amazonka-ml-1.6.1: Amazon Machine Learning SDK.

Copyright(c) 2013-2018 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone
LanguageHaskell2010

Network.AWS.MachineLearning.Types

Contents

Description

 
Synopsis

Service Configuration

machineLearning :: Service Source #

API version 2014-12-12 of the Amazon Machine Learning SDK configuration.

Errors

_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError Source #

Prism for InvalidTagException' errors.

_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the server occurred when trying to process a request.

_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the client occurred. Typically, the cause is an invalid input value.

_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) 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.

_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #

Prism for TagLimitExceededException' errors.

_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The exception is thrown when a predict request is made to an unmounted MLModel .

_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError Source #

A specified resource cannot be located.

_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource .

Algorithm

data Algorithm Source #

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

SGD 
Instances
Bounded Algorithm Source # 
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Enum Algorithm Source # 
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Eq Algorithm Source # 
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Data Algorithm Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Algorithm -> c Algorithm #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Algorithm #

toConstr :: Algorithm -> Constr #

dataTypeOf :: Algorithm -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c Algorithm) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Algorithm) #

gmapT :: (forall b. Data b => b -> b) -> Algorithm -> Algorithm #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Algorithm -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Algorithm -> r #

gmapQ :: (forall d. Data d => d -> u) -> Algorithm -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Algorithm -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

Ord Algorithm Source # 
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Read Algorithm Source # 
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Show Algorithm Source # 
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Generic Algorithm Source # 
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type Rep Algorithm :: Type -> Type #

Hashable Algorithm Source # 
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FromJSON Algorithm Source # 
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ToHeader Algorithm Source # 
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ToQuery Algorithm Source # 
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ToByteString Algorithm Source # 
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toBS :: Algorithm -> ByteString #

FromText Algorithm Source # 
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ToText Algorithm Source # 
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toText :: Algorithm -> Text #

NFData Algorithm Source # 
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rnf :: Algorithm -> () #

type Rep Algorithm Source # 
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type Rep Algorithm = D1 (MetaData "Algorithm" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "SGD" PrefixI False) (U1 :: Type -> Type))

BatchPredictionFilterVariable

data BatchPredictionFilterVariable Source #

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

  • CreatedAt - Sets the search criteria to BatchPrediction creation date. * Status - Sets the search criteria to BatchPrediction status. * Name - Sets the search criteria to the contents of BatchPrediction ____ Name . * IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. * MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction . * DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction . * DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction . The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
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Bounded BatchPredictionFilterVariable Source # 
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Enum BatchPredictionFilterVariable Source # 
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Eq BatchPredictionFilterVariable Source # 
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Data BatchPredictionFilterVariable Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> BatchPredictionFilterVariable -> c BatchPredictionFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c BatchPredictionFilterVariable #

toConstr :: BatchPredictionFilterVariable -> Constr #

dataTypeOf :: BatchPredictionFilterVariable -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c BatchPredictionFilterVariable) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c BatchPredictionFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> BatchPredictionFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> BatchPredictionFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> BatchPredictionFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> BatchPredictionFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

Ord BatchPredictionFilterVariable Source # 
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Read BatchPredictionFilterVariable Source # 
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Show BatchPredictionFilterVariable Source # 
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Generic BatchPredictionFilterVariable Source # 
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type Rep BatchPredictionFilterVariable :: Type -> Type #

Hashable BatchPredictionFilterVariable Source # 
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ToJSON BatchPredictionFilterVariable Source # 
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ToHeader BatchPredictionFilterVariable Source # 
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ToQuery BatchPredictionFilterVariable Source # 
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ToByteString BatchPredictionFilterVariable Source # 
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FromText BatchPredictionFilterVariable Source # 
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ToText BatchPredictionFilterVariable Source # 
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NFData BatchPredictionFilterVariable Source # 
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type Rep BatchPredictionFilterVariable Source # 
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type Rep BatchPredictionFilterVariable = D1 (MetaData "BatchPredictionFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (((C1 (MetaCons "BatchCreatedAt" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "BatchDataSourceId" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "BatchDataURI" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "BatchIAMUser" PrefixI False) (U1 :: Type -> Type))) :+: ((C1 (MetaCons "BatchLastUpdatedAt" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "BatchMLModelId" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "BatchName" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "BatchStatus" PrefixI False) (U1 :: Type -> Type))))

DataSourceFilterVariable

data DataSourceFilterVariable Source #

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

  • CreatedAt - Sets the search criteria to DataSource creation date. * Status - Sets the search criteria to DataSource status. * Name - Sets the search criteria to the contents of DataSource ____ Name . * DataUri - Sets the search criteria to the URI of data files used to create the DataSource . The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. * IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.
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Bounded DataSourceFilterVariable Source # 
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Enum DataSourceFilterVariable Source # 
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Eq DataSourceFilterVariable Source # 
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Data DataSourceFilterVariable Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DataSourceFilterVariable -> c DataSourceFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DataSourceFilterVariable #

toConstr :: DataSourceFilterVariable -> Constr #

dataTypeOf :: DataSourceFilterVariable -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c DataSourceFilterVariable) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DataSourceFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> DataSourceFilterVariable -> DataSourceFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DataSourceFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DataSourceFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> DataSourceFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DataSourceFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

Ord DataSourceFilterVariable Source # 
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Read DataSourceFilterVariable Source # 
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Show DataSourceFilterVariable Source # 
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Generic DataSourceFilterVariable Source # 
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type Rep DataSourceFilterVariable :: Type -> Type #

Hashable DataSourceFilterVariable Source # 
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ToJSON DataSourceFilterVariable Source # 
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ToHeader DataSourceFilterVariable Source # 
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ToQuery DataSourceFilterVariable Source # 
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ToByteString DataSourceFilterVariable Source # 
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FromText DataSourceFilterVariable Source # 
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ToText DataSourceFilterVariable Source # 
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NFData DataSourceFilterVariable Source # 
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type Rep DataSourceFilterVariable Source # 
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type Rep DataSourceFilterVariable = D1 (MetaData "DataSourceFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) ((C1 (MetaCons "DataCreatedAt" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "DataDATALOCATIONS3" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "DataIAMUser" PrefixI False) (U1 :: Type -> Type))) :+: (C1 (MetaCons "DataLastUpdatedAt" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "DataName" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "DataStatus" PrefixI False) (U1 :: Type -> Type))))

