amazonka-ml 1.4.3 → 1.4.4

raw patch · 42 files changed

+1326/−218 lines, 42 filesdep ~amazonka-coredep ~amazonka-mldep ~amazonka-testPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

Dependency ranges changed: amazonka-core, amazonka-ml, amazonka-test

API changes (from Hackage documentation)

Files

• README.md +1/−1
• amazonka-ml.cabal +9/−6
• fixture/AddTags.yaml +0/−0
• fixture/AddTagsResponse.proto +0/−0
• fixture/CreateDataSourceFromSResponse.proto +0/−0
• fixture/DeleteTags.yaml +0/−0
• fixture/DeleteTagsResponse.proto +0/−0
• fixture/DescribeTags.yaml +0/−0
• fixture/DescribeTagsResponse.proto +0/−0
• gen/Network/AWS/MachineLearning.hs +41/−0
• gen/Network/AWS/MachineLearning/AddTags.hs +164/−0
• gen/Network/AWS/MachineLearning/CreateBatchPrediction.hs +3/−3
• gen/Network/AWS/MachineLearning/CreateDataSourceFromRDS.hs +16/−16
• gen/Network/AWS/MachineLearning/CreateDataSourceFromRedshift.hs +17/−15
• gen/Network/AWS/MachineLearning/CreateDataSourceFromS3.hs +12/−12
• gen/Network/AWS/MachineLearning/CreateEvaluation.hs +4/−4
• gen/Network/AWS/MachineLearning/CreateMLModel.hs +19/−17
• gen/Network/AWS/MachineLearning/CreateRealtimeEndpoint.hs +1/−1
• gen/Network/AWS/MachineLearning/DeleteBatchPrediction.hs +2/−2
• gen/Network/AWS/MachineLearning/DeleteDataSource.hs +1/−1
• gen/Network/AWS/MachineLearning/DeleteEvaluation.hs +6/−4
• gen/Network/AWS/MachineLearning/DeleteMLModel.hs +4/−4
• gen/Network/AWS/MachineLearning/DeleteRealtimeEndpoint.hs +1/−1
• gen/Network/AWS/MachineLearning/DeleteTags.hs +166/−0
• gen/Network/AWS/MachineLearning/DescribeBatchPredictions.hs +25/−25
• gen/Network/AWS/MachineLearning/DescribeEvaluations.hs +2/−2
• gen/Network/AWS/MachineLearning/DescribeMLModels.hs +3/−3
• gen/Network/AWS/MachineLearning/DescribeTags.hs +164/−0
• gen/Network/AWS/MachineLearning/GetBatchPrediction.hs +52/−2
• gen/Network/AWS/MachineLearning/GetDataSource.hs +35/−5
• gen/Network/AWS/MachineLearning/GetEvaluation.hs +33/−3
• gen/Network/AWS/MachineLearning/GetMLModel.hs +50/−18
• gen/Network/AWS/MachineLearning/Types.hs +34/−0
• gen/Network/AWS/MachineLearning/Types/Product.hs +340/−41
• gen/Network/AWS/MachineLearning/Types/Sum.hs +53/−18
• gen/Network/AWS/MachineLearning/UpdateBatchPrediction.hs +3/−3
• gen/Network/AWS/MachineLearning/UpdateDataSource.hs +3/−3
• gen/Network/AWS/MachineLearning/UpdateEvaluation.hs +3/−3
• gen/Network/AWS/MachineLearning/UpdateMLModel.hs +3/−3
• gen/Network/AWS/MachineLearning/Waiters.hs +2/−2
• src/.gitkeep +0/−0
• test/Test/AWS/Gen/MachineLearning.hs +54/−0

@@ -8,7 +8,7 @@  ## Version -`1.4.3`+`1.4.4`   ## Description

@@ -1,5 +1,5 @@ name:                  amazonka-ml-version:               1.4.3+version:               1.4.4 synopsis:              Amazon Machine Learning SDK. homepage:              https://github.com/brendanhay/amazonka bug-reports:           https://github.com/brendanhay/amazonka/issues@@ -11,7 +11,7 @@ category:              Network, AWS, Cloud, Distributed Computing build-type:            Simple cabal-version:         >= 1.10-extra-source-files:    README.md fixture/*.yaml fixture/*.proto+extra-source-files:    README.md fixture/*.yaml fixture/*.proto src/.gitkeep description:     Definition of the public APIs exposed by Amazon Machine Learning     .@@ -41,6 +41,7 @@      exposed-modules:           Network.AWS.MachineLearning+        , Network.AWS.MachineLearning.AddTags         , Network.AWS.MachineLearning.CreateBatchPrediction         , Network.AWS.MachineLearning.CreateDataSourceFromRDS         , Network.AWS.MachineLearning.CreateDataSourceFromRedshift@@ -53,10 +54,12 @@         , Network.AWS.MachineLearning.DeleteEvaluation         , Network.AWS.MachineLearning.DeleteMLModel         , Network.AWS.MachineLearning.DeleteRealtimeEndpoint+        , Network.AWS.MachineLearning.DeleteTags         , Network.AWS.MachineLearning.DescribeBatchPredictions         , Network.AWS.MachineLearning.DescribeDataSources         , Network.AWS.MachineLearning.DescribeEvaluations         , Network.AWS.MachineLearning.DescribeMLModels+        , Network.AWS.MachineLearning.DescribeTags         , Network.AWS.MachineLearning.GetBatchPrediction         , Network.AWS.MachineLearning.GetDataSource         , Network.AWS.MachineLearning.GetEvaluation@@ -74,7 +77,7 @@         , Network.AWS.MachineLearning.Types.Sum      build-depends:-          amazonka-core == 1.4.3.*+          amazonka-core == 1.4.4.*         , base          >= 4.7     && < 5  test-suite amazonka-ml-test@@ -94,9 +97,9 @@         , Test.AWS.MachineLearning.Internal      build-depends:-          amazonka-core == 1.4.3.*-        , amazonka-test == 1.4.3.*-        , amazonka-ml == 1.4.3.*+          amazonka-core == 1.4.4.*+        , amazonka-test == 1.4.4.*+        , amazonka-ml == 1.4.4.*         , base         , bytestring         , tasty

@@ -20,6 +20,9 @@     -- * Errors     -- $errors +    -- ** InvalidTagException+    , _InvalidTagException+     -- ** InternalServerException     , _InternalServerException @@ -29,6 +32,9 @@     -- ** IdempotentParameterMismatchException     , _IdempotentParameterMismatchException +    -- ** TagLimitExceededException+    , _TagLimitExceededException+     -- ** PredictorNotMountedException     , _PredictorNotMountedException @@ -62,6 +68,9 @@     -- ** DeleteDataSource     , module Network.AWS.MachineLearning.DeleteDataSource +    -- ** DescribeTags+    , module Network.AWS.MachineLearning.DescribeTags+     -- ** CreateDataSourceFromRedshift     , module Network.AWS.MachineLearning.CreateDataSourceFromRedshift @@ -71,6 +80,9 @@     -- ** CreateMLModel     , module Network.AWS.MachineLearning.CreateMLModel +    -- ** DeleteTags+    , module Network.AWS.MachineLearning.DeleteTags+     -- ** DeleteBatchPrediction     , module Network.AWS.MachineLearning.DeleteBatchPrediction @@ -125,6 +137,9 @@     -- ** CreateRealtimeEndpoint     , module Network.AWS.MachineLearning.CreateRealtimeEndpoint +    -- ** AddTags+    , module Network.AWS.MachineLearning.AddTags+     -- ** DescribeMLModels (Paginated)     , module Network.AWS.MachineLearning.DescribeMLModels @@ -163,16 +178,24 @@     -- ** SortOrder     , SortOrder (..) +    -- ** TaggableResourceType+    , TaggableResourceType (..)+     -- ** BatchPrediction     , BatchPrediction     , batchPrediction     , bpStatus     , bpLastUpdatedAt     , bpCreatedAt+    , bpComputeTime     , bpInputDataLocationS3     , bpMLModelId     , bpBatchPredictionDataSourceId+    , bpTotalRecordCount+    , bpStartedAt     , bpBatchPredictionId+    , bpFinishedAt+    , bpInvalidRecordCount     , bpCreatedByIAMUser     , bpName     , bpMessage@@ -185,9 +208,12 @@     , dsNumberOfFiles     , dsLastUpdatedAt     , dsCreatedAt+    , dsComputeTime     , dsDataSourceId     , dsRDSMetadata     , dsDataSizeInBytes+    , dsStartedAt+    , dsFinishedAt     , dsCreatedByIAMUser     , dsName     , dsDataLocationS3@@ -204,8 +230,11 @@     , ePerformanceMetrics     , eLastUpdatedAt     , eCreatedAt+    , eComputeTime     , eInputDataLocationS3     , eMLModelId+    , eStartedAt+    , eFinishedAt     , eCreatedByIAMUser     , eName     , eEvaluationId@@ -220,10 +249,13 @@     , mlmTrainingParameters     , mlmScoreThresholdLastUpdatedAt     , mlmCreatedAt+    , mlmComputeTime     , mlmInputDataLocationS3     , mlmMLModelId     , mlmSizeInBytes+    , mlmStartedAt     , mlmScoreThreshold+    , mlmFinishedAt     , mlmAlgorithm     , mlmCreatedByIAMUser     , mlmName@@ -327,8 +359,15 @@     , sdsDataSchemaLocationS3     , sdsDataRearrangement     , sdsDataLocationS3++    -- ** Tag+    , Tag+    , tag+    , tagValue+    , tagKey     ) where +import           Network.AWS.MachineLearning.AddTags import           Network.AWS.MachineLearning.CreateBatchPrediction import           Network.AWS.MachineLearning.CreateDataSourceFromRDS import           Network.AWS.MachineLearning.CreateDataSourceFromRedshift@@ -341,10 +380,12 @@ import           Network.AWS.MachineLearning.DeleteEvaluation import           Network.AWS.MachineLearning.DeleteMLModel import           Network.AWS.MachineLearning.DeleteRealtimeEndpoint+import           Network.AWS.MachineLearning.DeleteTags import           Network.AWS.MachineLearning.DescribeBatchPredictions import           Network.AWS.MachineLearning.DescribeDataSources import           Network.AWS.MachineLearning.DescribeEvaluations import           Network.AWS.MachineLearning.DescribeMLModels+import           Network.AWS.MachineLearning.DescribeTags import           Network.AWS.MachineLearning.GetBatchPrediction import           Network.AWS.MachineLearning.GetDataSource import           Network.AWS.MachineLearning.GetEvaluation

@@ -0,0 +1,164 @@+{-# LANGUAGE DeriveDataTypeable #-}+{-# LANGUAGE DeriveGeneric      #-}+{-# LANGUAGE OverloadedStrings  #-}+{-# LANGUAGE RecordWildCards    #-}+{-# LANGUAGE TypeFamilies       #-}++{-# OPTIONS_GHC -fno-warn-unused-imports #-}+{-# OPTIONS_GHC -fno-warn-unused-binds   #-}+{-# OPTIONS_GHC -fno-warn-unused-matches #-}++-- Derived from AWS service descriptions, licensed under Apache 2.0.++-- |+-- Module      : Network.AWS.MachineLearning.AddTags+-- Copyright   : (c) 2013-2016 Brendan Hay+-- License     : Mozilla Public License, v. 2.0.+-- Maintainer  : Brendan Hay <brendan.g.hay@gmail.com>+-- Stability   : auto-generated+-- Portability : non-portable (GHC extensions)+--+-- 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 Network.AWS.MachineLearning.AddTags+    (+    -- * Creating a Request+      addTags+    , AddTags+    -- * Request Lenses+    , atTags+    , atResourceId+    , atResourceType++    -- * Destructuring the Response+    , addTagsResponse+    , AddTagsResponse+    -- * Response Lenses+    , atrsResourceId+    , atrsResourceType+    , atrsResponseStatus+    ) where++import           Network.AWS.Lens+import           Network.AWS.MachineLearning.Types+import           Network.AWS.MachineLearning.Types.Product+import           Network.AWS.Prelude+import           Network.AWS.Request+import           Network.AWS.Response++-- | /See:/ 'addTags' smart constructor.+data AddTags = AddTags'+    { _atTags         :: ![Tag]+    , _atResourceId   :: !Text+    , _atResourceType :: !TaggableResourceType+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'AddTags' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'atTags'+--+-- * 'atResourceId'+--+-- * 'atResourceType'+addTags+    :: Text -- ^ 'atResourceId'+    -> TaggableResourceType -- ^ 'atResourceType'+    -> AddTags+addTags pResourceId_ pResourceType_ =+    AddTags'+    { _atTags = mempty+    , _atResourceId = pResourceId_+    , _atResourceType = pResourceType_+    }++-- | 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.+atTags :: Lens' AddTags [Tag]+atTags = lens _atTags (\ s a -> s{_atTags = a}) . _Coerce;++-- | The ID of the ML object to tag. For example, 'exampleModelId'.+atResourceId :: Lens' AddTags Text+atResourceId = lens _atResourceId (\ s a -> s{_atResourceId = a});++-- | The type of the ML object to tag.+atResourceType :: Lens' AddTags TaggableResourceType+atResourceType = lens _atResourceType (\ s a -> s{_atResourceType = a});++instance AWSRequest AddTags where+        type Rs AddTags = AddTagsResponse+        request = postJSON machineLearning+        response+          = receiveJSON+              (\ s h x ->+                 AddTagsResponse' <$>+                   (x .?> "ResourceId") <*> (x .?> "ResourceType") <*>+                     (pure (fromEnum s)))++instance Hashable AddTags++instance NFData AddTags++instance ToHeaders AddTags where+        toHeaders+          = const+              (mconcat+                 ["X-Amz-Target" =#+                    ("AmazonML_20141212.AddTags" :: ByteString),+                  "Content-Type" =#+                    ("application/x-amz-json-1.1" :: ByteString)])++instance ToJSON AddTags where+        toJSON AddTags'{..}+          = object+              (catMaybes+                 [Just ("Tags" .= _atTags),+                  Just ("ResourceId" .= _atResourceId),+                  Just ("ResourceType" .= _atResourceType)])++instance ToPath AddTags where+        toPath = const "/"++instance ToQuery AddTags where+        toQuery = const mempty++-- | Amazon ML returns the following elements.+--+-- /See:/ 'addTagsResponse' smart constructor.+data AddTagsResponse = AddTagsResponse'+    { _atrsResourceId     :: !(Maybe Text)+    , _atrsResourceType   :: !(Maybe TaggableResourceType)+    , _atrsResponseStatus :: !Int+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'AddTagsResponse' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'atrsResourceId'+--+-- * 'atrsResourceType'+--+-- * 'atrsResponseStatus'+addTagsResponse+    :: Int -- ^ 'atrsResponseStatus'+    -> AddTagsResponse+addTagsResponse pResponseStatus_ =+    AddTagsResponse'+    { _atrsResourceId = Nothing+    , _atrsResourceType = Nothing+    , _atrsResponseStatus = pResponseStatus_+    }++-- | The ID of the ML object that was tagged.+atrsResourceId :: Lens' AddTagsResponse (Maybe Text)+atrsResourceId = lens _atrsResourceId (\ s a -> s{_atrsResourceId = a});++-- | The type of the ML object that was tagged.+atrsResourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType)+atrsResourceType = lens _atrsResourceType (\ s a -> s{_atrsResourceType = a});++-- | The response status code.+atrsResponseStatus :: Lens' AddTagsResponse Int+atrsResponseStatus = lens _atrsResponseStatus (\ s a -> s{_atrsResponseStatus = a});++instance NFData AddTagsResponse

