Network.AWS.MachineLearning.Types

Service Configuration

machineLearning :: Service

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

Errors

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

Prism for InvalidTagException' errors.

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

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

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

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

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

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

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

Prism for TagLimitExceededException' errors.

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

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

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

A specified resource cannot be located.

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

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

Algorithm

data Algorithm

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

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

Constructors

SGD

Instances

Bounded Algorithm

Methods

minBound :: Algorithm

maxBound :: Algorithm

Enum Algorithm

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

Methods

(==) :: Algorithm -> Algorithm -> Bool

(/=) :: Algorithm -> Algorithm -> Bool

Data Algorithm

Methods

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

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

toConstr :: Algorithm -> Constr

dataTypeOf :: Algorithm -> DataType

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

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

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

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

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

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

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

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

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

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

Ord Algorithm

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

Methods

readsPrec :: Int -> ReadS Algorithm

readList :: ReadS [Algorithm]

readPrec :: ReadPrec Algorithm

readListPrec :: ReadPrec [Algorithm]

Show Algorithm

Methods

showsPrec :: Int -> Algorithm -> ShowS

show :: Algorithm -> String

showList :: [Algorithm] -> ShowS

Generic Algorithm

Associated Types

type Rep Algorithm :: * -> *

Methods

from :: Algorithm -> Rep Algorithm x

to :: Rep Algorithm x -> Algorithm

Hashable Algorithm

Methods

hashWithSalt :: Int -> Algorithm -> Int

hash :: Algorithm -> Int

FromJSON Algorithm

Methods

parseJSON :: Value -> Parser Algorithm

parseJSONList :: Value -> Parser [Algorithm]

NFData Algorithm

Methods

rnf :: Algorithm -> ()

ToQuery Algorithm

Methods

toQuery :: Algorithm -> QueryString

ToHeader Algorithm

Methods

toHeader :: HeaderName -> Algorithm -> [Header]

ToByteString Algorithm

Methods

toBS :: Algorithm -> ByteString

FromText Algorithm

Methods

parser :: Parser Algorithm

ToText Algorithm

Methods

toText :: Algorithm -> Text

type Rep Algorithm

type Rep Algorithm = D1 (MetaData "Algorithm" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "SGD" PrefixI False) U1)

BatchPredictionFilterVariable

data BatchPredictionFilterVariable

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

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

Constructors

BatchCreatedAt

BatchDataSourceId

BatchDataURI

BatchIAMUser

BatchLastUpdatedAt

BatchMLModelId

BatchName

BatchStatus

Instances

Bounded BatchPredictionFilterVariable

Methods

minBound :: BatchPredictionFilterVariable

maxBound :: BatchPredictionFilterVariable

Enum BatchPredictionFilterVariable

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

Methods

(==) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool

(/=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool

Data BatchPredictionFilterVariable

Methods

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

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

toConstr :: BatchPredictionFilterVariable -> Constr

dataTypeOf :: BatchPredictionFilterVariable -> DataType

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

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

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

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

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

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

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

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

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

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

Ord BatchPredictionFilterVariable

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

Methods

readsPrec :: Int -> ReadS BatchPredictionFilterVariable

readList :: ReadS [BatchPredictionFilterVariable]

readPrec :: ReadPrec BatchPredictionFilterVariable

readListPrec :: ReadPrec [BatchPredictionFilterVariable]

Show BatchPredictionFilterVariable

Methods

showsPrec :: Int -> BatchPredictionFilterVariable -> ShowS

show :: BatchPredictionFilterVariable -> String

showList :: [BatchPredictionFilterVariable] -> ShowS

Generic BatchPredictionFilterVariable

Associated Types

type Rep BatchPredictionFilterVariable :: * -> *

Methods

from :: BatchPredictionFilterVariable -> Rep BatchPredictionFilterVariable x

to :: Rep BatchPredictionFilterVariable x -> BatchPredictionFilterVariable

Hashable BatchPredictionFilterVariable

Methods

hashWithSalt :: Int -> BatchPredictionFilterVariable -> Int

hash :: BatchPredictionFilterVariable -> Int

ToJSON BatchPredictionFilterVariable

Methods

toJSON :: BatchPredictionFilterVariable -> Value

toEncoding :: BatchPredictionFilterVariable -> Encoding

toJSONList :: [BatchPredictionFilterVariable] -> Value

toEncodingList :: [BatchPredictionFilterVariable] -> Encoding

NFData BatchPredictionFilterVariable

Methods

rnf :: BatchPredictionFilterVariable -> ()

ToQuery BatchPredictionFilterVariable

Methods

toQuery :: BatchPredictionFilterVariable -> QueryString

ToHeader BatchPredictionFilterVariable

Methods

toHeader :: HeaderName -> BatchPredictionFilterVariable -> [Header]

ToByteString BatchPredictionFilterVariable

Methods

toBS :: BatchPredictionFilterVariable -> ByteString

FromText BatchPredictionFilterVariable

Methods

parser :: Parser BatchPredictionFilterVariable

ToText BatchPredictionFilterVariable

Methods

toText :: BatchPredictionFilterVariable -> Text

type Rep BatchPredictionFilterVariable

type Rep BatchPredictionFilterVariable = D1 (MetaData "BatchPredictionFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "BatchCreatedAt" PrefixI False) U1) (C1 (MetaCons "BatchDataSourceId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchDataURI" PrefixI False) U1) (C1 (MetaCons "BatchIAMUser" PrefixI False) U1))) ((:+:) ((:+:) (C1 (MetaCons "BatchLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "BatchMLModelId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchName" PrefixI False) U1) (C1 (MetaCons "BatchStatus" PrefixI False) U1))))

DataSourceFilterVariable

data DataSourceFilterVariable

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

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

Constructors

DataCreatedAt

DataDATALOCATIONS3

DataIAMUser

DataLastUpdatedAt

DataName

DataStatus

Instances

Bounded DataSourceFilterVariable

Methods

minBound :: DataSourceFilterVariable

maxBound :: DataSourceFilterVariable

Enum DataSourceFilterVariable

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

Methods

(==) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool

(/=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool

Data DataSourceFilterVariable

Methods

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

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

toConstr :: DataSourceFilterVariable -> Constr

dataTypeOf :: DataSourceFilterVariable -> DataType

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

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

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

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

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

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

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

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

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

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

Ord DataSourceFilterVariable

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

Methods

readsPrec :: Int -> ReadS DataSourceFilterVariable

readList :: ReadS [DataSourceFilterVariable]

readPrec :: ReadPrec DataSourceFilterVariable

readListPrec :: ReadPrec [DataSourceFilterVariable]

Show DataSourceFilterVariable

Methods

showsPrec :: Int -> DataSourceFilterVariable -> ShowS

show :: DataSourceFilterVariable -> String

showList :: [DataSourceFilterVariable] -> ShowS

Generic DataSourceFilterVariable

Associated Types

type Rep DataSourceFilterVariable :: * -> *

Methods

from :: DataSourceFilterVariable -> Rep DataSourceFilterVariable x

to :: Rep DataSourceFilterVariable x -> DataSourceFilterVariable

Hashable DataSourceFilterVariable

Methods

hashWithSalt :: Int -> DataSourceFilterVariable -> Int

hash :: DataSourceFilterVariable -> Int

ToJSON DataSourceFilterVariable

Methods

toJSON :: DataSourceFilterVariable -> Value

toEncoding :: DataSourceFilterVariable -> Encoding

toJSONList :: [DataSourceFilterVariable] -> Value

toEncodingList :: [DataSourceFilterVariable] -> Encoding

NFData DataSourceFilterVariable

Methods

rnf :: DataSourceFilterVariable -> ()

ToQuery DataSourceFilterVariable

Methods

toQuery :: DataSourceFilterVariable -> QueryString

ToHeader DataSourceFilterVariable

Methods

toHeader :: HeaderName -> DataSourceFilterVariable -> [Header]

ToByteString DataSourceFilterVariable

Methods

toBS :: DataSourceFilterVariable -> ByteString

FromText DataSourceFilterVariable

Methods

parser :: Parser DataSourceFilterVariable

ToText DataSourceFilterVariable

Methods

toText :: DataSourceFilterVariable -> Text

type Rep DataSourceFilterVariable

type Rep DataSourceFilterVariable = D1 (MetaData "DataSourceFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) (C1 (MetaCons "DataCreatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataDATALOCATIONS3" PrefixI False) U1) (C1 (MetaCons "DataIAMUser" PrefixI False) U1))) ((:+:) (C1 (MetaCons "DataLastUpdatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataName" PrefixI False) U1) (C1 (MetaCons "DataStatus" PrefixI False) U1))))