DetailsAttributes

data DetailsAttributes Source #

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

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Bounded DetailsAttributes Source # 
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Enum DetailsAttributes Source # 
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Eq DetailsAttributes Source # 
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Data DetailsAttributes Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DetailsAttributes -> c DetailsAttributes #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DetailsAttributes #

toConstr :: DetailsAttributes -> Constr #

dataTypeOf :: DetailsAttributes -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c DetailsAttributes) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DetailsAttributes) #

gmapT :: (forall b. Data b => b -> b) -> DetailsAttributes -> DetailsAttributes #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DetailsAttributes -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DetailsAttributes -> r #

gmapQ :: (forall d. Data d => d -> u) -> DetailsAttributes -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DetailsAttributes -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

Ord DetailsAttributes Source # 
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Read DetailsAttributes Source # 
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Show DetailsAttributes Source # 
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Generic DetailsAttributes Source # 
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type Rep DetailsAttributes :: Type -> Type #

Hashable DetailsAttributes Source # 
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FromJSON DetailsAttributes Source # 
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ToHeader DetailsAttributes Source # 
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ToQuery DetailsAttributes Source # 
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ToByteString DetailsAttributes Source # 
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FromText DetailsAttributes Source # 
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ToText DetailsAttributes Source # 
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NFData DetailsAttributes Source # 
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rnf :: DetailsAttributes -> () #

type Rep DetailsAttributes Source # 
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type Rep DetailsAttributes = D1 (MetaData "DetailsAttributes" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "Algorithm" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "PredictiveModelType" PrefixI False) (U1 :: Type -> Type))

EntityStatus

data EntityStatus Source #

Object status with the following possible values:

  • PENDING * INPROGRESS * FAILED * COMPLETED * DELETED
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Bounded EntityStatus Source # 
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Enum EntityStatus Source # 
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Eq EntityStatus Source # 
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Data EntityStatus Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> EntityStatus -> c EntityStatus #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c EntityStatus #

toConstr :: EntityStatus -> Constr #

dataTypeOf :: EntityStatus -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c EntityStatus) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c EntityStatus) #

gmapT :: (forall b. Data b => b -> b) -> EntityStatus -> EntityStatus #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> EntityStatus -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> EntityStatus -> r #

gmapQ :: (forall d. Data d => d -> u) -> EntityStatus -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> EntityStatus -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

Ord EntityStatus Source # 
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Read EntityStatus Source # 
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Show EntityStatus Source # 
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Generic EntityStatus Source # 
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type Rep EntityStatus :: Type -> Type #

Hashable EntityStatus Source # 
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FromJSON EntityStatus Source # 
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ToHeader EntityStatus Source # 
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ToQuery EntityStatus Source # 
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ToByteString EntityStatus Source # 
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FromText EntityStatus Source # 
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ToText EntityStatus Source # 
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toText :: EntityStatus -> Text #

NFData EntityStatus Source # 
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rnf :: EntityStatus -> () #

type Rep EntityStatus Source # 
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type Rep EntityStatus = D1 (MetaData "EntityStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) ((C1 (MetaCons "ESCompleted" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "ESDeleted" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "ESFailed" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "ESInprogress" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "ESPending" PrefixI False) (U1 :: Type -> Type))))

EvaluationFilterVariable

data EvaluationFilterVariable Source #

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

  • CreatedAt - Sets the search criteria to Evaluation creation date. * Status - Sets the search criteria to Evaluation status. * Name - Sets the search criteria to the contents of Evaluation ____ Name . * IAMUser - Sets the search criteria to the user account that invoked an evaluation. * MLModelId - Sets the search criteria to the Predictor that was evaluated. * DataSourceId - Sets the search criteria to the DataSource used in evaluation. * DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
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Bounded EvaluationFilterVariable Source # 
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Enum EvaluationFilterVariable Source # 
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Eq EvaluationFilterVariable Source # 
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Data EvaluationFilterVariable Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> EvaluationFilterVariable -> c EvaluationFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c EvaluationFilterVariable #

toConstr :: EvaluationFilterVariable -> Constr #

dataTypeOf :: EvaluationFilterVariable -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c EvaluationFilterVariable) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c EvaluationFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> EvaluationFilterVariable -> EvaluationFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> EvaluationFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> EvaluationFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> EvaluationFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> EvaluationFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

Ord EvaluationFilterVariable Source # 
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Show EvaluationFilterVariable Source # 
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Generic EvaluationFilterVariable Source # 
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type Rep EvaluationFilterVariable :: Type -> Type #

Hashable EvaluationFilterVariable Source # 
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ToJSON EvaluationFilterVariable Source # 
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ToHeader EvaluationFilterVariable Source # 
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ToQuery EvaluationFilterVariable Source # 
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ToByteString EvaluationFilterVariable Source # 
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FromText EvaluationFilterVariable Source # 
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ToText EvaluationFilterVariable Source # 
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NFData EvaluationFilterVariable Source # 
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type Rep EvaluationFilterVariable Source # 
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type Rep EvaluationFilterVariable = D1 (MetaData "EvaluationFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (((C1 (MetaCons "EvalCreatedAt" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "EvalDataSourceId" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "EvalDataURI" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "EvalIAMUser" PrefixI False) (U1 :: Type -> Type))) :+: ((C1 (MetaCons "EvalLastUpdatedAt" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "EvalMLModelId" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "EvalName" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "EvalStatus" PrefixI False) (U1 :: Type -> Type))))

