amazonka-ml-1.4.5: Amazon Machine Learning SDK.

Copyright(c) 2013-2016 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone
LanguageHaskell2010

Network.AWS.MachineLearning

Contents

Description

Definition of the public APIs exposed by Amazon Machine Learning

Synopsis

Service Configuration

machineLearning :: Service #

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

Errors

Error matchers are designed for use with the functions provided by Control.Exception.Lens. This allows catching (and rethrowing) service specific errors returned by MachineLearning.

InvalidTagException

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

Prism for InvalidTagException' errors.

InternalServerException

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

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

InvalidInputException

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

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

IdempotentParameterMismatchException

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

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

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

Prism for TagLimitExceededException' errors.

PredictorNotMountedException

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

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

ResourceNotFoundException

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

A specified resource cannot be located.

LimitExceededException

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

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

Waiters

Waiters poll by repeatedly sending a request until some remote success condition configured by the Wait specification is fulfilled. The Wait specification determines how many attempts should be made, in addition to delay and retry strategies.

MLModelAvailable

mLModelAvailable :: Wait DescribeMLModels #

Polls DescribeMLModels every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

BatchPredictionAvailable

batchPredictionAvailable :: Wait DescribeBatchPredictions #

Polls DescribeBatchPredictions every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

DataSourceAvailable

dataSourceAvailable :: Wait DescribeDataSources #

Polls DescribeDataSources every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

EvaluationAvailable

evaluationAvailable :: Wait DescribeEvaluations #

Polls DescribeEvaluations every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

Operations

Some AWS operations return results that are incomplete and require subsequent requests in order to obtain the entire result set. The process of sending subsequent requests to continue where a previous request left off is called pagination. For example, the ListObjects operation of Amazon S3 returns up to 1000 objects at a time, and you must send subsequent requests with the appropriate Marker in order to retrieve the next page of results.

Operations that have an AWSPager instance can transparently perform subsequent requests, correctly setting Markers and other request facets to iterate through the entire result set of a truncated API operation. Operations which support this have an additional note in the documentation.

Many operations have the ability to filter results on the server side. See the individual operation parameters for details.

UpdateDataSource

DeleteDataSource

DescribeTags

CreateDataSourceFromRedshift

CreateDataSourceFromS3

CreateMLModel

DeleteTags

DeleteBatchPrediction

UpdateBatchPrediction

GetMLModel

GetDataSource

UpdateEvaluation

DeleteEvaluation

DeleteMLModel

UpdateMLModel

GetBatchPrediction

DescribeBatchPredictions (Paginated)

CreateDataSourceFromRDS

CreateEvaluation

Predict

DeleteRealtimeEndpoint

CreateBatchPrediction

GetEvaluation

DescribeEvaluations (Paginated)

CreateRealtimeEndpoint

AddTags

DescribeMLModels (Paginated)

DescribeDataSources (Paginated)

Types

Algorithm

data Algorithm #

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

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

Constructors

SGD 

Instances

Bounded Algorithm # 
Enum Algorithm # 
Eq Algorithm # 
Data Algorithm # 

Methods

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 # 
Read Algorithm # 
Show Algorithm # 
Generic Algorithm # 

Associated Types

type Rep Algorithm :: * -> * #

Hashable Algorithm # 
FromJSON Algorithm # 
NFData Algorithm # 

Methods

rnf :: Algorithm -> () #

ToQuery Algorithm # 
ToHeader Algorithm # 
ToByteString Algorithm # 

Methods

toBS :: Algorithm -> ByteString #

FromText Algorithm # 
ToText Algorithm # 

Methods

toText :: Algorithm -> Text #

type Rep Algorithm # 
type Rep Algorithm = D1 (MetaData "Algorithm" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "SGD" PrefixI False) U1)

BatchPredictionFilterVariable

data BatchPredictionFilterVariable #

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.

Instances

Bounded BatchPredictionFilterVariable # 
Enum BatchPredictionFilterVariable # 
Eq BatchPredictionFilterVariable # 
Data BatchPredictionFilterVariable # 

Methods

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 # 
Read BatchPredictionFilterVariable # 
Show BatchPredictionFilterVariable # 
Generic BatchPredictionFilterVariable # 
Hashable BatchPredictionFilterVariable # 
ToJSON BatchPredictionFilterVariable # 
NFData BatchPredictionFilterVariable # 
ToQuery BatchPredictionFilterVariable # 
ToHeader BatchPredictionFilterVariable # 
ToByteString BatchPredictionFilterVariable # 
FromText BatchPredictionFilterVariable # 
ToText BatchPredictionFilterVariable # 
type Rep BatchPredictionFilterVariable # 
type Rep BatchPredictionFilterVariable = D1 (MetaData "BatchPredictionFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "BatchCreatedAt" PrefixI False) U1) (C1 (MetaCons "BatchDataSourceId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchDataURI" PrefixI False) U1) (C1 (MetaCons "BatchIAMUser" PrefixI False) U1))) ((:+:) ((:+:) (C1 (MetaCons "BatchLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "BatchMLModelId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchName" PrefixI False) U1) (C1 (MetaCons "BatchStatus" PrefixI False) U1))))

DataSourceFilterVariable

data DataSourceFilterVariable #

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.