DetailsAttributes

data DetailsAttributes

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

Constructors

Algorithm

PredictiveModelType

Instances

Bounded DetailsAttributes

Methods

minBound :: DetailsAttributes

maxBound :: DetailsAttributes

Enum DetailsAttributes

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

Methods

(==) :: DetailsAttributes -> DetailsAttributes -> Bool

(/=) :: DetailsAttributes -> DetailsAttributes -> Bool

Data DetailsAttributes

Methods

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

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

toConstr :: DetailsAttributes -> Constr

dataTypeOf :: DetailsAttributes -> DataType

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

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

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

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

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

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

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

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

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

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

Ord DetailsAttributes

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

Methods

readsPrec :: Int -> ReadS DetailsAttributes

readList :: ReadS [DetailsAttributes]

readPrec :: ReadPrec DetailsAttributes

readListPrec :: ReadPrec [DetailsAttributes]

Show DetailsAttributes

Methods

showsPrec :: Int -> DetailsAttributes -> ShowS

show :: DetailsAttributes -> String

showList :: [DetailsAttributes] -> ShowS

Generic DetailsAttributes

Associated Types

type Rep DetailsAttributes :: * -> *

Methods

from :: DetailsAttributes -> Rep DetailsAttributes x

to :: Rep DetailsAttributes x -> DetailsAttributes

Hashable DetailsAttributes

Methods

hashWithSalt :: Int -> DetailsAttributes -> Int

hash :: DetailsAttributes -> Int

FromJSON DetailsAttributes

Methods

parseJSON :: Value -> Parser DetailsAttributes

parseJSONList :: Value -> Parser [DetailsAttributes]

NFData DetailsAttributes

Methods

rnf :: DetailsAttributes -> ()

ToQuery DetailsAttributes

Methods

toQuery :: DetailsAttributes -> QueryString

ToHeader DetailsAttributes

Methods

toHeader :: HeaderName -> DetailsAttributes -> [Header]

ToByteString DetailsAttributes

Methods

toBS :: DetailsAttributes -> ByteString

FromText DetailsAttributes

Methods

parser :: Parser DetailsAttributes

ToText DetailsAttributes

Methods

toText :: DetailsAttributes -> Text

type Rep DetailsAttributes

type Rep DetailsAttributes = D1 (MetaData "DetailsAttributes" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) (C1 (MetaCons "Algorithm" PrefixI False) U1) (C1 (MetaCons "PredictiveModelType" PrefixI False) U1))

EntityStatus

data EntityStatus

Object status with the following possible values:

• PENDING * INPROGRESS * FAILED * COMPLETED * DELETED

Constructors

ESCompleted

ESDeleted

ESFailed

ESInprogress

ESPending

Instances

Bounded EntityStatus

Methods

minBound :: EntityStatus

maxBound :: EntityStatus

Enum EntityStatus

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

Methods

(==) :: EntityStatus -> EntityStatus -> Bool

(/=) :: EntityStatus -> EntityStatus -> Bool

Data EntityStatus

Methods

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

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

toConstr :: EntityStatus -> Constr

dataTypeOf :: EntityStatus -> DataType

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

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

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

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

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

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

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

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

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

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

Ord EntityStatus

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

Methods

readsPrec :: Int -> ReadS EntityStatus

readList :: ReadS [EntityStatus]

readPrec :: ReadPrec EntityStatus

readListPrec :: ReadPrec [EntityStatus]

Show EntityStatus

Methods

showsPrec :: Int -> EntityStatus -> ShowS

show :: EntityStatus -> String

showList :: [EntityStatus] -> ShowS

Generic EntityStatus

Associated Types

type Rep EntityStatus :: * -> *

Methods

from :: EntityStatus -> Rep EntityStatus x

to :: Rep EntityStatus x -> EntityStatus

Hashable EntityStatus

Methods

hashWithSalt :: Int -> EntityStatus -> Int

hash :: EntityStatus -> Int

FromJSON EntityStatus

Methods

parseJSON :: Value -> Parser EntityStatus

parseJSONList :: Value -> Parser [EntityStatus]

NFData EntityStatus

Methods

rnf :: EntityStatus -> ()

ToQuery EntityStatus

Methods

toQuery :: EntityStatus -> QueryString

ToHeader EntityStatus

Methods

toHeader :: HeaderName -> EntityStatus -> [Header]

ToByteString EntityStatus

Methods

toBS :: EntityStatus -> ByteString

FromText EntityStatus

Methods

parser :: Parser EntityStatus

ToText EntityStatus

Methods

toText :: EntityStatus -> Text

type Rep EntityStatus

type Rep EntityStatus = D1 (MetaData "EntityStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) (C1 (MetaCons "ESCompleted" PrefixI False) U1) (C1 (MetaCons "ESDeleted" PrefixI False) U1)) ((:+:) (C1 (MetaCons "ESFailed" PrefixI False) U1) ((:+:) (C1 (MetaCons "ESInprogress" PrefixI False) U1) (C1 (MetaCons "ESPending" PrefixI False) U1))))

EvaluationFilterVariable

data EvaluationFilterVariable

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

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

Constructors

EvalCreatedAt

EvalDataSourceId

EvalDataURI

EvalIAMUser

EvalLastUpdatedAt

EvalMLModelId

EvalName

EvalStatus

Instances

Bounded EvaluationFilterVariable

Methods

minBound :: EvaluationFilterVariable

maxBound :: EvaluationFilterVariable

Enum EvaluationFilterVariable

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

Methods

(==) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool

(/=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool

Data EvaluationFilterVariable

Methods

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

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

toConstr :: EvaluationFilterVariable -> Constr

dataTypeOf :: EvaluationFilterVariable -> DataType

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

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

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

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

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

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

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

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

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

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

Ord EvaluationFilterVariable

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

Methods

readsPrec :: Int -> ReadS EvaluationFilterVariable

readList :: ReadS [EvaluationFilterVariable]

readPrec :: ReadPrec EvaluationFilterVariable

readListPrec :: ReadPrec [EvaluationFilterVariable]

Show EvaluationFilterVariable

Methods

showsPrec :: Int -> EvaluationFilterVariable -> ShowS

show :: EvaluationFilterVariable -> String

showList :: [EvaluationFilterVariable] -> ShowS

Generic EvaluationFilterVariable

Associated Types

type Rep EvaluationFilterVariable :: * -> *

Methods

from :: EvaluationFilterVariable -> Rep EvaluationFilterVariable x

to :: Rep EvaluationFilterVariable x -> EvaluationFilterVariable

Hashable EvaluationFilterVariable

Methods

hashWithSalt :: Int -> EvaluationFilterVariable -> Int

hash :: EvaluationFilterVariable -> Int

ToJSON EvaluationFilterVariable

Methods

toJSON :: EvaluationFilterVariable -> Value

toEncoding :: EvaluationFilterVariable -> Encoding

toJSONList :: [EvaluationFilterVariable] -> Value

toEncodingList :: [EvaluationFilterVariable] -> Encoding

NFData EvaluationFilterVariable

Methods

rnf :: EvaluationFilterVariable -> ()

ToQuery EvaluationFilterVariable

Methods

toQuery :: EvaluationFilterVariable -> QueryString

ToHeader EvaluationFilterVariable

Methods

toHeader :: HeaderName -> EvaluationFilterVariable -> [Header]

ToByteString EvaluationFilterVariable

Methods

toBS :: EvaluationFilterVariable -> ByteString

FromText EvaluationFilterVariable

Methods

parser :: Parser EvaluationFilterVariable

ToText EvaluationFilterVariable

Methods

toText :: EvaluationFilterVariable -> Text

type Rep EvaluationFilterVariable

type Rep EvaluationFilterVariable = D1 (MetaData "EvaluationFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

