| Copyright | (c) 2013-2023 Brendan Hay |
|---|---|
| License | Mozilla Public License, v. 2.0. |
| Maintainer | Brendan Hay |
| Stability | auto-generated |
| Portability | non-portable (GHC extensions) |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Amazonka.MachineLearning
Contents
- Service Configuration
- Errors
- Waiters
- Operations
- AddTags
- CreateBatchPrediction
- CreateDataSourceFromRDS
- CreateDataSourceFromRedshift
- CreateDataSourceFromS3
- CreateEvaluation
- CreateMLModel
- CreateRealtimeEndpoint
- DeleteBatchPrediction
- DeleteDataSource
- DeleteEvaluation
- DeleteMLModel
- DeleteRealtimeEndpoint
- DeleteTags
- DescribeBatchPredictions (Paginated)
- DescribeDataSources (Paginated)
- DescribeEvaluations (Paginated)
- DescribeMLModels (Paginated)
- DescribeTags
- GetBatchPrediction
- GetDataSource
- GetEvaluation
- GetMLModel
- Predict
- UpdateBatchPrediction
- UpdateDataSource
- UpdateEvaluation
- UpdateMLModel
- Types
- Algorithm
- BatchPredictionFilterVariable
- DataSourceFilterVariable
- DetailsAttributes
- EntityStatus
- EvaluationFilterVariable
- MLModelFilterVariable
- MLModelType
- RealtimeEndpointStatus
- SortOrder
- TaggableResourceType
- BatchPrediction
- DataSource
- Evaluation
- MLModel
- PerformanceMetrics
- Prediction
- RDSDataSpec
- RDSDatabase
- RDSDatabaseCredentials
- RDSMetadata
- RealtimeEndpointInfo
- RedshiftDataSpec
- RedshiftDatabase
- RedshiftDatabaseCredentials
- RedshiftMetadata
- S3DataSpec
- Tag
Description
Derived from API version 2014-12-12 of the AWS service descriptions, licensed under Apache 2.0.
Definition of the public APIs exposed by Amazon Machine Learning
Synopsis
- defaultService :: Service
- _IdempotentParameterMismatchException :: AsError a => Fold a ServiceError
- _InternalServerException :: AsError a => Fold a ServiceError
- _InvalidInputException :: AsError a => Fold a ServiceError
- _InvalidTagException :: AsError a => Fold a ServiceError
- _LimitExceededException :: AsError a => Fold a ServiceError
- _PredictorNotMountedException :: AsError a => Fold a ServiceError
- _ResourceNotFoundException :: AsError a => Fold a ServiceError
- _TagLimitExceededException :: AsError a => Fold a ServiceError
- newBatchPredictionAvailable :: Wait DescribeBatchPredictions
- newDataSourceAvailable :: Wait DescribeDataSources
- newEvaluationAvailable :: Wait DescribeEvaluations
- newMLModelAvailable :: Wait DescribeMLModels
- data AddTags = AddTags' [Tag] Text TaggableResourceType
- newAddTags :: Text -> TaggableResourceType -> AddTags
- data AddTagsResponse = AddTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int
- newAddTagsResponse :: Int -> AddTagsResponse
- data CreateBatchPrediction = CreateBatchPrediction' (Maybe Text) Text Text Text Text
- newCreateBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction
- data CreateBatchPredictionResponse = CreateBatchPredictionResponse' (Maybe Text) Int
- newCreateBatchPredictionResponse :: Int -> CreateBatchPredictionResponse
- data CreateDataSourceFromRDS = CreateDataSourceFromRDS' (Maybe Bool) (Maybe Text) Text RDSDataSpec Text
- newCreateDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
- data CreateDataSourceFromRDSResponse = CreateDataSourceFromRDSResponse' (Maybe Text) Int
- newCreateDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse
- data CreateDataSourceFromRedshift = CreateDataSourceFromRedshift' (Maybe Bool) (Maybe Text) Text RedshiftDataSpec Text
- newCreateDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
- data CreateDataSourceFromRedshiftResponse = CreateDataSourceFromRedshiftResponse' (Maybe Text) Int
- newCreateDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse
- data CreateDataSourceFromS3 = CreateDataSourceFromS3' (Maybe Bool) (Maybe Text) Text S3DataSpec
- newCreateDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3
- data CreateDataSourceFromS3Response = CreateDataSourceFromS3Response' (Maybe Text) Int
- newCreateDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response
- data CreateEvaluation = CreateEvaluation' (Maybe Text) Text Text Text
- newCreateEvaluation :: Text -> Text -> Text -> CreateEvaluation
- data CreateEvaluationResponse = CreateEvaluationResponse' (Maybe Text) Int
- newCreateEvaluationResponse :: Int -> CreateEvaluationResponse
- data CreateMLModel = CreateMLModel' (Maybe Text) (Maybe (HashMap Text Text)) (Maybe Text) (Maybe Text) Text MLModelType Text
- newCreateMLModel :: Text -> MLModelType -> Text -> CreateMLModel
- data CreateMLModelResponse = CreateMLModelResponse' (Maybe Text) Int
- newCreateMLModelResponse :: Int -> CreateMLModelResponse
- data CreateRealtimeEndpoint = CreateRealtimeEndpoint' Text
- newCreateRealtimeEndpoint :: Text -> CreateRealtimeEndpoint
- data CreateRealtimeEndpointResponse = CreateRealtimeEndpointResponse' (Maybe Text) (Maybe RealtimeEndpointInfo) Int
- newCreateRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse
- data DeleteBatchPrediction = DeleteBatchPrediction' Text
- newDeleteBatchPrediction :: Text -> DeleteBatchPrediction
- data DeleteBatchPredictionResponse = DeleteBatchPredictionResponse' (Maybe Text) Int
- newDeleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse
- data DeleteDataSource = DeleteDataSource' Text
- newDeleteDataSource :: Text -> DeleteDataSource
- data DeleteDataSourceResponse = DeleteDataSourceResponse' (Maybe Text) Int
- newDeleteDataSourceResponse :: Int -> DeleteDataSourceResponse
- data DeleteEvaluation = DeleteEvaluation' Text
- newDeleteEvaluation :: Text -> DeleteEvaluation
- data DeleteEvaluationResponse = DeleteEvaluationResponse' (Maybe Text) Int
- newDeleteEvaluationResponse :: Int -> DeleteEvaluationResponse
- data DeleteMLModel = DeleteMLModel' Text
- newDeleteMLModel :: Text -> DeleteMLModel
- data DeleteMLModelResponse = DeleteMLModelResponse' (Maybe Text) Int
- newDeleteMLModelResponse :: Int -> DeleteMLModelResponse
- data DeleteRealtimeEndpoint = DeleteRealtimeEndpoint' Text
- newDeleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint
- data DeleteRealtimeEndpointResponse = DeleteRealtimeEndpointResponse' (Maybe Text) (Maybe RealtimeEndpointInfo) Int
- newDeleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse
- data DeleteTags = DeleteTags' [Text] Text TaggableResourceType
- newDeleteTags :: Text -> TaggableResourceType -> DeleteTags
- data DeleteTagsResponse = DeleteTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int
- newDeleteTagsResponse :: Int -> DeleteTagsResponse
- data DescribeBatchPredictions = DescribeBatchPredictions' (Maybe Text) (Maybe BatchPredictionFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder)
- newDescribeBatchPredictions :: DescribeBatchPredictions
- data DescribeBatchPredictionsResponse = DescribeBatchPredictionsResponse' (Maybe Text) (Maybe [BatchPrediction]) Int
- newDescribeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse
- data DescribeDataSources = DescribeDataSources' (Maybe Text) (Maybe DataSourceFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder)
- newDescribeDataSources :: DescribeDataSources
- data DescribeDataSourcesResponse = DescribeDataSourcesResponse' (Maybe Text) (Maybe [DataSource]) Int
- newDescribeDataSourcesResponse :: Int -> DescribeDataSourcesResponse
- data DescribeEvaluations = DescribeEvaluations' (Maybe Text) (Maybe EvaluationFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder)
- newDescribeEvaluations :: DescribeEvaluations
- data DescribeEvaluationsResponse = DescribeEvaluationsResponse' (Maybe Text) (Maybe [Evaluation]) Int
- newDescribeEvaluationsResponse :: Int -> DescribeEvaluationsResponse
- data DescribeMLModels = DescribeMLModels' (Maybe Text) (Maybe MLModelFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder)
- newDescribeMLModels :: DescribeMLModels
- data DescribeMLModelsResponse = DescribeMLModelsResponse' (Maybe Text) (Maybe [MLModel]) Int
- newDescribeMLModelsResponse :: Int -> DescribeMLModelsResponse
- data DescribeTags = DescribeTags' Text TaggableResourceType
- newDescribeTags :: Text -> TaggableResourceType -> DescribeTags
- data DescribeTagsResponse = DescribeTagsResponse' (Maybe Text) (Maybe TaggableResourceType) (Maybe [Tag]) Int
- newDescribeTagsResponse :: Int -> DescribeTagsResponse
- data GetBatchPrediction = GetBatchPrediction' Text
- newGetBatchPrediction :: Text -> GetBatchPrediction
- data GetBatchPredictionResponse = GetBatchPredictionResponse' (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) (Maybe Integer) Int
- newGetBatchPredictionResponse :: Int -> GetBatchPredictionResponse
- data GetDataSource = GetDataSource' (Maybe Bool) Text
- newGetDataSource :: Text -> GetDataSource
- data GetDataSourceResponse = GetDataSourceResponse' (Maybe Bool) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe RDSMetadata) (Maybe RedshiftMetadata) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) Int
- newGetDataSourceResponse :: Int -> GetDataSourceResponse
- data GetEvaluation = GetEvaluation' Text
- newGetEvaluation :: Text -> GetEvaluation
- data GetEvaluationResponse = GetEvaluationResponse' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe EntityStatus) Int
- newGetEvaluationResponse :: Int -> GetEvaluationResponse
- data GetMLModel = GetMLModel' (Maybe Bool) Text
- newGetMLModel :: Text -> GetMLModel
- data GetMLModelResponse = GetMLModelResponse' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe MLModelType) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Double) (Maybe POSIX) (Maybe Integer) (Maybe POSIX) (Maybe EntityStatus) (Maybe Text) (Maybe (HashMap Text Text)) Int
- newGetMLModelResponse :: Int -> GetMLModelResponse
- data Predict = Predict' Text (HashMap Text Text) Text
- newPredict :: Text -> Text -> Predict
- data PredictResponse = PredictResponse' (Maybe Prediction) Int
- newPredictResponse :: Int -> PredictResponse
- data UpdateBatchPrediction = UpdateBatchPrediction' Text Text
- newUpdateBatchPrediction :: Text -> Text -> UpdateBatchPrediction
- data UpdateBatchPredictionResponse = UpdateBatchPredictionResponse' (Maybe Text) Int
- newUpdateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse
- data UpdateDataSource = UpdateDataSource' Text Text
- newUpdateDataSource :: Text -> Text -> UpdateDataSource
- data UpdateDataSourceResponse = UpdateDataSourceResponse' (Maybe Text) Int
- newUpdateDataSourceResponse :: Int -> UpdateDataSourceResponse
- data UpdateEvaluation = UpdateEvaluation' Text Text
- newUpdateEvaluation :: Text -> Text -> UpdateEvaluation
- data UpdateEvaluationResponse = UpdateEvaluationResponse' (Maybe Text) Int
- newUpdateEvaluationResponse :: Int -> UpdateEvaluationResponse
- data UpdateMLModel = UpdateMLModel' (Maybe Text) (Maybe Double) Text
- newUpdateMLModel :: Text -> UpdateMLModel
- data UpdateMLModelResponse = UpdateMLModelResponse' (Maybe Text) Int
- newUpdateMLModelResponse :: Int -> UpdateMLModelResponse
- newtype Algorithm where
- Algorithm' { }
- pattern Algorithm_Sgd :: Algorithm
- newtype BatchPredictionFilterVariable where
- BatchPredictionFilterVariable' { }
- pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable
- newtype DataSourceFilterVariable where
- DataSourceFilterVariable' { }
- pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable
- newtype DetailsAttributes where
- newtype EntityStatus where
- EntityStatus' { }
- pattern EntityStatus_COMPLETED :: EntityStatus
- pattern EntityStatus_DELETED :: EntityStatus
- pattern EntityStatus_FAILED :: EntityStatus
- pattern EntityStatus_INPROGRESS :: EntityStatus
- pattern EntityStatus_PENDING :: EntityStatus
- newtype EvaluationFilterVariable where
- EvaluationFilterVariable' { }
- pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable
- newtype MLModelFilterVariable where
- MLModelFilterVariable' { }
- pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
- pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
- pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
- pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
- pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
- pattern MLModelFilterVariable_Name :: MLModelFilterVariable
- pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
- pattern MLModelFilterVariable_Status :: MLModelFilterVariable
- pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
- pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable
- newtype MLModelType where
- MLModelType' { }
- pattern MLModelType_BINARY :: MLModelType
- pattern MLModelType_MULTICLASS :: MLModelType
- pattern MLModelType_REGRESSION :: MLModelType
- newtype RealtimeEndpointStatus where
- newtype SortOrder where
- SortOrder' { }
- pattern SortOrder_Asc :: SortOrder
- pattern SortOrder_Dsc :: SortOrder
- newtype TaggableResourceType where