Instances

Bounded DataSourceFilterVariable # 
Enum DataSourceFilterVariable # 
Eq DataSourceFilterVariable # 
Data DataSourceFilterVariable # 

Methods

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 # 
Read DataSourceFilterVariable # 
Show DataSourceFilterVariable # 
Generic DataSourceFilterVariable # 
Hashable DataSourceFilterVariable # 
ToJSON DataSourceFilterVariable # 
NFData DataSourceFilterVariable # 
ToQuery DataSourceFilterVariable # 
ToHeader DataSourceFilterVariable # 
ToByteString DataSourceFilterVariable # 
FromText DataSourceFilterVariable # 
ToText DataSourceFilterVariable # 
type Rep DataSourceFilterVariable # 
type Rep DataSourceFilterVariable = D1 (MetaData "DataSourceFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) (C1 (MetaCons "DataCreatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataDATALOCATIONS3" PrefixI False) U1) (C1 (MetaCons "DataIAMUser" PrefixI False) U1))) ((:+:) (C1 (MetaCons "DataLastUpdatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataName" PrefixI False) U1) (C1 (MetaCons "DataStatus" PrefixI False) U1))))

DetailsAttributes

data DetailsAttributes #

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

Instances

Bounded DetailsAttributes # 
Enum DetailsAttributes # 
Eq DetailsAttributes # 
Data DetailsAttributes # 

Methods

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 # 
Read DetailsAttributes # 
Show DetailsAttributes # 
Generic DetailsAttributes # 
Hashable DetailsAttributes # 
FromJSON DetailsAttributes # 
NFData DetailsAttributes # 

Methods

rnf :: DetailsAttributes -> () #

ToQuery DetailsAttributes # 
ToHeader DetailsAttributes # 
ToByteString DetailsAttributes # 
FromText DetailsAttributes # 
ToText DetailsAttributes # 
type Rep DetailsAttributes # 
type Rep DetailsAttributes = D1 (MetaData "DetailsAttributes" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) (C1 (MetaCons "Algorithm" PrefixI False) U1) (C1 (MetaCons "PredictiveModelType" PrefixI False) U1))

EntityStatus

data EntityStatus #

Object status with the following possible values:

  • PENDING * INPROGRESS * FAILED * COMPLETED * DELETED

Instances

Bounded EntityStatus # 
Enum EntityStatus # 
Eq EntityStatus # 
Data EntityStatus # 

Methods

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 # 
Read EntityStatus # 
Show EntityStatus # 
Generic EntityStatus # 

Associated Types

type Rep EntityStatus :: * -> * #

Hashable EntityStatus # 
FromJSON EntityStatus # 
NFData EntityStatus # 

Methods

rnf :: EntityStatus -> () #

ToQuery EntityStatus # 
ToHeader EntityStatus # 
ToByteString EntityStatus # 
FromText EntityStatus # 
ToText EntityStatus # 

Methods

toText :: EntityStatus -> Text #

type Rep EntityStatus # 
type Rep EntityStatus = D1 (MetaData "EntityStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) (C1 (MetaCons "ESCompleted" PrefixI False) U1) (C1 (MetaCons "ESDeleted" PrefixI False) U1)) ((:+:) (C1 (MetaCons "ESFailed" PrefixI False) U1) ((:+:) (C1 (MetaCons "ESInprogress" PrefixI False) U1) (C1 (MetaCons "ESPending" PrefixI False) U1))))

EvaluationFilterVariable

data EvaluationFilterVariable #

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.

Instances

Bounded EvaluationFilterVariable # 
Enum EvaluationFilterVariable # 
Eq EvaluationFilterVariable # 
Data EvaluationFilterVariable # 

Methods

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 # 
Read EvaluationFilterVariable # 
Show EvaluationFilterVariable # 
Generic EvaluationFilterVariable # 
Hashable EvaluationFilterVariable # 
ToJSON EvaluationFilterVariable # 
NFData EvaluationFilterVariable # 
ToQuery EvaluationFilterVariable # 
ToHeader EvaluationFilterVariable # 
ToByteString EvaluationFilterVariable # 
FromText EvaluationFilterVariable # 
ToText EvaluationFilterVariable # 
type Rep EvaluationFilterVariable # 
type Rep EvaluationFilterVariable = D1 (MetaData "EvaluationFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "EvalCreatedAt" PrefixI False) U1) (C1 (MetaCons "EvalDataSourceId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "EvalDataURI" PrefixI False) U1) (C1 (MetaCons "EvalIAMUser" PrefixI False) U1))) ((:+:) ((:+:) (C1 (MetaCons "EvalLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "EvalMLModelId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "EvalName" PrefixI False) U1) (C1 (MetaCons "EvalStatus" PrefixI False) U1))))