Constructors

MLMFVAlgorithm

MLMFVCreatedAt

MLMFVIAMUser

MLMFVLastUpdatedAt

MLMFVMLModelType

MLMFVName

MLMFVRealtimeEndpointStatus

MLMFVStatus

MLMFVTrainingDataSourceId

MLMFVTrainingDataURI

Instances

Bounded MLModelFilterVariable

Methods

minBound :: MLModelFilterVariable

maxBound :: MLModelFilterVariable

Enum MLModelFilterVariable

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

Methods

(==) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool

(/=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool

Data MLModelFilterVariable

Methods

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

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

toConstr :: MLModelFilterVariable -> Constr

dataTypeOf :: MLModelFilterVariable -> DataType

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

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

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

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

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

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

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

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

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

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

Ord MLModelFilterVariable

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

Methods

readsPrec :: Int -> ReadS MLModelFilterVariable

readList :: ReadS [MLModelFilterVariable]

readPrec :: ReadPrec MLModelFilterVariable

readListPrec :: ReadPrec [MLModelFilterVariable]

Show MLModelFilterVariable

Methods

showsPrec :: Int -> MLModelFilterVariable -> ShowS

show :: MLModelFilterVariable -> String

showList :: [MLModelFilterVariable] -> ShowS

Generic MLModelFilterVariable

Associated Types

type Rep MLModelFilterVariable :: * -> *

Methods

from :: MLModelFilterVariable -> Rep MLModelFilterVariable x

to :: Rep MLModelFilterVariable x -> MLModelFilterVariable

Hashable MLModelFilterVariable

Methods

hashWithSalt :: Int -> MLModelFilterVariable -> Int

hash :: MLModelFilterVariable -> Int

ToJSON MLModelFilterVariable

Methods

toJSON :: MLModelFilterVariable -> Value

toEncoding :: MLModelFilterVariable -> Encoding

toJSONList :: [MLModelFilterVariable] -> Value

toEncodingList :: [MLModelFilterVariable] -> Encoding

NFData MLModelFilterVariable

Methods

rnf :: MLModelFilterVariable -> ()

ToQuery MLModelFilterVariable

Methods

toQuery :: MLModelFilterVariable -> QueryString

ToHeader MLModelFilterVariable

Methods

toHeader :: HeaderName -> MLModelFilterVariable -> [Header]

ToByteString MLModelFilterVariable

Methods

toBS :: MLModelFilterVariable -> ByteString

FromText MLModelFilterVariable

Methods

parser :: Parser MLModelFilterVariable

ToText MLModelFilterVariable

Methods

toText :: MLModelFilterVariable -> Text

type Rep MLModelFilterVariable

type Rep MLModelFilterVariable = D1 (MetaData "MLModelFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "MLMFVAlgorithm" PrefixI False) U1) (C1 (MetaCons "MLMFVCreatedAt" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVIAMUser" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "MLMFVMLModelType" PrefixI False) U1)))) ((:+:) ((:+:) (C1 (MetaCons "MLMFVName" PrefixI False) U1) (C1 (MetaCons "MLMFVRealtimeEndpointStatus" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVStatus" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVTrainingDataSourceId" PrefixI False) U1) (C1 (MetaCons "MLMFVTrainingDataURI" PrefixI False) U1)))))

MLModelType

data MLModelType

Constructors

Binary

Multiclass

Regression

Instances

Bounded MLModelType

Methods

minBound :: MLModelType

maxBound :: MLModelType

Enum MLModelType

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

Methods

(==) :: MLModelType -> MLModelType -> Bool

(/=) :: MLModelType -> MLModelType -> Bool

Data MLModelType

Methods

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

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

toConstr :: MLModelType -> Constr

dataTypeOf :: MLModelType -> DataType

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

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

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

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

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

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

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

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

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

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

Ord MLModelType

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

Methods

readsPrec :: Int -> ReadS MLModelType

readList :: ReadS [MLModelType]

readPrec :: ReadPrec MLModelType

readListPrec :: ReadPrec [MLModelType]

Show MLModelType

Methods

showsPrec :: Int -> MLModelType -> ShowS

show :: MLModelType -> String

showList :: [MLModelType] -> ShowS

Generic MLModelType

Associated Types

type Rep MLModelType :: * -> *

Methods

from :: MLModelType -> Rep MLModelType x

to :: Rep MLModelType x -> MLModelType

Hashable MLModelType

Methods

hashWithSalt :: Int -> MLModelType -> Int

hash :: MLModelType -> Int

FromJSON MLModelType

Methods

parseJSON :: Value -> Parser MLModelType

parseJSONList :: Value -> Parser [MLModelType]

ToJSON MLModelType

Methods

toJSON :: MLModelType -> Value

toEncoding :: MLModelType -> Encoding

toJSONList :: [MLModelType] -> Value

toEncodingList :: [MLModelType] -> Encoding

NFData MLModelType

Methods

rnf :: MLModelType -> ()

ToQuery MLModelType

Methods

toQuery :: MLModelType -> QueryString

ToHeader MLModelType

Methods

toHeader :: HeaderName -> MLModelType -> [Header]

ToByteString MLModelType

Methods

toBS :: MLModelType -> ByteString

FromText MLModelType

Methods

parser :: Parser MLModelType

ToText MLModelType

Methods

toText :: MLModelType -> Text

type Rep MLModelType

type Rep MLModelType = D1 (MetaData "MLModelType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) (C1 (MetaCons "Binary" PrefixI False) U1) ((:+:) (C1 (MetaCons "Multiclass" PrefixI False) U1) (C1 (MetaCons "Regression" PrefixI False) U1)))

RealtimeEndpointStatus

data RealtimeEndpointStatus

Constructors

Failed

None

Ready

Updating

Instances

Bounded RealtimeEndpointStatus

Methods

minBound :: RealtimeEndpointStatus

maxBound :: RealtimeEndpointStatus

Enum RealtimeEndpointStatus

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

Methods

(==) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool

(/=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool

Data RealtimeEndpointStatus

Methods

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

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

toConstr :: RealtimeEndpointStatus -> Constr

dataTypeOf :: RealtimeEndpointStatus -> DataType

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

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

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

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

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

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

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

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

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

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

Ord RealtimeEndpointStatus

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

Methods

readsPrec :: Int -> ReadS RealtimeEndpointStatus

readList :: ReadS [RealtimeEndpointStatus]

readPrec :: ReadPrec RealtimeEndpointStatus

readListPrec :: ReadPrec [RealtimeEndpointStatus]

Show RealtimeEndpointStatus

Methods

showsPrec :: Int -> RealtimeEndpointStatus -> ShowS

show :: RealtimeEndpointStatus -> String

showList :: [RealtimeEndpointStatus] -> ShowS

Generic RealtimeEndpointStatus

Associated Types

type Rep RealtimeEndpointStatus :: * -> *

Methods

from :: RealtimeEndpointStatus -> Rep RealtimeEndpointStatus x

to :: Rep RealtimeEndpointStatus x -> RealtimeEndpointStatus

Hashable RealtimeEndpointStatus

Methods

hashWithSalt :: Int -> RealtimeEndpointStatus -> Int

hash :: RealtimeEndpointStatus -> Int

FromJSON RealtimeEndpointStatus

Methods

parseJSON :: Value -> Parser RealtimeEndpointStatus

parseJSONList :: Value -> Parser [RealtimeEndpointStatus]

NFData RealtimeEndpointStatus

Methods

rnf :: RealtimeEndpointStatus -> ()

ToQuery RealtimeEndpointStatus

Methods

toQuery :: RealtimeEndpointStatus -> QueryString

ToHeader RealtimeEndpointStatus

Methods

toHeader :: HeaderName -> RealtimeEndpointStatus -> [Header]

ToByteString RealtimeEndpointStatus

Methods

toBS :: RealtimeEndpointStatus -> ByteString

FromText RealtimeEndpointStatus

Methods

parser :: Parser RealtimeEndpointStatus

ToText RealtimeEndpointStatus

Methods

toText :: RealtimeEndpointStatus -> Text

type Rep RealtimeEndpointStatus

type Rep RealtimeEndpointStatus = D1 (MetaData "RealtimeEndpointStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) (C1 (MetaCons "Failed" PrefixI False) U1) (C1 (MetaCons "None" PrefixI False) U1)) ((:+:) (C1 (MetaCons "Ready" PrefixI False) U1) (C1 (MetaCons "Updating" PrefixI False) U1)))

SortOrder

data SortOrder

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

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

Constructors

Asc

Dsc

Instances

Bounded SortOrder

Methods

minBound :: SortOrder

maxBound :: SortOrder

Enum SortOrder

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

Methods

(==) :: SortOrder -> SortOrder -> Bool

(/=) :: SortOrder -> SortOrder -> Bool

Data SortOrder

Methods

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

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

toConstr :: SortOrder -> Constr

dataTypeOf :: SortOrder -> DataType

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

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

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

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

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

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

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

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

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

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

Ord SortOrder

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

Methods

readsPrec :: Int -> ReadS SortOrder

readList :: ReadS [SortOrder]

readPrec :: ReadPrec SortOrder

readListPrec :: ReadPrec [SortOrder]

