Copyright	(c) 2013-2018 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay <brendan.g.hay+amazonka@gmail.com>
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	None
Language	Haskell2010

Network.AWS.MachineLearning

Contents

Service Configuration
Errors
Waiters
Operations
Types

Description

Definition of the public APIs exposed by Amazon Machine Learning

Synopsis

Service Configuration

machineLearning :: Service Source #

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

Errors

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

InvalidTagException

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

Prism for InvalidTagException' errors.

InternalServerException

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

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

InvalidInputException

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

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

IdempotentParameterMismatchException

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

A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

TagLimitExceededException

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

Prism for TagLimitExceededException' errors.

PredictorNotMountedException

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

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

ResourceNotFoundException

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

A specified resource cannot be located.

LimitExceededException

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

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

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 Source #

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

BatchPredictionAvailable

batchPredictionAvailable :: Wait DescribeBatchPredictions Source #

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

DataSourceAvailable

dataSourceAvailable :: Wait DescribeDataSources Source #

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

EvaluationAvailable

evaluationAvailable :: Wait DescribeEvaluations Source #

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

module Network.AWS.MachineLearning.UpdateDataSource

DeleteDataSource

module Network.AWS.MachineLearning.DeleteDataSource

DescribeTags

module Network.AWS.MachineLearning.DescribeTags

CreateDataSourceFromRedshift

module Network.AWS.MachineLearning.CreateDataSourceFromRedshift

CreateDataSourceFromS3

module Network.AWS.MachineLearning.CreateDataSourceFromS3

CreateMLModel

module Network.AWS.MachineLearning.CreateMLModel

DeleteTags

module Network.AWS.MachineLearning.DeleteTags

DeleteBatchPrediction

module Network.AWS.MachineLearning.DeleteBatchPrediction

UpdateBatchPrediction

module Network.AWS.MachineLearning.UpdateBatchPrediction

GetMLModel

module Network.AWS.MachineLearning.GetMLModel

GetDataSource

module Network.AWS.MachineLearning.GetDataSource

UpdateEvaluation

module Network.AWS.MachineLearning.UpdateEvaluation

DeleteEvaluation

module Network.AWS.MachineLearning.DeleteEvaluation

DeleteMLModel

module Network.AWS.MachineLearning.DeleteMLModel

UpdateMLModel

module Network.AWS.MachineLearning.UpdateMLModel

GetBatchPrediction

module Network.AWS.MachineLearning.GetBatchPrediction

DescribeBatchPredictions (Paginated)

module Network.AWS.MachineLearning.DescribeBatchPredictions

CreateDataSourceFromRDS

module Network.AWS.MachineLearning.CreateDataSourceFromRDS

CreateEvaluation

module Network.AWS.MachineLearning.CreateEvaluation

Predict

module Network.AWS.MachineLearning.Predict

DeleteRealtimeEndpoint

module Network.AWS.MachineLearning.DeleteRealtimeEndpoint

CreateBatchPrediction

module Network.AWS.MachineLearning.CreateBatchPrediction

GetEvaluation

module Network.AWS.MachineLearning.GetEvaluation

DescribeEvaluations (Paginated)

module Network.AWS.MachineLearning.DescribeEvaluations

CreateRealtimeEndpoint

module Network.AWS.MachineLearning.CreateRealtimeEndpoint

AddTags

module Network.AWS.MachineLearning.AddTags

DescribeMLModels (Paginated)

module Network.AWS.MachineLearning.DescribeMLModels

DescribeDataSources (Paginated)

module Network.AWS.MachineLearning.DescribeDataSources

Types

Algorithm

data Algorithm Source #

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

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

Constructors

SGD

Instances

Bounded Algorithm Source #
Methods minBound :: Algorithm # maxBound :: Algorithm #
Enum Algorithm Source #
Methods succ :: Algorithm -> Algorithm # pred :: Algorithm -> Algorithm # toEnum :: Int -> Algorithm # fromEnum :: Algorithm -> Int # enumFrom :: Algorithm -> [Algorithm] # enumFromThen :: Algorithm -> Algorithm -> [Algorithm] # enumFromTo :: Algorithm -> Algorithm -> [Algorithm] # enumFromThenTo :: Algorithm -> Algorithm -> Algorithm -> [Algorithm] #
Eq Algorithm Source #
Methods (==) :: Algorithm -> Algorithm -> Bool # (/=) :: Algorithm -> Algorithm -> Bool #
Data Algorithm Source #
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 Source #
Methods compare :: Algorithm -> Algorithm -> Ordering # (<) :: Algorithm -> Algorithm -> Bool # (<=) :: Algorithm -> Algorithm -> Bool # (>) :: Algorithm -> Algorithm -> Bool # (>=) :: Algorithm -> Algorithm -> Bool # max :: Algorithm -> Algorithm -> Algorithm # min :: Algorithm -> Algorithm -> Algorithm #
Read Algorithm Source #
Methods readsPrec :: Int -> ReadS Algorithm # readList :: ReadS [Algorithm] # readPrec :: ReadPrec Algorithm # readListPrec :: ReadPrec [Algorithm] #
Show Algorithm Source #
Methods showsPrec :: Int -> Algorithm -> ShowS # show :: Algorithm -> String # showList :: [Algorithm] -> ShowS #
Generic Algorithm Source #
Associated Types type Rep Algorithm :: * -> * # Methods from :: Algorithm -> Rep Algorithm x # to :: Rep Algorithm x -> Algorithm #
Hashable Algorithm Source #
Methods hashWithSalt :: Int -> Algorithm -> Int # hash :: Algorithm -> Int #
FromJSON Algorithm Source #
Methods parseJSON :: Value -> Parser Algorithm # parseJSONList :: Value -> Parser [Algorithm] #
NFData Algorithm Source #
Methods rnf :: Algorithm -> () #
ToHeader Algorithm Source #
Methods toHeader :: HeaderName -> Algorithm -> [Header] #
ToQuery Algorithm Source #
Methods toQuery :: Algorithm -> QueryString #
ToByteString Algorithm Source #
Methods toBS :: Algorithm -> ByteString #
FromText Algorithm Source #
Methods parser :: Parser Algorithm #
ToText Algorithm Source #
Methods toText :: Algorithm -> Text #
type Rep Algorithm Source #
type Rep Algorithm = D1 * (MetaData "Algorithm" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" False) (C1 * (MetaCons "SGD" PrefixI False) (U1 *))

BatchPredictionFilterVariable

data BatchPredictionFilterVariable Source #

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

CreatedAt - Sets the search criteria to BatchPrediction creation date. * Status - Sets the search criteria to BatchPrediction status. * Name - Sets the search criteria to the contents of BatchPrediction ____ Name . * IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. * MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction . * DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction . * DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction . The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

Constructors

BatchCreatedAt
BatchDataSourceId
BatchDataURI
BatchIAMUser
BatchLastUpdatedAt
BatchMLModelId
BatchName
BatchStatus

Instances

Bounded BatchPredictionFilterVariable Source #
Methods minBound :: BatchPredictionFilterVariable # maxBound :: BatchPredictionFilterVariable #
Enum BatchPredictionFilterVariable Source #
Methods succ :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable # pred :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable # toEnum :: Int -> BatchPredictionFilterVariable # fromEnum :: BatchPredictionFilterVariable -> Int # enumFrom :: BatchPredictionFilterVariable -> [BatchPredictionFilterVariable] # enumFromThen :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> [BatchPredictionFilterVariable] # enumFromTo :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> [BatchPredictionFilterVariable] # enumFromThenTo :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> [BatchPredictionFilterVariable] #
Eq BatchPredictionFilterVariable Source #
Methods (==) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (/=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool #
Data BatchPredictionFilterVariable Source #
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 Source #
Methods compare :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Ordering # (<) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (<=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (>) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (>=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # max :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable # min :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable #
Read BatchPredictionFilterVariable Source #
Methods readsPrec :: Int -> ReadS BatchPredictionFilterVariable # readList :: ReadS [BatchPredictionFilterVariable] # readPrec :: ReadPrec BatchPredictionFilterVariable # readListPrec :: ReadPrec [BatchPredictionFilterVariable] #
Show BatchPredictionFilterVariable Source #
Methods showsPrec :: Int -> BatchPredictionFilterVariable -> ShowS # show :: BatchPredictionFilterVariable -> String # showList :: [BatchPredictionFilterVariable] -> ShowS #
Generic BatchPredictionFilterVariable Source #
Associated Types type Rep BatchPredictionFilterVariable :: * -> * # Methods from :: BatchPredictionFilterVariable -> Rep BatchPredictionFilterVariable x # to :: Rep BatchPredictionFilterVariable x -> BatchPredictionFilterVariable #
Hashable BatchPredictionFilterVariable Source #
Methods hashWithSalt :: Int -> BatchPredictionFilterVariable -> Int # hash :: BatchPredictionFilterVariable -> Int #
ToJSON BatchPredictionFilterVariable Source #
Methods toJSON :: BatchPredictionFilterVariable -> Value # toEncoding :: BatchPredictionFilterVariable -> Encoding # toJSONList :: [BatchPredictionFilterVariable] -> Value # toEncodingList :: [BatchPredictionFilterVariable] -> Encoding #
NFData BatchPredictionFilterVariable Source #
Methods rnf :: BatchPredictionFilterVariable -> () #
ToHeader BatchPredictionFilterVariable Source #
Methods toHeader :: HeaderName -> BatchPredictionFilterVariable -> [Header] #
ToQuery BatchPredictionFilterVariable Source #
Methods toQuery :: BatchPredictionFilterVariable -> QueryString #
ToByteString BatchPredictionFilterVariable Source #
Methods toBS :: BatchPredictionFilterVariable -> ByteString #
FromText BatchPredictionFilterVariable Source #
Methods parser :: Parser BatchPredictionFilterVariable #
ToText BatchPredictionFilterVariable Source #
Methods toText :: BatchPredictionFilterVariable -> Text #
type Rep BatchPredictionFilterVariable Source #
type Rep BatchPredictionFilterVariable = D1 * (MetaData "BatchPredictionFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

CreatedAt - Sets the search criteria to DataSource creation date. * Status - Sets the search criteria to DataSource status. * Name - Sets the search criteria to the contents of DataSource ____ Name . * DataUri - Sets the search criteria to the URI of data files used to create the DataSource . The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. * IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.