MLModelFilterVariable

data MLModelFilterVariable #

Instances

Bounded MLModelFilterVariable # 
Enum MLModelFilterVariable # 
Eq MLModelFilterVariable # 
Data MLModelFilterVariable # 

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 # 
Read MLModelFilterVariable # 
Show MLModelFilterVariable # 
Generic MLModelFilterVariable # 
Hashable MLModelFilterVariable # 
ToJSON MLModelFilterVariable # 
NFData MLModelFilterVariable # 

Methods

rnf :: MLModelFilterVariable -> () #

ToQuery MLModelFilterVariable # 
ToHeader MLModelFilterVariable # 
ToByteString MLModelFilterVariable # 
FromText MLModelFilterVariable # 
ToText MLModelFilterVariable # 
type Rep MLModelFilterVariable # 
type Rep MLModelFilterVariable = D1 (MetaData "MLModelFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "MLMFVAlgorithm" PrefixI False) U1) (C1 (MetaCons "MLMFVCreatedAt" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVIAMUser" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "MLMFVMLModelType" PrefixI False) U1)))) ((:+:) ((:+:) (C1 (MetaCons "MLMFVName" PrefixI False) U1) (C1 (MetaCons "MLMFVRealtimeEndpointStatus" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVStatus" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVTrainingDataSourceId" PrefixI False) U1) (C1 (MetaCons "MLMFVTrainingDataURI" PrefixI False) U1)))))

MLModelType

data MLModelType #

Constructors

Binary 
Multiclass 
Regression 

Instances

Bounded MLModelType # 
Enum MLModelType # 
Eq MLModelType # 
Data MLModelType # 

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 # 
Read MLModelType # 
Show MLModelType # 
Generic MLModelType # 

Associated Types

type Rep MLModelType :: * -> * #

Hashable MLModelType # 
ToJSON MLModelType # 
FromJSON MLModelType # 
NFData MLModelType # 

Methods

rnf :: MLModelType -> () #

ToQuery MLModelType # 
ToHeader MLModelType # 
ToByteString MLModelType # 
FromText MLModelType # 
ToText MLModelType # 

Methods

toText :: MLModelType -> Text #

type Rep MLModelType # 
type Rep MLModelType = D1 (MetaData "MLModelType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) (C1 (MetaCons "Binary" PrefixI False) U1) ((:+:) (C1 (MetaCons "Multiclass" PrefixI False) U1) (C1 (MetaCons "Regression" PrefixI False) U1)))

RealtimeEndpointStatus

data RealtimeEndpointStatus #

Constructors

Failed 
None 
Ready 
Updating 

Instances

Bounded RealtimeEndpointStatus # 
Enum RealtimeEndpointStatus # 
Eq RealtimeEndpointStatus # 
Data RealtimeEndpointStatus # 

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 # 
Read RealtimeEndpointStatus # 
Show RealtimeEndpointStatus # 
Generic RealtimeEndpointStatus # 
Hashable RealtimeEndpointStatus # 
FromJSON RealtimeEndpointStatus # 
NFData RealtimeEndpointStatus # 

Methods

rnf :: RealtimeEndpointStatus -> () #

ToQuery RealtimeEndpointStatus # 
ToHeader RealtimeEndpointStatus # 
ToByteString RealtimeEndpointStatus # 
FromText RealtimeEndpointStatus # 
ToText RealtimeEndpointStatus # 
type Rep RealtimeEndpointStatus # 
type Rep RealtimeEndpointStatus = D1 (MetaData "RealtimeEndpointStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) (C1 (MetaCons "Failed" PrefixI False) U1) (C1 (MetaCons "None" PrefixI False) U1)) ((:+:) (C1 (MetaCons "Ready" PrefixI False) U1) (C1 (MetaCons "Updating" PrefixI False) U1)))

SortOrder

data SortOrder #

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

  • asc - Present the information in ascending order (from A-Z). * dsc - Present the information in descending order (from Z-A).

Constructors

Asc 
Dsc 

Instances

Bounded SortOrder # 
Enum SortOrder # 
Eq SortOrder # 
Data SortOrder # 

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 # 
Read SortOrder # 
Show SortOrder # 
Generic SortOrder # 

Associated Types

type Rep SortOrder :: * -> * #

Hashable SortOrder # 
ToJSON SortOrder # 
NFData SortOrder # 

Methods

rnf :: SortOrder -> () #

ToQuery SortOrder # 
ToHeader SortOrder # 
ToByteString SortOrder # 

Methods

toBS :: SortOrder -> ByteString #

FromText SortOrder # 
ToText SortOrder # 

Methods

toText :: SortOrder -> Text #

type Rep SortOrder # 
type Rep SortOrder = D1 (MetaData "SortOrder" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) (C1 (MetaCons "Asc" PrefixI False) U1) (C1 (MetaCons "Dsc" PrefixI False) U1))

TaggableResourceType

data TaggableResourceType #

Instances

Bounded TaggableResourceType # 
Enum TaggableResourceType # 
Eq TaggableResourceType # 
Data TaggableResourceType # 