Show SortOrder

Methods

showsPrec :: Int -> SortOrder -> ShowS

show :: SortOrder -> String

showList :: [SortOrder] -> ShowS

Generic SortOrder

Associated Types

type Rep SortOrder :: * -> *

Methods

from :: SortOrder -> Rep SortOrder x

to :: Rep SortOrder x -> SortOrder

Hashable SortOrder

Methods

hashWithSalt :: Int -> SortOrder -> Int

hash :: SortOrder -> Int

ToJSON SortOrder

Methods

toJSON :: SortOrder -> Value

toEncoding :: SortOrder -> Encoding

toJSONList :: [SortOrder] -> Value

toEncodingList :: [SortOrder] -> Encoding

NFData SortOrder

Methods

rnf :: SortOrder -> ()

ToQuery SortOrder

Methods

toQuery :: SortOrder -> QueryString

ToHeader SortOrder

Methods

toHeader :: HeaderName -> SortOrder -> [Header]

ToByteString SortOrder

Methods

toBS :: SortOrder -> ByteString

FromText SortOrder

Methods

parser :: Parser SortOrder

ToText SortOrder

Methods

toText :: SortOrder -> Text

type Rep SortOrder

type Rep SortOrder = D1 (MetaData "SortOrder" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) (C1 (MetaCons "Asc" PrefixI False) U1) (C1 (MetaCons "Dsc" PrefixI False) U1))

TaggableResourceType

data TaggableResourceType

Constructors

BatchPrediction

DataSource

Evaluation

MLModel

Instances

Bounded TaggableResourceType

Methods

minBound :: TaggableResourceType

maxBound :: TaggableResourceType

Enum TaggableResourceType

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

Methods

(==) :: TaggableResourceType -> TaggableResourceType -> Bool

(/=) :: TaggableResourceType -> TaggableResourceType -> Bool

Data TaggableResourceType

Methods

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

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

toConstr :: TaggableResourceType -> Constr

dataTypeOf :: TaggableResourceType -> DataType

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

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

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

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

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

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

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

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

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

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

Ord TaggableResourceType

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

Methods

readsPrec :: Int -> ReadS TaggableResourceType

readList :: ReadS [TaggableResourceType]

readPrec :: ReadPrec TaggableResourceType

readListPrec :: ReadPrec [TaggableResourceType]

Show TaggableResourceType

Methods

showsPrec :: Int -> TaggableResourceType -> ShowS

show :: TaggableResourceType -> String

showList :: [TaggableResourceType] -> ShowS

Generic TaggableResourceType

Associated Types

type Rep TaggableResourceType :: * -> *

Methods

from :: TaggableResourceType -> Rep TaggableResourceType x

to :: Rep TaggableResourceType x -> TaggableResourceType

Hashable TaggableResourceType

Methods

hashWithSalt :: Int -> TaggableResourceType -> Int

hash :: TaggableResourceType -> Int

FromJSON TaggableResourceType

Methods

parseJSON :: Value -> Parser TaggableResourceType

parseJSONList :: Value -> Parser [TaggableResourceType]

ToJSON TaggableResourceType

Methods

toJSON :: TaggableResourceType -> Value

toEncoding :: TaggableResourceType -> Encoding

toJSONList :: [TaggableResourceType] -> Value

toEncodingList :: [TaggableResourceType] -> Encoding

NFData TaggableResourceType

Methods

rnf :: TaggableResourceType -> ()

ToQuery TaggableResourceType

Methods

toQuery :: TaggableResourceType -> QueryString

ToHeader TaggableResourceType

Methods

toHeader :: HeaderName -> TaggableResourceType -> [Header]

ToByteString TaggableResourceType

Methods

toBS :: TaggableResourceType -> ByteString

FromText TaggableResourceType

Methods

parser :: Parser TaggableResourceType

ToText TaggableResourceType

Methods

toText :: TaggableResourceType -> Text

type Rep TaggableResourceType

type Rep TaggableResourceType = D1 (MetaData "TaggableResourceType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) ((:+:) ((:+:) (C1 (MetaCons "BatchPrediction" PrefixI False) U1) (C1 (MetaCons "DataSource" PrefixI False) U1)) ((:+:) (C1 (MetaCons "Evaluation" PrefixI False) U1) (C1 (MetaCons "MLModel" PrefixI False) U1)))

BatchPrediction

data BatchPrediction

Represents the output of a GetBatchPrediction operation.

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

See: batchPrediction smart constructor.

Instances

Eq BatchPrediction

Methods

(==) :: BatchPrediction -> BatchPrediction -> Bool

(/=) :: BatchPrediction -> BatchPrediction -> Bool

Data BatchPrediction

Methods

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

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

toConstr :: BatchPrediction -> Constr

dataTypeOf :: BatchPrediction -> DataType

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

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

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

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

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

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

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

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

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

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

Read BatchPrediction

Methods

readsPrec :: Int -> ReadS BatchPrediction

readList :: ReadS [BatchPrediction]

readPrec :: ReadPrec BatchPrediction

readListPrec :: ReadPrec [BatchPrediction]

Show BatchPrediction

Methods

showsPrec :: Int -> BatchPrediction -> ShowS

show :: BatchPrediction -> String

showList :: [BatchPrediction] -> ShowS

Generic BatchPrediction

Associated Types

type Rep BatchPrediction :: * -> *

Methods

from :: BatchPrediction -> Rep BatchPrediction x

to :: Rep BatchPrediction x -> BatchPrediction

Hashable BatchPrediction

Methods

hashWithSalt :: Int -> BatchPrediction -> Int

hash :: BatchPrediction -> Int

FromJSON BatchPrediction

Methods

parseJSON :: Value -> Parser BatchPrediction

parseJSONList :: Value -> Parser [BatchPrediction]

NFData BatchPrediction

Methods

rnf :: BatchPrediction -> ()

type Rep BatchPrediction

type Rep BatchPrediction = D1 (MetaData "BatchPrediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "BatchPrediction'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_bpLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpBatchPredictionDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpTotalRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpBatchPredictionId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_bpInvalidRecordCount") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpOutputURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

batchPrediction :: BatchPrediction

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

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

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

bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)

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

bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)

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

bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)

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

bpComputeTime :: Lens' BatchPrediction (Maybe Integer)

Undocumented member.

bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)

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

bpMLModelId :: Lens' BatchPrediction (Maybe Text)

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

bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)

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

bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)

Undocumented member.

bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)

Undocumented member.

bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)

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

bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)

Undocumented member.

bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)

Undocumented member.

bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)

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

bpName :: Lens' BatchPrediction (Maybe Text)

A user-supplied name or description of the BatchPrediction .

bpMessage :: Lens' BatchPrediction (Maybe Text)

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

bpOutputURI :: Lens' BatchPrediction (Maybe Text)

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

DataSource

data DataSource

Represents the output of the GetDataSource operation.

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

See: dataSource smart constructor.

Instances

Eq DataSource

Methods

(==) :: DataSource -> DataSource -> Bool

(/=) :: DataSource -> DataSource -> Bool

Data DataSource

Methods

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

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

toConstr :: DataSource -> Constr

dataTypeOf :: DataSource -> DataType

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

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

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

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

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

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

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

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

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

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

Read DataSource

Methods

readsPrec :: Int -> ReadS DataSource

readList :: ReadS [DataSource]

readPrec :: ReadPrec DataSource

readListPrec :: ReadPrec [DataSource]

Show DataSource

Methods

showsPrec :: Int -> DataSource -> ShowS

show :: DataSource -> String

showList :: [DataSource] -> ShowS

Generic DataSource

Associated Types

type Rep DataSource :: * -> *

Methods

from :: DataSource -> Rep DataSource x

to :: Rep DataSource x -> DataSource

Hashable DataSource

Methods

hashWithSalt :: Int -> DataSource -> Int

hash :: DataSource -> Int

FromJSON DataSource

Methods

parseJSON :: Value -> Parser DataSource

parseJSONList :: Value -> Parser [DataSource]