- data BatchPrediction = BatchPrediction' (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) (Maybe Integer)
- newBatchPrediction :: BatchPrediction
- data DataSource = DataSource' (Maybe Bool) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe RDSMetadata) (Maybe RedshiftMetadata) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus)
- newDataSource :: DataSource
- data Evaluation = Evaluation' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe EntityStatus)
- newEvaluation :: Evaluation
- data MLModel = MLModel' (Maybe Algorithm) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe MLModelType) (Maybe Text) (Maybe Text) (Maybe Double) (Maybe POSIX) (Maybe Integer) (Maybe POSIX) (Maybe EntityStatus) (Maybe Text) (Maybe (HashMap Text Text))
- newMLModel :: MLModel
- data PerformanceMetrics = PerformanceMetrics' (Maybe (HashMap Text Text))
- newPerformanceMetrics :: PerformanceMetrics
- data Prediction = Prediction' (Maybe (HashMap DetailsAttributes Text)) (Maybe Text) (Maybe (HashMap Text Double)) (Maybe Double)
- newPrediction :: Prediction
- data RDSDataSpec = RDSDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RDSDatabase Text RDSDatabaseCredentials Text Text Text Text [Text]
- newRDSDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
- data RDSDatabase = RDSDatabase' Text Text
- newRDSDatabase :: Text -> Text -> RDSDatabase
- data RDSDatabaseCredentials = RDSDatabaseCredentials' Text Text
- newRDSDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
- data RDSMetadata = RDSMetadata' (Maybe Text) (Maybe RDSDatabase) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text)
- newRDSMetadata :: RDSMetadata
- data RealtimeEndpointInfo = RealtimeEndpointInfo' (Maybe POSIX) (Maybe RealtimeEndpointStatus) (Maybe Text) (Maybe Int)
- newRealtimeEndpointInfo :: RealtimeEndpointInfo
- data RedshiftDataSpec = RedshiftDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RedshiftDatabase Text RedshiftDatabaseCredentials Text
- newRedshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
- data RedshiftDatabase = RedshiftDatabase' Text Text
- newRedshiftDatabase :: Text -> Text -> RedshiftDatabase
- data RedshiftDatabaseCredentials = RedshiftDatabaseCredentials' Text Text
- newRedshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
- data RedshiftMetadata = RedshiftMetadata' (Maybe Text) (Maybe RedshiftDatabase) (Maybe Text)
- newRedshiftMetadata :: RedshiftMetadata
- data S3DataSpec = S3DataSpec' (Maybe Text) (Maybe Text) (Maybe Text) Text
- newS3DataSpec :: Text -> S3DataSpec
- data Tag = Tag' (Maybe Text) (Maybe Text)
- newTag :: Tag
Service Configuration
defaultService :: Service Source #
API version 2014-12-12 of the Amazon Machine Learning SDK configuration.
Errors
Error matchers are designed for use with the functions provided by
Control.Exception.Lens.
This allows catching (and rethrowing) service specific errors returned
by MachineLearning.
IdempotentParameterMismatchException
_IdempotentParameterMismatchException :: AsError a => Fold a ServiceError Source #
A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
InternalServerException
_InternalServerException :: AsError a => Fold a ServiceError Source #
An error on the server occurred when trying to process a request.
InvalidInputException
_InvalidInputException :: AsError a => Fold a ServiceError Source #
An error on the client occurred. Typically, the cause is an invalid input value.
InvalidTagException
_InvalidTagException :: AsError a => Fold a ServiceError Source #
Prism for InvalidTagException' errors.
LimitExceededException
_LimitExceededException :: AsError a => Fold a ServiceError Source #
The subscriber exceeded the maximum number of operations. This exception
can occur when listing objects such as DataSource.
PredictorNotMountedException
_PredictorNotMountedException :: AsError a => Fold a ServiceError Source #
The exception is thrown when a predict request is made to an unmounted
MLModel.
ResourceNotFoundException
_ResourceNotFoundException :: AsError a => Fold a ServiceError Source #
A specified resource cannot be located.
TagLimitExceededException
_TagLimitExceededException :: AsError a => Fold a ServiceError Source #
Prism for TagLimitExceededException' errors.
Waiters
Waiters poll by repeatedly sending a request until some remote success condition
configured by the Wait specification is fulfilled. The Wait specification
determines how many attempts should be made, in addition to delay and retry strategies.
BatchPredictionAvailable
newBatchPredictionAvailable :: Wait DescribeBatchPredictions Source #
Polls DescribeBatchPredictions every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
DataSourceAvailable
newDataSourceAvailable :: Wait DescribeDataSources Source #
Polls DescribeDataSources every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
EvaluationAvailable
newEvaluationAvailable :: Wait DescribeEvaluations Source #
Polls DescribeEvaluations every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
MLModelAvailable
newMLModelAvailable :: Wait DescribeMLModels Source #
Polls DescribeMLModels every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
Operations
Some AWS operations return results that are incomplete and require subsequent
requests in order to obtain the entire result set. The process of sending
subsequent requests to continue where a previous request left off is called
pagination. For example, the ListObjects operation of Amazon S3 returns up to
1000 objects at a time, and you must send subsequent requests with the
appropriate Marker in order to retrieve the next page of results.
Operations that have an AWSPager instance can transparently perform subsequent
requests, correctly setting Markers and other request facets to iterate through
the entire result set of a truncated API operation. Operations which support
this have an additional note in the documentation.
Many operations have the ability to filter results on the server side. See the individual operation parameters for details.
AddTags
See: newAddTags smart constructor.
Constructors
| AddTags' [Tag] Text TaggableResourceType |
Instances
Arguments
| :: Text | |
| -> TaggableResourceType | |
| -> AddTags |
Create a value of AddTags with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:tags:AddTags', addTags_tags - 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.
AddTags, addTags_resourceId - The ID of the ML object to tag. For example, exampleModelId.
AddTags, addTags_resourceType - The type of the ML object to tag.
data AddTagsResponse Source #
Amazon ML returns the following elements.
See: newAddTagsResponse smart constructor.
Constructors
| AddTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int |
Instances
Arguments
| :: Int | |
| -> AddTagsResponse |
Create a value of AddTagsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
AddTags, addTagsResponse_resourceId - The ID of the ML object that was tagged.
AddTags, addTagsResponse_resourceType - The type of the ML object that was tagged.
$sel:httpStatus:AddTagsResponse', addTagsResponse_httpStatus - The response's http status code.
CreateBatchPrediction
data CreateBatchPrediction Source #
See: newCreateBatchPrediction smart constructor.
Instances
newCreateBatchPrediction Source #
Arguments
| :: Text | |
| -> Text | |
| -> Text | |
| -> Text | |
| -> CreateBatchPrediction |
Create a value of CreateBatchPrediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:batchPredictionName:CreateBatchPrediction', createBatchPrediction_batchPredictionName - A user-supplied name or description of the BatchPrediction.
BatchPredictionName can only use the UTF-8 character set.
CreateBatchPrediction, createBatchPrediction_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction.
CreateBatchPrediction, createBatchPrediction_mLModelId - The ID of the MLModel that will generate predictions for the group of
observations.
CreateBatchPrediction, createBatchPrediction_batchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to
predict.
CreateBatchPrediction, createBatchPrediction_outputUri - 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.
data CreateBatchPredictionResponse Source #
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.
See: newCreateBatchPredictionResponse smart constructor.
Constructors
| CreateBatchPredictionResponse' (Maybe Text) Int |
Instances
newCreateBatchPredictionResponse Source #
Create a value of CreateBatchPredictionResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateBatchPrediction, createBatchPredictionResponse_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction. This
value is identical to the value of the BatchPredictionId in the
request.
$sel:httpStatus:CreateBatchPredictionResponse', createBatchPredictionResponse_httpStatus - The response's http status code.
CreateDataSourceFromRDS
data CreateDataSourceFromRDS Source #
See: newCreateDataSourceFromRDS smart constructor.
Constructors
| CreateDataSourceFromRDS' (Maybe Bool) (Maybe Text) Text RDSDataSpec Text |
Instances
newCreateDataSourceFromRDS Source #
Arguments
| :: Text | |
| -> RDSDataSpec | |
| -> Text | |
| -> CreateDataSourceFromRDS |
Create a value of CreateDataSourceFromRDS with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromRDS, createDataSourceFromRDS_computeStatistics - 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.
$sel:dataSourceName:CreateDataSourceFromRDS', createDataSourceFromRDS_dataSourceName - A user-supplied name or description of the DataSource.
CreateDataSourceFromRDS, createDataSourceFromRDS_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. Typically,
an Amazon Resource Number (ARN) becomes the ID for a DataSource.
$sel:rDSData:CreateDataSourceFromRDS', createDataSourceFromRDS_rDSData - 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 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 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
SelectSqlQueryis stored in this location. - DataSchemaUri - The Amazon S3 location of the
DataSchema. - DataSchema - A JSON string representing the schema. This is not
required if
DataSchemaUriis specified. DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the
Datasource.Sample -
"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
CreateDataSourceFromRDS, createDataSourceFromRDS_roleARN - 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.
data CreateDataSourceFromRDSResponse Source #
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.
See: newCreateDataSourceFromRDSResponse smart constructor.
Constructors
| CreateDataSourceFromRDSResponse' (Maybe Text) Int |
Instances
newCreateDataSourceFromRDSResponse Source #
Create a value of CreateDataSourceFromRDSResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromRDS, createDataSourceFromRDSResponse_dataSourceId - A user-supplied ID that uniquely identifies the datasource. This value
should be identical to the value of the DataSourceID in the request.
$sel:httpStatus:CreateDataSourceFromRDSResponse', createDataSourceFromRDSResponse_httpStatus - The response's http status code.
CreateDataSourceFromRedshift
data CreateDataSourceFromRedshift Source #
See: newCreateDataSourceFromRedshift smart constructor.
Constructors
| CreateDataSourceFromRedshift' (Maybe Bool) (Maybe Text) Text RedshiftDataSpec Text |
Instances
newCreateDataSourceFromRedshift Source #
Arguments
| :: Text | |
| -> RedshiftDataSpec | |
| -> Text | |
| -> CreateDataSourceFromRedshift |
Create a value of CreateDataSourceFromRedshift with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromRedshift, createDataSourceFromRedshift_computeStatistics - 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.
$sel:dataSourceName:CreateDataSourceFromRedshift', createDataSourceFromRedshift_dataSourceName - A user-supplied name or description of the DataSource.
CreateDataSourceFromRedshift, createDataSourceFromRedshift_dataSourceId - A user-supplied ID that uniquely identifies the DataSource.
$sel:dataSpec:CreateDataSourceFromRedshift', createDataSourceFromRedshift_dataSpec - 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
SelectSqlQueryquery 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
DataSchemaUriis specified. DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the
DataSource.Sample -
"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
CreateDataSourceFromRedshift, createDataSourceFromRedshift_roleARN - 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
SelectSqlQueryquery on an Amazon Redshift cluster - An Amazon S3 bucket policy to grant Amazon ML read/write
permissions on the
S3StagingLocation
data CreateDataSourceFromRedshiftResponse Source #
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.