Constructors

DataCreatedAt
DataDATALOCATIONS3
DataIAMUser
DataLastUpdatedAt
DataName
DataStatus

Instances

Bounded DataSourceFilterVariable Source #
Methods minBound :: DataSourceFilterVariable # maxBound :: DataSourceFilterVariable #
Enum DataSourceFilterVariable Source #
Methods succ :: DataSourceFilterVariable -> DataSourceFilterVariable # pred :: DataSourceFilterVariable -> DataSourceFilterVariable # toEnum :: Int -> DataSourceFilterVariable # fromEnum :: DataSourceFilterVariable -> Int # enumFrom :: DataSourceFilterVariable -> [DataSourceFilterVariable] # enumFromThen :: DataSourceFilterVariable -> DataSourceFilterVariable -> [DataSourceFilterVariable] # enumFromTo :: DataSourceFilterVariable -> DataSourceFilterVariable -> [DataSourceFilterVariable] # enumFromThenTo :: DataSourceFilterVariable -> DataSourceFilterVariable -> DataSourceFilterVariable -> [DataSourceFilterVariable] #
Eq DataSourceFilterVariable Source #
Methods (==) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (/=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool #
Data DataSourceFilterVariable Source #
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 Source #
Methods compare :: DataSourceFilterVariable -> DataSourceFilterVariable -> Ordering # (<) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (<=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (>) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (>=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # max :: DataSourceFilterVariable -> DataSourceFilterVariable -> DataSourceFilterVariable # min :: DataSourceFilterVariable -> DataSourceFilterVariable -> DataSourceFilterVariable #
Read DataSourceFilterVariable Source #
Methods readsPrec :: Int -> ReadS DataSourceFilterVariable # readList :: ReadS [DataSourceFilterVariable] # readPrec :: ReadPrec DataSourceFilterVariable # readListPrec :: ReadPrec [DataSourceFilterVariable] #
Show DataSourceFilterVariable Source #
Methods showsPrec :: Int -> DataSourceFilterVariable -> ShowS # show :: DataSourceFilterVariable -> String # showList :: [DataSourceFilterVariable] -> ShowS #
Generic DataSourceFilterVariable Source #
Associated Types type Rep DataSourceFilterVariable :: * -> * # Methods from :: DataSourceFilterVariable -> Rep DataSourceFilterVariable x # to :: Rep DataSourceFilterVariable x -> DataSourceFilterVariable #
Hashable DataSourceFilterVariable Source #
Methods hashWithSalt :: Int -> DataSourceFilterVariable -> Int # hash :: DataSourceFilterVariable -> Int #
ToJSON DataSourceFilterVariable Source #
Methods toJSON :: DataSourceFilterVariable -> Value # toEncoding :: DataSourceFilterVariable -> Encoding # toJSONList :: [DataSourceFilterVariable] -> Value # toEncodingList :: [DataSourceFilterVariable] -> Encoding #
NFData DataSourceFilterVariable Source #
Methods rnf :: DataSourceFilterVariable -> () #
ToHeader DataSourceFilterVariable Source #
Methods toHeader :: HeaderName -> DataSourceFilterVariable -> [Header] #
ToQuery DataSourceFilterVariable Source #
Methods toQuery :: DataSourceFilterVariable -> QueryString #
ToByteString DataSourceFilterVariable Source #
Methods toBS :: DataSourceFilterVariable -> ByteString #
FromText DataSourceFilterVariable Source #
Methods parser :: Parser DataSourceFilterVariable #
ToText DataSourceFilterVariable Source #
Methods toText :: DataSourceFilterVariable -> Text #
type Rep DataSourceFilterVariable Source #
type Rep DataSourceFilterVariable = D1 * (MetaData "DataSourceFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

Constructors

Algorithm
PredictiveModelType

Instances

Bounded DetailsAttributes Source #
Methods minBound :: DetailsAttributes # maxBound :: DetailsAttributes #
Enum DetailsAttributes Source #
Methods succ :: DetailsAttributes -> DetailsAttributes # pred :: DetailsAttributes -> DetailsAttributes # toEnum :: Int -> DetailsAttributes # fromEnum :: DetailsAttributes -> Int # enumFrom :: DetailsAttributes -> [DetailsAttributes] # enumFromThen :: DetailsAttributes -> DetailsAttributes -> [DetailsAttributes] # enumFromTo :: DetailsAttributes -> DetailsAttributes -> [DetailsAttributes] # enumFromThenTo :: DetailsAttributes -> DetailsAttributes -> DetailsAttributes -> [DetailsAttributes] #
Eq DetailsAttributes Source #
Methods (==) :: DetailsAttributes -> DetailsAttributes -> Bool # (/=) :: DetailsAttributes -> DetailsAttributes -> Bool #
Data DetailsAttributes Source #
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 Source #
Methods compare :: DetailsAttributes -> DetailsAttributes -> Ordering # (<) :: DetailsAttributes -> DetailsAttributes -> Bool # (<=) :: DetailsAttributes -> DetailsAttributes -> Bool # (>) :: DetailsAttributes -> DetailsAttributes -> Bool # (>=) :: DetailsAttributes -> DetailsAttributes -> Bool # max :: DetailsAttributes -> DetailsAttributes -> DetailsAttributes # min :: DetailsAttributes -> DetailsAttributes -> DetailsAttributes #
Read DetailsAttributes Source #
Methods readsPrec :: Int -> ReadS DetailsAttributes # readList :: ReadS [DetailsAttributes] # readPrec :: ReadPrec DetailsAttributes # readListPrec :: ReadPrec [DetailsAttributes] #
Show DetailsAttributes Source #
Methods showsPrec :: Int -> DetailsAttributes -> ShowS # show :: DetailsAttributes -> String # showList :: [DetailsAttributes] -> ShowS #
Generic DetailsAttributes Source #
Associated Types type Rep DetailsAttributes :: * -> * # Methods from :: DetailsAttributes -> Rep DetailsAttributes x # to :: Rep DetailsAttributes x -> DetailsAttributes #
Hashable DetailsAttributes Source #
Methods hashWithSalt :: Int -> DetailsAttributes -> Int # hash :: DetailsAttributes -> Int #
FromJSON DetailsAttributes Source #
Methods parseJSON :: Value -> Parser DetailsAttributes # parseJSONList :: Value -> Parser [DetailsAttributes] #
NFData DetailsAttributes Source #
Methods rnf :: DetailsAttributes -> () #
ToHeader DetailsAttributes Source #
Methods toHeader :: HeaderName -> DetailsAttributes -> [Header] #
ToQuery DetailsAttributes Source #
Methods toQuery :: DetailsAttributes -> QueryString #
ToByteString DetailsAttributes Source #
Methods toBS :: DetailsAttributes -> ByteString #
FromText DetailsAttributes Source #
Methods parser :: Parser DetailsAttributes #
ToText DetailsAttributes Source #
Methods toText :: DetailsAttributes -> Text #
type Rep DetailsAttributes Source #
type Rep DetailsAttributes = D1 * (MetaData "DetailsAttributes" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" False) ((:+:) * (C1 * (MetaCons "Algorithm" PrefixI False) (U1 )) (C1 (MetaCons "PredictiveModelType" PrefixI False) (U1 *)))

EntityStatus

data EntityStatus Source #

Object status with the following possible values:

PENDING * INPROGRESS * FAILED * COMPLETED * DELETED

Constructors

ESCompleted
ESDeleted
ESFailed
ESInprogress
ESPending

Instances

Bounded EntityStatus Source #
Methods minBound :: EntityStatus # maxBound :: EntityStatus #
Enum EntityStatus Source #
Methods succ :: EntityStatus -> EntityStatus # pred :: EntityStatus -> EntityStatus # toEnum :: Int -> EntityStatus # fromEnum :: EntityStatus -> Int # enumFrom :: EntityStatus -> [EntityStatus] # enumFromThen :: EntityStatus -> EntityStatus -> [EntityStatus] # enumFromTo :: EntityStatus -> EntityStatus -> [EntityStatus] # enumFromThenTo :: EntityStatus -> EntityStatus -> EntityStatus -> [EntityStatus] #
Eq EntityStatus Source #
Methods (==) :: EntityStatus -> EntityStatus -> Bool # (/=) :: EntityStatus -> EntityStatus -> Bool #
Data EntityStatus Source #
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 Source #
Methods compare :: EntityStatus -> EntityStatus -> Ordering # (<) :: EntityStatus -> EntityStatus -> Bool # (<=) :: EntityStatus -> EntityStatus -> Bool # (>) :: EntityStatus -> EntityStatus -> Bool # (>=) :: EntityStatus -> EntityStatus -> Bool # max :: EntityStatus -> EntityStatus -> EntityStatus # min :: EntityStatus -> EntityStatus -> EntityStatus #
Read EntityStatus Source #
Methods readsPrec :: Int -> ReadS EntityStatus # readList :: ReadS [EntityStatus] # readPrec :: ReadPrec EntityStatus # readListPrec :: ReadPrec [EntityStatus] #
Show EntityStatus Source #
Methods showsPrec :: Int -> EntityStatus -> ShowS # show :: EntityStatus -> String # showList :: [EntityStatus] -> ShowS #
Generic EntityStatus Source #
Associated Types type Rep EntityStatus :: * -> * # Methods from :: EntityStatus -> Rep EntityStatus x # to :: Rep EntityStatus x -> EntityStatus #
Hashable EntityStatus Source #
Methods hashWithSalt :: Int -> EntityStatus -> Int # hash :: EntityStatus -> Int #
FromJSON EntityStatus Source #
Methods parseJSON :: Value -> Parser EntityStatus # parseJSONList :: Value -> Parser [EntityStatus] #
NFData EntityStatus Source #
Methods rnf :: EntityStatus -> () #
ToHeader EntityStatus Source #
Methods toHeader :: HeaderName -> EntityStatus -> [Header] #
ToQuery EntityStatus Source #
Methods toQuery :: EntityStatus -> QueryString #
ToByteString EntityStatus Source #
Methods toBS :: EntityStatus -> ByteString #
FromText EntityStatus Source #
Methods parser :: Parser EntityStatus #
ToText EntityStatus Source #
Methods toText :: EntityStatus -> Text #
type Rep EntityStatus Source #
type Rep EntityStatus = D1 * (MetaData "EntityStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