NFData DataSource

Methods

rnf :: DataSource -> ()

type Rep DataSource

type Rep DataSource = D1 (MetaData "DataSource" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "DataSource'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_dsNumberOfFiles") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_dsCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) (S1 (MetaSel (Just Symbol "_dsDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsRDSMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSMetadata))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) (S1 (MetaSel (Just Symbol "_dsStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_dsCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsComputeStatistics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Bool))) (S1 (MetaSel (Just Symbol "_dsMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsRedshiftMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftMetadata))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsRoleARN") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))))

dataSource :: DataSource

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

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

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

dsStatus :: Lens' DataSource (Maybe EntityStatus)

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

dsNumberOfFiles :: Lens' DataSource (Maybe Integer)

The number of data files referenced by the DataSource .

dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)

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

dsCreatedAt :: Lens' DataSource (Maybe UTCTime)

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

dsComputeTime :: Lens' DataSource (Maybe Integer)

Undocumented member.

dsDataSourceId :: Lens' DataSource (Maybe Text)

The ID that is assigned to the DataSource during creation.

dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)

Undocumented member.

dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)

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

dsStartedAt :: Lens' DataSource (Maybe UTCTime)

Undocumented member.

dsFinishedAt :: Lens' DataSource (Maybe UTCTime)

Undocumented member.

dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)

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

dsName :: Lens' DataSource (Maybe Text)

A user-supplied name or description of the DataSource .

dsDataLocationS3 :: Lens' DataSource (Maybe Text)

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

dsComputeStatistics :: Lens' DataSource (Maybe Bool)

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

dsMessage :: Lens' DataSource (Maybe Text)

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

dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)

Undocumented member.

dsDataRearrangement :: Lens' DataSource (Maybe Text)

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

dsRoleARN :: Lens' DataSource (Maybe Text)

Undocumented member.

Evaluation

data Evaluation

Represents the output of GetEvaluation operation.

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

See: evaluation smart constructor.

Instances

Eq Evaluation

Methods

(==) :: Evaluation -> Evaluation -> Bool

(/=) :: Evaluation -> Evaluation -> Bool

Data Evaluation

Methods

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

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

toConstr :: Evaluation -> Constr

dataTypeOf :: Evaluation -> DataType

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

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

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

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

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

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

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

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

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

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

Read Evaluation

Methods

readsPrec :: Int -> ReadS Evaluation

readList :: ReadS [Evaluation]

readPrec :: ReadPrec Evaluation

readListPrec :: ReadPrec [Evaluation]

Show Evaluation

Methods

showsPrec :: Int -> Evaluation -> ShowS

show :: Evaluation -> String

showList :: [Evaluation] -> ShowS

Generic Evaluation

Associated Types

type Rep Evaluation :: * -> *

Methods

from :: Evaluation -> Rep Evaluation x

to :: Rep Evaluation x -> Evaluation

Hashable Evaluation

Methods

hashWithSalt :: Int -> Evaluation -> Int

hash :: Evaluation -> Int

FromJSON Evaluation

Methods

parseJSON :: Value -> Parser Evaluation

parseJSONList :: Value -> Parser [Evaluation]

NFData Evaluation

Methods

rnf :: Evaluation -> ()

type Rep Evaluation

type Rep Evaluation = D1 (MetaData "Evaluation" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "Evaluation'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) ((:*:) (S1 (MetaSel (Just Symbol "_ePerformanceMetrics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe PerformanceMetrics))) (S1 (MetaSel (Just Symbol "_eLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_eComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_eInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_eFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_eCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_eMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eEvaluationDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

evaluation :: Evaluation

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

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

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

eStatus :: Lens' Evaluation (Maybe EntityStatus)

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

ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)

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

eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)

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

eCreatedAt :: Lens' Evaluation (Maybe UTCTime)

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

eComputeTime :: Lens' Evaluation (Maybe Integer)

Undocumented member.

eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)

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

eMLModelId :: Lens' Evaluation (Maybe Text)

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

eStartedAt :: Lens' Evaluation (Maybe UTCTime)

Undocumented member.

eFinishedAt :: Lens' Evaluation (Maybe UTCTime)

Undocumented member.

eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)

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

eName :: Lens' Evaluation (Maybe Text)

A user-supplied name or description of the Evaluation .

eEvaluationId :: Lens' Evaluation (Maybe Text)

The ID that is assigned to the Evaluation at creation.

eMessage :: Lens' Evaluation (Maybe Text)

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

eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)

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

MLModel

data MLModel

Represents the output of a GetMLModel operation.

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

See: mLModel smart constructor.

Instances

Eq MLModel

Methods

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

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

Data MLModel

Methods

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

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

toConstr :: MLModel -> Constr

dataTypeOf :: MLModel -> DataType

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

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

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

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

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

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

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

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

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

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

Read MLModel

Methods

readsPrec :: Int -> ReadS MLModel

readList :: ReadS [MLModel]

readPrec :: ReadPrec MLModel

readListPrec :: ReadPrec [MLModel]

Show MLModel

Methods

showsPrec :: Int -> MLModel -> ShowS

show :: MLModel -> String

showList :: [MLModel] -> ShowS

Generic MLModel

Associated Types

type Rep MLModel :: * -> *

Methods

from :: MLModel -> Rep MLModel x

to :: Rep MLModel x -> MLModel

Hashable MLModel

Methods

hashWithSalt :: Int -> MLModel -> Int

hash :: MLModel -> Int

FromJSON MLModel

Methods

parseJSON :: Value -> Parser MLModel

parseJSONList :: Value -> Parser [MLModel]

NFData MLModel

Methods

rnf :: MLModel -> ()

type Rep MLModel

type Rep MLModel = D1 (MetaData "MLModel" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "MLModel'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_mlmLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmTrainingParameters") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Text)))) (S1 (MetaSel (Just Symbol "_mlmScoreThresholdLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_mlmComputeTime") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmStartedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_mlmScoreThreshold") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmFinishedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmAlgorithm") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Algorithm))) (S1 (MetaSel (Just Symbol "_mlmCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmEndpointInfo") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmTrainingDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmMLModelType") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe MLModelType)))))))))

mLModel :: MLModel

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

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

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

mlmStatus :: Lens' MLModel (Maybe EntityStatus)

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

mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

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

mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)

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

mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

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

mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)

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

mlmComputeTime :: Lens' MLModel (Maybe Integer)

Undocumented member.

mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)

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

mlmMLModelId :: Lens' MLModel (Maybe Text)

The ID assigned to the MLModel at creation.

mlmSizeInBytes :: Lens' MLModel (Maybe Integer)

Undocumented member.

mlmStartedAt :: Lens' MLModel (Maybe UTCTime)

Undocumented member.

mlmScoreThreshold :: Lens' MLModel (Maybe Double)

Undocumented member.

mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)

Undocumented member.

mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)

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

mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)

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

mlmName :: Lens' MLModel (Maybe Text)

A user-supplied name or description of the MLModel .

mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)

The current endpoint of the MLModel .

mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)

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

mlmMessage :: Lens' MLModel (Maybe Text)

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

mlmMLModelType :: Lens' MLModel (Maybe MLModelType)

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

PerformanceMetrics

data PerformanceMetrics

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

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

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

See: performanceMetrics smart constructor.

Instances

Eq PerformanceMetrics

Methods

(==) :: PerformanceMetrics -> PerformanceMetrics -> Bool

(/=) :: PerformanceMetrics -> PerformanceMetrics -> Bool

Data PerformanceMetrics

Methods

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

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

toConstr :: PerformanceMetrics -> Constr

dataTypeOf :: PerformanceMetrics -> DataType

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

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

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

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

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

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

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

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

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

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

Read PerformanceMetrics

Methods

readsPrec :: Int -> ReadS PerformanceMetrics

readList :: ReadS [PerformanceMetrics]

readPrec :: ReadPrec PerformanceMetrics

readListPrec :: ReadPrec [PerformanceMetrics]

Show PerformanceMetrics

Methods

showsPrec :: Int -> PerformanceMetrics -> ShowS

show :: PerformanceMetrics -> String

showList :: [PerformanceMetrics] -> ShowS

Generic PerformanceMetrics

Associated Types

type Rep PerformanceMetrics :: * -> *

Methods

from :: PerformanceMetrics -> Rep PerformanceMetrics x

to :: Rep PerformanceMetrics x -> PerformanceMetrics

Hashable PerformanceMetrics

Methods

hashWithSalt :: Int -> PerformanceMetrics -> Int

hash :: PerformanceMetrics -> Int

FromJSON PerformanceMetrics

Methods

parseJSON :: Value -> Parser PerformanceMetrics

parseJSONList :: Value -> Parser [PerformanceMetrics]

NFData PerformanceMetrics

Methods

rnf :: PerformanceMetrics -> ()

type Rep PerformanceMetrics

type Rep PerformanceMetrics = D1 (MetaData "PerformanceMetrics" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" True) (C1 (MetaCons "PerformanceMetrics'" PrefixI True) (S1 (MetaSel (Just Symbol "_pmProperties") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Maybe (Map Text Text)))))

performanceMetrics :: PerformanceMetrics

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

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

• pmProperties - Undocumented member.

pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)

Undocumented member.