@@ -103,7 +103,7 @@ cbpBatchPredictionDataSourceId :: Lens' CreateBatchPrediction Text cbpBatchPredictionDataSourceId = lens _cbpBatchPredictionDataSourceId (\ s a -> s{_cbpBatchPredictionDataSourceId = a}); --- | 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: \':\', \'\/\/\', \'\/.\/\', \'\/..\/\'.+-- | 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 <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>. cbpOutputURI :: Lens' CreateBatchPrediction Text@@ -152,9 +152,9 @@ instance ToQuery CreateBatchPrediction where         toQuery = const mempty --- | Represents the output of a < CreateBatchPrediction> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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.+-- 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:/ 'createBatchPredictionResponse' smart constructor. data CreateBatchPredictionResponse = CreateBatchPredictionResponse'

@@ -18,11 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a 'DataSource' object from an <http://aws.amazon.com/rds/ Amazon Relational Database Service> (Amazon RDS). A 'DataSource' references data that can be used to perform < CreateMLModel>, < CreateEvaluation>, or < CreateBatchPrediction> operations.+-- Creates a 'DataSource' object from an <http://aws.amazon.com/rds/ 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 'COMPLETED' or 'PENDING' status can only 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.+-- 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 Network.AWS.MachineLearning.CreateDataSourceFromRDS     (     -- * Creating a Request@@ -90,7 +90,7 @@ cdsfrdsDataSourceName :: Lens' CreateDataSourceFromRDS (Maybe Text) cdsfrdsDataSourceName = lens _cdsfrdsDataSourceName (\ s a -> s{_cdsfrdsDataSourceName = a}); --- | 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 an 'MLModel' training. This parameter must be set to 'true' if the ''DataSource'' needs to be used for 'MLModel' training.+-- | 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. cdsfrdsComputeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool) cdsfrdsComputeStatistics = lens _cdsfrdsComputeStatistics (\ s a -> s{_cdsfrdsComputeStatistics = a}); @@ -102,32 +102,32 @@ -- -- -   DatabaseInformation - -----     -   'DatabaseName ' - Name of the Amazon RDS database.---     -   ' InstanceIdentifier ' - Unique identifier for the Amazon RDS database instance.+--     -   '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 - Role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (EC2) instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines.+-- -   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 <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. ----- -   ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon Simple Storage Service (S3). For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html 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 <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. ----- -   SecurityInfo - Security information to use to access an Amazon RDS 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 Amazon RDS instance.+-- -   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 - Query that is used to retrieve the observation data for the 'Datasource'.+-- -   SelectSqlQuery - A query that is used to retrieve the observation data for the 'Datasource'. ----- -   S3StagingLocation - Amazon S3 location for staging RDS data. The data retrieved from Amazon RDS using 'SelectSqlQuery' is stored in this location.+-- -   S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using 'SelectSqlQuery' is stored in this location. ----- -   DataSchemaUri - Amazon S3 location of the 'DataSchema'.+-- -   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 representing the splitting requirement of a 'Datasource'.+-- -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'Datasource'. -- --     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"' -- cdsfrdsRDSData :: Lens' CreateDataSourceFromRDS RDSDataSpec cdsfrdsRDSData = lens _cdsfrdsRDSData (\ s a -> s{_cdsfrdsRDSData = a}); --- | 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.+-- | 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. -- cdsfrdsRoleARN :: Lens' CreateDataSourceFromRDS Text cdsfrdsRoleARN = lens _cdsfrdsRoleARN (\ s a -> s{_cdsfrdsRoleARN = a});@@ -173,9 +173,9 @@ instance ToQuery CreateDataSourceFromRDS where         toQuery = const mempty --- | Represents the output of a < CreateDataSourceFromRDS> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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.+-- 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:/ 'createDataSourceFromRDSResponse' smart constructor. data CreateDataSourceFromRDSResponse = CreateDataSourceFromRDSResponse'

@@ -18,15 +18,17 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a 'DataSource' from <http://aws.amazon.com/redshift/ Amazon Redshift>. A 'DataSource' references data that can be used to perform either < CreateMLModel>, < CreateEvaluation> or < CreateBatchPrediction> operations.+-- 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' status can only be used to perform < 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 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.+-- 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 exist in the database hosted on an Amazon Redshift cluster and should be specified by a 'SelectSqlQuery'. Amazon ML executes <http://docs.aws.amazon.com/redshift/latest/dg/t_Unloading_tables.html Unload> command in Amazon Redshift to transfer the result set of 'SelectSqlQuery' to 'S3StagingLocation.'+-- 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' is created, it\'s ready for use in evaluations and batch predictions. If you plan to use the 'DataSource' to train an 'MLModel', the 'DataSource' requires another item -- a recipe. A recipe describes the observation variables that participate in training an 'MLModel'. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.+-- 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 Network.AWS.MachineLearning.CreateDataSourceFromRedshift     (     -- * Creating a Request@@ -94,7 +96,7 @@ cdsfrDataSourceName :: Lens' CreateDataSourceFromRedshift (Maybe Text) cdsfrDataSourceName = lens _cdsfrDataSourceName (\ s a -> s{_cdsfrDataSourceName = a}); --- | 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+-- | 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. cdsfrComputeStatistics :: Lens' CreateDataSourceFromRedshift (Maybe Bool) cdsfrComputeStatistics = lens _cdsfrComputeStatistics (\ s a -> s{_cdsfrComputeStatistics = a}); @@ -106,19 +108,19 @@ -- -- -   DatabaseInformation - -----     -   'DatabaseName ' - Name of the Amazon Redshift database.---     -   ' ClusterIdentifier ' - Unique ID for the Amazon Redshift cluster.--- -   DatabaseCredentials - AWS Identity abd Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.+--     -   '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 - Query that is used to retrieve the observation data for the 'Datasource'.+-- -   SelectSqlQuery - The query that is used to retrieve the observation data for the 'Datasource'. ----- -   S3StagingLocation - Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Relational Database Service (Amazon RDS) using 'SelectSqlQuery' is stored in this location.+-- -   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 - Amazon S3 location of the 'DataSchema'.+-- -   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 representing the splitting requirement of a 'Datasource'.+-- -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'DataSource'. -- --     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"' --@@ -175,9 +177,9 @@ instance ToQuery CreateDataSourceFromRedshift where         toQuery = const mempty --- | Represents the output of a < CreateDataSourceFromRedshift> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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.+-- The 'CreateDataSourceFromRedshift' operation is asynchronous. You can poll for updates by using the 'GetBatchPrediction' operation and checking the 'Status' parameter. -- -- /See:/ 'createDataSourceFromRedshiftResponse' smart constructor. data CreateDataSourceFromRedshiftResponse = CreateDataSourceFromRedshiftResponse'

@@ -18,15 +18,15 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a 'DataSource' object. A 'DataSource' references data that can be used to perform < CreateMLModel>, < CreateEvaluation>, or < CreateBatchPrediction> operations.+-- 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' is created and ready for use, Amazon ML sets the 'Status' parameter to 'COMPLETED'. 'DataSource' in 'COMPLETED' or 'PENDING' status can only 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 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.+-- 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) bucket, 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'.+-- 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' requires another item: a recipe. A recipe describes the observation variables that participate in training an 'MLModel'. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+-- 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 Network.AWS.MachineLearning.CreateDataSourceFromS3     (     -- * Creating a Request@@ -88,7 +88,7 @@ cdsfsDataSourceName :: Lens' CreateDataSourceFromS3 (Maybe Text) cdsfsDataSourceName = lens _cdsfsDataSourceName (\ s a -> s{_cdsfsDataSourceName = a}); --- | 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 an 'MLModel' training. This parameter must be set to 'true' if the ''DataSource'' needs to be used for 'MLModel' training+-- | 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. cdsfsComputeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool) cdsfsComputeStatistics = lens _cdsfsComputeStatistics (\ s a -> s{_cdsfsComputeStatistics = a}); @@ -98,13 +98,13 @@  -- | The data specification of a 'DataSource': ----- -   DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.+-- -   DataLocationS3 - The Amazon S3 location of the observation data. ----- -   DataSchemaLocationS3 - Amazon S3 location of the 'DataSchema'.+-- -   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 representing the splitting requirement of a 'Datasource'.+-- -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'Datasource'. -- --     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"' --@@ -150,9 +150,9 @@ instance ToQuery CreateDataSourceFromS3 where         toQuery = const mempty --- | Represents the output of a < CreateDataSourceFromS3> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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.+-- The 'CreateDataSourceFromS3' operation is asynchronous. You can poll for updates by using the 'GetBatchPrediction' operation and checking the 'Status' parameter. -- -- /See:/ 'createDataSourceFromS3Response' smart constructor. data CreateDataSourceFromS3Response = CreateDataSourceFromS3Response'@@ -176,7 +176,7 @@     , _cdsfsrsResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the 'DataSourceID' in the request.+-- | A user-supplied ID that uniquely identifies the 'DataSource'. This value should be identical to the value of the 'DataSourceID' in the request. cdsfsrsDataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text) cdsfsrsDataSourceId = lens _cdsfsrsDataSourceId (\ s a -> s{_cdsfsrsDataSourceId = a}); 

@@ -18,11 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- 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'.+-- 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.+-- You can use the 'GetEvaluation' operation to check progress of the evaluation during the creation operation. module Network.AWS.MachineLearning.CreateEvaluation     (     -- * Creating a Request@@ -138,9 +138,9 @@ instance ToQuery CreateEvaluation where         toQuery = const mempty --- | Represents the output of a < CreateEvaluation> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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 < GetEvaluation> operation and checking the 'Status' parameter.+-- 'CreateEvaluation' operation is asynchronous. You can poll for status updates by using the 'GetEvcaluation' operation and checking the 'Status' parameter. -- -- /See:/ 'createEvaluationResponse' smart constructor. data CreateEvaluationResponse = CreateEvaluationResponse'

@@ -18,15 +18,15 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a new 'MLModel' using the data files and the recipe as information sources.+-- Creates a new 'MLModel' using the 'DataSource' and the recipe as information sources. ----- An 'MLModel' is nearly immutable. Users can only update the 'MLModelName' and the 'ScoreThreshold' in an 'MLModel' without creating a new 'MLModel'.+-- 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' is created and ready for use, Amazon ML sets the status to 'COMPLETED'.+-- '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 progress of the 'MLModel' during the creation operation.+-- 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.+-- 'CreateMLModel' requires a 'DataSource' with computed statistics, which can be created by setting 'ComputeStatistics' to 'true' in 'CreateDataSourceFromRDS', 'CreateDataSourceFromS3', or 'CreateDataSourceFromRedshift' operations. module Network.AWS.MachineLearning.CreateMLModel     (     -- * Creating a Request@@ -100,11 +100,11 @@     , _cmlmTrainingDataSourceId = pTrainingDataSourceId_     } --- | The data recipe for creating '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.+-- | 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. cmlmRecipe :: Lens' CreateMLModel (Maybe Text) cmlmRecipe = lens _cmlmRecipe (\ s a -> s{_cmlmRecipe = a}); --- | 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.+-- | 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. cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text) cmlmRecipeURI = lens _cmlmRecipeURI (\ s a -> s{_cmlmRecipeURI = a}); @@ -112,24 +112,26 @@ cmlmMLModelName :: Lens' CreateMLModel (Maybe Text) cmlmMLModelName = lens _cmlmMLModelName (\ s a -> s{_cmlmMLModelName = a}); --- | A list of the training parameters in the 'MLModel'. The list is implemented as a map of key\/value pairs.+-- | 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.l1RegularizationAmount' - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This 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.+-- -   '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 a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when 'L2' is specified. Use this parameter sparingly.+--     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'. ----- -   'sgd.l2RegularizationAmount' - 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.+-- -   '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'. -----     The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when 'L1' is specified. Use this parameter sparingly.+-- -   '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.maxPasses' - 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.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'. ----- -   'sgd.maxMLModelSizeInBytes' - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.+--     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. -----     The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.+-- -   '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.+-- cmlmParameters :: Lens' CreateMLModel (HashMap Text Text) cmlmParameters = lens _cmlmParameters (\ s a -> s{_cmlmParameters = a}) . _Default . _Map; @@ -193,9 +195,9 @@ instance ToQuery CreateMLModel where         toQuery = const mempty --- | Represents the output of a < CreateMLModel> operation, and is an acknowledgement that Amazon ML received the request.+-- | 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.+-- The 'CreateMLModel' operation is asynchronous. You can poll for status updates by using the 'GetMLModel' operation and checking the 'Status' parameter. -- -- /See:/ 'createMLModelResponse' smart constructor. data CreateMLModelResponse = CreateMLModelResponse'

@@ -102,7 +102,7 @@ instance ToQuery CreateRealtimeEndpoint where         toQuery = const mempty --- | Represents the output of an < CreateRealtimeEndpoint> operation.+-- | Represents the output of an 'CreateRealtimeEndpoint' operation. -- -- The result contains the 'MLModelId' and the endpoint information for the 'MLModel'. --

@@ -105,9 +105,9 @@ instance ToQuery DeleteBatchPrediction where         toQuery = const mempty --- | Represents the output of a < DeleteBatchPrediction> operation.+-- | 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'.+-- You can use the 'GetBatchPrediction' operation and check the value of the 'Status' parameter to see whether a 'BatchPrediction' is marked as 'DELETED'. -- -- /See:/ 'deleteBatchPredictionResponse' smart constructor. data DeleteBatchPredictionResponse = DeleteBatchPredictionResponse'

@@ -102,7 +102,7 @@ instance ToQuery DeleteDataSource where         toQuery = const mempty --- | Represents the output of a < DeleteDataSource> operation.+-- | Represents the output of a 'DeleteDataSource' operation. -- -- /See:/ 'deleteDataSourceResponse' smart constructor. data DeleteDataSourceResponse = DeleteDataSourceResponse'

@@ -20,9 +20,11 @@ -- -- 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'.+-- 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.+-- Caution+--+-- The results of the 'DeleteEvaluation' operation are irreversible. module Network.AWS.MachineLearning.DeleteEvaluation     (     -- * Creating a Request@@ -102,9 +104,9 @@ instance ToQuery DeleteEvaluation where         toQuery = const mempty --- | Represents the output of a < DeleteEvaluation> operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.+-- | 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'.+-- You can use the 'GetEvaluation' operation and check the value of the 'Status' parameter to see whether an 'Evaluation' is marked as 'DELETED'. -- -- /See:/ 'deleteEvaluationResponse' smart constructor. data DeleteEvaluationResponse = DeleteEvaluationResponse'

@@ -18,9 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Assigns the DELETED status to an 'MLModel', rendering it unusable.+-- 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.+-- 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 Network.AWS.MachineLearning.DeleteMLModel@@ -101,9 +101,9 @@ instance ToQuery DeleteMLModel where         toQuery = const mempty --- | Represents the output of a < DeleteMLModel> operation.+-- | 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'.+-- You can use the 'GetMLModel' operation and check the value of the 'Status' parameter to see whether an 'MLModel' is marked as 'DELETED'. -- -- /See:/ 'deleteMLModelResponse' smart constructor. data DeleteMLModelResponse = DeleteMLModelResponse'

@@ -102,7 +102,7 @@ instance ToQuery DeleteRealtimeEndpoint where         toQuery = const mempty --- | Represents the output of an < DeleteRealtimeEndpoint> operation.+-- | Represents the output of an 'DeleteRealtimeEndpoint' operation. -- -- The result contains the 'MLModelId' and the endpoint information for the 'MLModel'. --