See: newCreateDataSourceFromRedshiftResponse smart constructor.
Constructors
| CreateDataSourceFromRedshiftResponse' (Maybe Text) Int |
Instances
newCreateDataSourceFromRedshiftResponse Source #
Arguments
| :: Int | |
| -> CreateDataSourceFromRedshiftResponse |
Create a value of CreateDataSourceFromRedshiftResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromRedshift, createDataSourceFromRedshiftResponse_dataSourceId - A user-supplied ID that uniquely identifies the datasource. This value
should be identical to the value of the DataSourceID in the request.
$sel:httpStatus:CreateDataSourceFromRedshiftResponse', createDataSourceFromRedshiftResponse_httpStatus - The response's http status code.
CreateDataSourceFromS3
data CreateDataSourceFromS3 Source #
See: newCreateDataSourceFromS3 smart constructor.
Constructors
| CreateDataSourceFromS3' (Maybe Bool) (Maybe Text) Text S3DataSpec |
Instances
newCreateDataSourceFromS3 Source #
Arguments
| :: Text | |
| -> S3DataSpec | |
| -> CreateDataSourceFromS3 |
Create a value of CreateDataSourceFromS3 with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromS3, createDataSourceFromS3_computeStatistics - 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.
$sel:dataSourceName:CreateDataSourceFromS3', createDataSourceFromS3_dataSourceName - A user-supplied name or description of the DataSource.
CreateDataSourceFromS3, createDataSourceFromS3_dataSourceId - A user-supplied identifier that uniquely identifies the DataSource.
$sel:dataSpec:CreateDataSourceFromS3', createDataSourceFromS3_dataSpec - 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
DataSchemaUriis specified. DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the
Datasource.Sample -
"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
data CreateDataSourceFromS3Response Source #
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.
See: newCreateDataSourceFromS3Response smart constructor.
Constructors
| CreateDataSourceFromS3Response' (Maybe Text) Int |
Instances
newCreateDataSourceFromS3Response Source #
Create a value of CreateDataSourceFromS3Response with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateDataSourceFromS3, createDataSourceFromS3Response_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. This value
should be identical to the value of the DataSourceID in the request.
$sel:httpStatus:CreateDataSourceFromS3Response', createDataSourceFromS3Response_httpStatus - The response's http status code.
CreateEvaluation
data CreateEvaluation Source #
See: newCreateEvaluation smart constructor.
Instances
Arguments
| :: Text | |
| -> Text | |
| -> Text | |
| -> CreateEvaluation |
Create a value of CreateEvaluation with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:evaluationName:CreateEvaluation', createEvaluation_evaluationName - A user-supplied name or description of the Evaluation.
CreateEvaluation, createEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation.
CreateEvaluation, createEvaluation_mLModelId - 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.
CreateEvaluation, createEvaluation_evaluationDataSourceId - The ID of the DataSource for the evaluation. The schema of the
DataSource must match the schema used to create the MLModel.
data CreateEvaluationResponse Source #
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.
See: newCreateEvaluationResponse smart constructor.
Constructors
| CreateEvaluationResponse' (Maybe Text) Int |
Instances
newCreateEvaluationResponse Source #
Create a value of CreateEvaluationResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateEvaluation, createEvaluationResponse_evaluationId - The user-supplied ID that uniquely identifies the Evaluation. This
value should be identical to the value of the EvaluationId in the
request.
$sel:httpStatus:CreateEvaluationResponse', createEvaluationResponse_httpStatus - The response's http status code.
CreateMLModel
data CreateMLModel Source #
See: newCreateMLModel smart constructor.
Constructors
| CreateMLModel' (Maybe Text) (Maybe (HashMap Text Text)) (Maybe Text) (Maybe Text) Text MLModelType Text |
Instances
Arguments
| :: Text | |
| -> MLModelType | |
| -> Text | |
| -> CreateMLModel |
Create a value of CreateMLModel with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:mLModelName:CreateMLModel', createMLModel_mLModelName - A user-supplied name or description of the MLModel.
$sel:parameters:CreateMLModel', createMLModel_parameters - 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
100000to2147483648. The default value is33554432.sgd.maxPasses- The number of times that the training process traverses the observations to build theMLModel. The value is an integer that ranges from1to10000. The default value is10.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 areautoandnone. The default value isnone. 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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used whenL2is 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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used whenL1is specified. Use this parameter sparingly.
$sel:recipe:CreateMLModel', createMLModel_recipe - 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.
$sel:recipeUri:CreateMLModel', createMLModel_recipeUri - 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.
CreateMLModel, createMLModel_mLModelId - A user-supplied ID that uniquely identifies the MLModel.
CreateMLModel, createMLModel_mLModelType - The category of supervised learning that this MLModel will address.
Choose from the following types:
- Choose
REGRESSIONif theMLModelwill be used to predict a numeric value. - Choose
BINARYif theMLModelresult has two possible values. - Choose
MULTICLASSif theMLModelresult has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
CreateMLModel, createMLModel_trainingDataSourceId - The DataSource that points to the training data.
data CreateMLModelResponse Source #
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.
See: newCreateMLModelResponse smart constructor.
Constructors
| CreateMLModelResponse' (Maybe Text) Int |
Instances
newCreateMLModelResponse Source #
Create a value of CreateMLModelResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateMLModel, createMLModelResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value
should be identical to the value of the MLModelId in the request.
$sel:httpStatus:CreateMLModelResponse', createMLModelResponse_httpStatus - The response's http status code.
CreateRealtimeEndpoint
data CreateRealtimeEndpoint Source #
See: newCreateRealtimeEndpoint smart constructor.
Constructors
| CreateRealtimeEndpoint' Text |
Instances
newCreateRealtimeEndpoint Source #
Arguments
| :: Text | |
| -> CreateRealtimeEndpoint |
Create a value of CreateRealtimeEndpoint with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateRealtimeEndpoint, createRealtimeEndpoint_mLModelId - The ID assigned to the MLModel during creation.
data CreateRealtimeEndpointResponse Source #
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the
MLModel.
Note: The endpoint information includes the URI of the MLModel;
that is, the location to send online prediction requests for the
specified MLModel.
See: newCreateRealtimeEndpointResponse smart constructor.
Constructors
| CreateRealtimeEndpointResponse' (Maybe Text) (Maybe RealtimeEndpointInfo) Int |
Instances
newCreateRealtimeEndpointResponse Source #
Create a value of CreateRealtimeEndpointResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
CreateRealtimeEndpoint, createRealtimeEndpointResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value
should be identical to the value of the MLModelId in the request.
$sel:realtimeEndpointInfo:CreateRealtimeEndpointResponse', createRealtimeEndpointResponse_realtimeEndpointInfo - The endpoint information of the MLModel
$sel:httpStatus:CreateRealtimeEndpointResponse', createRealtimeEndpointResponse_httpStatus - The response's http status code.
DeleteBatchPrediction
data DeleteBatchPrediction Source #
See: newDeleteBatchPrediction smart constructor.
Constructors
| DeleteBatchPrediction' Text |
Instances
newDeleteBatchPrediction Source #
Arguments
| :: Text | |
| -> DeleteBatchPrediction |
Create a value of DeleteBatchPrediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteBatchPrediction, deleteBatchPrediction_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction.
data DeleteBatchPredictionResponse Source #
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.
See: newDeleteBatchPredictionResponse smart constructor.
Constructors
| DeleteBatchPredictionResponse' (Maybe Text) Int |
Instances
newDeleteBatchPredictionResponse Source #
Create a value of DeleteBatchPredictionResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteBatchPrediction, deleteBatchPredictionResponse_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction. This
value should be identical to the value of the BatchPredictionID in the
request.
$sel:httpStatus:DeleteBatchPredictionResponse', deleteBatchPredictionResponse_httpStatus - The response's http status code.
DeleteDataSource
data DeleteDataSource Source #
See: newDeleteDataSource smart constructor.
Constructors
| DeleteDataSource' Text |
Instances
Arguments
| :: Text | |
| -> DeleteDataSource |
Create a value of DeleteDataSource with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteDataSource, deleteDataSource_dataSourceId - A user-supplied ID that uniquely identifies the DataSource.
data DeleteDataSourceResponse Source #
Represents the output of a DeleteDataSource operation.
See: newDeleteDataSourceResponse smart constructor.
Constructors
| DeleteDataSourceResponse' (Maybe Text) Int |
Instances
newDeleteDataSourceResponse Source #
Create a value of DeleteDataSourceResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteDataSource, deleteDataSourceResponse_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. This value
should be identical to the value of the DataSourceID in the request.
$sel:httpStatus:DeleteDataSourceResponse', deleteDataSourceResponse_httpStatus - The response's http status code.
DeleteEvaluation
data DeleteEvaluation Source #
See: newDeleteEvaluation smart constructor.
Constructors
| DeleteEvaluation' Text |
Instances
Arguments
| :: Text | |
| -> DeleteEvaluation |
Create a value of DeleteEvaluation with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteEvaluation, deleteEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation to delete.
data DeleteEvaluationResponse Source #
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.
See: newDeleteEvaluationResponse smart constructor.
Constructors
| DeleteEvaluationResponse' (Maybe Text) Int |
Instances
newDeleteEvaluationResponse Source #
Create a value of DeleteEvaluationResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteEvaluation, deleteEvaluationResponse_evaluationId - A user-supplied ID that uniquely identifies the Evaluation. This value
should be identical to the value of the EvaluationId in the request.
$sel:httpStatus:DeleteEvaluationResponse', deleteEvaluationResponse_httpStatus - The response's http status code.
DeleteMLModel
data DeleteMLModel Source #
See: newDeleteMLModel smart constructor.
Constructors
| DeleteMLModel' Text |
Instances
Arguments
| :: Text | |
| -> DeleteMLModel |
Create a value of DeleteMLModel with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteMLModel, deleteMLModel_mLModelId - A user-supplied ID that uniquely identifies the MLModel.
data DeleteMLModelResponse Source #
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.
See: newDeleteMLModelResponse smart constructor.
Constructors
| DeleteMLModelResponse' (Maybe Text) Int |
Instances
newDeleteMLModelResponse Source #
Create a value of DeleteMLModelResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteMLModel, deleteMLModelResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value
should be identical to the value of the MLModelID in the request.
$sel:httpStatus:DeleteMLModelResponse', deleteMLModelResponse_httpStatus - The response's http status code.
DeleteRealtimeEndpoint
data DeleteRealtimeEndpoint Source #
See: newDeleteRealtimeEndpoint smart constructor.
Constructors
| DeleteRealtimeEndpoint' Text |
Instances
newDeleteRealtimeEndpoint Source #
Arguments
| :: Text | |
| -> DeleteRealtimeEndpoint |
Create a value of DeleteRealtimeEndpoint with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteRealtimeEndpoint, deleteRealtimeEndpoint_mLModelId - The ID assigned to the MLModel during creation.
data DeleteRealtimeEndpointResponse Source #
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the
MLModel.
See: newDeleteRealtimeEndpointResponse smart constructor.
Constructors
| DeleteRealtimeEndpointResponse' (Maybe Text) (Maybe RealtimeEndpointInfo) Int |
Instances
newDeleteRealtimeEndpointResponse Source #
Create a value of DeleteRealtimeEndpointResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteRealtimeEndpoint, deleteRealtimeEndpointResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value
should be identical to the value of the MLModelId in the request.
$sel:realtimeEndpointInfo:DeleteRealtimeEndpointResponse', deleteRealtimeEndpointResponse_realtimeEndpointInfo - The endpoint information of the MLModel
$sel:httpStatus:DeleteRealtimeEndpointResponse', deleteRealtimeEndpointResponse_httpStatus - The response's http status code.
DeleteTags
data DeleteTags Source #
See: newDeleteTags smart constructor.
Constructors
| DeleteTags' [Text] Text TaggableResourceType |
Instances
Arguments
| :: Text | |
| -> TaggableResourceType | |
| -> DeleteTags |
Create a value of DeleteTags with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:tagKeys:DeleteTags', deleteTags_tagKeys - One or more tags to delete.
