-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/
-- | Amazon Machine Learning SDK.
--
-- Derived from API version 2014-12-12 of the AWS service
-- descriptions, licensed under Apache 2.0.
--
-- The types from this library are intended to be used with
-- amazonka, which provides mechanisms for specifying AuthN/AuthZ
-- information, sending requests, and receiving responses.
--
-- It is recommended to use generic lenses or optics from packages such
-- as generic-lens or optics to modify optional fields and
-- deconstruct responses.
--
-- Generated lenses can be found in Amazonka.MachineLearning.Lens
-- and are suitable for use with a lens package such as lens or
-- lens-family-core.
--
-- See Amazonka.MachineLearning and the AWS documentation
-- to get started.
@package amazonka-ml
@version 2.0
module Amazonka.MachineLearning.Types.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.
--
newtype Algorithm
Algorithm' :: Text -> Algorithm
[fromAlgorithm] :: Algorithm -> Text
pattern Algorithm_Sgd :: Algorithm
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance GHC.Read.Read Amazonka.MachineLearning.Types.Algorithm.Algorithm
instance GHC.Show.Show Amazonka.MachineLearning.Types.Algorithm.Algorithm
module Amazonka.MachineLearning.Types.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.
--
newtype BatchPredictionFilterVariable
BatchPredictionFilterVariable' :: Text -> BatchPredictionFilterVariable
[fromBatchPredictionFilterVariable] :: BatchPredictionFilterVariable -> Text
pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance GHC.Read.Read Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
instance GHC.Show.Show Amazonka.MachineLearning.Types.BatchPredictionFilterVariable.BatchPredictionFilterVariable
module Amazonka.MachineLearning.Types.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.
--
--
-- Note: The variable names should match the variable names in the
-- DataSource.
newtype DataSourceFilterVariable
DataSourceFilterVariable' :: Text -> DataSourceFilterVariable
[fromDataSourceFilterVariable] :: DataSourceFilterVariable -> Text
pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance GHC.Read.Read Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
instance GHC.Show.Show Amazonka.MachineLearning.Types.DataSourceFilterVariable.DataSourceFilterVariable
module Amazonka.MachineLearning.Types.DetailsAttributes
-- | Contains the key values of DetailsMap:
--
--
-- - PredictiveModelType - Indicates the type of the
-- MLModel.
-- - Algorithm - Indicates the algorithm that was used for the
-- MLModel.
--
newtype DetailsAttributes
DetailsAttributes' :: Text -> DetailsAttributes
[fromDetailsAttributes] :: DetailsAttributes -> Text
pattern DetailsAttributes_Algorithm :: DetailsAttributes
pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance GHC.Read.Read Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
instance GHC.Show.Show Amazonka.MachineLearning.Types.DetailsAttributes.DetailsAttributes
module Amazonka.MachineLearning.Types.EntityStatus
-- | Object status with the following possible values:
--
--
-- PENDING
-- INPROGRESS
-- FAILED
-- COMPLETED
-- DELETED
--
newtype EntityStatus
EntityStatus' :: Text -> EntityStatus
[fromEntityStatus] :: EntityStatus -> Text
pattern EntityStatus_COMPLETED :: EntityStatus
pattern EntityStatus_DELETED :: EntityStatus
pattern EntityStatus_FAILED :: EntityStatus
pattern EntityStatus_INPROGRESS :: EntityStatus
pattern EntityStatus_PENDING :: EntityStatus
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance GHC.Read.Read Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
instance GHC.Show.Show Amazonka.MachineLearning.Types.EntityStatus.EntityStatus
module Amazonka.MachineLearning.Types.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: newBatchPrediction smart constructor.
data BatchPrediction
BatchPrediction' :: Maybe Text -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Maybe Integer -> BatchPrediction
-- | The ID of the DataSource that points to the group of
-- observations to predict.
[$sel:batchPredictionDataSourceId:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | The ID assigned to the BatchPrediction at creation. This
-- value should be identical to the value of the
-- BatchPredictionID in the request.
[$sel:batchPredictionId:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:computeTime:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | The time that the BatchPrediction was created. The time is
-- expressed in epoch time.
[$sel:createdAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:finishedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:invalidRecordCount:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
[$sel:lastUpdatedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
[$sel:mLModelId:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | A description of the most recent details about processing the batch
-- prediction request.
[$sel:message:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | A user-supplied name or description of the BatchPrediction.
[$sel:name:BatchPrediction'] :: 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: ':', '//',
-- '/./', '/../'.
[$sel:outputUri:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:startedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | 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.
--
[$sel:status:BatchPrediction'] :: BatchPrediction -> Maybe EntityStatus
[$sel:totalRecordCount:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | Create a value of BatchPrediction with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:batchPredictionDataSourceId:BatchPrediction',
-- batchPrediction_batchPredictionDataSourceId - The ID of the
-- DataSource that points to the group of observations to
-- predict.
--
-- $sel:batchPredictionId:BatchPrediction',
-- batchPrediction_batchPredictionId - The ID assigned to the
-- BatchPrediction at creation. This value should be identical
-- to the value of the BatchPredictionID in the request.
--
-- $sel:computeTime:BatchPrediction',
-- batchPrediction_computeTime - Undocumented member.
--
-- $sel:createdAt:BatchPrediction',
-- batchPrediction_createdAt - The time that the
-- BatchPrediction was created. The time is expressed in epoch
-- time.
--
-- $sel:createdByIamUser:BatchPrediction',
-- batchPrediction_createdByIamUser - 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.
--
-- $sel:finishedAt:BatchPrediction',
-- batchPrediction_finishedAt - Undocumented member.
--
-- $sel:inputDataLocationS3:BatchPrediction',
-- batchPrediction_inputDataLocationS3 - The location of the data
-- file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:invalidRecordCount:BatchPrediction',
-- batchPrediction_invalidRecordCount - Undocumented member.
--
-- $sel:lastUpdatedAt:BatchPrediction',
-- batchPrediction_lastUpdatedAt - The time of the most recent
-- edit to the BatchPrediction. The time is expressed in epoch
-- time.
--
-- $sel:mLModelId:BatchPrediction',
-- batchPrediction_mLModelId - The ID of the MLModel that
-- generated predictions for the BatchPrediction request.
--
-- $sel:message:BatchPrediction', batchPrediction_message -
-- A description of the most recent details about processing the batch
-- prediction request.
--
-- $sel:name:BatchPrediction', batchPrediction_name - A
-- user-supplied name or description of the BatchPrediction.
--
-- $sel:outputUri:BatchPrediction',
-- batchPrediction_outputUri - 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: ':', '//', '/./', '/../'.
--
-- $sel:startedAt:BatchPrediction',
-- batchPrediction_startedAt - Undocumented member.
--
-- $sel:status:BatchPrediction', batchPrediction_status -
-- 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.
--
--
-- $sel:totalRecordCount:BatchPrediction',
-- batchPrediction_totalRecordCount - Undocumented member.
newBatchPrediction :: BatchPrediction
-- | The ID of the DataSource that points to the group of
-- observations to predict.
batchPrediction_batchPredictionDataSourceId :: 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.
batchPrediction_batchPredictionId :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_computeTime :: Lens' BatchPrediction (Maybe Integer)
-- | The time that the BatchPrediction was created. The time is
-- expressed in epoch time.
batchPrediction_createdAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
batchPrediction_createdByIamUser :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_finishedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
batchPrediction_inputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_invalidRecordCount :: Lens' BatchPrediction (Maybe Integer)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
batchPrediction_lastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
batchPrediction_mLModelId :: Lens' BatchPrediction (Maybe Text)
-- | A description of the most recent details about processing the batch
-- prediction request.
batchPrediction_message :: Lens' BatchPrediction (Maybe Text)
-- | A user-supplied name or description of the BatchPrediction.
batchPrediction_name :: 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: ':', '//',
-- '/./', '/../'.
batchPrediction_outputUri :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_startedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
--
batchPrediction_status :: Lens' BatchPrediction (Maybe EntityStatus)
-- | Undocumented member.
batchPrediction_totalRecordCount :: Lens' BatchPrediction (Maybe Integer)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance GHC.Show.Show Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance GHC.Read.Read Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.BatchPrediction.BatchPrediction
module Amazonka.MachineLearning.Types.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.
--
newtype EvaluationFilterVariable
EvaluationFilterVariable' :: Text -> EvaluationFilterVariable
[fromEvaluationFilterVariable] :: EvaluationFilterVariable -> Text
pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance GHC.Read.Read Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
instance GHC.Show.Show Amazonka.MachineLearning.Types.EvaluationFilterVariable.EvaluationFilterVariable
module Amazonka.MachineLearning.Types.MLModelFilterVariable
newtype MLModelFilterVariable
MLModelFilterVariable' :: Text -> MLModelFilterVariable
[fromMLModelFilterVariable] :: MLModelFilterVariable -> Text
pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
pattern MLModelFilterVariable_Name :: MLModelFilterVariable
pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
pattern MLModelFilterVariable_Status :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance GHC.Read.Read Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
instance GHC.Show.Show Amazonka.MachineLearning.Types.MLModelFilterVariable.MLModelFilterVariable
module Amazonka.MachineLearning.Types.MLModelType
newtype MLModelType
MLModelType' :: Text -> MLModelType
[fromMLModelType] :: MLModelType -> Text
pattern MLModelType_BINARY :: MLModelType
pattern MLModelType_MULTICLASS :: MLModelType
pattern MLModelType_REGRESSION :: MLModelType
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance GHC.Read.Read Amazonka.MachineLearning.Types.MLModelType.MLModelType
instance GHC.Show.Show Amazonka.MachineLearning.Types.MLModelType.MLModelType
module Amazonka.MachineLearning.Types.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: newPerformanceMetrics smart constructor.
data PerformanceMetrics
PerformanceMetrics' :: Maybe (HashMap Text Text) -> PerformanceMetrics
[$sel:properties:PerformanceMetrics'] :: PerformanceMetrics -> Maybe (HashMap Text Text)
-- | Create a value of PerformanceMetrics with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:properties:PerformanceMetrics',
-- performanceMetrics_properties - Undocumented member.
newPerformanceMetrics :: PerformanceMetrics
-- | Undocumented member.
performanceMetrics_properties :: Lens' PerformanceMetrics (Maybe (HashMap Text Text))
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance GHC.Show.Show Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance GHC.Read.Read Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.PerformanceMetrics.PerformanceMetrics
module Amazonka.MachineLearning.Types.Evaluation
-- | Represents the output of GetEvaluation operation.
--
-- The content consists of the detailed metadata and data file
-- information and the current status of the Evaluation.
--
-- See: newEvaluation smart constructor.
data Evaluation
Evaluation' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe PerformanceMetrics -> Maybe POSIX -> Maybe EntityStatus -> Evaluation
[$sel:computeTime:Evaluation'] :: Evaluation -> Maybe Integer
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
[$sel:createdAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The AWS user account that invoked the evaluation. The account type can
-- be either an AWS root account or an AWS Identity and Access Management
-- (IAM) user account.
[$sel:createdByIamUser:Evaluation'] :: Evaluation -> Maybe Text
-- | The ID of the DataSource that is used to evaluate the
-- MLModel.
[$sel:evaluationDataSourceId:Evaluation'] :: Evaluation -> Maybe Text
-- | The ID that is assigned to the Evaluation at creation.
[$sel:evaluationId:Evaluation'] :: Evaluation -> Maybe Text
[$sel:finishedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The location and name of the data in Amazon Simple Storage Server
-- (Amazon S3) that is used in the evaluation.
[$sel:inputDataLocationS3:Evaluation'] :: Evaluation -> Maybe Text
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
[$sel:lastUpdatedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The ID of the MLModel that is the focus of the evaluation.
[$sel:mLModelId:Evaluation'] :: Evaluation -> Maybe Text
-- | A description of the most recent details about evaluating the
-- MLModel.
[$sel:message:Evaluation'] :: Evaluation -> Maybe Text
-- | A user-supplied name or description of the Evaluation.
[$sel:name:Evaluation'] :: Evaluation -> Maybe Text
-- | 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.
[$sel:performanceMetrics:Evaluation'] :: Evaluation -> Maybe PerformanceMetrics
[$sel:startedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | 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.
--
[$sel:status:Evaluation'] :: Evaluation -> Maybe EntityStatus
-- | Create a value of Evaluation with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeTime:Evaluation', evaluation_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:Evaluation', evaluation_createdAt - The
-- time that the Evaluation was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:Evaluation',
-- evaluation_createdByIamUser - 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.
--
-- $sel:evaluationDataSourceId:Evaluation',
-- evaluation_evaluationDataSourceId - The ID of the
-- DataSource that is used to evaluate the MLModel.
--
-- $sel:evaluationId:Evaluation', evaluation_evaluationId -
-- The ID that is assigned to the Evaluation at creation.
--
-- $sel:finishedAt:Evaluation', evaluation_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:Evaluation',
-- evaluation_inputDataLocationS3 - The location and name of the
-- data in Amazon Simple Storage Server (Amazon S3) that is used in the
-- evaluation.
--
-- $sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt
-- - The time of the most recent edit to the Evaluation. The
-- time is expressed in epoch time.
--
-- $sel:mLModelId:Evaluation', evaluation_mLModelId - The
-- ID of the MLModel that is the focus of the evaluation.
--
-- $sel:message:Evaluation', evaluation_message - A
-- description of the most recent details about evaluating the
-- MLModel.
--
-- $sel:name:Evaluation', evaluation_name - A user-supplied
-- name or description of the Evaluation.
--
-- $sel:performanceMetrics:Evaluation',
-- evaluation_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.
--
-- $sel:startedAt:Evaluation', evaluation_startedAt -
-- Undocumented member.
--
-- $sel:status:Evaluation', evaluation_status - 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.
--
newEvaluation :: Evaluation
-- | Undocumented member.
evaluation_computeTime :: Lens' Evaluation (Maybe Integer)
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
evaluation_createdAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
evaluation_createdByIamUser :: Lens' Evaluation (Maybe Text)
-- | The ID of the DataSource that is used to evaluate the
-- MLModel.
evaluation_evaluationDataSourceId :: Lens' Evaluation (Maybe Text)
-- | The ID that is assigned to the Evaluation at creation.
evaluation_evaluationId :: Lens' Evaluation (Maybe Text)
-- | Undocumented member.
evaluation_finishedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The location and name of the data in Amazon Simple Storage Server
-- (Amazon S3) that is used in the evaluation.
evaluation_inputDataLocationS3 :: Lens' Evaluation (Maybe Text)
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
evaluation_lastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The ID of the MLModel that is the focus of the evaluation.
evaluation_mLModelId :: Lens' Evaluation (Maybe Text)
-- | A description of the most recent details about evaluating the
-- MLModel.
evaluation_message :: Lens' Evaluation (Maybe Text)
-- | A user-supplied name or description of the Evaluation.
evaluation_name :: Lens' Evaluation (Maybe Text)
-- | 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.
evaluation_performanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)
-- | Undocumented member.
evaluation_startedAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
--
evaluation_status :: Lens' Evaluation (Maybe EntityStatus)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance GHC.Show.Show Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance GHC.Read.Read Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.Evaluation.Evaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.Evaluation.Evaluation
module Amazonka.MachineLearning.Types.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: newPrediction smart constructor.
data Prediction
Prediction' :: Maybe (HashMap DetailsAttributes Text) -> Maybe Text -> Maybe (HashMap Text Double) -> Maybe Double -> Prediction
[$sel:details:Prediction'] :: Prediction -> Maybe (HashMap DetailsAttributes Text)
-- | The prediction label for either a BINARY or
-- MULTICLASS MLModel.
[$sel:predictedLabel:Prediction'] :: Prediction -> Maybe Text
[$sel:predictedScores:Prediction'] :: Prediction -> Maybe (HashMap Text Double)
-- | The prediction value for REGRESSION MLModel.
[$sel:predictedValue:Prediction'] :: Prediction -> Maybe Double
-- | Create a value of Prediction with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:details:Prediction', prediction_details -
-- Undocumented member.
--
-- $sel:predictedLabel:Prediction',
-- prediction_predictedLabel - The prediction label for either a
-- BINARY or MULTICLASS MLModel.
--
-- $sel:predictedScores:Prediction',
-- prediction_predictedScores - Undocumented member.
--
-- $sel:predictedValue:Prediction',
-- prediction_predictedValue - The prediction value for
-- REGRESSION MLModel.
newPrediction :: Prediction
-- | Undocumented member.
prediction_details :: Lens' Prediction (Maybe (HashMap DetailsAttributes Text))
-- | The prediction label for either a BINARY or
-- MULTICLASS MLModel.
prediction_predictedLabel :: Lens' Prediction (Maybe Text)
-- | Undocumented member.
prediction_predictedScores :: Lens' Prediction (Maybe (HashMap Text Double))
-- | The prediction value for REGRESSION MLModel.
prediction_predictedValue :: Lens' Prediction (Maybe Double)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.Prediction.Prediction
instance GHC.Show.Show Amazonka.MachineLearning.Types.Prediction.Prediction
instance GHC.Read.Read Amazonka.MachineLearning.Types.Prediction.Prediction
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.Prediction.Prediction
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.Prediction.Prediction
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.Prediction.Prediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.Prediction.Prediction
module Amazonka.MachineLearning.Types.RDSDatabase
-- | The database details of an Amazon RDS database.
--
-- See: newRDSDatabase smart constructor.
data RDSDatabase
RDSDatabase' :: Text -> Text -> RDSDatabase
-- | The ID of an RDS DB instance.
[$sel:instanceIdentifier:RDSDatabase'] :: RDSDatabase -> Text
[$sel:databaseName:RDSDatabase'] :: RDSDatabase -> Text
-- | Create a value of RDSDatabase with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:instanceIdentifier:RDSDatabase',
-- rDSDatabase_instanceIdentifier - The ID of an RDS DB instance.
--
-- $sel:databaseName:RDSDatabase', rDSDatabase_databaseName
-- - Undocumented member.
newRDSDatabase :: Text -> Text -> RDSDatabase
-- | The ID of an RDS DB instance.
rDSDatabase_instanceIdentifier :: Lens' RDSDatabase Text
-- | Undocumented member.
rDSDatabase_databaseName :: Lens' RDSDatabase Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance GHC.Show.Show Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance GHC.Read.Read Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RDSDatabase.RDSDatabase
module Amazonka.MachineLearning.Types.RDSDatabaseCredentials
-- | The database credentials to connect to a database on an RDS DB
-- instance.
--
-- See: newRDSDatabaseCredentials smart constructor.
data RDSDatabaseCredentials
RDSDatabaseCredentials' :: Text -> Text -> RDSDatabaseCredentials
[$sel:username:RDSDatabaseCredentials'] :: RDSDatabaseCredentials -> Text
[$sel:password:RDSDatabaseCredentials'] :: RDSDatabaseCredentials -> Text
-- | Create a value of RDSDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_password - Undocumented member.
newRDSDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
-- | Undocumented member.
rDSDatabaseCredentials_username :: Lens' RDSDatabaseCredentials Text
-- | Undocumented member.
rDSDatabaseCredentials_password :: Lens' RDSDatabaseCredentials Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance GHC.Show.Show Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance GHC.Read.Read Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RDSDatabaseCredentials.RDSDatabaseCredentials
module Amazonka.MachineLearning.Types.RDSDataSpec
-- | The data specification of an Amazon Relational Database Service
-- (Amazon RDS) DataSource.
--
-- See: newRDSDataSpec smart constructor.
data RDSDataSpec
RDSDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> [Text] -> RDSDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:RDSDataSpec'] :: 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" ] }
[$sel:dataSchema:RDSDataSpec'] :: RDSDataSpec -> Maybe Text
-- | The Amazon S3 location of the DataSchema.
[$sel:dataSchemaUri:RDSDataSpec'] :: RDSDataSpec -> Maybe Text
-- | Describes the DatabaseName and InstanceIdentifier of
-- an Amazon RDS database.
[$sel:databaseInformation:RDSDataSpec'] :: RDSDataSpec -> RDSDatabase
-- | The query that is used to retrieve the observation data for the
-- DataSource.
[$sel:selectSqlQuery:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The AWS Identity and Access Management (IAM) credentials that are used
-- connect to the Amazon RDS database.
[$sel:databaseCredentials:RDSDataSpec'] :: RDSDataSpec -> RDSDatabaseCredentials
-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
-- from Amazon RDS using SelectSqlQuery is stored in this
-- location.
[$sel:s3StagingLocation:RDSDataSpec'] :: 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.
[$sel:resourceRole:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
-- service to monitor the progress of the copy task from Amazon RDS to
-- Amazon S3. For more information, see Role templates for data
-- pipelines.
[$sel:serviceRole:RDSDataSpec'] :: 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.
[$sel:subnetId:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The security group IDs to be used to access a VPC-based RDS DB
-- instance. Ensure that there are appropriate ingress rules set up to
-- allow access to the RDS DB instance. This attribute is used by Data
-- Pipeline to carry out the copy operation from Amazon RDS to an Amazon
-- S3 task.
[$sel:securityGroupIds:RDSDataSpec'] :: RDSDataSpec -> [Text]
-- | Create a value of RDSDataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RDSDataSpec',
-- rDSDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RDSDataSpec', rDSDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RDSDataSpec',
-- rDSDataSpec_dataSchemaUri - The Amazon S3 location of the
-- DataSchema.
--
-- $sel:databaseInformation:RDSDataSpec',
-- rDSDataSpec_databaseInformation - Describes the
-- DatabaseName and InstanceIdentifier of an Amazon RDS
-- database.
--
-- $sel:selectSqlQuery:RDSDataSpec',
-- rDSDataSpec_selectSqlQuery - The query that is used to retrieve
-- the observation data for the DataSource.
--
-- $sel:databaseCredentials:RDSDataSpec',
-- rDSDataSpec_databaseCredentials - The AWS Identity and Access
-- Management (IAM) credentials that are used connect to the Amazon RDS
-- database.
--
-- $sel:s3StagingLocation:RDSDataSpec',
-- rDSDataSpec_s3StagingLocation - The Amazon S3 location for
-- staging Amazon RDS data. The data retrieved from Amazon RDS using
-- SelectSqlQuery is stored in this location.
--
-- $sel:resourceRole:RDSDataSpec', rDSDataSpec_resourceRole
-- - 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.
--
-- $sel:serviceRole:RDSDataSpec', rDSDataSpec_serviceRole -
-- 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.
--
-- $sel:subnetId:RDSDataSpec', rDSDataSpec_subnetId - 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.
--
-- $sel:securityGroupIds:RDSDataSpec',
-- rDSDataSpec_securityGroupIds - 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.
newRDSDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
rDSDataSpec_dataRearrangement :: 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" ] }
rDSDataSpec_dataSchema :: Lens' RDSDataSpec (Maybe Text)
-- | The Amazon S3 location of the DataSchema.
rDSDataSpec_dataSchemaUri :: Lens' RDSDataSpec (Maybe Text)
-- | Describes the DatabaseName and InstanceIdentifier of
-- an Amazon RDS database.
rDSDataSpec_databaseInformation :: Lens' RDSDataSpec RDSDatabase
-- | The query that is used to retrieve the observation data for the
-- DataSource.
rDSDataSpec_selectSqlQuery :: Lens' RDSDataSpec Text
-- | The AWS Identity and Access Management (IAM) credentials that are used
-- connect to the Amazon RDS database.
rDSDataSpec_databaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials
-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
-- from Amazon RDS using SelectSqlQuery is stored in this
-- location.
rDSDataSpec_s3StagingLocation :: 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.
rDSDataSpec_resourceRole :: 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.
rDSDataSpec_serviceRole :: 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.
rDSDataSpec_subnetId :: 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.
rDSDataSpec_securityGroupIds :: Lens' RDSDataSpec [Text]
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance GHC.Show.Show Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance GHC.Read.Read Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RDSDataSpec.RDSDataSpec
module Amazonka.MachineLearning.Types.RDSMetadata
-- | The datasource details that are specific to Amazon RDS.
--
-- See: newRDSMetadata smart constructor.
data RDSMetadata
RDSMetadata' :: Maybe Text -> Maybe RDSDatabase -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> RDSMetadata
-- | 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.
[$sel:dataPipelineId:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | The database details required to connect to an Amazon RDS.
[$sel:database:RDSMetadata'] :: RDSMetadata -> Maybe RDSDatabase
[$sel:databaseUserName:RDSMetadata'] :: 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.
[$sel:resourceRole:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | The SQL query that is supplied during CreateDataSourceFromRDS. Returns
-- only if Verbose is true in GetDataSourceInput.
[$sel:selectSqlQuery:RDSMetadata'] :: 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.
[$sel:serviceRole:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | Create a value of RDSMetadata with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataPipelineId:RDSMetadata',
-- rDSMetadata_dataPipelineId - 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.
--
-- $sel:database:RDSMetadata', rDSMetadata_database - The
-- database details required to connect to an Amazon RDS.
--
-- $sel:databaseUserName:RDSMetadata',
-- rDSMetadata_databaseUserName - Undocumented member.
--
-- $sel:resourceRole:RDSMetadata', rDSMetadata_resourceRole
-- - 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.
--
-- $sel:selectSqlQuery:RDSMetadata',
-- rDSMetadata_selectSqlQuery - The SQL query that is supplied
-- during CreateDataSourceFromRDS. Returns only if Verbose is
-- true in GetDataSourceInput.
--
-- $sel:serviceRole:RDSMetadata', rDSMetadata_serviceRole -
-- 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.
newRDSMetadata :: RDSMetadata
-- | 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.
rDSMetadata_dataPipelineId :: Lens' RDSMetadata (Maybe Text)
-- | The database details required to connect to an Amazon RDS.
rDSMetadata_database :: Lens' RDSMetadata (Maybe RDSDatabase)
-- | Undocumented member.
rDSMetadata_databaseUserName :: 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.
rDSMetadata_resourceRole :: Lens' RDSMetadata (Maybe Text)
-- | The SQL query that is supplied during CreateDataSourceFromRDS. Returns
-- only if Verbose is true in GetDataSourceInput.
rDSMetadata_selectSqlQuery :: 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.
rDSMetadata_serviceRole :: Lens' RDSMetadata (Maybe Text)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance GHC.Show.Show Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance GHC.Read.Read Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RDSMetadata.RDSMetadata
module Amazonka.MachineLearning.Types.RealtimeEndpointStatus
newtype RealtimeEndpointStatus
RealtimeEndpointStatus' :: Text -> RealtimeEndpointStatus
[fromRealtimeEndpointStatus] :: RealtimeEndpointStatus -> Text
pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance GHC.Read.Read Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
instance GHC.Show.Show Amazonka.MachineLearning.Types.RealtimeEndpointStatus.RealtimeEndpointStatus
module Amazonka.MachineLearning.Types.RealtimeEndpointInfo
-- | Describes the real-time endpoint information for an MLModel.
--
-- See: newRealtimeEndpointInfo smart constructor.
data RealtimeEndpointInfo
RealtimeEndpointInfo' :: Maybe POSIX -> Maybe RealtimeEndpointStatus -> Maybe Text -> Maybe Int -> RealtimeEndpointInfo
-- | The time that the request to create the real-time endpoint for the
-- MLModel was received. The time is expressed in epoch time.
[$sel:createdAt:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe POSIX
-- | 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.
--
[$sel:endpointStatus:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe RealtimeEndpointStatus
-- | The URI that specifies where to send real-time prediction requests for
-- the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
[$sel:endpointUrl:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe Text
-- | The maximum processing rate for the real-time endpoint for
-- MLModel, measured in incoming requests per second.
[$sel:peakRequestsPerSecond:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe Int
-- | Create a value of RealtimeEndpointInfo with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:createdAt:RealtimeEndpointInfo',
-- realtimeEndpointInfo_createdAt - The time that the request to
-- create the real-time endpoint for the MLModel was received.
-- The time is expressed in epoch time.
--
-- $sel:endpointStatus:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointStatus - 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.
--
--
-- $sel:endpointUrl:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointUrl - The URI that specifies where
-- to send real-time prediction requests for the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
--
-- $sel:peakRequestsPerSecond:RealtimeEndpointInfo',
-- realtimeEndpointInfo_peakRequestsPerSecond - The maximum
-- processing rate for the real-time endpoint for MLModel,
-- measured in incoming requests per second.
newRealtimeEndpointInfo :: RealtimeEndpointInfo
-- | The time that the request to create the real-time endpoint for the
-- MLModel was received. The time is expressed in epoch time.
realtimeEndpointInfo_createdAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)
-- | 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.
--
realtimeEndpointInfo_endpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)
-- | The URI that specifies where to send real-time prediction requests for
-- the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
realtimeEndpointInfo_endpointUrl :: Lens' RealtimeEndpointInfo (Maybe Text)
-- | The maximum processing rate for the real-time endpoint for
-- MLModel, measured in incoming requests per second.
realtimeEndpointInfo_peakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance GHC.Show.Show Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance GHC.Read.Read Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RealtimeEndpointInfo.RealtimeEndpointInfo
module Amazonka.MachineLearning.Types.MLModel
-- | Represents the output of a GetMLModel operation.
--
-- The content consists of the detailed metadata and the current status
-- of the MLModel.
--
-- See: newMLModel smart constructor.
data MLModel
MLModel' :: Maybe Algorithm -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe RealtimeEndpointInfo -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe MLModelType -> Maybe Text -> Maybe Text -> Maybe Double -> Maybe POSIX -> Maybe Integer -> Maybe POSIX -> Maybe EntityStatus -> Maybe Text -> Maybe (HashMap Text Text) -> MLModel
-- | 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.
--
[$sel:algorithm:MLModel'] :: MLModel -> Maybe Algorithm
[$sel:computeTime:MLModel'] :: MLModel -> Maybe Integer
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
[$sel:createdAt:MLModel'] :: MLModel -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:MLModel'] :: MLModel -> Maybe Text
-- | The current endpoint of the MLModel.
[$sel:endpointInfo:MLModel'] :: MLModel -> Maybe RealtimeEndpointInfo
[$sel:finishedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:MLModel'] :: MLModel -> Maybe Text
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
[$sel:lastUpdatedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The ID assigned to the MLModel at creation.
[$sel:mLModelId:MLModel'] :: MLModel -> Maybe Text
-- | 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?".
--
[$sel:mLModelType:MLModel'] :: MLModel -> Maybe MLModelType
-- | A description of the most recent details about accessing the
-- MLModel.
[$sel:message:MLModel'] :: MLModel -> Maybe Text
-- | A user-supplied name or description of the MLModel.
[$sel:name:MLModel'] :: MLModel -> Maybe Text
[$sel:scoreThreshold:MLModel'] :: MLModel -> Maybe Double
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
[$sel:scoreThresholdLastUpdatedAt:MLModel'] :: MLModel -> Maybe POSIX
[$sel:sizeInBytes:MLModel'] :: MLModel -> Maybe Integer
[$sel:startedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The current status of an MLModel. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to create an MLModel.
-- - INPROGRESS - The creation process is underway.
-- - FAILED - The request to create an MLModel didn't
-- run to completion. The model isn't usable.
-- - COMPLETED - The creation process completed
-- successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
[$sel:status:MLModel'] :: MLModel -> Maybe EntityStatus
-- | The ID of the training DataSource. The CreateMLModel
-- operation uses the TrainingDataSourceId.
[$sel:trainingDataSourceId:MLModel'] :: MLModel -> Maybe Text
-- | A list of the training parameters in the MLModel. The list is
-- implemented as a map of key-value pairs.
--
-- The following is the current set of training parameters:
--
--
-- - sgd.maxMLModelSizeInBytes - The maximum allowed size of
-- the model. Depending on the input data, the size of the model might
-- affect its performance.The value is an integer that ranges from
-- 100000 to 2147483648. The default value is
-- 33554432.
-- - sgd.maxPasses - The number of times that the training
-- process traverses the observations to build the MLModel. The
-- value is an integer that ranges from 1 to 10000. The
-- default value is 10.
-- - sgd.shuffleType - Whether Amazon ML shuffles the training
-- data. Shuffling the data improves a model's ability to find the
-- optimal solution for a variety of data types. The valid values are
-- auto and none. The default value is
-- none.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm, which controls overfitting the data by
-- penalizing large coefficients. This parameter tends to drive
-- coefficients to zero, resulting in sparse feature set. If you use this
-- parameter, start by specifying a small value, such as
-- 1.0E-08.The value is a double that ranges from 0 to
-- MAX_DOUBLE. The default is to not use L1 normalization. This
-- parameter can't be used when L2 is specified. Use this
-- parameter sparingly.
-- - sgd.l2RegularizationAmount - The coefficient
-- regularization L2 norm, which controls overfitting the data by
-- penalizing large coefficients. This tends to drive coefficients to
-- small, nonzero values. If you use this parameter, start by specifying
-- a small value, such as 1.0E-08.The value is a double that
-- ranges from 0 to MAX_DOUBLE. The default is to not
-- use L2 normalization. This parameter can't be used when L1 is
-- specified. Use this parameter sparingly.
--
[$sel:trainingParameters:MLModel'] :: MLModel -> Maybe (HashMap Text Text)
-- | Create a value of MLModel with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:algorithm:MLModel', mLModel_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.
--
--
-- $sel:computeTime:MLModel', mLModel_computeTime -
-- Undocumented member.
--
-- MLModel, mLModel_createdAt - The time that the
-- MLModel was created. The time is expressed in epoch time.
--
-- $sel:createdByIamUser:MLModel', mLModel_createdByIamUser
-- - 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.
--
-- $sel:endpointInfo:MLModel', mLModel_endpointInfo - The
-- current endpoint of the MLModel.
--
-- $sel:finishedAt:MLModel', mLModel_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:MLModel',
-- mLModel_inputDataLocationS3 - The location of the data file or
-- directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:lastUpdatedAt:MLModel', mLModel_lastUpdatedAt - The
-- time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
--
-- $sel:mLModelId:MLModel', mLModel_mLModelId - The ID
-- assigned to the MLModel at creation.
--
-- $sel:mLModelType:MLModel', mLModel_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?".
--
--
-- $sel:message:MLModel', mLModel_message - A description
-- of the most recent details about accessing the MLModel.
--
-- $sel:name:MLModel', mLModel_name - A user-supplied name
-- or description of the MLModel.
--
-- $sel:scoreThreshold:MLModel', mLModel_scoreThreshold -
-- Undocumented member.
--
-- $sel:scoreThresholdLastUpdatedAt:MLModel',
-- mLModel_scoreThresholdLastUpdatedAt - The time of the most
-- recent edit to the ScoreThreshold. The time is expressed in
-- epoch time.
--
-- $sel:sizeInBytes:MLModel', mLModel_sizeInBytes -
-- Undocumented member.
--
-- $sel:startedAt:MLModel', mLModel_startedAt -
-- Undocumented member.
--
-- $sel:status:MLModel', mLModel_status - 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.
--
--
-- $sel:trainingDataSourceId:MLModel',
-- mLModel_trainingDataSourceId - The ID of the training
-- DataSource. The CreateMLModel operation uses the
-- TrainingDataSourceId.
--
-- $sel:trainingParameters:MLModel',
-- mLModel_trainingParameters - 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.
--
newMLModel :: MLModel
-- | 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.
--
mLModel_algorithm :: Lens' MLModel (Maybe Algorithm)
-- | Undocumented member.
mLModel_computeTime :: Lens' MLModel (Maybe Integer)
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
mLModel_createdAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
mLModel_createdByIamUser :: Lens' MLModel (Maybe Text)
-- | The current endpoint of the MLModel.
mLModel_endpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)
-- | Undocumented member.
mLModel_finishedAt :: Lens' MLModel (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
mLModel_inputDataLocationS3 :: Lens' MLModel (Maybe Text)
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
mLModel_lastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | The ID assigned to the MLModel at creation.
mLModel_mLModelId :: Lens' MLModel (Maybe Text)
-- | 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?".
--
mLModel_mLModelType :: Lens' MLModel (Maybe MLModelType)
-- | A description of the most recent details about accessing the
-- MLModel.
mLModel_message :: Lens' MLModel (Maybe Text)
-- | A user-supplied name or description of the MLModel.
mLModel_name :: Lens' MLModel (Maybe Text)
-- | Undocumented member.
mLModel_scoreThreshold :: Lens' MLModel (Maybe Double)
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
mLModel_scoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | Undocumented member.
mLModel_sizeInBytes :: Lens' MLModel (Maybe Integer)
-- | Undocumented member.
mLModel_startedAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
--
mLModel_status :: Lens' MLModel (Maybe EntityStatus)
-- | The ID of the training DataSource. The CreateMLModel
-- operation uses the TrainingDataSourceId.
mLModel_trainingDataSourceId :: Lens' MLModel (Maybe 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.
--
mLModel_trainingParameters :: Lens' MLModel (Maybe (HashMap Text Text))
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.MLModel.MLModel
instance GHC.Show.Show Amazonka.MachineLearning.Types.MLModel.MLModel
instance GHC.Read.Read Amazonka.MachineLearning.Types.MLModel.MLModel
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.MLModel.MLModel
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.MLModel.MLModel
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.MLModel.MLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.MLModel.MLModel
module Amazonka.MachineLearning.Types.RedshiftDatabase
-- | Describes the database details required to connect to an Amazon
-- Redshift database.
--
-- See: newRedshiftDatabase smart constructor.
data RedshiftDatabase
RedshiftDatabase' :: Text -> Text -> RedshiftDatabase
[$sel:databaseName:RedshiftDatabase'] :: RedshiftDatabase -> Text
[$sel:clusterIdentifier:RedshiftDatabase'] :: RedshiftDatabase -> Text
-- | Create a value of RedshiftDatabase with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseName:RedshiftDatabase',
-- redshiftDatabase_databaseName - Undocumented member.
--
-- $sel:clusterIdentifier:RedshiftDatabase',
-- redshiftDatabase_clusterIdentifier - Undocumented member.
newRedshiftDatabase :: Text -> Text -> RedshiftDatabase
-- | Undocumented member.
redshiftDatabase_databaseName :: Lens' RedshiftDatabase Text
-- | Undocumented member.
redshiftDatabase_clusterIdentifier :: Lens' RedshiftDatabase Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance GHC.Show.Show Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance GHC.Read.Read Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RedshiftDatabase.RedshiftDatabase
module Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials
-- | Describes the database credentials for connecting to a database on an
-- Amazon Redshift cluster.
--
-- See: newRedshiftDatabaseCredentials smart constructor.
data RedshiftDatabaseCredentials
RedshiftDatabaseCredentials' :: Text -> Text -> RedshiftDatabaseCredentials
[$sel:username:RedshiftDatabaseCredentials'] :: RedshiftDatabaseCredentials -> Text
[$sel:password:RedshiftDatabaseCredentials'] :: RedshiftDatabaseCredentials -> Text
-- | Create a value of RedshiftDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_password - Undocumented member.
newRedshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
-- | Undocumented member.
redshiftDatabaseCredentials_username :: Lens' RedshiftDatabaseCredentials Text
-- | Undocumented member.
redshiftDatabaseCredentials_password :: Lens' RedshiftDatabaseCredentials Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance GHC.Show.Show Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance GHC.Read.Read Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials.RedshiftDatabaseCredentials
module Amazonka.MachineLearning.Types.RedshiftDataSpec
-- | Describes the data specification of an Amazon Redshift
-- DataSource.
--
-- See: newRedshiftDataSpec smart constructor.
data RedshiftDataSpec
RedshiftDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:RedshiftDataSpec'] :: 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" ] }
[$sel:dataSchema:RedshiftDataSpec'] :: RedshiftDataSpec -> Maybe Text
-- | Describes the schema location for an Amazon Redshift
-- DataSource.
[$sel:dataSchemaUri:RedshiftDataSpec'] :: RedshiftDataSpec -> Maybe Text
-- | Describes the DatabaseName and ClusterIdentifier for
-- an Amazon Redshift DataSource.
[$sel:databaseInformation:RedshiftDataSpec'] :: RedshiftDataSpec -> RedshiftDatabase
-- | Describes the SQL Query to execute on an Amazon Redshift database for
-- an Amazon Redshift DataSource.
[$sel:selectSqlQuery:RedshiftDataSpec'] :: RedshiftDataSpec -> Text
-- | Describes AWS Identity and Access Management (IAM) credentials that
-- are used connect to the Amazon Redshift database.
[$sel:databaseCredentials:RedshiftDataSpec'] :: RedshiftDataSpec -> RedshiftDatabaseCredentials
-- | Describes an Amazon S3 location to store the result set of the
-- SelectSqlQuery query.
[$sel:s3StagingLocation:RedshiftDataSpec'] :: RedshiftDataSpec -> Text
-- | Create a value of RedshiftDataSpec with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RedshiftDataSpec',
-- redshiftDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RedshiftDataSpec',
-- redshiftDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RedshiftDataSpec',
-- redshiftDataSpec_dataSchemaUri - Describes the schema location
-- for an Amazon Redshift DataSource.
--
-- $sel:databaseInformation:RedshiftDataSpec',
-- redshiftDataSpec_databaseInformation - Describes the
-- DatabaseName and ClusterIdentifier for an Amazon
-- Redshift DataSource.
--
-- $sel:selectSqlQuery:RedshiftDataSpec',
-- redshiftDataSpec_selectSqlQuery - Describes the SQL Query to
-- execute on an Amazon Redshift database for an Amazon Redshift
-- DataSource.
--
-- $sel:databaseCredentials:RedshiftDataSpec',
-- redshiftDataSpec_databaseCredentials - Describes AWS Identity
-- and Access Management (IAM) credentials that are used connect to the
-- Amazon Redshift database.
--
-- $sel:s3StagingLocation:RedshiftDataSpec',
-- redshiftDataSpec_s3StagingLocation - Describes an Amazon S3
-- location to store the result set of the SelectSqlQuery query.
newRedshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
redshiftDataSpec_dataRearrangement :: 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" ] }
redshiftDataSpec_dataSchema :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the schema location for an Amazon Redshift
-- DataSource.
redshiftDataSpec_dataSchemaUri :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the DatabaseName and ClusterIdentifier for
-- an Amazon Redshift DataSource.
redshiftDataSpec_databaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase
-- | Describes the SQL Query to execute on an Amazon Redshift database for
-- an Amazon Redshift DataSource.
redshiftDataSpec_selectSqlQuery :: Lens' RedshiftDataSpec Text
-- | Describes AWS Identity and Access Management (IAM) credentials that
-- are used connect to the Amazon Redshift database.
redshiftDataSpec_databaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials
-- | Describes an Amazon S3 location to store the result set of the
-- SelectSqlQuery query.
redshiftDataSpec_s3StagingLocation :: Lens' RedshiftDataSpec Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance GHC.Show.Show Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance GHC.Read.Read Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.RedshiftDataSpec.RedshiftDataSpec
module Amazonka.MachineLearning.Types.RedshiftMetadata
-- | Describes the DataSource details specific to Amazon Redshift.
--
-- See: newRedshiftMetadata smart constructor.
data RedshiftMetadata
RedshiftMetadata' :: Maybe Text -> Maybe RedshiftDatabase -> Maybe Text -> RedshiftMetadata
[$sel:databaseUserName:RedshiftMetadata'] :: RedshiftMetadata -> Maybe Text
[$sel:redshiftDatabase:RedshiftMetadata'] :: RedshiftMetadata -> Maybe RedshiftDatabase
-- | The SQL query that is specified during CreateDataSourceFromRedshift.
-- Returns only if Verbose is true in GetDataSourceInput.
[$sel:selectSqlQuery:RedshiftMetadata'] :: RedshiftMetadata -> Maybe Text
-- | Create a value of RedshiftMetadata with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseUserName:RedshiftMetadata',
-- redshiftMetadata_databaseUserName - Undocumented member.
--
-- $sel:redshiftDatabase:RedshiftMetadata',
-- redshiftMetadata_redshiftDatabase - Undocumented member.
--
-- $sel:selectSqlQuery:RedshiftMetadata',
-- redshiftMetadata_selectSqlQuery - The SQL query that is
-- specified during CreateDataSourceFromRedshift. Returns only if
-- Verbose is true in GetDataSourceInput.
newRedshiftMetadata :: RedshiftMetadata
-- | Undocumented member.
redshiftMetadata_databaseUserName :: Lens' RedshiftMetadata (Maybe Text)
-- | Undocumented member.
redshiftMetadata_redshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)
-- | The SQL query that is specified during CreateDataSourceFromRedshift.
-- Returns only if Verbose is true in GetDataSourceInput.
redshiftMetadata_selectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance GHC.Show.Show Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance GHC.Read.Read Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.RedshiftMetadata.RedshiftMetadata
module Amazonka.MachineLearning.Types.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: newDataSource smart constructor.
data DataSource
DataSource' :: Maybe Bool -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe Text -> Maybe POSIX -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe RDSMetadata -> Maybe RedshiftMetadata -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> DataSource
-- | The parameter is true if statistics need to be generated from
-- the observation data.
[$sel:computeStatistics:DataSource'] :: DataSource -> Maybe Bool
[$sel:computeTime:DataSource'] :: DataSource -> Maybe Integer
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
[$sel:createdAt:DataSource'] :: DataSource -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:DataSource'] :: DataSource -> Maybe Text
-- | The location and name of the data in Amazon Simple Storage Service
-- (Amazon S3) that is used by a DataSource.
[$sel:dataLocationS3:DataSource'] :: DataSource -> Maybe Text
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
[$sel:dataRearrangement:DataSource'] :: DataSource -> Maybe Text
-- | The total number of observations contained in the data files that the
-- DataSource references.
[$sel:dataSizeInBytes:DataSource'] :: DataSource -> Maybe Integer
-- | The ID that is assigned to the DataSource during creation.
[$sel:dataSourceId:DataSource'] :: DataSource -> Maybe Text
[$sel:finishedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
[$sel:lastUpdatedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | A description of the most recent details about creating the
-- DataSource.
[$sel:message:DataSource'] :: DataSource -> Maybe Text
-- | A user-supplied name or description of the DataSource.
[$sel:name:DataSource'] :: DataSource -> Maybe Text
-- | The number of data files referenced by the DataSource.
[$sel:numberOfFiles:DataSource'] :: DataSource -> Maybe Integer
[$sel:rDSMetadata:DataSource'] :: DataSource -> Maybe RDSMetadata
[$sel:redshiftMetadata:DataSource'] :: DataSource -> Maybe RedshiftMetadata
[$sel:roleARN:DataSource'] :: DataSource -> Maybe Text
[$sel:startedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | 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.
--
[$sel:status:DataSource'] :: DataSource -> Maybe EntityStatus
-- | Create a value of DataSource with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeStatistics:DataSource',
-- dataSource_computeStatistics - The parameter is true
-- if statistics need to be generated from the observation data.
--
-- $sel:computeTime:DataSource', dataSource_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:DataSource', dataSource_createdAt - The
-- time that the DataSource was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:DataSource',
-- dataSource_createdByIamUser - 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.
--
-- $sel:dataLocationS3:DataSource',
-- dataSource_dataLocationS3 - The location and name of the data
-- in Amazon Simple Storage Service (Amazon S3) that is used by a
-- DataSource.
--
-- $sel:dataRearrangement:DataSource',
-- dataSource_dataRearrangement - A JSON string that represents
-- the splitting and rearrangement requirement used when this
-- DataSource was created.
--
-- $sel:dataSizeInBytes:DataSource',
-- dataSource_dataSizeInBytes - The total number of observations
-- contained in the data files that the DataSource references.
--
-- $sel:dataSourceId:DataSource', dataSource_dataSourceId -
-- The ID that is assigned to the DataSource during creation.
--
-- $sel:finishedAt:DataSource', dataSource_finishedAt -
-- Undocumented member.
--
-- $sel:lastUpdatedAt:DataSource', dataSource_lastUpdatedAt
-- - The time of the most recent edit to the BatchPrediction.
-- The time is expressed in epoch time.
--
-- $sel:message:DataSource', dataSource_message - A
-- description of the most recent details about creating the
-- DataSource.
--
-- $sel:name:DataSource', dataSource_name - A user-supplied
-- name or description of the DataSource.
--
-- $sel:numberOfFiles:DataSource', dataSource_numberOfFiles
-- - The number of data files referenced by the DataSource.
--
-- $sel:rDSMetadata:DataSource', dataSource_rDSMetadata -
-- Undocumented member.
--
-- $sel:redshiftMetadata:DataSource',
-- dataSource_redshiftMetadata - Undocumented member.
--
-- $sel:roleARN:DataSource', dataSource_roleARN -
-- Undocumented member.
--
-- $sel:startedAt:DataSource', dataSource_startedAt -
-- Undocumented member.
--
-- $sel:status:DataSource', dataSource_status - 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.