@@ -0,0 +1,166 @@+{-# LANGUAGE DeriveDataTypeable #-}+{-# LANGUAGE DeriveGeneric      #-}+{-# LANGUAGE OverloadedStrings  #-}+{-# LANGUAGE RecordWildCards    #-}+{-# LANGUAGE TypeFamilies       #-}++{-# OPTIONS_GHC -fno-warn-unused-imports #-}+{-# OPTIONS_GHC -fno-warn-unused-binds   #-}+{-# OPTIONS_GHC -fno-warn-unused-matches #-}++-- Derived from AWS service descriptions, licensed under Apache 2.0.++-- |+-- Module      : Network.AWS.MachineLearning.DeleteTags+-- Copyright   : (c) 2013-2016 Brendan Hay+-- License     : Mozilla Public License, v. 2.0.+-- Maintainer  : Brendan Hay <brendan.g.hay@gmail.com>+-- Stability   : auto-generated+-- Portability : non-portable (GHC extensions)+--+-- 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 Network.AWS.MachineLearning.DeleteTags+    (+    -- * Creating a Request+      deleteTags+    , DeleteTags+    -- * Request Lenses+    , dTagKeys+    , dResourceId+    , dResourceType++    -- * Destructuring the Response+    , deleteTagsResponse+    , DeleteTagsResponse+    -- * Response Lenses+    , drsResourceId+    , drsResourceType+    , drsResponseStatus+    ) where++import           Network.AWS.Lens+import           Network.AWS.MachineLearning.Types+import           Network.AWS.MachineLearning.Types.Product+import           Network.AWS.Prelude+import           Network.AWS.Request+import           Network.AWS.Response++-- | /See:/ 'deleteTags' smart constructor.+data DeleteTags = DeleteTags'+    { _dTagKeys      :: ![Text]+    , _dResourceId   :: !Text+    , _dResourceType :: !TaggableResourceType+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'DeleteTags' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'dTagKeys'+--+-- * 'dResourceId'+--+-- * 'dResourceType'+deleteTags+    :: Text -- ^ 'dResourceId'+    -> TaggableResourceType -- ^ 'dResourceType'+    -> DeleteTags+deleteTags pResourceId_ pResourceType_ =+    DeleteTags'+    { _dTagKeys = mempty+    , _dResourceId = pResourceId_+    , _dResourceType = pResourceType_+    }++-- | One or more tags to delete.+dTagKeys :: Lens' DeleteTags [Text]+dTagKeys = lens _dTagKeys (\ s a -> s{_dTagKeys = a}) . _Coerce;++-- | The ID of the tagged ML object. For example, 'exampleModelId'.+dResourceId :: Lens' DeleteTags Text+dResourceId = lens _dResourceId (\ s a -> s{_dResourceId = a});++-- | The type of the tagged ML object.+dResourceType :: Lens' DeleteTags TaggableResourceType+dResourceType = lens _dResourceType (\ s a -> s{_dResourceType = a});++instance AWSRequest DeleteTags where+        type Rs DeleteTags = DeleteTagsResponse+        request = postJSON machineLearning+        response+          = receiveJSON+              (\ s h x ->+                 DeleteTagsResponse' <$>+                   (x .?> "ResourceId") <*> (x .?> "ResourceType") <*>+                     (pure (fromEnum s)))++instance Hashable DeleteTags++instance NFData DeleteTags++instance ToHeaders DeleteTags where+        toHeaders+          = const+              (mconcat+                 ["X-Amz-Target" =#+                    ("AmazonML_20141212.DeleteTags" :: ByteString),+                  "Content-Type" =#+                    ("application/x-amz-json-1.1" :: ByteString)])++instance ToJSON DeleteTags where+        toJSON DeleteTags'{..}+          = object+              (catMaybes+                 [Just ("TagKeys" .= _dTagKeys),+                  Just ("ResourceId" .= _dResourceId),+                  Just ("ResourceType" .= _dResourceType)])++instance ToPath DeleteTags where+        toPath = const "/"++instance ToQuery DeleteTags where+        toQuery = const mempty++-- | Amazon ML returns the following elements.+--+-- /See:/ 'deleteTagsResponse' smart constructor.+data DeleteTagsResponse = DeleteTagsResponse'+    { _drsResourceId     :: !(Maybe Text)+    , _drsResourceType   :: !(Maybe TaggableResourceType)+    , _drsResponseStatus :: !Int+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'DeleteTagsResponse' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'drsResourceId'+--+-- * 'drsResourceType'+--+-- * 'drsResponseStatus'+deleteTagsResponse+    :: Int -- ^ 'drsResponseStatus'+    -> DeleteTagsResponse+deleteTagsResponse pResponseStatus_ =+    DeleteTagsResponse'+    { _drsResourceId = Nothing+    , _drsResourceType = Nothing+    , _drsResponseStatus = pResponseStatus_+    }++-- | The ID of the ML object from which tags were deleted.+drsResourceId :: Lens' DeleteTagsResponse (Maybe Text)+drsResourceId = lens _drsResourceId (\ s a -> s{_drsResourceId = a});++-- | The type of the ML object from which tags were deleted.+drsResourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType)+drsResourceType = lens _drsResourceType (\ s a -> s{_drsResourceType = a});++-- | The response status code.+drsResponseStatus :: Lens' DeleteTagsResponse Int+drsResponseStatus = lens _drsResponseStatus (\ s a -> s{_drsResponseStatus = a});++instance NFData DeleteTagsResponse

@@ -43,9 +43,9 @@     , describeBatchPredictionsResponse     , DescribeBatchPredictionsResponse     -- * Response Lenses-    , drsResults-    , drsNextToken-    , drsResponseStatus+    , dbpsrsResults+    , dbpsrsNextToken+    , dbpsrsResponseStatus     ) where  import           Network.AWS.Lens@@ -155,7 +155,7 @@ dbpSortOrder :: Lens' DescribeBatchPredictions (Maybe SortOrder) dbpSortOrder = lens _dbpSortOrder (\ s a -> s{_dbpSortOrder = a}); --- | 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.+-- | 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'. dbpLimit :: Lens' DescribeBatchPredictions (Maybe Natural) dbpLimit = lens _dbpLimit (\ s a -> s{_dbpLimit = a}) . mapping _Nat; @@ -181,10 +181,10 @@  instance AWSPager DescribeBatchPredictions where         page rq rs-          | stop (rs ^. drsNextToken) = Nothing-          | stop (rs ^. drsResults) = Nothing+          | stop (rs ^. dbpsrsNextToken) = Nothing+          | stop (rs ^. dbpsrsResults) = Nothing           | otherwise =-            Just $ rq & dbpNextToken .~ rs ^. drsNextToken+            Just $ rq & dbpNextToken .~ rs ^. dbpsrsNextToken  instance AWSRequest DescribeBatchPredictions where         type Rs DescribeBatchPredictions =@@ -230,44 +230,44 @@ instance ToQuery DescribeBatchPredictions where         toQuery = const mempty --- | Represents the output of a < DescribeBatchPredictions> operation. The content is essentially a list of 'BatchPrediction's.+-- | Represents the output of a 'DescribeBatchPredictions' operation. The content is essentially a list of 'BatchPrediction's. -- -- /See:/ 'describeBatchPredictionsResponse' smart constructor. data DescribeBatchPredictionsResponse = DescribeBatchPredictionsResponse'-    { _drsResults        :: !(Maybe [BatchPrediction])-    , _drsNextToken      :: !(Maybe Text)-    , _drsResponseStatus :: !Int+    { _dbpsrsResults        :: !(Maybe [BatchPrediction])+    , _dbpsrsNextToken      :: !(Maybe Text)+    , _dbpsrsResponseStatus :: !Int     } deriving (Eq,Read,Show,Data,Typeable,Generic)  -- | Creates a value of 'DescribeBatchPredictionsResponse' with the minimum fields required to make a request. -- -- Use one of the following lenses to modify other fields as desired: ----- * 'drsResults'+-- * 'dbpsrsResults' ----- * 'drsNextToken'+-- * 'dbpsrsNextToken' ----- * 'drsResponseStatus'+-- * 'dbpsrsResponseStatus' describeBatchPredictionsResponse-    :: Int -- ^ 'drsResponseStatus'+    :: Int -- ^ 'dbpsrsResponseStatus'     -> DescribeBatchPredictionsResponse describeBatchPredictionsResponse pResponseStatus_ =     DescribeBatchPredictionsResponse'-    { _drsResults = Nothing-    , _drsNextToken = Nothing-    , _drsResponseStatus = pResponseStatus_+    { _dbpsrsResults = Nothing+    , _dbpsrsNextToken = Nothing+    , _dbpsrsResponseStatus = pResponseStatus_     } --- | A list of < BatchPrediction> objects that meet the search criteria.-drsResults :: Lens' DescribeBatchPredictionsResponse [BatchPrediction]-drsResults = lens _drsResults (\ s a -> s{_drsResults = a}) . _Default . _Coerce;+-- | A list of 'BatchPrediction' objects that meet the search criteria.+dbpsrsResults :: Lens' DescribeBatchPredictionsResponse [BatchPrediction]+dbpsrsResults = lens _dbpsrsResults (\ s a -> s{_dbpsrsResults = a}) . _Default . _Coerce;  -- | The ID of the next page in the paginated results that indicates at least one more page follows.-drsNextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)-drsNextToken = lens _drsNextToken (\ s a -> s{_drsNextToken = a});+dbpsrsNextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)+dbpsrsNextToken = lens _dbpsrsNextToken (\ s a -> s{_dbpsrsNextToken = a});  -- | The response status code.-drsResponseStatus :: Lens' DescribeBatchPredictionsResponse Int-drsResponseStatus = lens _drsResponseStatus (\ s a -> s{_drsResponseStatus = a});+dbpsrsResponseStatus :: Lens' DescribeBatchPredictionsResponse Int+dbpsrsResponseStatus = lens _dbpsrsResponseStatus (\ s a -> s{_dbpsrsResponseStatus = a});  instance NFData DescribeBatchPredictionsResponse

@@ -230,7 +230,7 @@ instance ToQuery DescribeEvaluations where         toQuery = const mempty --- | Represents the query results from a < DescribeEvaluations> operation. The content is essentially a list of 'Evaluation'.+-- | Represents the query results from a 'DescribeEvaluations' operation. The content is essentially a list of 'Evaluation'. -- -- /See:/ 'describeEvaluationsResponse' smart constructor. data DescribeEvaluationsResponse = DescribeEvaluationsResponse'@@ -258,7 +258,7 @@     , _desrsResponseStatus = pResponseStatus_     } --- | A list of < Evaluation> that meet the search criteria.+-- | A list of 'Evaluation' that meet the search criteria. desrsResults :: Lens' DescribeEvaluationsResponse [Evaluation] desrsResults = lens _desrsResults (\ s a -> s{_desrsResults = a}) . _Default . _Coerce; 

@@ -155,7 +155,7 @@ dmlmSortOrder :: Lens' DescribeMLModels (Maybe SortOrder) dmlmSortOrder = lens _dmlmSortOrder (\ s a -> s{_dmlmSortOrder = a}); --- | 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.+-- | 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'. dmlmLimit :: Lens' DescribeMLModels (Maybe Natural) dmlmLimit = lens _dmlmLimit (\ s a -> s{_dmlmLimit = a}) . mapping _Nat; @@ -230,7 +230,7 @@ instance ToQuery DescribeMLModels where         toQuery = const mempty --- | Represents the output of a < DescribeMLModels> operation. The content is essentially a list of 'MLModel'.+-- | Represents the output of a 'DescribeMLModels' operation. The content is essentially a list of 'MLModel'. -- -- /See:/ 'describeMLModelsResponse' smart constructor. data DescribeMLModelsResponse = DescribeMLModelsResponse'@@ -258,7 +258,7 @@     , _dmlmsrsResponseStatus = pResponseStatus_     } --- | A list of < MLModel> that meet the search criteria.+-- | A list of 'MLModel' that meet the search criteria. dmlmsrsResults :: Lens' DescribeMLModelsResponse [MLModel] dmlmsrsResults = lens _dmlmsrsResults (\ s a -> s{_dmlmsrsResults = a}) . _Default . _Coerce; 

@@ -0,0 +1,164 @@+{-# LANGUAGE DeriveDataTypeable #-}+{-# LANGUAGE DeriveGeneric      #-}+{-# LANGUAGE OverloadedStrings  #-}+{-# LANGUAGE RecordWildCards    #-}+{-# LANGUAGE TypeFamilies       #-}++{-# OPTIONS_GHC -fno-warn-unused-imports #-}+{-# OPTIONS_GHC -fno-warn-unused-binds   #-}+{-# OPTIONS_GHC -fno-warn-unused-matches #-}++-- Derived from AWS service descriptions, licensed under Apache 2.0.++-- |+-- Module      : Network.AWS.MachineLearning.DescribeTags+-- Copyright   : (c) 2013-2016 Brendan Hay+-- License     : Mozilla Public License, v. 2.0.+-- Maintainer  : Brendan Hay <brendan.g.hay@gmail.com>+-- Stability   : auto-generated+-- Portability : non-portable (GHC extensions)+--+-- Describes one or more of the tags for your Amazon ML object.+module Network.AWS.MachineLearning.DescribeTags+    (+    -- * Creating a Request+      describeTags+    , DescribeTags+    -- * Request Lenses+    , dtResourceId+    , dtResourceType++    -- * Destructuring the Response+    , describeTagsResponse+    , DescribeTagsResponse+    -- * Response Lenses+    , dtrsResourceId+    , dtrsResourceType+    , dtrsTags+    , dtrsResponseStatus+    ) where++import           Network.AWS.Lens+import           Network.AWS.MachineLearning.Types+import           Network.AWS.MachineLearning.Types.Product+import           Network.AWS.Prelude+import           Network.AWS.Request+import           Network.AWS.Response++-- | /See:/ 'describeTags' smart constructor.+data DescribeTags = DescribeTags'+    { _dtResourceId   :: !Text+    , _dtResourceType :: !TaggableResourceType+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'DescribeTags' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'dtResourceId'+--+-- * 'dtResourceType'+describeTags+    :: Text -- ^ 'dtResourceId'+    -> TaggableResourceType -- ^ 'dtResourceType'+    -> DescribeTags+describeTags pResourceId_ pResourceType_ =+    DescribeTags'+    { _dtResourceId = pResourceId_+    , _dtResourceType = pResourceType_+    }++-- | The ID of the ML object. For example, 'exampleModelId'.+dtResourceId :: Lens' DescribeTags Text+dtResourceId = lens _dtResourceId (\ s a -> s{_dtResourceId = a});++-- | The type of the ML object.+dtResourceType :: Lens' DescribeTags TaggableResourceType+dtResourceType = lens _dtResourceType (\ s a -> s{_dtResourceType = a});++instance AWSRequest DescribeTags where+        type Rs DescribeTags = DescribeTagsResponse+        request = postJSON machineLearning+        response+          = receiveJSON+              (\ s h x ->+                 DescribeTagsResponse' <$>+                   (x .?> "ResourceId") <*> (x .?> "ResourceType") <*>+                     (x .?> "Tags" .!@ mempty)+                     <*> (pure (fromEnum s)))++instance Hashable DescribeTags++instance NFData DescribeTags++instance ToHeaders DescribeTags where+        toHeaders+          = const+              (mconcat+                 ["X-Amz-Target" =#+                    ("AmazonML_20141212.DescribeTags" :: ByteString),+                  "Content-Type" =#+                    ("application/x-amz-json-1.1" :: ByteString)])++instance ToJSON DescribeTags where+        toJSON DescribeTags'{..}+          = object+              (catMaybes+                 [Just ("ResourceId" .= _dtResourceId),+                  Just ("ResourceType" .= _dtResourceType)])++instance ToPath DescribeTags where+        toPath = const "/"++instance ToQuery DescribeTags where+        toQuery = const mempty++-- | Amazon ML returns the following elements.+--+-- /See:/ 'describeTagsResponse' smart constructor.+data DescribeTagsResponse = DescribeTagsResponse'+    { _dtrsResourceId     :: !(Maybe Text)+    , _dtrsResourceType   :: !(Maybe TaggableResourceType)+    , _dtrsTags           :: !(Maybe [Tag])+    , _dtrsResponseStatus :: !Int+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'DescribeTagsResponse' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'dtrsResourceId'+--+-- * 'dtrsResourceType'+--+-- * 'dtrsTags'+--+-- * 'dtrsResponseStatus'+describeTagsResponse+    :: Int -- ^ 'dtrsResponseStatus'+    -> DescribeTagsResponse+describeTagsResponse pResponseStatus_ =+    DescribeTagsResponse'+    { _dtrsResourceId = Nothing+    , _dtrsResourceType = Nothing+    , _dtrsTags = Nothing+    , _dtrsResponseStatus = pResponseStatus_+    }++-- | The ID of the tagged ML object.+dtrsResourceId :: Lens' DescribeTagsResponse (Maybe Text)+dtrsResourceId = lens _dtrsResourceId (\ s a -> s{_dtrsResourceId = a});++-- | The type of the tagged ML object.+dtrsResourceType :: Lens' DescribeTagsResponse (Maybe TaggableResourceType)+dtrsResourceType = lens _dtrsResourceType (\ s a -> s{_dtrsResourceType = a});++-- | A list of tags associated with the ML object.+dtrsTags :: Lens' DescribeTagsResponse [Tag]+dtrsTags = lens _dtrsTags (\ s a -> s{_dtrsTags = a}) . _Default . _Coerce;++-- | The response status code.+dtrsResponseStatus :: Lens' DescribeTagsResponse Int+dtrsResponseStatus = lens _dtrsResponseStatus (\ s a -> s{_dtrsResponseStatus = a});++instance NFData DescribeTagsResponse