DeleteTags, deleteTags_resourceId - The ID of the tagged ML object. For example, exampleModelId.
DeleteTags, deleteTags_resourceType - The type of the tagged ML object.
data DeleteTagsResponse Source #
Amazon ML returns the following elements.
See: newDeleteTagsResponse smart constructor.
Constructors
| DeleteTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int |
Instances
newDeleteTagsResponse Source #
Arguments
| :: Int | |
| -> DeleteTagsResponse |
Create a value of DeleteTagsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DeleteTags, deleteTagsResponse_resourceId - The ID of the ML object from which tags were deleted.
DeleteTags, deleteTagsResponse_resourceType - The type of the ML object from which tags were deleted.
$sel:httpStatus:DeleteTagsResponse', deleteTagsResponse_httpStatus - The response's http status code.
DescribeBatchPredictions (Paginated)
data DescribeBatchPredictions Source #
See: newDescribeBatchPredictions smart constructor.
Constructors
| DescribeBatchPredictions' (Maybe Text) (Maybe BatchPredictionFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) |
Instances
newDescribeBatchPredictions :: DescribeBatchPredictions Source #
Create a value of DescribeBatchPredictions with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:eq:DescribeBatchPredictions', describeBatchPredictions_eq - The equal to operator. The BatchPrediction results will have
FilterVariable values that exactly match the value specified with
EQ.
$sel:filterVariable:DescribeBatchPredictions', describeBatchPredictions_filterVariable - Use one of the following variables to filter a list of
BatchPrediction:
CreatedAt- Sets the search criteria to theBatchPredictioncreation date.Status- Sets the search criteria to theBatchPredictionstatus.Name- Sets the search criteria to the contents of theBatchPrediction____Name.IAMUser- Sets the search criteria to the user account that invoked theBatchPredictioncreation.MLModelId- Sets the search criteria to theMLModelused in theBatchPrediction.DataSourceId- Sets the search criteria to theDataSourceused in theBatchPrediction.DataURI- Sets the search criteria to the data file(s) used in theBatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.
$sel:ge:DescribeBatchPredictions', describeBatchPredictions_ge - 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.
$sel:gt:DescribeBatchPredictions', describeBatchPredictions_gt - The greater than operator. The BatchPrediction results will have
FilterVariable values that are greater than the value specified with
GT.
$sel:le:DescribeBatchPredictions', describeBatchPredictions_le - 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.
$sel:lt:DescribeBatchPredictions', describeBatchPredictions_lt - The less than operator. The BatchPrediction results will have
FilterVariable values that are less than the value specified with
LT.
$sel:limit:DescribeBatchPredictions', describeBatchPredictions_limit - 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.
$sel:ne:DescribeBatchPredictions', describeBatchPredictions_ne - The not equal to operator. The BatchPrediction results will have
FilterVariable values not equal to the value specified with NE.
DescribeBatchPredictions, describeBatchPredictions_nextToken - An ID of the page in the paginated results.
$sel:prefix:DescribeBatchPredictions', describeBatchPredictions_prefix - 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
$sel:sortOrder:DescribeBatchPredictions', describeBatchPredictions_sortOrder - 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.
data DescribeBatchPredictionsResponse Source #
Represents the output of a DescribeBatchPredictions operation. The
content is essentially a list of BatchPredictions.
See: newDescribeBatchPredictionsResponse smart constructor.
Constructors
| DescribeBatchPredictionsResponse' (Maybe Text) (Maybe [BatchPrediction]) Int |
Instances
newDescribeBatchPredictionsResponse Source #
Create a value of DescribeBatchPredictionsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeBatchPredictions, describeBatchPredictionsResponse_nextToken - The ID of the next page in the paginated results that indicates at least
one more page follows.
$sel:results:DescribeBatchPredictionsResponse', describeBatchPredictionsResponse_results - A list of BatchPrediction objects that meet the search criteria.
$sel:httpStatus:DescribeBatchPredictionsResponse', describeBatchPredictionsResponse_httpStatus - The response's http status code.
DescribeDataSources (Paginated)
data DescribeDataSources Source #
See: newDescribeDataSources smart constructor.
Constructors
| DescribeDataSources' (Maybe Text) (Maybe DataSourceFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) |
Instances
newDescribeDataSources :: DescribeDataSources Source #
Create a value of DescribeDataSources with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:eq:DescribeDataSources', describeDataSources_eq - The equal to operator. The DataSource results will have
FilterVariable values that exactly match the value specified with
EQ.
$sel:filterVariable:DescribeDataSources', describeDataSources_filterVariable - Use one of the following variables to filter a list of DataSource:
CreatedAt- Sets the search criteria toDataSourcecreation dates.Status- Sets the search criteria toDataSourcestatuses.Name- Sets the search criteria to the contents ofDataSourceName.DataUri- Sets the search criteria to the URI of data files used to create theDataSource. 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 theDataSourcecreation.
$sel:ge:DescribeDataSources', describeDataSources_ge - 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.
$sel:gt:DescribeDataSources', describeDataSources_gt - The greater than operator. The DataSource results will have
FilterVariable values that are greater than the value specified with
GT.
$sel:le:DescribeDataSources', describeDataSources_le - 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.
$sel:lt:DescribeDataSources', describeDataSources_lt - The less than operator. The DataSource results will have
FilterVariable values that are less than the value specified with
LT.
$sel:limit:DescribeDataSources', describeDataSources_limit - The maximum number of DataSource to include in the result.
$sel:ne:DescribeDataSources', describeDataSources_ne - The not equal to operator. The DataSource results will have
FilterVariable values not equal to the value specified with NE.
DescribeDataSources, describeDataSources_nextToken - The ID of the page in the paginated results.
$sel:prefix:DescribeDataSources', describeDataSources_prefix - 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
$sel:sortOrder:DescribeDataSources', describeDataSources_sortOrder - 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.
data DescribeDataSourcesResponse Source #
Represents the query results from a DescribeDataSources operation. The
content is essentially a list of DataSource.
See: newDescribeDataSourcesResponse smart constructor.
Constructors
| DescribeDataSourcesResponse' (Maybe Text) (Maybe [DataSource]) Int |
Instances
newDescribeDataSourcesResponse Source #
Create a value of DescribeDataSourcesResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeDataSources, describeDataSourcesResponse_nextToken - An ID of the next page in the paginated results that indicates at least
one more page follows.
$sel:results:DescribeDataSourcesResponse', describeDataSourcesResponse_results - A list of DataSource that meet the search criteria.
$sel:httpStatus:DescribeDataSourcesResponse', describeDataSourcesResponse_httpStatus - The response's http status code.
DescribeEvaluations (Paginated)
data DescribeEvaluations Source #
See: newDescribeEvaluations smart constructor.
Constructors
| DescribeEvaluations' (Maybe Text) (Maybe EvaluationFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) |
Instances
newDescribeEvaluations :: DescribeEvaluations Source #
Create a value of DescribeEvaluations with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:eq:DescribeEvaluations', describeEvaluations_eq - The equal to operator. The Evaluation results will have
FilterVariable values that exactly match the value specified with
EQ.
$sel:filterVariable:DescribeEvaluations', describeEvaluations_filterVariable - Use one of the following variable to filter a list of Evaluation
objects:
CreatedAt- Sets the search criteria to theEvaluationcreation date.Status- Sets the search criteria to theEvaluationstatus.Name- Sets the search criteria to the contents ofEvaluation____Name.IAMUser- Sets the search criteria to the user account that invoked anEvaluation.MLModelId- Sets the search criteria to theMLModelthat was evaluated.DataSourceId- Sets the search criteria to theDataSourceused inEvaluation.DataUri- Sets the search criteria to the data file(s) used inEvaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.
$sel:ge:DescribeEvaluations', describeEvaluations_ge - 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.
$sel:gt:DescribeEvaluations', describeEvaluations_gt - The greater than operator. The Evaluation results will have
FilterVariable values that are greater than the value specified with
GT.
$sel:le:DescribeEvaluations', describeEvaluations_le - 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.
$sel:lt:DescribeEvaluations', describeEvaluations_lt - The less than operator. The Evaluation results will have
FilterVariable values that are less than the value specified with
LT.
$sel:limit:DescribeEvaluations', describeEvaluations_limit - The maximum number of Evaluation to include in the result.
$sel:ne:DescribeEvaluations', describeEvaluations_ne - The not equal to operator. The Evaluation results will have
FilterVariable values not equal to the value specified with NE.
DescribeEvaluations, describeEvaluations_nextToken - The ID of the page in the paginated results.
$sel:prefix:DescribeEvaluations', describeEvaluations_prefix - 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
$sel:sortOrder:DescribeEvaluations', describeEvaluations_sortOrder - 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.
data DescribeEvaluationsResponse Source #
Represents the query results from a DescribeEvaluations operation. The
content is essentially a list of Evaluation.
See: newDescribeEvaluationsResponse smart constructor.
Constructors
| DescribeEvaluationsResponse' (Maybe Text) (Maybe [Evaluation]) Int |
Instances
newDescribeEvaluationsResponse Source #
Create a value of DescribeEvaluationsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeEvaluations, describeEvaluationsResponse_nextToken - The ID of the next page in the paginated results that indicates at least
one more page follows.
$sel:results:DescribeEvaluationsResponse', describeEvaluationsResponse_results - A list of Evaluation that meet the search criteria.
$sel:httpStatus:DescribeEvaluationsResponse', describeEvaluationsResponse_httpStatus - The response's http status code.
DescribeMLModels (Paginated)
data DescribeMLModels Source #
See: newDescribeMLModels smart constructor.
Constructors
| DescribeMLModels' (Maybe Text) (Maybe MLModelFilterVariable) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Natural) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) |
Instances
newDescribeMLModels :: DescribeMLModels Source #
Create a value of DescribeMLModels with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:eq:DescribeMLModels', describeMLModels_eq - The equal to operator. The MLModel results will have FilterVariable
values that exactly match the value specified with EQ.
$sel:filterVariable:DescribeMLModels', describeMLModels_filterVariable - Use one of the following variables to filter a list of MLModel:
CreatedAt- Sets the search criteria toMLModelcreation date.Status- Sets the search criteria toMLModelstatus.Name- Sets the search criteria to the contents ofMLModel____Name.IAMUser- Sets the search criteria to the user account that invoked theMLModelcreation.TrainingDataSourceId- Sets the search criteria to theDataSourceused to train one or moreMLModel.RealtimeEndpointStatus- Sets the search criteria to theMLModelreal-time endpoint status.MLModelType- Sets the search criteria toMLModeltype: binary, regression, or multi-class.Algorithm- Sets the search criteria to the algorithm that theMLModeluses.TrainingDataURI- Sets the search criteria to the data file(s) used in training aMLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
$sel:ge:DescribeMLModels', describeMLModels_ge - 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.
$sel:gt:DescribeMLModels', describeMLModels_gt - The greater than operator. The MLModel results will have
FilterVariable values that are greater than the value specified with
GT.
$sel:le:DescribeMLModels', describeMLModels_le - 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.
$sel:lt:DescribeMLModels', describeMLModels_lt - The less than operator. The MLModel results will have FilterVariable
values that are less than the value specified with LT.
$sel:limit:DescribeMLModels', describeMLModels_limit - 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.
$sel:ne:DescribeMLModels', describeMLModels_ne - The not equal to operator. The MLModel results will have
FilterVariable values not equal to the value specified with NE.
DescribeMLModels, describeMLModels_nextToken - The ID of the page in the paginated results.
$sel:prefix:DescribeMLModels', describeMLModels_prefix - 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
$sel:sortOrder:DescribeMLModels', describeMLModels_sortOrder - 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.
data DescribeMLModelsResponse Source #
Represents the output of a DescribeMLModels operation. The content is
essentially a list of MLModel.
See: newDescribeMLModelsResponse smart constructor.
Instances
newDescribeMLModelsResponse Source #
Create a value of DescribeMLModelsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeMLModels, describeMLModelsResponse_nextToken - The ID of the next page in the paginated results that indicates at least
one more page follows.
