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ResourceRole *string `min:"1" type:"string"` // The SQL query that is supplied during CreateDataSourceFromRDS. Returns only // if Verbose is true in GetDataSourceInput. SelectSqlQuery *string `min:"1" type:"string"` // The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to // monitor the progress of the copy task from Amazon RDS to Amazon S3. For more // information, see Role templates (http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html) // for data pipelines. ServiceRole *string `min:"1" type:"string"` // contains filtered or unexported fields }
The datasource details that are specific to Amazon RDS.
func (RDSMetadata) GoString ¶
func (s RDSMetadata) GoString() string
GoString returns the string representation
func (RDSMetadata) String ¶
func (s RDSMetadata) String() string
String returns the string representation
type RealtimeEndpointInfo ¶
type RealtimeEndpointInfo struct { // The time that the request to create the real-time endpoint for the MLModel // was received. The time is expressed in epoch time. CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"` // The current status of the real-time endpoint for the MLModel. This element // can have one of the following values: // // NONE - Endpoint does not exist or was previously deleted. READY - Endpoint // is ready to be used for real-time predictions. UPDATING - Updating/creating // the endpoint. EndpointStatus *string `type:"string" enum:"RealtimeEndpointStatus"` // The URI that specifies where to send real-time prediction requests for the // MLModel. // // Note The application must wait until the real-time endpoint is ready before // using this URI. EndpointUrl *string `type:"string"` // The maximum processing rate for the real-time endpoint for MLModel, measured // in incoming requests per second. PeakRequestsPerSecond *int64 `type:"integer"` // contains filtered or unexported fields }
Describes the real-time endpoint information for an MLModel.
func (RealtimeEndpointInfo) GoString ¶
func (s RealtimeEndpointInfo) GoString() string
GoString returns the string representation
func (RealtimeEndpointInfo) String ¶
func (s RealtimeEndpointInfo) String() string
String returns the string representation
type RedshiftDataSpec ¶
type RedshiftDataSpec struct { // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary datasources // for training and evaluation. To create a complementary datasource, use the // same values for percentBegin and percentEnd, along with the complement parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in the // input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting (for // example, you can use the S3 path to your data as the random seed string). // If you choose the random split strategy, Amazon ML assigns each row of data // a pseudo-random number between 0 and 100, and then selects the rows that // have an assigned number between percentBegin and percentEnd. Pseudo-random // numbers are assigned using both the input seed string value and the byte // offset as a seed, so changing the data results in a different split. Any // existing ordering is preserved. The random splitting strategy ensures that // variables in the training and evaluation data are distributed similarly. // It is useful in the cases where the input data may have an implicit sort // order, which would otherwise result in training and evaluation datasources // containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon Redshift DataSource. // The DataSchema defines the structure of the observation data in the data // file(s) referenced in the DataSource. // // A DataSchema is not required if you specify a DataSchemaUri. // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // Describes the schema location for an Amazon Redshift DataSource. DataSchemaUri *string `type:"string"` // Describes AWS Identity and Access Management (IAM) credentials that are used // connect to the Amazon Redshift database. DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"` // Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource. DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"` // Describes an Amazon S3 location to store the result set of the SelectSqlQuery // query. S3StagingLocation *string `type:"string" required:"true"` // Describes the SQL Query to execute on an Amazon Redshift database for an // Amazon Redshift DataSource. SelectSqlQuery *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the data specification of an Amazon Redshift DataSource.
func (RedshiftDataSpec) GoString ¶
func (s RedshiftDataSpec) GoString() string
GoString returns the string representation
func (RedshiftDataSpec) String ¶
func (s RedshiftDataSpec) String() string
String returns the string representation
func (*RedshiftDataSpec) Validate ¶
func (s *RedshiftDataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftDatabase ¶
type RedshiftDatabase struct { // The ID of an Amazon Redshift cluster. ClusterIdentifier *string `min:"1" type:"string" required:"true"` // The name of a database hosted on an Amazon Redshift cluster. DatabaseName *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the database details required to connect to an Amazon Redshift database.