@@ -34,10 +34,15 @@     , gbprsStatus     , gbprsLastUpdatedAt     , gbprsCreatedAt+    , gbprsComputeTime     , gbprsInputDataLocationS3     , gbprsMLModelId     , gbprsBatchPredictionDataSourceId+    , gbprsTotalRecordCount+    , gbprsStartedAt     , gbprsBatchPredictionId+    , gbprsFinishedAt+    , gbprsInvalidRecordCount     , gbprsCreatedByIAMUser     , gbprsName     , gbprsLogURI@@ -85,10 +90,15 @@                  GetBatchPredictionResponse' <$>                    (x .?> "Status") <*> (x .?> "LastUpdatedAt") <*>                      (x .?> "CreatedAt")+                     <*> (x .?> "ComputeTime")                      <*> (x .?> "InputDataLocationS3")                      <*> (x .?> "MLModelId")                      <*> (x .?> "BatchPredictionDataSourceId")+                     <*> (x .?> "TotalRecordCount")+                     <*> (x .?> "StartedAt")                      <*> (x .?> "BatchPredictionId")+                     <*> (x .?> "FinishedAt")+                     <*> (x .?> "InvalidRecordCount")                      <*> (x .?> "CreatedByIamUser")                      <*> (x .?> "Name")                      <*> (x .?> "LogUri")@@ -123,17 +133,22 @@ instance ToQuery GetBatchPrediction where         toQuery = const mempty --- | Represents the output of a < GetBatchPrediction> operation and describes a 'BatchPrediction'.+-- | Represents the output of a 'GetBatchPrediction' operation and describes a 'BatchPrediction'. -- -- /See:/ 'getBatchPredictionResponse' smart constructor. data GetBatchPredictionResponse = GetBatchPredictionResponse'     { _gbprsStatus                      :: !(Maybe EntityStatus)     , _gbprsLastUpdatedAt               :: !(Maybe POSIX)     , _gbprsCreatedAt                   :: !(Maybe POSIX)+    , _gbprsComputeTime                 :: !(Maybe Integer)     , _gbprsInputDataLocationS3         :: !(Maybe Text)     , _gbprsMLModelId                   :: !(Maybe Text)     , _gbprsBatchPredictionDataSourceId :: !(Maybe Text)+    , _gbprsTotalRecordCount            :: !(Maybe Integer)+    , _gbprsStartedAt                   :: !(Maybe POSIX)     , _gbprsBatchPredictionId           :: !(Maybe Text)+    , _gbprsFinishedAt                  :: !(Maybe POSIX)+    , _gbprsInvalidRecordCount          :: !(Maybe Integer)     , _gbprsCreatedByIAMUser            :: !(Maybe Text)     , _gbprsName                        :: !(Maybe Text)     , _gbprsLogURI                      :: !(Maybe Text)@@ -152,14 +167,24 @@ -- -- * 'gbprsCreatedAt' --+-- * 'gbprsComputeTime'+-- -- * 'gbprsInputDataLocationS3' -- -- * 'gbprsMLModelId' -- -- * 'gbprsBatchPredictionDataSourceId' --+-- * 'gbprsTotalRecordCount'+--+-- * 'gbprsStartedAt'+-- -- * 'gbprsBatchPredictionId' --+-- * 'gbprsFinishedAt'+--+-- * 'gbprsInvalidRecordCount'+-- -- * 'gbprsCreatedByIAMUser' -- -- * 'gbprsName'@@ -179,10 +204,15 @@     { _gbprsStatus = Nothing     , _gbprsLastUpdatedAt = Nothing     , _gbprsCreatedAt = Nothing+    , _gbprsComputeTime = Nothing     , _gbprsInputDataLocationS3 = Nothing     , _gbprsMLModelId = Nothing     , _gbprsBatchPredictionDataSourceId = Nothing+    , _gbprsTotalRecordCount = Nothing+    , _gbprsStartedAt = Nothing     , _gbprsBatchPredictionId = Nothing+    , _gbprsFinishedAt = Nothing+    , _gbprsInvalidRecordCount = Nothing     , _gbprsCreatedByIAMUser = Nothing     , _gbprsName = Nothing     , _gbprsLogURI = Nothing@@ -209,6 +239,10 @@ gbprsCreatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime) gbprsCreatedAt = lens _gbprsCreatedAt (\ s a -> s{_gbprsCreatedAt = a}) . mapping _Time; +-- | 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.+gbprsComputeTime :: Lens' GetBatchPredictionResponse (Maybe Integer)+gbprsComputeTime = lens _gbprsComputeTime (\ s a -> s{_gbprsComputeTime = a});+ -- | The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). gbprsInputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsInputDataLocationS3 = lens _gbprsInputDataLocationS3 (\ s a -> s{_gbprsInputDataLocationS3 = a});@@ -221,10 +255,26 @@ gbprsBatchPredictionDataSourceId :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsBatchPredictionDataSourceId = lens _gbprsBatchPredictionDataSourceId (\ s a -> s{_gbprsBatchPredictionDataSourceId = a}); +-- | The number of total records that Amazon Machine Learning saw while processing the 'BatchPrediction'.+gbprsTotalRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)+gbprsTotalRecordCount = lens _gbprsTotalRecordCount (\ s a -> s{_gbprsTotalRecordCount = a});++-- | The epoch time when Amazon Machine Learning marked the 'BatchPrediction' as 'INPROGRESS'. 'StartedAt' isn\'t available if the 'BatchPrediction' is in the 'PENDING' state.+gbprsStartedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)+gbprsStartedAt = lens _gbprsStartedAt (\ s a -> s{_gbprsStartedAt = a}) . mapping _Time;+ -- | An ID assigned to the 'BatchPrediction' at creation. This value should be identical to the value of the 'BatchPredictionID' in the request. gbprsBatchPredictionId :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsBatchPredictionId = lens _gbprsBatchPredictionId (\ s a -> s{_gbprsBatchPredictionId = a}); +-- | 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.+gbprsFinishedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)+gbprsFinishedAt = lens _gbprsFinishedAt (\ s a -> s{_gbprsFinishedAt = a}) . mapping _Time;++-- | The number of invalid records that Amazon Machine Learning saw while processing the 'BatchPrediction'.+gbprsInvalidRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)+gbprsInvalidRecordCount = lens _gbprsInvalidRecordCount (\ s a -> s{_gbprsInvalidRecordCount = a});+ -- | 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. gbprsCreatedByIAMUser :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsCreatedByIAMUser = lens _gbprsCreatedByIAMUser (\ s a -> s{_gbprsCreatedByIAMUser = a});@@ -233,7 +283,7 @@ gbprsName :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsName = lens _gbprsName (\ s a -> s{_gbprsName = a}); --- | A link to the file that contains logs of the < CreateBatchPrediction> operation.+-- | A link to the file that contains logs of the 'CreateBatchPrediction' operation. gbprsLogURI :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsLogURI = lens _gbprsLogURI (\ s a -> s{_gbprsLogURI = a}); 

@@ -38,10 +38,13 @@     , gdsrsNumberOfFiles     , gdsrsLastUpdatedAt     , gdsrsCreatedAt+    , gdsrsComputeTime     , gdsrsDataSourceId     , gdsrsRDSMetadata     , gdsrsDataSizeInBytes     , gdsrsDataSourceSchema+    , gdsrsStartedAt+    , gdsrsFinishedAt     , gdsrsCreatedByIAMUser     , gdsrsName     , gdsrsLogURI@@ -105,10 +108,13 @@                    (x .?> "Status") <*> (x .?> "NumberOfFiles") <*>                      (x .?> "LastUpdatedAt")                      <*> (x .?> "CreatedAt")+                     <*> (x .?> "ComputeTime")                      <*> (x .?> "DataSourceId")                      <*> (x .?> "RDSMetadata")                      <*> (x .?> "DataSizeInBytes")                      <*> (x .?> "DataSourceSchema")+                     <*> (x .?> "StartedAt")+                     <*> (x .?> "FinishedAt")                      <*> (x .?> "CreatedByIamUser")                      <*> (x .?> "Name")                      <*> (x .?> "LogUri")@@ -146,7 +152,7 @@ instance ToQuery GetDataSource where         toQuery = const mempty --- | Represents the output of a < GetDataSource> operation and describes a 'DataSource'.+-- | Represents the output of a 'GetDataSource' operation and describes a 'DataSource'. -- -- /See:/ 'getDataSourceResponse' smart constructor. data GetDataSourceResponse = GetDataSourceResponse'@@ -154,10 +160,13 @@     , _gdsrsNumberOfFiles     :: !(Maybe Integer)     , _gdsrsLastUpdatedAt     :: !(Maybe POSIX)     , _gdsrsCreatedAt         :: !(Maybe POSIX)+    , _gdsrsComputeTime       :: !(Maybe Integer)     , _gdsrsDataSourceId      :: !(Maybe Text)     , _gdsrsRDSMetadata       :: !(Maybe RDSMetadata)     , _gdsrsDataSizeInBytes   :: !(Maybe Integer)     , _gdsrsDataSourceSchema  :: !(Maybe Text)+    , _gdsrsStartedAt         :: !(Maybe POSIX)+    , _gdsrsFinishedAt        :: !(Maybe POSIX)     , _gdsrsCreatedByIAMUser  :: !(Maybe Text)     , _gdsrsName              :: !(Maybe Text)     , _gdsrsLogURI            :: !(Maybe Text)@@ -182,6 +191,8 @@ -- -- * 'gdsrsCreatedAt' --+-- * 'gdsrsComputeTime'+-- -- * 'gdsrsDataSourceId' -- -- * 'gdsrsRDSMetadata'@@ -190,6 +201,10 @@ -- -- * 'gdsrsDataSourceSchema' --+-- * 'gdsrsStartedAt'+--+-- * 'gdsrsFinishedAt'+-- -- * 'gdsrsCreatedByIAMUser' -- -- * 'gdsrsName'@@ -218,10 +233,13 @@     , _gdsrsNumberOfFiles = Nothing     , _gdsrsLastUpdatedAt = Nothing     , _gdsrsCreatedAt = Nothing+    , _gdsrsComputeTime = Nothing     , _gdsrsDataSourceId = Nothing     , _gdsrsRDSMetadata = Nothing     , _gdsrsDataSizeInBytes = Nothing     , _gdsrsDataSourceSchema = Nothing+    , _gdsrsStartedAt = Nothing+    , _gdsrsFinishedAt = Nothing     , _gdsrsCreatedByIAMUser = Nothing     , _gdsrsName = Nothing     , _gdsrsLogURI = Nothing@@ -236,7 +254,7 @@  -- | The current status of the 'DataSource'. This element can have one of the following values: ----- -   'PENDING' - Amazon Machine Language (Amazon ML) submitted a request to create a 'DataSource'.+-- -   '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.@@ -256,6 +274,10 @@ gdsrsCreatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime) gdsrsCreatedAt = lens _gdsrsCreatedAt (\ s a -> s{_gdsrsCreatedAt = a}) . mapping _Time; +-- | 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.+gdsrsComputeTime :: Lens' GetDataSourceResponse (Maybe Integer)+gdsrsComputeTime = lens _gdsrsComputeTime (\ s a -> s{_gdsrsComputeTime = a});+ -- | The ID assigned to the 'DataSource' at creation. This value should be identical to the value of the 'DataSourceId' in the request. gdsrsDataSourceId :: Lens' GetDataSourceResponse (Maybe Text) gdsrsDataSourceId = lens _gdsrsDataSourceId (\ s a -> s{_gdsrsDataSourceId = a});@@ -276,6 +298,14 @@ gdsrsDataSourceSchema :: Lens' GetDataSourceResponse (Maybe Text) gdsrsDataSourceSchema = lens _gdsrsDataSourceSchema (\ s a -> s{_gdsrsDataSourceSchema = a}); +-- | The epoch time when Amazon Machine Learning marked the 'DataSource' as 'INPROGRESS'. 'StartedAt' isn\'t available if the 'DataSource' is in the 'PENDING' state.+gdsrsStartedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)+gdsrsStartedAt = lens _gdsrsStartedAt (\ s a -> s{_gdsrsStartedAt = a}) . mapping _Time;++-- | 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.+gdsrsFinishedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)+gdsrsFinishedAt = lens _gdsrsFinishedAt (\ s a -> s{_gdsrsFinishedAt = a}) . mapping _Time;+ -- | 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. gdsrsCreatedByIAMUser :: Lens' GetDataSourceResponse (Maybe Text) gdsrsCreatedByIAMUser = lens _gdsrsCreatedByIAMUser (\ s a -> s{_gdsrsCreatedByIAMUser = a});@@ -284,7 +314,7 @@ gdsrsName :: Lens' GetDataSourceResponse (Maybe Text) gdsrsName = lens _gdsrsName (\ s a -> s{_gdsrsName = a}); --- | A link to the file containining logs of either create 'DataSource' operation.+-- | A link to the file containing logs of 'CreateDataSourceFrom*' operations. gdsrsLogURI :: Lens' GetDataSourceResponse (Maybe Text) gdsrsLogURI = lens _gdsrsLogURI (\ s a -> s{_gdsrsLogURI = a}); @@ -296,7 +326,7 @@ gdsrsComputeStatistics :: Lens' GetDataSourceResponse (Maybe Bool) gdsrsComputeStatistics = lens _gdsrsComputeStatistics (\ s a -> s{_gdsrsComputeStatistics = a}); --- | The description of the most recent details about creating the 'DataSource'.+-- | The user-supplied description of the most recent details about creating the 'DataSource'. gdsrsMessage :: Lens' GetDataSourceResponse (Maybe Text) gdsrsMessage = lens _gdsrsMessage (\ s a -> s{_gdsrsMessage = a}); @@ -304,7 +334,7 @@ gdsrsRedshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata) gdsrsRedshiftMetadata = lens _gdsrsRedshiftMetadata (\ s a -> s{_gdsrsRedshiftMetadata = a}); --- | A JSON string that captures the splitting rearrangement requirement of the 'DataSource'.+-- | A JSON string that represents the splitting and rearrangement requirement used when this 'DataSource' was created. gdsrsDataRearrangement :: Lens' GetDataSourceResponse (Maybe Text) gdsrsDataRearrangement = lens _gdsrsDataRearrangement (\ s a -> s{_gdsrsDataRearrangement = a}); 