--
newDataSource :: DataSource
-- | The parameter is true if statistics need to be generated from
-- the observation data.
dataSource_computeStatistics :: Lens' DataSource (Maybe Bool)
-- | Undocumented member.
dataSource_computeTime :: Lens' DataSource (Maybe Integer)
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
dataSource_createdAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
dataSource_createdByIamUser :: Lens' DataSource (Maybe Text)
-- | The location and name of the data in Amazon Simple Storage Service
-- (Amazon S3) that is used by a DataSource.
dataSource_dataLocationS3 :: Lens' DataSource (Maybe Text)
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
dataSource_dataRearrangement :: Lens' DataSource (Maybe Text)
-- | The total number of observations contained in the data files that the
-- DataSource references.
dataSource_dataSizeInBytes :: Lens' DataSource (Maybe Integer)
-- | The ID that is assigned to the DataSource during creation.
dataSource_dataSourceId :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_finishedAt :: Lens' DataSource (Maybe UTCTime)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
dataSource_lastUpdatedAt :: Lens' DataSource (Maybe UTCTime)
-- | A description of the most recent details about creating the
-- DataSource.
dataSource_message :: Lens' DataSource (Maybe Text)
-- | A user-supplied name or description of the DataSource.
dataSource_name :: Lens' DataSource (Maybe Text)
-- | The number of data files referenced by the DataSource.
dataSource_numberOfFiles :: Lens' DataSource (Maybe Integer)
-- | Undocumented member.
dataSource_rDSMetadata :: Lens' DataSource (Maybe RDSMetadata)
-- | Undocumented member.
dataSource_redshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)
-- | Undocumented member.
dataSource_roleARN :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_startedAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
--
dataSource_status :: Lens' DataSource (Maybe EntityStatus)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.DataSource.DataSource
instance GHC.Show.Show Amazonka.MachineLearning.Types.DataSource.DataSource
instance GHC.Read.Read Amazonka.MachineLearning.Types.DataSource.DataSource
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.DataSource.DataSource
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.DataSource.DataSource
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.DataSource.DataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.DataSource.DataSource
module Amazonka.MachineLearning.Types.S3DataSpec
-- | Describes the data specification of a DataSource.
--
-- See: newS3DataSpec smart constructor.
data S3DataSpec
S3DataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> Text -> S3DataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:S3DataSpec'] :: 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" ] }
[$sel:dataSchema:S3DataSpec'] :: S3DataSpec -> Maybe Text
-- | Describes the schema location in Amazon S3. You must provide either
-- the DataSchema or the DataSchemaLocationS3.
[$sel:dataSchemaLocationS3:S3DataSpec'] :: S3DataSpec -> Maybe 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.
[$sel:dataLocationS3:S3DataSpec'] :: S3DataSpec -> Text
-- | Create a value of S3DataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:S3DataSpec',
-- s3DataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:S3DataSpec', s3DataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaLocationS3:S3DataSpec',
-- s3DataSpec_dataSchemaLocationS3 - Describes the schema location
-- in Amazon S3. You must provide either the DataSchema or the
-- DataSchemaLocationS3.
--
-- $sel:dataLocationS3:S3DataSpec',
-- s3DataSpec_dataLocationS3 - 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.
newS3DataSpec :: Text -> S3DataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
s3DataSpec_dataRearrangement :: 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" ] }
s3DataSpec_dataSchema :: Lens' S3DataSpec (Maybe Text)
-- | Describes the schema location in Amazon S3. You must provide either
-- the DataSchema or the DataSchemaLocationS3.
s3DataSpec_dataSchemaLocationS3 :: Lens' S3DataSpec (Maybe 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.
s3DataSpec_dataLocationS3 :: Lens' S3DataSpec Text
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance GHC.Show.Show Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance GHC.Read.Read Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.S3DataSpec.S3DataSpec
module Amazonka.MachineLearning.Types.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).
--
newtype SortOrder
SortOrder' :: Text -> SortOrder
[fromSortOrder] :: SortOrder -> Text
pattern SortOrder_Asc :: SortOrder
pattern SortOrder_Dsc :: SortOrder
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance GHC.Read.Read Amazonka.MachineLearning.Types.SortOrder.SortOrder
instance GHC.Show.Show Amazonka.MachineLearning.Types.SortOrder.SortOrder
module Amazonka.MachineLearning.Types.Tag
-- | A custom key-value pair associated with an ML object, such as an ML
-- model.
--
-- See: newTag smart constructor.
data Tag
Tag' :: Maybe Text -> Maybe Text -> Tag
-- | A unique identifier for the tag. Valid characters include Unicode
-- letters, digits, white space, _, ., /, =, +, -, %, and @.
[$sel:key:Tag'] :: Tag -> Maybe Text
-- | An optional string, typically used to describe or define the tag.
-- Valid characters include Unicode letters, digits, white space, _, .,
-- /, =, +, -, %, and @.
[$sel:value:Tag'] :: Tag -> Maybe Text
-- | Create a value of Tag with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:key:Tag', tag_key - A unique identifier for the
-- tag. Valid characters include Unicode letters, digits, white space, _,
-- ., /, =, +, -, %, and @.
--
-- $sel:value:Tag', tag_value - An optional string,
-- typically used to describe or define the tag. Valid characters include
-- Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
newTag :: Tag
-- | A unique identifier for the tag. Valid characters include Unicode
-- letters, digits, white space, _, ., /, =, +, -, %, and @.
tag_key :: Lens' Tag (Maybe Text)
-- | An optional string, typically used to describe or define the tag.
-- Valid characters include Unicode letters, digits, white space, _, .,
-- /, =, +, -, %, and @.
tag_value :: Lens' Tag (Maybe Text)
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.Tag.Tag
instance GHC.Show.Show Amazonka.MachineLearning.Types.Tag.Tag
instance GHC.Read.Read Amazonka.MachineLearning.Types.Tag.Tag
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.Tag.Tag
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.Tag.Tag
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.Tag.Tag
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.Tag.Tag
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.Tag.Tag
module Amazonka.MachineLearning.Types.TaggableResourceType
newtype TaggableResourceType
TaggableResourceType' :: Text -> TaggableResourceType
[fromTaggableResourceType] :: TaggableResourceType -> Text
pattern TaggableResourceType_BatchPrediction :: TaggableResourceType
pattern TaggableResourceType_DataSource :: TaggableResourceType
pattern TaggableResourceType_Evaluation :: TaggableResourceType
pattern TaggableResourceType_MLModel :: TaggableResourceType
instance Amazonka.Data.XML.ToXML Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.XML.FromXML Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Data.Aeson.Types.ToJSON.ToJSONKey Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Data.Aeson.Types.FromJSON.FromJSONKey Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Data.Aeson.Types.FromJSON.FromJSON Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.Headers.ToHeader Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.Log.ToLog Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.ByteString.ToByteString Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.Text.ToText Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Amazonka.Data.Text.FromText Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance GHC.Generics.Generic Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance GHC.Classes.Ord Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance GHC.Classes.Eq Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance GHC.Read.Read Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
instance GHC.Show.Show Amazonka.MachineLearning.Types.TaggableResourceType.TaggableResourceType
module Amazonka.MachineLearning.Types
-- | API version 2014-12-12 of the Amazon Machine Learning SDK
-- configuration.
defaultService :: Service
-- | 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.
_IdempotentParameterMismatchException :: AsError a => Fold a ServiceError
-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Fold a ServiceError
-- | An error on the client occurred. Typically, the cause is an invalid
-- input value.
_InvalidInputException :: AsError a => Fold a ServiceError
-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Fold a ServiceError
-- | The subscriber exceeded the maximum number of operations. This
-- exception can occur when listing objects such as DataSource.
_LimitExceededException :: AsError a => Fold a ServiceError
-- | The exception is thrown when a predict request is made to an unmounted
-- MLModel.
_PredictorNotMountedException :: AsError a => Fold a ServiceError
-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Fold a ServiceError
-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Fold a ServiceError
-- | 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.
--
newtype Algorithm
Algorithm' :: Text -> Algorithm
[fromAlgorithm] :: Algorithm -> Text
pattern Algorithm_Sgd :: Algorithm
-- | 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.
--
newtype BatchPredictionFilterVariable
BatchPredictionFilterVariable' :: Text -> BatchPredictionFilterVariable
[fromBatchPredictionFilterVariable] :: BatchPredictionFilterVariable -> Text
pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable
-- | 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.
--
--
-- Note: The variable names should match the variable names in the
-- DataSource.
newtype DataSourceFilterVariable
DataSourceFilterVariable' :: Text -> DataSourceFilterVariable
[fromDataSourceFilterVariable] :: DataSourceFilterVariable -> Text
pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable
-- | Contains the key values of DetailsMap:
--
--
-- - PredictiveModelType - Indicates the type of the
-- MLModel.
-- - Algorithm - Indicates the algorithm that was used for the
-- MLModel.
--
newtype DetailsAttributes
DetailsAttributes' :: Text -> DetailsAttributes
[fromDetailsAttributes] :: DetailsAttributes -> Text
pattern DetailsAttributes_Algorithm :: DetailsAttributes
pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes
-- | Object status with the following possible values:
--
--
-- PENDING
-- INPROGRESS
-- FAILED
-- COMPLETED
-- DELETED
--
newtype EntityStatus
EntityStatus' :: Text -> EntityStatus
[fromEntityStatus] :: EntityStatus -> Text
pattern EntityStatus_COMPLETED :: EntityStatus
pattern EntityStatus_DELETED :: EntityStatus
pattern EntityStatus_FAILED :: EntityStatus
pattern EntityStatus_INPROGRESS :: EntityStatus
pattern EntityStatus_PENDING :: EntityStatus
-- | 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.
--
newtype EvaluationFilterVariable
EvaluationFilterVariable' :: Text -> EvaluationFilterVariable
[fromEvaluationFilterVariable] :: EvaluationFilterVariable -> Text
pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable
newtype MLModelFilterVariable
MLModelFilterVariable' :: Text -> MLModelFilterVariable
[fromMLModelFilterVariable] :: MLModelFilterVariable -> Text
pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
pattern MLModelFilterVariable_Name :: MLModelFilterVariable
pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
pattern MLModelFilterVariable_Status :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable
newtype MLModelType
MLModelType' :: Text -> MLModelType
[fromMLModelType] :: MLModelType -> Text
pattern MLModelType_BINARY :: MLModelType
pattern MLModelType_MULTICLASS :: MLModelType
pattern MLModelType_REGRESSION :: MLModelType
newtype RealtimeEndpointStatus
RealtimeEndpointStatus' :: Text -> RealtimeEndpointStatus
[fromRealtimeEndpointStatus] :: RealtimeEndpointStatus -> Text
pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus
-- | 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).
--
newtype SortOrder
SortOrder' :: Text -> SortOrder
[fromSortOrder] :: SortOrder -> Text
pattern SortOrder_Asc :: SortOrder
pattern SortOrder_Dsc :: SortOrder
newtype TaggableResourceType
TaggableResourceType' :: Text -> TaggableResourceType
[fromTaggableResourceType] :: TaggableResourceType -> Text
pattern TaggableResourceType_BatchPrediction :: TaggableResourceType
pattern TaggableResourceType_DataSource :: TaggableResourceType
pattern TaggableResourceType_Evaluation :: TaggableResourceType
pattern TaggableResourceType_MLModel :: TaggableResourceType
-- | 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: newBatchPrediction smart constructor.
data BatchPrediction
BatchPrediction' :: Maybe Text -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Maybe Integer -> BatchPrediction
-- | The ID of the DataSource that points to the group of
-- observations to predict.
[$sel:batchPredictionDataSourceId:BatchPrediction'] :: 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.
[$sel:batchPredictionId:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:computeTime:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | The time that the BatchPrediction was created. The time is
-- expressed in epoch time.
[$sel:createdAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The AWS user account that invoked the BatchPrediction. The
-- account type can be either an AWS root account or an AWS Identity and
-- Access Management (IAM) user account.
[$sel:createdByIamUser:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:finishedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:invalidRecordCount:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
[$sel:lastUpdatedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
[$sel:mLModelId:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | A description of the most recent details about processing the batch
-- prediction request.
[$sel:message:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | A user-supplied name or description of the BatchPrediction.
[$sel:name:BatchPrediction'] :: BatchPrediction -> Maybe Text
-- | The location of an Amazon S3 bucket or directory to receive the
-- operation results. The following substrings are not allowed in the
-- s3 key portion of the outputURI field: ':', '//',
-- '/./', '/../'.
[$sel:outputUri:BatchPrediction'] :: BatchPrediction -> Maybe Text
[$sel:startedAt:BatchPrediction'] :: BatchPrediction -> Maybe POSIX
-- | The status of the BatchPrediction. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate predictions for a batch of observations.
-- - INPROGRESS - The process is underway.
-- - FAILED - The request to perform a batch prediction did
-- not run to completion. It is not usable.
-- - COMPLETED - The batch prediction process completed
-- successfully.
-- - DELETED - The BatchPrediction is marked as
-- deleted. It is not usable.
--
[$sel:status:BatchPrediction'] :: BatchPrediction -> Maybe EntityStatus
[$sel:totalRecordCount:BatchPrediction'] :: BatchPrediction -> Maybe Integer
-- | Create a value of BatchPrediction with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:batchPredictionDataSourceId:BatchPrediction',
-- batchPrediction_batchPredictionDataSourceId - The ID of the
-- DataSource that points to the group of observations to
-- predict.
--
-- $sel:batchPredictionId:BatchPrediction',
-- batchPrediction_batchPredictionId - The ID assigned to the
-- BatchPrediction at creation. This value should be identical
-- to the value of the BatchPredictionID in the request.
--
-- $sel:computeTime:BatchPrediction',
-- batchPrediction_computeTime - Undocumented member.
--
-- $sel:createdAt:BatchPrediction',
-- batchPrediction_createdAt - The time that the
-- BatchPrediction was created. The time is expressed in epoch
-- time.
--
-- $sel:createdByIamUser:BatchPrediction',
-- batchPrediction_createdByIamUser - 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.
--
-- $sel:finishedAt:BatchPrediction',
-- batchPrediction_finishedAt - Undocumented member.
--
-- $sel:inputDataLocationS3:BatchPrediction',
-- batchPrediction_inputDataLocationS3 - The location of the data
-- file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:invalidRecordCount:BatchPrediction',
-- batchPrediction_invalidRecordCount - Undocumented member.
--
-- $sel:lastUpdatedAt:BatchPrediction',
-- batchPrediction_lastUpdatedAt - The time of the most recent
-- edit to the BatchPrediction. The time is expressed in epoch
-- time.
--
-- $sel:mLModelId:BatchPrediction',
-- batchPrediction_mLModelId - The ID of the MLModel that
-- generated predictions for the BatchPrediction request.
--
-- $sel:message:BatchPrediction', batchPrediction_message -
-- A description of the most recent details about processing the batch
-- prediction request.
--
-- $sel:name:BatchPrediction', batchPrediction_name - A
-- user-supplied name or description of the BatchPrediction.
--
-- $sel:outputUri:BatchPrediction',
-- batchPrediction_outputUri - 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: ':', '//', '/./', '/../'.
--
-- $sel:startedAt:BatchPrediction',
-- batchPrediction_startedAt - Undocumented member.
--
-- $sel:status:BatchPrediction', batchPrediction_status -
-- 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.
--
--
-- $sel:totalRecordCount:BatchPrediction',
-- batchPrediction_totalRecordCount - Undocumented member.
newBatchPrediction :: BatchPrediction
-- | The ID of the DataSource that points to the group of
-- observations to predict.
batchPrediction_batchPredictionDataSourceId :: 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.
batchPrediction_batchPredictionId :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_computeTime :: Lens' BatchPrediction (Maybe Integer)
-- | The time that the BatchPrediction was created. The time is
-- expressed in epoch time.
batchPrediction_createdAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
batchPrediction_createdByIamUser :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_finishedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
batchPrediction_inputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_invalidRecordCount :: Lens' BatchPrediction (Maybe Integer)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
batchPrediction_lastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
batchPrediction_mLModelId :: Lens' BatchPrediction (Maybe Text)
-- | A description of the most recent details about processing the batch
-- prediction request.
batchPrediction_message :: Lens' BatchPrediction (Maybe Text)
-- | A user-supplied name or description of the BatchPrediction.
batchPrediction_name :: 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: ':', '//',
-- '/./', '/../'.
batchPrediction_outputUri :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_startedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
--
batchPrediction_status :: Lens' BatchPrediction (Maybe EntityStatus)
-- | Undocumented member.
batchPrediction_totalRecordCount :: Lens' BatchPrediction (Maybe Integer)
-- | 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: newDataSource smart constructor.
data DataSource
DataSource' :: Maybe Bool -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe Text -> Maybe POSIX -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe RDSMetadata -> Maybe RedshiftMetadata -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> DataSource
-- | The parameter is true if statistics need to be generated from
-- the observation data.
[$sel:computeStatistics:DataSource'] :: DataSource -> Maybe Bool
[$sel:computeTime:DataSource'] :: DataSource -> Maybe Integer
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
[$sel:createdAt:DataSource'] :: DataSource -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:DataSource'] :: DataSource -> Maybe Text
-- | The location and name of the data in Amazon Simple Storage Service
-- (Amazon S3) that is used by a DataSource.
[$sel:dataLocationS3:DataSource'] :: DataSource -> Maybe Text
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
[$sel:dataRearrangement:DataSource'] :: DataSource -> Maybe Text
-- | The total number of observations contained in the data files that the
-- DataSource references.
[$sel:dataSizeInBytes:DataSource'] :: DataSource -> Maybe Integer
-- | The ID that is assigned to the DataSource during creation.
[$sel:dataSourceId:DataSource'] :: DataSource -> Maybe Text
[$sel:finishedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
[$sel:lastUpdatedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | A description of the most recent details about creating the
-- DataSource.
[$sel:message:DataSource'] :: DataSource -> Maybe Text
-- | A user-supplied name or description of the DataSource.
[$sel:name:DataSource'] :: DataSource -> Maybe Text
-- | The number of data files referenced by the DataSource.
[$sel:numberOfFiles:DataSource'] :: DataSource -> Maybe Integer
[$sel:rDSMetadata:DataSource'] :: DataSource -> Maybe RDSMetadata
[$sel:redshiftMetadata:DataSource'] :: DataSource -> Maybe RedshiftMetadata
[$sel:roleARN:DataSource'] :: DataSource -> Maybe Text
[$sel:startedAt:DataSource'] :: DataSource -> Maybe POSIX
-- | 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.
--
[$sel:status:DataSource'] :: DataSource -> Maybe EntityStatus
-- | Create a value of DataSource with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeStatistics:DataSource',
-- dataSource_computeStatistics - The parameter is true
-- if statistics need to be generated from the observation data.
--
-- $sel:computeTime:DataSource', dataSource_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:DataSource', dataSource_createdAt - The
-- time that the DataSource was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:DataSource',
-- dataSource_createdByIamUser - 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.
--
-- $sel:dataLocationS3:DataSource',
-- dataSource_dataLocationS3 - The location and name of the data
-- in Amazon Simple Storage Service (Amazon S3) that is used by a
-- DataSource.
--
-- $sel:dataRearrangement:DataSource',
-- dataSource_dataRearrangement - A JSON string that represents
-- the splitting and rearrangement requirement used when this
-- DataSource was created.
--
-- $sel:dataSizeInBytes:DataSource',
-- dataSource_dataSizeInBytes - The total number of observations
-- contained in the data files that the DataSource references.
--
-- $sel:dataSourceId:DataSource', dataSource_dataSourceId -
-- The ID that is assigned to the DataSource during creation.
--
-- $sel:finishedAt:DataSource', dataSource_finishedAt -
-- Undocumented member.
--
-- $sel:lastUpdatedAt:DataSource', dataSource_lastUpdatedAt
-- - The time of the most recent edit to the BatchPrediction.
-- The time is expressed in epoch time.
--
-- $sel:message:DataSource', dataSource_message - A
-- description of the most recent details about creating the
-- DataSource.
--
-- $sel:name:DataSource', dataSource_name - A user-supplied
-- name or description of the DataSource.
--
-- $sel:numberOfFiles:DataSource', dataSource_numberOfFiles
-- - The number of data files referenced by the DataSource.
--
-- $sel:rDSMetadata:DataSource', dataSource_rDSMetadata -
-- Undocumented member.
--
-- $sel:redshiftMetadata:DataSource',
-- dataSource_redshiftMetadata - Undocumented member.
--
-- $sel:roleARN:DataSource', dataSource_roleARN -
-- Undocumented member.
--
-- $sel:startedAt:DataSource', dataSource_startedAt -
-- Undocumented member.
--
-- $sel:status:DataSource', dataSource_status - 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.
--
newDataSource :: DataSource
-- | The parameter is true if statistics need to be generated from
-- the observation data.
dataSource_computeStatistics :: Lens' DataSource (Maybe Bool)
-- | Undocumented member.
dataSource_computeTime :: Lens' DataSource (Maybe Integer)
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
dataSource_createdAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
dataSource_createdByIamUser :: Lens' DataSource (Maybe Text)
-- | The location and name of the data in Amazon Simple Storage Service
-- (Amazon S3) that is used by a DataSource.
dataSource_dataLocationS3 :: Lens' DataSource (Maybe Text)
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
dataSource_dataRearrangement :: Lens' DataSource (Maybe Text)
-- | The total number of observations contained in the data files that the
-- DataSource references.
dataSource_dataSizeInBytes :: Lens' DataSource (Maybe Integer)
-- | The ID that is assigned to the DataSource during creation.
dataSource_dataSourceId :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_finishedAt :: Lens' DataSource (Maybe UTCTime)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
dataSource_lastUpdatedAt :: Lens' DataSource (Maybe UTCTime)
-- | A description of the most recent details about creating the
-- DataSource.
dataSource_message :: Lens' DataSource (Maybe Text)
-- | A user-supplied name or description of the DataSource.
dataSource_name :: Lens' DataSource (Maybe Text)
-- | The number of data files referenced by the DataSource.
dataSource_numberOfFiles :: Lens' DataSource (Maybe Integer)
-- | Undocumented member.
dataSource_rDSMetadata :: Lens' DataSource (Maybe RDSMetadata)
-- | Undocumented member.
dataSource_redshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)
-- | Undocumented member.
dataSource_roleARN :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_startedAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
--
dataSource_status :: Lens' DataSource (Maybe EntityStatus)
-- | 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: newEvaluation smart constructor.
data Evaluation
Evaluation' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe PerformanceMetrics -> Maybe POSIX -> Maybe EntityStatus -> Evaluation
[$sel:computeTime:Evaluation'] :: Evaluation -> Maybe Integer
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
[$sel:createdAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The AWS user account that invoked the evaluation. The account type can
-- be either an AWS root account or an AWS Identity and Access Management
-- (IAM) user account.
[$sel:createdByIamUser:Evaluation'] :: Evaluation -> Maybe Text
-- | The ID of the DataSource that is used to evaluate the
-- MLModel.
[$sel:evaluationDataSourceId:Evaluation'] :: Evaluation -> Maybe Text
-- | The ID that is assigned to the Evaluation at creation.
[$sel:evaluationId:Evaluation'] :: Evaluation -> Maybe Text
[$sel:finishedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The location and name of the data in Amazon Simple Storage Server
-- (Amazon S3) that is used in the evaluation.
[$sel:inputDataLocationS3:Evaluation'] :: Evaluation -> Maybe Text
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
[$sel:lastUpdatedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | The ID of the MLModel that is the focus of the evaluation.
[$sel:mLModelId:Evaluation'] :: Evaluation -> Maybe Text
-- | A description of the most recent details about evaluating the
-- MLModel.
[$sel:message:Evaluation'] :: Evaluation -> Maybe Text
-- | A user-supplied name or description of the Evaluation.
[$sel:name:Evaluation'] :: Evaluation -> Maybe Text
-- | 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.
[$sel:performanceMetrics:Evaluation'] :: Evaluation -> Maybe PerformanceMetrics
[$sel:startedAt:Evaluation'] :: Evaluation -> Maybe POSIX
-- | 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.
--
[$sel:status:Evaluation'] :: Evaluation -> Maybe EntityStatus
-- | Create a value of Evaluation with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeTime:Evaluation', evaluation_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:Evaluation', evaluation_createdAt - The
-- time that the Evaluation was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:Evaluation',
-- evaluation_createdByIamUser - 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.
--
-- $sel:evaluationDataSourceId:Evaluation',
-- evaluation_evaluationDataSourceId - The ID of the
-- DataSource that is used to evaluate the MLModel.
--
-- $sel:evaluationId:Evaluation', evaluation_evaluationId -
-- The ID that is assigned to the Evaluation at creation.
--
-- $sel:finishedAt:Evaluation', evaluation_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:Evaluation',
-- evaluation_inputDataLocationS3 - The location and name of the
-- data in Amazon Simple Storage Server (Amazon S3) that is used in the
-- evaluation.
--
-- $sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt
-- - The time of the most recent edit to the Evaluation. The
-- time is expressed in epoch time.
--
-- $sel:mLModelId:Evaluation', evaluation_mLModelId - The
-- ID of the MLModel that is the focus of the evaluation.
--
-- $sel:message:Evaluation', evaluation_message - A
-- description of the most recent details about evaluating the
-- MLModel.
--
-- $sel:name:Evaluation', evaluation_name - A user-supplied
-- name or description of the Evaluation.
--
-- $sel:performanceMetrics:Evaluation',
-- evaluation_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.
--
-- $sel:startedAt:Evaluation', evaluation_startedAt -
-- Undocumented member.
--
-- $sel:status:Evaluation', evaluation_status - 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.
--
newEvaluation :: Evaluation
-- | Undocumented member.
evaluation_computeTime :: Lens' Evaluation (Maybe Integer)
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
evaluation_createdAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
evaluation_createdByIamUser :: Lens' Evaluation (Maybe Text)
-- | The ID of the DataSource that is used to evaluate the
-- MLModel.
evaluation_evaluationDataSourceId :: Lens' Evaluation (Maybe Text)
-- | The ID that is assigned to the Evaluation at creation.
evaluation_evaluationId :: Lens' Evaluation (Maybe Text)
-- | Undocumented member.
evaluation_finishedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The location and name of the data in Amazon Simple Storage Server
-- (Amazon S3) that is used in the evaluation.
evaluation_inputDataLocationS3 :: Lens' Evaluation (Maybe Text)
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
evaluation_lastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The ID of the MLModel that is the focus of the evaluation.
evaluation_mLModelId :: Lens' Evaluation (Maybe Text)
-- | A description of the most recent details about evaluating the
-- MLModel.
evaluation_message :: Lens' Evaluation (Maybe Text)
-- | A user-supplied name or description of the Evaluation.
evaluation_name :: Lens' Evaluation (Maybe Text)
-- | 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.
evaluation_performanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)
-- | Undocumented member.
evaluation_startedAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
--
evaluation_status :: Lens' Evaluation (Maybe EntityStatus)
-- | Represents the output of a GetMLModel operation.
--
-- The content consists of the detailed metadata and the current status
-- of the MLModel.
--
-- See: newMLModel smart constructor.
data MLModel
MLModel' :: Maybe Algorithm -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe RealtimeEndpointInfo -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe MLModelType -> Maybe Text -> Maybe Text -> Maybe Double -> Maybe POSIX -> Maybe Integer -> Maybe POSIX -> Maybe EntityStatus -> Maybe Text -> Maybe (HashMap Text Text) -> MLModel
-- | 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.
--
[$sel:algorithm:MLModel'] :: MLModel -> Maybe Algorithm
[$sel:computeTime:MLModel'] :: MLModel -> Maybe Integer
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
[$sel:createdAt:MLModel'] :: MLModel -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:MLModel'] :: MLModel -> Maybe Text
-- | The current endpoint of the MLModel.
[$sel:endpointInfo:MLModel'] :: MLModel -> Maybe RealtimeEndpointInfo
[$sel:finishedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:MLModel'] :: MLModel -> Maybe Text
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
[$sel:lastUpdatedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The ID assigned to the MLModel at creation.
[$sel:mLModelId:MLModel'] :: MLModel -> Maybe Text
-- | 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?".
--
[$sel:mLModelType:MLModel'] :: MLModel -> Maybe MLModelType
-- | A description of the most recent details about accessing the
-- MLModel.
[$sel:message:MLModel'] :: MLModel -> Maybe Text
-- | A user-supplied name or description of the MLModel.
[$sel:name:MLModel'] :: MLModel -> Maybe Text
[$sel:scoreThreshold:MLModel'] :: MLModel -> Maybe Double
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
[$sel:scoreThresholdLastUpdatedAt:MLModel'] :: MLModel -> Maybe POSIX
[$sel:sizeInBytes:MLModel'] :: MLModel -> Maybe Integer
[$sel:startedAt:MLModel'] :: MLModel -> Maybe POSIX
-- | The current status of an MLModel. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to create an MLModel.
-- - INPROGRESS - The creation process is underway.
-- - FAILED - The request to create an MLModel didn't
-- run to completion. The model isn't usable.
-- - COMPLETED - The creation process completed
-- successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
[$sel:status:MLModel'] :: MLModel -> Maybe EntityStatus
-- | The ID of the training DataSource. The CreateMLModel
-- operation uses the TrainingDataSourceId.
[$sel:trainingDataSourceId:MLModel'] :: MLModel -> Maybe Text
-- | A list of the training parameters in the MLModel. The list is
-- implemented as a map of key-value pairs.
--
-- The following is the current set of training parameters:
--
--
-- - sgd.maxMLModelSizeInBytes - The maximum allowed size of
-- the model. Depending on the input data, the size of the model might
-- affect its performance.The value is an integer that ranges from
-- 100000 to 2147483648. The default value is
-- 33554432.
-- - sgd.maxPasses - The number of times that the training
-- process traverses the observations to build the MLModel. The
-- value is an integer that ranges from 1 to 10000. The
-- default value is 10.
-- - sgd.shuffleType - Whether Amazon ML shuffles the training
-- data. Shuffling the data improves a model's ability to find the
-- optimal solution for a variety of data types. The valid values are
-- auto and none. The default value is
-- none.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm, which controls overfitting the data by
-- penalizing large coefficients. This parameter tends to drive
-- coefficients to zero, resulting in sparse feature set. If you use this
-- parameter, start by specifying a small value, such as
-- 1.0E-08.The value is a double that ranges from 0 to
-- MAX_DOUBLE. The default is to not use L1 normalization. This
-- parameter can't be used when L2 is specified. Use this
-- parameter sparingly.
-- - sgd.l2RegularizationAmount - The coefficient
-- regularization L2 norm, which controls overfitting the data by
-- penalizing large coefficients. This tends to drive coefficients to
-- small, nonzero values. If you use this parameter, start by specifying
-- a small value, such as 1.0E-08.The value is a double that
-- ranges from 0 to MAX_DOUBLE. The default is to not
-- use L2 normalization. This parameter can't be used when L1 is
-- specified. Use this parameter sparingly.
--
[$sel:trainingParameters:MLModel'] :: MLModel -> Maybe (HashMap Text Text)
-- | Create a value of MLModel with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:algorithm:MLModel', mLModel_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.
--
--
-- $sel:computeTime:MLModel', mLModel_computeTime -
-- Undocumented member.
--
-- MLModel, mLModel_createdAt - The time that the
-- MLModel was created. The time is expressed in epoch time.
--
-- $sel:createdByIamUser:MLModel', mLModel_createdByIamUser
-- - 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.
--
-- $sel:endpointInfo:MLModel', mLModel_endpointInfo - The
-- current endpoint of the MLModel.
--
-- $sel:finishedAt:MLModel', mLModel_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:MLModel',
-- mLModel_inputDataLocationS3 - The location of the data file or
-- directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:lastUpdatedAt:MLModel', mLModel_lastUpdatedAt - The
-- time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
--
-- $sel:mLModelId:MLModel', mLModel_mLModelId - The ID
-- assigned to the MLModel at creation.
--
-- $sel:mLModelType:MLModel', mLModel_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?".
--
--
-- $sel:message:MLModel', mLModel_message - A description
-- of the most recent details about accessing the MLModel.
--
-- $sel:name:MLModel', mLModel_name - A user-supplied name
-- or description of the MLModel.
--
-- $sel:scoreThreshold:MLModel', mLModel_scoreThreshold -
-- Undocumented member.
--
-- $sel:scoreThresholdLastUpdatedAt:MLModel',
-- mLModel_scoreThresholdLastUpdatedAt - The time of the most
-- recent edit to the ScoreThreshold. The time is expressed in
-- epoch time.
--
-- $sel:sizeInBytes:MLModel', mLModel_sizeInBytes -
-- Undocumented member.
--
-- $sel:startedAt:MLModel', mLModel_startedAt -
-- Undocumented member.
--
-- $sel:status:MLModel', mLModel_status - 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.
--
--
-- $sel:trainingDataSourceId:MLModel',
-- mLModel_trainingDataSourceId - The ID of the training
-- DataSource. The CreateMLModel operation uses the
-- TrainingDataSourceId.
--
-- $sel:trainingParameters:MLModel',
-- mLModel_trainingParameters - 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.
--
newMLModel :: MLModel
-- | 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.
--
mLModel_algorithm :: Lens' MLModel (Maybe Algorithm)
-- | Undocumented member.
mLModel_computeTime :: Lens' MLModel (Maybe Integer)
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
mLModel_createdAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
mLModel_createdByIamUser :: Lens' MLModel (Maybe Text)
-- | The current endpoint of the MLModel.
mLModel_endpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)
-- | Undocumented member.
mLModel_finishedAt :: Lens' MLModel (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
mLModel_inputDataLocationS3 :: Lens' MLModel (Maybe Text)
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
mLModel_lastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | The ID assigned to the MLModel at creation.
mLModel_mLModelId :: Lens' MLModel (Maybe Text)
-- | 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?".
--
mLModel_mLModelType :: Lens' MLModel (Maybe MLModelType)
-- | A description of the most recent details about accessing the
-- MLModel.
mLModel_message :: Lens' MLModel (Maybe Text)
-- | A user-supplied name or description of the MLModel.
mLModel_name :: Lens' MLModel (Maybe Text)
-- | Undocumented member.
mLModel_scoreThreshold :: Lens' MLModel (Maybe Double)
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
mLModel_scoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | Undocumented member.
mLModel_sizeInBytes :: Lens' MLModel (Maybe Integer)
-- | Undocumented member.
mLModel_startedAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
--
mLModel_status :: Lens' MLModel (Maybe EntityStatus)
-- | The ID of the training DataSource. The CreateMLModel
-- operation uses the TrainingDataSourceId.
mLModel_trainingDataSourceId :: Lens' MLModel (Maybe 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.
--
mLModel_trainingParameters :: Lens' MLModel (Maybe (HashMap Text Text))
-- | 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: newPerformanceMetrics smart constructor.
data PerformanceMetrics
PerformanceMetrics' :: Maybe (HashMap Text Text) -> PerformanceMetrics
[$sel:properties:PerformanceMetrics'] :: PerformanceMetrics -> Maybe (HashMap Text Text)
-- | Create a value of PerformanceMetrics with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:properties:PerformanceMetrics',
-- performanceMetrics_properties - Undocumented member.
newPerformanceMetrics :: PerformanceMetrics
-- | Undocumented member.
performanceMetrics_properties :: Lens' PerformanceMetrics (Maybe (HashMap Text Text))
-- | The output from a Predict operation:
--
--
-- - Details - Contains the following attributes:
-- DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY |
-- MULTICLASS DetailsAttributes.ALGORITHM - SGD
-- - PredictedLabel - Present for either a BINARY or
-- MULTICLASS MLModel request.
-- - PredictedScores - Contains the raw classification score
-- corresponding to each label.
-- - PredictedValue - Present for a REGRESSION
-- MLModel request.
--
--
-- See: newPrediction smart constructor.
data Prediction
Prediction' :: Maybe (HashMap DetailsAttributes Text) -> Maybe Text -> Maybe (HashMap Text Double) -> Maybe Double -> Prediction
[$sel:details:Prediction'] :: Prediction -> Maybe (HashMap DetailsAttributes Text)
-- | The prediction label for either a BINARY or
-- MULTICLASS MLModel.
[$sel:predictedLabel:Prediction'] :: Prediction -> Maybe Text
[$sel:predictedScores:Prediction'] :: Prediction -> Maybe (HashMap Text Double)
-- | The prediction value for REGRESSION MLModel.
[$sel:predictedValue:Prediction'] :: Prediction -> Maybe Double
-- | Create a value of Prediction with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:details:Prediction', prediction_details -
-- Undocumented member.
--
-- $sel:predictedLabel:Prediction',
-- prediction_predictedLabel - The prediction label for either a
-- BINARY or MULTICLASS MLModel.
--
-- $sel:predictedScores:Prediction',
-- prediction_predictedScores - Undocumented member.
--
-- $sel:predictedValue:Prediction',
-- prediction_predictedValue - The prediction value for
-- REGRESSION MLModel.
newPrediction :: Prediction
-- | Undocumented member.
prediction_details :: Lens' Prediction (Maybe (HashMap DetailsAttributes Text))
-- | The prediction label for either a BINARY or
-- MULTICLASS MLModel.
prediction_predictedLabel :: Lens' Prediction (Maybe Text)
-- | Undocumented member.
prediction_predictedScores :: Lens' Prediction (Maybe (HashMap Text Double))
-- | The prediction value for REGRESSION MLModel.
prediction_predictedValue :: Lens' Prediction (Maybe Double)
-- | The data specification of an Amazon Relational Database Service
-- (Amazon RDS) DataSource.
--
-- See: newRDSDataSpec smart constructor.
data RDSDataSpec
RDSDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> [Text] -> RDSDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:RDSDataSpec'] :: 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" ] }
[$sel:dataSchema:RDSDataSpec'] :: RDSDataSpec -> Maybe Text
-- | The Amazon S3 location of the DataSchema.
[$sel:dataSchemaUri:RDSDataSpec'] :: RDSDataSpec -> Maybe Text
-- | Describes the DatabaseName and InstanceIdentifier of
-- an Amazon RDS database.
[$sel:databaseInformation:RDSDataSpec'] :: RDSDataSpec -> RDSDatabase
-- | The query that is used to retrieve the observation data for the
-- DataSource.
[$sel:selectSqlQuery:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The AWS Identity and Access Management (IAM) credentials that are used
-- connect to the Amazon RDS database.
[$sel:databaseCredentials:RDSDataSpec'] :: RDSDataSpec -> RDSDatabaseCredentials
-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
-- from Amazon RDS using SelectSqlQuery is stored in this
-- location.
[$sel:s3StagingLocation:RDSDataSpec'] :: 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.
[$sel:resourceRole:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
-- service to monitor the progress of the copy task from Amazon RDS to
-- Amazon S3. For more information, see Role templates for data
-- pipelines.
[$sel:serviceRole:RDSDataSpec'] :: 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.
[$sel:subnetId:RDSDataSpec'] :: RDSDataSpec -> Text
-- | The security group IDs to be used to access a VPC-based RDS DB
-- instance. Ensure that there are appropriate ingress rules set up to
-- allow access to the RDS DB instance. This attribute is used by Data
-- Pipeline to carry out the copy operation from Amazon RDS to an Amazon
-- S3 task.
[$sel:securityGroupIds:RDSDataSpec'] :: RDSDataSpec -> [Text]
-- | Create a value of RDSDataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RDSDataSpec',
-- rDSDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RDSDataSpec', rDSDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RDSDataSpec',
-- rDSDataSpec_dataSchemaUri - The Amazon S3 location of the
-- DataSchema.
--
-- $sel:databaseInformation:RDSDataSpec',
-- rDSDataSpec_databaseInformation - Describes the
-- DatabaseName and InstanceIdentifier of an Amazon RDS
-- database.
--
-- $sel:selectSqlQuery:RDSDataSpec',
-- rDSDataSpec_selectSqlQuery - The query that is used to retrieve
-- the observation data for the DataSource.
--
-- $sel:databaseCredentials:RDSDataSpec',
-- rDSDataSpec_databaseCredentials - The AWS Identity and Access
-- Management (IAM) credentials that are used connect to the Amazon RDS
-- database.
--
-- $sel:s3StagingLocation:RDSDataSpec',
-- rDSDataSpec_s3StagingLocation - The Amazon S3 location for
-- staging Amazon RDS data. The data retrieved from Amazon RDS using
-- SelectSqlQuery is stored in this location.
--
-- $sel:resourceRole:RDSDataSpec', rDSDataSpec_resourceRole
-- - 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.
--
-- $sel:serviceRole:RDSDataSpec', rDSDataSpec_serviceRole -
-- 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.
--
-- $sel:subnetId:RDSDataSpec', rDSDataSpec_subnetId - 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.
--
-- $sel:securityGroupIds:RDSDataSpec',
-- rDSDataSpec_securityGroupIds - 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.
newRDSDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
rDSDataSpec_dataRearrangement :: 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" ] }
rDSDataSpec_dataSchema :: Lens' RDSDataSpec (Maybe Text)
-- | The Amazon S3 location of the DataSchema.
rDSDataSpec_dataSchemaUri :: Lens' RDSDataSpec (Maybe Text)
-- | Describes the DatabaseName and InstanceIdentifier of
-- an Amazon RDS database.
rDSDataSpec_databaseInformation :: Lens' RDSDataSpec RDSDatabase
-- | The query that is used to retrieve the observation data for the
-- DataSource.
rDSDataSpec_selectSqlQuery :: Lens' RDSDataSpec Text
-- | The AWS Identity and Access Management (IAM) credentials that are used
-- connect to the Amazon RDS database.
rDSDataSpec_databaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials
-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
-- from Amazon RDS using SelectSqlQuery is stored in this
-- location.
rDSDataSpec_s3StagingLocation :: 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.
rDSDataSpec_resourceRole :: 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.
rDSDataSpec_serviceRole :: 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.
rDSDataSpec_subnetId :: 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.
rDSDataSpec_securityGroupIds :: Lens' RDSDataSpec [Text]
-- | The database details of an Amazon RDS database.
--
-- See: newRDSDatabase smart constructor.
data RDSDatabase
RDSDatabase' :: Text -> Text -> RDSDatabase
-- | The ID of an RDS DB instance.
[$sel:instanceIdentifier:RDSDatabase'] :: RDSDatabase -> Text
[$sel:databaseName:RDSDatabase'] :: RDSDatabase -> Text
-- | Create a value of RDSDatabase with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:instanceIdentifier:RDSDatabase',
-- rDSDatabase_instanceIdentifier - The ID of an RDS DB instance.
--
-- $sel:databaseName:RDSDatabase', rDSDatabase_databaseName
-- - Undocumented member.
newRDSDatabase :: Text -> Text -> RDSDatabase
-- | The ID of an RDS DB instance.
rDSDatabase_instanceIdentifier :: Lens' RDSDatabase Text
-- | Undocumented member.
rDSDatabase_databaseName :: Lens' RDSDatabase Text
-- | The database credentials to connect to a database on an RDS DB
-- instance.
--
-- See: newRDSDatabaseCredentials smart constructor.
data RDSDatabaseCredentials
RDSDatabaseCredentials' :: Text -> Text -> RDSDatabaseCredentials
[$sel:username:RDSDatabaseCredentials'] :: RDSDatabaseCredentials -> Text
[$sel:password:RDSDatabaseCredentials'] :: RDSDatabaseCredentials -> Text
-- | Create a value of RDSDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_password - Undocumented member.
newRDSDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
-- | Undocumented member.
rDSDatabaseCredentials_username :: Lens' RDSDatabaseCredentials Text
-- | Undocumented member.
rDSDatabaseCredentials_password :: Lens' RDSDatabaseCredentials Text
-- | The datasource details that are specific to Amazon RDS.
--
-- See: newRDSMetadata smart constructor.
data RDSMetadata
RDSMetadata' :: Maybe Text -> Maybe RDSDatabase -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> RDSMetadata
-- | The ID of the Data Pipeline instance that is used to carry to copy
-- data from Amazon RDS to Amazon S3. You can use the ID to find details
-- about the instance in the Data Pipeline console.
[$sel:dataPipelineId:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | The database details required to connect to an Amazon RDS.
[$sel:database:RDSMetadata'] :: RDSMetadata -> Maybe RDSDatabase
[$sel:databaseUserName:RDSMetadata'] :: 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.
[$sel:resourceRole:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | The SQL query that is supplied during CreateDataSourceFromRDS. Returns
-- only if Verbose is true in GetDataSourceInput.
[$sel:selectSqlQuery:RDSMetadata'] :: 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.
[$sel:serviceRole:RDSMetadata'] :: RDSMetadata -> Maybe Text
-- | Create a value of RDSMetadata with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataPipelineId:RDSMetadata',
-- rDSMetadata_dataPipelineId - 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.
--
-- $sel:database:RDSMetadata', rDSMetadata_database - The
-- database details required to connect to an Amazon RDS.
--
-- $sel:databaseUserName:RDSMetadata',
-- rDSMetadata_databaseUserName - Undocumented member.
--
-- $sel:resourceRole:RDSMetadata', rDSMetadata_resourceRole
-- - 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.
--
-- $sel:selectSqlQuery:RDSMetadata',
-- rDSMetadata_selectSqlQuery - The SQL query that is supplied
-- during CreateDataSourceFromRDS. Returns only if Verbose is
-- true in GetDataSourceInput.
--
-- $sel:serviceRole:RDSMetadata', rDSMetadata_serviceRole -
-- The role (DataPipelineDefaultRole) assumed by the Data Pipeline
-- service to monitor the progress of the copy task from Amazon RDS to
-- Amazon S3. For more information, see Role templates for data
-- pipelines.
newRDSMetadata :: RDSMetadata
-- | The ID of the Data Pipeline instance that is used to carry to copy
-- data from Amazon RDS to Amazon S3. You can use the ID to find details
-- about the instance in the Data Pipeline console.
rDSMetadata_dataPipelineId :: Lens' RDSMetadata (Maybe Text)
-- | The database details required to connect to an Amazon RDS.
rDSMetadata_database :: Lens' RDSMetadata (Maybe RDSDatabase)
-- | Undocumented member.
rDSMetadata_databaseUserName :: Lens' RDSMetadata (Maybe Text)
-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2
-- instance to carry out the copy task from Amazon RDS to Amazon S3. For
-- more information, see Role templates for data pipelines.
rDSMetadata_resourceRole :: Lens' RDSMetadata (Maybe Text)
-- | The SQL query that is supplied during CreateDataSourceFromRDS. Returns
-- only if Verbose is true in GetDataSourceInput.
rDSMetadata_selectSqlQuery :: Lens' RDSMetadata (Maybe Text)
-- | The role (DataPipelineDefaultRole) assumed by the Data Pipeline
-- service to monitor the progress of the copy task from Amazon RDS to
-- Amazon S3. For more information, see Role templates for data
-- pipelines.
rDSMetadata_serviceRole :: Lens' RDSMetadata (Maybe Text)
-- | Describes the real-time endpoint information for an MLModel.
--
-- See: newRealtimeEndpointInfo smart constructor.
data RealtimeEndpointInfo
RealtimeEndpointInfo' :: Maybe POSIX -> Maybe RealtimeEndpointStatus -> Maybe Text -> Maybe Int -> RealtimeEndpointInfo
-- | The time that the request to create the real-time endpoint for the
-- MLModel was received. The time is expressed in epoch time.
[$sel:createdAt:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe POSIX
-- | 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.
--
[$sel:endpointStatus:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe RealtimeEndpointStatus
-- | The URI that specifies where to send real-time prediction requests for
-- the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
[$sel:endpointUrl:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe Text
-- | The maximum processing rate for the real-time endpoint for
-- MLModel, measured in incoming requests per second.
[$sel:peakRequestsPerSecond:RealtimeEndpointInfo'] :: RealtimeEndpointInfo -> Maybe Int
-- | Create a value of RealtimeEndpointInfo with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:createdAt:RealtimeEndpointInfo',
-- realtimeEndpointInfo_createdAt - The time that the request to
-- create the real-time endpoint for the MLModel was received.
-- The time is expressed in epoch time.
--
-- $sel:endpointStatus:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointStatus - 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.
--
--
-- $sel:endpointUrl:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointUrl - The URI that specifies where
-- to send real-time prediction requests for the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
--
-- $sel:peakRequestsPerSecond:RealtimeEndpointInfo',
-- realtimeEndpointInfo_peakRequestsPerSecond - The maximum
-- processing rate for the real-time endpoint for MLModel,
-- measured in incoming requests per second.
newRealtimeEndpointInfo :: RealtimeEndpointInfo
-- | The time that the request to create the real-time endpoint for the
-- MLModel was received. The time is expressed in epoch time.
realtimeEndpointInfo_createdAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)
-- | 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.