$sel:results:DescribeMLModelsResponse', describeMLModelsResponse_results - A list of MLModel that meet the search criteria.
$sel:httpStatus:DescribeMLModelsResponse', describeMLModelsResponse_httpStatus - The response's http status code.
DescribeTags
data DescribeTags Source #
See: newDescribeTags smart constructor.
Constructors
| DescribeTags' Text TaggableResourceType |
Instances
Arguments
| :: Text | |
| -> TaggableResourceType | |
| -> DescribeTags |
Create a value of DescribeTags with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeTags, describeTags_resourceId - The ID of the ML object. For example, exampleModelId.
DescribeTags, describeTags_resourceType - The type of the ML object.
data DescribeTagsResponse Source #
Amazon ML returns the following elements.
See: newDescribeTagsResponse smart constructor.
Constructors
| DescribeTagsResponse' (Maybe Text) (Maybe TaggableResourceType) (Maybe [Tag]) Int |
Instances
newDescribeTagsResponse Source #
Arguments
| :: Int | |
| -> DescribeTagsResponse |
Create a value of DescribeTagsResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
DescribeTags, describeTagsResponse_resourceId - The ID of the tagged ML object.
DescribeTags, describeTagsResponse_resourceType - The type of the tagged ML object.
$sel:tags:DescribeTagsResponse', describeTagsResponse_tags - A list of tags associated with the ML object.
$sel:httpStatus:DescribeTagsResponse', describeTagsResponse_httpStatus - The response's http status code.
GetBatchPrediction
data GetBatchPrediction Source #
See: newGetBatchPrediction smart constructor.
Constructors
| GetBatchPrediction' Text |
Instances
newGetBatchPrediction Source #
Arguments
| :: Text | |
| -> GetBatchPrediction |
Create a value of GetBatchPrediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetBatchPrediction, getBatchPrediction_batchPredictionId - An ID assigned to the BatchPrediction at creation.
data GetBatchPredictionResponse Source #
Represents the output of a GetBatchPrediction operation and describes
a BatchPrediction.
See: newGetBatchPredictionResponse smart constructor.
Constructors
| GetBatchPredictionResponse' (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) (Maybe Integer) Int |
Instances
newGetBatchPredictionResponse Source #
Create a value of GetBatchPredictionResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetBatchPredictionResponse, getBatchPredictionResponse_batchPredictionDataSourceId - The ID of the DataSource that was used to create the
BatchPrediction.
GetBatchPrediction, getBatchPredictionResponse_batchPredictionId - An ID assigned to the BatchPrediction at creation. This value should
be identical to the value of the BatchPredictionID in the request.
GetBatchPredictionResponse, getBatchPredictionResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning
spent processing the BatchPrediction, normalized and scaled on
computation resources. ComputeTime is only available if the
BatchPrediction is in the COMPLETED state.
GetBatchPredictionResponse, getBatchPredictionResponse_createdAt - The time when the BatchPrediction was created. The time is expressed
in epoch time.
GetBatchPredictionResponse, getBatchPredictionResponse_createdByIamUser - 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.
GetBatchPredictionResponse, getBatchPredictionResponse_finishedAt - The epoch time when Amazon Machine Learning marked the BatchPrediction
as COMPLETED or FAILED. FinishedAt is only available when the
BatchPrediction is in the COMPLETED or FAILED state.
GetBatchPredictionResponse, getBatchPredictionResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
GetBatchPredictionResponse, getBatchPredictionResponse_invalidRecordCount - The number of invalid records that Amazon Machine Learning saw while
processing the BatchPrediction.
GetBatchPredictionResponse, getBatchPredictionResponse_lastUpdatedAt - The time of the most recent edit to BatchPrediction. The time is
expressed in epoch time.
$sel:logUri:GetBatchPredictionResponse', getBatchPredictionResponse_logUri - A link to the file that contains logs of the CreateBatchPrediction
operation.
GetBatchPredictionResponse, getBatchPredictionResponse_mLModelId - The ID of the MLModel that generated predictions for the
BatchPrediction request.
GetBatchPredictionResponse, getBatchPredictionResponse_message - A description of the most recent details about processing the batch
prediction request.
GetBatchPredictionResponse, getBatchPredictionResponse_name - A user-supplied name or description of the BatchPrediction.
GetBatchPredictionResponse, getBatchPredictionResponse_outputUri - The location of an Amazon S3 bucket or directory to receive the
operation results.
GetBatchPredictionResponse, getBatchPredictionResponse_startedAt - The epoch time when Amazon Machine Learning marked the BatchPrediction
as INPROGRESS. StartedAt isn't available if the BatchPrediction
is in the PENDING state.
GetBatchPredictionResponse, getBatchPredictionResponse_status - 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- TheBatchPredictionis marked as deleted. It is not usable.
GetBatchPredictionResponse, getBatchPredictionResponse_totalRecordCount - The number of total records that Amazon Machine Learning saw while
processing the BatchPrediction.
$sel:httpStatus:GetBatchPredictionResponse', getBatchPredictionResponse_httpStatus - The response's http status code.
GetDataSource
data GetDataSource Source #
See: newGetDataSource smart constructor.
Constructors
| GetDataSource' (Maybe Bool) Text |
Instances
Arguments
| :: Text | |
| -> GetDataSource |
Create a value of GetDataSource with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:verbose:GetDataSource', getDataSource_verbose - Specifies whether the GetDataSource operation should return
DataSourceSchema.
If true, DataSourceSchema is returned.
If false, DataSourceSchema is not returned.
GetDataSource, getDataSource_dataSourceId - The ID assigned to the DataSource at creation.
data GetDataSourceResponse Source #
Represents the output of a GetDataSource operation and describes a
DataSource.
See: newGetDataSourceResponse smart constructor.
Constructors
| GetDataSourceResponse' (Maybe Bool) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe RDSMetadata) (Maybe RedshiftMetadata) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) Int |
Instances
newGetDataSourceResponse Source #
Create a value of GetDataSourceResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetDataSourceResponse, getDataSourceResponse_computeStatistics - The parameter is true if statistics need to be generated from the
observation data.
GetDataSourceResponse, getDataSourceResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning
spent processing the DataSource, normalized and scaled on computation
resources. ComputeTime is only available if the DataSource is in the
COMPLETED state and the ComputeStatistics is set to true.
GetDataSourceResponse, getDataSourceResponse_createdAt - The time that the DataSource was created. The time is expressed in
epoch time.
GetDataSourceResponse, getDataSourceResponse_createdByIamUser - 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.
GetDataSourceResponse, getDataSourceResponse_dataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
GetDataSourceResponse, getDataSourceResponse_dataRearrangement - A JSON string that represents the splitting and rearrangement
requirement used when this DataSource was created.
GetDataSourceResponse, getDataSourceResponse_dataSizeInBytes - The total size of observations in the data files.
GetDataSource, getDataSourceResponse_dataSourceId - The ID assigned to the DataSource at creation. This value should be
identical to the value of the DataSourceId in the request.
$sel:dataSourceSchema:GetDataSourceResponse', getDataSourceResponse_dataSourceSchema - The schema used by all of the data files of this DataSource.
Note: This parameter is provided as part of the verbose format.
GetDataSourceResponse, getDataSourceResponse_finishedAt - The epoch time when Amazon Machine Learning marked the DataSource as
COMPLETED or FAILED. FinishedAt is only available when the
DataSource is in the COMPLETED or FAILED state.
GetDataSourceResponse, getDataSourceResponse_lastUpdatedAt - The time of the most recent edit to the DataSource. The time is
expressed in epoch time.
$sel:logUri:GetDataSourceResponse', getDataSourceResponse_logUri - A link to the file containing logs of CreateDataSourceFrom*
operations.
GetDataSourceResponse, getDataSourceResponse_message - The user-supplied description of the most recent details about creating
the DataSource.
GetDataSourceResponse, getDataSourceResponse_name - A user-supplied name or description of the DataSource.
GetDataSourceResponse, getDataSourceResponse_numberOfFiles - The number of data files referenced by the DataSource.
GetDataSourceResponse, getDataSourceResponse_rDSMetadata - Undocumented member.
GetDataSourceResponse, getDataSourceResponse_redshiftMetadata - Undocumented member.
GetDataSourceResponse, getDataSourceResponse_roleARN - Undocumented member.
GetDataSourceResponse, getDataSourceResponse_startedAt - The epoch time when Amazon Machine Learning marked the DataSource as
INPROGRESS. StartedAt isn't available if the DataSource is in the
PENDING state.
GetDataSourceResponse, getDataSourceResponse_status - The current status of the DataSource. This element can have one of the
following values:
PENDING- Amazon ML submitted a request to create aDataSource.INPROGRESS- The creation process is underway.FAILED- The request to create aDataSourcedid not run to completion. It is not usable.COMPLETED- The creation process completed successfully.DELETED- TheDataSourceis marked as deleted. It is not usable.
$sel:httpStatus:GetDataSourceResponse', getDataSourceResponse_httpStatus - The response's http status code.
GetEvaluation
data GetEvaluation Source #
See: newGetEvaluation smart constructor.
Constructors
| GetEvaluation' Text |
Instances
Arguments
| :: Text | |
| -> GetEvaluation |
Create a value of GetEvaluation with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetEvaluation, getEvaluation_evaluationId - 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.
data GetEvaluationResponse Source #
Represents the output of a GetEvaluation operation and describes an
Evaluation.
See: newGetEvaluationResponse smart constructor.
Constructors
| GetEvaluationResponse' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe EntityStatus) Int |
Instances
newGetEvaluationResponse Source #
Create a value of GetEvaluationResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetEvaluationResponse, getEvaluationResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning
spent processing the Evaluation, normalized and scaled on computation
resources. ComputeTime is only available if the Evaluation is in the
COMPLETED state.
GetEvaluationResponse, getEvaluationResponse_createdAt - The time that the Evaluation was created. The time is expressed in
epoch time.
GetEvaluationResponse, getEvaluationResponse_createdByIamUser - 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.
GetEvaluationResponse, getEvaluationResponse_evaluationDataSourceId - The DataSource used for this evaluation.
GetEvaluation, getEvaluationResponse_evaluationId - The evaluation ID which is same as the EvaluationId in the request.
GetEvaluationResponse, getEvaluationResponse_finishedAt - The epoch time when Amazon Machine Learning marked the Evaluation as
COMPLETED or FAILED. FinishedAt is only available when the
Evaluation is in the COMPLETED or FAILED state.
GetEvaluationResponse, getEvaluationResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
GetEvaluationResponse, getEvaluationResponse_lastUpdatedAt - The time of the most recent edit to the Evaluation. The time is
expressed in epoch time.
$sel:logUri:GetEvaluationResponse', getEvaluationResponse_logUri - A link to the file that contains logs of the CreateEvaluation
operation.
GetEvaluationResponse, getEvaluationResponse_mLModelId - The ID of the MLModel that was the focus of the evaluation.
GetEvaluationResponse, getEvaluationResponse_message - A description of the most recent details about evaluating the MLModel.
GetEvaluationResponse, getEvaluationResponse_name - A user-supplied name or description of the Evaluation.
GetEvaluationResponse, getEvaluationResponse_performanceMetrics - 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
MLModeluses the Area Under the Curve (AUC) technique to measure performance. - RegressionRMSE: A regression
MLModeluses 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
MLModeluses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
GetEvaluationResponse, getEvaluationResponse_startedAt - The epoch time when Amazon Machine Learning marked the Evaluation as
INPROGRESS. StartedAt isn't available if the Evaluation is in the
PENDING state.
GetEvaluationResponse, getEvaluationResponse_status - 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 anMLModel.INPROGRESS- The evaluation is underway.FAILED- The request to evaluate anMLModeldid not run to completion. It is not usable.COMPLETED- The evaluation process completed successfully.DELETED- TheEvaluationis marked as deleted. It is not usable.
$sel:httpStatus:GetEvaluationResponse', getEvaluationResponse_httpStatus - The response's http status code.
GetMLModel
data GetMLModel Source #
See: newGetMLModel smart constructor.
Constructors
| GetMLModel' (Maybe Bool) Text |
Instances
Arguments
| :: Text | |
| -> GetMLModel |
Create a value of GetMLModel with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:verbose:GetMLModel', getMLModel_verbose - Specifies whether the GetMLModel operation should return Recipe.