@@ -35,8 +35,11 @@     , gersPerformanceMetrics     , gersLastUpdatedAt     , gersCreatedAt+    , gersComputeTime     , gersInputDataLocationS3     , gersMLModelId+    , gersStartedAt+    , gersFinishedAt     , gersCreatedByIAMUser     , gersName     , gersLogURI@@ -85,8 +88,11 @@                    (x .?> "Status") <*> (x .?> "PerformanceMetrics") <*>                      (x .?> "LastUpdatedAt")                      <*> (x .?> "CreatedAt")+                     <*> (x .?> "ComputeTime")                      <*> (x .?> "InputDataLocationS3")                      <*> (x .?> "MLModelId")+                     <*> (x .?> "StartedAt")+                     <*> (x .?> "FinishedAt")                      <*> (x .?> "CreatedByIamUser")                      <*> (x .?> "Name")                      <*> (x .?> "LogUri")@@ -120,7 +126,7 @@ instance ToQuery GetEvaluation where         toQuery = const mempty --- | Represents the output of a < GetEvaluation> operation and describes an 'Evaluation'.+-- | Represents the output of a 'GetEvaluation' operation and describes an 'Evaluation'. -- -- /See:/ 'getEvaluationResponse' smart constructor. data GetEvaluationResponse = GetEvaluationResponse'@@ -128,8 +134,11 @@     , _gersPerformanceMetrics     :: !(Maybe PerformanceMetrics)     , _gersLastUpdatedAt          :: !(Maybe POSIX)     , _gersCreatedAt              :: !(Maybe POSIX)+    , _gersComputeTime            :: !(Maybe Integer)     , _gersInputDataLocationS3    :: !(Maybe Text)     , _gersMLModelId              :: !(Maybe Text)+    , _gersStartedAt              :: !(Maybe POSIX)+    , _gersFinishedAt             :: !(Maybe POSIX)     , _gersCreatedByIAMUser       :: !(Maybe Text)     , _gersName                   :: !(Maybe Text)     , _gersLogURI                 :: !(Maybe Text)@@ -151,10 +160,16 @@ -- -- * 'gersCreatedAt' --+-- * 'gersComputeTime'+-- -- * 'gersInputDataLocationS3' -- -- * 'gersMLModelId' --+-- * 'gersStartedAt'+--+-- * 'gersFinishedAt'+-- -- * 'gersCreatedByIAMUser' -- -- * 'gersName'@@ -177,8 +192,11 @@     , _gersPerformanceMetrics = Nothing     , _gersLastUpdatedAt = Nothing     , _gersCreatedAt = Nothing+    , _gersComputeTime = Nothing     , _gersInputDataLocationS3 = Nothing     , _gersMLModelId = Nothing+    , _gersStartedAt = Nothing+    , _gersFinishedAt = Nothing     , _gersCreatedByIAMUser = Nothing     , _gersName = Nothing     , _gersLogURI = Nothing@@ -210,7 +228,7 @@ gersPerformanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics) gersPerformanceMetrics = lens _gersPerformanceMetrics (\ s a -> s{_gersPerformanceMetrics = a}); --- | The time of the most recent edit to the 'BatchPrediction'. The time is expressed in epoch time.+-- | The time of the most recent edit to the 'Evaluation'. The time is expressed in epoch time. gersLastUpdatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime) gersLastUpdatedAt = lens _gersLastUpdatedAt (\ s a -> s{_gersLastUpdatedAt = a}) . mapping _Time; @@ -218,6 +236,10 @@ gersCreatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime) gersCreatedAt = lens _gersCreatedAt (\ s a -> s{_gersCreatedAt = a}) . mapping _Time; +-- | 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.+gersComputeTime :: Lens' GetEvaluationResponse (Maybe Integer)+gersComputeTime = lens _gersComputeTime (\ s a -> s{_gersComputeTime = a});+ -- | The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). gersInputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text) gersInputDataLocationS3 = lens _gersInputDataLocationS3 (\ s a -> s{_gersInputDataLocationS3 = a});@@ -226,6 +248,14 @@ gersMLModelId :: Lens' GetEvaluationResponse (Maybe Text) gersMLModelId = lens _gersMLModelId (\ s a -> s{_gersMLModelId = a}); +-- | The epoch time when Amazon Machine Learning marked the 'Evaluation' as 'INPROGRESS'. 'StartedAt' isn\'t available if the 'Evaluation' is in the 'PENDING' state.+gersStartedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)+gersStartedAt = lens _gersStartedAt (\ s a -> s{_gersStartedAt = a}) . mapping _Time;++-- | 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.+gersFinishedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)+gersFinishedAt = lens _gersFinishedAt (\ s a -> s{_gersFinishedAt = a}) . mapping _Time;+ -- | 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. gersCreatedByIAMUser :: Lens' GetEvaluationResponse (Maybe Text) gersCreatedByIAMUser = lens _gersCreatedByIAMUser (\ s a -> s{_gersCreatedByIAMUser = a});@@ -234,7 +264,7 @@ gersName :: Lens' GetEvaluationResponse (Maybe Text) gersName = lens _gersName (\ s a -> s{_gersName = a}); --- | A link to the file that contains logs of the < CreateEvaluation> operation.+-- | A link to the file that contains logs of the 'CreateEvaluation' operation. gersLogURI :: Lens' GetEvaluationResponse (Maybe Text) gersLogURI = lens _gersLogURI (\ s a -> s{_gersLogURI = a}); 

@@ -18,7 +18,7 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns an 'MLModel' that includes detailed metadata, and data source information as well as the current status of the 'MLModel'.+-- 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 Network.AWS.MachineLearning.GetMLModel@@ -39,12 +39,15 @@     , gmlmrsTrainingParameters     , gmlmrsScoreThresholdLastUpdatedAt     , gmlmrsCreatedAt+    , gmlmrsComputeTime     , gmlmrsRecipe     , gmlmrsInputDataLocationS3     , gmlmrsMLModelId     , gmlmrsSizeInBytes     , gmlmrsSchema+    , gmlmrsStartedAt     , gmlmrsScoreThreshold+    , gmlmrsFinishedAt     , gmlmrsCreatedByIAMUser     , gmlmrsName     , gmlmrsLogURI@@ -107,12 +110,15 @@                      (x .?> "TrainingParameters" .!@ mempty)                      <*> (x .?> "ScoreThresholdLastUpdatedAt")                      <*> (x .?> "CreatedAt")+                     <*> (x .?> "ComputeTime")                      <*> (x .?> "Recipe")                      <*> (x .?> "InputDataLocationS3")                      <*> (x .?> "MLModelId")                      <*> (x .?> "SizeInBytes")                      <*> (x .?> "Schema")+                     <*> (x .?> "StartedAt")                      <*> (x .?> "ScoreThreshold")+                     <*> (x .?> "FinishedAt")                      <*> (x .?> "CreatedByIamUser")                      <*> (x .?> "Name")                      <*> (x .?> "LogUri")@@ -148,7 +154,7 @@ instance ToQuery GetMLModel where         toQuery = const mempty --- | Represents the output of a < GetMLModel> operation, and provides detailed information about a 'MLModel'.+-- | Represents the output of a 'GetMLModel' operation, and provides detailed information about a 'MLModel'. -- -- /See:/ 'getMLModelResponse' smart constructor. data GetMLModelResponse = GetMLModelResponse'@@ -157,12 +163,15 @@     , _gmlmrsTrainingParameters          :: !(Maybe (Map Text Text))     , _gmlmrsScoreThresholdLastUpdatedAt :: !(Maybe POSIX)     , _gmlmrsCreatedAt                   :: !(Maybe POSIX)+    , _gmlmrsComputeTime                 :: !(Maybe Integer)     , _gmlmrsRecipe                      :: !(Maybe Text)     , _gmlmrsInputDataLocationS3         :: !(Maybe Text)     , _gmlmrsMLModelId                   :: !(Maybe Text)     , _gmlmrsSizeInBytes                 :: !(Maybe Integer)     , _gmlmrsSchema                      :: !(Maybe Text)+    , _gmlmrsStartedAt                   :: !(Maybe POSIX)     , _gmlmrsScoreThreshold              :: !(Maybe Double)+    , _gmlmrsFinishedAt                  :: !(Maybe POSIX)     , _gmlmrsCreatedByIAMUser            :: !(Maybe Text)     , _gmlmrsName                        :: !(Maybe Text)     , _gmlmrsLogURI                      :: !(Maybe Text)@@ -187,6 +196,8 @@ -- -- * 'gmlmrsCreatedAt' --+-- * 'gmlmrsComputeTime'+-- -- * 'gmlmrsRecipe' -- -- * 'gmlmrsInputDataLocationS3'@@ -197,8 +208,12 @@ -- -- * 'gmlmrsSchema' --+-- * 'gmlmrsStartedAt'+-- -- * 'gmlmrsScoreThreshold' --+-- * 'gmlmrsFinishedAt'+-- -- * 'gmlmrsCreatedByIAMUser' -- -- * 'gmlmrsName'@@ -224,12 +239,15 @@     , _gmlmrsTrainingParameters = Nothing     , _gmlmrsScoreThresholdLastUpdatedAt = Nothing     , _gmlmrsCreatedAt = Nothing+    , _gmlmrsComputeTime = Nothing     , _gmlmrsRecipe = Nothing     , _gmlmrsInputDataLocationS3 = Nothing     , _gmlmrsMLModelId = Nothing     , _gmlmrsSizeInBytes = Nothing     , _gmlmrsSchema = Nothing+    , _gmlmrsStartedAt = Nothing     , _gmlmrsScoreThreshold = Nothing+    , _gmlmrsFinishedAt = Nothing     , _gmlmrsCreatedByIAMUser = Nothing     , _gmlmrsName = Nothing     , _gmlmrsLogURI = Nothing@@ -244,9 +262,9 @@ -- -- -   '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. It is not usable.+-- -   '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 is not usable.+-- -   'DELETED' - The 'MLModel' is marked as deleted. It isn\'t usable. gmlmrsStatus :: Lens' GetMLModelResponse (Maybe EntityStatus) gmlmrsStatus = lens _gmlmrsStatus (\ s a -> s{_gmlmrsStatus = a}); @@ -254,24 +272,26 @@ gmlmrsLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) gmlmrsLastUpdatedAt = lens _gmlmrsLastUpdatedAt (\ s a -> s{_gmlmrsLastUpdatedAt = a}) . mapping _Time; --- | A list of the training parameters in the 'MLModel'. The list is implemented as a map of key\/value pairs.+-- | 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.l1RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.+-- -   '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 a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when 'L2' is specified. Use this parameter sparingly.+--     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'. ----- -   'sgd.l2RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.+-- -   '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'. -----     The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when 'L1' is specified. Use this parameter sparingly.+-- -   '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.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.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'. ----- -   'sgd.maxMLModelSizeInBytes' - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.+--     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. -----     The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.+-- -   '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.+-- gmlmrsTrainingParameters :: Lens' GetMLModelResponse (HashMap Text Text) gmlmrsTrainingParameters = lens _gmlmrsTrainingParameters (\ s a -> s{_gmlmrsTrainingParameters = a}) . _Default . _Map; @@ -283,7 +303,11 @@ gmlmrsCreatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) gmlmrsCreatedAt = lens _gmlmrsCreatedAt (\ s a -> s{_gmlmrsCreatedAt = a}) . mapping _Time; --- | The recipe to use when training the 'MLModel'. The 'Recipe' provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.+-- | 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.+gmlmrsComputeTime :: Lens' GetMLModelResponse (Maybe Integer)+gmlmrsComputeTime = lens _gmlmrsComputeTime (\ s a -> s{_gmlmrsComputeTime = a});++-- | 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 --@@ -295,7 +319,7 @@ gmlmrsInputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text) gmlmrsInputDataLocationS3 = lens _gmlmrsInputDataLocationS3 (\ s a -> s{_gmlmrsInputDataLocationS3 = a}); --- | The MLModel ID which is same as the 'MLModelId' in the request.+-- | The MLModel ID, which is same as the 'MLModelId' in the request. gmlmrsMLModelId :: Lens' GetMLModelResponse (Maybe Text) gmlmrsMLModelId = lens _gmlmrsMLModelId (\ s a -> s{_gmlmrsMLModelId = a}); @@ -311,12 +335,20 @@ gmlmrsSchema :: Lens' GetMLModelResponse (Maybe Text) gmlmrsSchema = lens _gmlmrsSchema (\ s a -> s{_gmlmrsSchema = a}); --- | The scoring threshold is used in binary classification 'MLModel's, and marks the boundary between a positive prediction and a negative prediction.+-- | The epoch time when Amazon Machine Learning marked the 'MLModel' as 'INPROGRESS'. 'StartedAt' isn\'t available if the 'MLModel' is in the 'PENDING' state.+gmlmrsStartedAt :: Lens' GetMLModelResponse (Maybe UTCTime)+gmlmrsStartedAt = lens _gmlmrsStartedAt (\ s a -> s{_gmlmrsStartedAt = a}) . mapping _Time;++-- | 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'. gmlmrsScoreThreshold :: Lens' GetMLModelResponse (Maybe Double) gmlmrsScoreThreshold = lens _gmlmrsScoreThreshold (\ s a -> s{_gmlmrsScoreThreshold = a}); +-- | 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.+gmlmrsFinishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)+gmlmrsFinishedAt = lens _gmlmrsFinishedAt (\ s a -> s{_gmlmrsFinishedAt = a}) . mapping _Time;+ -- | 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. gmlmrsCreatedByIAMUser :: Lens' GetMLModelResponse (Maybe Text) gmlmrsCreatedByIAMUser = lens _gmlmrsCreatedByIAMUser (\ s a -> s{_gmlmrsCreatedByIAMUser = a});@@ -337,15 +369,15 @@ gmlmrsTrainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text) gmlmrsTrainingDataSourceId = lens _gmlmrsTrainingDataSourceId (\ s a -> s{_gmlmrsTrainingDataSourceId = a}); --- | Description of the most recent details about accessing the 'MLModel'.+-- | A description of the most recent details about accessing the 'MLModel'. gmlmrsMessage :: Lens' GetMLModelResponse (Maybe Text) gmlmrsMessage = lens _gmlmrsMessage (\ s a -> s{_gmlmrsMessage = a});  -- | Identifies the 'MLModel' category. The following are the available types: ----- -   REGRESSION -- Produces a numeric result. For example, \"What listing price should a house have?\"+-- -   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 more than two possible results. For example, \"Is this a HIGH, LOW or MEDIUM risk trade?\"+-- -   MULTICLASS -- Produces one of several possible results. For example, \"Is this a HIGH, LOW or MEDIUM risk trade?\" gmlmrsMLModelType :: Lens' GetMLModelResponse (Maybe MLModelType) gmlmrsMLModelType = lens _gmlmrsMLModelType (\ s a -> s{_gmlmrsMLModelType = a}); 