CreatedAt - Sets the search criteria to Evaluation creation date. * Status - Sets the search criteria to Evaluation status. * Name - Sets the search criteria to the contents of Evaluation ____ Name . * IAMUser - Sets the search criteria to the user account that invoked an evaluation. * MLModelId - Sets the search criteria to the Predictor that was evaluated. * DataSourceId - Sets the search criteria to the DataSource used in evaluation. * DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

Constructors

EvalCreatedAt
EvalDataSourceId
EvalDataURI
EvalIAMUser
EvalLastUpdatedAt
EvalMLModelId
EvalName
EvalStatus

Instances

Bounded EvaluationFilterVariable Source #
Methods minBound :: EvaluationFilterVariable # maxBound :: EvaluationFilterVariable #
Enum EvaluationFilterVariable Source #
Methods succ :: EvaluationFilterVariable -> EvaluationFilterVariable # pred :: EvaluationFilterVariable -> EvaluationFilterVariable # toEnum :: Int -> EvaluationFilterVariable # fromEnum :: EvaluationFilterVariable -> Int # enumFrom :: EvaluationFilterVariable -> [EvaluationFilterVariable] # enumFromThen :: EvaluationFilterVariable -> EvaluationFilterVariable -> [EvaluationFilterVariable] # enumFromTo :: EvaluationFilterVariable -> EvaluationFilterVariable -> [EvaluationFilterVariable] # enumFromThenTo :: EvaluationFilterVariable -> EvaluationFilterVariable -> EvaluationFilterVariable -> [EvaluationFilterVariable] #
Eq EvaluationFilterVariable Source #
Methods (==) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (/=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool #
Data EvaluationFilterVariable Source #
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 Source #
Methods compare :: EvaluationFilterVariable -> EvaluationFilterVariable -> Ordering # (<) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (<=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (>) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (>=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # max :: EvaluationFilterVariable -> EvaluationFilterVariable -> EvaluationFilterVariable # min :: EvaluationFilterVariable -> EvaluationFilterVariable -> EvaluationFilterVariable #
Read EvaluationFilterVariable Source #
Methods readsPrec :: Int -> ReadS EvaluationFilterVariable # readList :: ReadS [EvaluationFilterVariable] # readPrec :: ReadPrec EvaluationFilterVariable # readListPrec :: ReadPrec [EvaluationFilterVariable] #
Show EvaluationFilterVariable Source #
Methods showsPrec :: Int -> EvaluationFilterVariable -> ShowS # show :: EvaluationFilterVariable -> String # showList :: [EvaluationFilterVariable] -> ShowS #
Generic EvaluationFilterVariable Source #
Associated Types type Rep EvaluationFilterVariable :: * -> * # Methods from :: EvaluationFilterVariable -> Rep EvaluationFilterVariable x # to :: Rep EvaluationFilterVariable x -> EvaluationFilterVariable #
Hashable EvaluationFilterVariable Source #
Methods hashWithSalt :: Int -> EvaluationFilterVariable -> Int # hash :: EvaluationFilterVariable -> Int #
ToJSON EvaluationFilterVariable Source #
Methods toJSON :: EvaluationFilterVariable -> Value # toEncoding :: EvaluationFilterVariable -> Encoding # toJSONList :: [EvaluationFilterVariable] -> Value # toEncodingList :: [EvaluationFilterVariable] -> Encoding #
NFData EvaluationFilterVariable Source #
Methods rnf :: EvaluationFilterVariable -> () #
ToHeader EvaluationFilterVariable Source #
Methods toHeader :: HeaderName -> EvaluationFilterVariable -> [Header] #
ToQuery EvaluationFilterVariable Source #
Methods toQuery :: EvaluationFilterVariable -> QueryString #
ToByteString EvaluationFilterVariable Source #
Methods toBS :: EvaluationFilterVariable -> ByteString #
FromText EvaluationFilterVariable Source #
Methods parser :: Parser EvaluationFilterVariable #
ToText EvaluationFilterVariable Source #
Methods toText :: EvaluationFilterVariable -> Text #
type Rep EvaluationFilterVariable Source #
type Rep EvaluationFilterVariable = D1 * (MetaData "EvaluationFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Constructors

MLMFVAlgorithm
MLMFVCreatedAt
MLMFVIAMUser
MLMFVLastUpdatedAt
MLMFVMLModelType
MLMFVName
MLMFVRealtimeEndpointStatus
MLMFVStatus
MLMFVTrainingDataSourceId
MLMFVTrainingDataURI

Instances

Bounded MLModelFilterVariable Source #
Methods minBound :: MLModelFilterVariable # maxBound :: MLModelFilterVariable #
Enum MLModelFilterVariable Source #
Methods succ :: MLModelFilterVariable -> MLModelFilterVariable # pred :: MLModelFilterVariable -> MLModelFilterVariable # toEnum :: Int -> MLModelFilterVariable # fromEnum :: MLModelFilterVariable -> Int # enumFrom :: MLModelFilterVariable -> [MLModelFilterVariable] # enumFromThen :: MLModelFilterVariable -> MLModelFilterVariable -> [MLModelFilterVariable] # enumFromTo :: MLModelFilterVariable -> MLModelFilterVariable -> [MLModelFilterVariable] # enumFromThenTo :: MLModelFilterVariable -> MLModelFilterVariable -> MLModelFilterVariable -> [MLModelFilterVariable] #
Eq MLModelFilterVariable Source #
Methods (==) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (/=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool #
Data MLModelFilterVariable Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelFilterVariable -> c MLModelFilterVariable # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelFilterVariable # toConstr :: MLModelFilterVariable -> Constr # dataTypeOf :: MLModelFilterVariable -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c MLModelFilterVariable) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelFilterVariable) # gmapT :: (forall b. Data b => b -> b) -> MLModelFilterVariable -> MLModelFilterVariable # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r # gmapQ :: (forall d. Data d => d -> u) -> MLModelFilterVariable -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelFilterVariable -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #
Ord MLModelFilterVariable Source #
Methods compare :: MLModelFilterVariable -> MLModelFilterVariable -> Ordering # (<) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (<=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (>) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (>=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # max :: MLModelFilterVariable -> MLModelFilterVariable -> MLModelFilterVariable # min :: MLModelFilterVariable -> MLModelFilterVariable -> MLModelFilterVariable #
Read MLModelFilterVariable Source #
Methods readsPrec :: Int -> ReadS MLModelFilterVariable # readList :: ReadS [MLModelFilterVariable] # readPrec :: ReadPrec MLModelFilterVariable # readListPrec :: ReadPrec [MLModelFilterVariable] #
Show MLModelFilterVariable Source #
Methods showsPrec :: Int -> MLModelFilterVariable -> ShowS # show :: MLModelFilterVariable -> String # showList :: [MLModelFilterVariable] -> ShowS #
Generic MLModelFilterVariable Source #
Associated Types type Rep MLModelFilterVariable :: * -> * # Methods from :: MLModelFilterVariable -> Rep MLModelFilterVariable x # to :: Rep MLModelFilterVariable x -> MLModelFilterVariable #
Hashable MLModelFilterVariable Source #
Methods hashWithSalt :: Int -> MLModelFilterVariable -> Int # hash :: MLModelFilterVariable -> Int #
ToJSON MLModelFilterVariable Source #
Methods toJSON :: MLModelFilterVariable -> Value # toEncoding :: MLModelFilterVariable -> Encoding # toJSONList :: [MLModelFilterVariable] -> Value # toEncodingList :: [MLModelFilterVariable] -> Encoding #
NFData MLModelFilterVariable Source #
Methods rnf :: MLModelFilterVariable -> () #
ToHeader MLModelFilterVariable Source #
Methods toHeader :: HeaderName -> MLModelFilterVariable -> [Header] #
ToQuery MLModelFilterVariable Source #
Methods toQuery :: MLModelFilterVariable -> QueryString #
ToByteString MLModelFilterVariable Source #
Methods toBS :: MLModelFilterVariable -> ByteString #
FromText MLModelFilterVariable Source #
Methods parser :: Parser MLModelFilterVariable #
ToText MLModelFilterVariable Source #
Methods toText :: MLModelFilterVariable -> Text #
type Rep MLModelFilterVariable Source #
type Rep MLModelFilterVariable = D1 * (MetaData "MLModelFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Constructors