Prediction

data Prediction

The output from a Predict operation:

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

See: prediction smart constructor.

Instances

Eq Prediction

Methods

(==) :: Prediction -> Prediction -> Bool

(/=) :: Prediction -> Prediction -> Bool

Data Prediction

Methods

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

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

toConstr :: Prediction -> Constr

dataTypeOf :: Prediction -> DataType

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

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

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

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

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

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

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

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

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

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

Read Prediction

Methods

readsPrec :: Int -> ReadS Prediction

readList :: ReadS [Prediction]

readPrec :: ReadPrec Prediction

readListPrec :: ReadPrec [Prediction]

Show Prediction

Methods

showsPrec :: Int -> Prediction -> ShowS

show :: Prediction -> String

showList :: [Prediction] -> ShowS

Generic Prediction

Associated Types

type Rep Prediction :: * -> *

Methods

from :: Prediction -> Rep Prediction x

to :: Rep Prediction x -> Prediction

Hashable Prediction

Methods

hashWithSalt :: Int -> Prediction -> Int

hash :: Prediction -> Int

FromJSON Prediction

Methods

parseJSON :: Value -> Parser Prediction

parseJSONList :: Value -> Parser [Prediction]

NFData Prediction

Methods

rnf :: Prediction -> ()

type Rep Prediction

type Rep Prediction = D1 (MetaData "Prediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "Prediction'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double))) (S1 (MetaSel (Just Symbol "_pPredictedLabel") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedScores") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Double)))) (S1 (MetaSel (Just Symbol "_pDetails") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map DetailsAttributes Text)))))))

prediction :: Prediction

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

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

• pPredictedValue - 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)

The prediction value for REGRESSION MLModel .

pPredictedLabel :: Lens' Prediction (Maybe Text)

The prediction label for either a BINARY or MULTICLASS MLModel .

pPredictedScores :: Lens' Prediction (HashMap Text Double)

Undocumented member.

pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)

Undocumented member.

RDSDataSpec

data RDSDataSpec

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

See: rdsDataSpec smart constructor.

Instances

Eq RDSDataSpec

Methods

(==) :: RDSDataSpec -> RDSDataSpec -> Bool

(/=) :: RDSDataSpec -> RDSDataSpec -> Bool

Data RDSDataSpec

Methods

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

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

toConstr :: RDSDataSpec -> Constr

dataTypeOf :: RDSDataSpec -> DataType

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

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

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

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

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

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

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

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

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

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

Read RDSDataSpec

Methods

readsPrec :: Int -> ReadS RDSDataSpec

readList :: ReadS [RDSDataSpec]

readPrec :: ReadPrec RDSDataSpec

readListPrec :: ReadPrec [RDSDataSpec]

Show RDSDataSpec

Methods

showsPrec :: Int -> RDSDataSpec -> ShowS

show :: RDSDataSpec -> String

showList :: [RDSDataSpec] -> ShowS

Generic RDSDataSpec

Associated Types

type Rep RDSDataSpec :: * -> *

Methods

from :: RDSDataSpec -> Rep RDSDataSpec x

to :: Rep RDSDataSpec x -> RDSDataSpec

Hashable RDSDataSpec

Methods

hashWithSalt :: Int -> RDSDataSpec -> Int

hash :: RDSDataSpec -> Int

ToJSON RDSDataSpec

Methods

toJSON :: RDSDataSpec -> Value

toEncoding :: RDSDataSpec -> Encoding

toJSONList :: [RDSDataSpec] -> Value

toEncodingList :: [RDSDataSpec] -> Encoding

NFData RDSDataSpec

Methods

rnf :: RDSDataSpec -> ()

type Rep RDSDataSpec

type Rep RDSDataSpec = D1 (MetaData "RDSDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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)

The Amazon S3 location of the DataSchema .

rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)

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

rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)

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

rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text

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

rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials

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

rdsdsS3StagingLocation :: Lens' RDSDataSpec Text

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

rdsdsResourceRole :: Lens' RDSDataSpec Text

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

rdsdsServiceRole :: Lens' RDSDataSpec Text

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

rdsdsSubnetId :: Lens' RDSDataSpec Text

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

rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]

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

RDSDatabase

data RDSDatabase

The database details of an Amazon RDS database.

See: rdsDatabase smart constructor.

Instances

Eq RDSDatabase

Methods

(==) :: RDSDatabase -> RDSDatabase -> Bool

(/=) :: RDSDatabase -> RDSDatabase -> Bool

Data RDSDatabase

Methods

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

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

toConstr :: RDSDatabase -> Constr

dataTypeOf :: RDSDatabase -> DataType

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

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

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

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

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

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

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

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

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

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

Read RDSDatabase

Methods

readsPrec :: Int -> ReadS RDSDatabase

readList :: ReadS [RDSDatabase]

readPrec :: ReadPrec RDSDatabase

readListPrec :: ReadPrec [RDSDatabase]

Show RDSDatabase

Methods

showsPrec :: Int -> RDSDatabase -> ShowS

show :: RDSDatabase -> String

showList :: [RDSDatabase] -> ShowS

Generic RDSDatabase

Associated Types

type Rep RDSDatabase :: * -> *

Methods

from :: RDSDatabase -> Rep RDSDatabase x

to :: Rep RDSDatabase x -> RDSDatabase

Hashable RDSDatabase

Methods

hashWithSalt :: Int -> RDSDatabase -> Int

hash :: RDSDatabase -> Int

FromJSON RDSDatabase

Methods

parseJSON :: Value -> Parser RDSDatabase

parseJSONList :: Value -> Parser [RDSDatabase]

ToJSON RDSDatabase

Methods

toJSON :: RDSDatabase -> Value

toEncoding :: RDSDatabase -> Encoding

toJSONList :: [RDSDatabase] -> Value

toEncodingList :: [RDSDatabase] -> Encoding

NFData RDSDatabase

Methods

rnf :: RDSDatabase -> ()

type Rep RDSDatabase

type Rep RDSDatabase = D1 (MetaData "RDSDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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

The ID of an RDS DB instance.

rdsdDatabaseName :: Lens' RDSDatabase Text

Undocumented member.

RDSDatabaseCredentials

data RDSDatabaseCredentials

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

See: rdsDatabaseCredentials smart constructor.

Instances

Eq RDSDatabaseCredentials

Methods

(==) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool

(/=) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool

Data RDSDatabaseCredentials

Methods

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

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

toConstr :: RDSDatabaseCredentials -> Constr

dataTypeOf :: RDSDatabaseCredentials -> DataType

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

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

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

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

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

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

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

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

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

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

Read RDSDatabaseCredentials

Methods

readsPrec :: Int -> ReadS RDSDatabaseCredentials

readList :: ReadS [RDSDatabaseCredentials]

readPrec :: ReadPrec RDSDatabaseCredentials

readListPrec :: ReadPrec [RDSDatabaseCredentials]

Show RDSDatabaseCredentials

Methods

showsPrec :: Int -> RDSDatabaseCredentials -> ShowS

show :: RDSDatabaseCredentials -> String

showList :: [RDSDatabaseCredentials] -> ShowS

Generic RDSDatabaseCredentials

Associated Types

type Rep RDSDatabaseCredentials :: * -> *

Methods

from :: RDSDatabaseCredentials -> Rep RDSDatabaseCredentials x

to :: Rep RDSDatabaseCredentials x -> RDSDatabaseCredentials

Hashable RDSDatabaseCredentials

Methods

hashWithSalt :: Int -> RDSDatabaseCredentials -> Int

hash :: RDSDatabaseCredentials -> Int

ToJSON RDSDatabaseCredentials

Methods

toJSON :: RDSDatabaseCredentials -> Value

toEncoding :: RDSDatabaseCredentials -> Encoding

toJSONList :: [RDSDatabaseCredentials] -> Value

toEncodingList :: [RDSDatabaseCredentials] -> Encoding

NFData RDSDatabaseCredentials

Methods

rnf :: RDSDatabaseCredentials -> ()

type Rep RDSDatabaseCredentials

type Rep RDSDatabaseCredentials = D1 (MetaData "RDSDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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

Undocumented member.

rdsdcPassword :: Lens' RDSDatabaseCredentials Text

Undocumented member.