Methods

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 # 
Read TaggableResourceType # 
Show TaggableResourceType # 
Generic TaggableResourceType # 
Hashable TaggableResourceType # 
ToJSON TaggableResourceType # 
FromJSON TaggableResourceType # 
NFData TaggableResourceType # 

Methods

rnf :: TaggableResourceType -> () #

ToQuery TaggableResourceType # 
ToHeader TaggableResourceType # 
ToByteString TaggableResourceType # 
FromText TaggableResourceType # 
ToText TaggableResourceType # 
type Rep TaggableResourceType # 
type Rep TaggableResourceType = D1 (MetaData "TaggableResourceType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) ((:+:) ((:+:) (C1 (MetaCons "BatchPrediction" PrefixI False) U1) (C1 (MetaCons "DataSource" PrefixI False) U1)) ((:+:) (C1 (MetaCons "Evaluation" PrefixI False) U1) (C1 (MetaCons "MLModel" PrefixI False) U1)))

BatchPrediction

data BatchPrediction #

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 # 
Data BatchPrediction # 

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 # 
Show BatchPrediction # 
Generic BatchPrediction # 
Hashable BatchPrediction # 
FromJSON BatchPrediction # 
NFData BatchPrediction # 

Methods

rnf :: BatchPrediction -> () #

type Rep BatchPrediction # 
type Rep BatchPrediction = D1 (MetaData "BatchPrediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "BatchPrediction'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_bpLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpBatchPredictionDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpTotalRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpBatchPredictionId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpInvalidRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpOutputURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

batchPrediction :: BatchPrediction #

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) #

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) #

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

bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) #

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

bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) #

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

bpMLModelId :: Lens' BatchPrediction (Maybe Text) #

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

bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) #

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

bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime) #

Undocumented member.

bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text) #

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

bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime) #

Undocumented member.

bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text) #

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) #

A user-supplied name or description of the BatchPrediction .

bpMessage :: Lens' BatchPrediction (Maybe Text) #

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

bpOutputURI :: Lens' BatchPrediction (Maybe Text) #

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 #

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 # 
Data DataSource # 

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 # 
Show DataSource # 
Generic DataSource # 

Associated Types

type Rep DataSource :: * -> * #

Hashable DataSource # 
FromJSON DataSource # 
NFData DataSource # 

Methods

rnf :: DataSource -> () #

type Rep DataSource # 
type Rep DataSource = D1 (MetaData "DataSource" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "DataSource'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_dsNumberOfFiles") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_dsCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) (S1 (MetaSel (Just Symbol "_dsDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsRDSMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSMetadata))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) (S1 (MetaSel (Just Symbol "_dsStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_dsCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsComputeStatistics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Bool))) (S1 (MetaSel (Just Symbol "_dsMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsRedshiftMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftMetadata))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsRoleARN") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))))

dataSource :: DataSource #

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) #

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) #

The number of data files referenced by the DataSource .

dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime) #

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

dsCreatedAt :: Lens' DataSource (Maybe UTCTime) #

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

dsComputeTime :: Lens' DataSource (Maybe Integer) #

Undocumented member.

dsDataSourceId :: Lens' DataSource (Maybe Text) #

The ID that is assigned to the DataSource during creation.

dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata) #

Undocumented member.

dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) #

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

dsStartedAt :: Lens' DataSource (Maybe UTCTime) #

Undocumented member.

dsFinishedAt :: Lens' DataSource (Maybe UTCTime) #

Undocumented member.

dsCreatedByIAMUser :: Lens' DataSource (Maybe Text) #

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) #

A user-supplied name or description of the DataSource .

dsDataLocationS3 :: Lens' DataSource (Maybe Text) #

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) #

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

dsMessage :: Lens' DataSource (Maybe Text) #

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

dsDataRearrangement :: Lens' DataSource (Maybe Text) #

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

dsRoleARN :: Lens' DataSource (Maybe Text) #

Undocumented member.

Evaluation

data Evaluation #

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 # 
Data Evaluation # 

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 # 
Show Evaluation # 
Generic Evaluation # 

Associated Types

type Rep Evaluation :: * -> * #

Hashable Evaluation # 
FromJSON Evaluation # 
NFData Evaluation # 

Methods

rnf :: Evaluation -> () #

type Rep Evaluation # 
type Rep Evaluation = D1 (MetaData "Evaluation" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "Evaluation'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) ((:*:) (S1 (MetaSel (Just Symbol "_ePerformanceMetrics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe PerformanceMetrics))) (S1 (MetaSel (Just Symbol "_eLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_eComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_eInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_eFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_eCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_eMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eEvaluationDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

evaluation :: Evaluation #

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) #

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) #

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) #

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

eCreatedAt :: Lens' Evaluation (Maybe UTCTime) #

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

eComputeTime :: Lens' Evaluation (Maybe Integer) #

Undocumented member.

eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) #

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) #

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

eStartedAt :: Lens' Evaluation (Maybe UTCTime) #

Undocumented member.

eFinishedAt :: Lens' Evaluation (Maybe UTCTime) #

Undocumented member.

eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) #

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) #

A user-supplied name or description of the Evaluation .

eEvaluationId :: Lens' Evaluation (Maybe Text) #

The ID that is assigned to the Evaluation at creation.

eMessage :: Lens' Evaluation (Maybe Text) #

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

eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text) #

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

MLModel

data MLModel #

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 # 

Methods

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

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

Data MLModel # 

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 # 
Show MLModel # 
Generic MLModel # 

Associated Types

type Rep MLModel :: * -> * #

Methods

from :: MLModel -> Rep MLModel x #

to :: Rep MLModel x -> MLModel #

Hashable MLModel # 

Methods

hashWithSalt :: Int -> MLModel -> Int #

hash :: MLModel -> Int #

FromJSON MLModel # 
NFData MLModel # 

Methods

rnf :: MLModel -> () #

type Rep MLModel # 
type Rep MLModel = D1 (MetaData "MLModel" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "MLModel'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_mlmLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmTrainingParameters") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Text)))) (S1 (MetaSel (Just Symbol "_mlmScoreThresholdLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_mlmComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_mlmScoreThreshold") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmAlgorithm") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Algorithm))) (S1 (MetaSel (Just Symbol "_mlmCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmEndpointInfo") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmTrainingDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmMLModelType") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe MLModelType)))))))))

mLModel :: MLModel #

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) #

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) #

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

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

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) #

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

mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) #

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

mlmComputeTime :: Lens' MLModel (Maybe Integer) #

Undocumented member.

mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) #

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

mlmMLModelId :: Lens' MLModel (Maybe Text) #

The ID assigned to the MLModel at creation.

mlmSizeInBytes :: Lens' MLModel (Maybe Integer) #

Undocumented member.

mlmStartedAt :: Lens' MLModel (Maybe UTCTime) #

Undocumented member.

mlmScoreThreshold :: Lens' MLModel (Maybe Double) #

Undocumented member.

mlmFinishedAt :: Lens' MLModel (Maybe UTCTime) #

Undocumented member.

mlmAlgorithm :: Lens' MLModel (Maybe Algorithm) #

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) #

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) #

A user-supplied name or description of the MLModel .

mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo) #

The current endpoint of the MLModel .

mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text) #

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

mlmMessage :: Lens' MLModel (Maybe Text) #

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

mlmMLModelType :: Lens' MLModel (Maybe MLModelType) #

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

PerformanceMetrics

data PerformanceMetrics #

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 # 
Data PerformanceMetrics # 

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 # 
Show PerformanceMetrics # 
Generic PerformanceMetrics # 
Hashable PerformanceMetrics # 
FromJSON PerformanceMetrics # 
NFData PerformanceMetrics # 

Methods

rnf :: PerformanceMetrics -> () #

type Rep PerformanceMetrics # 
type Rep PerformanceMetrics = D1 (MetaData "PerformanceMetrics" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" True) (C1 (MetaCons "PerformanceMetrics'" PrefixI True) (S1 (MetaSel (Just Symbol "_pmProperties") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Maybe (Map Text Text)))))

performanceMetrics :: PerformanceMetrics #

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 #

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 # 
Data Prediction # 

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 # 
Show Prediction # 
Generic Prediction # 

Associated Types

type Rep Prediction :: * -> * #

Hashable Prediction # 
FromJSON Prediction # 
NFData Prediction # 

Methods

rnf :: Prediction -> () #

type Rep Prediction # 
type Rep Prediction = D1 (MetaData "Prediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "Prediction'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double))) (S1 (MetaSel (Just Symbol "_pPredictedLabel") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedScores") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Double)))) (S1 (MetaSel (Just Symbol "_pDetails") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map DetailsAttributes Text)))))))

prediction :: Prediction #

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) #

The prediction value for REGRESSION MLModel .

pPredictedLabel :: Lens' Prediction (Maybe Text) #

The prediction label for either a BINARY or MULTICLASS MLModel .

RDSDataSpec

data RDSDataSpec #

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

See: rdsDataSpec smart constructor.

Instances

Eq RDSDataSpec # 
Data RDSDataSpec # 

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 # 
Show RDSDataSpec # 
Generic RDSDataSpec # 

Associated Types

type Rep RDSDataSpec :: * -> * #

Hashable RDSDataSpec # 
ToJSON RDSDataSpec # 
NFData RDSDataSpec # 

Methods

rnf :: RDSDataSpec -> () #

type Rep RDSDataSpec # 
type Rep RDSDataSpec = D1 (MetaData "RDSDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RDSDataSpec'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDataSchemaURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rdsdsDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDatabaseInformation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabase)) (S1 (MetaSel (Just Symbol "_rdsdsSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDatabaseCredentials") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabaseCredentials)) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsS3StagingLocation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdsResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsSubnetId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdsSecurityGroupIds") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 [Text])))))))

rdsDataSpec #

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) #

The Amazon S3 location of the DataSchema .

rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) #

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) #

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 #

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text #

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

rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials #

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

rdsdsS3StagingLocation :: Lens' RDSDataSpec Text #

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 #

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 #

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 #

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] #

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 #

The database details of an Amazon RDS database.