Binary
Multiclass
Regression

Instances

Bounded MLModelType Source #
Methods minBound :: MLModelType # maxBound :: MLModelType #
Enum MLModelType Source #
Methods succ :: MLModelType -> MLModelType # pred :: MLModelType -> MLModelType # toEnum :: Int -> MLModelType # fromEnum :: MLModelType -> Int # enumFrom :: MLModelType -> [MLModelType] # enumFromThen :: MLModelType -> MLModelType -> [MLModelType] # enumFromTo :: MLModelType -> MLModelType -> [MLModelType] # enumFromThenTo :: MLModelType -> MLModelType -> MLModelType -> [MLModelType] #
Eq MLModelType Source #
Methods (==) :: MLModelType -> MLModelType -> Bool # (/=) :: MLModelType -> MLModelType -> Bool #
Data MLModelType Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelType -> c MLModelType # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelType # toConstr :: MLModelType -> Constr # dataTypeOf :: MLModelType -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c MLModelType) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelType) # gmapT :: (forall b. Data b => b -> b) -> MLModelType -> MLModelType # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r # gmapQ :: (forall d. Data d => d -> u) -> MLModelType -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelType -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #
Ord MLModelType Source #
Methods compare :: MLModelType -> MLModelType -> Ordering # (<) :: MLModelType -> MLModelType -> Bool # (<=) :: MLModelType -> MLModelType -> Bool # (>) :: MLModelType -> MLModelType -> Bool # (>=) :: MLModelType -> MLModelType -> Bool # max :: MLModelType -> MLModelType -> MLModelType # min :: MLModelType -> MLModelType -> MLModelType #
Read MLModelType Source #
Methods readsPrec :: Int -> ReadS MLModelType # readList :: ReadS [MLModelType] # readPrec :: ReadPrec MLModelType # readListPrec :: ReadPrec [MLModelType] #
Show MLModelType Source #
Methods showsPrec :: Int -> MLModelType -> ShowS # show :: MLModelType -> String # showList :: [MLModelType] -> ShowS #
Generic MLModelType Source #
Associated Types type Rep MLModelType :: * -> * # Methods from :: MLModelType -> Rep MLModelType x # to :: Rep MLModelType x -> MLModelType #
Hashable MLModelType Source #
Methods hashWithSalt :: Int -> MLModelType -> Int # hash :: MLModelType -> Int #
ToJSON MLModelType Source #
Methods toJSON :: MLModelType -> Value # toEncoding :: MLModelType -> Encoding # toJSONList :: [MLModelType] -> Value # toEncodingList :: [MLModelType] -> Encoding #
FromJSON MLModelType Source #
Methods parseJSON :: Value -> Parser MLModelType # parseJSONList :: Value -> Parser [MLModelType] #
NFData MLModelType Source #
Methods rnf :: MLModelType -> () #
ToHeader MLModelType Source #
Methods toHeader :: HeaderName -> MLModelType -> [Header] #
ToQuery MLModelType Source #
Methods toQuery :: MLModelType -> QueryString #
ToByteString MLModelType Source #
Methods toBS :: MLModelType -> ByteString #
FromText MLModelType Source #
Methods parser :: Parser MLModelType #
ToText MLModelType Source #
Methods toText :: MLModelType -> Text #
type Rep MLModelType Source #
type Rep MLModelType = D1 * (MetaData "MLModelType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" False) ((:+:) * (C1 * (MetaCons "Binary" PrefixI False) (U1 )) ((:+:) (C1 * (MetaCons "Multiclass" PrefixI False) (U1 )) (C1 (MetaCons "Regression" PrefixI False) (U1 *))))

RealtimeEndpointStatus

data RealtimeEndpointStatus Source #

Constructors

Failed
None
Ready
Updating

Instances

Bounded RealtimeEndpointStatus Source #
Methods minBound :: RealtimeEndpointStatus # maxBound :: RealtimeEndpointStatus #
Enum RealtimeEndpointStatus Source #
Methods succ :: RealtimeEndpointStatus -> RealtimeEndpointStatus # pred :: RealtimeEndpointStatus -> RealtimeEndpointStatus # toEnum :: Int -> RealtimeEndpointStatus # fromEnum :: RealtimeEndpointStatus -> Int # enumFrom :: RealtimeEndpointStatus -> [RealtimeEndpointStatus] # enumFromThen :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> [RealtimeEndpointStatus] # enumFromTo :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> [RealtimeEndpointStatus] # enumFromThenTo :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> RealtimeEndpointStatus -> [RealtimeEndpointStatus] #
Eq RealtimeEndpointStatus Source #
Methods (==) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (/=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool #
Data RealtimeEndpointStatus Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RealtimeEndpointStatus -> c RealtimeEndpointStatus # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RealtimeEndpointStatus # toConstr :: RealtimeEndpointStatus -> Constr # dataTypeOf :: RealtimeEndpointStatus -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c RealtimeEndpointStatus) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RealtimeEndpointStatus) # gmapT :: (forall b. Data b => b -> b) -> RealtimeEndpointStatus -> RealtimeEndpointStatus # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r # gmapQ :: (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #
Ord RealtimeEndpointStatus Source #
Methods compare :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Ordering # (<) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (<=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (>) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (>=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # max :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> RealtimeEndpointStatus # min :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> RealtimeEndpointStatus #
Read RealtimeEndpointStatus Source #
Methods readsPrec :: Int -> ReadS RealtimeEndpointStatus # readList :: ReadS [RealtimeEndpointStatus] # readPrec :: ReadPrec RealtimeEndpointStatus # readListPrec :: ReadPrec [RealtimeEndpointStatus] #
Show RealtimeEndpointStatus Source #
Methods showsPrec :: Int -> RealtimeEndpointStatus -> ShowS # show :: RealtimeEndpointStatus -> String # showList :: [RealtimeEndpointStatus] -> ShowS #
Generic RealtimeEndpointStatus Source #
Associated Types type Rep RealtimeEndpointStatus :: * -> * # Methods from :: RealtimeEndpointStatus -> Rep RealtimeEndpointStatus x # to :: Rep RealtimeEndpointStatus x -> RealtimeEndpointStatus #
Hashable RealtimeEndpointStatus Source #
Methods hashWithSalt :: Int -> RealtimeEndpointStatus -> Int # hash :: RealtimeEndpointStatus -> Int #
FromJSON RealtimeEndpointStatus Source #
Methods parseJSON :: Value -> Parser RealtimeEndpointStatus # parseJSONList :: Value -> Parser [RealtimeEndpointStatus] #
NFData RealtimeEndpointStatus Source #
Methods rnf :: RealtimeEndpointStatus -> () #
ToHeader RealtimeEndpointStatus Source #
Methods toHeader :: HeaderName -> RealtimeEndpointStatus -> [Header] #
ToQuery RealtimeEndpointStatus Source #
Methods toQuery :: RealtimeEndpointStatus -> QueryString #
ToByteString RealtimeEndpointStatus Source #
Methods toBS :: RealtimeEndpointStatus -> ByteString #
FromText RealtimeEndpointStatus Source #
Methods parser :: Parser RealtimeEndpointStatus #
ToText RealtimeEndpointStatus Source #
Methods toText :: RealtimeEndpointStatus -> Text #
type Rep RealtimeEndpointStatus Source #
type Rep RealtimeEndpointStatus = D1 * (MetaData "RealtimeEndpointStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

Constructors

Asc
Dsc

Instances

Bounded SortOrder Source #
Methods minBound :: SortOrder # maxBound :: SortOrder #
Enum SortOrder Source #
Methods succ :: SortOrder -> SortOrder # pred :: SortOrder -> SortOrder # toEnum :: Int -> SortOrder # fromEnum :: SortOrder -> Int # enumFrom :: SortOrder -> [SortOrder] # enumFromThen :: SortOrder -> SortOrder -> [SortOrder] # enumFromTo :: SortOrder -> SortOrder -> [SortOrder] # enumFromThenTo :: SortOrder -> SortOrder -> SortOrder -> [SortOrder] #
Eq SortOrder Source #
Methods (==) :: SortOrder -> SortOrder -> Bool # (/=) :: SortOrder -> SortOrder -> Bool #
Data SortOrder Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> SortOrder -> c SortOrder # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c SortOrder # toConstr :: SortOrder -> Constr # dataTypeOf :: SortOrder -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c SortOrder) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c SortOrder) # gmapT :: (forall b. Data b => b -> b) -> SortOrder -> SortOrder # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r # gmapQ :: (forall d. Data d => d -> u) -> SortOrder -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> SortOrder -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #
Ord SortOrder Source #
Methods compare :: SortOrder -> SortOrder -> Ordering # (<) :: SortOrder -> SortOrder -> Bool # (<=) :: SortOrder -> SortOrder -> Bool # (>) :: SortOrder -> SortOrder -> Bool # (>=) :: SortOrder -> SortOrder -> Bool # max :: SortOrder -> SortOrder -> SortOrder # min :: SortOrder -> SortOrder -> SortOrder #
Read SortOrder Source #
Methods readsPrec :: Int -> ReadS SortOrder # readList :: ReadS [SortOrder] # readPrec :: ReadPrec SortOrder # readListPrec :: ReadPrec [SortOrder] #
Show SortOrder Source #
Methods showsPrec :: Int -> SortOrder -> ShowS # show :: SortOrder -> String # showList :: [SortOrder] -> ShowS #
Generic SortOrder Source #
Associated Types type Rep SortOrder :: * -> * # Methods from :: SortOrder -> Rep SortOrder x # to :: Rep SortOrder x -> SortOrder #
Hashable SortOrder Source #
Methods hashWithSalt :: Int -> SortOrder -> Int # hash :: SortOrder -> Int #
ToJSON SortOrder Source #
Methods toJSON :: SortOrder -> Value # toEncoding :: SortOrder -> Encoding # toJSONList :: [SortOrder] -> Value # toEncodingList :: [SortOrder] -> Encoding #
NFData SortOrder Source #
Methods rnf :: SortOrder -> () #
ToHeader SortOrder Source #
Methods toHeader :: HeaderName -> SortOrder -> [Header] #
ToQuery SortOrder Source #
Methods toQuery :: SortOrder -> QueryString #
ToByteString SortOrder Source #
Methods toBS :: SortOrder -> ByteString #
FromText SortOrder Source #
Methods parser :: Parser SortOrder #
ToText SortOrder Source #
Methods toText :: SortOrder -> Text #
type Rep SortOrder Source #
type Rep SortOrder = D1 * (MetaData "SortOrder" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" False) ((:+:) * (C1 * (MetaCons "Asc" PrefixI False) (U1 )) (C1 (MetaCons "Dsc" PrefixI False) (U1 *)))