MLModelFilterVariable

data MLModelFilterVariable Source #

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Bounded MLModelFilterVariable Source # 
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Enum MLModelFilterVariable Source # 
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Eq MLModelFilterVariable Source # 
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Data MLModelFilterVariable Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelFilterVariable -> c MLModelFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelFilterVariable #

toConstr :: MLModelFilterVariable -> Constr #

dataTypeOf :: MLModelFilterVariable -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c MLModelFilterVariable) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> MLModelFilterVariable -> MLModelFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModelFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

Ord MLModelFilterVariable Source # 
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Read MLModelFilterVariable Source # 
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Show MLModelFilterVariable Source # 
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Generic MLModelFilterVariable Source # 
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Associated Types

type Rep MLModelFilterVariable :: Type -> Type #

Hashable MLModelFilterVariable Source # 
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ToJSON MLModelFilterVariable Source # 
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ToHeader MLModelFilterVariable Source # 
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ToQuery MLModelFilterVariable Source # 
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ToByteString MLModelFilterVariable Source # 
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FromText MLModelFilterVariable Source # 
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ToText MLModelFilterVariable Source # 
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NFData MLModelFilterVariable Source # 
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Methods

rnf :: MLModelFilterVariable -> () #

type Rep MLModelFilterVariable Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

type Rep MLModelFilterVariable = D1 (MetaData "MLModelFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (((C1 (MetaCons "MLMFVAlgorithm" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "MLMFVCreatedAt" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "MLMFVIAMUser" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "MLMFVLastUpdatedAt" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "MLMFVMLModelType" PrefixI False) (U1 :: Type -> Type)))) :+: ((C1 (MetaCons "MLMFVName" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "MLMFVRealtimeEndpointStatus" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "MLMFVStatus" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "MLMFVTrainingDataSourceId" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "MLMFVTrainingDataURI" PrefixI False) (U1 :: Type -> Type)))))

MLModelType

data MLModelType Source #

Constructors

Binary 
Multiclass 
Regression 
Instances
Bounded MLModelType Source # 
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Enum MLModelType Source # 
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Eq MLModelType Source # 
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Data MLModelType Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelType -> c MLModelType #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelType #

toConstr :: MLModelType -> Constr #

dataTypeOf :: MLModelType -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c MLModelType) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelType) #

gmapT :: (forall b. Data b => b -> b) -> MLModelType -> MLModelType #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModelType -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelType -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

Ord MLModelType Source # 
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Read MLModelType Source # 
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Show MLModelType Source # 
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Generic MLModelType Source # 
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Associated Types

type Rep MLModelType :: Type -> Type #

Hashable MLModelType Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

ToJSON MLModelType Source # 
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FromJSON MLModelType Source # 
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ToHeader MLModelType Source # 
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ToQuery MLModelType Source # 
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ToByteString MLModelType Source # 
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FromText MLModelType Source # 
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ToText MLModelType Source # 
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toText :: MLModelType -> Text #

NFData MLModelType Source # 
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Methods

rnf :: MLModelType -> () #

type Rep MLModelType Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

type Rep MLModelType = D1 (MetaData "MLModelType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "Binary" PrefixI False) (U1 :: Type -> Type) :+: (C1 (MetaCons "Multiclass" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "Regression" PrefixI False) (U1 :: Type -> Type)))

RealtimeEndpointStatus

data RealtimeEndpointStatus Source #

Constructors

Failed 
None 
Ready 
Updating 
Instances
Bounded RealtimeEndpointStatus Source # 
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Enum RealtimeEndpointStatus Source # 
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Eq RealtimeEndpointStatus Source # 
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Data RealtimeEndpointStatus Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RealtimeEndpointStatus -> c RealtimeEndpointStatus #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RealtimeEndpointStatus #

toConstr :: RealtimeEndpointStatus -> Constr #

dataTypeOf :: RealtimeEndpointStatus -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c RealtimeEndpointStatus) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RealtimeEndpointStatus) #

gmapT :: (forall b. Data b => b -> b) -> RealtimeEndpointStatus -> RealtimeEndpointStatus #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r #

gmapQ :: (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

Ord RealtimeEndpointStatus Source # 
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Read RealtimeEndpointStatus Source # 
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Show RealtimeEndpointStatus Source # 
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Generic RealtimeEndpointStatus Source # 
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type Rep RealtimeEndpointStatus :: Type -> Type #

Hashable RealtimeEndpointStatus Source # 
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FromJSON RealtimeEndpointStatus Source # 
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ToHeader RealtimeEndpointStatus Source # 
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ToQuery RealtimeEndpointStatus Source # 
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ToByteString RealtimeEndpointStatus Source # 
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FromText RealtimeEndpointStatus Source # 
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ToText RealtimeEndpointStatus Source # 
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NFData RealtimeEndpointStatus Source # 
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rnf :: RealtimeEndpointStatus -> () #

type Rep RealtimeEndpointStatus Source # 
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type Rep RealtimeEndpointStatus = D1 (MetaData "RealtimeEndpointStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) ((C1 (MetaCons "Failed" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "None" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "Ready" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "Updating" PrefixI False) (U1 :: Type -> Type)))

SortOrder

data SortOrder Source #

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

Asc 
Dsc 
Instances
Bounded SortOrder Source # 
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Enum SortOrder Source # 
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Eq SortOrder Source # 
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Data SortOrder Source # 
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Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> SortOrder -> c SortOrder #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c SortOrder #

toConstr :: SortOrder -> Constr #

dataTypeOf :: SortOrder -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c SortOrder) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c SortOrder) #

gmapT :: (forall b. Data b => b -> b) -> SortOrder -> SortOrder #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r #

gmapQ :: (forall d. Data d => d -> u) -> SortOrder -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> SortOrder -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

Ord SortOrder Source # 
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Read SortOrder Source # 
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Show SortOrder Source # 
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Generic SortOrder Source # 
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Associated Types

type Rep SortOrder :: Type -> Type #

Hashable SortOrder Source # 
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ToJSON SortOrder Source # 
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ToHeader SortOrder Source # 
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ToQuery SortOrder Source # 
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ToByteString SortOrder Source # 
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toBS :: SortOrder -> ByteString #