--
realtimeEndpointInfo_endpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)
-- | The URI that specifies where to send real-time prediction requests for
-- the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
realtimeEndpointInfo_endpointUrl :: Lens' RealtimeEndpointInfo (Maybe Text)
-- | The maximum processing rate for the real-time endpoint for
-- MLModel, measured in incoming requests per second.
realtimeEndpointInfo_peakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)
-- | Describes the data specification of an Amazon Redshift
-- DataSource.
--
-- See: newRedshiftDataSpec smart constructor.
data RedshiftDataSpec
RedshiftDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:RedshiftDataSpec'] :: 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" ] }
[$sel:dataSchema:RedshiftDataSpec'] :: RedshiftDataSpec -> Maybe Text
-- | Describes the schema location for an Amazon Redshift
-- DataSource.
[$sel:dataSchemaUri:RedshiftDataSpec'] :: RedshiftDataSpec -> Maybe Text
-- | Describes the DatabaseName and ClusterIdentifier for
-- an Amazon Redshift DataSource.
[$sel:databaseInformation:RedshiftDataSpec'] :: RedshiftDataSpec -> RedshiftDatabase
-- | Describes the SQL Query to execute on an Amazon Redshift database for
-- an Amazon Redshift DataSource.
[$sel:selectSqlQuery:RedshiftDataSpec'] :: RedshiftDataSpec -> Text
-- | Describes AWS Identity and Access Management (IAM) credentials that
-- are used connect to the Amazon Redshift database.
[$sel:databaseCredentials:RedshiftDataSpec'] :: RedshiftDataSpec -> RedshiftDatabaseCredentials
-- | Describes an Amazon S3 location to store the result set of the
-- SelectSqlQuery query.
[$sel:s3StagingLocation:RedshiftDataSpec'] :: RedshiftDataSpec -> Text
-- | Create a value of RedshiftDataSpec with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RedshiftDataSpec',
-- redshiftDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RedshiftDataSpec',
-- redshiftDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RedshiftDataSpec',
-- redshiftDataSpec_dataSchemaUri - Describes the schema location
-- for an Amazon Redshift DataSource.
--
-- $sel:databaseInformation:RedshiftDataSpec',
-- redshiftDataSpec_databaseInformation - Describes the
-- DatabaseName and ClusterIdentifier for an Amazon
-- Redshift DataSource.
--
-- $sel:selectSqlQuery:RedshiftDataSpec',
-- redshiftDataSpec_selectSqlQuery - Describes the SQL Query to
-- execute on an Amazon Redshift database for an Amazon Redshift
-- DataSource.
--
-- $sel:databaseCredentials:RedshiftDataSpec',
-- redshiftDataSpec_databaseCredentials - Describes AWS Identity
-- and Access Management (IAM) credentials that are used connect to the
-- Amazon Redshift database.
--
-- $sel:s3StagingLocation:RedshiftDataSpec',
-- redshiftDataSpec_s3StagingLocation - Describes an Amazon S3
-- location to store the result set of the SelectSqlQuery query.
newRedshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
redshiftDataSpec_dataRearrangement :: 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" ] }
redshiftDataSpec_dataSchema :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the schema location for an Amazon Redshift
-- DataSource.
redshiftDataSpec_dataSchemaUri :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the DatabaseName and ClusterIdentifier for
-- an Amazon Redshift DataSource.
redshiftDataSpec_databaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase
-- | Describes the SQL Query to execute on an Amazon Redshift database for
-- an Amazon Redshift DataSource.
redshiftDataSpec_selectSqlQuery :: Lens' RedshiftDataSpec Text
-- | Describes AWS Identity and Access Management (IAM) credentials that
-- are used connect to the Amazon Redshift database.
redshiftDataSpec_databaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials
-- | Describes an Amazon S3 location to store the result set of the
-- SelectSqlQuery query.
redshiftDataSpec_s3StagingLocation :: Lens' RedshiftDataSpec Text
-- | Describes the database details required to connect to an Amazon
-- Redshift database.
--
-- See: newRedshiftDatabase smart constructor.
data RedshiftDatabase
RedshiftDatabase' :: Text -> Text -> RedshiftDatabase
[$sel:databaseName:RedshiftDatabase'] :: RedshiftDatabase -> Text
[$sel:clusterIdentifier:RedshiftDatabase'] :: RedshiftDatabase -> Text
-- | Create a value of RedshiftDatabase with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseName:RedshiftDatabase',
-- redshiftDatabase_databaseName - Undocumented member.
--
-- $sel:clusterIdentifier:RedshiftDatabase',
-- redshiftDatabase_clusterIdentifier - Undocumented member.
newRedshiftDatabase :: Text -> Text -> RedshiftDatabase
-- | Undocumented member.
redshiftDatabase_databaseName :: Lens' RedshiftDatabase Text
-- | Undocumented member.
redshiftDatabase_clusterIdentifier :: Lens' RedshiftDatabase Text
-- | Describes the database credentials for connecting to a database on an
-- Amazon Redshift cluster.
--
-- See: newRedshiftDatabaseCredentials smart constructor.
data RedshiftDatabaseCredentials
RedshiftDatabaseCredentials' :: Text -> Text -> RedshiftDatabaseCredentials
[$sel:username:RedshiftDatabaseCredentials'] :: RedshiftDatabaseCredentials -> Text
[$sel:password:RedshiftDatabaseCredentials'] :: RedshiftDatabaseCredentials -> Text
-- | Create a value of RedshiftDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_password - Undocumented member.
newRedshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
-- | Undocumented member.
redshiftDatabaseCredentials_username :: Lens' RedshiftDatabaseCredentials Text
-- | Undocumented member.
redshiftDatabaseCredentials_password :: Lens' RedshiftDatabaseCredentials Text
-- | Describes the DataSource details specific to Amazon Redshift.
--
-- See: newRedshiftMetadata smart constructor.
data RedshiftMetadata
RedshiftMetadata' :: Maybe Text -> Maybe RedshiftDatabase -> Maybe Text -> RedshiftMetadata
[$sel:databaseUserName:RedshiftMetadata'] :: RedshiftMetadata -> Maybe Text
[$sel:redshiftDatabase:RedshiftMetadata'] :: RedshiftMetadata -> Maybe RedshiftDatabase
-- | The SQL query that is specified during CreateDataSourceFromRedshift.
-- Returns only if Verbose is true in GetDataSourceInput.
[$sel:selectSqlQuery:RedshiftMetadata'] :: RedshiftMetadata -> Maybe Text
-- | Create a value of RedshiftMetadata with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseUserName:RedshiftMetadata',
-- redshiftMetadata_databaseUserName - Undocumented member.
--
-- $sel:redshiftDatabase:RedshiftMetadata',
-- redshiftMetadata_redshiftDatabase - Undocumented member.
--
-- $sel:selectSqlQuery:RedshiftMetadata',
-- redshiftMetadata_selectSqlQuery - The SQL query that is
-- specified during CreateDataSourceFromRedshift. Returns only if
-- Verbose is true in GetDataSourceInput.
newRedshiftMetadata :: RedshiftMetadata
-- | Undocumented member.
redshiftMetadata_databaseUserName :: Lens' RedshiftMetadata (Maybe Text)
-- | Undocumented member.
redshiftMetadata_redshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)
-- | The SQL query that is specified during CreateDataSourceFromRedshift.
-- Returns only if Verbose is true in GetDataSourceInput.
redshiftMetadata_selectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)
-- | Describes the data specification of a DataSource.
--
-- See: newS3DataSpec smart constructor.
data S3DataSpec
S3DataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> Text -> S3DataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
[$sel:dataRearrangement:S3DataSpec'] :: 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" ] }
[$sel:dataSchema:S3DataSpec'] :: S3DataSpec -> Maybe Text
-- | Describes the schema location in Amazon S3. You must provide either
-- the DataSchema or the DataSchemaLocationS3.
[$sel:dataSchemaLocationS3:S3DataSpec'] :: S3DataSpec -> Maybe 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.
[$sel:dataLocationS3:S3DataSpec'] :: S3DataSpec -> Text
-- | Create a value of S3DataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:S3DataSpec',
-- s3DataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:S3DataSpec', s3DataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaLocationS3:S3DataSpec',
-- s3DataSpec_dataSchemaLocationS3 - Describes the schema location
-- in Amazon S3. You must provide either the DataSchema or the
-- DataSchemaLocationS3.
--
-- $sel:dataLocationS3:S3DataSpec',
-- s3DataSpec_dataLocationS3 - 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.
newS3DataSpec :: Text -> S3DataSpec
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
s3DataSpec_dataRearrangement :: 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" ] }
s3DataSpec_dataSchema :: Lens' S3DataSpec (Maybe Text)
-- | Describes the schema location in Amazon S3. You must provide either
-- the DataSchema or the DataSchemaLocationS3.
s3DataSpec_dataSchemaLocationS3 :: Lens' S3DataSpec (Maybe 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.
s3DataSpec_dataLocationS3 :: Lens' S3DataSpec Text
-- | A custom key-value pair associated with an ML object, such as an ML
-- model.
--
-- See: newTag smart constructor.
data Tag
Tag' :: Maybe Text -> Maybe Text -> Tag
-- | A unique identifier for the tag. Valid characters include Unicode
-- letters, digits, white space, _, ., /, =, +, -, %, and @.
[$sel:key:Tag'] :: Tag -> Maybe Text
-- | An optional string, typically used to describe or define the tag.
-- Valid characters include Unicode letters, digits, white space, _, .,
-- /, =, +, -, %, and @.
[$sel:value:Tag'] :: Tag -> Maybe Text
-- | Create a value of Tag with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:key:Tag', tag_key - A unique identifier for the
-- tag. Valid characters include Unicode letters, digits, white space, _,
-- ., /, =, +, -, %, and @.
--
-- $sel:value:Tag', tag_value - An optional string,
-- typically used to describe or define the tag. Valid characters include
-- Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
newTag :: Tag
-- | A unique identifier for the tag. Valid characters include Unicode
-- letters, digits, white space, _, ., /, =, +, -, %, and @.
tag_key :: Lens' Tag (Maybe Text)
-- | An optional string, typically used to describe or define the tag.
-- Valid characters include Unicode letters, digits, white space, _, .,
-- /, =, +, -, %, and @.
tag_value :: Lens' Tag (Maybe Text)
-- | Generates a prediction for the observation using the specified ML
-- Model.
--
-- Note: Not all response parameters will be populated. Whether a
-- response parameter is populated depends on the type of model
-- requested.
module Amazonka.MachineLearning.Predict
-- | See: newPredict smart constructor.
data Predict
Predict' :: Text -> HashMap Text Text -> Text -> Predict
-- | A unique identifier of the MLModel.
[$sel:mLModelId:Predict'] :: Predict -> Text
[$sel:record:Predict'] :: Predict -> HashMap Text Text
[$sel:predictEndpoint:Predict'] :: Predict -> Text
-- | Create a value of Predict with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- Predict, predict_mLModelId - A unique identifier of the
-- MLModel.
--
-- $sel:record:Predict', predict_record - Undocumented
-- member.
--
-- $sel:predictEndpoint:Predict', predict_predictEndpoint -
-- Undocumented member.
newPredict :: Text -> Text -> Predict
-- | A unique identifier of the MLModel.
predict_mLModelId :: Lens' Predict Text
-- | Undocumented member.
predict_record :: Lens' Predict (HashMap Text Text)
-- | Undocumented member.
predict_predictEndpoint :: Lens' Predict Text
-- | See: newPredictResponse smart constructor.
data PredictResponse
PredictResponse' :: Maybe Prediction -> Int -> PredictResponse
[$sel:prediction:PredictResponse'] :: PredictResponse -> Maybe Prediction
-- | The response's http status code.
[$sel:httpStatus:PredictResponse'] :: PredictResponse -> Int
-- | Create a value of PredictResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:prediction:PredictResponse',
-- predictResponse_prediction - Undocumented member.
--
-- $sel:httpStatus:PredictResponse',
-- predictResponse_httpStatus - The response's http status code.
newPredictResponse :: Int -> PredictResponse
-- | Undocumented member.
predictResponse_prediction :: Lens' PredictResponse (Maybe Prediction)
-- | The response's http status code.
predictResponse_httpStatus :: Lens' PredictResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.Predict.Predict
instance GHC.Show.Show Amazonka.MachineLearning.Predict.Predict
instance GHC.Read.Read Amazonka.MachineLearning.Predict.Predict
instance GHC.Classes.Eq Amazonka.MachineLearning.Predict.Predict
instance GHC.Generics.Generic Amazonka.MachineLearning.Predict.PredictResponse
instance GHC.Show.Show Amazonka.MachineLearning.Predict.PredictResponse
instance GHC.Read.Read Amazonka.MachineLearning.Predict.PredictResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.Predict.PredictResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Predict.PredictResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Amazonka.MachineLearning.Predict.Predict
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.Predict.Predict
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.Predict.Predict
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.Predict.Predict
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.Predict.Predict
-- | Returns an MLModel that includes detailed metadata, data
-- source information, and the current status of the MLModel.
--
-- GetMLModel provides results in normal or verbose format.
module Amazonka.MachineLearning.GetMLModel
-- | See: newGetMLModel smart constructor.
data GetMLModel
GetMLModel' :: Maybe Bool -> Text -> GetMLModel
-- | Specifies whether the GetMLModel operation should return
-- Recipe.
--
-- If true, Recipe is returned.
--
-- If false, Recipe is not returned.
[$sel:verbose:GetMLModel'] :: GetMLModel -> Maybe Bool
-- | The ID assigned to the MLModel at creation.
[$sel:mLModelId:GetMLModel'] :: GetMLModel -> Text
-- | Create a value of GetMLModel with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:verbose:GetMLModel', getMLModel_verbose - Specifies
-- whether the GetMLModel operation should return
-- Recipe.
--
-- If true, Recipe is returned.
--
-- If false, Recipe is not returned.
--
-- GetMLModel, getMLModel_mLModelId - The ID assigned to
-- the MLModel at creation.
newGetMLModel :: Text -> GetMLModel
-- | Specifies whether the GetMLModel operation should return
-- Recipe.
--
-- If true, Recipe is returned.
--
-- If false, Recipe is not returned.
getMLModel_verbose :: Lens' GetMLModel (Maybe Bool)
-- | The ID assigned to the MLModel at creation.
getMLModel_mLModelId :: Lens' GetMLModel Text
-- | Represents the output of a GetMLModel operation, and provides
-- detailed information about a MLModel.
--
-- See: newGetMLModelResponse smart constructor.
data GetMLModelResponse
GetMLModelResponse' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe RealtimeEndpointInfo -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe MLModelType -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Double -> Maybe POSIX -> Maybe Integer -> Maybe POSIX -> Maybe EntityStatus -> Maybe Text -> Maybe (HashMap Text Text) -> Int -> GetMLModelResponse
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the MLModel, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- MLModel is in the COMPLETED state.
[$sel:computeTime:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Integer
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
[$sel:createdAt:GetMLModelResponse'] :: GetMLModelResponse -> Maybe POSIX
-- | The AWS user account from which the MLModel was created. The
-- account type can be either an AWS root account or an AWS Identity and
-- Access Management (IAM) user account.
[$sel:createdByIamUser:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The current endpoint of the MLModel
[$sel:endpointInfo:GetMLModelResponse'] :: GetMLModelResponse -> Maybe RealtimeEndpointInfo
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as COMPLETED or FAILED.
-- FinishedAt is only available when the MLModel is in
-- the COMPLETED or FAILED state.
[$sel:finishedAt:GetMLModelResponse'] :: GetMLModelResponse -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
[$sel:lastUpdatedAt:GetMLModelResponse'] :: GetMLModelResponse -> Maybe POSIX
-- | A link to the file that contains logs of the CreateMLModel
-- operation.
[$sel:logUri:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The MLModel ID, which is same as the MLModelId in the
-- request.
[$sel:mLModelId:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | 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 an e-commerce website?"
-- - MULTICLASS -- Produces one of several possible results. For
-- example, "Is this a HIGH, LOW or MEDIUM risk trade?"
--
[$sel:mLModelType:GetMLModelResponse'] :: GetMLModelResponse -> Maybe MLModelType
-- | A description of the most recent details about accessing the
-- MLModel.
[$sel:message:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | A user-supplied name or description of the MLModel.
[$sel:name:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The recipe to use when training the MLModel. The
-- Recipe provides detailed information about the observation
-- data to use during training, and manipulations to perform on the
-- observation data during training.
--
-- Note: This parameter is provided as part of the verbose format.
[$sel:recipe:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The schema used by all of the data files referenced by the
-- DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
[$sel:schema:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Text
-- | The scoring threshold is used in binary classification
-- MLModel models. It marks the boundary between a positive
-- prediction and a negative prediction.
--
-- Output values greater than or equal to the threshold receive a
-- positive result from the MLModel, such as true. Output values
-- less than the threshold receive a negative response from the MLModel,
-- such as false.
[$sel:scoreThreshold:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Double
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
[$sel:scoreThresholdLastUpdatedAt:GetMLModelResponse'] :: GetMLModelResponse -> Maybe POSIX
[$sel:sizeInBytes:GetMLModelResponse'] :: GetMLModelResponse -> Maybe Integer
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as INPROGRESS. StartedAt isn't
-- available if the MLModel is in the PENDING state.
[$sel:startedAt:GetMLModelResponse'] :: GetMLModelResponse -> Maybe POSIX
-- | The current status of the MLModel. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to describe a MLModel.
-- - INPROGRESS - The request is processing.
-- - FAILED - The request did not run to completion. The ML
-- model isn't usable.
-- - COMPLETED - The request completed successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
[$sel:status:GetMLModelResponse'] :: GetMLModelResponse -> Maybe EntityStatus
-- | The ID of the training DataSource.
[$sel:trainingDataSourceId:GetMLModelResponse'] :: GetMLModelResponse -> Maybe 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 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
[$sel:trainingParameters:GetMLModelResponse'] :: GetMLModelResponse -> Maybe (HashMap Text Text)
-- | The response's http status code.
[$sel:httpStatus:GetMLModelResponse'] :: GetMLModelResponse -> Int
-- | Create a value of GetMLModelResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetMLModelResponse, getMLModelResponse_computeTime - The
-- approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the MLModel, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- MLModel is in the COMPLETED state.
--
-- GetMLModelResponse, getMLModelResponse_createdAt - The
-- time that the MLModel was created. The time is expressed in
-- epoch time.
--
-- GetMLModelResponse, getMLModelResponse_createdByIamUser
-- - 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.
--
-- GetMLModelResponse, getMLModelResponse_endpointInfo -
-- The current endpoint of the MLModel
--
-- GetMLModelResponse, getMLModelResponse_finishedAt - The
-- epoch time when Amazon Machine Learning marked the MLModel as
-- COMPLETED or FAILED. FinishedAt is only
-- available when the MLModel is in the COMPLETED or
-- FAILED state.
--
-- GetMLModelResponse,
-- getMLModelResponse_inputDataLocationS3 - The location of the
-- data file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetMLModelResponse, getMLModelResponse_lastUpdatedAt -
-- The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
--
-- $sel:logUri:GetMLModelResponse',
-- getMLModelResponse_logUri - A link to the file that contains
-- logs of the CreateMLModel operation.
--
-- GetMLModel, getMLModelResponse_mLModelId - The MLModel
-- ID, which is same as the MLModelId in the request.
--
-- GetMLModelResponse, getMLModelResponse_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 an e-commerce website?"
-- - MULTICLASS -- Produces one of several possible results. For
-- example, "Is this a HIGH, LOW or MEDIUM risk trade?"
--
--
-- GetMLModelResponse, getMLModelResponse_message - A
-- description of the most recent details about accessing the
-- MLModel.
--
-- GetMLModelResponse, getMLModelResponse_name - A
-- user-supplied name or description of the MLModel.
--
-- $sel:recipe:GetMLModelResponse',
-- getMLModelResponse_recipe - The recipe to use when training the
-- MLModel. The Recipe provides detailed information
-- about the observation data to use during training, and manipulations
-- to perform on the observation data during training.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- $sel:schema:GetMLModelResponse',
-- getMLModelResponse_schema - The schema used by all of the data
-- files referenced by the DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- GetMLModelResponse, getMLModelResponse_scoreThreshold -
-- The scoring threshold is used in binary classification
-- MLModel models. It marks the boundary between a positive
-- prediction and a negative prediction.
--
-- Output values greater than or equal to the threshold receive a
-- positive result from the MLModel, such as true. Output values
-- less than the threshold receive a negative response from the MLModel,
-- such as false.
--
-- GetMLModelResponse,
-- getMLModelResponse_scoreThresholdLastUpdatedAt - The time of
-- the most recent edit to the ScoreThreshold. The time is
-- expressed in epoch time.
--
-- GetMLModelResponse, getMLModelResponse_sizeInBytes -
-- Undocumented member.
--
-- GetMLModelResponse, getMLModelResponse_startedAt - The
-- epoch time when Amazon Machine Learning marked the MLModel as
-- INPROGRESS. StartedAt isn't available if the
-- MLModel is in the PENDING state.
--
-- GetMLModelResponse, getMLModelResponse_status - The
-- current status of the MLModel. This element can have one of
-- the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to describe a MLModel.
-- - INPROGRESS - The request is processing.
-- - FAILED - The request did not run to completion. The ML
-- model isn't usable.
-- - COMPLETED - The request completed successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
--
-- GetMLModelResponse,
-- getMLModelResponse_trainingDataSourceId - The ID of the
-- training DataSource.
--
-- GetMLModelResponse,
-- getMLModelResponse_trainingParameters - 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 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
--
-- $sel:httpStatus:GetMLModelResponse',
-- getMLModelResponse_httpStatus - The response's http status
-- code.
newGetMLModelResponse :: Int -> GetMLModelResponse
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the MLModel, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- MLModel is in the COMPLETED state.
getMLModelResponse_computeTime :: Lens' GetMLModelResponse (Maybe Integer)
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
getMLModelResponse_createdAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | 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.
getMLModelResponse_createdByIamUser :: Lens' GetMLModelResponse (Maybe Text)
-- | The current endpoint of the MLModel
getMLModelResponse_endpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo)
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as COMPLETED or FAILED.
-- FinishedAt is only available when the MLModel is in
-- the COMPLETED or FAILED state.
getMLModelResponse_finishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getMLModelResponse_inputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text)
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
getMLModelResponse_lastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the CreateMLModel
-- operation.
getMLModelResponse_logUri :: Lens' GetMLModelResponse (Maybe Text)
-- | The MLModel ID, which is same as the MLModelId in the
-- request.
getMLModelResponse_mLModelId :: Lens' GetMLModelResponse (Maybe Text)
-- | 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 an e-commerce website?"
-- - MULTICLASS -- Produces one of several possible results. For
-- example, "Is this a HIGH, LOW or MEDIUM risk trade?"
--
getMLModelResponse_mLModelType :: Lens' GetMLModelResponse (Maybe MLModelType)
-- | A description of the most recent details about accessing the
-- MLModel.
getMLModelResponse_message :: Lens' GetMLModelResponse (Maybe Text)
-- | A user-supplied name or description of the MLModel.
getMLModelResponse_name :: Lens' GetMLModelResponse (Maybe Text)
-- | The recipe to use when training the MLModel. The
-- Recipe provides detailed information about the observation
-- data to use during training, and manipulations to perform on the
-- observation data during training.
--
-- Note: This parameter is provided as part of the verbose format.
getMLModelResponse_recipe :: Lens' GetMLModelResponse (Maybe Text)
-- | The schema used by all of the data files referenced by the
-- DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
getMLModelResponse_schema :: Lens' GetMLModelResponse (Maybe Text)
-- | The scoring threshold is used in binary classification
-- MLModel models. It marks the boundary between a positive
-- prediction and a negative prediction.
--
-- Output values greater than or equal to the threshold receive a
-- positive result from the MLModel, such as true. Output values
-- less than the threshold receive a negative response from the MLModel,
-- such as false.
getMLModelResponse_scoreThreshold :: Lens' GetMLModelResponse (Maybe Double)
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
getMLModelResponse_scoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | Undocumented member.
getMLModelResponse_sizeInBytes :: Lens' GetMLModelResponse (Maybe Integer)
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as INPROGRESS. StartedAt isn't
-- available if the MLModel is in the PENDING state.
getMLModelResponse_startedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | The current status of the MLModel. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to describe a MLModel.
-- - INPROGRESS - The request is processing.
-- - FAILED - The request did not run to completion. The ML
-- model isn't usable.
-- - COMPLETED - The request completed successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
getMLModelResponse_status :: Lens' GetMLModelResponse (Maybe EntityStatus)
-- | The ID of the training DataSource.
getMLModelResponse_trainingDataSourceId :: Lens' GetMLModelResponse (Maybe 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 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
getMLModelResponse_trainingParameters :: Lens' GetMLModelResponse (Maybe (HashMap Text Text))
-- | The response's http status code.
getMLModelResponse_httpStatus :: Lens' GetMLModelResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.GetMLModel.GetMLModel
instance GHC.Show.Show Amazonka.MachineLearning.GetMLModel.GetMLModel
instance GHC.Read.Read Amazonka.MachineLearning.GetMLModel.GetMLModel
instance GHC.Classes.Eq Amazonka.MachineLearning.GetMLModel.GetMLModel
instance GHC.Generics.Generic Amazonka.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Show.Show Amazonka.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Read.Read Amazonka.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.GetMLModel.GetMLModelResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetMLModel.GetMLModelResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.GetMLModel.GetMLModel
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.GetMLModel.GetMLModel
-- | Returns an Evaluation that includes metadata as well as the
-- current status of the Evaluation.
module Amazonka.MachineLearning.GetEvaluation
-- | See: newGetEvaluation smart constructor.
data GetEvaluation
GetEvaluation' :: Text -> GetEvaluation
-- | The ID of the Evaluation to retrieve. The evaluation of each
-- MLModel is recorded and cataloged. The ID provides the means
-- to access the information.
[$sel:evaluationId:GetEvaluation'] :: GetEvaluation -> Text
-- | Create a value of GetEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetEvaluation, getEvaluation_evaluationId - The ID of
-- the Evaluation to retrieve. The evaluation of each
-- MLModel is recorded and cataloged. The ID provides the means
-- to access the information.
newGetEvaluation :: Text -> GetEvaluation
-- | The ID of the Evaluation to retrieve. The evaluation of each
-- MLModel is recorded and cataloged. The ID provides the means
-- to access the information.
getEvaluation_evaluationId :: Lens' GetEvaluation Text
-- | Represents the output of a GetEvaluation operation and
-- describes an Evaluation.
--
-- See: newGetEvaluationResponse smart constructor.
data GetEvaluationResponse
GetEvaluationResponse' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe PerformanceMetrics -> Maybe POSIX -> Maybe EntityStatus -> Int -> GetEvaluationResponse
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the Evaluation, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- Evaluation is in the COMPLETED state.
[$sel:computeTime:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Integer
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
[$sel:createdAt:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | The DataSource used for this evaluation.
[$sel:evaluationDataSourceId:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | The evaluation ID which is same as the EvaluationId in the
-- request.
[$sel:evaluationId:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as COMPLETED or FAILED.
-- FinishedAt is only available when the Evaluation is
-- in the COMPLETED or FAILED state.
[$sel:finishedAt:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
[$sel:lastUpdatedAt:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe POSIX
-- | A link to the file that contains logs of the CreateEvaluation
-- operation.
[$sel:logUri:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | The ID of the MLModel that was the focus of the evaluation.
[$sel:mLModelId:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | A description of the most recent details about evaluating the
-- MLModel.
[$sel:message:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | A user-supplied name or description of the Evaluation.
[$sel:name:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe Text
-- | Measurements of how well the MLModel performed using
-- observations referenced by the DataSource. One of the
-- following metric 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.
[$sel:performanceMetrics:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe PerformanceMetrics
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as INPROGRESS. StartedAt isn't
-- available if the Evaluation is in the PENDING state.
[$sel:startedAt:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe POSIX
-- | The status of the evaluation. This element can have one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Language (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.
--
[$sel:status:GetEvaluationResponse'] :: GetEvaluationResponse -> Maybe EntityStatus
-- | The response's http status code.
[$sel:httpStatus:GetEvaluationResponse'] :: GetEvaluationResponse -> Int
-- | Create a value of GetEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetEvaluationResponse, getEvaluationResponse_computeTime
-- - The approximate CPU time in milliseconds that Amazon Machine
-- Learning spent processing the Evaluation, normalized and
-- scaled on computation resources. ComputeTime is only
-- available if the Evaluation is in the COMPLETED
-- state.
--
-- GetEvaluationResponse, getEvaluationResponse_createdAt -
-- The time that the Evaluation was created. The time is
-- expressed in epoch time.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_createdByIamUser - 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.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_evaluationDataSourceId - The
-- DataSource used for this evaluation.
--
-- GetEvaluation, getEvaluationResponse_evaluationId - The
-- evaluation ID which is same as the EvaluationId in the
-- request.
--
-- GetEvaluationResponse, getEvaluationResponse_finishedAt
-- - The epoch time when Amazon Machine Learning marked the
-- Evaluation as COMPLETED or FAILED.
-- FinishedAt is only available when the Evaluation is
-- in the COMPLETED or FAILED state.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_inputDataLocationS3 - The location of the
-- data file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetEvaluationResponse,
-- getEvaluationResponse_lastUpdatedAt - The time of the most
-- recent edit to the Evaluation. The time is expressed in epoch
-- time.
--
-- $sel:logUri:GetEvaluationResponse',
-- getEvaluationResponse_logUri - A link to the file that contains
-- logs of the CreateEvaluation operation.
--
-- GetEvaluationResponse, getEvaluationResponse_mLModelId -
-- The ID of the MLModel that was the focus of the evaluation.
--
-- GetEvaluationResponse, getEvaluationResponse_message - A
-- description of the most recent details about evaluating the
-- MLModel.
--
-- GetEvaluationResponse, getEvaluationResponse_name - A
-- user-supplied name or description of the Evaluation.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_performanceMetrics - Measurements of how
-- well the MLModel performed using observations referenced by
-- the DataSource. One of the following metric 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.
--
-- GetEvaluationResponse, getEvaluationResponse_startedAt -
-- The epoch time when Amazon Machine Learning marked the
-- Evaluation as INPROGRESS. StartedAt isn't
-- available if the Evaluation is in the PENDING state.
--
-- GetEvaluationResponse, getEvaluationResponse_status -
-- The status of the evaluation. This element can have one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Language (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.
--
--
-- $sel:httpStatus:GetEvaluationResponse',
-- getEvaluationResponse_httpStatus - The response's http status
-- code.
newGetEvaluationResponse :: Int -> GetEvaluationResponse
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the Evaluation, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- Evaluation is in the COMPLETED state.
getEvaluationResponse_computeTime :: Lens' GetEvaluationResponse (Maybe Integer)
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
getEvaluationResponse_createdAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | 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.
getEvaluationResponse_createdByIamUser :: Lens' GetEvaluationResponse (Maybe Text)
-- | The DataSource used for this evaluation.
getEvaluationResponse_evaluationDataSourceId :: Lens' GetEvaluationResponse (Maybe Text)
-- | The evaluation ID which is same as the EvaluationId in the
-- request.
getEvaluationResponse_evaluationId :: Lens' GetEvaluationResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as COMPLETED or FAILED.
-- FinishedAt is only available when the Evaluation is
-- in the COMPLETED or FAILED state.
getEvaluationResponse_finishedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getEvaluationResponse_inputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text)
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
getEvaluationResponse_lastUpdatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the CreateEvaluation
-- operation.
getEvaluationResponse_logUri :: Lens' GetEvaluationResponse (Maybe Text)
-- | The ID of the MLModel that was the focus of the evaluation.
getEvaluationResponse_mLModelId :: Lens' GetEvaluationResponse (Maybe Text)
-- | A description of the most recent details about evaluating the
-- MLModel.
getEvaluationResponse_message :: Lens' GetEvaluationResponse (Maybe Text)
-- | A user-supplied name or description of the Evaluation.
getEvaluationResponse_name :: Lens' GetEvaluationResponse (Maybe Text)
-- | Measurements of how well the MLModel performed using
-- observations referenced by the DataSource. One of the
-- following metric 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.
getEvaluationResponse_performanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics)
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as INPROGRESS. StartedAt isn't
-- available if the Evaluation is in the PENDING state.
getEvaluationResponse_startedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | The status of the evaluation. This element can have one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Language (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.
--
getEvaluationResponse_status :: Lens' GetEvaluationResponse (Maybe EntityStatus)
-- | The response's http status code.
getEvaluationResponse_httpStatus :: Lens' GetEvaluationResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Show.Show Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Read.Read Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Classes.Eq Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Generics.Generic Amazonka.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Show.Show Amazonka.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Read.Read Amazonka.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.GetEvaluation.GetEvaluationResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetEvaluation.GetEvaluationResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.GetEvaluation.GetEvaluation
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.GetEvaluation.GetEvaluation
-- | Returns a DataSource that includes metadata and data file
-- information, as well as the current status of the DataSource.
--
-- GetDataSource provides results in normal or verbose format.
-- The verbose format adds the schema description and the list of files
-- pointed to by the DataSource to the normal format.
module Amazonka.MachineLearning.GetDataSource
-- | See: newGetDataSource smart constructor.
data GetDataSource
GetDataSource' :: Maybe Bool -> Text -> GetDataSource
-- | Specifies whether the GetDataSource operation should return
-- DataSourceSchema.
--
-- If true, DataSourceSchema is returned.
--
-- If false, DataSourceSchema is not returned.
[$sel:verbose:GetDataSource'] :: GetDataSource -> Maybe Bool
-- | The ID assigned to the DataSource at creation.
[$sel:dataSourceId:GetDataSource'] :: GetDataSource -> Text
-- | Create a value of GetDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:verbose:GetDataSource', getDataSource_verbose -
-- Specifies whether the GetDataSource operation should return
-- DataSourceSchema.
--
-- If true, DataSourceSchema is returned.
--
-- If false, DataSourceSchema is not returned.
--
-- GetDataSource, getDataSource_dataSourceId - The ID
-- assigned to the DataSource at creation.
newGetDataSource :: Text -> GetDataSource
-- | Specifies whether the GetDataSource operation should return
-- DataSourceSchema.
--
-- If true, DataSourceSchema is returned.
--
-- If false, DataSourceSchema is not returned.
getDataSource_verbose :: Lens' GetDataSource (Maybe Bool)
-- | The ID assigned to the DataSource at creation.
getDataSource_dataSourceId :: Lens' GetDataSource Text
-- | Represents the output of a GetDataSource operation and
-- describes a DataSource.
--
-- See: newGetDataSourceResponse smart constructor.
data GetDataSourceResponse
GetDataSourceResponse' :: Maybe Bool -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe RDSMetadata -> Maybe RedshiftMetadata -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Int -> GetDataSourceResponse
-- | The parameter is true if statistics need to be generated from
-- the observation data.
[$sel:computeStatistics:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Bool
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the DataSource, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- DataSource is in the COMPLETED state and the
-- ComputeStatistics is set to true.
[$sel:computeTime:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Integer
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
[$sel:createdAt:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:dataLocationS3:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
[$sel:dataRearrangement:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The total size of observations in the data files.
[$sel:dataSizeInBytes:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Integer
-- | The ID assigned to the DataSource at creation. This value
-- should be identical to the value of the DataSourceId in the
-- request.
[$sel:dataSourceId:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The schema used by all of the data files of this DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
[$sel:dataSourceSchema:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as COMPLETED or FAILED.
-- FinishedAt is only available when the DataSource is
-- in the COMPLETED or FAILED state.
[$sel:finishedAt:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe POSIX
-- | The time of the most recent edit to the DataSource. The time
-- is expressed in epoch time.
[$sel:lastUpdatedAt:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe POSIX
-- | A link to the file containing logs of CreateDataSourceFrom*
-- operations.
[$sel:logUri:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The user-supplied description of the most recent details about
-- creating the DataSource.
[$sel:message:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | A user-supplied name or description of the DataSource.
[$sel:name:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The number of data files referenced by the DataSource.
[$sel:numberOfFiles:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Integer
[$sel:rDSMetadata:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe RDSMetadata
[$sel:redshiftMetadata:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe RedshiftMetadata
[$sel:roleARN:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe Text
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as INPROGRESS. StartedAt isn't
-- available if the DataSource is in the PENDING state.
[$sel:startedAt:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe POSIX
-- | The current status of the DataSource. This element can have
-- one of the following values:
--
--
-- - PENDING - 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.
--
[$sel:status:GetDataSourceResponse'] :: GetDataSourceResponse -> Maybe EntityStatus
-- | The response's http status code.
[$sel:httpStatus:GetDataSourceResponse'] :: GetDataSourceResponse -> Int
-- | Create a value of GetDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetDataSourceResponse,
-- getDataSourceResponse_computeStatistics - The parameter is
-- true if statistics need to be generated from the observation
-- data.
--
-- GetDataSourceResponse, getDataSourceResponse_computeTime
-- - The approximate CPU time in milliseconds that Amazon Machine
-- Learning spent processing the DataSource, normalized and
-- scaled on computation resources. ComputeTime is only
-- available if the DataSource is in the COMPLETED
-- state and the ComputeStatistics is set to true.
--
-- GetDataSourceResponse, getDataSourceResponse_createdAt -
-- The time that the DataSource was created. The time is
-- expressed in epoch time.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_createdByIamUser - 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.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataLocationS3 - The location of the data
-- file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataRearrangement - A JSON string that
-- represents the splitting and rearrangement requirement used when this
-- DataSource was created.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataSizeInBytes - The total size of
-- observations in the data files.
--
-- GetDataSource, getDataSourceResponse_dataSourceId - The
-- ID assigned to the DataSource at creation. This value should
-- be identical to the value of the DataSourceId in the request.
--
-- $sel:dataSourceSchema:GetDataSourceResponse',
-- getDataSourceResponse_dataSourceSchema - The schema used by all
-- of the data files of this DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- GetDataSourceResponse, getDataSourceResponse_finishedAt
-- - The epoch time when Amazon Machine Learning marked the
-- DataSource as COMPLETED or FAILED.
-- FinishedAt is only available when the DataSource is
-- in the COMPLETED or FAILED state.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_lastUpdatedAt - The time of the most
-- recent edit to the DataSource. The time is expressed in epoch
-- time.
--
-- $sel:logUri:GetDataSourceResponse',
-- getDataSourceResponse_logUri - A link to the file containing
-- logs of CreateDataSourceFrom* operations.
--
-- GetDataSourceResponse, getDataSourceResponse_message -
-- The user-supplied description of the most recent details about
-- creating the DataSource.
--
-- GetDataSourceResponse, getDataSourceResponse_name - A
-- user-supplied name or description of the DataSource.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_numberOfFiles - The number of data files
-- referenced by the DataSource.
--
-- GetDataSourceResponse, getDataSourceResponse_rDSMetadata
-- - Undocumented member.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_redshiftMetadata - Undocumented member.
--
-- GetDataSourceResponse, getDataSourceResponse_roleARN -
-- Undocumented member.
--
-- GetDataSourceResponse, getDataSourceResponse_startedAt -
-- The epoch time when Amazon Machine Learning marked the
-- DataSource as INPROGRESS. StartedAt isn't
-- available if the DataSource is in the PENDING state.
--
-- GetDataSourceResponse, getDataSourceResponse_status -
-- The current status of the DataSource. This element can have
-- one of the following values:
--
--
-- - PENDING - 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.
--
--
-- $sel:httpStatus:GetDataSourceResponse',
-- getDataSourceResponse_httpStatus - The response's http status
-- code.
newGetDataSourceResponse :: Int -> GetDataSourceResponse
-- | The parameter is true if statistics need to be generated from
-- the observation data.
getDataSourceResponse_computeStatistics :: Lens' GetDataSourceResponse (Maybe Bool)
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the DataSource, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- DataSource is in the COMPLETED state and the
-- ComputeStatistics is set to true.
getDataSourceResponse_computeTime :: Lens' GetDataSourceResponse (Maybe Integer)
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
getDataSourceResponse_createdAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | 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.
getDataSourceResponse_createdByIamUser :: Lens' GetDataSourceResponse (Maybe Text)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getDataSourceResponse_dataLocationS3 :: Lens' GetDataSourceResponse (Maybe Text)
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
getDataSourceResponse_dataRearrangement :: Lens' GetDataSourceResponse (Maybe Text)
-- | The total size of observations in the data files.
getDataSourceResponse_dataSizeInBytes :: Lens' GetDataSourceResponse (Maybe Integer)
-- | The ID assigned to the DataSource at creation. This value
-- should be identical to the value of the DataSourceId in the
-- request.
getDataSourceResponse_dataSourceId :: Lens' GetDataSourceResponse (Maybe Text)
-- | The schema used by all of the data files of this DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
getDataSourceResponse_dataSourceSchema :: Lens' GetDataSourceResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as COMPLETED or FAILED.
-- FinishedAt is only available when the DataSource is
-- in the COMPLETED or FAILED state.
getDataSourceResponse_finishedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | The time of the most recent edit to the DataSource. The time
-- is expressed in epoch time.
getDataSourceResponse_lastUpdatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | A link to the file containing logs of CreateDataSourceFrom*
-- operations.
getDataSourceResponse_logUri :: Lens' GetDataSourceResponse (Maybe Text)
-- | The user-supplied description of the most recent details about
-- creating the DataSource.
getDataSourceResponse_message :: Lens' GetDataSourceResponse (Maybe Text)
-- | A user-supplied name or description of the DataSource.
getDataSourceResponse_name :: Lens' GetDataSourceResponse (Maybe Text)
-- | The number of data files referenced by the DataSource.
getDataSourceResponse_numberOfFiles :: Lens' GetDataSourceResponse (Maybe Integer)
-- | Undocumented member.
getDataSourceResponse_rDSMetadata :: Lens' GetDataSourceResponse (Maybe RDSMetadata)
-- | Undocumented member.
getDataSourceResponse_redshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata)
-- | Undocumented member.
getDataSourceResponse_roleARN :: Lens' GetDataSourceResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as INPROGRESS. StartedAt isn't
-- available if the DataSource is in the PENDING state.
getDataSourceResponse_startedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | The current status of the DataSource. This element can have
-- one of the following values:
--
--
-- - PENDING - 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.
--
getDataSourceResponse_status :: Lens' GetDataSourceResponse (Maybe EntityStatus)
-- | The response's http status code.
getDataSourceResponse_httpStatus :: Lens' GetDataSourceResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.GetDataSource.GetDataSource
instance GHC.Show.Show Amazonka.MachineLearning.GetDataSource.GetDataSource
instance GHC.Read.Read Amazonka.MachineLearning.GetDataSource.GetDataSource
instance GHC.Classes.Eq Amazonka.MachineLearning.GetDataSource.GetDataSource
instance GHC.Generics.Generic Amazonka.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Show.Show Amazonka.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Read.Read Amazonka.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.GetDataSource.GetDataSourceResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetDataSource.GetDataSourceResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.GetDataSource.GetDataSource
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.GetDataSource.GetDataSource
-- | Returns a BatchPrediction that includes detailed metadata,
-- status, and data file information for a Batch Prediction
-- request.
module Amazonka.MachineLearning.GetBatchPrediction
-- | See: newGetBatchPrediction smart constructor.
data GetBatchPrediction
GetBatchPrediction' :: Text -> GetBatchPrediction
-- | An ID assigned to the BatchPrediction at creation.
[$sel:batchPredictionId:GetBatchPrediction'] :: GetBatchPrediction -> Text
-- | Create a value of GetBatchPrediction with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetBatchPrediction, getBatchPrediction_batchPredictionId
-- - An ID assigned to the BatchPrediction at creation.
newGetBatchPrediction :: Text -> GetBatchPrediction
-- | An ID assigned to the BatchPrediction at creation.
getBatchPrediction_batchPredictionId :: Lens' GetBatchPrediction Text
-- | Represents the output of a GetBatchPrediction operation and
-- describes a BatchPrediction.
--
-- See: newGetBatchPredictionResponse smart constructor.
data GetBatchPredictionResponse
GetBatchPredictionResponse' :: Maybe Text -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Maybe Integer -> Int -> GetBatchPredictionResponse
-- | The ID of the DataSource that was used to create the
-- BatchPrediction.
[$sel:batchPredictionDataSourceId:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | An ID assigned to the BatchPrediction at creation. This value
-- should be identical to the value of the BatchPredictionID in
-- the request.
[$sel:batchPredictionId:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the BatchPrediction, normalized and scaled
-- on computation resources. ComputeTime is only available if
-- the BatchPrediction is in the COMPLETED state.
[$sel:computeTime:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Integer
-- | The time when the BatchPrediction was created. The time is
-- expressed in epoch time.
[$sel:createdAt:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe POSIX
-- | 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.
[$sel:createdByIamUser:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as COMPLETED or FAILED.
-- FinishedAt is only available when the
-- BatchPrediction is in the COMPLETED or
-- FAILED state.
[$sel:finishedAt:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe POSIX
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
[$sel:inputDataLocationS3:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The number of invalid records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
[$sel:invalidRecordCount:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Integer
-- | The time of the most recent edit to BatchPrediction. The time
-- is expressed in epoch time.
[$sel:lastUpdatedAt:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe POSIX
-- | A link to the file that contains logs of the
-- CreateBatchPrediction operation.
[$sel:logUri:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
[$sel:mLModelId:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | A description of the most recent details about processing the batch
-- prediction request.
[$sel:message:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | A user-supplied name or description of the BatchPrediction.
[$sel:name:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The location of an Amazon S3 bucket or directory to receive the
-- operation results.
[$sel:outputUri:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Text
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as INPROGRESS. StartedAt
-- isn't available if the BatchPrediction is in the
-- PENDING state.
[$sel:startedAt:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe POSIX
-- | The status of the BatchPrediction, which can be one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate batch predictions.
-- - INPROGRESS - The batch predictions are in progress.
-- - FAILED - The request to perform a batch prediction did
-- not run to completion. It is not usable.
-- - COMPLETED - The batch prediction process completed
-- successfully.
-- - DELETED - The BatchPrediction is marked as
-- deleted. It is not usable.
--
[$sel:status:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe EntityStatus
-- | The number of total records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
[$sel:totalRecordCount:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Maybe Integer
-- | The response's http status code.
[$sel:httpStatus:GetBatchPredictionResponse'] :: GetBatchPredictionResponse -> Int
-- | Create a value of GetBatchPredictionResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_batchPredictionDataSourceId - The ID
-- of the DataSource that was used to create the
-- BatchPrediction.
--
-- GetBatchPrediction,
-- getBatchPredictionResponse_batchPredictionId - An ID assigned
-- to the BatchPrediction at creation. This value should be
-- identical to the value of the BatchPredictionID in the
-- request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_computeTime - The approximate CPU
-- time in milliseconds that Amazon Machine Learning spent processing the
-- BatchPrediction, normalized and scaled on computation
-- resources. ComputeTime is only available if the
-- BatchPrediction is in the COMPLETED state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_createdAt - The time when the
-- BatchPrediction was created. The time is expressed in epoch
-- time.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_createdByIamUser - 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.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_finishedAt - The epoch time when
-- Amazon Machine Learning marked the BatchPrediction as
-- COMPLETED or FAILED. FinishedAt is only
-- available when the BatchPrediction is in the
-- COMPLETED or FAILED state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_inputDataLocationS3 - The location
-- of the data file or directory in Amazon Simple Storage Service (Amazon
-- S3).
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_invalidRecordCount - The number of
-- invalid records that Amazon Machine Learning saw while processing the
-- BatchPrediction.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_lastUpdatedAt - The time of the most
-- recent edit to BatchPrediction. The time is expressed in
-- epoch time.
--
-- $sel:logUri:GetBatchPredictionResponse',
-- getBatchPredictionResponse_logUri - A link to the file that
-- contains logs of the CreateBatchPrediction operation.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_mLModelId - The ID of the
-- MLModel that generated predictions for the
-- BatchPrediction request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_message - A description of the most
-- recent details about processing the batch prediction request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_name - A user-supplied name or
-- description of the BatchPrediction.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_outputUri - The location of an
-- Amazon S3 bucket or directory to receive the operation results.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_startedAt - The epoch time when
-- Amazon Machine Learning marked the BatchPrediction as
-- INPROGRESS. StartedAt isn't available if the
-- BatchPrediction is in the PENDING state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_status - The status of the
-- BatchPrediction, which can be one of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate batch predictions.
-- - INPROGRESS - The batch predictions are in progress.
-- - 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.
--
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_totalRecordCount - The number of
-- total records that Amazon Machine Learning saw while processing the
-- BatchPrediction.