TaggableResourceType

data TaggableResourceType Source #

Constructors

BatchPrediction
DataSource
Evaluation
MLModel

Instances

Bounded TaggableResourceType Source #
Methods minBound :: TaggableResourceType # maxBound :: TaggableResourceType #
Enum TaggableResourceType Source #
Methods succ :: TaggableResourceType -> TaggableResourceType # pred :: TaggableResourceType -> TaggableResourceType # toEnum :: Int -> TaggableResourceType # fromEnum :: TaggableResourceType -> Int # enumFrom :: TaggableResourceType -> [TaggableResourceType] # enumFromThen :: TaggableResourceType -> TaggableResourceType -> [TaggableResourceType] # enumFromTo :: TaggableResourceType -> TaggableResourceType -> [TaggableResourceType] # enumFromThenTo :: TaggableResourceType -> TaggableResourceType -> TaggableResourceType -> [TaggableResourceType] #
Eq TaggableResourceType Source #
Methods (==) :: TaggableResourceType -> TaggableResourceType -> Bool # (/=) :: TaggableResourceType -> TaggableResourceType -> Bool #
Data TaggableResourceType Source #
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 Source #
Methods compare :: TaggableResourceType -> TaggableResourceType -> Ordering # (<) :: TaggableResourceType -> TaggableResourceType -> Bool # (<=) :: TaggableResourceType -> TaggableResourceType -> Bool # (>) :: TaggableResourceType -> TaggableResourceType -> Bool # (>=) :: TaggableResourceType -> TaggableResourceType -> Bool # max :: TaggableResourceType -> TaggableResourceType -> TaggableResourceType # min :: TaggableResourceType -> TaggableResourceType -> TaggableResourceType #
Read TaggableResourceType Source #
Methods readsPrec :: Int -> ReadS TaggableResourceType # readList :: ReadS [TaggableResourceType] # readPrec :: ReadPrec TaggableResourceType # readListPrec :: ReadPrec [TaggableResourceType] #
Show TaggableResourceType Source #
Methods showsPrec :: Int -> TaggableResourceType -> ShowS # show :: TaggableResourceType -> String # showList :: [TaggableResourceType] -> ShowS #
Generic TaggableResourceType Source #
Associated Types type Rep TaggableResourceType :: * -> * # Methods from :: TaggableResourceType -> Rep TaggableResourceType x # to :: Rep TaggableResourceType x -> TaggableResourceType #
Hashable TaggableResourceType Source #
Methods hashWithSalt :: Int -> TaggableResourceType -> Int # hash :: TaggableResourceType -> Int #
ToJSON TaggableResourceType Source #
Methods toJSON :: TaggableResourceType -> Value # toEncoding :: TaggableResourceType -> Encoding # toJSONList :: [TaggableResourceType] -> Value # toEncodingList :: [TaggableResourceType] -> Encoding #
FromJSON TaggableResourceType Source #
Methods parseJSON :: Value -> Parser TaggableResourceType # parseJSONList :: Value -> Parser [TaggableResourceType] #
NFData TaggableResourceType Source #
Methods rnf :: TaggableResourceType -> () #
ToHeader TaggableResourceType Source #
Methods toHeader :: HeaderName -> TaggableResourceType -> [Header] #
ToQuery TaggableResourceType Source #
Methods toQuery :: TaggableResourceType -> QueryString #
ToByteString TaggableResourceType Source #
Methods toBS :: TaggableResourceType -> ByteString #
FromText TaggableResourceType Source #
Methods parser :: Parser TaggableResourceType #
ToText TaggableResourceType Source #
Methods toText :: TaggableResourceType -> Text #
type Rep TaggableResourceType Source #
type Rep TaggableResourceType = D1 * (MetaData "TaggableResourceType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Represents the output of a GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction .

See: batchPrediction smart constructor.

Instances

Eq BatchPrediction Source #
Methods (==) :: BatchPrediction -> BatchPrediction -> Bool # (/=) :: BatchPrediction -> BatchPrediction -> Bool #
Data BatchPrediction Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> BatchPrediction -> c BatchPrediction # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c BatchPrediction # toConstr :: BatchPrediction -> Constr # dataTypeOf :: BatchPrediction -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c BatchPrediction) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c BatchPrediction) # gmapT :: (forall b. Data b => b -> b) -> BatchPrediction -> BatchPrediction # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r # gmapQ :: (forall d. Data d => d -> u) -> BatchPrediction -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> BatchPrediction -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #
Read BatchPrediction Source #
Methods readsPrec :: Int -> ReadS BatchPrediction # readList :: ReadS [BatchPrediction] # readPrec :: ReadPrec BatchPrediction # readListPrec :: ReadPrec [BatchPrediction] #
Show BatchPrediction Source #
Methods showsPrec :: Int -> BatchPrediction -> ShowS # show :: BatchPrediction -> String # showList :: [BatchPrediction] -> ShowS #
Generic BatchPrediction Source #
Associated Types type Rep BatchPrediction :: * -> * # Methods from :: BatchPrediction -> Rep BatchPrediction x # to :: Rep BatchPrediction x -> BatchPrediction #
Hashable BatchPrediction Source #
Methods hashWithSalt :: Int -> BatchPrediction -> Int # hash :: BatchPrediction -> Int #
FromJSON BatchPrediction Source #
Methods parseJSON :: Value -> Parser BatchPrediction # parseJSONList :: Value -> Parser [BatchPrediction] #
NFData BatchPrediction Source #
Methods rnf :: BatchPrediction -> () #
type Rep BatchPrediction Source #
type Rep BatchPrediction = D1 * (MetaData "BatchPrediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

bpStatus - The status of the BatchPrediction . This element can have one of the following values: * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. * INPROGRESS - The process is underway. * FAILED - The request to perform a batch prediction did not run to completion. It is not usable. * COMPLETED - The batch prediction process completed successfully. * DELETED - The BatchPrediction is marked as deleted. It is not usable.
bpLastUpdatedAt - The time of the most recent edit to the BatchPrediction . The time is expressed in epoch time.
bpCreatedAt - The time that the BatchPrediction was created. The time is expressed in epoch time.
bpComputeTime - Undocumented member.
bpInputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
bpMLModelId - The ID of the MLModel that generated predictions for the BatchPrediction request.
bpBatchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to predict.
bpTotalRecordCount - Undocumented member.
bpStartedAt - Undocumented member.
bpBatchPredictionId - The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.
bpFinishedAt - Undocumented member.
bpInvalidRecordCount - Undocumented member.
bpCreatedByIAMUser - The AWS user account that invoked the BatchPrediction . The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
bpName - A user-supplied name or description of the BatchPrediction .
bpMessage - A description of the most recent details about processing the batch prediction request.
bpOutputURI - The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the outputURI field: :, //, /./, /../.

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

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

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

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

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

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

bpComputeTime :: Lens' BatchPrediction (Maybe Integer) Source #

Undocumented member.

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

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

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

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

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

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

bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer) Source #

Undocumented member.

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

Undocumented member.

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

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

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

Undocumented member.

bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer) Source #

Undocumented member.

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

The AWS user account that invoked the BatchPrediction . The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

A user-supplied name or description of the BatchPrediction .

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

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

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

The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the outputURI field: :, //, /./, /../.

DataSource

data DataSource Source #

Represents the output of the GetDataSource operation.

The content consists of the detailed metadata and data file information and the current status of the DataSource .

See: dataSource smart constructor.

Instances

Eq DataSource Source #
Methods (==) :: DataSource -> DataSource -> Bool # (/=) :: DataSource -> DataSource -> Bool #
Data DataSource Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DataSource -> c DataSource # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DataSource # toConstr :: DataSource -> Constr # dataTypeOf :: DataSource -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c DataSource) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DataSource) # gmapT :: (forall b. Data b => b -> b) -> DataSource -> DataSource # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r # gmapQ :: (forall d. Data d => d -> u) -> DataSource -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> DataSource -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #
Read DataSource Source #
Methods readsPrec :: Int -> ReadS DataSource # readList :: ReadS [DataSource] # readPrec :: ReadPrec DataSource # readListPrec :: ReadPrec [DataSource] #
Show DataSource Source #
Methods showsPrec :: Int -> DataSource -> ShowS # show :: DataSource -> String # showList :: [DataSource] -> ShowS #
Generic DataSource Source #
Associated Types type Rep DataSource :: * -> * # Methods from :: DataSource -> Rep DataSource x # to :: Rep DataSource x -> DataSource #
Hashable DataSource Source #
Methods hashWithSalt :: Int -> DataSource -> Int # hash :: DataSource -> Int #
FromJSON DataSource Source #
Methods parseJSON :: Value -> Parser DataSource # parseJSONList :: Value -> Parser [DataSource] #
NFData DataSource Source #
Methods rnf :: DataSource -> () #
type Rep DataSource Source #
type Rep DataSource = D1 * (MetaData "DataSource" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

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

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

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

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

The number of data files referenced by the DataSource .

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

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

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

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

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

Undocumented member.

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

The ID that is assigned to the DataSource during creation.

dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata) Source #

Undocumented member.

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

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

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

Undocumented member.

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

Undocumented member.

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

The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

A user-supplied name or description of the DataSource .

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

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

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

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

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

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

dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata) Source #

Undocumented member.

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

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

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

Undocumented member.

Evaluation

data Evaluation Source #

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation .

See: evaluation smart constructor.

Instances

Eq Evaluation Source #
Methods (==) :: Evaluation -> Evaluation -> Bool # (/=) :: Evaluation -> Evaluation -> Bool #
Data Evaluation Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Evaluation -> c Evaluation # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Evaluation # toConstr :: Evaluation -> Constr # dataTypeOf :: Evaluation -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c Evaluation) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Evaluation) # gmapT :: (forall b. Data b => b -> b) -> Evaluation -> Evaluation # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r # gmapQ :: (forall d. Data d => d -> u) -> Evaluation -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> Evaluation -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #
Read Evaluation Source #
Methods readsPrec :: Int -> ReadS Evaluation # readList :: ReadS [Evaluation] # readPrec :: ReadPrec Evaluation # readListPrec :: ReadPrec [Evaluation] #
Show Evaluation Source #
Methods showsPrec :: Int -> Evaluation -> ShowS # show :: Evaluation -> String # showList :: [Evaluation] -> ShowS #
Generic Evaluation Source #
Associated Types type Rep Evaluation :: * -> * # Methods from :: Evaluation -> Rep Evaluation x # to :: Rep Evaluation x -> Evaluation #
Hashable Evaluation Source #
Methods hashWithSalt :: Int -> Evaluation -> Int # hash :: Evaluation -> Int #
FromJSON Evaluation Source #
Methods parseJSON :: Value -> Parser Evaluation # parseJSONList :: Value -> Parser [Evaluation] #
NFData Evaluation Source #
Methods rnf :: Evaluation -> () #
type Rep Evaluation Source #
type Rep Evaluation = D1 * (MetaData "Evaluation" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

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

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

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

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

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

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

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

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

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

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

Undocumented member.

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

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

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

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

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

Undocumented member.

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

Undocumented member.

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

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

A user-supplied name or description of the Evaluation .

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

The ID that is assigned to the Evaluation at creation.