FromText SortOrder Source # 
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ToText SortOrder Source # 
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toText :: SortOrder -> Text #

NFData SortOrder Source # 
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rnf :: SortOrder -> () #

type Rep SortOrder Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

type Rep SortOrder = D1 (MetaData "SortOrder" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "Asc" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "Dsc" PrefixI False) (U1 :: Type -> Type))

TaggableResourceType

data TaggableResourceType Source #

Instances
Bounded TaggableResourceType Source # 
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Enum TaggableResourceType Source # 
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Eq TaggableResourceType Source # 
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Data TaggableResourceType Source # 
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gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> TaggableResourceType -> c TaggableResourceType #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c TaggableResourceType #

toConstr :: TaggableResourceType -> Constr #

dataTypeOf :: TaggableResourceType -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c TaggableResourceType) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c TaggableResourceType) #

gmapT :: (forall b. Data b => b -> b) -> TaggableResourceType -> TaggableResourceType #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> TaggableResourceType -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> TaggableResourceType -> r #

gmapQ :: (forall d. Data d => d -> u) -> TaggableResourceType -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> TaggableResourceType -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> TaggableResourceType -> m TaggableResourceType #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> TaggableResourceType -> m TaggableResourceType #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> TaggableResourceType -> m TaggableResourceType #

Ord TaggableResourceType Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

Read TaggableResourceType Source # 
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Show TaggableResourceType Source # 
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Generic TaggableResourceType Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

Associated Types

type Rep TaggableResourceType :: Type -> Type #

Hashable TaggableResourceType Source # 
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ToJSON TaggableResourceType Source # 
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Defined in Network.AWS.MachineLearning.Types.Sum

FromJSON TaggableResourceType Source # 
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ToHeader TaggableResourceType Source # 
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ToQuery TaggableResourceType Source # 
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ToByteString TaggableResourceType Source # 
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FromText TaggableResourceType Source # 
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ToText TaggableResourceType Source # 
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NFData TaggableResourceType Source # 
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Methods

rnf :: TaggableResourceType -> () #

type Rep TaggableResourceType Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Sum

type Rep TaggableResourceType = D1 (MetaData "TaggableResourceType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) ((C1 (MetaCons "BatchPrediction" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "DataSource" PrefixI False) (U1 :: Type -> Type)) :+: (C1 (MetaCons "Evaluation" PrefixI False) (U1 :: Type -> Type) :+: C1 (MetaCons "MLModel" PrefixI False) (U1 :: Type -> Type)))

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: batchPrediction smart constructor.

Instances
Eq BatchPrediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data BatchPrediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> BatchPrediction -> c BatchPrediction #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c BatchPrediction #

toConstr :: BatchPrediction -> Constr #

dataTypeOf :: BatchPrediction -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c BatchPrediction) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c BatchPrediction) #

gmapT :: (forall b. Data b => b -> b) -> BatchPrediction -> BatchPrediction #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r #

gmapQ :: (forall d. Data d => d -> u) -> BatchPrediction -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> BatchPrediction -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

Read BatchPrediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show BatchPrediction Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Generic BatchPrediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep BatchPrediction :: Type -> Type #

Hashable BatchPrediction Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

FromJSON BatchPrediction Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

NFData BatchPrediction Source # 
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Methods

rnf :: BatchPrediction -> () #

type Rep BatchPrediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep BatchPrediction = D1 (MetaData "BatchPrediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "BatchPrediction'" PrefixI True) ((((S1 (MetaSel (Just "_bpStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus)) :*: S1 (MetaSel (Just "_bpLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) :*: (S1 (MetaSel (Just "_bpCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_bpComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) :*: ((S1 (MetaSel (Just "_bpInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_bpMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_bpBatchPredictionDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_bpTotalRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))) :*: (((S1 (MetaSel (Just "_bpStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_bpBatchPredictionId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_bpFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_bpInvalidRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) :*: ((S1 (MetaSel (Just "_bpCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_bpName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_bpMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_bpOutputURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))

batchPrediction :: BatchPrediction Source #

Creates a value of BatchPrediction with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

  • bpStatus - The status of the BatchPrediction . This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. * INPROGRESS - The process is underway. * FAILED - The request to perform a batch prediction did not run to completion. It is not usable. * COMPLETED - The batch prediction process completed successfully. * DELETED - The BatchPrediction is marked as deleted. It is not usable.
  • bpLastUpdatedAt - The time of the most recent edit to the BatchPrediction . The time is expressed in epoch time.
  • bpCreatedAt - The time that the BatchPrediction was created. The time is expressed in epoch time.
  • bpComputeTime - Undocumented member.
  • bpInputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  • bpMLModelId - The ID of the MLModel that generated predictions for the BatchPrediction request.
  • bpBatchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to predict.
  • bpTotalRecordCount - Undocumented member.
  • bpStartedAt - Undocumented member.
  • bpBatchPredictionId - The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.
  • bpFinishedAt - Undocumented member.
  • bpInvalidRecordCount - Undocumented member.
  • bpCreatedByIAMUser - 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.
  • bpName - A user-supplied name or description of the BatchPrediction .
  • bpMessage - A description of the most recent details about processing the batch prediction request.
  • bpOutputURI - 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: :, //, /./, /../.

bpStatus :: Lens' BatchPrediction (Maybe EntityStatus) Source #

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

bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #

The time of the most recent edit to the BatchPrediction . The time is expressed in epoch time.

bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #

The time that the BatchPrediction was created. The time is expressed in epoch time.

bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) Source #

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

bpMLModelId :: Lens' BatchPrediction (Maybe Text) Source #

The ID of the MLModel that generated predictions for the BatchPrediction request.

bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) Source #

The ID of the DataSource that points to the group of observations to predict.

bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text) Source #

The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text) Source #

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.

bpName :: Lens' BatchPrediction (Maybe Text) Source #

A user-supplied name or description of the BatchPrediction .

bpMessage :: Lens' BatchPrediction (Maybe Text) Source #

A description of the most recent details about processing the batch prediction request.

bpOutputURI :: Lens' BatchPrediction (Maybe Text) Source #

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: :, //, /./, /../.