--
-- $sel:httpStatus:GetBatchPredictionResponse',
-- getBatchPredictionResponse_httpStatus - The response's http
-- status code.
newGetBatchPredictionResponse :: Int -> GetBatchPredictionResponse
-- | The ID of the DataSource that was used to create the
-- BatchPrediction.
getBatchPredictionResponse_batchPredictionDataSourceId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | An ID assigned to the BatchPrediction at creation. This value
-- should be identical to the value of the BatchPredictionID in
-- the request.
getBatchPredictionResponse_batchPredictionId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the BatchPrediction, normalized and scaled
-- on computation resources. ComputeTime is only available if
-- the BatchPrediction is in the COMPLETED state.
getBatchPredictionResponse_computeTime :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The time when the BatchPrediction was created. The time is
-- expressed in epoch time.
getBatchPredictionResponse_createdAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | 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.
getBatchPredictionResponse_createdByIamUser :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as COMPLETED or FAILED.
-- FinishedAt is only available when the
-- BatchPrediction is in the COMPLETED or
-- FAILED state.
getBatchPredictionResponse_finishedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getBatchPredictionResponse_inputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The number of invalid records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
getBatchPredictionResponse_invalidRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The time of the most recent edit to BatchPrediction. The time
-- is expressed in epoch time.
getBatchPredictionResponse_lastUpdatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the
-- CreateBatchPrediction operation.
getBatchPredictionResponse_logUri :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
getBatchPredictionResponse_mLModelId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | A description of the most recent details about processing the batch
-- prediction request.
getBatchPredictionResponse_message :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | A user-supplied name or description of the BatchPrediction.
getBatchPredictionResponse_name :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The location of an Amazon S3 bucket or directory to receive the
-- operation results.
getBatchPredictionResponse_outputUri :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as INPROGRESS. StartedAt
-- isn't available if the BatchPrediction is in the
-- PENDING state.
getBatchPredictionResponse_startedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | The status of the BatchPrediction, which can be one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate batch predictions.
-- - INPROGRESS - The batch predictions are in progress.
-- - 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.
--
getBatchPredictionResponse_status :: Lens' GetBatchPredictionResponse (Maybe EntityStatus)
-- | The number of total records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
getBatchPredictionResponse_totalRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The response's http status code.
getBatchPredictionResponse_httpStatus :: Lens' GetBatchPredictionResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Show.Show Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Read.Read Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Classes.Eq Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Generics.Generic Amazonka.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Show.Show Amazonka.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Read.Read Amazonka.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.GetBatchPrediction.GetBatchPrediction
-- | Describes one or more of the tags for your Amazon ML object.
module Amazonka.MachineLearning.DescribeTags
-- | See: newDescribeTags smart constructor.
data DescribeTags
DescribeTags' :: Text -> TaggableResourceType -> DescribeTags
-- | The ID of the ML object. For example, exampleModelId.
[$sel:resourceId:DescribeTags'] :: DescribeTags -> Text
-- | The type of the ML object.
[$sel:resourceType:DescribeTags'] :: DescribeTags -> TaggableResourceType
-- | Create a value of DescribeTags with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeTags, describeTags_resourceId - The ID of the ML
-- object. For example, exampleModelId.
--
-- DescribeTags, describeTags_resourceType - The type of
-- the ML object.
newDescribeTags :: Text -> TaggableResourceType -> DescribeTags
-- | The ID of the ML object. For example, exampleModelId.
describeTags_resourceId :: Lens' DescribeTags Text
-- | The type of the ML object.
describeTags_resourceType :: Lens' DescribeTags TaggableResourceType
-- | Amazon ML returns the following elements.
--
-- See: newDescribeTagsResponse smart constructor.
data DescribeTagsResponse
DescribeTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Maybe [Tag] -> Int -> DescribeTagsResponse
-- | The ID of the tagged ML object.
[$sel:resourceId:DescribeTagsResponse'] :: DescribeTagsResponse -> Maybe Text
-- | The type of the tagged ML object.
[$sel:resourceType:DescribeTagsResponse'] :: DescribeTagsResponse -> Maybe TaggableResourceType
-- | A list of tags associated with the ML object.
[$sel:tags:DescribeTagsResponse'] :: DescribeTagsResponse -> Maybe [Tag]
-- | The response's http status code.
[$sel:httpStatus:DescribeTagsResponse'] :: DescribeTagsResponse -> Int
-- | Create a value of DescribeTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeTags, describeTagsResponse_resourceId - The ID
-- of the tagged ML object.
--
-- DescribeTags, describeTagsResponse_resourceType - The
-- type of the tagged ML object.
--
-- $sel:tags:DescribeTagsResponse',
-- describeTagsResponse_tags - A list of tags associated with the
-- ML object.
--
-- $sel:httpStatus:DescribeTagsResponse',
-- describeTagsResponse_httpStatus - The response's http status
-- code.
newDescribeTagsResponse :: Int -> DescribeTagsResponse
-- | The ID of the tagged ML object.
describeTagsResponse_resourceId :: Lens' DescribeTagsResponse (Maybe Text)
-- | The type of the tagged ML object.
describeTagsResponse_resourceType :: Lens' DescribeTagsResponse (Maybe TaggableResourceType)
-- | A list of tags associated with the ML object.
describeTagsResponse_tags :: Lens' DescribeTagsResponse (Maybe [Tag])
-- | The response's http status code.
describeTagsResponse_httpStatus :: Lens' DescribeTagsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeTags.DescribeTags
instance GHC.Show.Show Amazonka.MachineLearning.DescribeTags.DescribeTags
instance GHC.Read.Read Amazonka.MachineLearning.DescribeTags.DescribeTags
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeTags.DescribeTags
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Show.Show Amazonka.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Read.Read Amazonka.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeTags.DescribeTagsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeTags.DescribeTagsResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DescribeTags.DescribeTags
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DescribeTags.DescribeTags
-- | Returns a list of MLModel that match the search criteria in
-- the request.
--
-- This operation returns paginated results.
module Amazonka.MachineLearning.DescribeMLModels
-- | See: newDescribeMLModels smart constructor.
data DescribeMLModels
DescribeMLModels' :: Maybe Text -> Maybe MLModelFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeMLModels
-- | The equal to operator. The MLModel results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
[$sel:eq:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | Use one of the following variables to filter a list of
-- MLModel:
--
--
-- - CreatedAt - Sets the search criteria to MLModel
-- creation date.
-- - Status - Sets the search criteria to MLModel
-- status.
-- - Name - Sets the search criteria to the contents of
-- MLModel ____ Name.
-- - IAMUser - Sets the search criteria to the user account
-- that invoked the MLModel creation.
-- - TrainingDataSourceId - Sets the search criteria to the
-- DataSource used to train one or more MLModel.
-- - RealtimeEndpointStatus - Sets the search criteria to the
-- MLModel real-time endpoint status.
-- - MLModelType - Sets the search criteria to
-- MLModel type: binary, regression, or multi-class.
-- - Algorithm - Sets the search criteria to the algorithm
-- that the MLModel uses.
-- - TrainingDataURI - Sets the search criteria to the data
-- file(s) used in training a MLModel. The URL can identify
-- either a file or an Amazon Simple Storage Service (Amazon S3) bucket
-- or directory.
--
[$sel:filterVariable:DescribeMLModels'] :: DescribeMLModels -> Maybe MLModelFilterVariable
-- | The greater than or equal to operator. The MLModel results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
[$sel:ge:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | The greater than operator. The MLModel results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
[$sel:gt:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | The less than or equal to operator. The MLModel results will
-- have FilterVariable values that are less than or equal to the
-- value specified with LE.
[$sel:le:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | The less than operator. The MLModel results will have
-- FilterVariable values that are less than the value specified
-- with LT.
[$sel:lt:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
[$sel:limit:DescribeMLModels'] :: DescribeMLModels -> Maybe Natural
-- | The not equal to operator. The MLModel results will have
-- FilterVariable values not equal to the value specified with
-- NE.
[$sel:ne:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | The ID of the page in the paginated results.
[$sel:nextToken:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an MLModel could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- MLModel, select Name for the FilterVariable
-- and any of the following strings for the Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
[$sel:prefix:DescribeMLModels'] :: DescribeMLModels -> Maybe Text
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModel.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
[$sel:sortOrder:DescribeMLModels'] :: DescribeMLModels -> Maybe SortOrder
-- | Create a value of DescribeMLModels with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeMLModels', describeMLModels_eq - The
-- equal to operator. The MLModel results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeMLModels',
-- describeMLModels_filterVariable - Use one of the following
-- variables to filter a list of MLModel:
--
--
-- - CreatedAt - Sets the search criteria to MLModel
-- creation date.
-- - Status - Sets the search criteria to MLModel
-- status.
-- - Name - Sets the search criteria to the contents of
-- MLModel ____ Name.
-- - IAMUser - Sets the search criteria to the user account
-- that invoked the MLModel creation.
-- - TrainingDataSourceId - Sets the search criteria to the
-- DataSource used to train one or more MLModel.
-- - RealtimeEndpointStatus - Sets the search criteria to the
-- MLModel real-time endpoint status.
-- - MLModelType - Sets the search criteria to
-- MLModel type: binary, regression, or multi-class.
-- - Algorithm - Sets the search criteria to the algorithm
-- that the MLModel uses.
-- - TrainingDataURI - Sets the search criteria to the data
-- file(s) used in training a MLModel. The URL can identify
-- either a file or an Amazon Simple Storage Service (Amazon S3) bucket
-- or directory.
--
--
-- $sel:ge:DescribeMLModels', describeMLModels_ge - The
-- greater than or equal to operator. The MLModel results will
-- have FilterVariable values that are greater than or equal to
-- the value specified with GE.
--
-- $sel:gt:DescribeMLModels', describeMLModels_gt - The
-- greater than operator. The MLModel results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeMLModels', describeMLModels_le - The
-- less than or equal to operator. The MLModel results will have
-- FilterVariable values that are less than or equal to the
-- value specified with LE.
--
-- $sel:lt:DescribeMLModels', describeMLModels_lt - The
-- less than operator. The MLModel results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeMLModels', describeMLModels_limit -
-- The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
--
-- $sel:ne:DescribeMLModels', describeMLModels_ne - The not
-- equal to operator. The MLModel results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeMLModels, describeMLModels_nextToken - The ID of
-- the page in the paginated results.
--
-- $sel:prefix:DescribeMLModels', describeMLModels_prefix -
-- A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an MLModel could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- MLModel, select Name for the FilterVariable
-- and any of the following strings for the Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeMLModels',
-- describeMLModels_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of MLModel.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeMLModels :: DescribeMLModels
-- | The equal to operator. The MLModel results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeMLModels_eq :: Lens' DescribeMLModels (Maybe Text)
-- | Use one of the following variables to filter a list of
-- MLModel:
--
--
-- - CreatedAt - Sets the search criteria to MLModel
-- creation date.
-- - Status - Sets the search criteria to MLModel
-- status.
-- - Name - Sets the search criteria to the contents of
-- MLModel ____ Name.
-- - IAMUser - Sets the search criteria to the user account
-- that invoked the MLModel creation.
-- - TrainingDataSourceId - Sets the search criteria to the
-- DataSource used to train one or more MLModel.
-- - RealtimeEndpointStatus - Sets the search criteria to the
-- MLModel real-time endpoint status.
-- - MLModelType - Sets the search criteria to
-- MLModel type: binary, regression, or multi-class.
-- - Algorithm - Sets the search criteria to the algorithm
-- that the MLModel uses.
-- - TrainingDataURI - Sets the search criteria to the data
-- file(s) used in training a MLModel. The URL can identify
-- either a file or an Amazon Simple Storage Service (Amazon S3) bucket
-- or directory.
--
describeMLModels_filterVariable :: Lens' DescribeMLModels (Maybe MLModelFilterVariable)
-- | The greater than or equal to operator. The MLModel results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeMLModels_ge :: Lens' DescribeMLModels (Maybe Text)
-- | The greater than operator. The MLModel results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeMLModels_gt :: Lens' DescribeMLModels (Maybe Text)
-- | The less than or equal to operator. The MLModel results will
-- have FilterVariable values that are less than or equal to the
-- value specified with LE.
describeMLModels_le :: Lens' DescribeMLModels (Maybe Text)
-- | The less than operator. The MLModel results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeMLModels_lt :: Lens' DescribeMLModels (Maybe Text)
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
describeMLModels_limit :: Lens' DescribeMLModels (Maybe Natural)
-- | The not equal to operator. The MLModel results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeMLModels_ne :: Lens' DescribeMLModels (Maybe Text)
-- | The ID of the page in the paginated results.
describeMLModels_nextToken :: Lens' DescribeMLModels (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an MLModel could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- MLModel, select Name for the FilterVariable
-- and any of the following strings for the Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeMLModels_prefix :: Lens' DescribeMLModels (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModel.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeMLModels_sortOrder :: Lens' DescribeMLModels (Maybe SortOrder)
-- | Represents the output of a DescribeMLModels operation. The
-- content is essentially a list of MLModel.
--
-- See: newDescribeMLModelsResponse smart constructor.
data DescribeMLModelsResponse
DescribeMLModelsResponse' :: Maybe Text -> Maybe [MLModel] -> Int -> DescribeMLModelsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
[$sel:nextToken:DescribeMLModelsResponse'] :: DescribeMLModelsResponse -> Maybe Text
-- | A list of MLModel that meet the search criteria.
[$sel:results:DescribeMLModelsResponse'] :: DescribeMLModelsResponse -> Maybe [MLModel]
-- | The response's http status code.
[$sel:httpStatus:DescribeMLModelsResponse'] :: DescribeMLModelsResponse -> Int
-- | Create a value of DescribeMLModelsResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeMLModels, describeMLModelsResponse_nextToken -
-- The ID of the next page in the paginated results that indicates at
-- least one more page follows.
--
-- $sel:results:DescribeMLModelsResponse',
-- describeMLModelsResponse_results - A list of MLModel
-- that meet the search criteria.
--
-- $sel:httpStatus:DescribeMLModelsResponse',
-- describeMLModelsResponse_httpStatus - The response's http
-- status code.
newDescribeMLModelsResponse :: Int -> DescribeMLModelsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeMLModelsResponse_nextToken :: Lens' DescribeMLModelsResponse (Maybe Text)
-- | A list of MLModel that meet the search criteria.
describeMLModelsResponse_results :: Lens' DescribeMLModelsResponse (Maybe [MLModel])
-- | The response's http status code.
describeMLModelsResponse_httpStatus :: Lens' DescribeMLModelsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Show.Show Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Read.Read Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Show.Show Amazonka.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Read.Read Amazonka.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance Amazonka.Pager.AWSPager Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DescribeMLModels.DescribeMLModels
-- | Returns a list of DescribeEvaluations that match the search
-- criteria in the request.
--
-- This operation returns paginated results.
module Amazonka.MachineLearning.DescribeEvaluations
-- | See: newDescribeEvaluations smart constructor.
data DescribeEvaluations
DescribeEvaluations' :: Maybe Text -> Maybe EvaluationFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeEvaluations
-- | The equal to operator. The Evaluation results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
[$sel:eq:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | Use one of the following variable to filter a list of
-- Evaluation objects:
--
--
-- - CreatedAt - Sets the search criteria to the
-- Evaluation creation date.
-- - Status - Sets the search criteria to the
-- 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
-- MLModel 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 Solution (Amazon S3) bucket or directory.
--
[$sel:filterVariable:DescribeEvaluations'] :: DescribeEvaluations -> Maybe EvaluationFilterVariable
-- | The greater than or equal to operator. The Evaluation results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
[$sel:ge:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | The greater than operator. The Evaluation results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
[$sel:gt:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | The less than or equal to operator. The Evaluation results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
[$sel:le:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | The less than operator. The Evaluation results will have
-- FilterVariable values that are less than the value specified
-- with LT.
[$sel:lt:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | The maximum number of Evaluation to include in the result.
[$sel:limit:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Natural
-- | The not equal to operator. The Evaluation results will have
-- FilterVariable values not equal to the value specified with
-- NE.
[$sel:ne:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | The ID of the page in the paginated results.
[$sel:nextToken:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an Evaluation could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- Evaluation, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
[$sel:prefix:DescribeEvaluations'] :: DescribeEvaluations -> Maybe Text
-- | A two-value parameter that determines the sequence of the resulting
-- list of Evaluation.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
[$sel:sortOrder:DescribeEvaluations'] :: DescribeEvaluations -> Maybe SortOrder
-- | Create a value of DescribeEvaluations with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeEvaluations', describeEvaluations_eq -
-- The equal to operator. The Evaluation results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeEvaluations',
-- describeEvaluations_filterVariable - Use one of the following
-- variable to filter a list of Evaluation objects:
--
--
-- - CreatedAt - Sets the search criteria to the
-- Evaluation creation date.
-- - Status - Sets the search criteria to the
-- 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
-- MLModel 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 Solution (Amazon S3) bucket or directory.
--
--
-- $sel:ge:DescribeEvaluations', describeEvaluations_ge -
-- The greater than or equal to operator. The Evaluation results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
--
-- $sel:gt:DescribeEvaluations', describeEvaluations_gt -
-- The greater than operator. The Evaluation results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeEvaluations', describeEvaluations_le -
-- The less than or equal to operator. The Evaluation results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
--
-- $sel:lt:DescribeEvaluations', describeEvaluations_lt -
-- The less than operator. The Evaluation results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeEvaluations',
-- describeEvaluations_limit - The maximum number of
-- Evaluation to include in the result.
--
-- $sel:ne:DescribeEvaluations', describeEvaluations_ne -
-- The not equal to operator. The Evaluation results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeEvaluations, describeEvaluations_nextToken - The
-- ID of the page in the paginated results.
--
-- $sel:prefix:DescribeEvaluations',
-- describeEvaluations_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, an Evaluation could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- Evaluation, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeEvaluations',
-- describeEvaluations_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of Evaluation.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeEvaluations :: DescribeEvaluations
-- | The equal to operator. The Evaluation results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeEvaluations_eq :: Lens' DescribeEvaluations (Maybe Text)
-- | Use one of the following variable to filter a list of
-- Evaluation objects:
--
--
-- - CreatedAt - Sets the search criteria to the
-- Evaluation creation date.
-- - Status - Sets the search criteria to the
-- 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
-- MLModel 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 Solution (Amazon S3) bucket or directory.
--
describeEvaluations_filterVariable :: Lens' DescribeEvaluations (Maybe EvaluationFilterVariable)
-- | The greater than or equal to operator. The Evaluation results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeEvaluations_ge :: Lens' DescribeEvaluations (Maybe Text)
-- | The greater than operator. The Evaluation results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeEvaluations_gt :: Lens' DescribeEvaluations (Maybe Text)
-- | The less than or equal to operator. The Evaluation results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
describeEvaluations_le :: Lens' DescribeEvaluations (Maybe Text)
-- | The less than operator. The Evaluation results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeEvaluations_lt :: Lens' DescribeEvaluations (Maybe Text)
-- | The maximum number of Evaluation to include in the result.
describeEvaluations_limit :: Lens' DescribeEvaluations (Maybe Natural)
-- | The not equal to operator. The Evaluation results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeEvaluations_ne :: Lens' DescribeEvaluations (Maybe Text)
-- | The ID of the page in the paginated results.
describeEvaluations_nextToken :: Lens' DescribeEvaluations (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an Evaluation could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- Evaluation, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeEvaluations_prefix :: Lens' DescribeEvaluations (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of Evaluation.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeEvaluations_sortOrder :: Lens' DescribeEvaluations (Maybe SortOrder)
-- | Represents the query results from a DescribeEvaluations
-- operation. The content is essentially a list of Evaluation.
--
-- See: newDescribeEvaluationsResponse smart constructor.
data DescribeEvaluationsResponse
DescribeEvaluationsResponse' :: Maybe Text -> Maybe [Evaluation] -> Int -> DescribeEvaluationsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
[$sel:nextToken:DescribeEvaluationsResponse'] :: DescribeEvaluationsResponse -> Maybe Text
-- | A list of Evaluation that meet the search criteria.
[$sel:results:DescribeEvaluationsResponse'] :: DescribeEvaluationsResponse -> Maybe [Evaluation]
-- | The response's http status code.
[$sel:httpStatus:DescribeEvaluationsResponse'] :: DescribeEvaluationsResponse -> Int
-- | Create a value of DescribeEvaluationsResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeEvaluations,
-- describeEvaluationsResponse_nextToken - The ID of the next page
-- in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeEvaluationsResponse',
-- describeEvaluationsResponse_results - A list of
-- Evaluation that meet the search criteria.
--
-- $sel:httpStatus:DescribeEvaluationsResponse',
-- describeEvaluationsResponse_httpStatus - The response's http
-- status code.
newDescribeEvaluationsResponse :: Int -> DescribeEvaluationsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeEvaluationsResponse_nextToken :: Lens' DescribeEvaluationsResponse (Maybe Text)
-- | A list of Evaluation that meet the search criteria.
describeEvaluationsResponse_results :: Lens' DescribeEvaluationsResponse (Maybe [Evaluation])
-- | The response's http status code.
describeEvaluationsResponse_httpStatus :: Lens' DescribeEvaluationsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Show.Show Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Read.Read Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Show.Show Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Read.Read Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance Amazonka.Pager.AWSPager Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DescribeEvaluations.DescribeEvaluations
-- | Returns a list of DataSource that match the search criteria
-- in the request.
--
-- This operation returns paginated results.
module Amazonka.MachineLearning.DescribeDataSources
-- | See: newDescribeDataSources smart constructor.
data DescribeDataSources
DescribeDataSources' :: Maybe Text -> Maybe DataSourceFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeDataSources
-- | The equal to operator. The DataSource results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
[$sel:eq:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | Use one of the following variables to filter a list of
-- DataSource:
--
--
-- - CreatedAt - Sets the search criteria to
-- DataSource creation dates.
-- - Status - Sets the search criteria to DataSource
-- statuses.
-- - 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.
--
[$sel:filterVariable:DescribeDataSources'] :: DescribeDataSources -> Maybe DataSourceFilterVariable
-- | The greater than or equal to operator. The DataSource results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
[$sel:ge:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | The greater than operator. The DataSource results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
[$sel:gt:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | The less than or equal to operator. The DataSource results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
[$sel:le:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | The less than operator. The DataSource results will have
-- FilterVariable values that are less than the value specified
-- with LT.
[$sel:lt:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | The maximum number of DataSource to include in the result.
[$sel:limit:DescribeDataSources'] :: DescribeDataSources -> Maybe Natural
-- | The not equal to operator. The DataSource results will have
-- FilterVariable values not equal to the value specified with
-- NE.
[$sel:ne:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | The ID of the page in the paginated results.
[$sel:nextToken:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a DataSource could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- DataSource, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
[$sel:prefix:DescribeDataSources'] :: DescribeDataSources -> Maybe Text
-- | A two-value parameter that determines the sequence of the resulting
-- list of DataSource.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
[$sel:sortOrder:DescribeDataSources'] :: DescribeDataSources -> Maybe SortOrder
-- | Create a value of DescribeDataSources with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeDataSources', describeDataSources_eq -
-- The equal to operator. The DataSource results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeDataSources',
-- describeDataSources_filterVariable - Use one of the following
-- variables to filter a list of DataSource:
--
--
-- - CreatedAt - Sets the search criteria to
-- DataSource creation dates.
-- - Status - Sets the search criteria to DataSource
-- statuses.
-- - 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.
--
--
-- $sel:ge:DescribeDataSources', describeDataSources_ge -
-- The greater than or equal to operator. The DataSource results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
--
-- $sel:gt:DescribeDataSources', describeDataSources_gt -
-- The greater than operator. The DataSource results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeDataSources', describeDataSources_le -
-- The less than or equal to operator. The DataSource results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
--
-- $sel:lt:DescribeDataSources', describeDataSources_lt -
-- The less than operator. The DataSource results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeDataSources',
-- describeDataSources_limit - The maximum number of
-- DataSource to include in the result.
--
-- $sel:ne:DescribeDataSources', describeDataSources_ne -
-- The not equal to operator. The DataSource results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeDataSources, describeDataSources_nextToken - The
-- ID of the page in the paginated results.
--
-- $sel:prefix:DescribeDataSources',
-- describeDataSources_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, a DataSource could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- DataSource, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeDataSources',
-- describeDataSources_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of DataSource.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeDataSources :: DescribeDataSources
-- | The equal to operator. The DataSource results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeDataSources_eq :: Lens' DescribeDataSources (Maybe Text)
-- | Use one of the following variables to filter a list of
-- DataSource:
--
--
-- - CreatedAt - Sets the search criteria to
-- DataSource creation dates.
-- - Status - Sets the search criteria to DataSource
-- statuses.
-- - 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.
--
describeDataSources_filterVariable :: Lens' DescribeDataSources (Maybe DataSourceFilterVariable)
-- | The greater than or equal to operator. The DataSource results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeDataSources_ge :: Lens' DescribeDataSources (Maybe Text)
-- | The greater than operator. The DataSource results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeDataSources_gt :: Lens' DescribeDataSources (Maybe Text)
-- | The less than or equal to operator. The DataSource results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
describeDataSources_le :: Lens' DescribeDataSources (Maybe Text)
-- | The less than operator. The DataSource results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeDataSources_lt :: Lens' DescribeDataSources (Maybe Text)
-- | The maximum number of DataSource to include in the result.
describeDataSources_limit :: Lens' DescribeDataSources (Maybe Natural)
-- | The not equal to operator. The DataSource results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeDataSources_ne :: Lens' DescribeDataSources (Maybe Text)
-- | The ID of the page in the paginated results.
describeDataSources_nextToken :: Lens' DescribeDataSources (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a DataSource could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- DataSource, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeDataSources_prefix :: Lens' DescribeDataSources (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of DataSource.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeDataSources_sortOrder :: Lens' DescribeDataSources (Maybe SortOrder)
-- | Represents the query results from a DescribeDataSources operation. The
-- content is essentially a list of DataSource.
--
-- See: newDescribeDataSourcesResponse smart constructor.
data DescribeDataSourcesResponse
DescribeDataSourcesResponse' :: Maybe Text -> Maybe [DataSource] -> Int -> DescribeDataSourcesResponse
-- | An ID of the next page in the paginated results that indicates at
-- least one more page follows.
[$sel:nextToken:DescribeDataSourcesResponse'] :: DescribeDataSourcesResponse -> Maybe Text
-- | A list of DataSource that meet the search criteria.
[$sel:results:DescribeDataSourcesResponse'] :: DescribeDataSourcesResponse -> Maybe [DataSource]
-- | The response's http status code.
[$sel:httpStatus:DescribeDataSourcesResponse'] :: DescribeDataSourcesResponse -> Int
-- | Create a value of DescribeDataSourcesResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeDataSources,
-- describeDataSourcesResponse_nextToken - An ID of the next page
-- in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeDataSourcesResponse',
-- describeDataSourcesResponse_results - A list of
-- DataSource that meet the search criteria.
--
-- $sel:httpStatus:DescribeDataSourcesResponse',
-- describeDataSourcesResponse_httpStatus - The response's http
-- status code.
newDescribeDataSourcesResponse :: Int -> DescribeDataSourcesResponse
-- | An ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeDataSourcesResponse_nextToken :: Lens' DescribeDataSourcesResponse (Maybe Text)
-- | A list of DataSource that meet the search criteria.
describeDataSourcesResponse_results :: Lens' DescribeDataSourcesResponse (Maybe [DataSource])
-- | The response's http status code.
describeDataSourcesResponse_httpStatus :: Lens' DescribeDataSourcesResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Show.Show Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Read.Read Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Show.Show Amazonka.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Read.Read Amazonka.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance Amazonka.Pager.AWSPager Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DescribeDataSources.DescribeDataSources
-- | Returns a list of BatchPrediction operations that match the
-- search criteria in the request.
--
-- This operation returns paginated results.
module Amazonka.MachineLearning.DescribeBatchPredictions
-- | See: newDescribeBatchPredictions smart constructor.
data DescribeBatchPredictions
DescribeBatchPredictions' :: Maybe Text -> Maybe BatchPredictionFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeBatchPredictions
-- | The equal to operator. The BatchPrediction results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
[$sel:eq:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | Use one of the following variables to filter a list of
-- BatchPrediction:
--
--
-- - CreatedAt - Sets the search criteria to the
-- BatchPrediction creation date.
-- - Status - Sets the search criteria to the
-- BatchPrediction status.
-- - Name - Sets the search criteria to the contents of the
-- 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 Solution (Amazon S3) bucket or
-- directory.
--
[$sel:filterVariable:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe BatchPredictionFilterVariable
-- | The greater than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are greater than
-- or equal to the value specified with GE.
[$sel:ge:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | The greater than operator. The BatchPrediction results will
-- have FilterVariable values that are greater than the value
-- specified with GT.
[$sel:gt:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | The less than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are less than or
-- equal to the value specified with LE.
[$sel:le:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | The less than operator. The BatchPrediction results will have
-- FilterVariable values that are less than the value specified
-- with LT.
[$sel:lt:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
[$sel:limit:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Natural
-- | The not equal to operator. The BatchPrediction results will
-- have FilterVariable values not equal to the value specified
-- with NE.
[$sel:ne:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | An ID of the page in the paginated results.
[$sel:nextToken:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a Batch Prediction operation could have the
-- Name 2014-09-09-HolidayGiftMailer. To search for
-- this BatchPrediction, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
[$sel:prefix:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe Text
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModels.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
[$sel:sortOrder:DescribeBatchPredictions'] :: DescribeBatchPredictions -> Maybe SortOrder
-- | Create a value of DescribeBatchPredictions with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeBatchPredictions',
-- describeBatchPredictions_eq - The equal to operator. The
-- BatchPrediction results will have FilterVariable
-- values that exactly match the value specified with EQ.
--
-- $sel:filterVariable:DescribeBatchPredictions',
-- describeBatchPredictions_filterVariable - Use one of the
-- following variables to filter a list of BatchPrediction:
--
--
-- - CreatedAt - Sets the search criteria to the
-- BatchPrediction creation date.
-- - Status - Sets the search criteria to the
-- BatchPrediction status.
-- - Name - Sets the search criteria to the contents of the
-- 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 Solution (Amazon S3) bucket or
-- directory.
--
--
-- $sel:ge:DescribeBatchPredictions',
-- describeBatchPredictions_ge - The greater than or equal to
-- operator. The BatchPrediction results will have
-- FilterVariable values that are greater than or equal to the
-- value specified with GE.
--
-- $sel:gt:DescribeBatchPredictions',
-- describeBatchPredictions_gt - The greater than operator. The
-- BatchPrediction results will have FilterVariable
-- values that are greater than the value specified with GT.
--
-- $sel:le:DescribeBatchPredictions',
-- describeBatchPredictions_le - The less than or equal to
-- operator. The BatchPrediction results will have
-- FilterVariable values that are less than or equal to the
-- value specified with LE.
--
-- $sel:lt:DescribeBatchPredictions',
-- describeBatchPredictions_lt - The less than operator. The
-- BatchPrediction results will have FilterVariable
-- values that are less than the value specified with LT.
--
-- $sel:limit:DescribeBatchPredictions',
-- describeBatchPredictions_limit - The number of pages of
-- information to include in the result. The range of acceptable values
-- is 1 through 100. The default value is 100.
--
-- $sel:ne:DescribeBatchPredictions',
-- describeBatchPredictions_ne - The not equal to operator. The
-- BatchPrediction results will have FilterVariable
-- values not equal to the value specified with NE.
--
-- DescribeBatchPredictions,
-- describeBatchPredictions_nextToken - An ID of the page in the
-- paginated results.
--
-- $sel:prefix:DescribeBatchPredictions',
-- describeBatchPredictions_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, a Batch Prediction operation could have the
-- Name 2014-09-09-HolidayGiftMailer. To search for
-- this BatchPrediction, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeBatchPredictions',
-- describeBatchPredictions_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of MLModels.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeBatchPredictions :: DescribeBatchPredictions
-- | The equal to operator. The BatchPrediction results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeBatchPredictions_eq :: Lens' DescribeBatchPredictions (Maybe Text)
-- | Use one of the following variables to filter a list of
-- BatchPrediction:
--
--
-- - CreatedAt - Sets the search criteria to the
-- BatchPrediction creation date.
-- - Status - Sets the search criteria to the
-- BatchPrediction status.
-- - Name - Sets the search criteria to the contents of the
-- 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 Solution (Amazon S3) bucket or
-- directory.
--
describeBatchPredictions_filterVariable :: Lens' DescribeBatchPredictions (Maybe BatchPredictionFilterVariable)
-- | The greater than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are greater than
-- or equal to the value specified with GE.
describeBatchPredictions_ge :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The greater than operator. The BatchPrediction results will
-- have FilterVariable values that are greater than the value
-- specified with GT.
describeBatchPredictions_gt :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The less than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are less than or
-- equal to the value specified with LE.
describeBatchPredictions_le :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The less than operator. The BatchPrediction results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeBatchPredictions_lt :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
describeBatchPredictions_limit :: Lens' DescribeBatchPredictions (Maybe Natural)
-- | The not equal to operator. The BatchPrediction results will
-- have FilterVariable values not equal to the value specified
-- with NE.
describeBatchPredictions_ne :: Lens' DescribeBatchPredictions (Maybe Text)
-- | An ID of the page in the paginated results.
describeBatchPredictions_nextToken :: Lens' DescribeBatchPredictions (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a Batch Prediction operation could have the
-- Name 2014-09-09-HolidayGiftMailer. To search for
-- this BatchPrediction, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeBatchPredictions_prefix :: Lens' DescribeBatchPredictions (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModels.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeBatchPredictions_sortOrder :: Lens' DescribeBatchPredictions (Maybe SortOrder)
-- | Represents the output of a DescribeBatchPredictions
-- operation. The content is essentially a list of
-- BatchPredictions.
--
-- See: newDescribeBatchPredictionsResponse smart
-- constructor.
data DescribeBatchPredictionsResponse
DescribeBatchPredictionsResponse' :: Maybe Text -> Maybe [BatchPrediction] -> Int -> DescribeBatchPredictionsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
[$sel:nextToken:DescribeBatchPredictionsResponse'] :: DescribeBatchPredictionsResponse -> Maybe Text
-- | A list of BatchPrediction objects that meet the search
-- criteria.
[$sel:results:DescribeBatchPredictionsResponse'] :: DescribeBatchPredictionsResponse -> Maybe [BatchPrediction]
-- | The response's http status code.
[$sel:httpStatus:DescribeBatchPredictionsResponse'] :: DescribeBatchPredictionsResponse -> Int
-- | Create a value of DescribeBatchPredictionsResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeBatchPredictions,
-- describeBatchPredictionsResponse_nextToken - The ID of the next
-- page in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeBatchPredictionsResponse',
-- describeBatchPredictionsResponse_results - A list of
-- BatchPrediction objects that meet the search criteria.
--
-- $sel:httpStatus:DescribeBatchPredictionsResponse',
-- describeBatchPredictionsResponse_httpStatus - The response's
-- http status code.
newDescribeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeBatchPredictionsResponse_nextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)
-- | A list of BatchPrediction objects that meet the search
-- criteria.
describeBatchPredictionsResponse_results :: Lens' DescribeBatchPredictionsResponse (Maybe [BatchPrediction])
-- | The response's http status code.
describeBatchPredictionsResponse_httpStatus :: Lens' DescribeBatchPredictionsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Show.Show Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Read.Read Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Generics.Generic Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Show.Show Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Read.Read Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance Amazonka.Pager.AWSPager Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
-- | Deletes the specified tags associated with an ML object. After this
-- operation is complete, you can't recover deleted tags.
--
-- If you specify a tag that doesn't exist, Amazon ML ignores it.
module Amazonka.MachineLearning.DeleteTags
-- | See: newDeleteTags smart constructor.
data DeleteTags
DeleteTags' :: [Text] -> Text -> TaggableResourceType -> DeleteTags
-- | One or more tags to delete.
[$sel:tagKeys:DeleteTags'] :: DeleteTags -> [Text]
-- | The ID of the tagged ML object. For example, exampleModelId.
[$sel:resourceId:DeleteTags'] :: DeleteTags -> Text
-- | The type of the tagged ML object.
[$sel:resourceType:DeleteTags'] :: DeleteTags -> TaggableResourceType
-- | Create a value of DeleteTags with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:tagKeys:DeleteTags', deleteTags_tagKeys - One or
-- more tags to delete.
--
-- DeleteTags, deleteTags_resourceId - The ID of the tagged
-- ML object. For example, exampleModelId.
--
-- DeleteTags, deleteTags_resourceType - The type of the
-- tagged ML object.
newDeleteTags :: Text -> TaggableResourceType -> DeleteTags
-- | One or more tags to delete.
deleteTags_tagKeys :: Lens' DeleteTags [Text]
-- | The ID of the tagged ML object. For example, exampleModelId.
deleteTags_resourceId :: Lens' DeleteTags Text
-- | The type of the tagged ML object.
deleteTags_resourceType :: Lens' DeleteTags TaggableResourceType
-- | Amazon ML returns the following elements.
--
-- See: newDeleteTagsResponse smart constructor.
data DeleteTagsResponse
DeleteTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Int -> DeleteTagsResponse
-- | The ID of the ML object from which tags were deleted.
[$sel:resourceId:DeleteTagsResponse'] :: DeleteTagsResponse -> Maybe Text
-- | The type of the ML object from which tags were deleted.
[$sel:resourceType:DeleteTagsResponse'] :: DeleteTagsResponse -> Maybe TaggableResourceType
-- | The response's http status code.
[$sel:httpStatus:DeleteTagsResponse'] :: DeleteTagsResponse -> Int
-- | Create a value of DeleteTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteTags, deleteTagsResponse_resourceId - The ID of
-- the ML object from which tags were deleted.
--
-- DeleteTags, deleteTagsResponse_resourceType - The type
-- of the ML object from which tags were deleted.
--
-- $sel:httpStatus:DeleteTagsResponse',
-- deleteTagsResponse_httpStatus - The response's http status
-- code.
newDeleteTagsResponse :: Int -> DeleteTagsResponse
-- | The ID of the ML object from which tags were deleted.
deleteTagsResponse_resourceId :: Lens' DeleteTagsResponse (Maybe Text)
-- | The type of the ML object from which tags were deleted.
deleteTagsResponse_resourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType)
-- | The response's http status code.
deleteTagsResponse_httpStatus :: Lens' DeleteTagsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteTags.DeleteTags
instance GHC.Show.Show Amazonka.MachineLearning.DeleteTags.DeleteTags
instance GHC.Read.Read Amazonka.MachineLearning.DeleteTags.DeleteTags
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteTags.DeleteTags
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteTags.DeleteTagsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteTags.DeleteTagsResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteTags.DeleteTags
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteTags.DeleteTags
-- | Deletes a real time endpoint of an MLModel.
module Amazonka.MachineLearning.DeleteRealtimeEndpoint
-- | See: newDeleteRealtimeEndpoint smart constructor.
data DeleteRealtimeEndpoint
DeleteRealtimeEndpoint' :: Text -> DeleteRealtimeEndpoint
-- | The ID assigned to the MLModel during creation.
[$sel:mLModelId:DeleteRealtimeEndpoint'] :: DeleteRealtimeEndpoint -> Text
-- | Create a value of DeleteRealtimeEndpoint with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteRealtimeEndpoint, deleteRealtimeEndpoint_mLModelId
-- - The ID assigned to the MLModel during creation.
newDeleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint
-- | The ID assigned to the MLModel during creation.
deleteRealtimeEndpoint_mLModelId :: Lens' DeleteRealtimeEndpoint Text
-- | Represents the output of an DeleteRealtimeEndpoint operation.
--
-- The result contains the MLModelId and the endpoint
-- information for the MLModel.
--
-- See: newDeleteRealtimeEndpointResponse smart
-- constructor.
data DeleteRealtimeEndpointResponse
DeleteRealtimeEndpointResponse' :: Maybe Text -> Maybe RealtimeEndpointInfo -> Int -> DeleteRealtimeEndpointResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
[$sel:mLModelId:DeleteRealtimeEndpointResponse'] :: DeleteRealtimeEndpointResponse -> Maybe Text
-- | The endpoint information of the MLModel
[$sel:realtimeEndpointInfo:DeleteRealtimeEndpointResponse'] :: DeleteRealtimeEndpointResponse -> Maybe RealtimeEndpointInfo
-- | The response's http status code.
[$sel:httpStatus:DeleteRealtimeEndpointResponse'] :: DeleteRealtimeEndpointResponse -> Int
-- | Create a value of DeleteRealtimeEndpointResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteRealtimeEndpoint,
-- deleteRealtimeEndpointResponse_mLModelId - A user-supplied ID
-- that uniquely identifies the MLModel. This value should be
-- identical to the value of the MLModelId in the request.
--
-- $sel:realtimeEndpointInfo:DeleteRealtimeEndpointResponse',
-- deleteRealtimeEndpointResponse_realtimeEndpointInfo - The
-- endpoint information of the MLModel
--
-- $sel:httpStatus:DeleteRealtimeEndpointResponse',
-- deleteRealtimeEndpointResponse_httpStatus - The response's http
-- status code.
newDeleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
deleteRealtimeEndpointResponse_mLModelId :: Lens' DeleteRealtimeEndpointResponse (Maybe Text)
-- | The endpoint information of the MLModel
deleteRealtimeEndpointResponse_realtimeEndpointInfo :: Lens' DeleteRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)
-- | The response's http status code.
deleteRealtimeEndpointResponse_httpStatus :: Lens' DeleteRealtimeEndpointResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Show.Show Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Read.Read Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
-- | Assigns the DELETED status to an MLModel, rendering
-- it unusable.
--
-- After using the DeleteMLModel operation, you can use the
-- GetMLModel operation to verify that the status of the
-- MLModel changed to DELETED.
--
-- Caution: The result of the DeleteMLModel operation is
-- irreversible.
module Amazonka.MachineLearning.DeleteMLModel
-- | See: newDeleteMLModel smart constructor.
data DeleteMLModel
DeleteMLModel' :: Text -> DeleteMLModel
-- | A user-supplied ID that uniquely identifies the MLModel.
[$sel:mLModelId:DeleteMLModel'] :: DeleteMLModel -> Text
-- | Create a value of DeleteMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteMLModel, deleteMLModel_mLModelId - A user-supplied
-- ID that uniquely identifies the MLModel.
newDeleteMLModel :: Text -> DeleteMLModel
-- | A user-supplied ID that uniquely identifies the MLModel.
deleteMLModel_mLModelId :: Lens' DeleteMLModel Text
-- | Represents the output of a DeleteMLModel operation.
--
-- You can use the GetMLModel operation and check the value of
-- the Status parameter to see whether an MLModel is
-- marked as DELETED.
--
-- See: newDeleteMLModelResponse smart constructor.
data DeleteMLModelResponse
DeleteMLModelResponse' :: Maybe Text -> Int -> DeleteMLModelResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelID in
-- the request.
[$sel:mLModelId:DeleteMLModelResponse'] :: DeleteMLModelResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:DeleteMLModelResponse'] :: DeleteMLModelResponse -> Int
-- | Create a value of DeleteMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteMLModel, deleteMLModelResponse_mLModelId - A
-- user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelID in
-- the request.
--
-- $sel:httpStatus:DeleteMLModelResponse',
-- deleteMLModelResponse_httpStatus - The response's http status
-- code.
newDeleteMLModelResponse :: Int -> DeleteMLModelResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelID in
-- the request.
deleteMLModelResponse_mLModelId :: Lens' DeleteMLModelResponse (Maybe Text)
-- | The response's http status code.
deleteMLModelResponse_httpStatus :: Lens' DeleteMLModelResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Show.Show Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Read.Read Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteMLModel.DeleteMLModel
-- | Assigns the DELETED status to an Evaluation,
-- rendering it unusable.
--
-- After invoking the DeleteEvaluation operation, you can use
-- the GetEvaluation operation to verify that the status of the
-- Evaluation changed to DELETED.
--
-- Caution: The results of the DeleteEvaluation operation
-- are irreversible.
module Amazonka.MachineLearning.DeleteEvaluation
-- | See: newDeleteEvaluation smart constructor.
data DeleteEvaluation
DeleteEvaluation' :: Text -> DeleteEvaluation
-- | A user-supplied ID that uniquely identifies the Evaluation to
-- delete.
[$sel:evaluationId:DeleteEvaluation'] :: DeleteEvaluation -> Text
-- | Create a value of DeleteEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteEvaluation, deleteEvaluation_evaluationId - A
-- user-supplied ID that uniquely identifies the Evaluation to
-- delete.
newDeleteEvaluation :: Text -> DeleteEvaluation
-- | A user-supplied ID that uniquely identifies the Evaluation to
-- delete.
deleteEvaluation_evaluationId :: Lens' DeleteEvaluation Text
-- | Represents the output of a DeleteEvaluation operation. The
-- output indicates that Amazon Machine Learning (Amazon ML) received the
-- request.
--
-- You can use the GetEvaluation operation and check the value
-- of the Status parameter to see whether an Evaluation
-- is marked as DELETED.
--
-- See: newDeleteEvaluationResponse smart constructor.
data DeleteEvaluationResponse
DeleteEvaluationResponse' :: Maybe Text -> Int -> DeleteEvaluationResponse
-- | A user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
[$sel:evaluationId:DeleteEvaluationResponse'] :: DeleteEvaluationResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:DeleteEvaluationResponse'] :: DeleteEvaluationResponse -> Int
-- | Create a value of DeleteEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteEvaluation, deleteEvaluationResponse_evaluationId
-- - A user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
--
-- $sel:httpStatus:DeleteEvaluationResponse',
-- deleteEvaluationResponse_httpStatus - The response's http
-- status code.
newDeleteEvaluationResponse :: Int -> DeleteEvaluationResponse
-- | A user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
deleteEvaluationResponse_evaluationId :: Lens' DeleteEvaluationResponse (Maybe Text)
-- | The response's http status code.
deleteEvaluationResponse_httpStatus :: Lens' DeleteEvaluationResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Show.Show Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Read.Read Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteEvaluation.DeleteEvaluation
-- | Assigns the DELETED status to a DataSource, rendering it
-- unusable.
--
-- After using the DeleteDataSource operation, you can use the
-- GetDataSource operation to verify that the status of the
-- DataSource changed to DELETED.
--
-- Caution: The results of the DeleteDataSource operation
-- are irreversible.
module Amazonka.MachineLearning.DeleteDataSource
-- | See: newDeleteDataSource smart constructor.
data DeleteDataSource
DeleteDataSource' :: Text -> DeleteDataSource
-- | A user-supplied ID that uniquely identifies the DataSource.
[$sel:dataSourceId:DeleteDataSource'] :: DeleteDataSource -> Text
-- | Create a value of DeleteDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteDataSource, deleteDataSource_dataSourceId - A
-- user-supplied ID that uniquely identifies the DataSource.
newDeleteDataSource :: Text -> DeleteDataSource
-- | A user-supplied ID that uniquely identifies the DataSource.
deleteDataSource_dataSourceId :: Lens' DeleteDataSource Text
-- | Represents the output of a DeleteDataSource operation.
--
-- See: newDeleteDataSourceResponse smart constructor.
data DeleteDataSourceResponse
DeleteDataSourceResponse' :: Maybe Text -> Int -> DeleteDataSourceResponse
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
[$sel:dataSourceId:DeleteDataSourceResponse'] :: DeleteDataSourceResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:DeleteDataSourceResponse'] :: DeleteDataSourceResponse -> Int
-- | Create a value of DeleteDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteDataSource, deleteDataSourceResponse_dataSourceId
-- - A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
--
-- $sel:httpStatus:DeleteDataSourceResponse',
-- deleteDataSourceResponse_httpStatus - The response's http
-- status code.
newDeleteDataSourceResponse :: Int -> DeleteDataSourceResponse
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
deleteDataSourceResponse_dataSourceId :: Lens' DeleteDataSourceResponse (Maybe Text)
-- | The response's http status code.
deleteDataSourceResponse_httpStatus :: Lens' DeleteDataSourceResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Show.Show Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Read.Read Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteDataSource.DeleteDataSource
-- | Assigns the DELETED status to a BatchPrediction, rendering it
-- unusable.
--
-- After using the DeleteBatchPrediction operation, you can use
-- the GetBatchPrediction operation to verify that the status of the
-- BatchPrediction changed to DELETED.
--
-- Caution: The result of the DeleteBatchPrediction
-- operation is irreversible.
module Amazonka.MachineLearning.DeleteBatchPrediction
-- | See: newDeleteBatchPrediction smart constructor.
data DeleteBatchPrediction
DeleteBatchPrediction' :: Text -> DeleteBatchPrediction
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
[$sel:batchPredictionId:DeleteBatchPrediction'] :: DeleteBatchPrediction -> Text
-- | Create a value of DeleteBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteBatchPrediction,
-- deleteBatchPrediction_batchPredictionId - A user-supplied ID
-- that uniquely identifies the BatchPrediction.
newDeleteBatchPrediction :: Text -> DeleteBatchPrediction
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
deleteBatchPrediction_batchPredictionId :: Lens' DeleteBatchPrediction Text
-- | Represents the output of a DeleteBatchPrediction operation.