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

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

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

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

MLModel

data MLModel Source #

Represents the output of a GetMLModel operation.

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

See: mLModel smart constructor.

Instances

Eq MLModel Source #
Methods (==) :: MLModel -> MLModel -> Bool # (/=) :: MLModel -> MLModel -> Bool #
Data MLModel Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModel -> c MLModel # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModel # toConstr :: MLModel -> Constr # dataTypeOf :: MLModel -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c MLModel) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModel) # gmapT :: (forall b. Data b => b -> b) -> MLModel -> MLModel # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r # gmapQ :: (forall d. Data d => d -> u) -> MLModel -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModel -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #
Read MLModel Source #
Methods readsPrec :: Int -> ReadS MLModel # readList :: ReadS [MLModel] # readPrec :: ReadPrec MLModel # readListPrec :: ReadPrec [MLModel] #
Show MLModel Source #
Methods showsPrec :: Int -> MLModel -> ShowS # show :: MLModel -> String # showList :: [MLModel] -> ShowS #
Generic MLModel Source #
Associated Types type Rep MLModel :: * -> * # Methods from :: MLModel -> Rep MLModel x # to :: Rep MLModel x -> MLModel #
Hashable MLModel Source #
Methods hashWithSalt :: Int -> MLModel -> Int # hash :: MLModel -> Int #
FromJSON MLModel Source #
Methods parseJSON :: Value -> Parser MLModel # parseJSONList :: Value -> Parser [MLModel] #
NFData MLModel Source #
Methods rnf :: MLModel -> () #
type Rep MLModel Source #
type Rep MLModel = D1 * (MetaData "MLModel" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Undocumented member.

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

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

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

The ID assigned to the MLModel at creation.

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

Undocumented member.

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

Undocumented member.

mlmScoreThreshold :: Lens' MLModel (Maybe Double) Source #

Undocumented member.

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

Undocumented member.

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

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

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

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

A user-supplied name or description of the MLModel .

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

The current endpoint of the MLModel .

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

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

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

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

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

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

PerformanceMetrics

data PerformanceMetrics Source #

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel :

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

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

See: performanceMetrics smart constructor.

Instances

Eq PerformanceMetrics Source #
Methods (==) :: PerformanceMetrics -> PerformanceMetrics -> Bool # (/=) :: PerformanceMetrics -> PerformanceMetrics -> Bool #
Data PerformanceMetrics Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> PerformanceMetrics -> c PerformanceMetrics # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c PerformanceMetrics # toConstr :: PerformanceMetrics -> Constr # dataTypeOf :: PerformanceMetrics -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c PerformanceMetrics) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c PerformanceMetrics) # gmapT :: (forall b. Data b => b -> b) -> PerformanceMetrics -> PerformanceMetrics # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r # gmapQ :: (forall d. Data d => d -> u) -> PerformanceMetrics -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> PerformanceMetrics -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #
Read PerformanceMetrics Source #
Methods readsPrec :: Int -> ReadS PerformanceMetrics # readList :: ReadS [PerformanceMetrics] # readPrec :: ReadPrec PerformanceMetrics # readListPrec :: ReadPrec [PerformanceMetrics] #
Show PerformanceMetrics Source #
Methods showsPrec :: Int -> PerformanceMetrics -> ShowS # show :: PerformanceMetrics -> String # showList :: [PerformanceMetrics] -> ShowS #
Generic PerformanceMetrics Source #
Associated Types type Rep PerformanceMetrics :: * -> * # Methods from :: PerformanceMetrics -> Rep PerformanceMetrics x # to :: Rep PerformanceMetrics x -> PerformanceMetrics #
Hashable PerformanceMetrics Source #
Methods hashWithSalt :: Int -> PerformanceMetrics -> Int # hash :: PerformanceMetrics -> Int #
FromJSON PerformanceMetrics Source #
Methods parseJSON :: Value -> Parser PerformanceMetrics # parseJSONList :: Value -> Parser [PerformanceMetrics] #
NFData PerformanceMetrics Source #
Methods rnf :: PerformanceMetrics -> () #
type Rep PerformanceMetrics Source #
type Rep PerformanceMetrics = D1 * (MetaData "PerformanceMetrics" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" True) (C1 * (MetaCons "PerformanceMetrics'" PrefixI True) (S1 * (MetaSel (Just Symbol "_pmProperties") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 * (Maybe (Map Text Text)))))

performanceMetrics :: PerformanceMetrics Source #

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

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

pmProperties - Undocumented member.

pmProperties :: Lens' PerformanceMetrics (HashMap Text Text) Source #

Undocumented member.

Prediction

data Prediction Source #

The output from a Predict operation:

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

See: prediction smart constructor.

Instances

Eq Prediction Source #
Methods (==) :: Prediction -> Prediction -> Bool # (/=) :: Prediction -> Prediction -> Bool #
Data Prediction Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Prediction -> c Prediction # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Prediction # toConstr :: Prediction -> Constr # dataTypeOf :: Prediction -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c Prediction) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Prediction) # gmapT :: (forall b. Data b => b -> b) -> Prediction -> Prediction # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r # gmapQ :: (forall d. Data d => d -> u) -> Prediction -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> Prediction -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #
Read Prediction Source #
Methods readsPrec :: Int -> ReadS Prediction # readList :: ReadS [Prediction] # readPrec :: ReadPrec Prediction # readListPrec :: ReadPrec [Prediction] #
Show Prediction Source #
Methods showsPrec :: Int -> Prediction -> ShowS # show :: Prediction -> String # showList :: [Prediction] -> ShowS #
Generic Prediction Source #
Associated Types type Rep Prediction :: * -> * # Methods from :: Prediction -> Rep Prediction x # to :: Rep Prediction x -> Prediction #
Hashable Prediction Source #
Methods hashWithSalt :: Int -> Prediction -> Int # hash :: Prediction -> Int #
FromJSON Prediction Source #
Methods parseJSON :: Value -> Parser Prediction # parseJSONList :: Value -> Parser [Prediction] #
NFData Prediction Source #
Methods rnf :: Prediction -> () #
type Rep Prediction Source #
type Rep Prediction = D1 * (MetaData "Prediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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

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

pPredictedValue - The prediction value for REGRESSION MLModel .
pPredictedLabel - The prediction label for either a BINARY or MULTICLASS MLModel .
pPredictedScores - Undocumented member.
pDetails - Undocumented member.

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

The prediction value for REGRESSION MLModel .

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

The prediction label for either a BINARY or MULTICLASS MLModel .

pPredictedScores :: Lens' Prediction (HashMap Text Double) Source #

Undocumented member.

pDetails :: Lens' Prediction (HashMap DetailsAttributes Text) Source #

Undocumented member.

RDSDataSpec

data RDSDataSpec Source #

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

See: rdsDataSpec smart constructor.

Instances

Eq RDSDataSpec Source #
Methods (==) :: RDSDataSpec -> RDSDataSpec -> Bool # (/=) :: RDSDataSpec -> RDSDataSpec -> Bool #
Data RDSDataSpec Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDataSpec -> c RDSDataSpec # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDataSpec # toConstr :: RDSDataSpec -> Constr # dataTypeOf :: RDSDataSpec -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDataSpec) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDataSpec) # gmapT :: (forall b. Data b => b -> b) -> RDSDataSpec -> RDSDataSpec # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r # gmapQ :: (forall d. Data d => d -> u) -> RDSDataSpec -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDataSpec -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #
Read RDSDataSpec Source #
Methods readsPrec :: Int -> ReadS RDSDataSpec # readList :: ReadS [RDSDataSpec] # readPrec :: ReadPrec RDSDataSpec # readListPrec :: ReadPrec [RDSDataSpec] #
Show RDSDataSpec Source #
Methods showsPrec :: Int -> RDSDataSpec -> ShowS # show :: RDSDataSpec -> String # showList :: [RDSDataSpec] -> ShowS #
Generic RDSDataSpec Source #
Associated Types type Rep RDSDataSpec :: * -> * # Methods from :: RDSDataSpec -> Rep RDSDataSpec x # to :: Rep RDSDataSpec x -> RDSDataSpec #
Hashable RDSDataSpec Source #
Methods hashWithSalt :: Int -> RDSDataSpec -> Int # hash :: RDSDataSpec -> Int #
ToJSON RDSDataSpec Source #
Methods toJSON :: RDSDataSpec -> Value # toEncoding :: RDSDataSpec -> Encoding # toJSONList :: [RDSDataSpec] -> Value # toEncodingList :: [RDSDataSpec] -> Encoding #
NFData RDSDataSpec Source #
Methods rnf :: RDSDataSpec -> () #
type Rep RDSDataSpec Source #
type Rep RDSDataSpec = D1 * (MetaData "RDSDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: RDSDatabase	`rdsdsDatabaseInformation`
-> Text	`rdsdsSelectSqlQuery`
-> RDSDatabaseCredentials	`rdsdsDatabaseCredentials`
-> Text	`rdsdsS3StagingLocation`
-> Text	`rdsdsResourceRole`
-> Text	`rdsdsServiceRole`
-> Text	`rdsdsSubnetId`
-> 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) Source #

The Amazon S3 location of the DataSchema .

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

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

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

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

rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase Source #

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text Source #

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

rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials Source #

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

rdsdsS3StagingLocation :: Lens' RDSDataSpec Text Source #

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

rdsdsResourceRole :: Lens' RDSDataSpec Text Source #

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

rdsdsServiceRole :: Lens' RDSDataSpec Text Source #

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

rdsdsSubnetId :: Lens' RDSDataSpec Text Source #

The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text] Source #

The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

RDSDatabase

data RDSDatabase Source #

The database details of an Amazon RDS database.

See: rdsDatabase smart constructor.