RDSMetadata

data RDSMetadata

The datasource details that are specific to Amazon RDS.

See: rdsMetadata smart constructor.

Instances

Eq RDSMetadata

Methods

(==) :: RDSMetadata -> RDSMetadata -> Bool

(/=) :: RDSMetadata -> RDSMetadata -> Bool

Data RDSMetadata

Methods

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

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

toConstr :: RDSMetadata -> Constr

dataTypeOf :: RDSMetadata -> DataType

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

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

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

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

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

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

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

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

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

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

Read RDSMetadata

Methods

readsPrec :: Int -> ReadS RDSMetadata

readList :: ReadS [RDSMetadata]

readPrec :: ReadPrec RDSMetadata

readListPrec :: ReadPrec [RDSMetadata]

Show RDSMetadata

Methods

showsPrec :: Int -> RDSMetadata -> ShowS

show :: RDSMetadata -> String

showList :: [RDSMetadata] -> ShowS

Generic RDSMetadata

Associated Types

type Rep RDSMetadata :: * -> *

Methods

from :: RDSMetadata -> Rep RDSMetadata x

to :: Rep RDSMetadata x -> RDSMetadata

Hashable RDSMetadata

Methods

hashWithSalt :: Int -> RDSMetadata -> Int

hash :: RDSMetadata -> Int

FromJSON RDSMetadata

Methods

parseJSON :: Value -> Parser RDSMetadata

parseJSONList :: Value -> Parser [RDSMetadata]

NFData RDSMetadata

Methods

rnf :: RDSMetadata -> ()

type Rep RDSMetadata

type Rep RDSMetadata = D1 (MetaData "RDSMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "RDSMetadata'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rmSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDataPipelineId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSDatabase))))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))

rdsMetadata :: RDSMetadata

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

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

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

rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)

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

rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)

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

rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)

The database details required to connect to an Amazon RDS.

rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)

Undocumented member.

rmResourceRole :: Lens' RDSMetadata (Maybe Text)

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

rmServiceRole :: Lens' RDSMetadata (Maybe Text)

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

RealtimeEndpointInfo

data RealtimeEndpointInfo

Describes the real-time endpoint information for an MLModel .

See: realtimeEndpointInfo smart constructor.

Instances

Eq RealtimeEndpointInfo

Methods

(==) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool

(/=) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool

Data RealtimeEndpointInfo

Methods

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

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

toConstr :: RealtimeEndpointInfo -> Constr

dataTypeOf :: RealtimeEndpointInfo -> DataType

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

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

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

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

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

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

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

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

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

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

Read RealtimeEndpointInfo

Methods

readsPrec :: Int -> ReadS RealtimeEndpointInfo

readList :: ReadS [RealtimeEndpointInfo]

readPrec :: ReadPrec RealtimeEndpointInfo

readListPrec :: ReadPrec [RealtimeEndpointInfo]

Show RealtimeEndpointInfo

Methods

showsPrec :: Int -> RealtimeEndpointInfo -> ShowS

show :: RealtimeEndpointInfo -> String

showList :: [RealtimeEndpointInfo] -> ShowS

Generic RealtimeEndpointInfo

Associated Types

type Rep RealtimeEndpointInfo :: * -> *

Methods

from :: RealtimeEndpointInfo -> Rep RealtimeEndpointInfo x

to :: Rep RealtimeEndpointInfo x -> RealtimeEndpointInfo

Hashable RealtimeEndpointInfo

Methods

hashWithSalt :: Int -> RealtimeEndpointInfo -> Int

hash :: RealtimeEndpointInfo -> Int

FromJSON RealtimeEndpointInfo

Methods

parseJSON :: Value -> Parser RealtimeEndpointInfo

parseJSONList :: Value -> Parser [RealtimeEndpointInfo]

NFData RealtimeEndpointInfo

Methods

rnf :: RealtimeEndpointInfo -> ()

type Rep RealtimeEndpointInfo

type Rep RealtimeEndpointInfo = D1 (MetaData "RealtimeEndpointInfo" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "RealtimeEndpointInfo'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_reiCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_reiEndpointURL") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_reiEndpointStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointStatus))) (S1 (MetaSel (Just Symbol "_reiPeakRequestsPerSecond") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Int))))))

realtimeEndpointInfo :: RealtimeEndpointInfo

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

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

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

reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)

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

reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)

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

reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)

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

reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)

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

RedshiftDataSpec

data RedshiftDataSpec

Describes the data specification of an Amazon Redshift DataSource .

See: redshiftDataSpec smart constructor.

Instances

Eq RedshiftDataSpec

Methods

(==) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool

(/=) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool

Data RedshiftDataSpec

Methods

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

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

toConstr :: RedshiftDataSpec -> Constr

dataTypeOf :: RedshiftDataSpec -> DataType

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

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

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

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

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

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

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

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

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

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

Read RedshiftDataSpec

Methods

readsPrec :: Int -> ReadS RedshiftDataSpec

readList :: ReadS [RedshiftDataSpec]

readPrec :: ReadPrec RedshiftDataSpec

readListPrec :: ReadPrec [RedshiftDataSpec]

Show RedshiftDataSpec

Methods

showsPrec :: Int -> RedshiftDataSpec -> ShowS

show :: RedshiftDataSpec -> String

showList :: [RedshiftDataSpec] -> ShowS

Generic RedshiftDataSpec

Associated Types

type Rep RedshiftDataSpec :: * -> *

Methods

from :: RedshiftDataSpec -> Rep RedshiftDataSpec x

to :: Rep RedshiftDataSpec x -> RedshiftDataSpec

Hashable RedshiftDataSpec

Methods

hashWithSalt :: Int -> RedshiftDataSpec -> Int

hash :: RedshiftDataSpec -> Int

ToJSON RedshiftDataSpec

Methods

toJSON :: RedshiftDataSpec -> Value

toEncoding :: RedshiftDataSpec -> Encoding

toJSONList :: [RedshiftDataSpec] -> Value

toEncodingList :: [RedshiftDataSpec] -> Encoding

NFData RedshiftDataSpec

Methods

rnf :: RedshiftDataSpec -> ()

type Rep RedshiftDataSpec

type Rep RedshiftDataSpec = D1 (MetaData "RedshiftDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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)

Describes the schema location for an Amazon Redshift DataSource .

rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)

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

rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)

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

rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource .

rSelectSqlQuery :: Lens' RedshiftDataSpec Text

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

rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials

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

rS3StagingLocation :: Lens' RedshiftDataSpec Text

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

RedshiftDatabase

data RedshiftDatabase

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

See: redshiftDatabase smart constructor.

Instances

Eq RedshiftDatabase

Methods

(==) :: RedshiftDatabase -> RedshiftDatabase -> Bool

(/=) :: RedshiftDatabase -> RedshiftDatabase -> Bool

Data RedshiftDatabase

Methods

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

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

toConstr :: RedshiftDatabase -> Constr

dataTypeOf :: RedshiftDatabase -> DataType

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

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

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

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

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

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

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

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

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

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

Read RedshiftDatabase

Methods

readsPrec :: Int -> ReadS RedshiftDatabase

readList :: ReadS [RedshiftDatabase]

readPrec :: ReadPrec RedshiftDatabase

readListPrec :: ReadPrec [RedshiftDatabase]

Show RedshiftDatabase

Methods

showsPrec :: Int -> RedshiftDatabase -> ShowS

show :: RedshiftDatabase -> String

showList :: [RedshiftDatabase] -> ShowS

Generic RedshiftDatabase

Associated Types

type Rep RedshiftDatabase :: * -> *

Methods

from :: RedshiftDatabase -> Rep RedshiftDatabase x

to :: Rep RedshiftDatabase x -> RedshiftDatabase

Hashable RedshiftDatabase

Methods

hashWithSalt :: Int -> RedshiftDatabase -> Int

hash :: RedshiftDatabase -> Int

FromJSON RedshiftDatabase

Methods

parseJSON :: Value -> Parser RedshiftDatabase

parseJSONList :: Value -> Parser [RedshiftDatabase]

ToJSON RedshiftDatabase

Methods

toJSON :: RedshiftDatabase -> Value

toEncoding :: RedshiftDatabase -> Encoding

toJSONList :: [RedshiftDatabase] -> Value

toEncodingList :: [RedshiftDatabase] -> Encoding

NFData RedshiftDatabase

Methods

rnf :: RedshiftDatabase -> ()

type Rep RedshiftDatabase

type Rep RedshiftDatabase = D1 (MetaData "RedshiftDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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

Undocumented member.

rdClusterIdentifier :: Lens' RedshiftDatabase Text

Undocumented member.