See: rdsDatabase smart constructor.

Instances

Eq RDSDatabase # 
Data RDSDatabase # 

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 # 
Show RDSDatabase # 
Generic RDSDatabase # 

Associated Types

type Rep RDSDatabase :: * -> * #

Hashable RDSDatabase # 
ToJSON RDSDatabase # 
FromJSON RDSDatabase # 
NFData RDSDatabase # 

Methods

rnf :: RDSDatabase -> () #

type Rep RDSDatabase # 
type Rep RDSDatabase = D1 (MetaData "RDSDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RDSDatabase'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdInstanceIdentifier") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdDatabaseName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

rdsDatabase #

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 #

The ID of an RDS DB instance.

rdsdDatabaseName :: Lens' RDSDatabase Text #

Undocumented member.

RDSDatabaseCredentials

data RDSDatabaseCredentials #

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

See: rdsDatabaseCredentials smart constructor.

Instances

Eq RDSDatabaseCredentials # 
Data RDSDatabaseCredentials # 

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 # 
Show RDSDatabaseCredentials # 
Generic RDSDatabaseCredentials # 
Hashable RDSDatabaseCredentials # 
ToJSON RDSDatabaseCredentials # 
NFData RDSDatabaseCredentials # 

Methods

rnf :: RDSDatabaseCredentials -> () #

type Rep RDSDatabaseCredentials # 
type Rep RDSDatabaseCredentials = D1 (MetaData "RDSDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RDSDatabaseCredentials'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdcUsername") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdcPassword") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

rdsDatabaseCredentials #

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 #

The datasource details that are specific to Amazon RDS.

See: rdsMetadata smart constructor.

Instances

Eq RDSMetadata # 
Data RDSMetadata # 

Methods

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

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

toConstr :: RDSMetadata -> Constr #

dataTypeOf :: RDSMetadata -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RDSMetadata # 
Show RDSMetadata # 
Generic RDSMetadata # 

Associated Types

type Rep RDSMetadata :: * -> * #

Hashable RDSMetadata # 
FromJSON RDSMetadata # 
NFData RDSMetadata # 

Methods

rnf :: RDSMetadata -> () #

type Rep RDSMetadata # 
type Rep RDSMetadata = D1 (MetaData "RDSMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RDSMetadata'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rmSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDataPipelineId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSDatabase))))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))

rdsMetadata :: RDSMetadata #

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

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

  • rmSelectSqlQuery - The SQL query that is supplied during CreateDataSourceFromRDS . Returns only if Verbose is true in GetDataSourceInput .
  • rmDataPipelineId - The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.
  • rmDatabase - The database details required to connect to an Amazon RDS.
  • rmDatabaseUserName - Undocumented member.
  • rmResourceRole - The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
  • rmServiceRole - The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text) #

The SQL query that is supplied during CreateDataSourceFromRDS . Returns only if Verbose is true in GetDataSourceInput .

rmDataPipelineId :: Lens' RDSMetadata (Maybe Text) #

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase) #

The database details required to connect to an Amazon RDS.

rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text) #

Undocumented member.

rmResourceRole :: Lens' RDSMetadata (Maybe Text) #

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

rmServiceRole :: Lens' RDSMetadata (Maybe Text) #

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

RealtimeEndpointInfo

data RealtimeEndpointInfo #

Describes the real-time endpoint information for an MLModel .

See: realtimeEndpointInfo smart constructor.

Instances

Eq RealtimeEndpointInfo # 
Data RealtimeEndpointInfo # 

Methods

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

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

toConstr :: RealtimeEndpointInfo -> Constr #

dataTypeOf :: RealtimeEndpointInfo -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RealtimeEndpointInfo # 
Show RealtimeEndpointInfo # 
Generic RealtimeEndpointInfo # 
Hashable RealtimeEndpointInfo # 
FromJSON RealtimeEndpointInfo # 
NFData RealtimeEndpointInfo # 

Methods

rnf :: RealtimeEndpointInfo -> () #

type Rep RealtimeEndpointInfo # 
type Rep RealtimeEndpointInfo = D1 (MetaData "RealtimeEndpointInfo" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RealtimeEndpointInfo'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_reiCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_reiEndpointURL") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_reiEndpointStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointStatus))) (S1 (MetaSel (Just Symbol "_reiPeakRequestsPerSecond") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Int))))))

realtimeEndpointInfo :: RealtimeEndpointInfo #

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

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

  • reiCreatedAt - The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.
  • reiEndpointURL - The URI that specifies where to send real-time prediction requests for the MLModel .
  • reiEndpointStatus - The current status of the real-time endpoint for the MLModel . This element can have one of the following values: * NONE - Endpoint does not exist or was previously deleted. * READY - Endpoint is ready to be used for real-time predictions. * UPDATING - Updating/creating the endpoint.
  • reiPeakRequestsPerSecond - The maximum processing rate for the real-time endpoint for MLModel , measured in incoming requests per second.

reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime) #

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text) #

The URI that specifies where to send real-time prediction requests for the MLModel .

reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) #

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

reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int) #

The maximum processing rate for the real-time endpoint for MLModel , measured in incoming requests per second.