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: dataSource smart constructor.

Instances
Eq DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DataSource -> c DataSource #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DataSource #

toConstr :: DataSource -> Constr #

dataTypeOf :: DataSource -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c DataSource) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DataSource) #

gmapT :: (forall b. Data b => b -> b) -> DataSource -> DataSource #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r #

gmapQ :: (forall d. Data d => d -> u) -> DataSource -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DataSource -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

Read DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Generic DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep DataSource :: Type -> Type #

Hashable DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

FromJSON DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

NFData DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: DataSource -> () #

type Rep DataSource Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep DataSource = D1 (MetaData "DataSource" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "DataSource'" PrefixI True) ((((S1 (MetaSel (Just "_dsStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus)) :*: S1 (MetaSel (Just "_dsNumberOfFiles") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) :*: (S1 (MetaSel (Just "_dsLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_dsCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) :*: ((S1 (MetaSel (Just "_dsComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)) :*: S1 (MetaSel (Just "_dsDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_dsRDSMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSMetadata)) :*: (S1 (MetaSel (Just "_dsDataSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)) :*: S1 (MetaSel (Just "_dsStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))))) :*: (((S1 (MetaSel (Just "_dsFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_dsCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_dsName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_dsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) :*: ((S1 (MetaSel (Just "_dsComputeStatistics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Bool)) :*: S1 (MetaSel (Just "_dsMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_dsRedshiftMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftMetadata)) :*: (S1 (MetaSel (Just "_dsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_dsRoleARN") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

dataSource :: DataSource Source #

Creates a value of DataSource with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

  • dsStatus - The current status of the DataSource . This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource . * INPROGRESS - The creation process is underway. * FAILED - The request to create a DataSource did not run to completion. It is not usable. * COMPLETED - The creation process completed successfully. * DELETED - The DataSource is marked as deleted. It is not usable.
  • dsNumberOfFiles - The number of data files referenced by the DataSource .
  • dsLastUpdatedAt - The time of the most recent edit to the BatchPrediction . The time is expressed in epoch time.
  • dsCreatedAt - The time that the DataSource was created. The time is expressed in epoch time.
  • dsComputeTime - Undocumented member.
  • dsDataSourceId - The ID that is assigned to the DataSource during creation.
  • dsRDSMetadata - Undocumented member.
  • dsDataSizeInBytes - The total number of observations contained in the data files that the DataSource references.
  • dsStartedAt - Undocumented member.
  • dsFinishedAt - Undocumented member.
  • dsCreatedByIAMUser - 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.
  • dsName - A user-supplied name or description of the DataSource .
  • dsDataLocationS3 - The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource .
  • dsComputeStatistics - The parameter is true if statistics need to be generated from the observation data.
  • dsMessage - A description of the most recent details about creating the DataSource .
  • dsRedshiftMetadata - Undocumented member.
  • dsDataRearrangement - A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.
  • dsRoleARN - Undocumented member.

dsStatus :: Lens' DataSource (Maybe EntityStatus) Source #

The current status of the DataSource . This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource . * INPROGRESS - The creation process is underway. * FAILED - The request to create a DataSource did not run to completion. It is not usable. * COMPLETED - The creation process completed successfully. * DELETED - The DataSource is marked as deleted. It is not usable.

dsNumberOfFiles :: Lens' DataSource (Maybe Integer) Source #

The number of data files referenced by the DataSource .

dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime) Source #

The time of the most recent edit to the BatchPrediction . The time is expressed in epoch time.

dsCreatedAt :: Lens' DataSource (Maybe UTCTime) Source #

The time that the DataSource was created. The time is expressed in epoch time.

dsDataSourceId :: Lens' DataSource (Maybe Text) Source #

The ID that is assigned to the DataSource during creation.

dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) Source #

The total number of observations contained in the data files that the DataSource references.

dsStartedAt :: Lens' DataSource (Maybe UTCTime) Source #

Undocumented member.

dsFinishedAt :: Lens' DataSource (Maybe UTCTime) Source #

Undocumented member.

dsCreatedByIAMUser :: Lens' DataSource (Maybe Text) Source #

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.

dsName :: Lens' DataSource (Maybe Text) Source #

A user-supplied name or description of the DataSource .

dsDataLocationS3 :: Lens' DataSource (Maybe Text) Source #

The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource .

dsComputeStatistics :: Lens' DataSource (Maybe Bool) Source #

The parameter is true if statistics need to be generated from the observation data.

dsMessage :: Lens' DataSource (Maybe Text) Source #

A description of the most recent details about creating the DataSource .

dsDataRearrangement :: Lens' DataSource (Maybe Text) Source #

A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

dsRoleARN :: Lens' DataSource (Maybe Text) Source #

Undocumented member.

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: evaluation smart constructor.

Instances
Eq Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Evaluation -> c Evaluation #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Evaluation #

toConstr :: Evaluation -> Constr #

dataTypeOf :: Evaluation -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c Evaluation) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Evaluation) #

gmapT :: (forall b. Data b => b -> b) -> Evaluation -> Evaluation #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r #

gmapQ :: (forall d. Data d => d -> u) -> Evaluation -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Evaluation -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

Read Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Generic Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep Evaluation :: Type -> Type #

Hashable Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

FromJSON Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

NFData Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: Evaluation -> () #

type Rep Evaluation Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep Evaluation = D1 (MetaData "Evaluation" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "Evaluation'" PrefixI True) (((S1 (MetaSel (Just "_eStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus)) :*: (S1 (MetaSel (Just "_ePerformanceMetrics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe PerformanceMetrics)) :*: S1 (MetaSel (Just "_eLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) :*: ((S1 (MetaSel (Just "_eCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_eComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) :*: (S1 (MetaSel (Just "_eInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_eMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) :*: ((S1 (MetaSel (Just "_eStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: (S1 (MetaSel (Just "_eFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_eCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) :*: ((S1 (MetaSel (Just "_eName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_eEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_eMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_eEvaluationDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))

evaluation :: Evaluation Source #

Creates a value of Evaluation with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