func (RedshiftDatabase) GoString ¶
func (s RedshiftDatabase) GoString() string
GoString returns the string representation
func (RedshiftDatabase) String ¶
func (s RedshiftDatabase) String() string
String returns the string representation
func (*RedshiftDatabase) Validate ¶
func (s *RedshiftDatabase) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftDatabaseCredentials ¶
type RedshiftDatabaseCredentials struct { // A password to be used by Amazon ML to connect to a database on an Amazon // Redshift cluster. The password should have sufficient permissions to execute // a RedshiftSelectSqlQuery query. The password should be valid for an Amazon // Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). Password *string `min:"8" type:"string" required:"true"` // A username to be used by Amazon Machine Learning (Amazon ML)to connect to // a database on an Amazon Redshift cluster. The username should have sufficient // permissions to execute the RedshiftSelectSqlQuery query. The username should // be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). Username *string `min:"1" type:"string" required:"true"` // contains filtered or unexported fields }
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
func (RedshiftDatabaseCredentials) GoString ¶
func (s RedshiftDatabaseCredentials) GoString() string
GoString returns the string representation
func (RedshiftDatabaseCredentials) String ¶
func (s RedshiftDatabaseCredentials) String() string
String returns the string representation
func (*RedshiftDatabaseCredentials) Validate ¶
func (s *RedshiftDatabaseCredentials) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type RedshiftMetadata ¶
type RedshiftMetadata struct { // A username to be used by Amazon Machine Learning (Amazon ML)to connect to // a database on an Amazon Redshift cluster. The username should have sufficient // permissions to execute the RedshiftSelectSqlQuery query. The username should // be valid for an Amazon Redshift USER (http://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_USER.html). DatabaseUserName *string `min:"1" type:"string"` // Describes the database details required to connect to an Amazon Redshift // database. RedshiftDatabase *RedshiftDatabase `type:"structure"` // The SQL query that is specified during CreateDataSourceFromRedshift. Returns // only if Verbose is true in GetDataSourceInput. SelectSqlQuery *string `min:"1" type:"string"` // contains filtered or unexported fields }
Describes the DataSource details specific to Amazon Redshift.
func (RedshiftMetadata) GoString ¶
func (s RedshiftMetadata) GoString() string
GoString returns the string representation
func (RedshiftMetadata) String ¶
func (s RedshiftMetadata) String() string
String returns the string representation
type S3DataSpec ¶
type S3DataSpec struct { // The location of the data file(s) used by a DataSource. The URI specifies // a data file or an Amazon Simple Storage Service (Amazon S3) directory or // bucket containing data files. DataLocationS3 *string `type:"string" required:"true"` // A JSON string that represents the splitting and rearrangement processing // to be applied to a DataSource. If the DataRearrangement parameter is not // provided, all of the input data is used to create the Datasource. // // There are multiple parameters that control what data is used to create a // datasource: // // percentBegin // // Use percentBegin to indicate the beginning of the range of the data used // to create the Datasource. If you do not include percentBegin and percentEnd, // Amazon ML includes all of the data when creating the datasource. // // percentEnd // // Use percentEnd to indicate the end of the range of the data used to create // the Datasource. If you do not include percentBegin and percentEnd, Amazon // ML includes all of the data when creating the datasource. // // complement // // The complement parameter instructs Amazon ML to use the data that is not // included in the range of percentBegin to percentEnd to create a datasource. // The complement parameter is useful if you need to create complementary datasources // for training and evaluation. To create a complementary datasource, use the // same values for percentBegin and percentEnd, along with the complement parameter. // // For example, the following two datasources do not share any data, and can // be