RedshiftDatabaseCredentials

data RedshiftDatabaseCredentials

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

See: redshiftDatabaseCredentials smart constructor.

Instances

Eq RedshiftDatabaseCredentials

Methods

(==) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool

(/=) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool

Data RedshiftDatabaseCredentials

Methods

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

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

toConstr :: RedshiftDatabaseCredentials -> Constr

dataTypeOf :: RedshiftDatabaseCredentials -> DataType

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

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

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

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

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

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

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

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

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

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

Read RedshiftDatabaseCredentials

Methods

readsPrec :: Int -> ReadS RedshiftDatabaseCredentials

readList :: ReadS [RedshiftDatabaseCredentials]

readPrec :: ReadPrec RedshiftDatabaseCredentials

readListPrec :: ReadPrec [RedshiftDatabaseCredentials]

Show RedshiftDatabaseCredentials

Methods

showsPrec :: Int -> RedshiftDatabaseCredentials -> ShowS

show :: RedshiftDatabaseCredentials -> String

showList :: [RedshiftDatabaseCredentials] -> ShowS

Generic RedshiftDatabaseCredentials

Associated Types

type Rep RedshiftDatabaseCredentials :: * -> *

Methods

from :: RedshiftDatabaseCredentials -> Rep RedshiftDatabaseCredentials x

to :: Rep RedshiftDatabaseCredentials x -> RedshiftDatabaseCredentials

Hashable RedshiftDatabaseCredentials

Methods

hashWithSalt :: Int -> RedshiftDatabaseCredentials -> Int

hash :: RedshiftDatabaseCredentials -> Int

ToJSON RedshiftDatabaseCredentials

Methods

toJSON :: RedshiftDatabaseCredentials -> Value

toEncoding :: RedshiftDatabaseCredentials -> Encoding

toJSONList :: [RedshiftDatabaseCredentials] -> Value

toEncodingList :: [RedshiftDatabaseCredentials] -> Encoding

NFData RedshiftDatabaseCredentials

Methods

rnf :: RedshiftDatabaseCredentials -> ()

type Rep RedshiftDatabaseCredentials

type Rep RedshiftDatabaseCredentials = D1 (MetaData "RedshiftDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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

Undocumented member.

rdcPassword :: Lens' RedshiftDatabaseCredentials Text

Undocumented member.

RedshiftMetadata

data RedshiftMetadata

Describes the DataSource details specific to Amazon Redshift.

See: redshiftMetadata smart constructor.

Instances

Eq RedshiftMetadata

Methods

(==) :: RedshiftMetadata -> RedshiftMetadata -> Bool

(/=) :: RedshiftMetadata -> RedshiftMetadata -> Bool

Data RedshiftMetadata

Methods

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

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

toConstr :: RedshiftMetadata -> Constr

dataTypeOf :: RedshiftMetadata -> DataType

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

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

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

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

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

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

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

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

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

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

Read RedshiftMetadata

Methods

readsPrec :: Int -> ReadS RedshiftMetadata

readList :: ReadS [RedshiftMetadata]

readPrec :: ReadPrec RedshiftMetadata

readListPrec :: ReadPrec [RedshiftMetadata]

Show RedshiftMetadata

Methods

showsPrec :: Int -> RedshiftMetadata -> ShowS

show :: RedshiftMetadata -> String

showList :: [RedshiftMetadata] -> ShowS

Generic RedshiftMetadata

Associated Types

type Rep RedshiftMetadata :: * -> *

Methods

from :: RedshiftMetadata -> Rep RedshiftMetadata x

to :: Rep RedshiftMetadata x -> RedshiftMetadata

Hashable RedshiftMetadata

Methods

hashWithSalt :: Int -> RedshiftMetadata -> Int

hash :: RedshiftMetadata -> Int

FromJSON RedshiftMetadata

Methods

parseJSON :: Value -> Parser RedshiftMetadata

parseJSONList :: Value -> Parser [RedshiftMetadata]

NFData RedshiftMetadata

Methods

rnf :: RedshiftMetadata -> ()

type Rep RedshiftMetadata

type Rep RedshiftMetadata = D1 (MetaData "RedshiftMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "RedshiftMetadata'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_redSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_redRedshiftDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftDatabase))) (S1 (MetaSel (Just Symbol "_redDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))

redshiftMetadata :: RedshiftMetadata

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

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

• redSelectSqlQuery - 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)

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

redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)

Undocumented member.

redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)

Undocumented member.

S3DataSpec

data S3DataSpec

Describes the data specification of a DataSource .

See: s3DataSpec smart constructor.

Instances

Eq S3DataSpec

Methods

(==) :: S3DataSpec -> S3DataSpec -> Bool

(/=) :: S3DataSpec -> S3DataSpec -> Bool

Data S3DataSpec

Methods

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

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

toConstr :: S3DataSpec -> Constr

dataTypeOf :: S3DataSpec -> DataType

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

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

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

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

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

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

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

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

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

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

Read S3DataSpec

Methods

readsPrec :: Int -> ReadS S3DataSpec

readList :: ReadS [S3DataSpec]

readPrec :: ReadPrec S3DataSpec

readListPrec :: ReadPrec [S3DataSpec]

Show S3DataSpec

Methods

showsPrec :: Int -> S3DataSpec -> ShowS

show :: S3DataSpec -> String

showList :: [S3DataSpec] -> ShowS

Generic S3DataSpec

Associated Types

type Rep S3DataSpec :: * -> *

Methods

from :: S3DataSpec -> Rep S3DataSpec x

to :: Rep S3DataSpec x -> S3DataSpec

Hashable S3DataSpec

Methods

hashWithSalt :: Int -> S3DataSpec -> Int

hash :: S3DataSpec -> Int

ToJSON S3DataSpec

Methods

toJSON :: S3DataSpec -> Value

toEncoding :: S3DataSpec -> Encoding

toJSONList :: [S3DataSpec] -> Value

toEncodingList :: [S3DataSpec] -> Encoding

NFData S3DataSpec

Methods

rnf :: S3DataSpec -> ()

type Rep S3DataSpec

type Rep S3DataSpec = D1 (MetaData "S3DataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" 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

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)

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

sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)

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

sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)

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

sdsDataLocationS3 :: Lens' S3DataSpec Text

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

Tag

data Tag

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

See: tag smart constructor.

Instances

Eq Tag

Methods

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

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

Data Tag

Methods

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

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

toConstr :: Tag -> Constr

dataTypeOf :: Tag -> DataType

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

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

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

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

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

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

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

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

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

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

Read Tag

Methods

readsPrec :: Int -> ReadS Tag

readList :: ReadS [Tag]

readPrec :: ReadPrec Tag

readListPrec :: ReadPrec [Tag]

Show Tag

Methods

showsPrec :: Int -> Tag -> ShowS

show :: Tag -> String

showList :: [Tag] -> ShowS

Generic Tag

Associated Types

type Rep Tag :: * -> *

Methods

from :: Tag -> Rep Tag x

to :: Rep Tag x -> Tag

Hashable Tag

Methods

hashWithSalt :: Int -> Tag -> Int

hash :: Tag -> Int

FromJSON Tag

Methods

parseJSON :: Value -> Parser Tag

parseJSONList :: Value -> Parser [Tag]

ToJSON Tag

Methods

toJSON :: Tag -> Value

toEncoding :: Tag -> Encoding

toJSONList :: [Tag] -> Value

toEncodingList :: [Tag] -> Encoding

NFData Tag

Methods

rnf :: Tag -> ()

type Rep Tag

type Rep Tag = D1 (MetaData "Tag" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.5.0-CbUNVP31vJd1Lf9wTXn86Z" False) (C1 (MetaCons "Tag'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_tagValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_tagKey") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))

tag :: Tag

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

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

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

tagValue :: Lens' Tag (Maybe Text)

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

tagKey :: Lens' Tag (Maybe Text)

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