  • eStatus - The status of the evaluation. This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel . * INPROGRESS - The evaluation is underway. * FAILED - The request to evaluate an MLModel did not run to completion. It is not usable. * COMPLETED - The evaluation process completed successfully. * DELETED - The Evaluation is marked as deleted. It is not usable.
  • ePerformanceMetrics - Measurements of how well the MLModel performed, using observations referenced by the DataSource . One of the following metrics is returned, based on the type of the MLModel : * BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance. * RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. * MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide .
  • eLastUpdatedAt - The time of the most recent edit to the Evaluation . The time is expressed in epoch time.
  • eCreatedAt - The time that the Evaluation was created. The time is expressed in epoch time.
  • eComputeTime - Undocumented member.
  • eInputDataLocationS3 - The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
  • eMLModelId - The ID of the MLModel that is the focus of the evaluation.
  • eStartedAt - Undocumented member.
  • eFinishedAt - Undocumented member.
  • eCreatedByIAMUser - 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.
  • eName - A user-supplied name or description of the Evaluation .
  • eEvaluationId - The ID that is assigned to the Evaluation at creation.
  • eMessage - A description of the most recent details about evaluating the MLModel .
  • eEvaluationDataSourceId - The ID of the DataSource that is used to evaluate the MLModel .

eStatus :: Lens' Evaluation (Maybe EntityStatus) Source #

The status of the evaluation. This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel . * INPROGRESS - The evaluation is underway. * FAILED - The request to evaluate an MLModel did not run to completion. It is not usable. * COMPLETED - The evaluation process completed successfully. * DELETED - The Evaluation is marked as deleted. It is not usable.

ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics) Source #

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

eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime) Source #

The time of the most recent edit to the Evaluation . The time is expressed in epoch time.

eCreatedAt :: Lens' Evaluation (Maybe UTCTime) Source #

The time that the Evaluation was created. The time is expressed in epoch time.

eComputeTime :: Lens' Evaluation (Maybe Integer) Source #

Undocumented member.

eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) Source #

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

eMLModelId :: Lens' Evaluation (Maybe Text) Source #

The ID of the MLModel that is the focus of the evaluation.

eStartedAt :: Lens' Evaluation (Maybe UTCTime) Source #

Undocumented member.

eFinishedAt :: Lens' Evaluation (Maybe UTCTime) Source #

Undocumented member.

eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) Source #

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.

eName :: Lens' Evaluation (Maybe Text) Source #

A user-supplied name or description of the Evaluation .

eEvaluationId :: Lens' Evaluation (Maybe Text) Source #

The ID that is assigned to the Evaluation at creation.

eMessage :: Lens' Evaluation (Maybe Text) Source #

A description of the most recent details about evaluating the MLModel .

eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text) Source #

The ID of the DataSource that is used to evaluate the MLModel .

MLModel

data MLModel Source #

Represents the output of a GetMLModel operation.

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

See: mLModel smart constructor.

Instances
Eq MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

(==) :: MLModel -> MLModel -> Bool #

(/=) :: MLModel -> MLModel -> Bool #

Data MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModel -> c MLModel #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModel #

toConstr :: MLModel -> Constr #

dataTypeOf :: MLModel -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c MLModel) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModel) #

gmapT :: (forall b. Data b => b -> b) -> MLModel -> MLModel #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModel -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModel -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

Read MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Generic MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep MLModel :: Type -> Type #

Methods

from :: MLModel -> Rep MLModel x #

to :: Rep MLModel x -> MLModel #

Hashable MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

hashWithSalt :: Int -> MLModel -> Int #

hash :: MLModel -> Int #

FromJSON MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

NFData MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: MLModel -> () #

type Rep MLModel Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep MLModel = D1 (MetaData "MLModel" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "MLModel'" PrefixI True) ((((S1 (MetaSel (Just "_mlmStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus)) :*: S1 (MetaSel (Just "_mlmLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) :*: (S1 (MetaSel (Just "_mlmTrainingParameters") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Text))) :*: S1 (MetaSel (Just "_mlmScoreThresholdLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) :*: ((S1 (MetaSel (Just "_mlmCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_mlmComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) :*: (S1 (MetaSel (Just "_mlmInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 (MetaSel (Just "_mlmMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_mlmSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))))) :*: (((S1 (MetaSel (Just "_mlmStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 (MetaSel (Just "_mlmScoreThreshold") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double))) :*: (S1 (MetaSel (Just "_mlmFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)) :*: (S1 (MetaSel (Just "_mlmAlgorithm") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Algorithm)) :*: S1 (MetaSel (Just "_mlmCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) :*: ((S1 (MetaSel (Just "_mlmName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_mlmEndpointInfo") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo))) :*: (S1 (MetaSel (Just "_mlmTrainingDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 (MetaSel (Just "_mlmMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_mlmMLModelType") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe MLModelType))))))))

mLModel :: MLModel Source #

Creates a value of MLModel with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