If true, Recipe is returned.
If false, Recipe is not returned.
GetMLModel, getMLModel_mLModelId - The ID assigned to the MLModel at creation.
data GetMLModelResponse Source #
Represents the output of a GetMLModel operation, and provides detailed
information about a MLModel.
See: newGetMLModelResponse smart constructor.
Constructors
| GetMLModelResponse' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe MLModelType) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Double) (Maybe POSIX) (Maybe Integer) (Maybe POSIX) (Maybe EntityStatus) (Maybe Text) (Maybe (HashMap Text Text)) Int |
Instances
newGetMLModelResponse Source #
Arguments
| :: Int | |
| -> GetMLModelResponse |
Create a value of GetMLModelResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
GetMLModelResponse, getMLModelResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning
spent processing the MLModel, normalized and scaled on computation
resources. ComputeTime is only available if the MLModel is in the
COMPLETED state.
GetMLModelResponse, getMLModelResponse_createdAt - The time that the MLModel was created. The time is expressed in epoch
time.
GetMLModelResponse, getMLModelResponse_createdByIamUser - 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.
GetMLModelResponse, getMLModelResponse_endpointInfo - The current endpoint of the MLModel
GetMLModelResponse, getMLModelResponse_finishedAt - The epoch time when Amazon Machine Learning marked the MLModel as
COMPLETED or FAILED. FinishedAt is only available when the
MLModel is in the COMPLETED or FAILED state.
GetMLModelResponse, getMLModelResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
GetMLModelResponse, getMLModelResponse_lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed
in epoch time.
$sel:logUri:GetMLModelResponse', getMLModelResponse_logUri - A link to the file that contains logs of the CreateMLModel operation.
GetMLModel, getMLModelResponse_mLModelId - The MLModel ID, which is same as the MLModelId in the request.
GetMLModelResponse, getMLModelResponse_mLModelType - 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?"
GetMLModelResponse, getMLModelResponse_message - A description of the most recent details about accessing the MLModel.
GetMLModelResponse, getMLModelResponse_name - A user-supplied name or description of the MLModel.
$sel:recipe:GetMLModelResponse', getMLModelResponse_recipe - 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.
$sel:schema:GetMLModelResponse', getMLModelResponse_schema - The schema used by all of the data files referenced by the DataSource.
Note: This parameter is provided as part of the verbose format.
GetMLModelResponse, getMLModelResponse_scoreThreshold - 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.
GetMLModelResponse, getMLModelResponse_scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is
expressed in epoch time.
GetMLModelResponse, getMLModelResponse_sizeInBytes - Undocumented member.
GetMLModelResponse, getMLModelResponse_startedAt - The epoch time when Amazon Machine Learning marked the MLModel as
INPROGRESS. StartedAt isn't available if the MLModel is in the
PENDING state.
GetMLModelResponse, getMLModelResponse_status - 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 aMLModel.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- TheMLModelis marked as deleted. It isn't usable.
GetMLModelResponse, getMLModelResponse_trainingDataSourceId - The ID of the training DataSource.
GetMLModelResponse, getMLModelResponse_trainingParameters - 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
100000to2147483648. The default value is33554432.sgd.maxPasses- The number of times that the training process traverses the observations to build theMLModel. The value is an integer that ranges from1to10000. The default value is10.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 areautoandnone. The default value isnone. 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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used whenL2is 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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used whenL1is specified. Use this parameter sparingly.
$sel:httpStatus:GetMLModelResponse', getMLModelResponse_httpStatus - The response's http status code.
Predict
See: newPredict smart constructor.
Instances
Create a value of Predict with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
Predict, predict_mLModelId - A unique identifier of the MLModel.
$sel:record:Predict', predict_record - Undocumented member.
$sel:predictEndpoint:Predict', predict_predictEndpoint - Undocumented member.
data PredictResponse Source #
See: newPredictResponse smart constructor.
Constructors
| PredictResponse' (Maybe Prediction) Int |
Instances
Arguments
| :: Int | |
| -> PredictResponse |
Create a value of PredictResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:prediction:PredictResponse', predictResponse_prediction - Undocumented member.
$sel:httpStatus:PredictResponse', predictResponse_httpStatus - The response's http status code.
UpdateBatchPrediction
data UpdateBatchPrediction Source #
See: newUpdateBatchPrediction smart constructor.
Constructors
| UpdateBatchPrediction' Text Text |
Instances
newUpdateBatchPrediction Source #
Arguments
| :: Text | |
| -> Text | |
| -> UpdateBatchPrediction |
Create a value of UpdateBatchPrediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateBatchPrediction, updateBatchPrediction_batchPredictionId - The ID assigned to the BatchPrediction during creation.
$sel:batchPredictionName:UpdateBatchPrediction', updateBatchPrediction_batchPredictionName - A new user-supplied name or description of the BatchPrediction.
data UpdateBatchPredictionResponse Source #
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction
operation.
See: newUpdateBatchPredictionResponse smart constructor.
Constructors
| UpdateBatchPredictionResponse' (Maybe Text) Int |
Instances
newUpdateBatchPredictionResponse Source #
Create a value of UpdateBatchPredictionResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateBatchPrediction, updateBatchPredictionResponse_batchPredictionId - The ID assigned to the BatchPrediction during creation. This value
should be identical to the value of the BatchPredictionId in the
request.
$sel:httpStatus:UpdateBatchPredictionResponse', updateBatchPredictionResponse_httpStatus - The response's http status code.
UpdateDataSource
data UpdateDataSource Source #
See: newUpdateDataSource smart constructor.
Constructors
| UpdateDataSource' Text Text |
Instances
Create a value of UpdateDataSource with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateDataSource, updateDataSource_dataSourceId - The ID assigned to the DataSource during creation.
$sel:dataSourceName:UpdateDataSource', updateDataSource_dataSourceName - A new user-supplied name or description of the DataSource that will
replace the current description.
data UpdateDataSourceResponse Source #
Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction
operation.
See: newUpdateDataSourceResponse smart constructor.
Constructors
| UpdateDataSourceResponse' (Maybe Text) Int |
Instances
newUpdateDataSourceResponse Source #
Create a value of UpdateDataSourceResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateDataSource, updateDataSourceResponse_dataSourceId - The ID assigned to the DataSource during creation. This value should
be identical to the value of the DataSourceID in the request.
$sel:httpStatus:UpdateDataSourceResponse', updateDataSourceResponse_httpStatus - The response's http status code.
UpdateEvaluation
data UpdateEvaluation Source #
See: newUpdateEvaluation smart constructor.
Constructors
| UpdateEvaluation' Text Text |
Instances
Create a value of UpdateEvaluation with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateEvaluation, updateEvaluation_evaluationId - The ID assigned to the Evaluation during creation.
$sel:evaluationName:UpdateEvaluation', updateEvaluation_evaluationName - A new user-supplied name or description of the Evaluation that will
replace the current content.
data UpdateEvaluationResponse Source #
Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
See: newUpdateEvaluationResponse smart constructor.
Constructors
| UpdateEvaluationResponse' (Maybe Text) Int |
Instances
newUpdateEvaluationResponse Source #
Create a value of UpdateEvaluationResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateEvaluation, updateEvaluationResponse_evaluationId - The ID assigned to the Evaluation during creation. This value should
be identical to the value of the Evaluation in the request.
$sel:httpStatus:UpdateEvaluationResponse', updateEvaluationResponse_httpStatus - The response's http status code.
UpdateMLModel
data UpdateMLModel Source #
See: newUpdateMLModel smart constructor.
Instances
Arguments
| :: Text | |
| -> UpdateMLModel |
Create a value of UpdateMLModel with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:mLModelName:UpdateMLModel', updateMLModel_mLModelName - A user-supplied name or description of the MLModel.
UpdateMLModel, updateMLModel_scoreThreshold - 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.
UpdateMLModel, updateMLModel_mLModelId - The ID assigned to the MLModel during creation.
data UpdateMLModelResponse Source #
Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
See: newUpdateMLModelResponse smart constructor.
Constructors
| UpdateMLModelResponse' (Maybe Text) Int |
Instances
newUpdateMLModelResponse Source #
Create a value of UpdateMLModelResponse with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
UpdateMLModel, updateMLModelResponse_mLModelId - The ID assigned to the MLModel during creation. This value should be
identical to the value of the MLModelID in the request.
$sel:httpStatus:UpdateMLModelResponse', updateMLModelResponse_httpStatus - The response's http status code.
Types
Algorithm
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.
Constructors
| Algorithm' | |
Fields | |
Bundled Patterns
| pattern Algorithm_Sgd :: Algorithm |
Instances
BatchPredictionFilterVariable
newtype BatchPredictionFilterVariable Source #
A list of the variables to use in searching or filtering
BatchPrediction.
CreatedAt- Sets the search criteria toBatchPredictioncreation date.Status- Sets the search criteria toBatchPredictionstatus.Name- Sets the search criteria to the contents ofBatchPredictionName.IAMUser- Sets the search criteria to the user account that invoked theBatchPredictioncreation.MLModelId- Sets the search criteria to theMLModelused in theBatchPrediction.DataSourceId- Sets the search criteria to theDataSourceused in theBatchPrediction.DataURI- Sets the search criteria to the data file(s) used in theBatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
Constructors
| BatchPredictionFilterVariable' | |
Fields | |
Bundled Patterns
Instances
DataSourceFilterVariable
newtype DataSourceFilterVariable Source #
A list of the variables to use in searching or filtering DataSource.
CreatedAt- Sets the search criteria toDataSourcecreation date.Status- Sets the search criteria toDataSourcestatus.Name- Sets the search criteria to the contents ofDataSourceName.DataUri- Sets the search criteria to the URI of data files used to create theDataSource. 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 theDataSourcecreation.
Note: The variable names should match the variable names in the
DataSource.
Constructors
| DataSourceFilterVariable' | |
Fields | |
Bundled Patterns
Instances
DetailsAttributes
newtype DetailsAttributes Source #
Contains the key values of DetailsMap:
PredictiveModelType- Indicates the type of theMLModel.Algorithm- Indicates the algorithm that was used for theMLModel.
Constructors
| DetailsAttributes' | |
Fields | |
Bundled Patterns
| pattern DetailsAttributes_Algorithm :: DetailsAttributes | |
| pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes |
Instances
EntityStatus
newtype EntityStatus Source #
Object status with the following possible values:
PENDING
INPROGRESS
FAILED
COMPLETED
DELETED
Constructors
| EntityStatus' | |
Fields | |
Bundled Patterns
| pattern EntityStatus_COMPLETED :: EntityStatus | |
| pattern EntityStatus_DELETED :: EntityStatus | |
| pattern EntityStatus_FAILED :: EntityStatus | |
| pattern EntityStatus_INPROGRESS :: EntityStatus | |
| pattern EntityStatus_PENDING :: EntityStatus |
Instances
EvaluationFilterVariable
newtype EvaluationFilterVariable Source #
A list of the variables to use in searching or filtering Evaluation.
CreatedAt- Sets the search criteria toEvaluationcreation date.Status- Sets the search criteria toEvaluationstatus.Name- Sets the search criteria to the contents ofEvaluation____Name.IAMUser- Sets the search criteria to the user account that invoked an evaluation.MLModelId- Sets the search criteria to thePredictorthat was evaluated.DataSourceId- Sets the search criteria to theDataSourceused 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.
Constructors
| EvaluationFilterVariable' | |
Fields | |
Bundled Patterns
Instances
MLModelFilterVariable
newtype MLModelFilterVariable Source #
Constructors
| MLModelFilterVariable' | |
Fields | |
Bundled Patterns
Instances
MLModelType
newtype MLModelType Source #
Constructors
| MLModelType' | |
Fields | |
Bundled Patterns
| pattern MLModelType_BINARY :: MLModelType | |
| pattern MLModelType_MULTICLASS :: MLModelType | |
| pattern MLModelType_REGRESSION :: MLModelType |
Instances
RealtimeEndpointStatus
newtype RealtimeEndpointStatus Source #
Constructors
| RealtimeEndpointStatus' | |
Fields | |
Bundled Patterns
| pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus | |
| pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus | |
| pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus | |
| pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus |
Instances
SortOrder
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).