--
-- You can use the GetBatchPrediction operation and check the
-- value of the Status parameter to see whether a
-- BatchPrediction is marked as DELETED.
--
-- See: newDeleteBatchPredictionResponse smart constructor.
data DeleteBatchPredictionResponse
DeleteBatchPredictionResponse' :: Maybe Text -> Int -> DeleteBatchPredictionResponse
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value should be identical to the value
-- of the BatchPredictionID in the request.
[$sel:batchPredictionId:DeleteBatchPredictionResponse'] :: DeleteBatchPredictionResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:DeleteBatchPredictionResponse'] :: DeleteBatchPredictionResponse -> Int
-- | Create a value of DeleteBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteBatchPrediction,
-- deleteBatchPredictionResponse_batchPredictionId - A
-- user-supplied ID that uniquely identifies the
-- BatchPrediction. This value should be identical to the value
-- of the BatchPredictionID in the request.
--
-- $sel:httpStatus:DeleteBatchPredictionResponse',
-- deleteBatchPredictionResponse_httpStatus - The response's http
-- status code.
newDeleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value should be identical to the value
-- of the BatchPredictionID in the request.
deleteBatchPredictionResponse_batchPredictionId :: Lens' DeleteBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
deleteBatchPredictionResponse_httpStatus :: Lens' DeleteBatchPredictionResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Show.Show Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Read.Read Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Generics.Generic Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Show.Show Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Read.Read Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
-- | Creates a real-time endpoint for the MLModel. The endpoint
-- contains the URI of the MLModel; that is, the location to
-- send real-time prediction requests for the specified MLModel.
module Amazonka.MachineLearning.CreateRealtimeEndpoint
-- | See: newCreateRealtimeEndpoint smart constructor.
data CreateRealtimeEndpoint
CreateRealtimeEndpoint' :: Text -> CreateRealtimeEndpoint
-- | The ID assigned to the MLModel during creation.
[$sel:mLModelId:CreateRealtimeEndpoint'] :: CreateRealtimeEndpoint -> Text
-- | Create a value of CreateRealtimeEndpoint with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateRealtimeEndpoint, createRealtimeEndpoint_mLModelId
-- - The ID assigned to the MLModel during creation.
newCreateRealtimeEndpoint :: Text -> CreateRealtimeEndpoint
-- | The ID assigned to the MLModel during creation.
createRealtimeEndpoint_mLModelId :: Lens' CreateRealtimeEndpoint Text
-- | Represents the output of an CreateRealtimeEndpoint operation.
--
-- The result contains the MLModelId and the endpoint
-- information for the MLModel.
--
-- Note: The endpoint information includes the URI of the
-- MLModel; that is, the location to send online prediction
-- requests for the specified MLModel.
--
-- See: newCreateRealtimeEndpointResponse smart
-- constructor.
data CreateRealtimeEndpointResponse
CreateRealtimeEndpointResponse' :: Maybe Text -> Maybe RealtimeEndpointInfo -> Int -> CreateRealtimeEndpointResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
[$sel:mLModelId:CreateRealtimeEndpointResponse'] :: CreateRealtimeEndpointResponse -> Maybe Text
-- | The endpoint information of the MLModel
[$sel:realtimeEndpointInfo:CreateRealtimeEndpointResponse'] :: CreateRealtimeEndpointResponse -> Maybe RealtimeEndpointInfo
-- | The response's http status code.
[$sel:httpStatus:CreateRealtimeEndpointResponse'] :: CreateRealtimeEndpointResponse -> Int
-- | Create a value of CreateRealtimeEndpointResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateRealtimeEndpoint,
-- createRealtimeEndpointResponse_mLModelId - A user-supplied ID
-- that uniquely identifies the MLModel. This value should be
-- identical to the value of the MLModelId in the request.
--
-- $sel:realtimeEndpointInfo:CreateRealtimeEndpointResponse',
-- createRealtimeEndpointResponse_realtimeEndpointInfo - The
-- endpoint information of the MLModel
--
-- $sel:httpStatus:CreateRealtimeEndpointResponse',
-- createRealtimeEndpointResponse_httpStatus - The response's http
-- status code.
newCreateRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
createRealtimeEndpointResponse_mLModelId :: Lens' CreateRealtimeEndpointResponse (Maybe Text)
-- | The endpoint information of the MLModel
createRealtimeEndpointResponse_realtimeEndpointInfo :: Lens' CreateRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)
-- | The response's http status code.
createRealtimeEndpointResponse_httpStatus :: Lens' CreateRealtimeEndpointResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Show.Show Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Read.Read Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
-- | Creates a new MLModel using the DataSource and the
-- recipe as information sources.
--
-- An MLModel is nearly immutable. Users can update only the
-- MLModelName and the ScoreThreshold in an
-- MLModel without creating a new MLModel.
--
-- CreateMLModel is an asynchronous operation. In response to
-- CreateMLModel, Amazon Machine Learning (Amazon ML)
-- immediately returns and sets the MLModel status to
-- PENDING. After the MLModel has been created and
-- ready is for use, Amazon ML sets the status to COMPLETED.
--
-- You can use the GetMLModel operation to check the progress of
-- the MLModel during the creation operation.
--
-- CreateMLModel requires a DataSource with computed
-- statistics, which can be created by setting ComputeStatistics
-- to true in CreateDataSourceFromRDS,
-- CreateDataSourceFromS3, or
-- CreateDataSourceFromRedshift operations.
module Amazonka.MachineLearning.CreateMLModel
-- | See: newCreateMLModel smart constructor.
data CreateMLModel
CreateMLModel' :: Maybe Text -> Maybe (HashMap Text Text) -> Maybe Text -> Maybe Text -> Text -> MLModelType -> Text -> CreateMLModel
-- | A user-supplied name or description of the MLModel.
[$sel:mLModelName:CreateMLModel'] :: CreateMLModel -> Maybe 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
[$sel:parameters:CreateMLModel'] :: CreateMLModel -> Maybe (HashMap Text Text)
-- | The data recipe for creating the MLModel. You must specify
-- either the recipe or its URI. If you don't specify a recipe or its
-- URI, Amazon ML creates a default.
[$sel:recipe:CreateMLModel'] :: CreateMLModel -> Maybe Text
-- | The Amazon Simple Storage Service (Amazon S3) location and file name
-- that contains the MLModel recipe. You must specify either the
-- recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
-- creates a default.
[$sel:recipeUri:CreateMLModel'] :: CreateMLModel -> Maybe Text
-- | A user-supplied ID that uniquely identifies the MLModel.
[$sel:mLModelId:CreateMLModel'] :: CreateMLModel -> Text
-- | The category of supervised learning that this MLModel will
-- address. Choose from the following types:
--
--
-- - Choose REGRESSION if the MLModel will be used to
-- predict a numeric value.
-- - Choose BINARY if the MLModel result has two
-- possible values.
-- - Choose MULTICLASS if the MLModel result has a
-- limited number of values.
--
--
-- For more information, see the Amazon Machine Learning Developer
-- Guide.
[$sel:mLModelType:CreateMLModel'] :: CreateMLModel -> MLModelType
-- | The DataSource that points to the training data.
[$sel:trainingDataSourceId:CreateMLModel'] :: CreateMLModel -> Text
-- | Create a value of CreateMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:mLModelName:CreateMLModel',
-- createMLModel_mLModelName - A user-supplied name or description
-- of the MLModel.
--
-- $sel:parameters:CreateMLModel', createMLModel_parameters
-- - 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
--
-- $sel:recipe:CreateMLModel', createMLModel_recipe - The
-- data recipe for creating the MLModel. You must specify either
-- the recipe or its URI. If you don't specify a recipe or its URI,
-- Amazon ML creates a default.
--
-- $sel:recipeUri:CreateMLModel', createMLModel_recipeUri -
-- The Amazon Simple Storage Service (Amazon S3) location and file name
-- that contains the MLModel recipe. You must specify either the
-- recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
-- creates a default.
--
-- CreateMLModel, createMLModel_mLModelId - A user-supplied
-- ID that uniquely identifies the MLModel.
--
-- CreateMLModel, createMLModel_mLModelType - The category
-- of supervised learning that this MLModel will address. Choose
-- from the following types:
--
--
-- - Choose REGRESSION if the MLModel will be used to
-- predict a numeric value.
-- - Choose BINARY if the MLModel result has two
-- possible values.
-- - Choose MULTICLASS if the MLModel result has a
-- limited number of values.
--
--
-- For more information, see the Amazon Machine Learning Developer
-- Guide.
--
-- CreateMLModel, createMLModel_trainingDataSourceId - The
-- DataSource that points to the training data.
newCreateMLModel :: Text -> MLModelType -> Text -> CreateMLModel
-- | A user-supplied name or description of the MLModel.
createMLModel_mLModelName :: Lens' CreateMLModel (Maybe 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
createMLModel_parameters :: Lens' CreateMLModel (Maybe (HashMap Text Text))
-- | The data recipe for creating the MLModel. You must specify
-- either the recipe or its URI. If you don't specify a recipe or its
-- URI, Amazon ML creates a default.
createMLModel_recipe :: Lens' CreateMLModel (Maybe Text)
-- | The Amazon Simple Storage Service (Amazon S3) location and file name
-- that contains the MLModel recipe. You must specify either the
-- recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
-- creates a default.
createMLModel_recipeUri :: Lens' CreateMLModel (Maybe Text)
-- | A user-supplied ID that uniquely identifies the MLModel.
createMLModel_mLModelId :: Lens' CreateMLModel Text
-- | The category of supervised learning that this MLModel will
-- address. Choose from the following types:
--
--
-- - Choose REGRESSION if the MLModel will be used to
-- predict a numeric value.
-- - Choose BINARY if the MLModel result has two
-- possible values.
-- - Choose MULTICLASS if the MLModel result has a
-- limited number of values.
--
--
-- For more information, see the Amazon Machine Learning Developer
-- Guide.
createMLModel_mLModelType :: Lens' CreateMLModel MLModelType
-- | The DataSource that points to the training data.
createMLModel_trainingDataSourceId :: Lens' CreateMLModel Text
-- | Represents the output of a CreateMLModel operation, and is an
-- acknowledgement that Amazon ML received the request.
--
-- The CreateMLModel operation is asynchronous. You can poll for
-- status updates by using the GetMLModel operation and checking
-- the Status parameter.
--
-- See: newCreateMLModelResponse smart constructor.
data CreateMLModelResponse
CreateMLModelResponse' :: Maybe Text -> Int -> CreateMLModelResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
[$sel:mLModelId:CreateMLModelResponse'] :: CreateMLModelResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateMLModelResponse'] :: CreateMLModelResponse -> Int
-- | Create a value of CreateMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateMLModel, createMLModelResponse_mLModelId - A
-- user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
--
-- $sel:httpStatus:CreateMLModelResponse',
-- createMLModelResponse_httpStatus - The response's http status
-- code.
newCreateMLModelResponse :: Int -> CreateMLModelResponse
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
createMLModelResponse_mLModelId :: Lens' CreateMLModelResponse (Maybe Text)
-- | The response's http status code.
createMLModelResponse_httpStatus :: Lens' CreateMLModelResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Show.Show Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Read.Read Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateMLModel.CreateMLModelResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateMLModel.CreateMLModelResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateMLModel.CreateMLModel
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateMLModel.CreateMLModel
-- | 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.
module Amazonka.MachineLearning.CreateEvaluation
-- | See: newCreateEvaluation smart constructor.
data CreateEvaluation
CreateEvaluation' :: Maybe Text -> Text -> Text -> Text -> CreateEvaluation
-- | A user-supplied name or description of the Evaluation.
[$sel:evaluationName:CreateEvaluation'] :: CreateEvaluation -> Maybe Text
-- | A user-supplied ID that uniquely identifies the Evaluation.
[$sel:evaluationId:CreateEvaluation'] :: CreateEvaluation -> Text
-- | The ID of the MLModel to evaluate.
--
-- The schema used in creating the MLModel must match the schema
-- of the DataSource used in the Evaluation.
[$sel:mLModelId:CreateEvaluation'] :: CreateEvaluation -> Text
-- | The ID of the DataSource for the evaluation. The schema of
-- the DataSource must match the schema used to create the
-- MLModel.
[$sel:evaluationDataSourceId:CreateEvaluation'] :: CreateEvaluation -> Text
-- | Create a value of CreateEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:evaluationName:CreateEvaluation',
-- createEvaluation_evaluationName - A user-supplied name or
-- description of the Evaluation.
--
-- CreateEvaluation, createEvaluation_evaluationId - A
-- user-supplied ID that uniquely identifies the Evaluation.
--
-- CreateEvaluation, createEvaluation_mLModelId - 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.
--
-- CreateEvaluation,
-- createEvaluation_evaluationDataSourceId - The ID of the
-- DataSource for the evaluation. The schema of the
-- DataSource must match the schema used to create the
-- MLModel.
newCreateEvaluation :: Text -> Text -> Text -> CreateEvaluation
-- | A user-supplied name or description of the Evaluation.
createEvaluation_evaluationName :: Lens' CreateEvaluation (Maybe Text)
-- | A user-supplied ID that uniquely identifies the Evaluation.
createEvaluation_evaluationId :: 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.
createEvaluation_mLModelId :: 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.
createEvaluation_evaluationDataSourceId :: Lens' CreateEvaluation Text
-- | 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: newCreateEvaluationResponse smart constructor.
data CreateEvaluationResponse
CreateEvaluationResponse' :: Maybe Text -> Int -> CreateEvaluationResponse
-- | The user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
[$sel:evaluationId:CreateEvaluationResponse'] :: CreateEvaluationResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateEvaluationResponse'] :: CreateEvaluationResponse -> Int
-- | Create a value of CreateEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateEvaluation, createEvaluationResponse_evaluationId
-- - The user-supplied ID that uniquely identifies the
-- Evaluation. This value should be identical to the value of
-- the EvaluationId in the request.
--
-- $sel:httpStatus:CreateEvaluationResponse',
-- createEvaluationResponse_httpStatus - The response's http
-- status code.
newCreateEvaluationResponse :: Int -> CreateEvaluationResponse
-- | The user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
createEvaluationResponse_evaluationId :: Lens' CreateEvaluationResponse (Maybe Text)
-- | The response's http status code.
createEvaluationResponse_httpStatus :: Lens' CreateEvaluationResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Show.Show Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Read.Read Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateEvaluation.CreateEvaluation
-- | Creates a DataSource object. A DataSource references
-- data that can be used to perform CreateMLModel,
-- CreateEvaluation, or CreateBatchPrediction
-- operations.
--
-- CreateDataSourceFromS3 is an asynchronous operation. In
-- response to CreateDataSourceFromS3, Amazon Machine Learning
-- (Amazon ML) immediately returns and sets the DataSource
-- status to PENDING. After the DataSource has been
-- created and is ready for use, Amazon ML sets the Status
-- parameter to COMPLETED. DataSource in the
-- COMPLETED or PENDING state can be used to perform
-- only CreateMLModel, CreateEvaluation or
-- CreateBatchPrediction operations.
--
-- If Amazon ML can't accept the input source, it sets the
-- Status parameter to FAILED and includes an error
-- message in the Message attribute of the
-- GetDataSource operation response.
--
-- The observation data used in a DataSource should be ready to
-- use; that is, it should have a consistent structure, and missing data
-- values should be kept to a minimum. The observation data must reside
-- in one or more .csv files in an Amazon Simple Storage Service (Amazon
-- S3) location, along with a schema that describes the data items by
-- name and type. The same schema must be used for all of the data files
-- referenced by the DataSource.
--
-- After the DataSource has been created, it's ready to use in
-- evaluations and batch predictions. If you plan to use the
-- DataSource to train an MLModel, the
-- DataSource also needs a recipe. A recipe describes how each
-- input variable will be used in training an MLModel. Will the
-- variable be included or excluded from training? Will the variable be
-- manipulated; for example, will it be combined with another variable or
-- will it be split apart into word combinations? The recipe provides
-- answers to these questions.
module Amazonka.MachineLearning.CreateDataSourceFromS3
-- | See: newCreateDataSourceFromS3 smart constructor.
data CreateDataSourceFromS3
CreateDataSourceFromS3' :: Maybe Bool -> Maybe Text -> Text -> S3DataSpec -> CreateDataSourceFromS3
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
[$sel:computeStatistics:CreateDataSourceFromS3'] :: CreateDataSourceFromS3 -> Maybe Bool
-- | A user-supplied name or description of the DataSource.
[$sel:dataSourceName:CreateDataSourceFromS3'] :: CreateDataSourceFromS3 -> Maybe Text
-- | A user-supplied identifier that uniquely identifies the
-- DataSource.
[$sel:dataSourceId:CreateDataSourceFromS3'] :: CreateDataSourceFromS3 -> Text
-- | The data specification of a DataSource:
--
--
-- - DataLocationS3 - The Amazon S3 location of the observation
-- data.
-- - DataSchemaLocationS3 - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
[$sel:dataSpec:CreateDataSourceFromS3'] :: CreateDataSourceFromS3 -> S3DataSpec
-- | Create a value of CreateDataSourceFromS3 with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource needs to
-- be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromS3',
-- createDataSourceFromS3_dataSourceName - A user-supplied name or
-- description of the DataSource.
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3_dataSourceId - A user-supplied
-- identifier that uniquely identifies the DataSource.
--
-- $sel:dataSpec:CreateDataSourceFromS3',
-- createDataSourceFromS3_dataSpec - The data specification of a
-- DataSource:
--
--
-- - DataLocationS3 - The Amazon S3 location of the observation
-- data.
-- - DataSchemaLocationS3 - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
newCreateDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
createDataSourceFromS3_computeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromS3_dataSourceName :: Lens' CreateDataSourceFromS3 (Maybe Text)
-- | A user-supplied identifier that uniquely identifies the
-- DataSource.
createDataSourceFromS3_dataSourceId :: Lens' CreateDataSourceFromS3 Text
-- | The data specification of a DataSource:
--
--
-- - DataLocationS3 - The Amazon S3 location of the observation
-- data.
-- - DataSchemaLocationS3 - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromS3_dataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec
-- | Represents the output of a CreateDataSourceFromS3 operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateDataSourceFromS3 operation is asynchronous. You can
-- poll for updates by using the GetBatchPrediction operation
-- and checking the Status parameter.
--
-- See: newCreateDataSourceFromS3Response smart
-- constructor.
data CreateDataSourceFromS3Response
CreateDataSourceFromS3Response' :: Maybe Text -> Int -> CreateDataSourceFromS3Response
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
[$sel:dataSourceId:CreateDataSourceFromS3Response'] :: CreateDataSourceFromS3Response -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateDataSourceFromS3Response'] :: CreateDataSourceFromS3Response -> Int
-- | Create a value of CreateDataSourceFromS3Response with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3Response_dataSourceId - A user-supplied
-- ID that uniquely identifies the DataSource. This value should
-- be identical to the value of the DataSourceID in the request.
--
-- $sel:httpStatus:CreateDataSourceFromS3Response',
-- createDataSourceFromS3Response_httpStatus - The response's http
-- status code.
newCreateDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
createDataSourceFromS3Response_dataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text)
-- | The response's http status code.
createDataSourceFromS3Response_httpStatus :: Lens' CreateDataSourceFromS3Response Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
-- | Creates a DataSource from a database hosted on an Amazon
-- Redshift cluster. A DataSource references data that can be
-- used to perform either CreateMLModel,
-- CreateEvaluation, or CreateBatchPrediction
-- operations.
--
-- CreateDataSourceFromRedshift is an asynchronous operation. In
-- response to CreateDataSourceFromRedshift, Amazon Machine
-- Learning (Amazon ML) immediately returns and sets the
-- DataSource status to PENDING. After the
-- DataSource is created and ready for use, Amazon ML sets the
-- Status parameter to COMPLETED. DataSource
-- in COMPLETED or PENDING states can be used to
-- perform only CreateMLModel, CreateEvaluation, or
-- CreateBatchPrediction operations.
--
-- If Amazon ML can't accept the input source, it sets the
-- Status parameter to FAILED and includes an error
-- message in the Message attribute of the
-- GetDataSource operation response.
--
-- The observations should be contained in the database hosted on an
-- Amazon Redshift cluster and should be specified by a
-- SelectSqlQuery query. Amazon ML executes an Unload
-- command in Amazon Redshift to transfer the result set of the
-- SelectSqlQuery query to S3StagingLocation.
--
-- After the DataSource has been created, it's ready for use in
-- evaluations and batch predictions. If you plan to use the
-- DataSource to train an MLModel, the
-- DataSource also requires a recipe. A recipe describes how
-- each input variable will be used in training an MLModel. Will
-- the variable be included or excluded from training? Will the variable
-- be manipulated; for example, will it be combined with another variable
-- or will it be split apart into word combinations? The recipe provides
-- answers to these questions.
--
-- You can't change an existing datasource, but you can copy and modify
-- the settings from an existing Amazon Redshift datasource to create a
-- new datasource. To do so, call GetDataSource for an existing
-- datasource and copy the values to a CreateDataSource call.
-- Change the settings that you want to change and make sure that all
-- required fields have the appropriate values.
module Amazonka.MachineLearning.CreateDataSourceFromRedshift
-- | See: newCreateDataSourceFromRedshift smart constructor.
data CreateDataSourceFromRedshift
CreateDataSourceFromRedshift' :: Maybe Bool -> Maybe Text -> Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel
-- training.
[$sel:computeStatistics:CreateDataSourceFromRedshift'] :: CreateDataSourceFromRedshift -> Maybe Bool
-- | A user-supplied name or description of the DataSource.
[$sel:dataSourceName:CreateDataSourceFromRedshift'] :: CreateDataSourceFromRedshift -> Maybe Text
-- | A user-supplied ID that uniquely identifies the DataSource.
[$sel:dataSourceId:CreateDataSourceFromRedshift'] :: CreateDataSourceFromRedshift -> Text
-- | The data specification of an Amazon Redshift DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon Redshift database.
- ClusterIdentifier -
-- The unique ID for the Amazon Redshift cluster.
-- - DatabaseCredentials - The AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon Redshift
-- database.
-- - SelectSqlQuery - The query that is used to retrieve the
-- observation data for the Datasource.
-- - S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
-- location for staging Amazon Redshift data. The data retrieved from
-- Amazon Redshift using the SelectSqlQuery query is stored in
-- this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the DataSource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
[$sel:dataSpec:CreateDataSourceFromRedshift'] :: CreateDataSourceFromRedshift -> RedshiftDataSpec
-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes
-- the role on behalf of the user to create the following:
--
--
-- - A security group to allow Amazon ML to execute the
-- SelectSqlQuery query on an Amazon Redshift cluster
-- - An Amazon S3 bucket policy to grant Amazon ML read/write
-- permissions on the S3StagingLocation
--
[$sel:roleARN:CreateDataSourceFromRedshift'] :: CreateDataSourceFromRedshift -> Text
-- | Create a value of CreateDataSourceFromRedshift with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource
-- needs to be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromRedshift',
-- createDataSourceFromRedshift_dataSourceName - A user-supplied
-- name or description of the DataSource.
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_dataSourceId - A user-supplied ID
-- that uniquely identifies the DataSource.
--
-- $sel:dataSpec:CreateDataSourceFromRedshift',
-- createDataSourceFromRedshift_dataSpec - The data specification
-- of an Amazon Redshift DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon Redshift database.
- ClusterIdentifier -
-- The unique ID for the Amazon Redshift cluster.
-- - DatabaseCredentials - The AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon Redshift
-- database.
-- - SelectSqlQuery - The query that is used to retrieve the
-- observation data for the Datasource.
-- - S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
-- location for staging Amazon Redshift data. The data retrieved from
-- Amazon Redshift using the SelectSqlQuery query is stored in
-- this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the DataSource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_roleARN - A fully specified role
-- Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of
-- the user to create the following:
--
--
-- - A security group to allow Amazon ML to execute the
-- SelectSqlQuery query on an Amazon Redshift cluster
-- - An Amazon S3 bucket policy to grant Amazon ML read/write
-- permissions on the S3StagingLocation
--
newCreateDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel
-- training.
createDataSourceFromRedshift_computeStatistics :: Lens' CreateDataSourceFromRedshift (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromRedshift_dataSourceName :: Lens' CreateDataSourceFromRedshift (Maybe Text)
-- | A user-supplied ID that uniquely identifies the DataSource.
createDataSourceFromRedshift_dataSourceId :: Lens' CreateDataSourceFromRedshift Text
-- | The data specification of an Amazon Redshift DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon Redshift database.
- ClusterIdentifier -
-- The unique ID for the Amazon Redshift cluster.
-- - DatabaseCredentials - The AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon Redshift
-- database.
-- - SelectSqlQuery - The query that is used to retrieve the
-- observation data for the Datasource.
-- - S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
-- location for staging Amazon Redshift data. The data retrieved from
-- Amazon Redshift using the SelectSqlQuery query is stored in
-- this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the DataSource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromRedshift_dataSpec :: Lens' CreateDataSourceFromRedshift RedshiftDataSpec
-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes
-- the role on behalf of the user to create the following:
--
--
-- - A security group to allow Amazon ML to execute the
-- SelectSqlQuery query on an Amazon Redshift cluster
-- - An Amazon S3 bucket policy to grant Amazon ML read/write
-- permissions on the S3StagingLocation
--
createDataSourceFromRedshift_roleARN :: Lens' CreateDataSourceFromRedshift Text
-- | Represents the output of a CreateDataSourceFromRedshift
-- operation, and is an acknowledgement that Amazon ML received the
-- request.
--
-- The CreateDataSourceFromRedshift operation is asynchronous.
-- You can poll for updates by using the GetBatchPrediction
-- operation and checking the Status parameter.
--
-- See: newCreateDataSourceFromRedshiftResponse smart
-- constructor.
data CreateDataSourceFromRedshiftResponse
CreateDataSourceFromRedshiftResponse' :: Maybe Text -> Int -> CreateDataSourceFromRedshiftResponse
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
[$sel:dataSourceId:CreateDataSourceFromRedshiftResponse'] :: CreateDataSourceFromRedshiftResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateDataSourceFromRedshiftResponse'] :: CreateDataSourceFromRedshiftResponse -> Int
-- | Create a value of CreateDataSourceFromRedshiftResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshiftResponse_dataSourceId - A
-- user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
--
-- $sel:httpStatus:CreateDataSourceFromRedshiftResponse',
-- createDataSourceFromRedshiftResponse_httpStatus - The
-- response's http status code.
newCreateDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
createDataSourceFromRedshiftResponse_dataSourceId :: Lens' CreateDataSourceFromRedshiftResponse (Maybe Text)
-- | The response's http status code.
createDataSourceFromRedshiftResponse_httpStatus :: Lens' CreateDataSourceFromRedshiftResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
-- | Creates a DataSource object from an Amazon Relational
-- Database Service (Amazon RDS). A DataSource references
-- data that can be used to perform CreateMLModel,
-- CreateEvaluation, or CreateBatchPrediction
-- operations.
--
-- CreateDataSourceFromRDS is an asynchronous operation. In
-- response to CreateDataSourceFromRDS, Amazon Machine Learning
-- (Amazon ML) immediately returns and sets the DataSource
-- status to PENDING. After the DataSource is created
-- and ready for use, Amazon ML sets the Status parameter to
-- COMPLETED. DataSource in the COMPLETED or
-- PENDING state can be used only to perform
-- >CreateMLModel>, CreateEvaluation, or
-- CreateBatchPrediction operations.
--
-- If Amazon ML cannot accept the input source, it sets the
-- Status parameter to FAILED and includes an error
-- message in the Message attribute of the
-- GetDataSource operation response.
module Amazonka.MachineLearning.CreateDataSourceFromRDS
-- | See: newCreateDataSourceFromRDS smart constructor.
data CreateDataSourceFromRDS
CreateDataSourceFromRDS' :: Maybe Bool -> Maybe Text -> Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
[$sel:computeStatistics:CreateDataSourceFromRDS'] :: CreateDataSourceFromRDS -> Maybe Bool
-- | A user-supplied name or description of the DataSource.
[$sel:dataSourceName:CreateDataSourceFromRDS'] :: CreateDataSourceFromRDS -> Maybe Text
-- | A user-supplied ID that uniquely identifies the DataSource.
-- Typically, an Amazon Resource Number (ARN) becomes the ID for a
-- DataSource.
[$sel:dataSourceId:CreateDataSourceFromRDS'] :: CreateDataSourceFromRDS -> Text
-- | The data specification of an Amazon RDS DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon RDS database.
- InstanceIdentifier - A
-- unique identifier for the Amazon RDS database instance.
-- - DatabaseCredentials - AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon RDS database.
-- - ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by
-- an EC2 instance to carry out the copy task from Amazon RDS to Amazon
-- Simple Storage Service (Amazon S3). For more information, see Role
-- templates for data pipelines.
-- - ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
-- - SecurityInfo - The security information to use to access an RDS DB
-- instance. You need to set up appropriate ingress rules for the
-- security entity IDs provided to allow access to the Amazon RDS
-- instance. Specify a [SubnetId, SecurityGroupIds]
-- pair for a VPC-based RDS DB instance.
-- - SelectSqlQuery - A query that is used to retrieve the observation
-- data for the Datasource.
-- - S3StagingLocation - The Amazon S3 location for staging Amazon RDS
-- data. The data retrieved from Amazon RDS using SelectSqlQuery
-- is stored in this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
[$sel:rDSData:CreateDataSourceFromRDS'] :: CreateDataSourceFromRDS -> RDSDataSpec
-- | The role that Amazon ML assumes on behalf of the user to create and
-- activate a data pipeline in the user's account and copy data using the
-- SelectSqlQuery query from Amazon RDS to Amazon S3.
[$sel:roleARN:CreateDataSourceFromRDS'] :: CreateDataSourceFromRDS -> Text
-- | Create a value of CreateDataSourceFromRDS with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDS_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource needs to
-- be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromRDS',
-- createDataSourceFromRDS_dataSourceName - A user-supplied name
-- or description of the DataSource.
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDS_dataSourceId - A user-supplied ID that
-- uniquely identifies the DataSource. Typically, an Amazon
-- Resource Number (ARN) becomes the ID for a DataSource.
--
-- $sel:rDSData:CreateDataSourceFromRDS',
-- createDataSourceFromRDS_rDSData - The data specification of an
-- Amazon RDS DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon RDS database.
- InstanceIdentifier - A
-- unique identifier for the Amazon RDS database instance.
-- - DatabaseCredentials - AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon RDS database.
-- - ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by
-- an EC2 instance to carry out the copy task from Amazon RDS to Amazon
-- Simple Storage Service (Amazon S3). For more information, see Role
-- templates for data pipelines.
-- - ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
-- - SecurityInfo - The security information to use to access an RDS DB
-- instance. You need to set up appropriate ingress rules for the
-- security entity IDs provided to allow access to the Amazon RDS
-- instance. Specify a [SubnetId, SecurityGroupIds]
-- pair for a VPC-based RDS DB instance.
-- - SelectSqlQuery - A query that is used to retrieve the observation
-- data for the Datasource.
-- - S3StagingLocation - The Amazon S3 location for staging Amazon RDS
-- data. The data retrieved from Amazon RDS using SelectSqlQuery
-- is stored in this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
--
-- CreateDataSourceFromRDS, createDataSourceFromRDS_roleARN
-- - The role that Amazon ML assumes on behalf of the user to create and
-- activate a data pipeline in the user's account and copy data using the
-- SelectSqlQuery query from Amazon RDS to Amazon S3.
newCreateDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
createDataSourceFromRDS_computeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromRDS_dataSourceName :: Lens' CreateDataSourceFromRDS (Maybe Text)
-- | A user-supplied ID that uniquely identifies the DataSource.
-- Typically, an Amazon Resource Number (ARN) becomes the ID for a
-- DataSource.
createDataSourceFromRDS_dataSourceId :: Lens' CreateDataSourceFromRDS Text
-- | The data specification of an Amazon RDS DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon RDS database.
- InstanceIdentifier - A
-- unique identifier for the Amazon RDS database instance.
-- - DatabaseCredentials - AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon RDS database.
-- - ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by
-- an EC2 instance to carry out the copy task from Amazon RDS to Amazon
-- Simple Storage Service (Amazon S3). For more information, see Role
-- templates for data pipelines.
-- - ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
-- - SecurityInfo - The security information to use to access an RDS DB
-- instance. You need to set up appropriate ingress rules for the
-- security entity IDs provided to allow access to the Amazon RDS
-- instance. Specify a [SubnetId, SecurityGroupIds]
-- pair for a VPC-based RDS DB instance.
-- - SelectSqlQuery - A query that is used to retrieve the observation
-- data for the Datasource.
-- - S3StagingLocation - The Amazon S3 location for staging Amazon RDS
-- data. The data retrieved from Amazon RDS using SelectSqlQuery
-- is stored in this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromRDS_rDSData :: Lens' CreateDataSourceFromRDS RDSDataSpec
-- | The role that Amazon ML assumes on behalf of the user to create and
-- activate a data pipeline in the user's account and copy data using the
-- SelectSqlQuery query from Amazon RDS to Amazon S3.
createDataSourceFromRDS_roleARN :: Lens' CreateDataSourceFromRDS Text
-- | Represents the output of a CreateDataSourceFromRDS operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateDataSourceFromRDS> operation is asynchronous.
-- You can poll for updates by using the GetBatchPrediction
-- operation and checking the Status parameter. You can inspect
-- the Message when Status shows up as FAILED.
-- You can also check the progress of the copy operation by going to the
-- DataPipeline console and looking up the pipeline using the
-- pipelineId from the describe call.
--
-- See: newCreateDataSourceFromRDSResponse smart
-- constructor.
data CreateDataSourceFromRDSResponse
CreateDataSourceFromRDSResponse' :: Maybe Text -> Int -> CreateDataSourceFromRDSResponse
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
[$sel:dataSourceId:CreateDataSourceFromRDSResponse'] :: CreateDataSourceFromRDSResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateDataSourceFromRDSResponse'] :: CreateDataSourceFromRDSResponse -> Int
-- | Create a value of CreateDataSourceFromRDSResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDSResponse_dataSourceId - A user-supplied
-- ID that uniquely identifies the datasource. This value should be
-- identical to the value of the DataSourceID in the request.
--
-- $sel:httpStatus:CreateDataSourceFromRDSResponse',
-- createDataSourceFromRDSResponse_httpStatus - The response's
-- http status code.
newCreateDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
createDataSourceFromRDSResponse_dataSourceId :: Lens' CreateDataSourceFromRDSResponse (Maybe Text)
-- | The response's http status code.
createDataSourceFromRDSResponse_httpStatus :: Lens' CreateDataSourceFromRDSResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
-- | Generates predictions for a group of observations. The observations to
-- process exist in one or more data files referenced by a
-- DataSource. This operation creates a new
-- BatchPrediction, and uses an MLModel and the data
-- files referenced by the DataSource as information sources.
--
-- CreateBatchPrediction is an asynchronous operation. In
-- response to CreateBatchPrediction, Amazon Machine Learning
-- (Amazon ML) immediately returns and sets the BatchPrediction
-- status to PENDING. After the BatchPrediction
-- completes, Amazon ML sets the status to COMPLETED.
--
-- You can poll for status updates by using the GetBatchPrediction
-- operation and checking the Status parameter of the result.
-- After the COMPLETED status appears, the results are available
-- in the location specified by the OutputUri parameter.
module Amazonka.MachineLearning.CreateBatchPrediction
-- | See: newCreateBatchPrediction smart constructor.
data CreateBatchPrediction
CreateBatchPrediction' :: Maybe Text -> Text -> Text -> Text -> Text -> CreateBatchPrediction
-- | A user-supplied name or description of the BatchPrediction.
-- BatchPredictionName can only use the UTF-8 character set.
[$sel:batchPredictionName:CreateBatchPrediction'] :: CreateBatchPrediction -> Maybe Text
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
[$sel:batchPredictionId:CreateBatchPrediction'] :: CreateBatchPrediction -> Text
-- | The ID of the MLModel that will generate predictions for the
-- group of observations.
[$sel:mLModelId:CreateBatchPrediction'] :: CreateBatchPrediction -> Text
-- | The ID of the DataSource that points to the group of
-- observations to predict.
[$sel:batchPredictionDataSourceId:CreateBatchPrediction'] :: CreateBatchPrediction -> Text
-- | The location of an Amazon Simple Storage Service (Amazon S3) bucket or
-- directory to store the batch prediction results. The following
-- substrings are not allowed in the s3 key portion of the
-- outputURI field: ':', '//', '/./', '/../'.
--
-- Amazon ML needs permissions to store and retrieve the logs on your
-- behalf. For information about how to set permissions, see the
-- Amazon Machine Learning Developer Guide.
[$sel:outputUri:CreateBatchPrediction'] :: CreateBatchPrediction -> Text
-- | Create a value of CreateBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:batchPredictionName:CreateBatchPrediction',
-- createBatchPrediction_batchPredictionName - A user-supplied
-- name or description of the BatchPrediction.
-- BatchPredictionName can only use the UTF-8 character set.
--
-- CreateBatchPrediction,
-- createBatchPrediction_batchPredictionId - A user-supplied ID
-- that uniquely identifies the BatchPrediction.
--
-- CreateBatchPrediction, createBatchPrediction_mLModelId -
-- The ID of the MLModel that will generate predictions for the
-- group of observations.
--
-- CreateBatchPrediction,
-- createBatchPrediction_batchPredictionDataSourceId - The ID of
-- the DataSource that points to the group of observations to
-- predict.
--
-- CreateBatchPrediction, createBatchPrediction_outputUri -
-- The location of an Amazon Simple Storage Service (Amazon S3) bucket or
-- directory to store the batch prediction results. The following
-- substrings are not allowed in the s3 key portion of the
-- outputURI field: ':', '//', '/./', '/../'.
--
-- Amazon ML needs permissions to store and retrieve the logs on your
-- behalf. For information about how to set permissions, see the
-- Amazon Machine Learning Developer Guide.
newCreateBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction
-- | A user-supplied name or description of the BatchPrediction.
-- BatchPredictionName can only use the UTF-8 character set.
createBatchPrediction_batchPredictionName :: Lens' CreateBatchPrediction (Maybe Text)
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
createBatchPrediction_batchPredictionId :: Lens' CreateBatchPrediction Text
-- | The ID of the MLModel that will generate predictions for the
-- group of observations.
createBatchPrediction_mLModelId :: Lens' CreateBatchPrediction Text
-- | The ID of the DataSource that points to the group of
-- observations to predict.
createBatchPrediction_batchPredictionDataSourceId :: Lens' CreateBatchPrediction Text
-- | The location of an Amazon Simple Storage Service (Amazon S3) bucket or
-- directory to store the batch prediction results. The following
-- substrings are not allowed in the s3 key portion of the
-- outputURI field: ':', '//', '/./', '/../'.
--
-- Amazon ML needs permissions to store and retrieve the logs on your
-- behalf. For information about how to set permissions, see the
-- Amazon Machine Learning Developer Guide.
createBatchPrediction_outputUri :: Lens' CreateBatchPrediction Text
-- | Represents the output of a CreateBatchPrediction operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateBatchPrediction operation is asynchronous. You can
-- poll for status updates by using the >GetBatchPrediction
-- operation and checking the Status parameter of the result.
--
-- See: newCreateBatchPredictionResponse smart constructor.
data CreateBatchPredictionResponse
CreateBatchPredictionResponse' :: Maybe Text -> Int -> CreateBatchPredictionResponse
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value is identical to the value of the
-- BatchPredictionId in the request.
[$sel:batchPredictionId:CreateBatchPredictionResponse'] :: CreateBatchPredictionResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:CreateBatchPredictionResponse'] :: CreateBatchPredictionResponse -> Int
-- | Create a value of CreateBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateBatchPrediction,
-- createBatchPredictionResponse_batchPredictionId - A
-- user-supplied ID that uniquely identifies the
-- BatchPrediction. This value is identical to the value of the
-- BatchPredictionId in the request.
--
-- $sel:httpStatus:CreateBatchPredictionResponse',
-- createBatchPredictionResponse_httpStatus - The response's http
-- status code.
newCreateBatchPredictionResponse :: Int -> CreateBatchPredictionResponse
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value is identical to the value of the
-- BatchPredictionId in the request.
createBatchPredictionResponse_batchPredictionId :: Lens' CreateBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
createBatchPredictionResponse_httpStatus :: Lens' CreateBatchPredictionResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Show.Show Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Read.Read Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Generics.Generic Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Show.Show Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Read.Read Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
-- | Adds one or more tags to an object, up to a limit of 10. Each tag
-- consists of a key and an optional value. If you add a tag using a key
-- that is already associated with the ML object, AddTags
-- updates the tag's value.
module Amazonka.MachineLearning.AddTags
-- | See: newAddTags smart constructor.
data AddTags
AddTags' :: [Tag] -> Text -> TaggableResourceType -> AddTags
-- | The key-value pairs to use to create tags. If you specify a key
-- without specifying a value, Amazon ML creates a tag with the specified
-- key and a value of null.
[$sel:tags:AddTags'] :: AddTags -> [Tag]
-- | The ID of the ML object to tag. For example, exampleModelId.
[$sel:resourceId:AddTags'] :: AddTags -> Text
-- | The type of the ML object to tag.
[$sel:resourceType:AddTags'] :: AddTags -> TaggableResourceType
-- | Create a value of AddTags with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:tags:AddTags', addTags_tags - The key-value pairs
-- to use to create tags. If you specify a key without specifying a
-- value, Amazon ML creates a tag with the specified key and a value of
-- null.
--
-- AddTags, addTags_resourceId - The ID of the ML object to
-- tag. For example, exampleModelId.
--
-- AddTags, addTags_resourceType - The type of the ML
-- object to tag.
newAddTags :: Text -> TaggableResourceType -> AddTags
-- | The key-value pairs to use to create tags. If you specify a key
-- without specifying a value, Amazon ML creates a tag with the specified
-- key and a value of null.
addTags_tags :: Lens' AddTags [Tag]
-- | The ID of the ML object to tag. For example, exampleModelId.
addTags_resourceId :: Lens' AddTags Text
-- | The type of the ML object to tag.
addTags_resourceType :: Lens' AddTags TaggableResourceType
-- | Amazon ML returns the following elements.
--
-- See: newAddTagsResponse smart constructor.
data AddTagsResponse
AddTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Int -> AddTagsResponse
-- | The ID of the ML object that was tagged.
[$sel:resourceId:AddTagsResponse'] :: AddTagsResponse -> Maybe Text
-- | The type of the ML object that was tagged.
[$sel:resourceType:AddTagsResponse'] :: AddTagsResponse -> Maybe TaggableResourceType
-- | The response's http status code.
[$sel:httpStatus:AddTagsResponse'] :: AddTagsResponse -> Int
-- | Create a value of AddTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- AddTags, addTagsResponse_resourceId - The ID of the ML
-- object that was tagged.
--
-- AddTags, addTagsResponse_resourceType - The type of the
-- ML object that was tagged.
--
-- $sel:httpStatus:AddTagsResponse',
-- addTagsResponse_httpStatus - The response's http status code.
newAddTagsResponse :: Int -> AddTagsResponse
-- | The ID of the ML object that was tagged.
addTagsResponse_resourceId :: Lens' AddTagsResponse (Maybe Text)
-- | The type of the ML object that was tagged.
addTagsResponse_resourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType)
-- | The response's http status code.
addTagsResponse_httpStatus :: Lens' AddTagsResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.AddTags.AddTags
instance GHC.Show.Show Amazonka.MachineLearning.AddTags.AddTags
instance GHC.Read.Read Amazonka.MachineLearning.AddTags.AddTags
instance GHC.Classes.Eq Amazonka.MachineLearning.AddTags.AddTags
instance GHC.Generics.Generic Amazonka.MachineLearning.AddTags.AddTagsResponse
instance GHC.Show.Show Amazonka.MachineLearning.AddTags.AddTagsResponse
instance GHC.Read.Read Amazonka.MachineLearning.AddTags.AddTagsResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.AddTags.AddTagsResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.AddTags.AddTagsResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Amazonka.MachineLearning.AddTags.AddTags
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.AddTags.AddTags
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.AddTags.AddTags
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.AddTags.AddTags
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.AddTags.AddTags
-- | Updates the BatchPredictionName of a
-- BatchPrediction.
--
-- You can use the GetBatchPrediction operation to view the
-- contents of the updated data element.
module Amazonka.MachineLearning.UpdateBatchPrediction
-- | See: newUpdateBatchPrediction smart constructor.
data UpdateBatchPrediction
UpdateBatchPrediction' :: Text -> Text -> UpdateBatchPrediction
-- | The ID assigned to the BatchPrediction during creation.
[$sel:batchPredictionId:UpdateBatchPrediction'] :: UpdateBatchPrediction -> Text
-- | A new user-supplied name or description of the
-- BatchPrediction.
[$sel:batchPredictionName:UpdateBatchPrediction'] :: UpdateBatchPrediction -> Text
-- | Create a value of UpdateBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateBatchPrediction,
-- updateBatchPrediction_batchPredictionId - The ID assigned to
-- the BatchPrediction during creation.
--
-- $sel:batchPredictionName:UpdateBatchPrediction',
-- updateBatchPrediction_batchPredictionName - A new user-supplied
-- name or description of the BatchPrediction.
newUpdateBatchPrediction :: Text -> Text -> UpdateBatchPrediction
-- | The ID assigned to the BatchPrediction during creation.
updateBatchPrediction_batchPredictionId :: Lens' UpdateBatchPrediction Text
-- | A new user-supplied name or description of the
-- BatchPrediction.
updateBatchPrediction_batchPredictionName :: Lens' UpdateBatchPrediction Text
-- | Represents the output of an UpdateBatchPrediction operation.
--
-- You can see the updated content by using the
-- GetBatchPrediction operation.
--
-- See: newUpdateBatchPredictionResponse smart constructor.
data UpdateBatchPredictionResponse
UpdateBatchPredictionResponse' :: Maybe Text -> Int -> UpdateBatchPredictionResponse
-- | The ID assigned to the BatchPrediction during creation. This
-- value should be identical to the value of the
-- BatchPredictionId in the request.
[$sel:batchPredictionId:UpdateBatchPredictionResponse'] :: UpdateBatchPredictionResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:UpdateBatchPredictionResponse'] :: UpdateBatchPredictionResponse -> Int
-- | Create a value of UpdateBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateBatchPrediction,
-- updateBatchPredictionResponse_batchPredictionId - The ID
-- assigned to the BatchPrediction during creation. This value
-- should be identical to the value of the BatchPredictionId in
-- the request.
--
-- $sel:httpStatus:UpdateBatchPredictionResponse',
-- updateBatchPredictionResponse_httpStatus - The response's http
-- status code.
newUpdateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse
-- | The ID assigned to the BatchPrediction during creation. This
-- value should be identical to the value of the
-- BatchPredictionId in the request.
updateBatchPredictionResponse_batchPredictionId :: Lens' UpdateBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
updateBatchPredictionResponse_httpStatus :: Lens' UpdateBatchPredictionResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Show.Show Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Read.Read Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Show.Show Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Read.Read Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
-- | Updates the DataSourceName of a DataSource.
--
-- You can use the GetDataSource operation to view the contents
-- of the updated data element.
module Amazonka.MachineLearning.UpdateDataSource
-- | See: newUpdateDataSource smart constructor.
data UpdateDataSource
UpdateDataSource' :: Text -> Text -> UpdateDataSource
-- | The ID assigned to the DataSource during creation.
[$sel:dataSourceId:UpdateDataSource'] :: UpdateDataSource -> Text
-- | A new user-supplied name or description of the DataSource
-- that will replace the current description.
[$sel:dataSourceName:UpdateDataSource'] :: UpdateDataSource -> Text
-- | Create a value of UpdateDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateDataSource, updateDataSource_dataSourceId - The ID
-- assigned to the DataSource during creation.
--
-- $sel:dataSourceName:UpdateDataSource',
-- updateDataSource_dataSourceName - A new user-supplied name or
-- description of the DataSource that will replace the current
-- description.
newUpdateDataSource :: Text -> Text -> UpdateDataSource
-- | The ID assigned to the DataSource during creation.
updateDataSource_dataSourceId :: Lens' UpdateDataSource Text
-- | A new user-supplied name or description of the DataSource
-- that will replace the current description.
updateDataSource_dataSourceName :: Lens' UpdateDataSource Text
-- | Represents the output of an UpdateDataSource operation.