used to train and evaluate a model. The first datasource has 25 percent // of the data, and the second one has 75 percent of the data. // // Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} // // Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, // "complement":"true"}} // // strategy // // To change how Amazon ML splits the data for a datasource, use the strategy // parameter. // // The default value for the strategy parameter is sequential, meaning that // Amazon ML takes all of the data records between the percentBegin and percentEnd // parameters for the datasource, in the order that the records appear in the // input data. // // The following two DataRearrangement lines are examples of sequentially ordered // training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"sequential", "complement":"true"}} // // To randomly split the input data into the proportions indicated by the percentBegin // and percentEnd parameters, set the strategy parameter to random and provide // a string that is used as the seed value for the random data splitting (for // example, you can use the S3 path to your data as the random seed string). // If you choose the random split strategy, Amazon ML assigns each row of data // a pseudo-random number between 0 and 100, and then selects the rows that // have an assigned number between percentBegin and percentEnd. Pseudo-random // numbers are assigned using both the input seed string value and the byte // offset as a seed, so changing the data results in a different split. Any // existing ordering is preserved. The random splitting strategy ensures that // variables in the training and evaluation data are distributed similarly. // It is useful in the cases where the input data may have an implicit sort // order, which would otherwise result in training and evaluation datasources // containing non-similar data records. // // The following two DataRearrangement lines are examples of non-sequentially // ordered training and evaluation datasources: // // Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}} // // Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, // "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} DataRearrangement *string `type:"string"` // A JSON string that represents the schema for an Amazon S3 DataSource. The // DataSchema defines the structure of the observation data in the data file(s) // referenced in the DataSource. // // You must provide either the DataSchema or the DataSchemaLocationS3. // // Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames // have an array of key-value pairs for their value. Use the following format // to define your DataSchema. // // { "version": "1.0", // // "recordAnnotationFieldName": "F1", // // "recordWeightFieldName": "F2", // // "targetFieldName": "F3", // // "dataFormat": "CSV", // // "dataFileContainsHeader": true, // // "attributes": [ // // { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": // "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": // "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" // }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": // "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" // } ], // // "excludedVariableNames": [ "F6" ] } DataSchema *string `type:"string"` // Describes the schema location in Amazon S3. You must provide either the DataSchema // or the DataSchemaLocationS3. DataSchemaLocationS3 *string `type:"string"` // contains filtered or unexported fields }
Describes the data specification of a DataSource.
func (S3DataSpec) GoString ¶
func (s S3DataSpec) GoString() string
GoString returns the string representation
func (S3DataSpec) String ¶
func (s S3DataSpec) String() string
String returns the string representation
func (*S3DataSpec) Validate ¶
func (s *S3DataSpec) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type Tag ¶
type Tag struct { // A unique identifier for the tag. Valid characters include Unicode letters, // digits, white space, _, ., /, =, +, -, %, and @. Key *string `min:"1" type:"string"` // An optional string, typically used to describe or define the tag. Valid characters // include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. Value *string `type:"string"` // contains filtered or unexported fields }
A custom key-value pair associated with an ML object, such as an ML model.