RedshiftDataSpec

data RedshiftDataSpec #

Describes the data specification of an Amazon Redshift DataSource .

See: redshiftDataSpec smart constructor.

Instances

Eq RedshiftDataSpec # 
Data RedshiftDataSpec # 

Methods

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

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

toConstr :: RedshiftDataSpec -> Constr #

dataTypeOf :: RedshiftDataSpec -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RedshiftDataSpec # 
Show RedshiftDataSpec # 
Generic RedshiftDataSpec # 
Hashable RedshiftDataSpec # 
ToJSON RedshiftDataSpec # 
NFData RedshiftDataSpec # 

Methods

rnf :: RedshiftDataSpec -> () #

type Rep RedshiftDataSpec # 
type Rep RedshiftDataSpec = D1 (MetaData "RedshiftDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RedshiftDataSpec'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rDataSchemaURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rDatabaseInformation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RedshiftDatabase)) (S1 (MetaSel (Just Symbol "_rSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rDatabaseCredentials") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RedshiftDatabaseCredentials)) (S1 (MetaSel (Just Symbol "_rS3StagingLocation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))))

redshiftDataSpec #

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

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

  • rDataSchemaURI - Describes the schema location for an Amazon Redshift DataSource .
  • rDataSchema - A JSON string that represents the schema for an Amazon Redshift DataSource . The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource . A DataSchema is not required if you specify a DataSchemaUri . Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema . { "version": "1.0", "recordAnnotationFieldName": F1, "recordWeightFieldName": F2, "targetFieldName": F3, "dataFormat": CSV, "dataFileContainsHeader": true, "attributes": [ { "fieldName": F1, "fieldType": TEXT }, { "fieldName": F2, "fieldType": NUMERIC }, { "fieldName": F3, "fieldType": CATEGORICAL }, { "fieldName": F4, "fieldType": NUMERIC }, { "fieldName": F5, "fieldType": CATEGORICAL }, { "fieldName": F6, "fieldType": TEXT }, { "fieldName": F7, "fieldType": WEIGHTED_INT_SEQUENCE }, { "fieldName": F8, "fieldType": WEIGHTED_STRING_SEQUENCE } ], "excludedVariableNames": [ F6 ] }
  • rDataRearrangement - 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"}}
  • rDatabaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource .
  • rSelectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource .
  • rDatabaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
  • rS3StagingLocation - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text) #

Describes the schema location for an Amazon Redshift DataSource .

rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) #

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

rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text) #

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"}}

rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase #

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource .

rSelectSqlQuery :: Lens' RedshiftDataSpec Text #

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource .

rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials #

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

rS3StagingLocation :: Lens' RedshiftDataSpec Text #

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

RedshiftDatabase

data RedshiftDatabase #

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

See: redshiftDatabase smart constructor.

Instances

Eq RedshiftDatabase # 
Data RedshiftDatabase # 

Methods

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

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

toConstr :: RedshiftDatabase -> Constr #

dataTypeOf :: RedshiftDatabase -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RedshiftDatabase # 
Show RedshiftDatabase # 
Generic RedshiftDatabase # 
Hashable RedshiftDatabase # 
ToJSON RedshiftDatabase # 
FromJSON RedshiftDatabase # 
NFData RedshiftDatabase # 

Methods

rnf :: RedshiftDatabase -> () #

type Rep RedshiftDatabase # 
type Rep RedshiftDatabase = D1 (MetaData "RedshiftDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RedshiftDatabase'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdDatabaseName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdClusterIdentifier") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

redshiftDatabase #

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

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

rdDatabaseName :: Lens' RedshiftDatabase Text #

Undocumented member.

RedshiftDatabaseCredentials

data RedshiftDatabaseCredentials #

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

See: redshiftDatabaseCredentials smart constructor.

Instances

Eq RedshiftDatabaseCredentials # 
Data RedshiftDatabaseCredentials # 

Methods

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

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

toConstr :: RedshiftDatabaseCredentials -> Constr #

dataTypeOf :: RedshiftDatabaseCredentials -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RedshiftDatabaseCredentials # 
Show RedshiftDatabaseCredentials # 
Generic RedshiftDatabaseCredentials # 
Hashable RedshiftDatabaseCredentials # 
ToJSON RedshiftDatabaseCredentials # 
NFData RedshiftDatabaseCredentials # 
type Rep RedshiftDatabaseCredentials # 
type Rep RedshiftDatabaseCredentials = D1 (MetaData "RedshiftDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RedshiftDatabaseCredentials'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdcUsername") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdcPassword") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

redshiftDatabaseCredentials #

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

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

RedshiftMetadata

data RedshiftMetadata #

Describes the DataSource details specific to Amazon Redshift.

See: redshiftMetadata smart constructor.