--
-- You can see the updated content by using the
-- GetBatchPrediction operation.
--
-- See: newUpdateDataSourceResponse smart constructor.
data UpdateDataSourceResponse
UpdateDataSourceResponse' :: Maybe Text -> Int -> UpdateDataSourceResponse
-- | The ID assigned to the DataSource during creation. This value
-- should be identical to the value of the DataSourceID in the
-- request.
[$sel:dataSourceId:UpdateDataSourceResponse'] :: UpdateDataSourceResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:UpdateDataSourceResponse'] :: UpdateDataSourceResponse -> Int
-- | Create a value of UpdateDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateDataSource, updateDataSourceResponse_dataSourceId
-- - The ID assigned to the DataSource during creation. This
-- value should be identical to the value of the DataSourceID in
-- the request.
--
-- $sel:httpStatus:UpdateDataSourceResponse',
-- updateDataSourceResponse_httpStatus - The response's http
-- status code.
newUpdateDataSourceResponse :: Int -> UpdateDataSourceResponse
-- | The ID assigned to the DataSource during creation. This value
-- should be identical to the value of the DataSourceID in the
-- request.
updateDataSourceResponse_dataSourceId :: Lens' UpdateDataSourceResponse (Maybe Text)
-- | The response's http status code.
updateDataSourceResponse_httpStatus :: Lens' UpdateDataSourceResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Show.Show Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Read.Read Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Show.Show Amazonka.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Read.Read Amazonka.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.UpdateDataSource.UpdateDataSource
-- | Updates the EvaluationName of an Evaluation.
--
-- You can use the GetEvaluation operation to view the contents
-- of the updated data element.
module Amazonka.MachineLearning.UpdateEvaluation
-- | See: newUpdateEvaluation smart constructor.
data UpdateEvaluation
UpdateEvaluation' :: Text -> Text -> UpdateEvaluation
-- | The ID assigned to the Evaluation during creation.
[$sel:evaluationId:UpdateEvaluation'] :: UpdateEvaluation -> Text
-- | A new user-supplied name or description of the Evaluation
-- that will replace the current content.
[$sel:evaluationName:UpdateEvaluation'] :: UpdateEvaluation -> Text
-- | Create a value of UpdateEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateEvaluation, updateEvaluation_evaluationId - The ID
-- assigned to the Evaluation during creation.
--
-- $sel:evaluationName:UpdateEvaluation',
-- updateEvaluation_evaluationName - A new user-supplied name or
-- description of the Evaluation that will replace the current
-- content.
newUpdateEvaluation :: Text -> Text -> UpdateEvaluation
-- | The ID assigned to the Evaluation during creation.
updateEvaluation_evaluationId :: Lens' UpdateEvaluation Text
-- | A new user-supplied name or description of the Evaluation
-- that will replace the current content.
updateEvaluation_evaluationName :: Lens' UpdateEvaluation Text
-- | Represents the output of an UpdateEvaluation operation.
--
-- You can see the updated content by using the GetEvaluation
-- operation.
--
-- See: newUpdateEvaluationResponse smart constructor.
data UpdateEvaluationResponse
UpdateEvaluationResponse' :: Maybe Text -> Int -> UpdateEvaluationResponse
-- | The ID assigned to the Evaluation during creation. This value
-- should be identical to the value of the Evaluation in the
-- request.
[$sel:evaluationId:UpdateEvaluationResponse'] :: UpdateEvaluationResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:UpdateEvaluationResponse'] :: UpdateEvaluationResponse -> Int
-- | Create a value of UpdateEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateEvaluation, updateEvaluationResponse_evaluationId
-- - The ID assigned to the Evaluation during creation. This
-- value should be identical to the value of the Evaluation in
-- the request.
--
-- $sel:httpStatus:UpdateEvaluationResponse',
-- updateEvaluationResponse_httpStatus - The response's http
-- status code.
newUpdateEvaluationResponse :: Int -> UpdateEvaluationResponse
-- | The ID assigned to the Evaluation during creation. This value
-- should be identical to the value of the Evaluation in the
-- request.
updateEvaluationResponse_evaluationId :: Lens' UpdateEvaluationResponse (Maybe Text)
-- | The response's http status code.
updateEvaluationResponse_httpStatus :: Lens' UpdateEvaluationResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Show.Show Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Read.Read Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Show.Show Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Read.Read Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.UpdateEvaluation.UpdateEvaluation
-- | Updates the MLModelName and the ScoreThreshold of an
-- MLModel.
--
-- You can use the GetMLModel operation to view the contents of
-- the updated data element.
module Amazonka.MachineLearning.UpdateMLModel
-- | See: newUpdateMLModel smart constructor.
data UpdateMLModel
UpdateMLModel' :: Maybe Text -> Maybe Double -> Text -> UpdateMLModel
-- | A user-supplied name or description of the MLModel.
[$sel:mLModelName:UpdateMLModel'] :: UpdateMLModel -> Maybe Text
-- | The ScoreThreshold used in binary classification
-- MLModel that marks the boundary between a positive prediction
-- and a negative prediction.
--
-- Output values greater than or equal to the ScoreThreshold
-- receive a positive result from the MLModel, such as
-- true. Output values less than the ScoreThreshold
-- receive a negative response from the MLModel, such as
-- false.
[$sel:scoreThreshold:UpdateMLModel'] :: UpdateMLModel -> Maybe Double
-- | The ID assigned to the MLModel during creation.
[$sel:mLModelId:UpdateMLModel'] :: UpdateMLModel -> Text
-- | Create a value of UpdateMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:mLModelName:UpdateMLModel',
-- updateMLModel_mLModelName - A user-supplied name or description
-- of the MLModel.
--
-- UpdateMLModel, updateMLModel_scoreThreshold - The
-- ScoreThreshold used in binary classification MLModel
-- that marks the boundary between a positive prediction and a negative
-- prediction.
--
-- Output values greater than or equal to the ScoreThreshold
-- receive a positive result from the MLModel, such as
-- true. Output values less than the ScoreThreshold
-- receive a negative response from the MLModel, such as
-- false.
--
-- UpdateMLModel, updateMLModel_mLModelId - The ID assigned
-- to the MLModel during creation.
newUpdateMLModel :: Text -> UpdateMLModel
-- | A user-supplied name or description of the MLModel.
updateMLModel_mLModelName :: Lens' UpdateMLModel (Maybe Text)
-- | The ScoreThreshold used in binary classification
-- MLModel that marks the boundary between a positive prediction
-- and a negative prediction.
--
-- Output values greater than or equal to the ScoreThreshold
-- receive a positive result from the MLModel, such as
-- true. Output values less than the ScoreThreshold
-- receive a negative response from the MLModel, such as
-- false.
updateMLModel_scoreThreshold :: Lens' UpdateMLModel (Maybe Double)
-- | The ID assigned to the MLModel during creation.
updateMLModel_mLModelId :: Lens' UpdateMLModel Text
-- | Represents the output of an UpdateMLModel operation.
--
-- You can see the updated content by using the GetMLModel
-- operation.
--
-- See: newUpdateMLModelResponse smart constructor.
data UpdateMLModelResponse
UpdateMLModelResponse' :: Maybe Text -> Int -> UpdateMLModelResponse
-- | The ID assigned to the MLModel during creation. This value
-- should be identical to the value of the MLModelID in the
-- request.
[$sel:mLModelId:UpdateMLModelResponse'] :: UpdateMLModelResponse -> Maybe Text
-- | The response's http status code.
[$sel:httpStatus:UpdateMLModelResponse'] :: UpdateMLModelResponse -> Int
-- | Create a value of UpdateMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateMLModel, updateMLModelResponse_mLModelId - The ID
-- assigned to the MLModel during creation. This value should be
-- identical to the value of the MLModelID in the request.
--
-- $sel:httpStatus:UpdateMLModelResponse',
-- updateMLModelResponse_httpStatus - The response's http status
-- code.
newUpdateMLModelResponse :: Int -> UpdateMLModelResponse
-- | The ID assigned to the MLModel during creation. This value
-- should be identical to the value of the MLModelID in the
-- request.
updateMLModelResponse_mLModelId :: Lens' UpdateMLModelResponse (Maybe Text)
-- | The response's http status code.
updateMLModelResponse_httpStatus :: Lens' UpdateMLModelResponse Int
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Show.Show Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Read.Read Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Generics.Generic Amazonka.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Show.Show Amazonka.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Read.Read Amazonka.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Classes.Eq Amazonka.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance Amazonka.Types.AWSRequest Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance Data.Hashable.Class.Hashable Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Amazonka.Data.Headers.ToHeaders Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Amazonka.Data.Path.ToPath Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
instance Amazonka.Data.Query.ToQuery Amazonka.MachineLearning.UpdateMLModel.UpdateMLModel
module Amazonka.MachineLearning.Lens
-- | The key-value pairs to use to create tags. If you specify a key
-- without specifying a value, Amazon ML creates a tag with the specified
-- key and a value of null.
addTags_tags :: Lens' AddTags [Tag]
-- | The ID of the ML object to tag. For example, exampleModelId.
addTags_resourceId :: Lens' AddTags Text
-- | The type of the ML object to tag.
addTags_resourceType :: Lens' AddTags TaggableResourceType
-- | The ID of the ML object that was tagged.
addTagsResponse_resourceId :: Lens' AddTagsResponse (Maybe Text)
-- | The type of the ML object that was tagged.
addTagsResponse_resourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType)
-- | The response's http status code.
addTagsResponse_httpStatus :: Lens' AddTagsResponse Int
-- | A user-supplied name or description of the BatchPrediction.
-- BatchPredictionName can only use the UTF-8 character set.
createBatchPrediction_batchPredictionName :: Lens' CreateBatchPrediction (Maybe Text)
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
createBatchPrediction_batchPredictionId :: Lens' CreateBatchPrediction Text
-- | The ID of the MLModel that will generate predictions for the
-- group of observations.
createBatchPrediction_mLModelId :: Lens' CreateBatchPrediction Text
-- | The ID of the DataSource that points to the group of
-- observations to predict.
createBatchPrediction_batchPredictionDataSourceId :: Lens' CreateBatchPrediction Text
-- | The location of an Amazon Simple Storage Service (Amazon S3) bucket or
-- directory to store the batch prediction results. The following
-- substrings are not allowed in the s3 key portion of the
-- outputURI field: ':', '//', '/./', '/../'.
--
-- Amazon ML needs permissions to store and retrieve the logs on your
-- behalf. For information about how to set permissions, see the
-- Amazon Machine Learning Developer Guide.
createBatchPrediction_outputUri :: Lens' CreateBatchPrediction Text
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value is identical to the value of the
-- BatchPredictionId in the request.
createBatchPredictionResponse_batchPredictionId :: Lens' CreateBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
createBatchPredictionResponse_httpStatus :: Lens' CreateBatchPredictionResponse Int
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
createDataSourceFromRDS_computeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromRDS_dataSourceName :: Lens' CreateDataSourceFromRDS (Maybe Text)
-- | A user-supplied ID that uniquely identifies the DataSource.
-- Typically, an Amazon Resource Number (ARN) becomes the ID for a
-- DataSource.
createDataSourceFromRDS_dataSourceId :: Lens' CreateDataSourceFromRDS Text
-- | The data specification of an Amazon RDS DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon RDS database.
- InstanceIdentifier - A
-- unique identifier for the Amazon RDS database instance.
-- - DatabaseCredentials - AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon RDS database.
-- - ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by
-- an EC2 instance to carry out the copy task from Amazon RDS to Amazon
-- Simple Storage Service (Amazon S3). For more information, see Role
-- templates for data pipelines.
-- - ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
-- - SecurityInfo - The security information to use to access an RDS DB
-- instance. You need to set up appropriate ingress rules for the
-- security entity IDs provided to allow access to the Amazon RDS
-- instance. Specify a [SubnetId, SecurityGroupIds]
-- pair for a VPC-based RDS DB instance.
-- - SelectSqlQuery - A query that is used to retrieve the observation
-- data for the Datasource.
-- - S3StagingLocation - The Amazon S3 location for staging Amazon RDS
-- data. The data retrieved from Amazon RDS using SelectSqlQuery
-- is stored in this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromRDS_rDSData :: Lens' CreateDataSourceFromRDS RDSDataSpec
-- | The role that Amazon ML assumes on behalf of the user to create and
-- activate a data pipeline in the user's account and copy data using the
-- SelectSqlQuery query from Amazon RDS to Amazon S3.
createDataSourceFromRDS_roleARN :: Lens' CreateDataSourceFromRDS Text
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
createDataSourceFromRDSResponse_dataSourceId :: Lens' CreateDataSourceFromRDSResponse (Maybe Text)
-- | The response's http status code.
createDataSourceFromRDSResponse_httpStatus :: Lens' CreateDataSourceFromRDSResponse Int
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel
-- training.
createDataSourceFromRedshift_computeStatistics :: Lens' CreateDataSourceFromRedshift (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromRedshift_dataSourceName :: Lens' CreateDataSourceFromRedshift (Maybe Text)
-- | A user-supplied ID that uniquely identifies the DataSource.
createDataSourceFromRedshift_dataSourceId :: Lens' CreateDataSourceFromRedshift Text
-- | The data specification of an Amazon Redshift DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon Redshift database.
- ClusterIdentifier -
-- The unique ID for the Amazon Redshift cluster.
-- - DatabaseCredentials - The AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon Redshift
-- database.
-- - SelectSqlQuery - The query that is used to retrieve the
-- observation data for the Datasource.
-- - S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
-- location for staging Amazon Redshift data. The data retrieved from
-- Amazon Redshift using the SelectSqlQuery query is stored in
-- this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the DataSource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromRedshift_dataSpec :: Lens' CreateDataSourceFromRedshift RedshiftDataSpec
-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes
-- the role on behalf of the user to create the following:
--
--
-- - A security group to allow Amazon ML to execute the
-- SelectSqlQuery query on an Amazon Redshift cluster
-- - An Amazon S3 bucket policy to grant Amazon ML read/write
-- permissions on the S3StagingLocation
--
createDataSourceFromRedshift_roleARN :: Lens' CreateDataSourceFromRedshift Text
-- | A user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
createDataSourceFromRedshiftResponse_dataSourceId :: Lens' CreateDataSourceFromRedshiftResponse (Maybe Text)
-- | The response's http status code.
createDataSourceFromRedshiftResponse_httpStatus :: Lens' CreateDataSourceFromRedshiftResponse Int
-- | The compute statistics for a DataSource. The statistics are
-- generated from the observation data referenced by a
-- DataSource. Amazon ML uses the statistics internally during
-- MLModel training. This parameter must be set to true
-- if the DataSource needs to be used for MLModel training.
createDataSourceFromS3_computeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool)
-- | A user-supplied name or description of the DataSource.
createDataSourceFromS3_dataSourceName :: Lens' CreateDataSourceFromS3 (Maybe Text)
-- | A user-supplied identifier that uniquely identifies the
-- DataSource.
createDataSourceFromS3_dataSourceId :: Lens' CreateDataSourceFromS3 Text
-- | The data specification of a DataSource:
--
--
-- - DataLocationS3 - The Amazon S3 location of the observation
-- data.
-- - DataSchemaLocationS3 - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
createDataSourceFromS3_dataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
createDataSourceFromS3Response_dataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text)
-- | The response's http status code.
createDataSourceFromS3Response_httpStatus :: Lens' CreateDataSourceFromS3Response Int
-- | A user-supplied name or description of the Evaluation.
createEvaluation_evaluationName :: Lens' CreateEvaluation (Maybe Text)
-- | A user-supplied ID that uniquely identifies the Evaluation.
createEvaluation_evaluationId :: 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.
createEvaluation_mLModelId :: 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.
createEvaluation_evaluationDataSourceId :: Lens' CreateEvaluation Text
-- | The user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
createEvaluationResponse_evaluationId :: Lens' CreateEvaluationResponse (Maybe Text)
-- | The response's http status code.
createEvaluationResponse_httpStatus :: Lens' CreateEvaluationResponse Int
-- | A user-supplied name or description of the MLModel.
createMLModel_mLModelName :: Lens' CreateMLModel (Maybe 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
createMLModel_parameters :: Lens' CreateMLModel (Maybe (HashMap Text Text))
-- | The data recipe for creating the MLModel. You must specify
-- either the recipe or its URI. If you don't specify a recipe or its
-- URI, Amazon ML creates a default.
createMLModel_recipe :: Lens' CreateMLModel (Maybe Text)
-- | The Amazon Simple Storage Service (Amazon S3) location and file name
-- that contains the MLModel recipe. You must specify either the
-- recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
-- creates a default.
createMLModel_recipeUri :: Lens' CreateMLModel (Maybe Text)
-- | A user-supplied ID that uniquely identifies the MLModel.
createMLModel_mLModelId :: Lens' CreateMLModel Text
-- | The category of supervised learning that this MLModel will
-- address. Choose from the following types:
--
--
-- - Choose REGRESSION if the MLModel will be used to
-- predict a numeric value.
-- - Choose BINARY if the MLModel result has two
-- possible values.
-- - Choose MULTICLASS if the MLModel result has a
-- limited number of values.
--
--
-- For more information, see the Amazon Machine Learning Developer
-- Guide.
createMLModel_mLModelType :: Lens' CreateMLModel MLModelType
-- | The DataSource that points to the training data.
createMLModel_trainingDataSourceId :: Lens' CreateMLModel Text
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
createMLModelResponse_mLModelId :: Lens' CreateMLModelResponse (Maybe Text)
-- | The response's http status code.
createMLModelResponse_httpStatus :: Lens' CreateMLModelResponse Int
-- | The ID assigned to the MLModel during creation.
createRealtimeEndpoint_mLModelId :: Lens' CreateRealtimeEndpoint Text
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
createRealtimeEndpointResponse_mLModelId :: Lens' CreateRealtimeEndpointResponse (Maybe Text)
-- | The endpoint information of the MLModel
createRealtimeEndpointResponse_realtimeEndpointInfo :: Lens' CreateRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)
-- | The response's http status code.
createRealtimeEndpointResponse_httpStatus :: Lens' CreateRealtimeEndpointResponse Int
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction.
deleteBatchPrediction_batchPredictionId :: Lens' DeleteBatchPrediction Text
-- | A user-supplied ID that uniquely identifies the
-- BatchPrediction. This value should be identical to the value
-- of the BatchPredictionID in the request.
deleteBatchPredictionResponse_batchPredictionId :: Lens' DeleteBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
deleteBatchPredictionResponse_httpStatus :: Lens' DeleteBatchPredictionResponse Int
-- | A user-supplied ID that uniquely identifies the DataSource.
deleteDataSource_dataSourceId :: Lens' DeleteDataSource Text
-- | A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
deleteDataSourceResponse_dataSourceId :: Lens' DeleteDataSourceResponse (Maybe Text)
-- | The response's http status code.
deleteDataSourceResponse_httpStatus :: Lens' DeleteDataSourceResponse Int
-- | A user-supplied ID that uniquely identifies the Evaluation to
-- delete.
deleteEvaluation_evaluationId :: Lens' DeleteEvaluation Text
-- | A user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
deleteEvaluationResponse_evaluationId :: Lens' DeleteEvaluationResponse (Maybe Text)
-- | The response's http status code.
deleteEvaluationResponse_httpStatus :: Lens' DeleteEvaluationResponse Int
-- | A user-supplied ID that uniquely identifies the MLModel.
deleteMLModel_mLModelId :: Lens' DeleteMLModel Text
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelID in
-- the request.
deleteMLModelResponse_mLModelId :: Lens' DeleteMLModelResponse (Maybe Text)
-- | The response's http status code.
deleteMLModelResponse_httpStatus :: Lens' DeleteMLModelResponse Int
-- | The ID assigned to the MLModel during creation.
deleteRealtimeEndpoint_mLModelId :: Lens' DeleteRealtimeEndpoint Text
-- | A user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
deleteRealtimeEndpointResponse_mLModelId :: Lens' DeleteRealtimeEndpointResponse (Maybe Text)
-- | The endpoint information of the MLModel
deleteRealtimeEndpointResponse_realtimeEndpointInfo :: Lens' DeleteRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)
-- | The response's http status code.
deleteRealtimeEndpointResponse_httpStatus :: Lens' DeleteRealtimeEndpointResponse Int
-- | One or more tags to delete.
deleteTags_tagKeys :: Lens' DeleteTags [Text]
-- | The ID of the tagged ML object. For example, exampleModelId.
deleteTags_resourceId :: Lens' DeleteTags Text
-- | The type of the tagged ML object.
deleteTags_resourceType :: Lens' DeleteTags TaggableResourceType
-- | The ID of the ML object from which tags were deleted.
deleteTagsResponse_resourceId :: Lens' DeleteTagsResponse (Maybe Text)
-- | The type of the ML object from which tags were deleted.
deleteTagsResponse_resourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType)
-- | The response's http status code.
deleteTagsResponse_httpStatus :: Lens' DeleteTagsResponse Int
-- | The equal to operator. The BatchPrediction results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeBatchPredictions_eq :: Lens' DescribeBatchPredictions (Maybe Text)
-- | Use one of the following variables to filter a list of
-- BatchPrediction:
--
--
-- - CreatedAt - Sets the search criteria to the
-- BatchPrediction creation date.
-- - Status - Sets the search criteria to the
-- BatchPrediction status.
-- - Name - Sets the search criteria to the contents of the
-- 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 Solution (Amazon S3) bucket or
-- directory.
--
describeBatchPredictions_filterVariable :: Lens' DescribeBatchPredictions (Maybe BatchPredictionFilterVariable)
-- | The greater than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are greater than
-- or equal to the value specified with GE.
describeBatchPredictions_ge :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The greater than operator. The BatchPrediction results will
-- have FilterVariable values that are greater than the value
-- specified with GT.
describeBatchPredictions_gt :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The less than or equal to operator. The BatchPrediction
-- results will have FilterVariable values that are less than or
-- equal to the value specified with LE.
describeBatchPredictions_le :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The less than operator. The BatchPrediction results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeBatchPredictions_lt :: Lens' DescribeBatchPredictions (Maybe Text)
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
describeBatchPredictions_limit :: Lens' DescribeBatchPredictions (Maybe Natural)
-- | The not equal to operator. The BatchPrediction results will
-- have FilterVariable values not equal to the value specified
-- with NE.
describeBatchPredictions_ne :: Lens' DescribeBatchPredictions (Maybe Text)
-- | An ID of the page in the paginated results.
describeBatchPredictions_nextToken :: Lens' DescribeBatchPredictions (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a Batch Prediction operation could have the
-- Name 2014-09-09-HolidayGiftMailer. To search for
-- this BatchPrediction, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeBatchPredictions_prefix :: Lens' DescribeBatchPredictions (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModels.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeBatchPredictions_sortOrder :: Lens' DescribeBatchPredictions (Maybe SortOrder)
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeBatchPredictionsResponse_nextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)
-- | A list of BatchPrediction objects that meet the search
-- criteria.
describeBatchPredictionsResponse_results :: Lens' DescribeBatchPredictionsResponse (Maybe [BatchPrediction])
-- | The response's http status code.
describeBatchPredictionsResponse_httpStatus :: Lens' DescribeBatchPredictionsResponse Int
-- | The equal to operator. The DataSource results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeDataSources_eq :: Lens' DescribeDataSources (Maybe Text)
-- | Use one of the following variables to filter a list of
-- DataSource:
--
--
-- - CreatedAt - Sets the search criteria to
-- DataSource creation dates.
-- - Status - Sets the search criteria to DataSource
-- statuses.
-- - 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.
--
describeDataSources_filterVariable :: Lens' DescribeDataSources (Maybe DataSourceFilterVariable)
-- | The greater than or equal to operator. The DataSource results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeDataSources_ge :: Lens' DescribeDataSources (Maybe Text)
-- | The greater than operator. The DataSource results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeDataSources_gt :: Lens' DescribeDataSources (Maybe Text)
-- | The less than or equal to operator. The DataSource results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
describeDataSources_le :: Lens' DescribeDataSources (Maybe Text)
-- | The less than operator. The DataSource results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeDataSources_lt :: Lens' DescribeDataSources (Maybe Text)
-- | The maximum number of DataSource to include in the result.
describeDataSources_limit :: Lens' DescribeDataSources (Maybe Natural)
-- | The not equal to operator. The DataSource results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeDataSources_ne :: Lens' DescribeDataSources (Maybe Text)
-- | The ID of the page in the paginated results.
describeDataSources_nextToken :: Lens' DescribeDataSources (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, a DataSource could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- DataSource, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeDataSources_prefix :: Lens' DescribeDataSources (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of DataSource.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeDataSources_sortOrder :: Lens' DescribeDataSources (Maybe SortOrder)
-- | An ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeDataSourcesResponse_nextToken :: Lens' DescribeDataSourcesResponse (Maybe Text)
-- | A list of DataSource that meet the search criteria.
describeDataSourcesResponse_results :: Lens' DescribeDataSourcesResponse (Maybe [DataSource])
-- | The response's http status code.
describeDataSourcesResponse_httpStatus :: Lens' DescribeDataSourcesResponse Int
-- | The equal to operator. The Evaluation results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeEvaluations_eq :: Lens' DescribeEvaluations (Maybe Text)
-- | Use one of the following variable to filter a list of
-- Evaluation objects:
--
--
-- - CreatedAt - Sets the search criteria to the
-- Evaluation creation date.
-- - Status - Sets the search criteria to the
-- 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
-- MLModel 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 Solution (Amazon S3) bucket or directory.
--
describeEvaluations_filterVariable :: Lens' DescribeEvaluations (Maybe EvaluationFilterVariable)
-- | The greater than or equal to operator. The Evaluation results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeEvaluations_ge :: Lens' DescribeEvaluations (Maybe Text)
-- | The greater than operator. The Evaluation results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeEvaluations_gt :: Lens' DescribeEvaluations (Maybe Text)
-- | The less than or equal to operator. The Evaluation results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
describeEvaluations_le :: Lens' DescribeEvaluations (Maybe Text)
-- | The less than operator. The Evaluation results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeEvaluations_lt :: Lens' DescribeEvaluations (Maybe Text)
-- | The maximum number of Evaluation to include in the result.
describeEvaluations_limit :: Lens' DescribeEvaluations (Maybe Natural)
-- | The not equal to operator. The Evaluation results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeEvaluations_ne :: Lens' DescribeEvaluations (Maybe Text)
-- | The ID of the page in the paginated results.
describeEvaluations_nextToken :: Lens' DescribeEvaluations (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an Evaluation could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- Evaluation, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeEvaluations_prefix :: Lens' DescribeEvaluations (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of Evaluation.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeEvaluations_sortOrder :: Lens' DescribeEvaluations (Maybe SortOrder)
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeEvaluationsResponse_nextToken :: Lens' DescribeEvaluationsResponse (Maybe Text)
-- | A list of Evaluation that meet the search criteria.
describeEvaluationsResponse_results :: Lens' DescribeEvaluationsResponse (Maybe [Evaluation])
-- | The response's http status code.
describeEvaluationsResponse_httpStatus :: Lens' DescribeEvaluationsResponse Int
-- | The equal to operator. The MLModel results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
describeMLModels_eq :: Lens' DescribeMLModels (Maybe Text)
-- | Use one of the following variables to filter a list of
-- MLModel:
--
--
-- - CreatedAt - Sets the search criteria to MLModel
-- creation date.
-- - Status - Sets the search criteria to MLModel
-- status.
-- - Name - Sets the search criteria to the contents of
-- MLModel ____ Name.
-- - IAMUser - Sets the search criteria to the user account
-- that invoked the MLModel creation.
-- - TrainingDataSourceId - Sets the search criteria to the
-- DataSource used to train one or more MLModel.
-- - RealtimeEndpointStatus - Sets the search criteria to the
-- MLModel real-time endpoint status.
-- - MLModelType - Sets the search criteria to
-- MLModel type: binary, regression, or multi-class.
-- - Algorithm - Sets the search criteria to the algorithm
-- that the MLModel uses.
-- - TrainingDataURI - Sets the search criteria to the data
-- file(s) used in training a MLModel. The URL can identify
-- either a file or an Amazon Simple Storage Service (Amazon S3) bucket
-- or directory.
--
describeMLModels_filterVariable :: Lens' DescribeMLModels (Maybe MLModelFilterVariable)
-- | The greater than or equal to operator. The MLModel results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
describeMLModels_ge :: Lens' DescribeMLModels (Maybe Text)
-- | The greater than operator. The MLModel results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
describeMLModels_gt :: Lens' DescribeMLModels (Maybe Text)
-- | The less than or equal to operator. The MLModel results will
-- have FilterVariable values that are less than or equal to the
-- value specified with LE.
describeMLModels_le :: Lens' DescribeMLModels (Maybe Text)
-- | The less than operator. The MLModel results will have
-- FilterVariable values that are less than the value specified
-- with LT.
describeMLModels_lt :: Lens' DescribeMLModels (Maybe Text)
-- | The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
describeMLModels_limit :: Lens' DescribeMLModels (Maybe Natural)
-- | The not equal to operator. The MLModel results will have
-- FilterVariable values not equal to the value specified with
-- NE.
describeMLModels_ne :: Lens' DescribeMLModels (Maybe Text)
-- | The ID of the page in the paginated results.
describeMLModels_nextToken :: Lens' DescribeMLModels (Maybe Text)
-- | A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an MLModel could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- MLModel, select Name for the FilterVariable
-- and any of the following strings for the Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
describeMLModels_prefix :: Lens' DescribeMLModels (Maybe Text)
-- | A two-value parameter that determines the sequence of the resulting
-- list of MLModel.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
describeMLModels_sortOrder :: Lens' DescribeMLModels (Maybe SortOrder)
-- | The ID of the next page in the paginated results that indicates at
-- least one more page follows.
describeMLModelsResponse_nextToken :: Lens' DescribeMLModelsResponse (Maybe Text)
-- | A list of MLModel that meet the search criteria.
describeMLModelsResponse_results :: Lens' DescribeMLModelsResponse (Maybe [MLModel])
-- | The response's http status code.
describeMLModelsResponse_httpStatus :: Lens' DescribeMLModelsResponse Int
-- | The ID of the ML object. For example, exampleModelId.
describeTags_resourceId :: Lens' DescribeTags Text
-- | The type of the ML object.
describeTags_resourceType :: Lens' DescribeTags TaggableResourceType
-- | The ID of the tagged ML object.
describeTagsResponse_resourceId :: Lens' DescribeTagsResponse (Maybe Text)
-- | The type of the tagged ML object.
describeTagsResponse_resourceType :: Lens' DescribeTagsResponse (Maybe TaggableResourceType)
-- | A list of tags associated with the ML object.
describeTagsResponse_tags :: Lens' DescribeTagsResponse (Maybe [Tag])
-- | The response's http status code.
describeTagsResponse_httpStatus :: Lens' DescribeTagsResponse Int
-- | An ID assigned to the BatchPrediction at creation.
getBatchPrediction_batchPredictionId :: Lens' GetBatchPrediction Text
-- | The ID of the DataSource that was used to create the
-- BatchPrediction.
getBatchPredictionResponse_batchPredictionDataSourceId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | An ID assigned to the BatchPrediction at creation. This value
-- should be identical to the value of the BatchPredictionID in
-- the request.
getBatchPredictionResponse_batchPredictionId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the BatchPrediction, normalized and scaled
-- on computation resources. ComputeTime is only available if
-- the BatchPrediction is in the COMPLETED state.
getBatchPredictionResponse_computeTime :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The time when the BatchPrediction was created. The time is
-- expressed in epoch time.
getBatchPredictionResponse_createdAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | 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.
getBatchPredictionResponse_createdByIamUser :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as COMPLETED or FAILED.
-- FinishedAt is only available when the
-- BatchPrediction is in the COMPLETED or
-- FAILED state.
getBatchPredictionResponse_finishedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getBatchPredictionResponse_inputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The number of invalid records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
getBatchPredictionResponse_invalidRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The time of the most recent edit to BatchPrediction. The time
-- is expressed in epoch time.
getBatchPredictionResponse_lastUpdatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the
-- CreateBatchPrediction operation.
getBatchPredictionResponse_logUri :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
getBatchPredictionResponse_mLModelId :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | A description of the most recent details about processing the batch
-- prediction request.
getBatchPredictionResponse_message :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | A user-supplied name or description of the BatchPrediction.
getBatchPredictionResponse_name :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The location of an Amazon S3 bucket or directory to receive the
-- operation results.
getBatchPredictionResponse_outputUri :: Lens' GetBatchPredictionResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- BatchPrediction as INPROGRESS. StartedAt
-- isn't available if the BatchPrediction is in the
-- PENDING state.
getBatchPredictionResponse_startedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)
-- | The status of the BatchPrediction, which can be one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate batch predictions.
-- - INPROGRESS - The batch predictions are in progress.
-- - 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.
--
getBatchPredictionResponse_status :: Lens' GetBatchPredictionResponse (Maybe EntityStatus)
-- | The number of total records that Amazon Machine Learning saw while
-- processing the BatchPrediction.
getBatchPredictionResponse_totalRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)
-- | The response's http status code.
getBatchPredictionResponse_httpStatus :: Lens' GetBatchPredictionResponse Int
-- | Specifies whether the GetDataSource operation should return
-- DataSourceSchema.
--
-- If true, DataSourceSchema is returned.
--
-- If false, DataSourceSchema is not returned.
getDataSource_verbose :: Lens' GetDataSource (Maybe Bool)
-- | The ID assigned to the DataSource at creation.
getDataSource_dataSourceId :: Lens' GetDataSource Text
-- | The parameter is true if statistics need to be generated from
-- the observation data.
getDataSourceResponse_computeStatistics :: Lens' GetDataSourceResponse (Maybe Bool)
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the DataSource, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- DataSource is in the COMPLETED state and the
-- ComputeStatistics is set to true.
getDataSourceResponse_computeTime :: Lens' GetDataSourceResponse (Maybe Integer)
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
getDataSourceResponse_createdAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | 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.
getDataSourceResponse_createdByIamUser :: Lens' GetDataSourceResponse (Maybe Text)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getDataSourceResponse_dataLocationS3 :: Lens' GetDataSourceResponse (Maybe Text)
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
getDataSourceResponse_dataRearrangement :: Lens' GetDataSourceResponse (Maybe Text)
-- | The total size of observations in the data files.
getDataSourceResponse_dataSizeInBytes :: Lens' GetDataSourceResponse (Maybe Integer)
-- | The ID assigned to the DataSource at creation. This value
-- should be identical to the value of the DataSourceId in the
-- request.
getDataSourceResponse_dataSourceId :: Lens' GetDataSourceResponse (Maybe Text)
-- | The schema used by all of the data files of this DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
getDataSourceResponse_dataSourceSchema :: Lens' GetDataSourceResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as COMPLETED or FAILED.
-- FinishedAt is only available when the DataSource is
-- in the COMPLETED or FAILED state.
getDataSourceResponse_finishedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | The time of the most recent edit to the DataSource. The time
-- is expressed in epoch time.
getDataSourceResponse_lastUpdatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | A link to the file containing logs of CreateDataSourceFrom*
-- operations.
getDataSourceResponse_logUri :: Lens' GetDataSourceResponse (Maybe Text)
-- | The user-supplied description of the most recent details about
-- creating the DataSource.
getDataSourceResponse_message :: Lens' GetDataSourceResponse (Maybe Text)
-- | A user-supplied name or description of the DataSource.
getDataSourceResponse_name :: Lens' GetDataSourceResponse (Maybe Text)
-- | The number of data files referenced by the DataSource.
getDataSourceResponse_numberOfFiles :: Lens' GetDataSourceResponse (Maybe Integer)
-- | Undocumented member.
getDataSourceResponse_rDSMetadata :: Lens' GetDataSourceResponse (Maybe RDSMetadata)
-- | Undocumented member.
getDataSourceResponse_redshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata)
-- | Undocumented member.
getDataSourceResponse_roleARN :: Lens' GetDataSourceResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- DataSource as INPROGRESS. StartedAt isn't
-- available if the DataSource is in the PENDING state.
getDataSourceResponse_startedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)
-- | The current status of the DataSource. This element can have
-- one of the following values:
--
--
-- - PENDING - 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.
--
getDataSourceResponse_status :: Lens' GetDataSourceResponse (Maybe EntityStatus)
-- | The response's http status code.
getDataSourceResponse_httpStatus :: Lens' GetDataSourceResponse Int
-- | The ID of the Evaluation to retrieve. The evaluation of each
-- MLModel is recorded and cataloged. The ID provides the means
-- to access the information.
getEvaluation_evaluationId :: Lens' GetEvaluation Text
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the Evaluation, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- Evaluation is in the COMPLETED state.
getEvaluationResponse_computeTime :: Lens' GetEvaluationResponse (Maybe Integer)
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
getEvaluationResponse_createdAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | 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.
getEvaluationResponse_createdByIamUser :: Lens' GetEvaluationResponse (Maybe Text)
-- | The DataSource used for this evaluation.
getEvaluationResponse_evaluationDataSourceId :: Lens' GetEvaluationResponse (Maybe Text)
-- | The evaluation ID which is same as the EvaluationId in the
-- request.
getEvaluationResponse_evaluationId :: Lens' GetEvaluationResponse (Maybe Text)
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as COMPLETED or FAILED.
-- FinishedAt is only available when the Evaluation is
-- in the COMPLETED or FAILED state.
getEvaluationResponse_finishedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getEvaluationResponse_inputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text)
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
getEvaluationResponse_lastUpdatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the CreateEvaluation
-- operation.
getEvaluationResponse_logUri :: Lens' GetEvaluationResponse (Maybe Text)
-- | The ID of the MLModel that was the focus of the evaluation.
getEvaluationResponse_mLModelId :: Lens' GetEvaluationResponse (Maybe Text)
-- | A description of the most recent details about evaluating the
-- MLModel.
getEvaluationResponse_message :: Lens' GetEvaluationResponse (Maybe Text)
-- | A user-supplied name or description of the Evaluation.
getEvaluationResponse_name :: Lens' GetEvaluationResponse (Maybe Text)
-- | Measurements of how well the MLModel performed using
-- observations referenced by the DataSource. One of the
-- following metric 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.
getEvaluationResponse_performanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics)
-- | The epoch time when Amazon Machine Learning marked the
-- Evaluation as INPROGRESS. StartedAt isn't
-- available if the Evaluation is in the PENDING state.
getEvaluationResponse_startedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)
-- | The status of the evaluation. This element can have one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Language (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.
--
getEvaluationResponse_status :: Lens' GetEvaluationResponse (Maybe EntityStatus)
-- | The response's http status code.
getEvaluationResponse_httpStatus :: Lens' GetEvaluationResponse Int
-- | Specifies whether the GetMLModel operation should return
-- Recipe.
--
-- If true, Recipe is returned.
--
-- If false, Recipe is not returned.
getMLModel_verbose :: Lens' GetMLModel (Maybe Bool)
-- | The ID assigned to the MLModel at creation.
getMLModel_mLModelId :: Lens' GetMLModel Text
-- | The approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the MLModel, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- MLModel is in the COMPLETED state.
getMLModelResponse_computeTime :: Lens' GetMLModelResponse (Maybe Integer)
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
getMLModelResponse_createdAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | 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.
getMLModelResponse_createdByIamUser :: Lens' GetMLModelResponse (Maybe Text)
-- | The current endpoint of the MLModel
getMLModelResponse_endpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo)
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as COMPLETED or FAILED.
-- FinishedAt is only available when the MLModel is in
-- the COMPLETED or FAILED state.
getMLModelResponse_finishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
getMLModelResponse_inputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text)
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
getMLModelResponse_lastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | A link to the file that contains logs of the CreateMLModel
-- operation.
getMLModelResponse_logUri :: Lens' GetMLModelResponse (Maybe Text)
-- | The MLModel ID, which is same as the MLModelId in the
-- request.
getMLModelResponse_mLModelId :: Lens' GetMLModelResponse (Maybe Text)
-- | 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 an e-commerce website?"
-- - MULTICLASS -- Produces one of several possible results. For
-- example, "Is this a HIGH, LOW or MEDIUM risk trade?"
--
getMLModelResponse_mLModelType :: Lens' GetMLModelResponse (Maybe MLModelType)
-- | A description of the most recent details about accessing the
-- MLModel.
getMLModelResponse_message :: Lens' GetMLModelResponse (Maybe Text)
-- | A user-supplied name or description of the MLModel.
getMLModelResponse_name :: Lens' GetMLModelResponse (Maybe Text)
-- | The recipe to use when training the MLModel. The
-- Recipe provides detailed information about the observation
-- data to use during training, and manipulations to perform on the
-- observation data during training.
--
-- Note: This parameter is provided as part of the verbose format.
getMLModelResponse_recipe :: Lens' GetMLModelResponse (Maybe Text)
-- | The schema used by all of the data files referenced by the
-- DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
getMLModelResponse_schema :: Lens' GetMLModelResponse (Maybe Text)
-- | The scoring threshold is used in binary classification
-- MLModel models. It marks the boundary between a positive
-- prediction and a negative prediction.
--
-- Output values greater than or equal to the threshold receive a
-- positive result from the MLModel, such as true. Output values
-- less than the threshold receive a negative response from the MLModel,
-- such as false.
getMLModelResponse_scoreThreshold :: Lens' GetMLModelResponse (Maybe Double)
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
getMLModelResponse_scoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | Undocumented member.
getMLModelResponse_sizeInBytes :: Lens' GetMLModelResponse (Maybe Integer)
-- | The epoch time when Amazon Machine Learning marked the
-- MLModel as INPROGRESS. StartedAt isn't
-- available if the MLModel is in the PENDING state.
getMLModelResponse_startedAt :: Lens' GetMLModelResponse (Maybe UTCTime)
-- | The current status of the MLModel. This element can have one
-- of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to describe a MLModel.
-- - INPROGRESS - The request is processing.
-- - FAILED - The request did not run to completion. The ML
-- model isn't usable.
-- - COMPLETED - The request completed successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
getMLModelResponse_status :: Lens' GetMLModelResponse (Maybe EntityStatus)
-- | The ID of the training DataSource.
getMLModelResponse_trainingDataSourceId :: Lens' GetMLModelResponse (Maybe 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 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
getMLModelResponse_trainingParameters :: Lens' GetMLModelResponse (Maybe (HashMap Text Text))
-- | The response's http status code.
getMLModelResponse_httpStatus :: Lens' GetMLModelResponse Int
-- | A unique identifier of the MLModel.
predict_mLModelId :: Lens' Predict Text
-- | Undocumented member.
predict_record :: Lens' Predict (HashMap Text Text)
-- | Undocumented member.
predict_predictEndpoint :: Lens' Predict Text
-- | Undocumented member.
predictResponse_prediction :: Lens' PredictResponse (Maybe Prediction)
-- | The response's http status code.
predictResponse_httpStatus :: Lens' PredictResponse Int
-- | The ID assigned to the BatchPrediction during creation.
updateBatchPrediction_batchPredictionId :: Lens' UpdateBatchPrediction Text
-- | A new user-supplied name or description of the
-- BatchPrediction.
updateBatchPrediction_batchPredictionName :: Lens' UpdateBatchPrediction Text
-- | The ID assigned to the BatchPrediction during creation. This
-- value should be identical to the value of the
-- BatchPredictionId in the request.
updateBatchPredictionResponse_batchPredictionId :: Lens' UpdateBatchPredictionResponse (Maybe Text)
-- | The response's http status code.
updateBatchPredictionResponse_httpStatus :: Lens' UpdateBatchPredictionResponse Int
-- | The ID assigned to the DataSource during creation.
updateDataSource_dataSourceId :: Lens' UpdateDataSource Text
-- | A new user-supplied name or description of the DataSource
-- that will replace the current description.
updateDataSource_dataSourceName :: Lens' UpdateDataSource Text
-- | The ID assigned to the DataSource during creation. This value
-- should be identical to the value of the DataSourceID in the
-- request.
updateDataSourceResponse_dataSourceId :: Lens' UpdateDataSourceResponse (Maybe Text)
-- | The response's http status code.
updateDataSourceResponse_httpStatus :: Lens' UpdateDataSourceResponse Int
-- | The ID assigned to the Evaluation during creation.
updateEvaluation_evaluationId :: Lens' UpdateEvaluation Text
-- | A new user-supplied name or description of the Evaluation
-- that will replace the current content.
updateEvaluation_evaluationName :: Lens' UpdateEvaluation Text
-- | The ID assigned to the Evaluation during creation. This value
-- should be identical to the value of the Evaluation in the
-- request.
updateEvaluationResponse_evaluationId :: Lens' UpdateEvaluationResponse (Maybe Text)
-- | The response's http status code.
updateEvaluationResponse_httpStatus :: Lens' UpdateEvaluationResponse Int
-- | A user-supplied name or description of the MLModel.
updateMLModel_mLModelName :: Lens' UpdateMLModel (Maybe Text)
-- | The ScoreThreshold used in binary classification
-- MLModel that marks the boundary between a positive prediction
-- and a negative prediction.
--
-- Output values greater than or equal to the ScoreThreshold
-- receive a positive result from the MLModel, such as
-- true. Output values less than the ScoreThreshold
-- receive a negative response from the MLModel, such as
-- false.
updateMLModel_scoreThreshold :: Lens' UpdateMLModel (Maybe Double)
-- | The ID assigned to the MLModel during creation.
updateMLModel_mLModelId :: Lens' UpdateMLModel Text
-- | The ID assigned to the MLModel during creation. This value
-- should be identical to the value of the MLModelID in the
-- request.
updateMLModelResponse_mLModelId :: Lens' UpdateMLModelResponse (Maybe Text)
-- | The response's http status code.
updateMLModelResponse_httpStatus :: Lens' UpdateMLModelResponse Int
-- | The ID of the DataSource that points to the group of
-- observations to predict.
batchPrediction_batchPredictionDataSourceId :: 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.
batchPrediction_batchPredictionId :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_computeTime :: Lens' BatchPrediction (Maybe Integer)
-- | The time that the BatchPrediction was created. The time is
-- expressed in epoch time.
batchPrediction_createdAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
batchPrediction_createdByIamUser :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_finishedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
batchPrediction_inputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_invalidRecordCount :: Lens' BatchPrediction (Maybe Integer)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
batchPrediction_lastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | The ID of the MLModel that generated predictions for the
-- BatchPrediction request.
batchPrediction_mLModelId :: Lens' BatchPrediction (Maybe Text)
-- | A description of the most recent details about processing the batch
-- prediction request.
batchPrediction_message :: Lens' BatchPrediction (Maybe Text)
-- | A user-supplied name or description of the BatchPrediction.
batchPrediction_name :: 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: ':', '//',
-- '/./', '/../'.
batchPrediction_outputUri :: Lens' BatchPrediction (Maybe Text)
-- | Undocumented member.
batchPrediction_startedAt :: Lens' BatchPrediction (Maybe UTCTime)
-- | 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.
--
batchPrediction_status :: Lens' BatchPrediction (Maybe EntityStatus)
-- | Undocumented member.
batchPrediction_totalRecordCount :: Lens' BatchPrediction (Maybe Integer)
-- | The parameter is true if statistics need to be generated from
-- the observation data.
dataSource_computeStatistics :: Lens' DataSource (Maybe Bool)
-- | Undocumented member.
dataSource_computeTime :: Lens' DataSource (Maybe Integer)
-- | The time that the DataSource was created. The time is
-- expressed in epoch time.
dataSource_createdAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
dataSource_createdByIamUser :: Lens' DataSource (Maybe Text)
-- | The location and name of the data in Amazon Simple Storage Service
-- (Amazon S3) that is used by a DataSource.
dataSource_dataLocationS3 :: Lens' DataSource (Maybe Text)
-- | A JSON string that represents the splitting and rearrangement
-- requirement used when this DataSource was created.
dataSource_dataRearrangement :: Lens' DataSource (Maybe Text)
-- | The total number of observations contained in the data files that the
-- DataSource references.
dataSource_dataSizeInBytes :: Lens' DataSource (Maybe Integer)
-- | The ID that is assigned to the DataSource during creation.
dataSource_dataSourceId :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_finishedAt :: Lens' DataSource (Maybe UTCTime)
-- | The time of the most recent edit to the BatchPrediction. The
-- time is expressed in epoch time.
dataSource_lastUpdatedAt :: Lens' DataSource (Maybe UTCTime)
-- | A description of the most recent details about creating the
-- DataSource.
dataSource_message :: Lens' DataSource (Maybe Text)
-- | A user-supplied name or description of the DataSource.
dataSource_name :: Lens' DataSource (Maybe Text)
-- | The number of data files referenced by the DataSource.
dataSource_numberOfFiles :: Lens' DataSource (Maybe Integer)
-- | Undocumented member.
dataSource_rDSMetadata :: Lens' DataSource (Maybe RDSMetadata)
-- | Undocumented member.
dataSource_redshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)
-- | Undocumented member.
dataSource_roleARN :: Lens' DataSource (Maybe Text)
-- | Undocumented member.
dataSource_startedAt :: Lens' DataSource (Maybe UTCTime)
-- | 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.
