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

Network.AWS.MachineLearning.CreateEvaluation

Contents

Creating a Request
Request Lenses
Destructuring the Response
Response Lenses

Description

Creates a new Evaluation of an MLModel . An MLModel is evaluated on a set of observations associated to a DataSource . Like a DataSource for an MLModel , the DataSource for an Evaluation contains values for the Target Variable . The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType : BINARY , REGRESSION or MULTICLASS .

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation , Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING . After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED .

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Creating a Request

createEvaluation #

Arguments

:: Text	`ceEvaluationId`
-> Text	`ceMLModelId`
-> Text	`ceEvaluationDataSourceId`
-> CreateEvaluation

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

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

ceEvaluationName - A user-supplied name or description of the Evaluation .
ceEvaluationId - A user-supplied ID that uniquely identifies the Evaluation .
ceMLModelId - The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation .
ceEvaluationDataSourceId - The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel .

data CreateEvaluation #

See: createEvaluation smart constructor.

Instances

Eq CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods (==) :: CreateEvaluation -> CreateEvaluation -> Bool # (/=) :: CreateEvaluation -> CreateEvaluation -> Bool #
Data CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> CreateEvaluation -> c CreateEvaluation # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c CreateEvaluation # toConstr :: CreateEvaluation -> Constr # dataTypeOf :: CreateEvaluation -> DataType # dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c CreateEvaluation) # dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c CreateEvaluation) # gmapT :: (forall b. Data b => b -> b) -> CreateEvaluation -> CreateEvaluation # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> CreateEvaluation -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> CreateEvaluation -> r # gmapQ :: (forall d. Data d => d -> u) -> CreateEvaluation -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> CreateEvaluation -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> CreateEvaluation -> m CreateEvaluation # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> CreateEvaluation -> m CreateEvaluation # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> CreateEvaluation -> m CreateEvaluation #
Read CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods readsPrec :: Int -> ReadS CreateEvaluation # readList :: ReadS [CreateEvaluation] # readPrec :: ReadPrec CreateEvaluation # readListPrec :: ReadPrec [CreateEvaluation] #
Show CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods showsPrec :: Int -> CreateEvaluation -> ShowS # show :: CreateEvaluation -> String # showList :: [CreateEvaluation] -> ShowS #
Generic CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Associated Types type Rep CreateEvaluation :: * -> * # Methods from :: CreateEvaluation -> Rep CreateEvaluation x # to :: Rep CreateEvaluation x -> CreateEvaluation #
Hashable CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods hashWithSalt :: Int -> CreateEvaluation -> Int # hash :: CreateEvaluation -> Int #
ToJSON CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods toJSON :: CreateEvaluation -> Value # toEncoding :: CreateEvaluation -> Encoding # toJSONList :: [CreateEvaluation] -> Value # toEncodingList :: [CreateEvaluation] -> Encoding #
AWSRequest CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Associated Types type Rs CreateEvaluation :: * # Methods request :: CreateEvaluation -> Request CreateEvaluation # response :: (MonadResource m, MonadThrow m) => Logger -> Service -> Proxy CreateEvaluation -> ClientResponse -> m (Response CreateEvaluation) #
ToHeaders CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods toHeaders :: CreateEvaluation -> [Header] #
ToPath CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods toPath :: CreateEvaluation -> ByteString #
ToQuery CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods toQuery :: CreateEvaluation -> QueryString #
NFData CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods rnf :: CreateEvaluation -> () #
type Rep CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation type Rep CreateEvaluation = D1 (MetaData "CreateEvaluation" "Network.AWS.MachineLearning.CreateEvaluation" "amazonka-ml-1.6.0-TdNGQduaCI3599xkq7Zfu" False) (C1 (MetaCons "CreateEvaluation'" PrefixI True) ((S1 (MetaSel (Just "_ceEvaluationName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :: S1 (MetaSel (Just "_ceEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) :: (S1 (MetaSel (Just "_ceMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text) :*: S1 (MetaSel (Just "_ceEvaluationDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))
type Rs CreateEvaluation #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation type Rs CreateEvaluation = CreateEvaluationResponse

Request Lenses

ceEvaluationName :: Lens' CreateEvaluation (Maybe Text) #

A user-supplied name or description of the Evaluation .

ceEvaluationId :: Lens' CreateEvaluation Text #

A user-supplied ID that uniquely identifies the Evaluation .

ceMLModelId :: Lens' CreateEvaluation Text #

The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation .

ceEvaluationDataSourceId :: Lens' CreateEvaluation Text #

The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel .

Destructuring the Response

createEvaluationResponse #

Arguments

:: Int	`cersResponseStatus`
-> CreateEvaluationResponse

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

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

cersEvaluationId - The user-supplied ID that uniquely identifies the Evaluation . This value should be identical to the value of the EvaluationId in the request.
cersResponseStatus - -- | The response status code.

data CreateEvaluationResponse #

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

See: createEvaluationResponse smart constructor.

Instances

Eq CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods (==) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool # (/=) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool #
Data CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> CreateEvaluationResponse -> c CreateEvaluationResponse # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c CreateEvaluationResponse # toConstr :: CreateEvaluationResponse -> Constr # dataTypeOf :: CreateEvaluationResponse -> DataType # dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c CreateEvaluationResponse) # dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c CreateEvaluationResponse) # gmapT :: (forall b. Data b => b -> b) -> CreateEvaluationResponse -> CreateEvaluationResponse # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> CreateEvaluationResponse -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> CreateEvaluationResponse -> r # gmapQ :: (forall d. Data d => d -> u) -> CreateEvaluationResponse -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> CreateEvaluationResponse -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> CreateEvaluationResponse -> m CreateEvaluationResponse # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> CreateEvaluationResponse -> m CreateEvaluationResponse # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> CreateEvaluationResponse -> m CreateEvaluationResponse #
Read CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods readsPrec :: Int -> ReadS CreateEvaluationResponse # readList :: ReadS [CreateEvaluationResponse] # readPrec :: ReadPrec CreateEvaluationResponse # readListPrec :: ReadPrec [CreateEvaluationResponse] #
Show CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods showsPrec :: Int -> CreateEvaluationResponse -> ShowS # show :: CreateEvaluationResponse -> String # showList :: [CreateEvaluationResponse] -> ShowS #
Generic CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Associated Types type Rep CreateEvaluationResponse :: * -> * # Methods from :: CreateEvaluationResponse -> Rep CreateEvaluationResponse x # to :: Rep CreateEvaluationResponse x -> CreateEvaluationResponse #
NFData CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation Methods rnf :: CreateEvaluationResponse -> () #
type Rep CreateEvaluationResponse #
Instance details Defined in Network.AWS.MachineLearning.CreateEvaluation type Rep CreateEvaluationResponse = D1 (MetaData "CreateEvaluationResponse" "Network.AWS.MachineLearning.CreateEvaluation" "amazonka-ml-1.6.0-TdNGQduaCI3599xkq7Zfu" False) (C1 (MetaCons "CreateEvaluationResponse'" PrefixI True) (S1 (MetaSel (Just "_cersEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)) :*: S1 (MetaSel (Just "_cersResponseStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Int)))

Response Lenses

cersEvaluationId :: Lens' CreateEvaluationResponse (Maybe Text) #

The user-supplied ID that uniquely identifies the Evaluation . This value should be identical to the value of the EvaluationId in the request.

cersResponseStatus :: Lens' CreateEvaluationResponse Int #

- | The response status code.