Constructors
| SortOrder' | |
Fields | |
Bundled Patterns
| pattern SortOrder_Asc :: SortOrder | |
| pattern SortOrder_Dsc :: SortOrder |
Instances
TaggableResourceType
newtype TaggableResourceType Source #
Constructors
| TaggableResourceType' | |
Fields | |
Bundled Patterns
Instances
BatchPrediction
data BatchPrediction Source #
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.
See: newBatchPrediction smart constructor.
Constructors
| BatchPrediction' (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) (Maybe Integer) |
Instances
newBatchPrediction :: BatchPrediction Source #
Create a value of BatchPrediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:batchPredictionDataSourceId:BatchPrediction', batchPrediction_batchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to
predict.
$sel:batchPredictionId:BatchPrediction', batchPrediction_batchPredictionId - The ID assigned to the BatchPrediction at creation. This value should
be identical to the value of the BatchPredictionID in the request.
$sel:computeTime:BatchPrediction', batchPrediction_computeTime - Undocumented member.
$sel:createdAt:BatchPrediction', batchPrediction_createdAt - The time that the BatchPrediction was created. The time is expressed
in epoch time.
$sel:createdByIamUser:BatchPrediction', batchPrediction_createdByIamUser - 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.
$sel:finishedAt:BatchPrediction', batchPrediction_finishedAt - Undocumented member.
$sel:inputDataLocationS3:BatchPrediction', batchPrediction_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
$sel:invalidRecordCount:BatchPrediction', batchPrediction_invalidRecordCount - Undocumented member.
$sel:lastUpdatedAt:BatchPrediction', batchPrediction_lastUpdatedAt - The time of the most recent edit to the BatchPrediction. The time is
expressed in epoch time.
$sel:mLModelId:BatchPrediction', batchPrediction_mLModelId - The ID of the MLModel that generated predictions for the
BatchPrediction request.
$sel:message:BatchPrediction', batchPrediction_message - A description of the most recent details about processing the batch
prediction request.
$sel:name:BatchPrediction', batchPrediction_name - A user-supplied name or description of the BatchPrediction.
$sel:outputUri:BatchPrediction', batchPrediction_outputUri - 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: ':', '//', '/./',
'/../'.
$sel:startedAt:BatchPrediction', batchPrediction_startedAt - Undocumented member.
$sel:status:BatchPrediction', batchPrediction_status - 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- TheBatchPredictionis marked as deleted. It is not usable.
$sel:totalRecordCount:BatchPrediction', batchPrediction_totalRecordCount - Undocumented member.
DataSource
data DataSource Source #
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.
See: newDataSource smart constructor.
Constructors
| DataSource' (Maybe Bool) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe RDSMetadata) (Maybe RedshiftMetadata) (Maybe Text) (Maybe POSIX) (Maybe EntityStatus) |
Instances
newDataSource :: DataSource Source #
Create a value of DataSource with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:computeStatistics:DataSource', dataSource_computeStatistics - The parameter is true if statistics need to be generated from the
observation data.
$sel:computeTime:DataSource', dataSource_computeTime - Undocumented member.
$sel:createdAt:DataSource', dataSource_createdAt - The time that the DataSource was created. The time is expressed in
epoch time.
$sel:createdByIamUser:DataSource', dataSource_createdByIamUser - 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.
$sel:dataLocationS3:DataSource', dataSource_dataLocationS3 - The location and name of the data in Amazon Simple Storage Service
(Amazon S3) that is used by a DataSource.
$sel:dataRearrangement:DataSource', dataSource_dataRearrangement - A JSON string that represents the splitting and rearrangement
requirement used when this DataSource was created.
$sel:dataSizeInBytes:DataSource', dataSource_dataSizeInBytes - The total number of observations contained in the data files that the
DataSource references.
$sel:dataSourceId:DataSource', dataSource_dataSourceId - The ID that is assigned to the DataSource during creation.
$sel:finishedAt:DataSource', dataSource_finishedAt - Undocumented member.
$sel:lastUpdatedAt:DataSource', dataSource_lastUpdatedAt - The time of the most recent edit to the BatchPrediction. The time is
expressed in epoch time.
$sel:message:DataSource', dataSource_message - A description of the most recent details about creating the
DataSource.
$sel:name:DataSource', dataSource_name - A user-supplied name or description of the DataSource.
$sel:numberOfFiles:DataSource', dataSource_numberOfFiles - The number of data files referenced by the DataSource.
$sel:rDSMetadata:DataSource', dataSource_rDSMetadata - Undocumented member.
$sel:redshiftMetadata:DataSource', dataSource_redshiftMetadata - Undocumented member.
$sel:roleARN:DataSource', dataSource_roleARN - Undocumented member.
$sel:startedAt:DataSource', dataSource_startedAt - Undocumented member.
$sel:status:DataSource', dataSource_status - 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
DataSourcedid not run to completion. It is not usable. - COMPLETED - The creation process completed successfully.
- DELETED - The
DataSourceis marked as deleted. It is not usable.
Evaluation
data Evaluation Source #
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information
and the current status of the Evaluation.
See: newEvaluation smart constructor.
Constructors
| Evaluation' (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe EntityStatus) |
Instances
newEvaluation :: Evaluation Source #
Create a value of Evaluation with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:computeTime:Evaluation', evaluation_computeTime - Undocumented member.
$sel:createdAt:Evaluation', evaluation_createdAt - The time that the Evaluation was created. The time is expressed in
epoch time.
$sel:createdByIamUser:Evaluation', evaluation_createdByIamUser - 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.
$sel:evaluationDataSourceId:Evaluation', evaluation_evaluationDataSourceId - The ID of the DataSource that is used to evaluate the MLModel.
$sel:evaluationId:Evaluation', evaluation_evaluationId - The ID that is assigned to the Evaluation at creation.
$sel:finishedAt:Evaluation', evaluation_finishedAt - Undocumented member.
$sel:inputDataLocationS3:Evaluation', evaluation_inputDataLocationS3 - The location and name of the data in Amazon Simple Storage Server
(Amazon S3) that is used in the evaluation.
$sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt - The time of the most recent edit to the Evaluation. The time is
expressed in epoch time.
$sel:mLModelId:Evaluation', evaluation_mLModelId - The ID of the MLModel that is the focus of the evaluation.
$sel:message:Evaluation', evaluation_message - A description of the most recent details about evaluating the MLModel.
$sel:name:Evaluation', evaluation_name - A user-supplied name or description of the Evaluation.
$sel:performanceMetrics:Evaluation', evaluation_performanceMetrics - 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
MLModeluses the Area Under the Curve (AUC) technique to measure performance. - RegressionRMSE: A regression
MLModeluses 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
MLModeluses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
$sel:startedAt:Evaluation', evaluation_startedAt - Undocumented member.
$sel:status:Evaluation', evaluation_status - 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 anMLModel.INPROGRESS- The evaluation is underway.FAILED- The request to evaluate anMLModeldid not run to completion. It is not usable.COMPLETED- The evaluation process completed successfully.DELETED- TheEvaluationis marked as deleted. It is not usable.
MLModel
Represents the output of a GetMLModel operation.
The content consists of the detailed metadata and the current status of
the MLModel.
See: newMLModel smart constructor.
Constructors
| MLModel' (Maybe Algorithm) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Text) (Maybe MLModelType) (Maybe Text) (Maybe Text) (Maybe Double) (Maybe POSIX) (Maybe Integer) (Maybe POSIX) (Maybe EntityStatus) (Maybe Text) (Maybe (HashMap Text Text)) |
Instances
newMLModel :: MLModel Source #
Create a value of MLModel with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:algorithm:MLModel', mLModel_algorithm - The algorithm used to train the MLModel. The following algorithm is
supported:
SGD-- Stochastic gradient descent. The goal ofSGDis to minimize the gradient of the loss function.
$sel:computeTime:MLModel', mLModel_computeTime - Undocumented member.
MLModel, mLModel_createdAt - The time that the MLModel was created. The time is expressed in epoch
time.
$sel:createdByIamUser:MLModel', mLModel_createdByIamUser - 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.
$sel:endpointInfo:MLModel', mLModel_endpointInfo - The current endpoint of the MLModel.
$sel:finishedAt:MLModel', mLModel_finishedAt - Undocumented member.
$sel:inputDataLocationS3:MLModel', mLModel_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage
Service (Amazon S3).
$sel:lastUpdatedAt:MLModel', mLModel_lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed
in epoch time.
$sel:mLModelId:MLModel', mLModel_mLModelId - The ID assigned to the MLModel at creation.
$sel:mLModelType:MLModel', mLModel_mLModelType - 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?".
$sel:message:MLModel', mLModel_message - A description of the most recent details about accessing the MLModel.
$sel:name:MLModel', mLModel_name - A user-supplied name or description of the MLModel.
$sel:scoreThreshold:MLModel', mLModel_scoreThreshold - Undocumented member.
$sel:scoreThresholdLastUpdatedAt:MLModel', mLModel_scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is
expressed in epoch time.
$sel:sizeInBytes:MLModel', mLModel_sizeInBytes - Undocumented member.
$sel:startedAt:MLModel', mLModel_startedAt - Undocumented member.
$sel:status:MLModel', mLModel_status - 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 anMLModel.INPROGRESS- The creation process is underway.FAILED- The request to create anMLModeldidn't run to completion. The model isn't usable.COMPLETED- The creation process completed successfully.DELETED- TheMLModelis marked as deleted. It isn't usable.
$sel:trainingDataSourceId:MLModel', mLModel_trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses
the TrainingDataSourceId.
$sel:trainingParameters:MLModel', mLModel_trainingParameters - 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
100000to2147483648. The default value is33554432.sgd.maxPasses- The number of times that the training process traverses the observations to build theMLModel. The value is an integer that ranges from1to10000. The default value is10.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 areautoandnone. The default value isnone.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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used whenL2is 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 as1.0E-08.The value is a double that ranges from
0toMAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used whenL1is specified. Use this parameter sparingly.
PerformanceMetrics
data PerformanceMetrics Source #
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
MLModeluses the Area Under the Curve (AUC) technique to measure performance. - RegressionRMSE: The regression
MLModeluses 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
MLModeluses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
See: newPerformanceMetrics smart constructor.
Constructors
| PerformanceMetrics' (Maybe (HashMap Text Text)) |
Instances
newPerformanceMetrics :: PerformanceMetrics Source #
Create a value of PerformanceMetrics with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:properties:PerformanceMetrics', performanceMetrics_properties - Undocumented member.
Prediction
data Prediction Source #
The output from a Predict operation:
Details- Contains the following attributes:DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASSDetailsAttributes.ALGORITHM - SGDPredictedLabel- Present for either aBINARYorMULTICLASSMLModelrequest.PredictedScores- Contains the raw classification score corresponding to each label.PredictedValue- Present for aREGRESSIONMLModelrequest.
See: newPrediction smart constructor.
Constructors
| Prediction' (Maybe (HashMap DetailsAttributes Text)) (Maybe Text) (Maybe (HashMap Text Double)) (Maybe Double) |
Instances
newPrediction :: Prediction Source #
Create a value of Prediction with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:details:Prediction', prediction_details - Undocumented member.
$sel:predictedLabel:Prediction', prediction_predictedLabel - The prediction label for either a BINARY or MULTICLASS MLModel.
$sel:predictedScores:Prediction', prediction_predictedScores - Undocumented member.
$sel:predictedValue:Prediction', prediction_predictedValue - The prediction value for REGRESSION MLModel.
RDSDataSpec
data RDSDataSpec Source #
The data specification of an Amazon Relational Database Service (Amazon
RDS) DataSource.
See: newRDSDataSpec smart constructor.