  • mlmStatus - The current status of an MLModel . This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel . * INPROGRESS - The creation process is underway. * FAILED - The request to create an MLModel didn't run to completion. The model isn't usable. * COMPLETED - The creation process completed successfully. * DELETED - The MLModel is marked as deleted. It isn't usable.
  • mlmLastUpdatedAt - The time of the most recent edit to the MLModel . The time is expressed in epoch time.
  • mlmTrainingParameters - A list of the training parameters in the MLModel . The list is implemented as a map of key-value pairs. The following is the current set of training parameters: * sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648 . The default value is 33554432 . * sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel . The value is an integer that ranges from 1 to 10000 . The default value is 10 . * sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none . The default value is none . * sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08 . The value is a double that ranges from 0 to MAX_DOUBLE . The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly. * sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08 . The value is a double that ranges from 0 to MAX_DOUBLE . The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
  • mlmScoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold . The time is expressed in epoch time.
  • mlmCreatedAt - The time that the MLModel was created. The time is expressed in epoch time.
  • mlmComputeTime - Undocumented member.
  • mlmInputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  • mlmMLModelId - The ID assigned to the MLModel at creation.
  • mlmSizeInBytes - Undocumented member.
  • mlmStartedAt - Undocumented member.
  • mlmScoreThreshold - Undocumented member.
  • mlmFinishedAt - Undocumented member.
  • mlmAlgorithm - The algorithm used to train the MLModel . The following algorithm is supported: * SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
  • mlmCreatedByIAMUser - 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.
  • mlmName - A user-supplied name or description of the MLModel .
  • mlmEndpointInfo - The current endpoint of the MLModel .
  • mlmTrainingDataSourceId - The ID of the training DataSource . The CreateMLModel operation uses the TrainingDataSourceId .
  • mlmMessage - A description of the most recent details about accessing the MLModel .
  • mlmMLModelType - 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?".

mlmStatus :: Lens' MLModel (Maybe EntityStatus) Source #

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

mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time of the most recent edit to the MLModel . The time is expressed in epoch time.

mlmTrainingParameters :: Lens' MLModel (HashMap Text Text) Source #

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

mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time of the most recent edit to the ScoreThreshold . The time is expressed in epoch time.

mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time that the MLModel was created. The time is expressed in epoch time.

mlmComputeTime :: Lens' MLModel (Maybe Integer) Source #

Undocumented member.

mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) Source #

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

mlmMLModelId :: Lens' MLModel (Maybe Text) Source #

The ID assigned to the MLModel at creation.

mlmSizeInBytes :: Lens' MLModel (Maybe Integer) Source #

Undocumented member.

mlmStartedAt :: Lens' MLModel (Maybe UTCTime) Source #

Undocumented member.

mlmFinishedAt :: Lens' MLModel (Maybe UTCTime) Source #

Undocumented member.

mlmAlgorithm :: Lens' MLModel (Maybe Algorithm) Source #

The algorithm used to train the MLModel . The following algorithm is supported: * SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text) Source #

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.

mlmName :: Lens' MLModel (Maybe Text) Source #

A user-supplied name or description of the MLModel .

mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo) Source #

The current endpoint of the MLModel .

mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text) Source #

The ID of the training DataSource . The CreateMLModel operation uses the TrainingDataSourceId .

mlmMessage :: Lens' MLModel (Maybe Text) Source #

A description of the most recent details about accessing the MLModel .

mlmMLModelType :: Lens' MLModel (Maybe MLModelType) Source #

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?".

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 MLModel uses the Area Under the Curve (AUC) technique to measure performance.
  • RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
  • MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide .

See: performanceMetrics smart constructor.

Instances
Eq PerformanceMetrics Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data PerformanceMetrics Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> PerformanceMetrics -> c PerformanceMetrics #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c PerformanceMetrics #

toConstr :: PerformanceMetrics -> Constr #

dataTypeOf :: PerformanceMetrics -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c PerformanceMetrics) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c PerformanceMetrics) #

gmapT :: (forall b. Data b => b -> b) -> PerformanceMetrics -> PerformanceMetrics #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r #

gmapQ :: (forall d. Data d => d -> u) -> PerformanceMetrics -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> PerformanceMetrics -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

Read PerformanceMetrics Source # 
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Show PerformanceMetrics Source # 
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Generic PerformanceMetrics Source # 
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Associated Types

type Rep PerformanceMetrics :: Type -> Type #

Hashable PerformanceMetrics Source # 
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FromJSON PerformanceMetrics Source # 
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NFData PerformanceMetrics Source # 
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Methods

rnf :: PerformanceMetrics -> () #

type Rep PerformanceMetrics Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

type Rep PerformanceMetrics = D1 (MetaData "PerformanceMetrics" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" True) (C1 (MetaCons "PerformanceMetrics'" PrefixI True) (S1 (MetaSel (Just "_pmProperties") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Maybe (Map Text Text)))))

performanceMetrics :: PerformanceMetrics Source #

Creates a value of PerformanceMetrics with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

Prediction

data Prediction Source #

The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
  • PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
  • PredictedScores - Contains the raw classification score corresponding to each label.
  • PredictedValue - Present for a REGRESSION MLModel request.

See: prediction smart constructor.

Instances
Eq Prediction Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data Prediction Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Prediction -> c Prediction #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Prediction #

toConstr :: Prediction -> Constr #

dataTypeOf :: Prediction -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c Prediction) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Prediction) #

gmapT :: (forall b. Data b => b -> b) -> Prediction -> Prediction #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r #

gmapQ :: (forall d. Data d => d -> u) -> Prediction -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Prediction -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

Read Prediction Source # 
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Show Prediction Source # 
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Generic Prediction Source # 
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Associated Types

type Rep Prediction :: Type -> Type #

Hashable Prediction Source # 
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FromJSON Prediction Source # 
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NFData Prediction Source # 
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Methods

rnf :: Prediction -> () #

type Rep Prediction Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

type Rep Prediction = D1 (MetaData "Prediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "Prediction'" PrefixI True) ((S1 (MetaSel (Just "_pPredictedValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double)) :*: S1 (MetaSel (Just "_pPredictedLabel") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_pPredictedScores") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Double))) :*: S1 (MetaSel (Just "_pDetails") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map DetailsAttributes Text))))))

prediction :: Prediction Source #

Creates a value of Prediction with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

pPredictedValue :: Lens' Prediction (Maybe Double) Source #

The prediction value for REGRESSION MLModel .

pPredictedLabel :: Lens' Prediction (Maybe Text) Source #

The prediction label for either a BINARY or MULTICLASS MLModel .

RDSDataSpec

data RDSDataSpec Source #

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

See: rdsDataSpec smart constructor.