--
dataSource_status :: Lens' DataSource (Maybe EntityStatus)
-- | Undocumented member.
evaluation_computeTime :: Lens' Evaluation (Maybe Integer)
-- | The time that the Evaluation was created. The time is
-- expressed in epoch time.
evaluation_createdAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
evaluation_createdByIamUser :: Lens' Evaluation (Maybe Text)
-- | The ID of the DataSource that is used to evaluate the
-- MLModel.
evaluation_evaluationDataSourceId :: Lens' Evaluation (Maybe Text)
-- | The ID that is assigned to the Evaluation at creation.
evaluation_evaluationId :: Lens' Evaluation (Maybe Text)
-- | Undocumented member.
evaluation_finishedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The location and name of the data in Amazon Simple Storage Server
-- (Amazon S3) that is used in the evaluation.
evaluation_inputDataLocationS3 :: Lens' Evaluation (Maybe Text)
-- | The time of the most recent edit to the Evaluation. The time
-- is expressed in epoch time.
evaluation_lastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)
-- | The ID of the MLModel that is the focus of the evaluation.
evaluation_mLModelId :: Lens' Evaluation (Maybe Text)
-- | A description of the most recent details about evaluating the
-- MLModel.
evaluation_message :: Lens' Evaluation (Maybe Text)
-- | A user-supplied name or description of the Evaluation.
evaluation_name :: Lens' Evaluation (Maybe Text)
-- | 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.
evaluation_performanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)
-- | Undocumented member.
evaluation_startedAt :: Lens' Evaluation (Maybe UTCTime)
-- | 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.
--
evaluation_status :: Lens' Evaluation (Maybe EntityStatus)
-- | 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.
--
mLModel_algorithm :: Lens' MLModel (Maybe Algorithm)
-- | Undocumented member.
mLModel_computeTime :: Lens' MLModel (Maybe Integer)
-- | The time that the MLModel was created. The time is expressed
-- in epoch time.
mLModel_createdAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
mLModel_createdByIamUser :: Lens' MLModel (Maybe Text)
-- | The current endpoint of the MLModel.
mLModel_endpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)
-- | Undocumented member.
mLModel_finishedAt :: Lens' MLModel (Maybe UTCTime)
-- | The location of the data file or directory in Amazon Simple Storage
-- Service (Amazon S3).
mLModel_inputDataLocationS3 :: Lens' MLModel (Maybe Text)
-- | The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
mLModel_lastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | The ID assigned to the MLModel at creation.
mLModel_mLModelId :: Lens' MLModel (Maybe Text)
-- | 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?".
--
mLModel_mLModelType :: Lens' MLModel (Maybe MLModelType)
-- | A description of the most recent details about accessing the
-- MLModel.
mLModel_message :: Lens' MLModel (Maybe Text)
-- | A user-supplied name or description of the MLModel.
mLModel_name :: Lens' MLModel (Maybe Text)
-- | Undocumented member.
mLModel_scoreThreshold :: Lens' MLModel (Maybe Double)
-- | The time of the most recent edit to the ScoreThreshold. The
-- time is expressed in epoch time.
mLModel_scoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)
-- | Undocumented member.
mLModel_sizeInBytes :: Lens' MLModel (Maybe Integer)
-- | Undocumented member.
mLModel_startedAt :: Lens' MLModel (Maybe UTCTime)
-- | 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.
--
mLModel_status :: Lens' MLModel (Maybe EntityStatus)
-- | The ID of the training DataSource. The CreateMLModel
-- operation uses the TrainingDataSourceId.
mLModel_trainingDataSourceId :: Lens' MLModel (Maybe 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.
--
mLModel_trainingParameters :: Lens' MLModel (Maybe (HashMap Text Text))
-- | Undocumented member.
performanceMetrics_properties :: Lens' PerformanceMetrics (Maybe (HashMap Text Text))
-- | Undocumented member.
prediction_details :: Lens' Prediction (Maybe (HashMap DetailsAttributes Text))
-- | The prediction label for either a BINARY or
-- MULTICLASS MLModel.
prediction_predictedLabel :: Lens' Prediction (Maybe Text)
-- | Undocumented member.
prediction_predictedScores :: Lens' Prediction (Maybe (HashMap Text Double))
-- | The prediction value for REGRESSION MLModel.
prediction_predictedValue :: Lens' Prediction (Maybe Double)
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
rDSDataSpec_dataRearrangement :: 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" ] }
rDSDataSpec_dataSchema :: Lens' RDSDataSpec (Maybe Text)
-- | The Amazon S3 location of the DataSchema.
rDSDataSpec_dataSchemaUri :: Lens' RDSDataSpec (Maybe Text)
-- | Describes the DatabaseName and InstanceIdentifier of
-- an Amazon RDS database.
rDSDataSpec_databaseInformation :: Lens' RDSDataSpec RDSDatabase
-- | The query that is used to retrieve the observation data for the
-- DataSource.
rDSDataSpec_selectSqlQuery :: Lens' RDSDataSpec Text
-- | The AWS Identity and Access Management (IAM) credentials that are used
-- connect to the Amazon RDS database.
rDSDataSpec_databaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials
-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
-- from Amazon RDS using SelectSqlQuery is stored in this
-- location.
rDSDataSpec_s3StagingLocation :: 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.
rDSDataSpec_resourceRole :: 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.
rDSDataSpec_serviceRole :: 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.
rDSDataSpec_subnetId :: 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.
rDSDataSpec_securityGroupIds :: Lens' RDSDataSpec [Text]
-- | The ID of an RDS DB instance.
rDSDatabase_instanceIdentifier :: Lens' RDSDatabase Text
-- | Undocumented member.
rDSDatabase_databaseName :: Lens' RDSDatabase Text
-- | Undocumented member.
rDSDatabaseCredentials_username :: Lens' RDSDatabaseCredentials Text
-- | Undocumented member.
rDSDatabaseCredentials_password :: Lens' RDSDatabaseCredentials 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.
rDSMetadata_dataPipelineId :: Lens' RDSMetadata (Maybe Text)
-- | The database details required to connect to an Amazon RDS.
rDSMetadata_database :: Lens' RDSMetadata (Maybe RDSDatabase)
-- | Undocumented member.
rDSMetadata_databaseUserName :: 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.
rDSMetadata_resourceRole :: Lens' RDSMetadata (Maybe Text)
-- | The SQL query that is supplied during CreateDataSourceFromRDS. Returns
-- only if Verbose is true in GetDataSourceInput.
rDSMetadata_selectSqlQuery :: 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.
rDSMetadata_serviceRole :: Lens' RDSMetadata (Maybe Text)
-- | The time that the request to create the real-time endpoint for the
-- MLModel was received. The time is expressed in epoch time.
realtimeEndpointInfo_createdAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)
-- | 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.
--
realtimeEndpointInfo_endpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)
-- | The URI that specifies where to send real-time prediction requests for
-- the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
realtimeEndpointInfo_endpointUrl :: Lens' RealtimeEndpointInfo (Maybe Text)
-- | The maximum processing rate for the real-time endpoint for
-- MLModel, measured in incoming requests per second.
realtimeEndpointInfo_peakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)
-- | 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
redshiftDataSpec_dataRearrangement :: 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" ] }
redshiftDataSpec_dataSchema :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the schema location for an Amazon Redshift
-- DataSource.
redshiftDataSpec_dataSchemaUri :: Lens' RedshiftDataSpec (Maybe Text)
-- | Describes the DatabaseName and ClusterIdentifier for
-- an Amazon Redshift DataSource.
redshiftDataSpec_databaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase
-- | Describes the SQL Query to execute on an Amazon Redshift database for
-- an Amazon Redshift DataSource.
redshiftDataSpec_selectSqlQuery :: Lens' RedshiftDataSpec Text
-- | Describes AWS Identity and Access Management (IAM) credentials that
-- are used connect to the Amazon Redshift database.
redshiftDataSpec_databaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials
-- | Describes an Amazon S3 location to store the result set of the
-- SelectSqlQuery query.
redshiftDataSpec_s3StagingLocation :: Lens' RedshiftDataSpec Text
-- | Undocumented member.
redshiftDatabase_databaseName :: Lens' RedshiftDatabase Text
-- | Undocumented member.
redshiftDatabase_clusterIdentifier :: Lens' RedshiftDatabase Text
-- | Undocumented member.
redshiftDatabaseCredentials_username :: Lens' RedshiftDatabaseCredentials Text
-- | Undocumented member.
redshiftDatabaseCredentials_password :: Lens' RedshiftDatabaseCredentials Text
-- | Undocumented member.
redshiftMetadata_databaseUserName :: Lens' RedshiftMetadata (Maybe Text)
-- | Undocumented member.
redshiftMetadata_redshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)
-- | The SQL query that is specified during CreateDataSourceFromRedshift.
-- Returns only if Verbose is true in GetDataSourceInput.
redshiftMetadata_selectSqlQuery :: Lens' RedshiftMetadata (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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
s3DataSpec_dataRearrangement :: 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" ] }
s3DataSpec_dataSchema :: Lens' S3DataSpec (Maybe Text)
-- | Describes the schema location in Amazon S3. You must provide either
-- the DataSchema or the DataSchemaLocationS3.
s3DataSpec_dataSchemaLocationS3 :: Lens' S3DataSpec (Maybe 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.
s3DataSpec_dataLocationS3 :: Lens' S3DataSpec Text
-- | A unique identifier for the tag. Valid characters include Unicode
-- letters, digits, white space, _, ., /, =, +, -, %, and @.
tag_key :: Lens' Tag (Maybe Text)
-- | An optional string, typically used to describe or define the tag.
-- Valid characters include Unicode letters, digits, white space, _, .,
-- /, =, +, -, %, and @.
tag_value :: Lens' Tag (Maybe Text)
module Amazonka.MachineLearning.Waiters
-- | Polls DescribeBatchPredictions every 30 seconds until a
-- successful state is reached. An error is returned after 60 failed
-- checks.
newBatchPredictionAvailable :: Wait DescribeBatchPredictions
-- | Polls DescribeDataSources every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newDataSourceAvailable :: Wait DescribeDataSources
-- | Polls DescribeEvaluations every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newEvaluationAvailable :: Wait DescribeEvaluations
-- | Polls DescribeMLModels every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newMLModelAvailable :: Wait DescribeMLModels
-- | Derived from API version 2014-12-12 of the AWS service
-- descriptions, licensed under Apache 2.0.
--
-- Definition of the public APIs exposed by Amazon Machine Learning
module Amazonka.MachineLearning
-- | API version 2014-12-12 of the Amazon Machine Learning SDK
-- configuration.
defaultService :: Service
-- | 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.
_IdempotentParameterMismatchException :: AsError a => Fold a ServiceError
-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Fold a ServiceError
-- | An error on the client occurred. Typically, the cause is an invalid
-- input value.
_InvalidInputException :: AsError a => Fold a ServiceError
-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Fold a ServiceError
-- | The subscriber exceeded the maximum number of operations. This
-- exception can occur when listing objects such as DataSource.
_LimitExceededException :: AsError a => Fold a ServiceError
-- | The exception is thrown when a predict request is made to an unmounted
-- MLModel.
_PredictorNotMountedException :: AsError a => Fold a ServiceError
-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Fold a ServiceError
-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Fold a ServiceError
-- | Polls DescribeBatchPredictions every 30 seconds until a
-- successful state is reached. An error is returned after 60 failed
-- checks.
newBatchPredictionAvailable :: Wait DescribeBatchPredictions
-- | Polls DescribeDataSources every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newDataSourceAvailable :: Wait DescribeDataSources
-- | Polls DescribeEvaluations every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newEvaluationAvailable :: Wait DescribeEvaluations
-- | Polls DescribeMLModels every 30 seconds until a successful
-- state is reached. An error is returned after 60 failed checks.
newMLModelAvailable :: Wait DescribeMLModels
-- | See: newAddTags smart constructor.
data AddTags
AddTags' :: [Tag] -> Text -> TaggableResourceType -> AddTags
-- | Create a value of AddTags with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:tags:AddTags', addTags_tags - The key-value pairs
-- to use to create tags. If you specify a key without specifying a
-- value, Amazon ML creates a tag with the specified key and a value of
-- null.
--
-- AddTags, addTags_resourceId - The ID of the ML object to
-- tag. For example, exampleModelId.
--
-- AddTags, addTags_resourceType - The type of the ML
-- object to tag.
newAddTags :: Text -> TaggableResourceType -> AddTags
-- | Amazon ML returns the following elements.
--
-- See: newAddTagsResponse smart constructor.
data AddTagsResponse
AddTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Int -> AddTagsResponse
-- | Create a value of AddTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- AddTags, addTagsResponse_resourceId - The ID of the ML
-- object that was tagged.
--
-- AddTags, addTagsResponse_resourceType - The type of the
-- ML object that was tagged.
--
-- $sel:httpStatus:AddTagsResponse',
-- addTagsResponse_httpStatus - The response's http status code.
newAddTagsResponse :: Int -> AddTagsResponse
-- | See: newCreateBatchPrediction smart constructor.
data CreateBatchPrediction
CreateBatchPrediction' :: Maybe Text -> Text -> Text -> Text -> Text -> CreateBatchPrediction
-- | Create a value of CreateBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:batchPredictionName:CreateBatchPrediction',
-- createBatchPrediction_batchPredictionName - A user-supplied
-- name or description of the BatchPrediction.
-- BatchPredictionName can only use the UTF-8 character set.
--
-- CreateBatchPrediction,
-- createBatchPrediction_batchPredictionId - A user-supplied ID
-- that uniquely identifies the BatchPrediction.
--
-- CreateBatchPrediction, createBatchPrediction_mLModelId -
-- The ID of the MLModel that will generate predictions for the
-- group of observations.
--
-- CreateBatchPrediction,
-- createBatchPrediction_batchPredictionDataSourceId - The ID of
-- the DataSource that points to the group of observations to
-- predict.
--
-- CreateBatchPrediction, createBatchPrediction_outputUri -
-- The location of an Amazon Simple Storage Service (Amazon S3) bucket or
-- directory to store the batch prediction results. The following
-- substrings are not allowed in the s3 key portion of the
-- outputURI field: ':', '//', '/./', '/../'.
--
-- Amazon ML needs permissions to store and retrieve the logs on your
-- behalf. For information about how to set permissions, see the
-- Amazon Machine Learning Developer Guide.
newCreateBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction
-- | Represents the output of a CreateBatchPrediction operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateBatchPrediction operation is asynchronous. You can
-- poll for status updates by using the >GetBatchPrediction
-- operation and checking the Status parameter of the result.
--
-- See: newCreateBatchPredictionResponse smart constructor.
data CreateBatchPredictionResponse
CreateBatchPredictionResponse' :: Maybe Text -> Int -> CreateBatchPredictionResponse
-- | Create a value of CreateBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateBatchPrediction,
-- createBatchPredictionResponse_batchPredictionId - A
-- user-supplied ID that uniquely identifies the
-- BatchPrediction. This value is identical to the value of the
-- BatchPredictionId in the request.
--
-- $sel:httpStatus:CreateBatchPredictionResponse',
-- createBatchPredictionResponse_httpStatus - The response's http
-- status code.
newCreateBatchPredictionResponse :: Int -> CreateBatchPredictionResponse
-- | See: newCreateDataSourceFromRDS smart constructor.
data CreateDataSourceFromRDS
CreateDataSourceFromRDS' :: Maybe Bool -> Maybe Text -> Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
-- | Create a value of CreateDataSourceFromRDS with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDS_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource needs to
-- be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromRDS',
-- createDataSourceFromRDS_dataSourceName - A user-supplied name
-- or description of the DataSource.
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDS_dataSourceId - A user-supplied ID that
-- uniquely identifies the DataSource. Typically, an Amazon
-- Resource Number (ARN) becomes the ID for a DataSource.
--
-- $sel:rDSData:CreateDataSourceFromRDS',
-- createDataSourceFromRDS_rDSData - The data specification of an
-- Amazon RDS DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon RDS database.
- InstanceIdentifier - A
-- unique identifier for the Amazon RDS database instance.
-- - DatabaseCredentials - AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon RDS database.
-- - ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by
-- an EC2 instance to carry out the copy task from Amazon RDS to Amazon
-- Simple Storage Service (Amazon S3). For more information, see Role
-- templates for data pipelines.
-- - ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
-- - SecurityInfo - The security information to use to access an RDS DB
-- instance. You need to set up appropriate ingress rules for the
-- security entity IDs provided to allow access to the Amazon RDS
-- instance. Specify a [SubnetId, SecurityGroupIds]
-- pair for a VPC-based RDS DB instance.
-- - SelectSqlQuery - A query that is used to retrieve the observation
-- data for the Datasource.
-- - S3StagingLocation - The Amazon S3 location for staging Amazon RDS
-- data. The data retrieved from Amazon RDS using SelectSqlQuery
-- is stored in this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
--
-- CreateDataSourceFromRDS, createDataSourceFromRDS_roleARN
-- - The role that Amazon ML assumes on behalf of the user to create and
-- activate a data pipeline in the user's account and copy data using the
-- SelectSqlQuery query from Amazon RDS to Amazon S3.
newCreateDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
-- | Represents the output of a CreateDataSourceFromRDS operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateDataSourceFromRDS> operation is asynchronous.
-- You can poll for updates by using the GetBatchPrediction
-- operation and checking the Status parameter. You can inspect
-- the Message when Status shows up as FAILED.
-- You can also check the progress of the copy operation by going to the
-- DataPipeline console and looking up the pipeline using the
-- pipelineId from the describe call.
--
-- See: newCreateDataSourceFromRDSResponse smart
-- constructor.
data CreateDataSourceFromRDSResponse
CreateDataSourceFromRDSResponse' :: Maybe Text -> Int -> CreateDataSourceFromRDSResponse
-- | Create a value of CreateDataSourceFromRDSResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRDS,
-- createDataSourceFromRDSResponse_dataSourceId - A user-supplied
-- ID that uniquely identifies the datasource. This value should be
-- identical to the value of the DataSourceID in the request.
--
-- $sel:httpStatus:CreateDataSourceFromRDSResponse',
-- createDataSourceFromRDSResponse_httpStatus - The response's
-- http status code.
newCreateDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse
-- | See: newCreateDataSourceFromRedshift smart constructor.
data CreateDataSourceFromRedshift
CreateDataSourceFromRedshift' :: Maybe Bool -> Maybe Text -> Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
-- | Create a value of CreateDataSourceFromRedshift with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource
-- needs to be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromRedshift',
-- createDataSourceFromRedshift_dataSourceName - A user-supplied
-- name or description of the DataSource.
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_dataSourceId - A user-supplied ID
-- that uniquely identifies the DataSource.
--
-- $sel:dataSpec:CreateDataSourceFromRedshift',
-- createDataSourceFromRedshift_dataSpec - The data specification
-- of an Amazon Redshift DataSource:
--
--
-- - DatabaseInformation -
- DatabaseName - The name of
-- the Amazon Redshift database.
- ClusterIdentifier -
-- The unique ID for the Amazon Redshift cluster.
-- - DatabaseCredentials - The AWS Identity and Access Management (IAM)
-- credentials that are used to connect to the Amazon Redshift
-- database.
-- - SelectSqlQuery - The query that is used to retrieve the
-- observation data for the Datasource.
-- - S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
-- location for staging Amazon Redshift data. The data retrieved from
-- Amazon Redshift using the SelectSqlQuery query is stored in
-- this location.
-- - DataSchemaUri - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the DataSource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshift_roleARN - A fully specified role
-- Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of
-- the user to create the following:
--
--
-- - A security group to allow Amazon ML to execute the
-- SelectSqlQuery query on an Amazon Redshift cluster
-- - An Amazon S3 bucket policy to grant Amazon ML read/write
-- permissions on the S3StagingLocation
--
newCreateDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
-- | Represents the output of a CreateDataSourceFromRedshift
-- operation, and is an acknowledgement that Amazon ML received the
-- request.
--
-- The CreateDataSourceFromRedshift operation is asynchronous.
-- You can poll for updates by using the GetBatchPrediction
-- operation and checking the Status parameter.
--
-- See: newCreateDataSourceFromRedshiftResponse smart
-- constructor.
data CreateDataSourceFromRedshiftResponse
CreateDataSourceFromRedshiftResponse' :: Maybe Text -> Int -> CreateDataSourceFromRedshiftResponse
-- | Create a value of CreateDataSourceFromRedshiftResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromRedshift,
-- createDataSourceFromRedshiftResponse_dataSourceId - A
-- user-supplied ID that uniquely identifies the datasource. This value
-- should be identical to the value of the DataSourceID in the
-- request.
--
-- $sel:httpStatus:CreateDataSourceFromRedshiftResponse',
-- createDataSourceFromRedshiftResponse_httpStatus - The
-- response's http status code.
newCreateDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse
-- | See: newCreateDataSourceFromS3 smart constructor.
data CreateDataSourceFromS3
CreateDataSourceFromS3' :: Maybe Bool -> Maybe Text -> Text -> S3DataSpec -> CreateDataSourceFromS3
-- | Create a value of CreateDataSourceFromS3 with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3_computeStatistics - The compute
-- statistics for a DataSource. The statistics are generated
-- from the observation data referenced by a DataSource. Amazon
-- ML uses the statistics internally during MLModel training.
-- This parameter must be set to true if the DataSource needs to
-- be used for MLModel training.
--
-- $sel:dataSourceName:CreateDataSourceFromS3',
-- createDataSourceFromS3_dataSourceName - A user-supplied name or
-- description of the DataSource.
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3_dataSourceId - A user-supplied
-- identifier that uniquely identifies the DataSource.
--
-- $sel:dataSpec:CreateDataSourceFromS3',
-- createDataSourceFromS3_dataSpec - The data specification of a
-- DataSource:
--
--
-- - DataLocationS3 - The Amazon S3 location of the observation
-- data.
-- - DataSchemaLocationS3 - The Amazon S3 location of the
-- DataSchema.
-- - DataSchema - A JSON string representing the schema. This is not
-- required if DataSchemaUri is specified.
-- - DataRearrangement - A JSON string that represents the splitting
-- and rearrangement requirements for the Datasource.Sample -
--
-- "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
--
newCreateDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3
-- | Represents the output of a CreateDataSourceFromS3 operation,
-- and is an acknowledgement that Amazon ML received the request.
--
-- The CreateDataSourceFromS3 operation is asynchronous. You can
-- poll for updates by using the GetBatchPrediction operation
-- and checking the Status parameter.
--
-- See: newCreateDataSourceFromS3Response smart
-- constructor.
data CreateDataSourceFromS3Response
CreateDataSourceFromS3Response' :: Maybe Text -> Int -> CreateDataSourceFromS3Response
-- | Create a value of CreateDataSourceFromS3Response with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateDataSourceFromS3,
-- createDataSourceFromS3Response_dataSourceId - A user-supplied
-- ID that uniquely identifies the DataSource. This value should
-- be identical to the value of the DataSourceID in the request.
--
-- $sel:httpStatus:CreateDataSourceFromS3Response',
-- createDataSourceFromS3Response_httpStatus - The response's http
-- status code.
newCreateDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response
-- | See: newCreateEvaluation smart constructor.
data CreateEvaluation
CreateEvaluation' :: Maybe Text -> Text -> Text -> Text -> CreateEvaluation
-- | Create a value of CreateEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:evaluationName:CreateEvaluation',
-- createEvaluation_evaluationName - A user-supplied name or
-- description of the Evaluation.
--
-- CreateEvaluation, createEvaluation_evaluationId - A
-- user-supplied ID that uniquely identifies the Evaluation.
--
-- CreateEvaluation, createEvaluation_mLModelId - 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.
--
-- CreateEvaluation,
-- createEvaluation_evaluationDataSourceId - The ID of the
-- DataSource for the evaluation. The schema of the
-- DataSource must match the schema used to create the
-- MLModel.
newCreateEvaluation :: Text -> Text -> Text -> CreateEvaluation
-- | 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: newCreateEvaluationResponse smart constructor.
data CreateEvaluationResponse
CreateEvaluationResponse' :: Maybe Text -> Int -> CreateEvaluationResponse
-- | Create a value of CreateEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateEvaluation, createEvaluationResponse_evaluationId
-- - The user-supplied ID that uniquely identifies the
-- Evaluation. This value should be identical to the value of
-- the EvaluationId in the request.
--
-- $sel:httpStatus:CreateEvaluationResponse',
-- createEvaluationResponse_httpStatus - The response's http
-- status code.
newCreateEvaluationResponse :: Int -> CreateEvaluationResponse
-- | See: newCreateMLModel smart constructor.
data CreateMLModel
CreateMLModel' :: Maybe Text -> Maybe (HashMap Text Text) -> Maybe Text -> Maybe Text -> Text -> MLModelType -> Text -> CreateMLModel
-- | Create a value of CreateMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:mLModelName:CreateMLModel',
-- createMLModel_mLModelName - A user-supplied name or description
-- of the MLModel.
--
-- $sel:parameters:CreateMLModel', createMLModel_parameters
-- - 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
--
-- $sel:recipe:CreateMLModel', createMLModel_recipe - The
-- data recipe for creating the MLModel. You must specify either
-- the recipe or its URI. If you don't specify a recipe or its URI,
-- Amazon ML creates a default.
--
-- $sel:recipeUri:CreateMLModel', createMLModel_recipeUri -
-- The Amazon Simple Storage Service (Amazon S3) location and file name
-- that contains the MLModel recipe. You must specify either the
-- recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
-- creates a default.
--
-- CreateMLModel, createMLModel_mLModelId - A user-supplied
-- ID that uniquely identifies the MLModel.
--
-- CreateMLModel, createMLModel_mLModelType - The category
-- of supervised learning that this MLModel will address. Choose
-- from the following types:
--
--
-- - Choose REGRESSION if the MLModel will be used to
-- predict a numeric value.
-- - Choose BINARY if the MLModel result has two
-- possible values.
-- - Choose MULTICLASS if the MLModel result has a
-- limited number of values.
--
--
-- For more information, see the Amazon Machine Learning Developer
-- Guide.
--
-- CreateMLModel, createMLModel_trainingDataSourceId - The
-- DataSource that points to the training data.
newCreateMLModel :: Text -> MLModelType -> Text -> CreateMLModel
-- | Represents the output of a CreateMLModel operation, and is an
-- acknowledgement that Amazon ML received the request.
--
-- The CreateMLModel operation is asynchronous. You can poll for
-- status updates by using the GetMLModel operation and checking
-- the Status parameter.
--
-- See: newCreateMLModelResponse smart constructor.
data CreateMLModelResponse
CreateMLModelResponse' :: Maybe Text -> Int -> CreateMLModelResponse
-- | Create a value of CreateMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateMLModel, createMLModelResponse_mLModelId - A
-- user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelId in
-- the request.
--
-- $sel:httpStatus:CreateMLModelResponse',
-- createMLModelResponse_httpStatus - The response's http status
-- code.
newCreateMLModelResponse :: Int -> CreateMLModelResponse
-- | See: newCreateRealtimeEndpoint smart constructor.
data CreateRealtimeEndpoint
CreateRealtimeEndpoint' :: Text -> CreateRealtimeEndpoint
-- | Create a value of CreateRealtimeEndpoint with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateRealtimeEndpoint, createRealtimeEndpoint_mLModelId
-- - The ID assigned to the MLModel during creation.
newCreateRealtimeEndpoint :: Text -> CreateRealtimeEndpoint
-- | Represents the output of an CreateRealtimeEndpoint operation.
--
-- The result contains the MLModelId and the endpoint
-- information for the MLModel.
--
-- Note: The endpoint information includes the URI of the
-- MLModel; that is, the location to send online prediction
-- requests for the specified MLModel.
--
-- See: newCreateRealtimeEndpointResponse smart
-- constructor.
data CreateRealtimeEndpointResponse
CreateRealtimeEndpointResponse' :: Maybe Text -> Maybe RealtimeEndpointInfo -> Int -> CreateRealtimeEndpointResponse
-- | Create a value of CreateRealtimeEndpointResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- CreateRealtimeEndpoint,
-- createRealtimeEndpointResponse_mLModelId - A user-supplied ID
-- that uniquely identifies the MLModel. This value should be
-- identical to the value of the MLModelId in the request.
--
-- $sel:realtimeEndpointInfo:CreateRealtimeEndpointResponse',
-- createRealtimeEndpointResponse_realtimeEndpointInfo - The
-- endpoint information of the MLModel
--
-- $sel:httpStatus:CreateRealtimeEndpointResponse',
-- createRealtimeEndpointResponse_httpStatus - The response's http
-- status code.
newCreateRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse
-- | See: newDeleteBatchPrediction smart constructor.
data DeleteBatchPrediction
DeleteBatchPrediction' :: Text -> DeleteBatchPrediction
-- | Create a value of DeleteBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteBatchPrediction,
-- deleteBatchPrediction_batchPredictionId - A user-supplied ID
-- that uniquely identifies the BatchPrediction.
newDeleteBatchPrediction :: Text -> DeleteBatchPrediction
-- | Represents the output of a DeleteBatchPrediction operation.
--
-- You can use the GetBatchPrediction operation and check the
-- value of the Status parameter to see whether a
-- BatchPrediction is marked as DELETED.
--
-- See: newDeleteBatchPredictionResponse smart constructor.
data DeleteBatchPredictionResponse
DeleteBatchPredictionResponse' :: Maybe Text -> Int -> DeleteBatchPredictionResponse
-- | Create a value of DeleteBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteBatchPrediction,
-- deleteBatchPredictionResponse_batchPredictionId - A
-- user-supplied ID that uniquely identifies the
-- BatchPrediction. This value should be identical to the value
-- of the BatchPredictionID in the request.
--
-- $sel:httpStatus:DeleteBatchPredictionResponse',
-- deleteBatchPredictionResponse_httpStatus - The response's http
-- status code.
newDeleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse
-- | See: newDeleteDataSource smart constructor.
data DeleteDataSource
DeleteDataSource' :: Text -> DeleteDataSource
-- | Create a value of DeleteDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteDataSource, deleteDataSource_dataSourceId - A
-- user-supplied ID that uniquely identifies the DataSource.
newDeleteDataSource :: Text -> DeleteDataSource
-- | Represents the output of a DeleteDataSource operation.
--
-- See: newDeleteDataSourceResponse smart constructor.
data DeleteDataSourceResponse
DeleteDataSourceResponse' :: Maybe Text -> Int -> DeleteDataSourceResponse
-- | Create a value of DeleteDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteDataSource, deleteDataSourceResponse_dataSourceId
-- - A user-supplied ID that uniquely identifies the DataSource.
-- This value should be identical to the value of the
-- DataSourceID in the request.
--
-- $sel:httpStatus:DeleteDataSourceResponse',
-- deleteDataSourceResponse_httpStatus - The response's http
-- status code.
newDeleteDataSourceResponse :: Int -> DeleteDataSourceResponse
-- | See: newDeleteEvaluation smart constructor.
data DeleteEvaluation
DeleteEvaluation' :: Text -> DeleteEvaluation
-- | Create a value of DeleteEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteEvaluation, deleteEvaluation_evaluationId - A
-- user-supplied ID that uniquely identifies the Evaluation to
-- delete.
newDeleteEvaluation :: Text -> DeleteEvaluation
-- | Represents the output of a DeleteEvaluation operation. The
-- output indicates that Amazon Machine Learning (Amazon ML) received the
-- request.
--
-- You can use the GetEvaluation operation and check the value
-- of the Status parameter to see whether an Evaluation
-- is marked as DELETED.
--
-- See: newDeleteEvaluationResponse smart constructor.
data DeleteEvaluationResponse
DeleteEvaluationResponse' :: Maybe Text -> Int -> DeleteEvaluationResponse
-- | Create a value of DeleteEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteEvaluation, deleteEvaluationResponse_evaluationId
-- - A user-supplied ID that uniquely identifies the Evaluation.
-- This value should be identical to the value of the
-- EvaluationId in the request.
--
-- $sel:httpStatus:DeleteEvaluationResponse',
-- deleteEvaluationResponse_httpStatus - The response's http
-- status code.
newDeleteEvaluationResponse :: Int -> DeleteEvaluationResponse
-- | See: newDeleteMLModel smart constructor.
data DeleteMLModel
DeleteMLModel' :: Text -> DeleteMLModel
-- | Create a value of DeleteMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteMLModel, deleteMLModel_mLModelId - A user-supplied
-- ID that uniquely identifies the MLModel.
newDeleteMLModel :: Text -> DeleteMLModel
-- | Represents the output of a DeleteMLModel operation.
--
-- You can use the GetMLModel operation and check the value of
-- the Status parameter to see whether an MLModel is
-- marked as DELETED.
--
-- See: newDeleteMLModelResponse smart constructor.
data DeleteMLModelResponse
DeleteMLModelResponse' :: Maybe Text -> Int -> DeleteMLModelResponse
-- | Create a value of DeleteMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteMLModel, deleteMLModelResponse_mLModelId - A
-- user-supplied ID that uniquely identifies the MLModel. This
-- value should be identical to the value of the MLModelID in
-- the request.
--
-- $sel:httpStatus:DeleteMLModelResponse',
-- deleteMLModelResponse_httpStatus - The response's http status
-- code.
newDeleteMLModelResponse :: Int -> DeleteMLModelResponse
-- | See: newDeleteRealtimeEndpoint smart constructor.
data DeleteRealtimeEndpoint
DeleteRealtimeEndpoint' :: Text -> DeleteRealtimeEndpoint
-- | Create a value of DeleteRealtimeEndpoint with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteRealtimeEndpoint, deleteRealtimeEndpoint_mLModelId
-- - The ID assigned to the MLModel during creation.
newDeleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint
-- | Represents the output of an DeleteRealtimeEndpoint operation.
--
-- The result contains the MLModelId and the endpoint
-- information for the MLModel.
--
-- See: newDeleteRealtimeEndpointResponse smart
-- constructor.
data DeleteRealtimeEndpointResponse
DeleteRealtimeEndpointResponse' :: Maybe Text -> Maybe RealtimeEndpointInfo -> Int -> DeleteRealtimeEndpointResponse
-- | Create a value of DeleteRealtimeEndpointResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteRealtimeEndpoint,
-- deleteRealtimeEndpointResponse_mLModelId - A user-supplied ID
-- that uniquely identifies the MLModel. This value should be
-- identical to the value of the MLModelId in the request.
--
-- $sel:realtimeEndpointInfo:DeleteRealtimeEndpointResponse',
-- deleteRealtimeEndpointResponse_realtimeEndpointInfo - The
-- endpoint information of the MLModel
--
-- $sel:httpStatus:DeleteRealtimeEndpointResponse',
-- deleteRealtimeEndpointResponse_httpStatus - The response's http
-- status code.
newDeleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse
-- | See: newDeleteTags smart constructor.
data DeleteTags
DeleteTags' :: [Text] -> Text -> TaggableResourceType -> DeleteTags
-- | Create a value of DeleteTags with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:tagKeys:DeleteTags', deleteTags_tagKeys - One or
-- more tags to delete.
--
-- DeleteTags, deleteTags_resourceId - The ID of the tagged
-- ML object. For example, exampleModelId.
--
-- DeleteTags, deleteTags_resourceType - The type of the
-- tagged ML object.
newDeleteTags :: Text -> TaggableResourceType -> DeleteTags
-- | Amazon ML returns the following elements.
--
-- See: newDeleteTagsResponse smart constructor.
data DeleteTagsResponse
DeleteTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Int -> DeleteTagsResponse
-- | Create a value of DeleteTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DeleteTags, deleteTagsResponse_resourceId - The ID of
-- the ML object from which tags were deleted.
--
-- DeleteTags, deleteTagsResponse_resourceType - The type
-- of the ML object from which tags were deleted.
--
-- $sel:httpStatus:DeleteTagsResponse',
-- deleteTagsResponse_httpStatus - The response's http status
-- code.
newDeleteTagsResponse :: Int -> DeleteTagsResponse
-- | See: newDescribeBatchPredictions smart constructor.
data DescribeBatchPredictions
DescribeBatchPredictions' :: Maybe Text -> Maybe BatchPredictionFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeBatchPredictions
-- | Create a value of DescribeBatchPredictions with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeBatchPredictions',
-- describeBatchPredictions_eq - The equal to operator. The
-- BatchPrediction results will have FilterVariable
-- values that exactly match the value specified with EQ.
--
-- $sel:filterVariable:DescribeBatchPredictions',
-- describeBatchPredictions_filterVariable - Use one of the
-- following variables to filter a list of BatchPrediction:
--
--
-- - CreatedAt - Sets the search criteria to the
-- BatchPrediction creation date.
-- - Status - Sets the search criteria to the
-- BatchPrediction status.
-- - Name - Sets the search criteria to the contents of the
-- 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 Solution (Amazon S3) bucket or
-- directory.
--
--
-- $sel:ge:DescribeBatchPredictions',
-- describeBatchPredictions_ge - The greater than or equal to
-- operator. The BatchPrediction results will have
-- FilterVariable values that are greater than or equal to the
-- value specified with GE.
--
-- $sel:gt:DescribeBatchPredictions',
-- describeBatchPredictions_gt - The greater than operator. The
-- BatchPrediction results will have FilterVariable
-- values that are greater than the value specified with GT.
--
-- $sel:le:DescribeBatchPredictions',
-- describeBatchPredictions_le - The less than or equal to
-- operator. The BatchPrediction results will have
-- FilterVariable values that are less than or equal to the
-- value specified with LE.
--
-- $sel:lt:DescribeBatchPredictions',
-- describeBatchPredictions_lt - The less than operator. The
-- BatchPrediction results will have FilterVariable
-- values that are less than the value specified with LT.
--
-- $sel:limit:DescribeBatchPredictions',
-- describeBatchPredictions_limit - The number of pages of
-- information to include in the result. The range of acceptable values
-- is 1 through 100. The default value is 100.
--
-- $sel:ne:DescribeBatchPredictions',
-- describeBatchPredictions_ne - The not equal to operator. The
-- BatchPrediction results will have FilterVariable
-- values not equal to the value specified with NE.
--
-- DescribeBatchPredictions,
-- describeBatchPredictions_nextToken - An ID of the page in the
-- paginated results.
--
-- $sel:prefix:DescribeBatchPredictions',
-- describeBatchPredictions_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, a Batch Prediction operation could have the
-- Name 2014-09-09-HolidayGiftMailer. To search for
-- this BatchPrediction, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeBatchPredictions',
-- describeBatchPredictions_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of MLModels.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeBatchPredictions :: DescribeBatchPredictions
-- | Represents the output of a DescribeBatchPredictions
-- operation. The content is essentially a list of
-- BatchPredictions.
--
-- See: newDescribeBatchPredictionsResponse smart
-- constructor.
data DescribeBatchPredictionsResponse
DescribeBatchPredictionsResponse' :: Maybe Text -> Maybe [BatchPrediction] -> Int -> DescribeBatchPredictionsResponse
-- | Create a value of DescribeBatchPredictionsResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeBatchPredictions,
-- describeBatchPredictionsResponse_nextToken - The ID of the next
-- page in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeBatchPredictionsResponse',
-- describeBatchPredictionsResponse_results - A list of
-- BatchPrediction objects that meet the search criteria.
--
-- $sel:httpStatus:DescribeBatchPredictionsResponse',
-- describeBatchPredictionsResponse_httpStatus - The response's
-- http status code.
newDescribeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse
-- | See: newDescribeDataSources smart constructor.
data DescribeDataSources
DescribeDataSources' :: Maybe Text -> Maybe DataSourceFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeDataSources
-- | Create a value of DescribeDataSources with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeDataSources', describeDataSources_eq -
-- The equal to operator. The DataSource results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeDataSources',
-- describeDataSources_filterVariable - Use one of the following
-- variables to filter a list of DataSource:
--
--
-- - CreatedAt - Sets the search criteria to
-- DataSource creation dates.
-- - Status - Sets the search criteria to DataSource
-- statuses.
-- - 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.
--
--
-- $sel:ge:DescribeDataSources', describeDataSources_ge -
-- The greater than or equal to operator. The DataSource results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
--
-- $sel:gt:DescribeDataSources', describeDataSources_gt -
-- The greater than operator. The DataSource results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeDataSources', describeDataSources_le -
-- The less than or equal to operator. The DataSource results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
--
-- $sel:lt:DescribeDataSources', describeDataSources_lt -
-- The less than operator. The DataSource results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeDataSources',
-- describeDataSources_limit - The maximum number of
-- DataSource to include in the result.
--
-- $sel:ne:DescribeDataSources', describeDataSources_ne -
-- The not equal to operator. The DataSource results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeDataSources, describeDataSources_nextToken - The
-- ID of the page in the paginated results.
--
-- $sel:prefix:DescribeDataSources',
-- describeDataSources_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, a DataSource could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- DataSource, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeDataSources',
-- describeDataSources_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of DataSource.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeDataSources :: DescribeDataSources
-- | Represents the query results from a DescribeDataSources operation. The
-- content is essentially a list of DataSource.
--
-- See: newDescribeDataSourcesResponse smart constructor.
data DescribeDataSourcesResponse
DescribeDataSourcesResponse' :: Maybe Text -> Maybe [DataSource] -> Int -> DescribeDataSourcesResponse
-- | Create a value of DescribeDataSourcesResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeDataSources,
-- describeDataSourcesResponse_nextToken - An ID of the next page
-- in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeDataSourcesResponse',
-- describeDataSourcesResponse_results - A list of
-- DataSource that meet the search criteria.
--
-- $sel:httpStatus:DescribeDataSourcesResponse',
-- describeDataSourcesResponse_httpStatus - The response's http
-- status code.
newDescribeDataSourcesResponse :: Int -> DescribeDataSourcesResponse
-- | See: newDescribeEvaluations smart constructor.
data DescribeEvaluations
DescribeEvaluations' :: Maybe Text -> Maybe EvaluationFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeEvaluations
-- | Create a value of DescribeEvaluations with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeEvaluations', describeEvaluations_eq -
-- The equal to operator. The Evaluation results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeEvaluations',
-- describeEvaluations_filterVariable - Use one of the following
-- variable to filter a list of Evaluation objects:
--
--
-- - CreatedAt - Sets the search criteria to the
-- Evaluation creation date.
-- - Status - Sets the search criteria to the
-- 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
-- MLModel 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 Solution (Amazon S3) bucket or directory.
--
--
-- $sel:ge:DescribeEvaluations', describeEvaluations_ge -
-- The greater than or equal to operator. The Evaluation results
-- will have FilterVariable values that are greater than or
-- equal to the value specified with GE.
--
-- $sel:gt:DescribeEvaluations', describeEvaluations_gt -
-- The greater than operator. The Evaluation results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeEvaluations', describeEvaluations_le -
-- The less than or equal to operator. The Evaluation results
-- will have FilterVariable values that are less than or equal
-- to the value specified with LE.
--
-- $sel:lt:DescribeEvaluations', describeEvaluations_lt -
-- The less than operator. The Evaluation results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeEvaluations',
-- describeEvaluations_limit - The maximum number of
-- Evaluation to include in the result.
--
-- $sel:ne:DescribeEvaluations', describeEvaluations_ne -
-- The not equal to operator. The Evaluation results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeEvaluations, describeEvaluations_nextToken - The
-- ID of the page in the paginated results.
--
-- $sel:prefix:DescribeEvaluations',
-- describeEvaluations_prefix - A string that is found at the
-- beginning of a variable, such as Name or Id.
--
-- For example, an Evaluation could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- Evaluation, select Name for the
-- FilterVariable and any of the following strings for the
-- Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeEvaluations',
-- describeEvaluations_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of Evaluation.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeEvaluations :: DescribeEvaluations
-- | Represents the query results from a DescribeEvaluations
-- operation. The content is essentially a list of Evaluation.
--
-- See: newDescribeEvaluationsResponse smart constructor.
data DescribeEvaluationsResponse
DescribeEvaluationsResponse' :: Maybe Text -> Maybe [Evaluation] -> Int -> DescribeEvaluationsResponse
-- | Create a value of DescribeEvaluationsResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeEvaluations,
-- describeEvaluationsResponse_nextToken - The ID of the next page
-- in the paginated results that indicates at least one more page
-- follows.
--
-- $sel:results:DescribeEvaluationsResponse',
-- describeEvaluationsResponse_results - A list of
-- Evaluation that meet the search criteria.
--
-- $sel:httpStatus:DescribeEvaluationsResponse',
-- describeEvaluationsResponse_httpStatus - The response's http
-- status code.
newDescribeEvaluationsResponse :: Int -> DescribeEvaluationsResponse
-- | See: newDescribeMLModels smart constructor.
data DescribeMLModels
DescribeMLModels' :: Maybe Text -> Maybe MLModelFilterVariable -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Natural -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe SortOrder -> DescribeMLModels
-- | Create a value of DescribeMLModels with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:eq:DescribeMLModels', describeMLModels_eq - The
-- equal to operator. The MLModel results will have
-- FilterVariable values that exactly match the value specified
-- with EQ.
--
-- $sel:filterVariable:DescribeMLModels',
-- describeMLModels_filterVariable - Use one of the following
-- variables to filter a list of MLModel:
--
--
-- - CreatedAt - Sets the search criteria to MLModel
-- creation date.
-- - Status - Sets the search criteria to MLModel
-- status.
-- - Name - Sets the search criteria to the contents of
-- MLModel ____ Name.
-- - IAMUser - Sets the search criteria to the user account
-- that invoked the MLModel creation.
-- - TrainingDataSourceId - Sets the search criteria to the
-- DataSource used to train one or more MLModel.
-- - RealtimeEndpointStatus - Sets the search criteria to the
-- MLModel real-time endpoint status.
-- - MLModelType - Sets the search criteria to
-- MLModel type: binary, regression, or multi-class.
-- - Algorithm - Sets the search criteria to the algorithm
-- that the MLModel uses.
-- - TrainingDataURI - Sets the search criteria to the data
-- file(s) used in training a MLModel. The URL can identify
-- either a file or an Amazon Simple Storage Service (Amazon S3) bucket
-- or directory.
--
--
-- $sel:ge:DescribeMLModels', describeMLModels_ge - The
-- greater than or equal to operator. The MLModel results will
-- have FilterVariable values that are greater than or equal to
-- the value specified with GE.
--
-- $sel:gt:DescribeMLModels', describeMLModels_gt - The
-- greater than operator. The MLModel results will have
-- FilterVariable values that are greater than the value
-- specified with GT.
--
-- $sel:le:DescribeMLModels', describeMLModels_le - The
-- less than or equal to operator. The MLModel results will have
-- FilterVariable values that are less than or equal to the
-- value specified with LE.
--
-- $sel:lt:DescribeMLModels', describeMLModels_lt - The
-- less than operator. The MLModel results will have
-- FilterVariable values that are less than the value specified
-- with LT.
--
-- $sel:limit:DescribeMLModels', describeMLModels_limit -
-- The number of pages of information to include in the result. The range
-- of acceptable values is 1 through 100. The default
-- value is 100.
--
-- $sel:ne:DescribeMLModels', describeMLModels_ne - The not
-- equal to operator. The MLModel results will have
-- FilterVariable values not equal to the value specified with
-- NE.
--
-- DescribeMLModels, describeMLModels_nextToken - The ID of
-- the page in the paginated results.
--
-- $sel:prefix:DescribeMLModels', describeMLModels_prefix -
-- A string that is found at the beginning of a variable, such as
-- Name or Id.
--
-- For example, an MLModel could have the Name
-- 2014-09-09-HolidayGiftMailer. To search for this
-- MLModel, select Name for the FilterVariable
-- and any of the following strings for the Prefix:
--
--
-- - 2014-09
-- - 2014-09-09
-- - 2014-09-09-Holiday
--
--
-- $sel:sortOrder:DescribeMLModels',
-- describeMLModels_sortOrder - A two-value parameter that
-- determines the sequence of the resulting list of MLModel.
--
--
-- - asc - Arranges the list in ascending order (A-Z,
-- 0-9).
-- - dsc - Arranges the list in descending order (Z-A,
-- 9-0).
--
--
-- Results are sorted by FilterVariable.
newDescribeMLModels :: DescribeMLModels
-- | Represents the output of a DescribeMLModels operation. The
-- content is essentially a list of MLModel.