Instances

Eq RDSDatabase Source #
Methods (==) :: RDSDatabase -> RDSDatabase -> Bool # (/=) :: RDSDatabase -> RDSDatabase -> Bool #
Data RDSDatabase Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabase -> c RDSDatabase # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabase # toConstr :: RDSDatabase -> Constr # dataTypeOf :: RDSDatabase -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabase) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabase) # gmapT :: (forall b. Data b => b -> b) -> RDSDatabase -> RDSDatabase # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r # gmapQ :: (forall d. Data d => d -> u) -> RDSDatabase -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabase -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #
Read RDSDatabase Source #
Methods readsPrec :: Int -> ReadS RDSDatabase # readList :: ReadS [RDSDatabase] # readPrec :: ReadPrec RDSDatabase # readListPrec :: ReadPrec [RDSDatabase] #
Show RDSDatabase Source #
Methods showsPrec :: Int -> RDSDatabase -> ShowS # show :: RDSDatabase -> String # showList :: [RDSDatabase] -> ShowS #
Generic RDSDatabase Source #
Associated Types type Rep RDSDatabase :: * -> * # Methods from :: RDSDatabase -> Rep RDSDatabase x # to :: Rep RDSDatabase x -> RDSDatabase #
Hashable RDSDatabase Source #
Methods hashWithSalt :: Int -> RDSDatabase -> Int # hash :: RDSDatabase -> Int #
ToJSON RDSDatabase Source #
Methods toJSON :: RDSDatabase -> Value # toEncoding :: RDSDatabase -> Encoding # toJSONList :: [RDSDatabase] -> Value # toEncodingList :: [RDSDatabase] -> Encoding #
FromJSON RDSDatabase Source #
Methods parseJSON :: Value -> Parser RDSDatabase # parseJSONList :: Value -> Parser [RDSDatabase] #
NFData RDSDatabase Source #
Methods rnf :: RDSDatabase -> () #
type Rep RDSDatabase Source #
type Rep RDSDatabase = D1 * (MetaData "RDSDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: Text	`rdsdInstanceIdentifier`
-> Text	`rdsdDatabaseName`
-> 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 - The ID of an RDS DB instance.
rdsdDatabaseName - Undocumented member.

rdsdInstanceIdentifier :: Lens' RDSDatabase Text Source #

The ID of an RDS DB instance.

rdsdDatabaseName :: Lens' RDSDatabase Text Source #

Undocumented member.

RDSDatabaseCredentials

data RDSDatabaseCredentials Source #

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

See: rdsDatabaseCredentials smart constructor.

Instances

Eq RDSDatabaseCredentials Source #
Methods (==) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool # (/=) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool #
Data RDSDatabaseCredentials Source #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabaseCredentials -> c RDSDatabaseCredentials # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabaseCredentials # toConstr :: RDSDatabaseCredentials -> Constr # dataTypeOf :: RDSDatabaseCredentials -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabaseCredentials) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabaseCredentials) # gmapT :: (forall b. Data b => b -> b) -> RDSDatabaseCredentials -> RDSDatabaseCredentials # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r # gmapQ :: (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #
Read RDSDatabaseCredentials Source #
Methods readsPrec :: Int -> ReadS RDSDatabaseCredentials # readList :: ReadS [RDSDatabaseCredentials] # readPrec :: ReadPrec RDSDatabaseCredentials # readListPrec :: ReadPrec [RDSDatabaseCredentials] #
Show RDSDatabaseCredentials Source #
Methods showsPrec :: Int -> RDSDatabaseCredentials -> ShowS # show :: RDSDatabaseCredentials -> String # showList :: [RDSDatabaseCredentials] -> ShowS #
Generic RDSDatabaseCredentials Source #
Associated Types type Rep RDSDatabaseCredentials :: * -> * # Methods from :: RDSDatabaseCredentials -> Rep RDSDatabaseCredentials x # to :: Rep RDSDatabaseCredentials x -> RDSDatabaseCredentials #
Hashable RDSDatabaseCredentials Source #
Methods hashWithSalt :: Int -> RDSDatabaseCredentials -> Int # hash :: RDSDatabaseCredentials -> Int #
ToJSON RDSDatabaseCredentials Source #
Methods toJSON :: RDSDatabaseCredentials -> Value # toEncoding :: RDSDatabaseCredentials -> Encoding # toJSONList :: [RDSDatabaseCredentials] -> Value # toEncodingList :: [RDSDatabaseCredentials] -> Encoding #
NFData RDSDatabaseCredentials Source #
Methods rnf :: RDSDatabaseCredentials -> () #
type Rep RDSDatabaseCredentials Source #
type Rep RDSDatabaseCredentials = D1 * (MetaData "RDSDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: Text	`rdsdcUsername`
-> Text	`rdsdcPassword`
-> 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:

rdsdcUsername - Undocumented member.
rdsdcPassword - Undocumented member.

rdsdcUsername :: Lens' RDSDatabaseCredentials Text Source #

Undocumented member.

rdsdcPassword :: Lens' RDSDatabaseCredentials Text Source #

Undocumented member.

RDSMetadata

data RDSMetadata Source #

The datasource details that are specific to Amazon RDS.

See: rdsMetadata smart constructor.

Instances

Eq RDSMetadata Source #
Methods (==) :: RDSMetadata -> RDSMetadata -> Bool # (/=) :: RDSMetadata -> RDSMetadata -> Bool #
Data RDSMetadata Source #
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 Source #
Methods readsPrec :: Int -> ReadS RDSMetadata # readList :: ReadS [RDSMetadata] # readPrec :: ReadPrec RDSMetadata # readListPrec :: ReadPrec [RDSMetadata] #
Show RDSMetadata Source #
Methods showsPrec :: Int -> RDSMetadata -> ShowS # show :: RDSMetadata -> String # showList :: [RDSMetadata] -> ShowS #
Generic RDSMetadata Source #
Associated Types type Rep RDSMetadata :: * -> * # Methods from :: RDSMetadata -> Rep RDSMetadata x # to :: Rep RDSMetadata x -> RDSMetadata #
Hashable RDSMetadata Source #
Methods hashWithSalt :: Int -> RDSMetadata -> Int # hash :: RDSMetadata -> Int #
FromJSON RDSMetadata Source #
Methods parseJSON :: Value -> Parser RDSMetadata # parseJSONList :: Value -> Parser [RDSMetadata] #
NFData RDSMetadata Source #
Methods rnf :: RDSMetadata -> () #
type Rep RDSMetadata Source #
type Rep RDSMetadata = D1 * (MetaData "RDSMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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) Source #

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

rmDataPipelineId :: Lens' RDSMetadata (Maybe Text) Source #

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) Source #

The database details required to connect to an Amazon RDS.

rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text) Source #

Undocumented member.

rmResourceRole :: Lens' RDSMetadata (Maybe Text) Source #

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) Source #

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

RealtimeEndpointInfo

data RealtimeEndpointInfo Source #

Describes the real-time endpoint information for an MLModel .

See: realtimeEndpointInfo smart constructor.

Instances

Eq RealtimeEndpointInfo Source #
Methods (==) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool # (/=) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool #
Data RealtimeEndpointInfo Source #
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 Source #
Methods readsPrec :: Int -> ReadS RealtimeEndpointInfo # readList :: ReadS [RealtimeEndpointInfo] # readPrec :: ReadPrec RealtimeEndpointInfo # readListPrec :: ReadPrec [RealtimeEndpointInfo] #
Show RealtimeEndpointInfo Source #
Methods showsPrec :: Int -> RealtimeEndpointInfo -> ShowS # show :: RealtimeEndpointInfo -> String # showList :: [RealtimeEndpointInfo] -> ShowS #
Generic RealtimeEndpointInfo Source #
Associated Types type Rep RealtimeEndpointInfo :: * -> * # Methods from :: RealtimeEndpointInfo -> Rep RealtimeEndpointInfo x # to :: Rep RealtimeEndpointInfo x -> RealtimeEndpointInfo #
Hashable RealtimeEndpointInfo Source #
Methods hashWithSalt :: Int -> RealtimeEndpointInfo -> Int # hash :: RealtimeEndpointInfo -> Int #
FromJSON RealtimeEndpointInfo Source #
Methods parseJSON :: Value -> Parser RealtimeEndpointInfo # parseJSONList :: Value -> Parser [RealtimeEndpointInfo] #
NFData RealtimeEndpointInfo Source #
Methods rnf :: RealtimeEndpointInfo -> () #
type Rep RealtimeEndpointInfo Source #
type Rep RealtimeEndpointInfo = D1 * (MetaData "RealtimeEndpointInfo" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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) Source #

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) Source #

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

reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) Source #

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) Source #

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

RedshiftDataSpec

data RedshiftDataSpec Source #

Describes the data specification of an Amazon Redshift DataSource .

See: redshiftDataSpec smart constructor.

Instances

Eq RedshiftDataSpec Source #
Methods (==) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool # (/=) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool #
Data RedshiftDataSpec Source #
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 Source #
Methods readsPrec :: Int -> ReadS RedshiftDataSpec # readList :: ReadS [RedshiftDataSpec] # readPrec :: ReadPrec RedshiftDataSpec # readListPrec :: ReadPrec [RedshiftDataSpec] #
Show RedshiftDataSpec Source #
Methods showsPrec :: Int -> RedshiftDataSpec -> ShowS # show :: RedshiftDataSpec -> String # showList :: [RedshiftDataSpec] -> ShowS #
Generic RedshiftDataSpec Source #
Associated Types type Rep RedshiftDataSpec :: * -> * # Methods from :: RedshiftDataSpec -> Rep RedshiftDataSpec x # to :: Rep RedshiftDataSpec x -> RedshiftDataSpec #
Hashable RedshiftDataSpec Source #
Methods hashWithSalt :: Int -> RedshiftDataSpec -> Int # hash :: RedshiftDataSpec -> Int #
ToJSON RedshiftDataSpec Source #
Methods toJSON :: RedshiftDataSpec -> Value # toEncoding :: RedshiftDataSpec -> Encoding # toJSONList :: [RedshiftDataSpec] -> Value # toEncodingList :: [RedshiftDataSpec] -> Encoding #
NFData RedshiftDataSpec Source #
Methods rnf :: RedshiftDataSpec -> () #
type Rep RedshiftDataSpec Source #
type Rep RedshiftDataSpec = D1 * (MetaData "RedshiftDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: RedshiftDatabase	`rDatabaseInformation`
-> Text	`rSelectSqlQuery`
-> RedshiftDatabaseCredentials	`rDatabaseCredentials`
-> Text	`rS3StagingLocation`
-> 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) Source #

Describes the schema location for an Amazon Redshift DataSource .

rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) Source #

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) Source #

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

rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase Source #

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource .

rSelectSqlQuery :: Lens' RedshiftDataSpec Text Source #

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

rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials Source #

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

rS3StagingLocation :: Lens' RedshiftDataSpec Text Source #

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

RedshiftDatabase

data RedshiftDatabase Source #

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

See: redshiftDatabase smart constructor.