Constructors
| RDSDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RDSDatabase Text RDSDatabaseCredentials Text Text Text Text [Text] |
Instances
Arguments
| :: RDSDatabase | |
| -> Text | |
| -> RDSDatabaseCredentials | |
| -> Text | |
| -> Text | |
| -> Text | |
| -> Text | |
| -> RDSDataSpec |
Create a value of RDSDataSpec with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:dataRearrangement:RDSDataSpec', rDSDataSpec_dataRearrangement - 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:
percentBeginUse
percentBeginto indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.percentEndUse
percentEndto indicate the end of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.complementThe
complementparameter instructs Amazon ML to use the data that is not included in the range ofpercentBegintopercentEndto create a datasource. Thecomplementparameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBeginandpercentEnd, along with thecomplementparameter.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"}}strategyTo change how Amazon ML splits the data for a datasource, use the
strategyparameter.The default value for the
strategyparameter issequential, meaning that Amazon ML takes all of the data records between thepercentBeginandpercentEndparameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangementlines 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
strategyparameter torandomand 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 betweenpercentBeginandpercentEnd. 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
DataRearrangementlines 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"}}
$sel:dataSchema:RDSDataSpec', rDSDataSpec_dataSchema - 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" ] }
$sel:dataSchemaUri:RDSDataSpec', rDSDataSpec_dataSchemaUri - The Amazon S3 location of the DataSchema.
$sel:databaseInformation:RDSDataSpec', rDSDataSpec_databaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS
database.
$sel:selectSqlQuery:RDSDataSpec', rDSDataSpec_selectSqlQuery - The query that is used to retrieve the observation data for the
DataSource.
$sel:databaseCredentials:RDSDataSpec', rDSDataSpec_databaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used
connect to the Amazon RDS database.
$sel:s3StagingLocation:RDSDataSpec', rDSDataSpec_s3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved
from Amazon RDS using SelectSqlQuery is stored in this location.
$sel:resourceRole:RDSDataSpec', rDSDataSpec_resourceRole - 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
for data pipelines.
$sel:serviceRole:RDSDataSpec', rDSDataSpec_serviceRole - 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
for data pipelines.
$sel:subnetId:RDSDataSpec', rDSDataSpec_subnetId - 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.
$sel:securityGroupIds:RDSDataSpec', rDSDataSpec_securityGroupIds - 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.
RDSDatabase
data RDSDatabase Source #
The database details of an Amazon RDS database.
See: newRDSDatabase smart constructor.
Constructors
| RDSDatabase' Text Text |
Instances
Arguments
| :: Text | |
| -> Text | |
| -> RDSDatabase |
Create a value of RDSDatabase with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:instanceIdentifier:RDSDatabase', rDSDatabase_instanceIdentifier - The ID of an RDS DB instance.
$sel:databaseName:RDSDatabase', rDSDatabase_databaseName - Undocumented member.
RDSDatabaseCredentials
data RDSDatabaseCredentials Source #
The database credentials to connect to a database on an RDS DB instance.
See: newRDSDatabaseCredentials smart constructor.
Constructors
| RDSDatabaseCredentials' Text Text |
Instances
newRDSDatabaseCredentials Source #
Arguments
| :: Text | |
| -> Text | |
| -> RDSDatabaseCredentials |
Create a value of RDSDatabaseCredentials with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:username:RDSDatabaseCredentials', rDSDatabaseCredentials_username - Undocumented member.
$sel:password:RDSDatabaseCredentials', rDSDatabaseCredentials_password - Undocumented member.
RDSMetadata
data RDSMetadata Source #
The datasource details that are specific to Amazon RDS.
See: newRDSMetadata smart constructor.
Constructors
| RDSMetadata' (Maybe Text) (Maybe RDSDatabase) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) |
Instances
newRDSMetadata :: RDSMetadata Source #
Create a value of RDSMetadata with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:dataPipelineId:RDSMetadata', rDSMetadata_dataPipelineId - 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.
$sel:database:RDSMetadata', rDSMetadata_database - The database details required to connect to an Amazon RDS.
$sel:databaseUserName:RDSMetadata', rDSMetadata_databaseUserName - Undocumented member.
$sel:resourceRole:RDSMetadata', rDSMetadata_resourceRole - 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
for data pipelines.
$sel:selectSqlQuery:RDSMetadata', rDSMetadata_selectSqlQuery - The SQL query that is supplied during CreateDataSourceFromRDS. Returns
only if Verbose is true in GetDataSourceInput.
$sel:serviceRole:RDSMetadata', rDSMetadata_serviceRole - 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
for data pipelines.
RealtimeEndpointInfo
data RealtimeEndpointInfo Source #
Describes the real-time endpoint information for an MLModel.
See: newRealtimeEndpointInfo smart constructor.
Constructors
| RealtimeEndpointInfo' (Maybe POSIX) (Maybe RealtimeEndpointStatus) (Maybe Text) (Maybe Int) |
Instances
newRealtimeEndpointInfo :: RealtimeEndpointInfo Source #
Create a value of RealtimeEndpointInfo with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:createdAt:RealtimeEndpointInfo', realtimeEndpointInfo_createdAt - The time that the request to create the real-time endpoint for the
MLModel was received. The time is expressed in epoch time.
$sel:endpointStatus:RealtimeEndpointInfo', realtimeEndpointInfo_endpointStatus - 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.
$sel:endpointUrl:RealtimeEndpointInfo', realtimeEndpointInfo_endpointUrl - 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.
$sel:peakRequestsPerSecond:RealtimeEndpointInfo', realtimeEndpointInfo_peakRequestsPerSecond - The maximum processing rate for the real-time endpoint for MLModel,
measured in incoming requests per second.
RedshiftDataSpec
data RedshiftDataSpec Source #
Describes the data specification of an Amazon Redshift DataSource.
See: newRedshiftDataSpec smart constructor.
Constructors
| RedshiftDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RedshiftDatabase Text RedshiftDatabaseCredentials Text |
Instances
Arguments
| :: RedshiftDatabase | |
| -> Text | |
| -> RedshiftDatabaseCredentials | |
| -> Text | |
| -> RedshiftDataSpec |
Create a value of RedshiftDataSpec with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:dataRearrangement:RedshiftDataSpec', redshiftDataSpec_dataRearrangement - 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:
percentBeginUse
percentBeginto indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.percentEndUse
percentEndto indicate the end of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.complementThe
complementparameter instructs Amazon ML to use the data that is not included in the range ofpercentBegintopercentEndto create a datasource. Thecomplementparameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBeginandpercentEnd, along with thecomplementparameter.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"}}strategyTo change how Amazon ML splits the data for a datasource, use the
strategyparameter.The default value for the
strategyparameter issequential, meaning that Amazon ML takes all of the data records between thepercentBeginandpercentEndparameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangementlines 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
strategyparameter torandomand 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 betweenpercentBeginandpercentEnd. 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
DataRearrangementlines 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"}}
$sel:dataSchema:RedshiftDataSpec', redshiftDataSpec_dataSchema - 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" ] }
$sel:dataSchemaUri:RedshiftDataSpec', redshiftDataSpec_dataSchemaUri - Describes the schema location for an Amazon Redshift DataSource.
$sel:databaseInformation:RedshiftDataSpec', redshiftDataSpec_databaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon
Redshift DataSource.
$sel:selectSqlQuery:RedshiftDataSpec', redshiftDataSpec_selectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an
Amazon Redshift DataSource.
$sel:databaseCredentials:RedshiftDataSpec', redshiftDataSpec_databaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are
used connect to the Amazon Redshift database.
$sel:s3StagingLocation:RedshiftDataSpec', redshiftDataSpec_s3StagingLocation - Describes an Amazon S3 location to store the result set of the
SelectSqlQuery query.
RedshiftDatabase
data RedshiftDatabase Source #
Describes the database details required to connect to an Amazon Redshift database.
See: newRedshiftDatabase smart constructor.
Constructors
| RedshiftDatabase' Text Text |
Instances
Arguments
| :: Text | |
| -> Text | |
| -> RedshiftDatabase |
Create a value of RedshiftDatabase with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:databaseName:RedshiftDatabase', redshiftDatabase_databaseName - Undocumented member.
$sel:clusterIdentifier:RedshiftDatabase', redshiftDatabase_clusterIdentifier - Undocumented member.
RedshiftDatabaseCredentials
data RedshiftDatabaseCredentials Source #
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
See: newRedshiftDatabaseCredentials smart constructor.
Constructors
| RedshiftDatabaseCredentials' Text Text |
Instances
newRedshiftDatabaseCredentials Source #
Arguments
| :: Text | |
| -> Text | |
| -> RedshiftDatabaseCredentials |
Create a value of RedshiftDatabaseCredentials with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:username:RedshiftDatabaseCredentials', redshiftDatabaseCredentials_username - Undocumented member.
$sel:password:RedshiftDatabaseCredentials', redshiftDatabaseCredentials_password - Undocumented member.
RedshiftMetadata
data RedshiftMetadata Source #
Describes the DataSource details specific to Amazon Redshift.
See: newRedshiftMetadata smart constructor.
Constructors
| RedshiftMetadata' (Maybe Text) (Maybe RedshiftDatabase) (Maybe Text) |
Instances
newRedshiftMetadata :: RedshiftMetadata Source #
Create a value of RedshiftMetadata with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:databaseUserName:RedshiftMetadata', redshiftMetadata_databaseUserName - Undocumented member.
$sel:redshiftDatabase:RedshiftMetadata', redshiftMetadata_redshiftDatabase - Undocumented member.
$sel:selectSqlQuery:RedshiftMetadata', redshiftMetadata_selectSqlQuery - The SQL query that is specified during CreateDataSourceFromRedshift.
Returns only if Verbose is true in GetDataSourceInput.
S3DataSpec
data S3DataSpec Source #
Describes the data specification of a DataSource.
See: newS3DataSpec smart constructor.
Instances
Arguments
| :: Text | |
| -> S3DataSpec |
Create a value of S3DataSpec with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:dataRearrangement:S3DataSpec', s3DataSpec_dataRearrangement - 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:
percentBeginUse
percentBeginto indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.percentEndUse
percentEndto indicate the end of the range of the data used to create the Datasource. If you do not includepercentBeginandpercentEnd, Amazon ML includes all of the data when creating the datasource.complementThe
complementparameter instructs Amazon ML to use the data that is not included in the range ofpercentBegintopercentEndto create a datasource. Thecomplementparameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBeginandpercentEnd, along with thecomplementparameter.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"}}strategyTo change how Amazon ML splits the data for a datasource, use the
strategyparameter.The default value for the
strategyparameter issequential, meaning that Amazon ML takes all of the data records between thepercentBeginandpercentEndparameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangementlines 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
strategyparameter torandomand 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 betweenpercentBeginandpercentEnd. 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
DataRearrangementlines 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"}}
$sel:dataSchema:S3DataSpec', s3DataSpec_dataSchema - 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" ] }
$sel:dataSchemaLocationS3:S3DataSpec', s3DataSpec_dataSchemaLocationS3 - Describes the schema location in Amazon S3. You must provide either the
DataSchema or the DataSchemaLocationS3.
$sel:dataLocationS3:S3DataSpec', s3DataSpec_dataLocationS3 - 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.
Tag
A custom key-value pair associated with an ML object, such as an ML model.
See: newTag smart constructor.
Instances
| FromJSON Tag Source # | |
| ToJSON Tag Source # | |
Defined in Amazonka.MachineLearning.Types.Tag | |
| Generic Tag Source # | |
| Read Tag Source # | |
| Show Tag Source # | |
| NFData Tag Source # | |
Defined in Amazonka.MachineLearning.Types.Tag | |
| Eq Tag Source # | |
| Hashable Tag Source # | |
Defined in Amazonka.MachineLearning.Types.Tag | |
| type Rep Tag Source # | |
Defined in Amazonka.MachineLearning.Types.Tag type Rep Tag = D1 ('MetaData "Tag" "Amazonka.MachineLearning.Types.Tag" "amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA" 'False) (C1 ('MetaCons "Tag'" 'PrefixI 'True) (S1 ('MetaSel ('Just "key") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "value") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) | |
Create a value of Tag with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:key:Tag', tag_key - A unique identifier for the tag. Valid characters include Unicode
letters, digits, white space, _, ., /, =, +, -, %, and @.
$sel:value:Tag', tag_value - An optional string, typically used to describe or define the tag. Valid
characters include Unicode letters, digits, white space, _, ., /, =, +,
-, %, and @.