Instances

Eq RedshiftMetadata # 
Data RedshiftMetadata # 

Methods

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

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

toConstr :: RedshiftMetadata -> Constr #

dataTypeOf :: RedshiftMetadata -> DataType #

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

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

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

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

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

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

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

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

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

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

Read RedshiftMetadata # 
Show RedshiftMetadata # 
Generic RedshiftMetadata # 
Hashable RedshiftMetadata # 
FromJSON RedshiftMetadata # 
NFData RedshiftMetadata # 

Methods

rnf :: RedshiftMetadata -> () #

type Rep RedshiftMetadata # 
type Rep RedshiftMetadata = D1 (MetaData "RedshiftMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "RedshiftMetadata'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_redSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_redRedshiftDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftDatabase))) (S1 (MetaSel (Just Symbol "_redDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))

redshiftMetadata :: RedshiftMetadata #

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

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

redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text) #

The SQL query that is specified during CreateDataSourceFromRedshift . Returns only if Verbose is true in GetDataSourceInput.

S3DataSpec

data S3DataSpec #

Describes the data specification of a DataSource .

See: s3DataSpec smart constructor.

Instances

Eq S3DataSpec # 
Data S3DataSpec # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> S3DataSpec -> c S3DataSpec #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c S3DataSpec #

toConstr :: S3DataSpec -> Constr #

dataTypeOf :: S3DataSpec -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c S3DataSpec) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c S3DataSpec) #

gmapT :: (forall b. Data b => b -> b) -> S3DataSpec -> S3DataSpec #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> S3DataSpec -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> S3DataSpec -> r #

gmapQ :: (forall d. Data d => d -> u) -> S3DataSpec -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> S3DataSpec -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

Read S3DataSpec # 
Show S3DataSpec # 
Generic S3DataSpec # 

Associated Types

type Rep S3DataSpec :: * -> * #

Hashable S3DataSpec # 
ToJSON S3DataSpec # 
NFData S3DataSpec # 

Methods

rnf :: S3DataSpec -> () #

type Rep S3DataSpec # 
type Rep S3DataSpec = D1 (MetaData "S3DataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "S3DataSpec'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_sdsDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_sdsDataSchemaLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_sdsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_sdsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))))

s3DataSpec #

Creates a value of S3DataSpec with the minimum fields required to make a request.

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

  • sdsDataSchema - A JSON string that represents the schema for an Amazon S3 DataSource . The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource . You must provide either the DataSchema or the DataSchemaLocationS3 . Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema . { "version": "1.0", "recordAnnotationFieldName": F1, "recordWeightFieldName": F2, "targetFieldName": F3, "dataFormat": CSV, "dataFileContainsHeader": true, "attributes": [ { "fieldName": F1, "fieldType": TEXT }, { "fieldName": F2, "fieldType": NUMERIC }, { "fieldName": F3, "fieldType": CATEGORICAL }, { "fieldName": F4, "fieldType": NUMERIC }, { "fieldName": F5, "fieldType": CATEGORICAL }, { "fieldName": F6, "fieldType": TEXT }, { "fieldName": F7, "fieldType": WEIGHTED_INT_SEQUENCE }, { "fieldName": F8, "fieldType": WEIGHTED_STRING_SEQUENCE } ], "excludedVariableNames": [ F6 ] }
  • sdsDataSchemaLocationS3 - Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3 .
  • sdsDataRearrangement - 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"}}
  • sdsDataLocationS3 - The location of the data file(s) used by a DataSource . The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

sdsDataSchema :: Lens' S3DataSpec (Maybe Text) #

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

sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text) #

Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3 .

sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text) #

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"}}

sdsDataLocationS3 :: Lens' S3DataSpec Text #

The location of the data file(s) used by a DataSource . The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

Tag

data Tag #

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

See: tag smart constructor.

Instances

Eq Tag # 

Methods

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

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

Data Tag # 

Methods

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

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

toConstr :: Tag -> Constr #

dataTypeOf :: Tag -> DataType #

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

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

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

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

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

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

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

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

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

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

Read Tag # 
Show Tag # 

Methods

showsPrec :: Int -> Tag -> ShowS #

show :: Tag -> String #

showList :: [Tag] -> ShowS #

Generic Tag # 

Associated Types

type Rep Tag :: * -> * #

Methods

from :: Tag -> Rep Tag x #

to :: Rep Tag x -> Tag #

Hashable Tag # 

Methods

hashWithSalt :: Int -> Tag -> Int #

hash :: Tag -> Int #

ToJSON Tag # 
FromJSON Tag # 
NFData Tag # 

Methods

rnf :: Tag -> () #

type Rep Tag # 
type Rep Tag = D1 (MetaData "Tag" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.5-CevT0Y7DDZXCSb8Nqca7UU" False) (C1 (MetaCons "Tag'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_tagValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_tagKey") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))

tag :: Tag #

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

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

  • tagValue - An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
  • tagKey - A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

tagValue :: Lens' Tag (Maybe Text) #

An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

tagKey :: Lens' Tag (Maybe Text) #

A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.