Instances

Eq RedshiftDatabase Source #
Methods (==) :: RedshiftDatabase -> RedshiftDatabase -> Bool # (/=) :: RedshiftDatabase -> RedshiftDatabase -> Bool #
Data RedshiftDatabase Source #
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 Source #
Methods readsPrec :: Int -> ReadS RedshiftDatabase # readList :: ReadS [RedshiftDatabase] # readPrec :: ReadPrec RedshiftDatabase # readListPrec :: ReadPrec [RedshiftDatabase] #
Show RedshiftDatabase Source #
Methods showsPrec :: Int -> RedshiftDatabase -> ShowS # show :: RedshiftDatabase -> String # showList :: [RedshiftDatabase] -> ShowS #
Generic RedshiftDatabase Source #
Associated Types type Rep RedshiftDatabase :: * -> * # Methods from :: RedshiftDatabase -> Rep RedshiftDatabase x # to :: Rep RedshiftDatabase x -> RedshiftDatabase #
Hashable RedshiftDatabase Source #
Methods hashWithSalt :: Int -> RedshiftDatabase -> Int # hash :: RedshiftDatabase -> Int #
ToJSON RedshiftDatabase Source #
Methods toJSON :: RedshiftDatabase -> Value # toEncoding :: RedshiftDatabase -> Encoding # toJSONList :: [RedshiftDatabase] -> Value # toEncodingList :: [RedshiftDatabase] -> Encoding #
FromJSON RedshiftDatabase Source #
Methods parseJSON :: Value -> Parser RedshiftDatabase # parseJSONList :: Value -> Parser [RedshiftDatabase] #
NFData RedshiftDatabase Source #
Methods rnf :: RedshiftDatabase -> () #
type Rep RedshiftDatabase Source #
type Rep RedshiftDatabase = D1 * (MetaData "RedshiftDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: Text	`rdDatabaseName`
-> Text	`rdClusterIdentifier`
-> 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 - Undocumented member.
rdClusterIdentifier - Undocumented member.

rdDatabaseName :: Lens' RedshiftDatabase Text Source #

Undocumented member.

rdClusterIdentifier :: Lens' RedshiftDatabase Text Source #

Undocumented member.

RedshiftDatabaseCredentials

data RedshiftDatabaseCredentials Source #

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

See: redshiftDatabaseCredentials smart constructor.

Instances

Eq RedshiftDatabaseCredentials Source #
Methods (==) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool # (/=) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool #
Data RedshiftDatabaseCredentials Source #
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 Source #
Methods readsPrec :: Int -> ReadS RedshiftDatabaseCredentials # readList :: ReadS [RedshiftDatabaseCredentials] # readPrec :: ReadPrec RedshiftDatabaseCredentials # readListPrec :: ReadPrec [RedshiftDatabaseCredentials] #
Show RedshiftDatabaseCredentials Source #
Methods showsPrec :: Int -> RedshiftDatabaseCredentials -> ShowS # show :: RedshiftDatabaseCredentials -> String # showList :: [RedshiftDatabaseCredentials] -> ShowS #
Generic RedshiftDatabaseCredentials Source #
Associated Types type Rep RedshiftDatabaseCredentials :: * -> * # Methods from :: RedshiftDatabaseCredentials -> Rep RedshiftDatabaseCredentials x # to :: Rep RedshiftDatabaseCredentials x -> RedshiftDatabaseCredentials #
Hashable RedshiftDatabaseCredentials Source #
Methods hashWithSalt :: Int -> RedshiftDatabaseCredentials -> Int # hash :: RedshiftDatabaseCredentials -> Int #
ToJSON RedshiftDatabaseCredentials Source #
Methods toJSON :: RedshiftDatabaseCredentials -> Value # toEncoding :: RedshiftDatabaseCredentials -> Encoding # toJSONList :: [RedshiftDatabaseCredentials] -> Value # toEncodingList :: [RedshiftDatabaseCredentials] -> Encoding #
NFData RedshiftDatabaseCredentials Source #
Methods rnf :: RedshiftDatabaseCredentials -> () #
type Rep RedshiftDatabaseCredentials Source #
type Rep RedshiftDatabaseCredentials = D1 * (MetaData "RedshiftDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: Text	`rdcUsername`
-> Text	`rdcPassword`
-> 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:

rdcUsername - Undocumented member.
rdcPassword - Undocumented member.

rdcUsername :: Lens' RedshiftDatabaseCredentials Text Source #

Undocumented member.

rdcPassword :: Lens' RedshiftDatabaseCredentials Text Source #

Undocumented member.

RedshiftMetadata

data RedshiftMetadata Source #

Describes the DataSource details specific to Amazon Redshift.

See: redshiftMetadata smart constructor.

Instances

Eq RedshiftMetadata Source #
Methods (==) :: RedshiftMetadata -> RedshiftMetadata -> Bool # (/=) :: RedshiftMetadata -> RedshiftMetadata -> Bool #
Data RedshiftMetadata Source #
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 Source #
Methods readsPrec :: Int -> ReadS RedshiftMetadata # readList :: ReadS [RedshiftMetadata] # readPrec :: ReadPrec RedshiftMetadata # readListPrec :: ReadPrec [RedshiftMetadata] #
Show RedshiftMetadata Source #
Methods showsPrec :: Int -> RedshiftMetadata -> ShowS # show :: RedshiftMetadata -> String # showList :: [RedshiftMetadata] -> ShowS #
Generic RedshiftMetadata Source #
Associated Types type Rep RedshiftMetadata :: * -> * # Methods from :: RedshiftMetadata -> Rep RedshiftMetadata x # to :: Rep RedshiftMetadata x -> RedshiftMetadata #
Hashable RedshiftMetadata Source #
Methods hashWithSalt :: Int -> RedshiftMetadata -> Int # hash :: RedshiftMetadata -> Int #
FromJSON RedshiftMetadata Source #
Methods parseJSON :: Value -> Parser RedshiftMetadata # parseJSONList :: Value -> Parser [RedshiftMetadata] #
NFData RedshiftMetadata Source #
Methods rnf :: RedshiftMetadata -> () #
type Rep RedshiftMetadata Source #
type Rep RedshiftMetadata = D1 * (MetaData "RedshiftMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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 - The SQL query that is specified during CreateDataSourceFromRedshift . Returns only if Verbose is true in GetDataSourceInput.
redRedshiftDatabase - Undocumented member.
redDatabaseUserName - Undocumented member.

redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text) Source #

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

redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase) Source #

Undocumented member.

redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text) Source #

Undocumented member.

S3DataSpec

data S3DataSpec Source #

Describes the data specification of a DataSource .

See: s3DataSpec smart constructor.

Instances

Eq S3DataSpec Source #
Methods (==) :: S3DataSpec -> S3DataSpec -> Bool # (/=) :: S3DataSpec -> S3DataSpec -> Bool #
Data S3DataSpec Source #
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 Source #
Methods readsPrec :: Int -> ReadS S3DataSpec # readList :: ReadS [S3DataSpec] # readPrec :: ReadPrec S3DataSpec # readListPrec :: ReadPrec [S3DataSpec] #
Show S3DataSpec Source #
Methods showsPrec :: Int -> S3DataSpec -> ShowS # show :: S3DataSpec -> String # showList :: [S3DataSpec] -> ShowS #
Generic S3DataSpec Source #
Associated Types type Rep S3DataSpec :: * -> * # Methods from :: S3DataSpec -> Rep S3DataSpec x # to :: Rep S3DataSpec x -> S3DataSpec #
Hashable S3DataSpec Source #
Methods hashWithSalt :: Int -> S3DataSpec -> Int # hash :: S3DataSpec -> Int #
ToJSON S3DataSpec Source #
Methods toJSON :: S3DataSpec -> Value # toEncoding :: S3DataSpec -> Encoding # toJSONList :: [S3DataSpec] -> Value # toEncodingList :: [S3DataSpec] -> Encoding #
NFData S3DataSpec Source #
Methods rnf :: S3DataSpec -> () #
type Rep S3DataSpec Source #
type Rep S3DataSpec = D1 * (MetaData "S3DataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

Arguments

:: Text	`sdsDataLocationS3`
-> 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) Source #

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) Source #

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

sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text) Source #

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

sdsDataLocationS3 :: Lens' S3DataSpec Text Source #

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 Source #

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

See: tag smart constructor.

Instances

Eq Tag Source #
Methods (==) :: Tag -> Tag -> Bool # (/=) :: Tag -> Tag -> Bool #
Data Tag Source #
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 Source #
Methods readsPrec :: Int -> ReadS Tag # readList :: ReadS [Tag] # readPrec :: ReadPrec Tag # readListPrec :: ReadPrec [Tag] #
Show Tag Source #
Methods showsPrec :: Int -> Tag -> ShowS # show :: Tag -> String # showList :: [Tag] -> ShowS #
Generic Tag Source #
Associated Types type Rep Tag :: * -> * # Methods from :: Tag -> Rep Tag x # to :: Rep Tag x -> Tag #
Hashable Tag Source #
Methods hashWithSalt :: Int -> Tag -> Int # hash :: Tag -> Int #
ToJSON Tag Source #
Methods toJSON :: Tag -> Value # toEncoding :: Tag -> Encoding # toJSONList :: [Tag] -> Value # toEncodingList :: [Tag] -> Encoding #
FromJSON Tag Source #
Methods parseJSON :: Value -> Parser Tag # parseJSONList :: Value -> Parser [Tag] #
NFData Tag Source #
Methods rnf :: Tag -> () #
type Rep Tag Source #
type Rep Tag = D1 * (MetaData "Tag" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.6.0-Ieesuz5Kri8FW4cNPxVPkB" 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 Source #

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) Source #

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) Source #

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