type UpdateBatchPredictionInput ¶
type UpdateBatchPredictionInput struct { // The ID assigned to the BatchPrediction during creation. BatchPredictionId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the BatchPrediction. BatchPredictionName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateBatchPredictionInput) GoString ¶
func (s UpdateBatchPredictionInput) GoString() string
GoString returns the string representation
func (UpdateBatchPredictionInput) String ¶
func (s UpdateBatchPredictionInput) String() string
String returns the string representation
func (*UpdateBatchPredictionInput) Validate ¶
func (s *UpdateBatchPredictionInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateBatchPredictionOutput ¶
type UpdateBatchPredictionOutput struct { // The ID assigned to the BatchPrediction during creation. This value should // be identical to the value of the BatchPredictionId in the request. BatchPredictionId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
func (UpdateBatchPredictionOutput) GoString ¶
func (s UpdateBatchPredictionOutput) GoString() string
GoString returns the string representation
func (UpdateBatchPredictionOutput) String ¶
func (s UpdateBatchPredictionOutput) String() string
String returns the string representation
type UpdateDataSourceInput ¶
type UpdateDataSourceInput struct { // The ID assigned to the DataSource during creation. DataSourceId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the DataSource that will replace // the current description. DataSourceName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateDataSourceInput) GoString ¶
func (s UpdateDataSourceInput) GoString() string
GoString returns the string representation
func (UpdateDataSourceInput) String ¶
func (s UpdateDataSourceInput) String() string
String returns the string representation
func (*UpdateDataSourceInput) Validate ¶
func (s *UpdateDataSourceInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateDataSourceOutput ¶
type UpdateDataSourceOutput struct { // The ID assigned to the DataSource during creation. This value should be identical // to the value of the DataSourceID in the request. DataSourceId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
func (UpdateDataSourceOutput) GoString ¶
func (s UpdateDataSourceOutput) GoString() string
GoString returns the string representation
func (UpdateDataSourceOutput) String ¶
func (s UpdateDataSourceOutput) String() string
String returns the string representation
type UpdateEvaluationInput ¶
type UpdateEvaluationInput struct { // The ID assigned to the Evaluation during creation. EvaluationId *string `min:"1" type:"string" required:"true"` // A new user-supplied name or description of the Evaluation that will replace // the current content. EvaluationName *string `type:"string" required:"true"` // contains filtered or unexported fields }
func (UpdateEvaluationInput) GoString ¶
func (s UpdateEvaluationInput) GoString() string
GoString returns the string representation
func (UpdateEvaluationInput) String ¶
func (s UpdateEvaluationInput) String() string
String returns the string representation
func (*UpdateEvaluationInput) Validate ¶
func (s *UpdateEvaluationInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateEvaluationOutput ¶
type UpdateEvaluationOutput struct { // The ID assigned to the Evaluation during creation. This value should be identical // to the value of the Evaluation in the request. EvaluationId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
func (UpdateEvaluationOutput) GoString ¶
func (s UpdateEvaluationOutput) GoString() string
GoString returns the string representation
func (UpdateEvaluationOutput) String ¶
func (s UpdateEvaluationOutput) String() string
String returns the string representation
type UpdateMLModelInput ¶
type UpdateMLModelInput struct { // The ID assigned to the MLModel during creation. MLModelId *string `min:"1" type:"string" required:"true"` // A user-supplied name or description of the MLModel. MLModelName *string `type:"string"` // The ScoreThreshold used in binary classification MLModel that marks the boundary // between a positive prediction and a negative prediction. // // Output values greater than or equal to the ScoreThreshold receive a positive // result from the MLModel, such as true. Output values less than the ScoreThreshold // receive a negative response from the MLModel, such as false. ScoreThreshold *float64 `type:"float"` // contains filtered or unexported fields }
func (UpdateMLModelInput) GoString ¶
func (s UpdateMLModelInput) GoString() string
GoString returns the string representation
func (UpdateMLModelInput) String ¶
func (s UpdateMLModelInput) String() string
String returns the string representation
func (*UpdateMLModelInput) Validate ¶
func (s *UpdateMLModelInput) Validate() error
Validate inspects the fields of the type to determine if they are valid.
type UpdateMLModelOutput ¶
type UpdateMLModelOutput struct { // The ID assigned to the MLModel during creation. This value should be identical // to the value of the MLModelID in the request. MLModelId *string `min:"1" type:"string"` // contains filtered or unexported fields }
Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
func (UpdateMLModelOutput) GoString ¶
func (s UpdateMLModelOutput) GoString() string
GoString returns the string representation
func (UpdateMLModelOutput) String ¶
func (s UpdateMLModelOutput) String() string
String returns the string representation
Directories
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Package machinelearningiface provides an interface for the Amazon Machine Learning.
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Package machinelearningiface provides an interface for the Amazon Machine Learning. |