@@ -16,9 +16,11 @@       machineLearning      -- * Errors+    , _InvalidTagException     , _InternalServerException     , _InvalidInputException     , _IdempotentParameterMismatchException+    , _TagLimitExceededException     , _PredictorNotMountedException     , _ResourceNotFoundException     , _LimitExceededException@@ -53,16 +55,24 @@     -- * SortOrder     , SortOrder (..) +    -- * TaggableResourceType+    , TaggableResourceType (..)+     -- * BatchPrediction     , BatchPrediction     , batchPrediction     , bpStatus     , bpLastUpdatedAt     , bpCreatedAt+    , bpComputeTime     , bpInputDataLocationS3     , bpMLModelId     , bpBatchPredictionDataSourceId+    , bpTotalRecordCount+    , bpStartedAt     , bpBatchPredictionId+    , bpFinishedAt+    , bpInvalidRecordCount     , bpCreatedByIAMUser     , bpName     , bpMessage@@ -75,9 +85,12 @@     , dsNumberOfFiles     , dsLastUpdatedAt     , dsCreatedAt+    , dsComputeTime     , dsDataSourceId     , dsRDSMetadata     , dsDataSizeInBytes+    , dsStartedAt+    , dsFinishedAt     , dsCreatedByIAMUser     , dsName     , dsDataLocationS3@@ -94,8 +107,11 @@     , ePerformanceMetrics     , eLastUpdatedAt     , eCreatedAt+    , eComputeTime     , eInputDataLocationS3     , eMLModelId+    , eStartedAt+    , eFinishedAt     , eCreatedByIAMUser     , eName     , eEvaluationId@@ -110,10 +126,13 @@     , mlmTrainingParameters     , mlmScoreThresholdLastUpdatedAt     , mlmCreatedAt+    , mlmComputeTime     , mlmInputDataLocationS3     , mlmMLModelId     , mlmSizeInBytes+    , mlmStartedAt     , mlmScoreThreshold+    , mlmFinishedAt     , mlmAlgorithm     , mlmCreatedByIAMUser     , mlmName@@ -217,6 +236,12 @@     , sdsDataSchemaLocationS3     , sdsDataRearrangement     , sdsDataLocationS3++    -- * Tag+    , Tag+    , tag+    , tagValue+    , tagKey     ) where  import           Network.AWS.Lens@@ -259,6 +284,10 @@       | has (hasStatus 509) e = Just "limit_exceeded"       | otherwise = Nothing +-- | Prism for InvalidTagException' errors.+_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError+_InvalidTagException = _ServiceError . hasCode "InvalidTagException"+ -- | An error on the server occurred when trying to process a request. _InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError _InternalServerException =@@ -274,6 +303,11 @@ _IdempotentParameterMismatchException =     _ServiceError .     hasStatus 400 . hasCode "IdempotentParameterMismatchException"++-- | Prism for TagLimitExceededException' errors.+_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError+_TagLimitExceededException =+    _ServiceError . hasCode "TagLimitExceededException"  -- | The exception is thrown when a predict request is made to an unmounted 'MLModel'. _PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError

@@ -21,19 +21,24 @@ import           Network.AWS.MachineLearning.Types.Sum import           Network.AWS.Prelude --- | Represents the output of < GetBatchPrediction> operation.+-- | 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/.+-- The content consists of the detailed metadata, the status, and the data file information of a 'Batch Prediction'. -- -- /See:/ 'batchPrediction' smart constructor. data BatchPrediction = BatchPrediction'     { _bpStatus                      :: !(Maybe EntityStatus)     , _bpLastUpdatedAt               :: !(Maybe POSIX)     , _bpCreatedAt                   :: !(Maybe POSIX)+    , _bpComputeTime                 :: !(Maybe Integer)     , _bpInputDataLocationS3         :: !(Maybe Text)     , _bpMLModelId                   :: !(Maybe Text)     , _bpBatchPredictionDataSourceId :: !(Maybe Text)+    , _bpTotalRecordCount            :: !(Maybe Integer)+    , _bpStartedAt                   :: !(Maybe POSIX)     , _bpBatchPredictionId           :: !(Maybe Text)+    , _bpFinishedAt                  :: !(Maybe POSIX)+    , _bpInvalidRecordCount          :: !(Maybe Integer)     , _bpCreatedByIAMUser            :: !(Maybe Text)     , _bpName                        :: !(Maybe Text)     , _bpMessage                     :: !(Maybe Text)@@ -50,14 +55,24 @@ -- -- * 'bpCreatedAt' --+-- * 'bpComputeTime'+-- -- * 'bpInputDataLocationS3' -- -- * 'bpMLModelId' -- -- * 'bpBatchPredictionDataSourceId' --+-- * 'bpTotalRecordCount'+--+-- * 'bpStartedAt'+-- -- * 'bpBatchPredictionId' --+-- * 'bpFinishedAt'+--+-- * 'bpInvalidRecordCount'+-- -- * 'bpCreatedByIAMUser' -- -- * 'bpName'@@ -72,10 +87,15 @@     { _bpStatus = Nothing     , _bpLastUpdatedAt = Nothing     , _bpCreatedAt = Nothing+    , _bpComputeTime = Nothing     , _bpInputDataLocationS3 = Nothing     , _bpMLModelId = Nothing     , _bpBatchPredictionDataSourceId = Nothing+    , _bpTotalRecordCount = Nothing+    , _bpStartedAt = Nothing     , _bpBatchPredictionId = Nothing+    , _bpFinishedAt = Nothing+    , _bpInvalidRecordCount = Nothing     , _bpCreatedByIAMUser = Nothing     , _bpName = Nothing     , _bpMessage = Nothing@@ -86,7 +106,7 @@ -- -- -   '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 peform a batch prediction did not run to completion. It is not usable.+-- -   '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. bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)@@ -100,6 +120,10 @@ bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) bpCreatedAt = lens _bpCreatedAt (\ s a -> s{_bpCreatedAt = a}) . mapping _Time; +-- | Undocumented member.+bpComputeTime :: Lens' BatchPrediction (Maybe Integer)+bpComputeTime = lens _bpComputeTime (\ s a -> s{_bpComputeTime = a});+ -- | The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) bpInputDataLocationS3 = lens _bpInputDataLocationS3 (\ s a -> s{_bpInputDataLocationS3 = a});@@ -112,10 +136,26 @@ bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) bpBatchPredictionDataSourceId = lens _bpBatchPredictionDataSourceId (\ s a -> s{_bpBatchPredictionDataSourceId = a}); +-- | Undocumented member.+bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)+bpTotalRecordCount = lens _bpTotalRecordCount (\ s a -> s{_bpTotalRecordCount = a});++-- | Undocumented member.+bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)+bpStartedAt = lens _bpStartedAt (\ s a -> s{_bpStartedAt = a}) . mapping _Time;+ -- | The ID assigned to the 'BatchPrediction' at creation. This value should be identical to the value of the 'BatchPredictionID' in the request. bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text) bpBatchPredictionId = lens _bpBatchPredictionId (\ s a -> s{_bpBatchPredictionId = a}); +-- | Undocumented member.+bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)+bpFinishedAt = lens _bpFinishedAt (\ s a -> s{_bpFinishedAt = a}) . mapping _Time;++-- | Undocumented member.+bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)+bpInvalidRecordCount = lens _bpInvalidRecordCount (\ s a -> s{_bpInvalidRecordCount = a});+ -- | 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. bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text) bpCreatedByIAMUser = lens _bpCreatedByIAMUser (\ s a -> s{_bpCreatedByIAMUser = a});@@ -128,7 +168,7 @@ bpMessage :: Lens' BatchPrediction (Maybe Text) bpMessage = lens _bpMessage (\ s a -> s{_bpMessage = a}); --- | 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: \':\', \'\/\/\', \'\/.\/\', \'\/..\/\'.+-- | 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: \':\', \'\/\/\', \'\/.\/\', \'\/..\/\'. bpOutputURI :: Lens' BatchPrediction (Maybe Text) bpOutputURI = lens _bpOutputURI (\ s a -> s{_bpOutputURI = a}); @@ -139,10 +179,15 @@                  BatchPrediction' <$>                    (x .:? "Status") <*> (x .:? "LastUpdatedAt") <*>                      (x .:? "CreatedAt")+                     <*> (x .:? "ComputeTime")                      <*> (x .:? "InputDataLocationS3")                      <*> (x .:? "MLModelId")                      <*> (x .:? "BatchPredictionDataSourceId")+                     <*> (x .:? "TotalRecordCount")+                     <*> (x .:? "StartedAt")                      <*> (x .:? "BatchPredictionId")+                     <*> (x .:? "FinishedAt")+                     <*> (x .:? "InvalidRecordCount")                      <*> (x .:? "CreatedByIamUser")                      <*> (x .:? "Name")                      <*> (x .:? "Message")@@ -152,7 +197,7 @@  instance NFData BatchPrediction --- | Represents the output of the < GetDataSource> operation.+-- | 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'. --@@ -162,9 +207,12 @@     , _dsNumberOfFiles     :: !(Maybe Integer)     , _dsLastUpdatedAt     :: !(Maybe POSIX)     , _dsCreatedAt         :: !(Maybe POSIX)+    , _dsComputeTime       :: !(Maybe Integer)     , _dsDataSourceId      :: !(Maybe Text)     , _dsRDSMetadata       :: !(Maybe RDSMetadata)     , _dsDataSizeInBytes   :: !(Maybe Integer)+    , _dsStartedAt         :: !(Maybe POSIX)+    , _dsFinishedAt        :: !(Maybe POSIX)     , _dsCreatedByIAMUser  :: !(Maybe Text)     , _dsName              :: !(Maybe Text)     , _dsDataLocationS3    :: !(Maybe Text)@@ -187,12 +235,18 @@ -- -- * 'dsCreatedAt' --+-- * 'dsComputeTime'+-- -- * 'dsDataSourceId' -- -- * 'dsRDSMetadata' -- -- * 'dsDataSizeInBytes' --+-- * 'dsStartedAt'+--+-- * 'dsFinishedAt'+-- -- * 'dsCreatedByIAMUser' -- -- * 'dsName'@@ -216,9 +270,12 @@     , _dsNumberOfFiles = Nothing     , _dsLastUpdatedAt = Nothing     , _dsCreatedAt = Nothing+    , _dsComputeTime = Nothing     , _dsDataSourceId = Nothing     , _dsRDSMetadata = Nothing     , _dsDataSizeInBytes = Nothing+    , _dsStartedAt = Nothing+    , _dsFinishedAt = Nothing     , _dsCreatedByIAMUser = Nothing     , _dsName = Nothing     , _dsDataLocationS3 = Nothing@@ -251,6 +308,10 @@ dsCreatedAt :: Lens' DataSource (Maybe UTCTime) dsCreatedAt = lens _dsCreatedAt (\ s a -> s{_dsCreatedAt = a}) . mapping _Time; +-- | Undocumented member.+dsComputeTime :: Lens' DataSource (Maybe Integer)+dsComputeTime = lens _dsComputeTime (\ s a -> s{_dsComputeTime = a});+ -- | The ID that is assigned to the 'DataSource' during creation. dsDataSourceId :: Lens' DataSource (Maybe Text) dsDataSourceId = lens _dsDataSourceId (\ s a -> s{_dsDataSourceId = a});@@ -263,6 +324,14 @@ dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) dsDataSizeInBytes = lens _dsDataSizeInBytes (\ s a -> s{_dsDataSizeInBytes = a}); +-- | Undocumented member.+dsStartedAt :: Lens' DataSource (Maybe UTCTime)+dsStartedAt = lens _dsStartedAt (\ s a -> s{_dsStartedAt = a}) . mapping _Time;++-- | Undocumented member.+dsFinishedAt :: Lens' DataSource (Maybe UTCTime)+dsFinishedAt = lens _dsFinishedAt (\ s a -> s{_dsFinishedAt = a}) . mapping _Time;+ -- | 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. dsCreatedByIAMUser :: Lens' DataSource (Maybe Text) dsCreatedByIAMUser = lens _dsCreatedByIAMUser (\ s a -> s{_dsCreatedByIAMUser = a});@@ -287,7 +356,7 @@ dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata) dsRedshiftMetadata = lens _dsRedshiftMetadata (\ s a -> s{_dsRedshiftMetadata = a}); --- | A JSON string that represents the splitting requirement of a 'Datasource'.+-- | A JSON string that represents the splitting and rearrangement requirement used when this 'DataSource' was created. dsDataRearrangement :: Lens' DataSource (Maybe Text) dsDataRearrangement = lens _dsDataRearrangement (\ s a -> s{_dsDataRearrangement = a}); @@ -303,9 +372,12 @@                    (x .:? "Status") <*> (x .:? "NumberOfFiles") <*>                      (x .:? "LastUpdatedAt")                      <*> (x .:? "CreatedAt")+                     <*> (x .:? "ComputeTime")                      <*> (x .:? "DataSourceId")                      <*> (x .:? "RDSMetadata")                      <*> (x .:? "DataSizeInBytes")+                     <*> (x .:? "StartedAt")+                     <*> (x .:? "FinishedAt")                      <*> (x .:? "CreatedByIamUser")                      <*> (x .:? "Name")                      <*> (x .:? "DataLocationS3")@@ -319,7 +391,7 @@  instance NFData DataSource --- | Represents the output of < GetEvaluation> operation.+-- | Represents the output of 'GetEvaluation' operation. -- -- The content consists of the detailed metadata and data file information and the current status of the 'Evaluation'. --@@ -329,8 +401,11 @@     , _ePerformanceMetrics     :: !(Maybe PerformanceMetrics)     , _eLastUpdatedAt          :: !(Maybe POSIX)     , _eCreatedAt              :: !(Maybe POSIX)+    , _eComputeTime            :: !(Maybe Integer)     , _eInputDataLocationS3    :: !(Maybe Text)     , _eMLModelId              :: !(Maybe Text)+    , _eStartedAt              :: !(Maybe POSIX)+    , _eFinishedAt             :: !(Maybe POSIX)     , _eCreatedByIAMUser       :: !(Maybe Text)     , _eName                   :: !(Maybe Text)     , _eEvaluationId           :: !(Maybe Text)@@ -350,10 +425,16 @@ -- -- * 'eCreatedAt' --+-- * 'eComputeTime'+-- -- * 'eInputDataLocationS3' -- -- * 'eMLModelId' --+-- * 'eStartedAt'+--+-- * 'eFinishedAt'+-- -- * 'eCreatedByIAMUser' -- -- * 'eName'@@ -371,8 +452,11 @@     , _ePerformanceMetrics = Nothing     , _eLastUpdatedAt = Nothing     , _eCreatedAt = Nothing+    , _eComputeTime = Nothing     , _eInputDataLocationS3 = Nothing     , _eMLModelId = Nothing+    , _eStartedAt = Nothing+    , _eFinishedAt = Nothing     , _eCreatedByIAMUser = Nothing     , _eName = Nothing     , _eEvaluationId = Nothing@@ -390,7 +474,7 @@ eStatus :: Lens' Evaluation (Maybe EntityStatus) eStatus = lens _eStatus (\ s a -> s{_eStatus = a}); --- | 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:+-- | 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. --@@ -410,6 +494,10 @@ eCreatedAt :: Lens' Evaluation (Maybe UTCTime) eCreatedAt = lens _eCreatedAt (\ s a -> s{_eCreatedAt = a}) . mapping _Time; +-- | Undocumented member.+eComputeTime :: Lens' Evaluation (Maybe Integer)+eComputeTime = lens _eComputeTime (\ s a -> s{_eComputeTime = a});+ -- | The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) eInputDataLocationS3 = lens _eInputDataLocationS3 (\ s a -> s{_eInputDataLocationS3 = a});@@ -418,6 +506,14 @@ eMLModelId :: Lens' Evaluation (Maybe Text) eMLModelId = lens _eMLModelId (\ s a -> s{_eMLModelId = a}); +-- | Undocumented member.+eStartedAt :: Lens' Evaluation (Maybe UTCTime)+eStartedAt = lens _eStartedAt (\ s a -> s{_eStartedAt = a}) . mapping _Time;++-- | Undocumented member.+eFinishedAt :: Lens' Evaluation (Maybe UTCTime)+eFinishedAt = lens _eFinishedAt (\ s a -> s{_eFinishedAt = a}) . mapping _Time;+ -- | 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. eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) eCreatedByIAMUser = lens _eCreatedByIAMUser (\ s a -> s{_eCreatedByIAMUser = a});@@ -446,8 +542,11 @@                    (x .:? "Status") <*> (x .:? "PerformanceMetrics") <*>                      (x .:? "LastUpdatedAt")                      <*> (x .:? "CreatedAt")+                     <*> (x .:? "ComputeTime")                      <*> (x .:? "InputDataLocationS3")                      <*> (x .:? "MLModelId")+                     <*> (x .:? "StartedAt")+                     <*> (x .:? "FinishedAt")                      <*> (x .:? "CreatedByIamUser")                      <*> (x .:? "Name")                      <*> (x .:? "EvaluationId")@@ -458,7 +557,7 @@  instance NFData Evaluation --- | Represents the output of a < GetMLModel> operation.+-- | Represents the output of a 'GetMLModel' operation. -- -- The content consists of the detailed metadata and the current status of the 'MLModel'. --@@ -469,10 +568,13 @@     , _mlmTrainingParameters          :: !(Maybe (Map Text Text))     , _mlmScoreThresholdLastUpdatedAt :: !(Maybe POSIX)     , _mlmCreatedAt                   :: !(Maybe POSIX)+    , _mlmComputeTime                 :: !(Maybe Integer)     , _mlmInputDataLocationS3         :: !(Maybe Text)     , _mlmMLModelId                   :: !(Maybe Text)     , _mlmSizeInBytes                 :: !(Maybe Integer)+    , _mlmStartedAt                   :: !(Maybe POSIX)     , _mlmScoreThreshold              :: !(Maybe Double)+    , _mlmFinishedAt                  :: !(Maybe POSIX)     , _mlmAlgorithm                   :: !(Maybe Algorithm)     , _mlmCreatedByIAMUser            :: !(Maybe Text)     , _mlmName                        :: !(Maybe Text)@@ -496,14 +598,20 @@ -- -- * 'mlmCreatedAt' --+-- * 'mlmComputeTime'+-- -- * 'mlmInputDataLocationS3' -- -- * 'mlmMLModelId' -- -- * 'mlmSizeInBytes' --+-- * 'mlmStartedAt'+-- -- * 'mlmScoreThreshold' --+-- * 'mlmFinishedAt'+-- -- * 'mlmAlgorithm' -- -- * 'mlmCreatedByIAMUser'@@ -526,10 +634,13 @@     , _mlmTrainingParameters = Nothing     , _mlmScoreThresholdLastUpdatedAt = Nothing     , _mlmCreatedAt = Nothing+    , _mlmComputeTime = Nothing     , _mlmInputDataLocationS3 = Nothing     , _mlmMLModelId = Nothing     , _mlmSizeInBytes = Nothing+    , _mlmStartedAt = Nothing     , _mlmScoreThreshold = Nothing+    , _mlmFinishedAt = Nothing     , _mlmAlgorithm = Nothing     , _mlmCreatedByIAMUser = Nothing     , _mlmName = Nothing@@ -541,11 +652,11 @@  -- | 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' did not run to completion. It is not usable.--- -   COMPLETED - The creation process completed successfully.--- -   DELETED - The 'MLModel' is marked as deleted. It is not usable.+-- -   '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. mlmStatus :: Lens' MLModel (Maybe EntityStatus) mlmStatus = lens _mlmStatus (\ s a -> s{_mlmStatus = a}); @@ -553,24 +664,26 @@ mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) mlmLastUpdatedAt = lens _mlmLastUpdatedAt (\ s a -> s{_mlmLastUpdatedAt = a}) . mapping _Time; --- | A list of the training parameters in the 'MLModel'. The list is implemented as a map of key\/value pairs.+-- | 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.l1RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.+-- -   '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 a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when 'L2' is specified. Use this parameter sparingly.+--     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'. ----- -   'sgd.l2RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.+-- -   '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'. -----     The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when 'L1' is specified. Use this parameter sparingly.+-- -   '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.maxPasses' - 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.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'. ----- -   'sgd.maxMLModelSizeInBytes' - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.+--     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. -----     The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.+-- -   '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.+-- mlmTrainingParameters :: Lens' MLModel (HashMap Text Text) mlmTrainingParameters = lens _mlmTrainingParameters (\ s a -> s{_mlmTrainingParameters = a}) . _Default . _Map; @@ -582,6 +695,10 @@ mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) mlmCreatedAt = lens _mlmCreatedAt (\ s a -> s{_mlmCreatedAt = a}) . mapping _Time; +-- | Undocumented member.+mlmComputeTime :: Lens' MLModel (Maybe Integer)+mlmComputeTime = lens _mlmComputeTime (\ s a -> s{_mlmComputeTime = a});+ -- | The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) mlmInputDataLocationS3 = lens _mlmInputDataLocationS3 (\ s a -> s{_mlmInputDataLocationS3 = a});@@ -595,12 +712,20 @@ mlmSizeInBytes = lens _mlmSizeInBytes (\ s a -> s{_mlmSizeInBytes = a});  -- | Undocumented member.+mlmStartedAt :: Lens' MLModel (Maybe UTCTime)+mlmStartedAt = lens _mlmStartedAt (\ s a -> s{_mlmStartedAt = a}) . mapping _Time;++-- | Undocumented member. mlmScoreThreshold :: Lens' MLModel (Maybe Double) mlmScoreThreshold = lens _mlmScoreThreshold (\ s a -> s{_mlmScoreThreshold = a}); +-- | Undocumented member.+mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)+mlmFinishedAt = lens _mlmFinishedAt (\ s a -> s{_mlmFinishedAt = a}) . mapping _Time;+ -- | 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.+-- -   'SGD' -- Stochastic gradient descent. The goal of 'SGD' is to minimize the gradient of the loss function. mlmAlgorithm :: Lens' MLModel (Maybe Algorithm) mlmAlgorithm = lens _mlmAlgorithm (\ s a -> s{_mlmAlgorithm = a}); @@ -616,7 +741,7 @@ mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo) mlmEndpointInfo = lens _mlmEndpointInfo (\ s a -> s{_mlmEndpointInfo = a}); --- | The ID of the training 'DataSource'. The < CreateMLModel> operation uses the 'TrainingDataSourceId'.+-- | The ID of the training 'DataSource'. The 'CreateMLModel' operation uses the 'TrainingDataSourceId'. mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text) mlmTrainingDataSourceId = lens _mlmTrainingDataSourceId (\ s a -> s{_mlmTrainingDataSourceId = a}); @@ -626,9 +751,11 @@  -- | Identifies the 'MLModel' category. The following are the available types: ----- -   REGRESSION - Produces a numeric result. For example, \"What listing price should a house have?\".--- -   BINARY - Produces one of two possible results. For example, \"Is this a child-friendly web site?\".--- -   MULTICLASS - Produces more than two possible results. For example, \"Is this a HIGH, LOW or MEDIUM risk trade?\".+-- -   '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?\". mlmMLModelType :: Lens' MLModel (Maybe MLModelType) mlmMLModelType = lens _mlmMLModelType (\ s a -> s{_mlmMLModelType = a}); @@ -641,10 +768,13 @@                      (x .:? "TrainingParameters" .!= mempty)                      <*> (x .:? "ScoreThresholdLastUpdatedAt")                      <*> (x .:? "CreatedAt")+                     <*> (x .:? "ComputeTime")                      <*> (x .:? "InputDataLocationS3")                      <*> (x .:? "MLModelId")                      <*> (x .:? "SizeInBytes")+                     <*> (x .:? "StartedAt")                      <*> (x .:? "ScoreThreshold")+                     <*> (x .:? "FinishedAt")                      <*> (x .:? "Algorithm")                      <*> (x .:? "CreatedByIamUser")                      <*> (x .:? "Name")@@ -701,13 +831,13 @@  -- | The output from a 'Predict' operation: ----- -   'Details' - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD+-- -   '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.+-- -   '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.+-- -   'PredictedValue' - Present for a 'REGRESSION' 'MLModel' request. -- -- -- /See:/ 'prediction' smart constructor.@@ -739,11 +869,11 @@     , _pDetails = Nothing     } --- | The prediction value for REGRESSION 'MLModel'.+-- | The prediction value for 'REGRESSION' 'MLModel'. pPredictedValue :: Lens' Prediction (Maybe Double) pPredictedValue = lens _pPredictedValue (\ s a -> s{_pPredictedValue = a}); --- | The prediction label for either a BINARY or MULTICLASS 'MLModel'.+-- | The prediction label for either a 'BINARY' or 'MULTICLASS' 'MLModel'. pPredictedLabel :: Lens' Prediction (Maybe Text) pPredictedLabel = lens _pPredictedLabel (\ s a -> s{_pPredictedLabel = a}); @@ -864,13 +994,52 @@ rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) rdsdsDataSchema = lens _rdsdsDataSchema (\ s a -> s{_rdsdsDataSchema = a}); --- | DataRearrangement - A JSON string that represents the splitting requirement of a 'DataSource'.+-- | 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'. ----- Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'+-- There are multiple parameters that control what data is used to create a datasource:+--+-- -   __'percentBegin'__+--+--     Use 'percentBegin' to indicate the beginning of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'percentEnd'__+--+--     Use 'percentEnd' to indicate the end of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'complement'__+--+--     The 'complement' parameter instructs Amazon ML to use the data that is not included in the range of 'percentBegin' to 'percentEnd' to create a datasource. The 'complement' parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for 'percentBegin' and 'percentEnd', along with the 'complement' parameter.+--+--     For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}'+--+-- -   __'strategy'__+--+--     To change how Amazon ML splits the data for a datasource, use the 'strategy' parameter.+--+--     The default value for the 'strategy' parameter is 'sequential', meaning that Amazon ML takes all of the data records between the 'percentBegin' and 'percentEnd' parameters for the datasource, in the order that the records appear in the input data.+--+--     The following two 'DataRearrangement' lines are examples of sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}'+--+--     To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the 'strategy' parameter to 'random' and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between 'percentBegin' and 'percentEnd'. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.+--+--     The following two 'DataRearrangement' lines are examples of non-sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\", \"complement\":\"true\"}}'+-- rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text) rdsdsDataRearrangement = lens _rdsdsDataRearrangement (\ s a -> s{_rdsdsDataRearrangement = a}); --- | Describes the 'DatabaseName' and 'InstanceIdentifier' of an an Amazon RDS database.+-- | Describes the 'DatabaseName' and 'InstanceIdentifier' of an Amazon RDS database. rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase rdsdsDatabaseInformation = lens _rdsdsDatabaseInformation (\ s a -> s{_rdsdsDatabaseInformation = a}); @@ -1145,9 +1314,9 @@  -- | 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.+-- -   '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. reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) reiEndpointStatus = lens _reiEndpointStatus (\ s a -> s{_reiEndpointStatus = a}); @@ -1245,7 +1414,48 @@ rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) rDataSchema = lens _rDataSchema (\ s a -> s{_rDataSchema = a}); --- | Describes the splitting specifications for a 'DataSource'.+-- | A JSON string that represents the splitting and rearrangement processing to be applied to a 'DataSource'. If the 'DataRearrangement' parameter is not provided, all of the input data is used to create the 'Datasource'.+--+-- There are multiple parameters that control what data is used to create a datasource:+--+-- -   __'percentBegin'__+--+--     Use 'percentBegin' to indicate the beginning of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'percentEnd'__+--+--     Use 'percentEnd' to indicate the end of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'complement'__+--+--     The 'complement' parameter instructs Amazon ML to use the data that is not included in the range of 'percentBegin' to 'percentEnd' to create a datasource. The 'complement' parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for 'percentBegin' and 'percentEnd', along with the 'complement' parameter.+--+--     For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}'+--+-- -   __'strategy'__+--+--     To change how Amazon ML splits the data for a datasource, use the 'strategy' parameter.+--+--     The default value for the 'strategy' parameter is 'sequential', meaning that Amazon ML takes all of the data records between the 'percentBegin' and 'percentEnd' parameters for the datasource, in the order that the records appear in the input data.+--+--     The following two 'DataRearrangement' lines are examples of sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}'+--+--     To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the 'strategy' parameter to 'random' and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between 'percentBegin' and 'percentEnd'. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.+--+--     The following two 'DataRearrangement' lines are examples of non-sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\", \"complement\":\"true\"}}'+-- rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text) rDataRearrangement = lens _rDataRearrangement (\ s a -> s{_rDataRearrangement = a}); @@ -1464,6 +1674,8 @@  -- | 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\",@@ -1486,11 +1698,52 @@ sdsDataSchema :: Lens' S3DataSpec (Maybe Text) sdsDataSchema = lens _sdsDataSchema (\ s a -> s{_sdsDataSchema = a}); --- | Describes the schema Location in Amazon S3.+-- | Describes the schema location in Amazon S3. You must provide either the 'DataSchema' or the 'DataSchemaLocationS3'. sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text) sdsDataSchemaLocationS3 = lens _sdsDataSchemaLocationS3 (\ s a -> s{_sdsDataSchemaLocationS3 = a}); --- | Describes the splitting requirement of a 'Datasource'.+-- | A JSON string that represents the splitting and rearrangement processing to be applied to a 'DataSource'. If the 'DataRearrangement' parameter is not provided, all of the input data is used to create the 'Datasource'.+--+-- There are multiple parameters that control what data is used to create a datasource:+--+-- -   __'percentBegin'__+--+--     Use 'percentBegin' to indicate the beginning of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'percentEnd'__+--+--     Use 'percentEnd' to indicate the end of the range of the data used to create the Datasource. If you do not include 'percentBegin' and 'percentEnd', Amazon ML includes all of the data when creating the datasource.+--+-- -   __'complement'__+--+--     The 'complement' parameter instructs Amazon ML to use the data that is not included in the range of 'percentBegin' to 'percentEnd' to create a datasource. The 'complement' parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for 'percentBegin' and 'percentEnd', along with the 'complement' parameter.+--+--     For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":0, \"percentEnd\":25, \"complement\":\"true\"}}'+--+-- -   __'strategy'__+--+--     To change how Amazon ML splits the data for a datasource, use the 'strategy' parameter.+--+--     The default value for the 'strategy' parameter is 'sequential', meaning that Amazon ML takes all of the data records between the 'percentBegin' and 'percentEnd' parameters for the datasource, in the order that the records appear in the input data.+--+--     The following two 'DataRearrangement' lines are examples of sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"sequential\", \"complement\":\"true\"}}'+--+--     To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the 'strategy' parameter to 'random' and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between 'percentBegin' and 'percentEnd'. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.+--+--     The following two 'DataRearrangement' lines are examples of non-sequentially ordered training and evaluation datasources:+--+--     Datasource for evaluation: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\"}}'+--+--     Datasource for training: '{\"splitting\":{\"percentBegin\":70, \"percentEnd\":100, \"strategy\":\"random\", \"randomSeed\"=\"s3:\/\/my_s3_path\/bucket\/file.csv\", \"complement\":\"true\"}}'+-- sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text) sdsDataRearrangement = lens _sdsDataRearrangement (\ s a -> s{_sdsDataRearrangement = a}); @@ -1511,3 +1764,49 @@                     _sdsDataSchemaLocationS3,                   ("DataRearrangement" .=) <$> _sdsDataRearrangement,                   Just ("DataLocationS3" .= _sdsDataLocationS3)])++-- | A custom key-value pair associated with an ML object, such as an ML model.+--+-- /See:/ 'tag' smart constructor.+data Tag = Tag'+    { _tagValue :: !(Maybe Text)+    , _tagKey   :: !(Maybe Text)+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'Tag' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'tagValue'+--+-- * 'tagKey'+tag+    :: Tag+tag =+    Tag'+    { _tagValue = Nothing+    , _tagKey = Nothing+    }++-- | An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., \/, =, +, -, %, and \'.+tagValue :: Lens' Tag (Maybe Text)+tagValue = lens _tagValue (\ s a -> s{_tagValue = a});++-- | A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., \/, =, +, -, %, and \'.+tagKey :: Lens' Tag (Maybe Text)+tagKey = lens _tagKey (\ s a -> s{_tagKey = a});++instance FromJSON Tag where+        parseJSON+          = withObject "Tag"+              (\ x -> Tag' <$> (x .:? "Value") <*> (x .:? "Key"))++instance Hashable Tag++instance NFData Tag++instance ToJSON Tag where+        toJSON Tag'{..}+          = object+              (catMaybes+                 [("Value" .=) <$> _tagValue, ("Key" .=) <$> _tagKey])