--
-- See: newDescribeMLModelsResponse smart constructor.
data DescribeMLModelsResponse
DescribeMLModelsResponse' :: Maybe Text -> Maybe [MLModel] -> Int -> DescribeMLModelsResponse
-- | Create a value of DescribeMLModelsResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeMLModels, describeMLModelsResponse_nextToken -
-- The ID of the next page in the paginated results that indicates at
-- least one more page follows.
--
-- $sel:results:DescribeMLModelsResponse',
-- describeMLModelsResponse_results - A list of MLModel
-- that meet the search criteria.
--
-- $sel:httpStatus:DescribeMLModelsResponse',
-- describeMLModelsResponse_httpStatus - The response's http
-- status code.
newDescribeMLModelsResponse :: Int -> DescribeMLModelsResponse
-- | See: newDescribeTags smart constructor.
data DescribeTags
DescribeTags' :: Text -> TaggableResourceType -> DescribeTags
-- | Create a value of DescribeTags with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeTags, describeTags_resourceId - The ID of the ML
-- object. For example, exampleModelId.
--
-- DescribeTags, describeTags_resourceType - The type of
-- the ML object.
newDescribeTags :: Text -> TaggableResourceType -> DescribeTags
-- | Amazon ML returns the following elements.
--
-- See: newDescribeTagsResponse smart constructor.
data DescribeTagsResponse
DescribeTagsResponse' :: Maybe Text -> Maybe TaggableResourceType -> Maybe [Tag] -> Int -> DescribeTagsResponse
-- | Create a value of DescribeTagsResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- DescribeTags, describeTagsResponse_resourceId - The ID
-- of the tagged ML object.
--
-- DescribeTags, describeTagsResponse_resourceType - The
-- type of the tagged ML object.
--
-- $sel:tags:DescribeTagsResponse',
-- describeTagsResponse_tags - A list of tags associated with the
-- ML object.
--
-- $sel:httpStatus:DescribeTagsResponse',
-- describeTagsResponse_httpStatus - The response's http status
-- code.
newDescribeTagsResponse :: Int -> DescribeTagsResponse
-- | See: newGetBatchPrediction smart constructor.
data GetBatchPrediction
GetBatchPrediction' :: Text -> GetBatchPrediction
-- | Create a value of GetBatchPrediction with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetBatchPrediction, getBatchPrediction_batchPredictionId
-- - An ID assigned to the BatchPrediction at creation.
newGetBatchPrediction :: Text -> GetBatchPrediction
-- | Represents the output of a GetBatchPrediction operation and
-- describes a BatchPrediction.
--
-- See: newGetBatchPredictionResponse smart constructor.
data GetBatchPredictionResponse
GetBatchPredictionResponse' :: Maybe Text -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Maybe Integer -> Int -> GetBatchPredictionResponse
-- | Create a value of GetBatchPredictionResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_batchPredictionDataSourceId - The ID
-- of the DataSource that was used to create the
-- BatchPrediction.
--
-- GetBatchPrediction,
-- getBatchPredictionResponse_batchPredictionId - An ID assigned
-- to the BatchPrediction at creation. This value should be
-- identical to the value of the BatchPredictionID in the
-- request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_computeTime - The approximate CPU
-- time in milliseconds that Amazon Machine Learning spent processing the
-- BatchPrediction, normalized and scaled on computation
-- resources. ComputeTime is only available if the
-- BatchPrediction is in the COMPLETED state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_createdAt - The time when the
-- BatchPrediction was created. The time is expressed in epoch
-- time.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_createdByIamUser - 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.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_finishedAt - The epoch time when
-- Amazon Machine Learning marked the BatchPrediction as
-- COMPLETED or FAILED. FinishedAt is only
-- available when the BatchPrediction is in the
-- COMPLETED or FAILED state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_inputDataLocationS3 - The location
-- of the data file or directory in Amazon Simple Storage Service (Amazon
-- S3).
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_invalidRecordCount - The number of
-- invalid records that Amazon Machine Learning saw while processing the
-- BatchPrediction.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_lastUpdatedAt - The time of the most
-- recent edit to BatchPrediction. The time is expressed in
-- epoch time.
--
-- $sel:logUri:GetBatchPredictionResponse',
-- getBatchPredictionResponse_logUri - A link to the file that
-- contains logs of the CreateBatchPrediction operation.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_mLModelId - The ID of the
-- MLModel that generated predictions for the
-- BatchPrediction request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_message - A description of the most
-- recent details about processing the batch prediction request.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_name - A user-supplied name or
-- description of the BatchPrediction.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_outputUri - The location of an
-- Amazon S3 bucket or directory to receive the operation results.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_startedAt - The epoch time when
-- Amazon Machine Learning marked the BatchPrediction as
-- INPROGRESS. StartedAt isn't available if the
-- BatchPrediction is in the PENDING state.
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_status - The status of the
-- BatchPrediction, which can be one of the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to generate batch predictions.
-- - INPROGRESS - The batch predictions are in progress.
-- - 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.
--
--
-- GetBatchPredictionResponse,
-- getBatchPredictionResponse_totalRecordCount - The number of
-- total records that Amazon Machine Learning saw while processing the
-- BatchPrediction.
--
-- $sel:httpStatus:GetBatchPredictionResponse',
-- getBatchPredictionResponse_httpStatus - The response's http
-- status code.
newGetBatchPredictionResponse :: Int -> GetBatchPredictionResponse
-- | See: newGetDataSource smart constructor.
data GetDataSource
GetDataSource' :: Maybe Bool -> Text -> GetDataSource
-- | Create a value of GetDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:verbose:GetDataSource', getDataSource_verbose -
-- Specifies whether the GetDataSource operation should return
-- DataSourceSchema.
--
-- If true, DataSourceSchema is returned.
--
-- If false, DataSourceSchema is not returned.
--
-- GetDataSource, getDataSource_dataSourceId - The ID
-- assigned to the DataSource at creation.
newGetDataSource :: Text -> GetDataSource
-- | Represents the output of a GetDataSource operation and
-- describes a DataSource.
--
-- See: newGetDataSourceResponse smart constructor.
data GetDataSourceResponse
GetDataSourceResponse' :: Maybe Bool -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe RDSMetadata -> Maybe RedshiftMetadata -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Int -> GetDataSourceResponse
-- | Create a value of GetDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetDataSourceResponse,
-- getDataSourceResponse_computeStatistics - The parameter is
-- true if statistics need to be generated from the observation
-- data.
--
-- GetDataSourceResponse, getDataSourceResponse_computeTime
-- - The approximate CPU time in milliseconds that Amazon Machine
-- Learning spent processing the DataSource, normalized and
-- scaled on computation resources. ComputeTime is only
-- available if the DataSource is in the COMPLETED
-- state and the ComputeStatistics is set to true.
--
-- GetDataSourceResponse, getDataSourceResponse_createdAt -
-- The time that the DataSource was created. The time is
-- expressed in epoch time.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_createdByIamUser - 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.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataLocationS3 - The location of the data
-- file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataRearrangement - A JSON string that
-- represents the splitting and rearrangement requirement used when this
-- DataSource was created.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_dataSizeInBytes - The total size of
-- observations in the data files.
--
-- GetDataSource, getDataSourceResponse_dataSourceId - The
-- ID assigned to the DataSource at creation. This value should
-- be identical to the value of the DataSourceId in the request.
--
-- $sel:dataSourceSchema:GetDataSourceResponse',
-- getDataSourceResponse_dataSourceSchema - The schema used by all
-- of the data files of this DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- GetDataSourceResponse, getDataSourceResponse_finishedAt
-- - The epoch time when Amazon Machine Learning marked the
-- DataSource as COMPLETED or FAILED.
-- FinishedAt is only available when the DataSource is
-- in the COMPLETED or FAILED state.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_lastUpdatedAt - The time of the most
-- recent edit to the DataSource. The time is expressed in epoch
-- time.
--
-- $sel:logUri:GetDataSourceResponse',
-- getDataSourceResponse_logUri - A link to the file containing
-- logs of CreateDataSourceFrom* operations.
--
-- GetDataSourceResponse, getDataSourceResponse_message -
-- The user-supplied description of the most recent details about
-- creating the DataSource.
--
-- GetDataSourceResponse, getDataSourceResponse_name - A
-- user-supplied name or description of the DataSource.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_numberOfFiles - The number of data files
-- referenced by the DataSource.
--
-- GetDataSourceResponse, getDataSourceResponse_rDSMetadata
-- - Undocumented member.
--
-- GetDataSourceResponse,
-- getDataSourceResponse_redshiftMetadata - Undocumented member.
--
-- GetDataSourceResponse, getDataSourceResponse_roleARN -
-- Undocumented member.
--
-- GetDataSourceResponse, getDataSourceResponse_startedAt -
-- The epoch time when Amazon Machine Learning marked the
-- DataSource as INPROGRESS. StartedAt isn't
-- available if the DataSource is in the PENDING state.
--
-- GetDataSourceResponse, getDataSourceResponse_status -
-- The current status of the DataSource. This element can have
-- one of the following values:
--
--
-- - PENDING - 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.
--
--
-- $sel:httpStatus:GetDataSourceResponse',
-- getDataSourceResponse_httpStatus - The response's http status
-- code.
newGetDataSourceResponse :: Int -> GetDataSourceResponse
-- | See: newGetEvaluation smart constructor.
data GetEvaluation
GetEvaluation' :: Text -> GetEvaluation
-- | Create a value of GetEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetEvaluation, getEvaluation_evaluationId - The ID of
-- the Evaluation to retrieve. The evaluation of each
-- MLModel is recorded and cataloged. The ID provides the means
-- to access the information.
newGetEvaluation :: Text -> GetEvaluation
-- | Represents the output of a GetEvaluation operation and
-- describes an Evaluation.
--
-- See: newGetEvaluationResponse smart constructor.
data GetEvaluationResponse
GetEvaluationResponse' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe PerformanceMetrics -> Maybe POSIX -> Maybe EntityStatus -> Int -> GetEvaluationResponse
-- | Create a value of GetEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetEvaluationResponse, getEvaluationResponse_computeTime
-- - The approximate CPU time in milliseconds that Amazon Machine
-- Learning spent processing the Evaluation, normalized and
-- scaled on computation resources. ComputeTime is only
-- available if the Evaluation is in the COMPLETED
-- state.
--
-- GetEvaluationResponse, getEvaluationResponse_createdAt -
-- The time that the Evaluation was created. The time is
-- expressed in epoch time.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_createdByIamUser - 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.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_evaluationDataSourceId - The
-- DataSource used for this evaluation.
--
-- GetEvaluation, getEvaluationResponse_evaluationId - The
-- evaluation ID which is same as the EvaluationId in the
-- request.
--
-- GetEvaluationResponse, getEvaluationResponse_finishedAt
-- - The epoch time when Amazon Machine Learning marked the
-- Evaluation as COMPLETED or FAILED.
-- FinishedAt is only available when the Evaluation is
-- in the COMPLETED or FAILED state.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_inputDataLocationS3 - The location of the
-- data file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetEvaluationResponse,
-- getEvaluationResponse_lastUpdatedAt - The time of the most
-- recent edit to the Evaluation. The time is expressed in epoch
-- time.
--
-- $sel:logUri:GetEvaluationResponse',
-- getEvaluationResponse_logUri - A link to the file that contains
-- logs of the CreateEvaluation operation.
--
-- GetEvaluationResponse, getEvaluationResponse_mLModelId -
-- The ID of the MLModel that was the focus of the evaluation.
--
-- GetEvaluationResponse, getEvaluationResponse_message - A
-- description of the most recent details about evaluating the
-- MLModel.
--
-- GetEvaluationResponse, getEvaluationResponse_name - A
-- user-supplied name or description of the Evaluation.
--
-- GetEvaluationResponse,
-- getEvaluationResponse_performanceMetrics - Measurements of how
-- well the MLModel performed using observations referenced by
-- the DataSource. One of the following metric 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.
--
-- GetEvaluationResponse, getEvaluationResponse_startedAt -
-- The epoch time when Amazon Machine Learning marked the
-- Evaluation as INPROGRESS. StartedAt isn't
-- available if the Evaluation is in the PENDING state.
--
-- GetEvaluationResponse, getEvaluationResponse_status -
-- The status of the evaluation. This element can have one of the
-- following values:
--
--
-- - PENDING - Amazon Machine Language (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.
--
--
-- $sel:httpStatus:GetEvaluationResponse',
-- getEvaluationResponse_httpStatus - The response's http status
-- code.
newGetEvaluationResponse :: Int -> GetEvaluationResponse
-- | See: newGetMLModel smart constructor.
data GetMLModel
GetMLModel' :: Maybe Bool -> Text -> GetMLModel
-- | Create a value of GetMLModel with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:verbose:GetMLModel', getMLModel_verbose - Specifies
-- whether the GetMLModel operation should return
-- Recipe.
--
-- If true, Recipe is returned.
--
-- If false, Recipe is not returned.
--
-- GetMLModel, getMLModel_mLModelId - The ID assigned to
-- the MLModel at creation.
newGetMLModel :: Text -> GetMLModel
-- | Represents the output of a GetMLModel operation, and provides
-- detailed information about a MLModel.
--
-- See: newGetMLModelResponse smart constructor.
data GetMLModelResponse
GetMLModelResponse' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe RealtimeEndpointInfo -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe MLModelType -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Double -> Maybe POSIX -> Maybe Integer -> Maybe POSIX -> Maybe EntityStatus -> Maybe Text -> Maybe (HashMap Text Text) -> Int -> GetMLModelResponse
-- | Create a value of GetMLModelResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- GetMLModelResponse, getMLModelResponse_computeTime - The
-- approximate CPU time in milliseconds that Amazon Machine Learning
-- spent processing the MLModel, normalized and scaled on
-- computation resources. ComputeTime is only available if the
-- MLModel is in the COMPLETED state.
--
-- GetMLModelResponse, getMLModelResponse_createdAt - The
-- time that the MLModel was created. The time is expressed in
-- epoch time.
--
-- GetMLModelResponse, getMLModelResponse_createdByIamUser
-- - 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.
--
-- GetMLModelResponse, getMLModelResponse_endpointInfo -
-- The current endpoint of the MLModel
--
-- GetMLModelResponse, getMLModelResponse_finishedAt - The
-- epoch time when Amazon Machine Learning marked the MLModel as
-- COMPLETED or FAILED. FinishedAt is only
-- available when the MLModel is in the COMPLETED or
-- FAILED state.
--
-- GetMLModelResponse,
-- getMLModelResponse_inputDataLocationS3 - The location of the
-- data file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- GetMLModelResponse, getMLModelResponse_lastUpdatedAt -
-- The time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
--
-- $sel:logUri:GetMLModelResponse',
-- getMLModelResponse_logUri - A link to the file that contains
-- logs of the CreateMLModel operation.
--
-- GetMLModel, getMLModelResponse_mLModelId - The MLModel
-- ID, which is same as the MLModelId in the request.
--
-- GetMLModelResponse, getMLModelResponse_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 an e-commerce website?"
-- - MULTICLASS -- Produces one of several possible results. For
-- example, "Is this a HIGH, LOW or MEDIUM risk trade?"
--
--
-- GetMLModelResponse, getMLModelResponse_message - A
-- description of the most recent details about accessing the
-- MLModel.
--
-- GetMLModelResponse, getMLModelResponse_name - A
-- user-supplied name or description of the MLModel.
--
-- $sel:recipe:GetMLModelResponse',
-- getMLModelResponse_recipe - The recipe to use when training the
-- MLModel. The Recipe provides detailed information
-- about the observation data to use during training, and manipulations
-- to perform on the observation data during training.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- $sel:schema:GetMLModelResponse',
-- getMLModelResponse_schema - The schema used by all of the data
-- files referenced by the DataSource.
--
-- Note: This parameter is provided as part of the verbose format.
--
-- GetMLModelResponse, getMLModelResponse_scoreThreshold -
-- The scoring threshold is used in binary classification
-- MLModel models. It marks the boundary between a positive
-- prediction and a negative prediction.
--
-- Output values greater than or equal to the threshold receive a
-- positive result from the MLModel, such as true. Output values
-- less than the threshold receive a negative response from the MLModel,
-- such as false.
--
-- GetMLModelResponse,
-- getMLModelResponse_scoreThresholdLastUpdatedAt - The time of
-- the most recent edit to the ScoreThreshold. The time is
-- expressed in epoch time.
--
-- GetMLModelResponse, getMLModelResponse_sizeInBytes -
-- Undocumented member.
--
-- GetMLModelResponse, getMLModelResponse_startedAt - The
-- epoch time when Amazon Machine Learning marked the MLModel as
-- INPROGRESS. StartedAt isn't available if the
-- MLModel is in the PENDING state.
--
-- GetMLModelResponse, getMLModelResponse_status - The
-- current status of the MLModel. This element can have one of
-- the following values:
--
--
-- - PENDING - Amazon Machine Learning (Amazon ML) submitted a
-- request to describe a MLModel.
-- - INPROGRESS - The request is processing.
-- - FAILED - The request did not run to completion. The ML
-- model isn't usable.
-- - COMPLETED - The request completed successfully.
-- - DELETED - The MLModel is marked as deleted. It
-- isn't usable.
--
--
-- GetMLModelResponse,
-- getMLModelResponse_trainingDataSourceId - The ID of the
-- training DataSource.
--
-- GetMLModelResponse,
-- getMLModelResponse_trainingParameters - 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 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.
-- We strongly recommend that you shuffle your data.
-- - sgd.l1RegularizationAmount - The coefficient
-- regularization L1 norm. It controls overfitting the data by penalizing
-- large coefficients. This tends to drive coefficients to zero,
-- resulting in a 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. It 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.
--
--
-- $sel:httpStatus:GetMLModelResponse',
-- getMLModelResponse_httpStatus - The response's http status
-- code.
newGetMLModelResponse :: Int -> GetMLModelResponse
-- | See: newPredict smart constructor.
data Predict
Predict' :: Text -> HashMap Text Text -> Text -> Predict
-- | Create a value of Predict with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- Predict, predict_mLModelId - A unique identifier of the
-- MLModel.
--
-- $sel:record:Predict', predict_record - Undocumented
-- member.
--
-- $sel:predictEndpoint:Predict', predict_predictEndpoint -
-- Undocumented member.
newPredict :: Text -> Text -> Predict
-- | See: newPredictResponse smart constructor.
data PredictResponse
PredictResponse' :: Maybe Prediction -> Int -> PredictResponse
-- | Create a value of PredictResponse with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:prediction:PredictResponse',
-- predictResponse_prediction - Undocumented member.
--
-- $sel:httpStatus:PredictResponse',
-- predictResponse_httpStatus - The response's http status code.
newPredictResponse :: Int -> PredictResponse
-- | See: newUpdateBatchPrediction smart constructor.
data UpdateBatchPrediction
UpdateBatchPrediction' :: Text -> Text -> UpdateBatchPrediction
-- | Create a value of UpdateBatchPrediction with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateBatchPrediction,
-- updateBatchPrediction_batchPredictionId - The ID assigned to
-- the BatchPrediction during creation.
--
-- $sel:batchPredictionName:UpdateBatchPrediction',
-- updateBatchPrediction_batchPredictionName - A new user-supplied
-- name or description of the BatchPrediction.
newUpdateBatchPrediction :: Text -> Text -> UpdateBatchPrediction
-- | Represents the output of an UpdateBatchPrediction operation.
--
-- You can see the updated content by using the
-- GetBatchPrediction operation.
--
-- See: newUpdateBatchPredictionResponse smart constructor.
data UpdateBatchPredictionResponse
UpdateBatchPredictionResponse' :: Maybe Text -> Int -> UpdateBatchPredictionResponse
-- | Create a value of UpdateBatchPredictionResponse with all
-- optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateBatchPrediction,
-- updateBatchPredictionResponse_batchPredictionId - The ID
-- assigned to the BatchPrediction during creation. This value
-- should be identical to the value of the BatchPredictionId in
-- the request.
--
-- $sel:httpStatus:UpdateBatchPredictionResponse',
-- updateBatchPredictionResponse_httpStatus - The response's http
-- status code.
newUpdateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse
-- | See: newUpdateDataSource smart constructor.
data UpdateDataSource
UpdateDataSource' :: Text -> Text -> UpdateDataSource
-- | Create a value of UpdateDataSource with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateDataSource, updateDataSource_dataSourceId - The ID
-- assigned to the DataSource during creation.
--
-- $sel:dataSourceName:UpdateDataSource',
-- updateDataSource_dataSourceName - A new user-supplied name or
-- description of the DataSource that will replace the current
-- description.
newUpdateDataSource :: Text -> Text -> UpdateDataSource
-- | Represents the output of an UpdateDataSource operation.
--
-- You can see the updated content by using the
-- GetBatchPrediction operation.
--
-- See: newUpdateDataSourceResponse smart constructor.
data UpdateDataSourceResponse
UpdateDataSourceResponse' :: Maybe Text -> Int -> UpdateDataSourceResponse
-- | Create a value of UpdateDataSourceResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateDataSource, updateDataSourceResponse_dataSourceId
-- - The ID assigned to the DataSource during creation. This
-- value should be identical to the value of the DataSourceID in
-- the request.
--
-- $sel:httpStatus:UpdateDataSourceResponse',
-- updateDataSourceResponse_httpStatus - The response's http
-- status code.
newUpdateDataSourceResponse :: Int -> UpdateDataSourceResponse
-- | See: newUpdateEvaluation smart constructor.
data UpdateEvaluation
UpdateEvaluation' :: Text -> Text -> UpdateEvaluation
-- | Create a value of UpdateEvaluation with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateEvaluation, updateEvaluation_evaluationId - The ID
-- assigned to the Evaluation during creation.
--
-- $sel:evaluationName:UpdateEvaluation',
-- updateEvaluation_evaluationName - A new user-supplied name or
-- description of the Evaluation that will replace the current
-- content.
newUpdateEvaluation :: Text -> Text -> UpdateEvaluation
-- | Represents the output of an UpdateEvaluation operation.
--
-- You can see the updated content by using the GetEvaluation
-- operation.
--
-- See: newUpdateEvaluationResponse smart constructor.
data UpdateEvaluationResponse
UpdateEvaluationResponse' :: Maybe Text -> Int -> UpdateEvaluationResponse
-- | Create a value of UpdateEvaluationResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateEvaluation, updateEvaluationResponse_evaluationId
-- - The ID assigned to the Evaluation during creation. This
-- value should be identical to the value of the Evaluation in
-- the request.
--
-- $sel:httpStatus:UpdateEvaluationResponse',
-- updateEvaluationResponse_httpStatus - The response's http
-- status code.
newUpdateEvaluationResponse :: Int -> UpdateEvaluationResponse
-- | See: newUpdateMLModel smart constructor.
data UpdateMLModel
UpdateMLModel' :: Maybe Text -> Maybe Double -> Text -> UpdateMLModel
-- | Create a value of UpdateMLModel with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:mLModelName:UpdateMLModel',
-- updateMLModel_mLModelName - A user-supplied name or description
-- of the MLModel.
--
-- UpdateMLModel, updateMLModel_scoreThreshold - The
-- ScoreThreshold used in binary classification MLModel
-- that marks the boundary between a positive prediction and a negative
-- prediction.
--
-- Output values greater than or equal to the ScoreThreshold
-- receive a positive result from the MLModel, such as
-- true. Output values less than the ScoreThreshold
-- receive a negative response from the MLModel, such as
-- false.
--
-- UpdateMLModel, updateMLModel_mLModelId - The ID assigned
-- to the MLModel during creation.
newUpdateMLModel :: Text -> UpdateMLModel
-- | Represents the output of an UpdateMLModel operation.
--
-- You can see the updated content by using the GetMLModel
-- operation.
--
-- See: newUpdateMLModelResponse smart constructor.
data UpdateMLModelResponse
UpdateMLModelResponse' :: Maybe Text -> Int -> UpdateMLModelResponse
-- | Create a value of UpdateMLModelResponse with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- UpdateMLModel, updateMLModelResponse_mLModelId - The ID
-- assigned to the MLModel during creation. This value should be
-- identical to the value of the MLModelID in the request.
--
-- $sel:httpStatus:UpdateMLModelResponse',
-- updateMLModelResponse_httpStatus - The response's http status
-- code.
newUpdateMLModelResponse :: Int -> UpdateMLModelResponse
-- | 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.
--
newtype Algorithm
Algorithm' :: Text -> Algorithm
[fromAlgorithm] :: Algorithm -> Text
pattern Algorithm_Sgd :: Algorithm
-- | 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.
--
newtype BatchPredictionFilterVariable
BatchPredictionFilterVariable' :: Text -> BatchPredictionFilterVariable
[fromBatchPredictionFilterVariable] :: BatchPredictionFilterVariable -> Text
pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable
-- | 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.
--
--
-- Note: The variable names should match the variable names in the
-- DataSource.
newtype DataSourceFilterVariable
DataSourceFilterVariable' :: Text -> DataSourceFilterVariable
[fromDataSourceFilterVariable] :: DataSourceFilterVariable -> Text
pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable
-- | Contains the key values of DetailsMap:
--
--
-- - PredictiveModelType - Indicates the type of the
-- MLModel.
-- - Algorithm - Indicates the algorithm that was used for the
-- MLModel.
--
newtype DetailsAttributes
DetailsAttributes' :: Text -> DetailsAttributes
[fromDetailsAttributes] :: DetailsAttributes -> Text
pattern DetailsAttributes_Algorithm :: DetailsAttributes
pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes
-- | Object status with the following possible values:
--
--
-- PENDING
-- INPROGRESS
-- FAILED
-- COMPLETED
-- DELETED
--
newtype EntityStatus
EntityStatus' :: Text -> EntityStatus
[fromEntityStatus] :: EntityStatus -> Text
pattern EntityStatus_COMPLETED :: EntityStatus
pattern EntityStatus_DELETED :: EntityStatus
pattern EntityStatus_FAILED :: EntityStatus
pattern EntityStatus_INPROGRESS :: EntityStatus
pattern EntityStatus_PENDING :: EntityStatus
-- | 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.
--
newtype EvaluationFilterVariable
EvaluationFilterVariable' :: Text -> EvaluationFilterVariable
[fromEvaluationFilterVariable] :: EvaluationFilterVariable -> Text
pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable
newtype MLModelFilterVariable
MLModelFilterVariable' :: Text -> MLModelFilterVariable
[fromMLModelFilterVariable] :: MLModelFilterVariable -> Text
pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
pattern MLModelFilterVariable_Name :: MLModelFilterVariable
pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
pattern MLModelFilterVariable_Status :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable
newtype MLModelType
MLModelType' :: Text -> MLModelType
[fromMLModelType] :: MLModelType -> Text
pattern MLModelType_BINARY :: MLModelType
pattern MLModelType_MULTICLASS :: MLModelType
pattern MLModelType_REGRESSION :: MLModelType
newtype RealtimeEndpointStatus
RealtimeEndpointStatus' :: Text -> RealtimeEndpointStatus
[fromRealtimeEndpointStatus] :: RealtimeEndpointStatus -> Text
pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus
-- | 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).
--
newtype SortOrder
SortOrder' :: Text -> SortOrder
[fromSortOrder] :: SortOrder -> Text
pattern SortOrder_Asc :: SortOrder
pattern SortOrder_Dsc :: SortOrder
newtype TaggableResourceType
TaggableResourceType' :: Text -> TaggableResourceType
[fromTaggableResourceType] :: TaggableResourceType -> Text
pattern TaggableResourceType_BatchPrediction :: TaggableResourceType
pattern TaggableResourceType_DataSource :: TaggableResourceType
pattern TaggableResourceType_Evaluation :: TaggableResourceType
pattern TaggableResourceType_MLModel :: TaggableResourceType
-- | 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: newBatchPrediction smart constructor.
data BatchPrediction
BatchPrediction' :: Maybe Text -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> Maybe Integer -> BatchPrediction
-- | Create a value of BatchPrediction with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:batchPredictionDataSourceId:BatchPrediction',
-- batchPrediction_batchPredictionDataSourceId - The ID of the
-- DataSource that points to the group of observations to
-- predict.
--
-- $sel:batchPredictionId:BatchPrediction',
-- batchPrediction_batchPredictionId - The ID assigned to the
-- BatchPrediction at creation. This value should be identical
-- to the value of the BatchPredictionID in the request.
--
-- $sel:computeTime:BatchPrediction',
-- batchPrediction_computeTime - Undocumented member.
--
-- $sel:createdAt:BatchPrediction',
-- batchPrediction_createdAt - The time that the
-- BatchPrediction was created. The time is expressed in epoch
-- time.
--
-- $sel:createdByIamUser:BatchPrediction',
-- batchPrediction_createdByIamUser - 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.
--
-- $sel:finishedAt:BatchPrediction',
-- batchPrediction_finishedAt - Undocumented member.
--
-- $sel:inputDataLocationS3:BatchPrediction',
-- batchPrediction_inputDataLocationS3 - The location of the data
-- file or directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:invalidRecordCount:BatchPrediction',
-- batchPrediction_invalidRecordCount - Undocumented member.
--
-- $sel:lastUpdatedAt:BatchPrediction',
-- batchPrediction_lastUpdatedAt - The time of the most recent
-- edit to the BatchPrediction. The time is expressed in epoch
-- time.
--
-- $sel:mLModelId:BatchPrediction',
-- batchPrediction_mLModelId - The ID of the MLModel that
-- generated predictions for the BatchPrediction request.
--
-- $sel:message:BatchPrediction', batchPrediction_message -
-- A description of the most recent details about processing the batch
-- prediction request.
--
-- $sel:name:BatchPrediction', batchPrediction_name - A
-- user-supplied name or description of the BatchPrediction.
--
-- $sel:outputUri:BatchPrediction',
-- batchPrediction_outputUri - 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: ':', '//', '/./', '/../'.
--
-- $sel:startedAt:BatchPrediction',
-- batchPrediction_startedAt - Undocumented member.
--
-- $sel:status:BatchPrediction', batchPrediction_status -
-- 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.
--
--
-- $sel:totalRecordCount:BatchPrediction',
-- batchPrediction_totalRecordCount - Undocumented member.
newBatchPrediction :: BatchPrediction
-- | 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: newDataSource smart constructor.
data DataSource
DataSource' :: Maybe Bool -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe Text -> Maybe POSIX -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Integer -> Maybe RDSMetadata -> Maybe RedshiftMetadata -> Maybe Text -> Maybe POSIX -> Maybe EntityStatus -> DataSource
-- | Create a value of DataSource with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeStatistics:DataSource',
-- dataSource_computeStatistics - The parameter is true
-- if statistics need to be generated from the observation data.
--
-- $sel:computeTime:DataSource', dataSource_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:DataSource', dataSource_createdAt - The
-- time that the DataSource was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:DataSource',
-- dataSource_createdByIamUser - 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.
--
-- $sel:dataLocationS3:DataSource',
-- dataSource_dataLocationS3 - The location and name of the data
-- in Amazon Simple Storage Service (Amazon S3) that is used by a
-- DataSource.
--
-- $sel:dataRearrangement:DataSource',
-- dataSource_dataRearrangement - A JSON string that represents
-- the splitting and rearrangement requirement used when this
-- DataSource was created.
--
-- $sel:dataSizeInBytes:DataSource',
-- dataSource_dataSizeInBytes - The total number of observations
-- contained in the data files that the DataSource references.
--
-- $sel:dataSourceId:DataSource', dataSource_dataSourceId -
-- The ID that is assigned to the DataSource during creation.
--
-- $sel:finishedAt:DataSource', dataSource_finishedAt -
-- Undocumented member.
--
-- $sel:lastUpdatedAt:DataSource', dataSource_lastUpdatedAt
-- - The time of the most recent edit to the BatchPrediction.
-- The time is expressed in epoch time.
--
-- $sel:message:DataSource', dataSource_message - A
-- description of the most recent details about creating the
-- DataSource.
--
-- $sel:name:DataSource', dataSource_name - A user-supplied
-- name or description of the DataSource.
--
-- $sel:numberOfFiles:DataSource', dataSource_numberOfFiles
-- - The number of data files referenced by the DataSource.
--
-- $sel:rDSMetadata:DataSource', dataSource_rDSMetadata -
-- Undocumented member.
--
-- $sel:redshiftMetadata:DataSource',
-- dataSource_redshiftMetadata - Undocumented member.
--
-- $sel:roleARN:DataSource', dataSource_roleARN -
-- Undocumented member.
--
-- $sel:startedAt:DataSource', dataSource_startedAt -
-- Undocumented member.
--
-- $sel:status:DataSource', dataSource_status - 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.
--
newDataSource :: DataSource
-- | 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: newEvaluation smart constructor.
data Evaluation
Evaluation' :: Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe PerformanceMetrics -> Maybe POSIX -> Maybe EntityStatus -> Evaluation
-- | Create a value of Evaluation with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:computeTime:Evaluation', evaluation_computeTime -
-- Undocumented member.
--
-- $sel:createdAt:Evaluation', evaluation_createdAt - The
-- time that the Evaluation was created. The time is expressed
-- in epoch time.
--
-- $sel:createdByIamUser:Evaluation',
-- evaluation_createdByIamUser - 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.
--
-- $sel:evaluationDataSourceId:Evaluation',
-- evaluation_evaluationDataSourceId - The ID of the
-- DataSource that is used to evaluate the MLModel.
--
-- $sel:evaluationId:Evaluation', evaluation_evaluationId -
-- The ID that is assigned to the Evaluation at creation.
--
-- $sel:finishedAt:Evaluation', evaluation_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:Evaluation',
-- evaluation_inputDataLocationS3 - The location and name of the
-- data in Amazon Simple Storage Server (Amazon S3) that is used in the
-- evaluation.
--
-- $sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt
-- - The time of the most recent edit to the Evaluation. The
-- time is expressed in epoch time.
--
-- $sel:mLModelId:Evaluation', evaluation_mLModelId - The
-- ID of the MLModel that is the focus of the evaluation.
--
-- $sel:message:Evaluation', evaluation_message - A
-- description of the most recent details about evaluating the
-- MLModel.
--
-- $sel:name:Evaluation', evaluation_name - A user-supplied
-- name or description of the Evaluation.
--
-- $sel:performanceMetrics:Evaluation',
-- evaluation_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.
--
-- $sel:startedAt:Evaluation', evaluation_startedAt -
-- Undocumented member.
--
-- $sel:status:Evaluation', evaluation_status - 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.
--
newEvaluation :: Evaluation
-- | Represents the output of a GetMLModel operation.
--
-- The content consists of the detailed metadata and the current status
-- of the MLModel.
--
-- See: newMLModel smart constructor.
data MLModel
MLModel' :: Maybe Algorithm -> Maybe Integer -> Maybe POSIX -> Maybe Text -> Maybe RealtimeEndpointInfo -> Maybe POSIX -> Maybe Text -> Maybe POSIX -> Maybe Text -> Maybe MLModelType -> Maybe Text -> Maybe Text -> Maybe Double -> Maybe POSIX -> Maybe Integer -> Maybe POSIX -> Maybe EntityStatus -> Maybe Text -> Maybe (HashMap Text Text) -> MLModel
-- | Create a value of MLModel with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:algorithm:MLModel', mLModel_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.
--
--
-- $sel:computeTime:MLModel', mLModel_computeTime -
-- Undocumented member.
--
-- MLModel, mLModel_createdAt - The time that the
-- MLModel was created. The time is expressed in epoch time.
--
-- $sel:createdByIamUser:MLModel', mLModel_createdByIamUser
-- - 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.
--
-- $sel:endpointInfo:MLModel', mLModel_endpointInfo - The
-- current endpoint of the MLModel.
--
-- $sel:finishedAt:MLModel', mLModel_finishedAt -
-- Undocumented member.
--
-- $sel:inputDataLocationS3:MLModel',
-- mLModel_inputDataLocationS3 - The location of the data file or
-- directory in Amazon Simple Storage Service (Amazon S3).
--
-- $sel:lastUpdatedAt:MLModel', mLModel_lastUpdatedAt - The
-- time of the most recent edit to the MLModel. The time is
-- expressed in epoch time.
--
-- $sel:mLModelId:MLModel', mLModel_mLModelId - The ID
-- assigned to the MLModel at creation.
--
-- $sel:mLModelType:MLModel', mLModel_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?".
--
--
-- $sel:message:MLModel', mLModel_message - A description
-- of the most recent details about accessing the MLModel.
--
-- $sel:name:MLModel', mLModel_name - A user-supplied name
-- or description of the MLModel.
--
-- $sel:scoreThreshold:MLModel', mLModel_scoreThreshold -
-- Undocumented member.
--
-- $sel:scoreThresholdLastUpdatedAt:MLModel',
-- mLModel_scoreThresholdLastUpdatedAt - The time of the most
-- recent edit to the ScoreThreshold. The time is expressed in
-- epoch time.
--
-- $sel:sizeInBytes:MLModel', mLModel_sizeInBytes -
-- Undocumented member.
--
-- $sel:startedAt:MLModel', mLModel_startedAt -
-- Undocumented member.
--
-- $sel:status:MLModel', mLModel_status - 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.
--
--
-- $sel:trainingDataSourceId:MLModel',
-- mLModel_trainingDataSourceId - The ID of the training
-- DataSource. The CreateMLModel operation uses the
-- TrainingDataSourceId.
--
-- $sel:trainingParameters:MLModel',
-- mLModel_trainingParameters - 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.
--
newMLModel :: MLModel
-- | 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: newPerformanceMetrics smart constructor.
data PerformanceMetrics
PerformanceMetrics' :: Maybe (HashMap Text Text) -> PerformanceMetrics
-- | Create a value of PerformanceMetrics with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:properties:PerformanceMetrics',
-- performanceMetrics_properties - Undocumented member.
newPerformanceMetrics :: PerformanceMetrics
-- | 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: newPrediction smart constructor.
data Prediction
Prediction' :: Maybe (HashMap DetailsAttributes Text) -> Maybe Text -> Maybe (HashMap Text Double) -> Maybe Double -> Prediction
-- | Create a value of Prediction with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:details:Prediction', prediction_details -
-- Undocumented member.
--
-- $sel:predictedLabel:Prediction',
-- prediction_predictedLabel - The prediction label for either a
-- BINARY or MULTICLASS MLModel.
--
-- $sel:predictedScores:Prediction',
-- prediction_predictedScores - Undocumented member.
--
-- $sel:predictedValue:Prediction',
-- prediction_predictedValue - The prediction value for
-- REGRESSION MLModel.
newPrediction :: Prediction
-- | The data specification of an Amazon Relational Database Service
-- (Amazon RDS) DataSource.
--
-- See: newRDSDataSpec smart constructor.
data RDSDataSpec
RDSDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> [Text] -> RDSDataSpec
-- | Create a value of RDSDataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RDSDataSpec',
-- rDSDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RDSDataSpec', rDSDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RDSDataSpec',
-- rDSDataSpec_dataSchemaUri - The Amazon S3 location of the
-- DataSchema.
--
-- $sel:databaseInformation:RDSDataSpec',
-- rDSDataSpec_databaseInformation - Describes the
-- DatabaseName and InstanceIdentifier of an Amazon RDS
-- database.
--
-- $sel:selectSqlQuery:RDSDataSpec',
-- rDSDataSpec_selectSqlQuery - The query that is used to retrieve
-- the observation data for the DataSource.
--
-- $sel:databaseCredentials:RDSDataSpec',
-- rDSDataSpec_databaseCredentials - The AWS Identity and Access
-- Management (IAM) credentials that are used connect to the Amazon RDS
-- database.
--
-- $sel:s3StagingLocation:RDSDataSpec',
-- rDSDataSpec_s3StagingLocation - The Amazon S3 location for
-- staging Amazon RDS data. The data retrieved from Amazon RDS using
-- SelectSqlQuery is stored in this location.
--
-- $sel:resourceRole:RDSDataSpec', rDSDataSpec_resourceRole
-- - 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.
--
-- $sel:serviceRole:RDSDataSpec', rDSDataSpec_serviceRole -
-- 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.
--
-- $sel:subnetId:RDSDataSpec', rDSDataSpec_subnetId - 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.
--
-- $sel:securityGroupIds:RDSDataSpec',
-- rDSDataSpec_securityGroupIds - 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.
newRDSDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
-- | The database details of an Amazon RDS database.
--
-- See: newRDSDatabase smart constructor.
data RDSDatabase
RDSDatabase' :: Text -> Text -> RDSDatabase
-- | Create a value of RDSDatabase with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:instanceIdentifier:RDSDatabase',
-- rDSDatabase_instanceIdentifier - The ID of an RDS DB instance.
--
-- $sel:databaseName:RDSDatabase', rDSDatabase_databaseName
-- - Undocumented member.
newRDSDatabase :: Text -> Text -> RDSDatabase
-- | The database credentials to connect to a database on an RDS DB
-- instance.
--
-- See: newRDSDatabaseCredentials smart constructor.
data RDSDatabaseCredentials
RDSDatabaseCredentials' :: Text -> Text -> RDSDatabaseCredentials
-- | Create a value of RDSDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RDSDatabaseCredentials',
-- rDSDatabaseCredentials_password - Undocumented member.
newRDSDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
-- | The datasource details that are specific to Amazon RDS.
--
-- See: newRDSMetadata smart constructor.
data RDSMetadata
RDSMetadata' :: Maybe Text -> Maybe RDSDatabase -> Maybe Text -> Maybe Text -> Maybe Text -> Maybe Text -> RDSMetadata
-- | Create a value of RDSMetadata with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataPipelineId:RDSMetadata',
-- rDSMetadata_dataPipelineId - 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.
--
-- $sel:database:RDSMetadata', rDSMetadata_database - The
-- database details required to connect to an Amazon RDS.
--
-- $sel:databaseUserName:RDSMetadata',
-- rDSMetadata_databaseUserName - Undocumented member.
--
-- $sel:resourceRole:RDSMetadata', rDSMetadata_resourceRole
-- - 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.
--
-- $sel:selectSqlQuery:RDSMetadata',
-- rDSMetadata_selectSqlQuery - The SQL query that is supplied
-- during CreateDataSourceFromRDS. Returns only if Verbose is
-- true in GetDataSourceInput.
--
-- $sel:serviceRole:RDSMetadata', rDSMetadata_serviceRole -
-- 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.
newRDSMetadata :: RDSMetadata
-- | Describes the real-time endpoint information for an MLModel.
--
-- See: newRealtimeEndpointInfo smart constructor.
data RealtimeEndpointInfo
RealtimeEndpointInfo' :: Maybe POSIX -> Maybe RealtimeEndpointStatus -> Maybe Text -> Maybe Int -> RealtimeEndpointInfo
-- | Create a value of RealtimeEndpointInfo with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:createdAt:RealtimeEndpointInfo',
-- realtimeEndpointInfo_createdAt - The time that the request to
-- create the real-time endpoint for the MLModel was received.
-- The time is expressed in epoch time.
--
-- $sel:endpointStatus:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointStatus - 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.
--
--
-- $sel:endpointUrl:RealtimeEndpointInfo',
-- realtimeEndpointInfo_endpointUrl - The URI that specifies where
-- to send real-time prediction requests for the MLModel.
--
-- Note: The application must wait until the real-time endpoint is
-- ready before using this URI.
--
-- $sel:peakRequestsPerSecond:RealtimeEndpointInfo',
-- realtimeEndpointInfo_peakRequestsPerSecond - The maximum
-- processing rate for the real-time endpoint for MLModel,
-- measured in incoming requests per second.
newRealtimeEndpointInfo :: RealtimeEndpointInfo
-- | Describes the data specification of an Amazon Redshift
-- DataSource.
--
-- See: newRedshiftDataSpec smart constructor.
data RedshiftDataSpec
RedshiftDataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | Create a value of RedshiftDataSpec with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:RedshiftDataSpec',
-- redshiftDataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:RedshiftDataSpec',
-- redshiftDataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaUri:RedshiftDataSpec',
-- redshiftDataSpec_dataSchemaUri - Describes the schema location
-- for an Amazon Redshift DataSource.
--
-- $sel:databaseInformation:RedshiftDataSpec',
-- redshiftDataSpec_databaseInformation - Describes the
-- DatabaseName and ClusterIdentifier for an Amazon
-- Redshift DataSource.
--
-- $sel:selectSqlQuery:RedshiftDataSpec',
-- redshiftDataSpec_selectSqlQuery - Describes the SQL Query to
-- execute on an Amazon Redshift database for an Amazon Redshift
-- DataSource.
--
-- $sel:databaseCredentials:RedshiftDataSpec',
-- redshiftDataSpec_databaseCredentials - Describes AWS Identity
-- and Access Management (IAM) credentials that are used connect to the
-- Amazon Redshift database.
--
-- $sel:s3StagingLocation:RedshiftDataSpec',
-- redshiftDataSpec_s3StagingLocation - Describes an Amazon S3
-- location to store the result set of the SelectSqlQuery query.
newRedshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
-- | Describes the database details required to connect to an Amazon
-- Redshift database.
--
-- See: newRedshiftDatabase smart constructor.
data RedshiftDatabase
RedshiftDatabase' :: Text -> Text -> RedshiftDatabase
-- | Create a value of RedshiftDatabase with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseName:RedshiftDatabase',
-- redshiftDatabase_databaseName - Undocumented member.
--
-- $sel:clusterIdentifier:RedshiftDatabase',
-- redshiftDatabase_clusterIdentifier - Undocumented member.
newRedshiftDatabase :: Text -> Text -> RedshiftDatabase
-- | Describes the database credentials for connecting to a database on an
-- Amazon Redshift cluster.
--
-- See: newRedshiftDatabaseCredentials smart constructor.
data RedshiftDatabaseCredentials
RedshiftDatabaseCredentials' :: Text -> Text -> RedshiftDatabaseCredentials
-- | Create a value of RedshiftDatabaseCredentials with all optional
-- fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:username:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_username - Undocumented member.
--
-- $sel:password:RedshiftDatabaseCredentials',
-- redshiftDatabaseCredentials_password - Undocumented member.
newRedshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
-- | Describes the DataSource details specific to Amazon Redshift.
--
-- See: newRedshiftMetadata smart constructor.
data RedshiftMetadata
RedshiftMetadata' :: Maybe Text -> Maybe RedshiftDatabase -> Maybe Text -> RedshiftMetadata
-- | Create a value of RedshiftMetadata with all optional fields
-- omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:databaseUserName:RedshiftMetadata',
-- redshiftMetadata_databaseUserName - Undocumented member.
--
-- $sel:redshiftDatabase:RedshiftMetadata',
-- redshiftMetadata_redshiftDatabase - Undocumented member.
--
-- $sel:selectSqlQuery:RedshiftMetadata',
-- redshiftMetadata_selectSqlQuery - The SQL query that is
-- specified during CreateDataSourceFromRedshift. Returns only if
-- Verbose is true in GetDataSourceInput.
newRedshiftMetadata :: RedshiftMetadata
-- | Describes the data specification of a DataSource.
--
-- See: newS3DataSpec smart constructor.
data S3DataSpec
S3DataSpec' :: Maybe Text -> Maybe Text -> Maybe Text -> Text -> S3DataSpec
-- | Create a value of S3DataSpec with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:dataRearrangement:S3DataSpec',
-- s3DataSpec_dataRearrangement - 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:
--
--
-- - percentBeginUse 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.
-- - percentEndUse 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.
-- - complementThe 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"}}
-- - strategyTo 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_path/bucket/file.csv"}}Datasource for
-- training: {"splitting":{"percentBegin":70, "percentEnd":100,
-- "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv",
-- "complement":"true"}}
--
--
-- $sel:dataSchema:S3DataSpec', s3DataSpec_dataSchema - 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" ] }
--
-- $sel:dataSchemaLocationS3:S3DataSpec',
-- s3DataSpec_dataSchemaLocationS3 - Describes the schema location
-- in Amazon S3. You must provide either the DataSchema or the
-- DataSchemaLocationS3.
--
-- $sel:dataLocationS3:S3DataSpec',
-- s3DataSpec_dataLocationS3 - 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.
newS3DataSpec :: Text -> S3DataSpec
-- | A custom key-value pair associated with an ML object, such as an ML
-- model.
--
-- See: newTag smart constructor.
data Tag
Tag' :: Maybe Text -> Maybe Text -> Tag
-- | Create a value of Tag with all optional fields omitted.
--
-- Use generic-lens or optics to modify other optional
-- fields.
--
-- The following record fields are available, with the corresponding
-- lenses provided for backwards compatibility:
--
-- $sel:key:Tag', tag_key - A unique identifier for the
-- tag. Valid characters include Unicode letters, digits, white space, _,
-- ., /, =, +, -, %, and @.
--
-- $sel:value:Tag', tag_value - An optional string,
-- typically used to describe or define the tag. Valid characters include
-- Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
newTag :: Tag