Instances
Eq RDSDataSpec Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data RDSDataSpec Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDataSpec -> c RDSDataSpec #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDataSpec #

toConstr :: RDSDataSpec -> Constr #

dataTypeOf :: RDSDataSpec -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c RDSDataSpec) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDataSpec) #

gmapT :: (forall b. Data b => b -> b) -> RDSDataSpec -> RDSDataSpec #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDataSpec -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDataSpec -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

Read RDSDataSpec Source # 
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Show RDSDataSpec Source # 
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Generic RDSDataSpec Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep RDSDataSpec :: Type -> Type #

Hashable RDSDataSpec Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

ToJSON RDSDataSpec Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

NFData RDSDataSpec Source # 
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Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: RDSDataSpec -> () #

type Rep RDSDataSpec Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep RDSDataSpec = D1 (MetaData "RDSDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "RDSDataSpec'" PrefixI True) (((S1 (MetaSel (Just "_rdsdsDataSchemaURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_rdsdsDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 (MetaSel (Just "_rdsdsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 (MetaSel (Just "_rdsdsDatabaseInformation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabase) :*: S1 (MetaSel (Just "_rdsdsSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))) :*: ((S1 (MetaSel (Just "_rdsdsDatabaseCredentials") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabaseCredentials) :*: (S1 (MetaSel (Just "_rdsdsS3StagingLocation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: S1 (MetaSel (Just "_rdsdsResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))) :*: (S1 (MetaSel (Just "_rdsdsServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: (S1 (MetaSel (Just "_rdsdsSubnetId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: S1 (MetaSel (Just "_rdsdsSecurityGroupIds") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 [Text]))))))

rdsDataSpec Source #

Creates a value of RDSDataSpec with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

  • rdsdsDataSchemaURI - The Amazon S3 location of the DataSchema .
  • rdsdsDataSchema - 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 ] }
  • rdsdsDataRearrangement - A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource . There are multiple parameters that control what data is used to create a datasource: * percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource. * percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource. * complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd , along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} * strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential , meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3:/my_s3_pathbucket/file.csv"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3:/my_s3_pathbucket/file.csv", "complement":"true"}}
  • rdsdsDatabaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
  • rdsdsSelectSqlQuery - The query that is used to retrieve the observation data for the DataSource .
  • rdsdsDatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
  • rdsdsS3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
  • rdsdsResourceRole - 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.
  • rdsdsServiceRole - 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.
  • rdsdsSubnetId - 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.
  • rdsdsSecurityGroupIds - 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.

rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text) Source #

The Amazon S3 location of the DataSchema .

rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) Source #

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 ] }

rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text) Source #

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource . There are multiple parameters that control what data is used to create a datasource: * percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource. * percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd , Amazon ML includes all of the data when creating the datasource. * complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd , along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} * strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential , meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3:/my_s3_pathbucket/file.csv"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3:/my_s3_pathbucket/file.csv", "complement":"true"}}

rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase Source #

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text Source #

The query that is used to retrieve the observation data for the DataSource .

rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials Source #

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

rdsdsS3StagingLocation :: Lens' RDSDataSpec Text Source #

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

rdsdsResourceRole :: Lens' RDSDataSpec Text Source #

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.

rdsdsServiceRole :: Lens' RDSDataSpec Text Source #

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.

rdsdsSubnetId :: Lens' RDSDataSpec Text Source #

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.

rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text] Source #

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: rdsDatabase smart constructor.

Instances
Eq RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabase -> c RDSDatabase #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabase #

toConstr :: RDSDatabase -> Constr #

dataTypeOf :: RDSDatabase -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabase) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabase) #

gmapT :: (forall b. Data b => b -> b) -> RDSDatabase -> RDSDatabase #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDatabase -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabase -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

Read RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Generic RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep RDSDatabase :: Type -> Type #

Hashable RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

ToJSON RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

FromJSON RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

NFData RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: RDSDatabase -> () #

type Rep RDSDatabase Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep RDSDatabase = D1 (MetaData "RDSDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "RDSDatabase'" PrefixI True) (S1 (MetaSel (Just "_rdsdInstanceIdentifier") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: S1 (MetaSel (Just "_rdsdDatabaseName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))

rdsDatabase Source #

Creates a value of RDSDatabase with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

rdsdInstanceIdentifier :: Lens' RDSDatabase Text Source #

The ID of an RDS DB instance.

RDSDatabaseCredentials

data RDSDatabaseCredentials Source #

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

See: rdsDatabaseCredentials smart constructor.

Instances
Eq RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabaseCredentials -> c RDSDatabaseCredentials #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabaseCredentials #

toConstr :: RDSDatabaseCredentials -> Constr #

dataTypeOf :: RDSDatabaseCredentials -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabaseCredentials) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabaseCredentials) #

gmapT :: (forall b. Data b => b -> b) -> RDSDatabaseCredentials -> RDSDatabaseCredentials #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

Read RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Show RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Generic RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Associated Types

type Rep RDSDatabaseCredentials :: Type -> Type #

Hashable RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

ToJSON RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

NFData RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

rnf :: RDSDatabaseCredentials -> () #

type Rep RDSDatabaseCredentials Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

type Rep RDSDatabaseCredentials = D1 (MetaData "RDSDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.1-CNBnEKh3aOlK9oNc02t7Bw" False) (C1 (MetaCons "RDSDatabaseCredentials'" PrefixI True) (S1 (MetaSel (Just "_rdsdcUsername") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: S1 (MetaSel (Just "_rdsdcPassword") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))

rdsDatabaseCredentials Source #

Creates a value of RDSDatabaseCredentials with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

RDSMetadata

data RDSMetadata Source #

The datasource details that are specific to Amazon RDS.

See: rdsMetadata smart constructor.

Instances
Eq RDSMetadata Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Data RDSMetadata Source # 
Instance details

Defined in Network.AWS.MachineLearning.Types.Product

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSMetadata -> c RDSMetadata #