@@ -19,10 +19,10 @@  import           Network.AWS.Prelude --- | The function used to train a 'MLModel'. Training choices supported by Amazon ML include the following:+-- | 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.+-- -   'SGD' - Stochastic Gradient Descent.+-- -   'RandomForest' - Random forest of decision trees. data Algorithm =     SGD     deriving (Eq,Ord,Read,Show,Enum,Bounded,Data,Typeable,Generic)@@ -77,7 +77,7 @@         "name" -> pure BatchName         "status" -> pure BatchStatus         e -> fromTextError $ "Failure parsing BatchPredictionFilterVariable from value: '" <> e-           <> "'. Accepted values: CreatedAt, DataSourceId, DataURI, IAMUser, LastUpdatedAt, MLModelId, Name, Status"+           <> "'. Accepted values: createdat, datasourceid, datauri, iamuser, lastupdatedat, mlmodelid, name, status"  instance ToText BatchPredictionFilterVariable where     toText = \case@@ -128,7 +128,7 @@         "name" -> pure DataName         "status" -> pure DataStatus         e -> fromTextError $ "Failure parsing DataSourceFilterVariable from value: '" <> e-           <> "'. Accepted values: CreatedAt, DataLocationS3, IAMUser, LastUpdatedAt, Name, Status"+           <> "'. Accepted values: createdat, datalocations3, iamuser, lastupdatedat, name, status"  instance ToText DataSourceFilterVariable where     toText = \case@@ -148,7 +148,7 @@ instance ToJSON DataSourceFilterVariable where     toJSON = toJSONText --- | Contains the key values of 'DetailsMap': PredictiveModelType - Indicates the type of the 'MLModel'. Algorithm - Indicates the algorithm was used for the 'MLModel'.+-- | Contains the key values of 'DetailsMap': 'PredictiveModelType' - Indicates the type of the 'MLModel'. 'Algorithm' - Indicates the algorithm that was used for the 'MLModel'. data DetailsAttributes     = Algorithm     | PredictiveModelType@@ -159,7 +159,7 @@         "algorithm" -> pure Algorithm         "predictivemodeltype" -> pure PredictiveModelType         e -> fromTextError $ "Failure parsing DetailsAttributes from value: '" <> e-           <> "'. Accepted values: Algorithm, PredictiveModelType"+           <> "'. Accepted values: algorithm, predictivemodeltype"  instance ToText DetailsAttributes where     toText = \case@@ -175,13 +175,13 @@ instance FromJSON DetailsAttributes where     parseJSON = parseJSONText "DetailsAttributes" --- | Entity status with the following possible values:+-- | Object status with the following possible values: ----- -   PENDING--- -   INPROGRESS--- -   FAILED--- -   COMPLETED--- -   DELETED+-- -   'PENDING'+-- -   'INPROGRESS'+-- -   'FAILED'+-- -   'COMPLETED'+-- -   'DELETED' data EntityStatus     = ESCompleted     | ESDeleted@@ -198,7 +198,7 @@         "inprogress" -> pure ESInprogress         "pending" -> pure ESPending         e -> fromTextError $ "Failure parsing EntityStatus from value: '" <> e-           <> "'. Accepted values: COMPLETED, DELETED, FAILED, INPROGRESS, PENDING"+           <> "'. Accepted values: completed, deleted, failed, inprogress, pending"  instance ToText EntityStatus where     toText = \case@@ -248,7 +248,7 @@         "name" -> pure EvalName         "status" -> pure EvalStatus         e -> fromTextError $ "Failure parsing EvaluationFilterVariable from value: '" <> e-           <> "'. Accepted values: CreatedAt, DataSourceId, DataURI, IAMUser, LastUpdatedAt, MLModelId, Name, Status"+           <> "'. Accepted values: createdat, datasourceid, datauri, iamuser, lastupdatedat, mlmodelid, name, status"  instance ToText EvaluationFilterVariable where     toText = \case@@ -296,7 +296,7 @@         "trainingdatasourceid" -> pure MLMFVTrainingDataSourceId         "trainingdatauri" -> pure MLMFVTrainingDataURI         e -> fromTextError $ "Failure parsing MLModelFilterVariable from value: '" <> e-           <> "'. Accepted values: Algorithm, CreatedAt, IAMUser, LastUpdatedAt, MLModelType, Name, RealtimeEndpointStatus, Status, TrainingDataSourceId, TrainingDataURI"+           <> "'. Accepted values: algorithm, createdat, iamuser, lastupdatedat, mlmodeltype, name, realtimeendpointstatus, status, trainingdatasourceid, trainingdatauri"  instance ToText MLModelFilterVariable where     toText = \case@@ -332,7 +332,7 @@         "multiclass" -> pure Multiclass         "regression" -> pure Regression         e -> fromTextError $ "Failure parsing MLModelType from value: '" <> e-           <> "'. Accepted values: BINARY, MULTICLASS, REGRESSION"+           <> "'. Accepted values: binary, multiclass, regression"  instance ToText MLModelType where     toText = \case@@ -366,7 +366,7 @@         "ready" -> pure Ready         "updating" -> pure Updating         e -> fromTextError $ "Failure parsing RealtimeEndpointStatus from value: '" <> e-           <> "'. Accepted values: FAILED, NONE, READY, UPDATING"+           <> "'. Accepted values: failed, none, ready, updating"  instance ToText RealtimeEndpointStatus where     toText = \case@@ -413,3 +413,38 @@  instance ToJSON SortOrder where     toJSON = toJSONText++data TaggableResourceType+    = BatchPrediction+    | DataSource+    | Evaluation+    | MLModel+    deriving (Eq,Ord,Read,Show,Enum,Bounded,Data,Typeable,Generic)++instance FromText TaggableResourceType where+    parser = takeLowerText >>= \case+        "batchprediction" -> pure BatchPrediction+        "datasource" -> pure DataSource+        "evaluation" -> pure Evaluation+        "mlmodel" -> pure MLModel+        e -> fromTextError $ "Failure parsing TaggableResourceType from value: '" <> e+           <> "'. Accepted values: batchprediction, datasource, evaluation, mlmodel"++instance ToText TaggableResourceType where+    toText = \case+        BatchPrediction -> "BatchPrediction"+        DataSource -> "DataSource"+        Evaluation -> "Evaluation"+        MLModel -> "MLModel"++instance Hashable     TaggableResourceType+instance NFData       TaggableResourceType+instance ToByteString TaggableResourceType+instance ToQuery      TaggableResourceType+instance ToHeader     TaggableResourceType++instance ToJSON TaggableResourceType where+    toJSON = toJSONText++instance FromJSON TaggableResourceType where+    parseJSON = parseJSONText "TaggableResourceType"

@@ -20,7 +20,7 @@ -- -- Updates the 'BatchPredictionName' of a 'BatchPrediction'. ----- You can use the < GetBatchPrediction> operation to view the contents of the updated data element.+-- You can use the 'GetBatchPrediction' operation to view the contents of the updated data element. module Network.AWS.MachineLearning.UpdateBatchPrediction     (     -- * Creating a Request@@ -114,9 +114,9 @@ instance ToQuery UpdateBatchPrediction where         toQuery = const mempty --- | Represents the output of an < UpdateBatchPrediction> operation.+-- | Represents the output of an 'UpdateBatchPrediction' operation. ----- You can see the updated content by using the < GetBatchPrediction> operation.+-- You can see the updated content by using the 'GetBatchPrediction' operation. -- -- /See:/ 'updateBatchPredictionResponse' smart constructor. data UpdateBatchPredictionResponse = UpdateBatchPredictionResponse'

@@ -20,7 +20,7 @@ -- -- Updates the 'DataSourceName' of a 'DataSource'. ----- You can use the < GetDataSource> operation to view the contents of the updated data element.+-- You can use the 'GetDataSource' operation to view the contents of the updated data element. module Network.AWS.MachineLearning.UpdateDataSource     (     -- * Creating a Request@@ -111,9 +111,9 @@ instance ToQuery UpdateDataSource where         toQuery = const mempty --- | Represents the output of an < UpdateDataSource> operation.+-- | Represents the output of an 'UpdateDataSource' operation. ----- You can see the updated content by using the < GetBatchPrediction> operation.+-- You can see the updated content by using the 'GetBatchPrediction' operation. -- -- /See:/ 'updateDataSourceResponse' smart constructor. data UpdateDataSourceResponse = UpdateDataSourceResponse'

@@ -20,7 +20,7 @@ -- -- Updates the 'EvaluationName' of an 'Evaluation'. ----- You can use the < GetEvaluation> operation to view the contents of the updated data element.+-- You can use the 'GetEvaluation' operation to view the contents of the updated data element. module Network.AWS.MachineLearning.UpdateEvaluation     (     -- * Creating a Request@@ -111,9 +111,9 @@ instance ToQuery UpdateEvaluation where         toQuery = const mempty --- | Represents the output of an < UpdateEvaluation> operation.+-- | Represents the output of an 'UpdateEvaluation' operation. ----- You can see the updated content by using the < GetEvaluation> operation.+-- You can see the updated content by using the 'GetEvaluation' operation. -- -- /See:/ 'updateEvaluationResponse' smart constructor. data UpdateEvaluationResponse = UpdateEvaluationResponse'

@@ -20,7 +20,7 @@ -- -- Updates the 'MLModelName' and the 'ScoreThreshold' of an 'MLModel'. ----- You can use the < GetMLModel> operation to view the contents of the updated data element.+-- You can use the 'GetMLModel' operation to view the contents of the updated data element. module Network.AWS.MachineLearning.UpdateMLModel     (     -- * Creating a Request@@ -122,9 +122,9 @@ instance ToQuery UpdateMLModel where         toQuery = const mempty --- | Represents the output of an < UpdateMLModel> operation.+-- | Represents the output of an 'UpdateMLModel' operation. ----- You can see the updated content by using the < GetMLModel> operation.+-- You can see the updated content by using the 'GetMLModel' operation. -- -- /See:/ 'updateMLModelResponse' smart constructor. data UpdateMLModelResponse = UpdateMLModelResponse'

@@ -55,12 +55,12 @@     , _waitAcceptors = [ matchAll                              "COMPLETED"                              AcceptSuccess-                             (folding (concatOf drsResults) .+                             (folding (concatOf dbpsrsResults) .                               bpStatus . _Just . to toTextCI)                        , matchAny                              "FAILED"                              AcceptFailure-                             (folding (concatOf drsResults) .+                             (folding (concatOf dbpsrsResults) .                               bpStatus . _Just . to toTextCI)]     } 

@@ -34,6 +34,9 @@ --         , requestDeleteDataSource $ --             deleteDataSource --+--         , requestDescribeTags $+--             describeTags+-- --         , requestCreateDataSourceFromRedshift $ --             createDataSourceFromRedshift --@@ -43,6 +46,9 @@ --         , requestCreateMLModel $ --             createMLModel --+--         , requestDeleteTags $+--             deleteTags+-- --         , requestDeleteBatchPrediction $ --             deleteBatchPrediction --@@ -97,6 +103,9 @@ --         , requestCreateRealtimeEndpoint $ --             createRealtimeEndpoint --+--         , requestAddTags $+--             addTags+-- --         , requestDescribeMLModels $ --             describeMLModels --@@ -112,6 +121,9 @@ --         , responseDeleteDataSource $ --             deleteDataSourceResponse --+--         , responseDescribeTags $+--             describeTagsResponse+-- --         , responseCreateDataSourceFromRedshift $ --             createDataSourceFromRedshiftResponse --@@ -121,6 +133,9 @@ --         , responseCreateMLModel $ --             createMLModelResponse --+--         , responseDeleteTags $+--             deleteTagsResponse+-- --         , responseDeleteBatchPrediction $ --             deleteBatchPredictionResponse --@@ -175,6 +190,9 @@ --         , responseCreateRealtimeEndpoint $ --             createRealtimeEndpointResponse --+--         , responseAddTags $+--             addTagsResponse+-- --         , responseDescribeMLModels $ --             describeMLModelsResponse --@@ -196,6 +214,11 @@     "DeleteDataSource"     "fixture/DeleteDataSource.yaml" +requestDescribeTags :: DescribeTags -> TestTree+requestDescribeTags = req+    "DescribeTags"+    "fixture/DescribeTags.yaml"+ requestCreateDataSourceFromRedshift :: CreateDataSourceFromRedshift -> TestTree requestCreateDataSourceFromRedshift = req     "CreateDataSourceFromRedshift"@@ -211,6 +234,11 @@     "CreateMLModel"     "fixture/CreateMLModel.yaml" +requestDeleteTags :: DeleteTags -> TestTree+requestDeleteTags = req+    "DeleteTags"+    "fixture/DeleteTags.yaml"+ requestDeleteBatchPrediction :: DeleteBatchPrediction -> TestTree requestDeleteBatchPrediction = req     "DeleteBatchPrediction"@@ -301,6 +329,11 @@     "CreateRealtimeEndpoint"     "fixture/CreateRealtimeEndpoint.yaml" +requestAddTags :: AddTags -> TestTree+requestAddTags = req+    "AddTags"+    "fixture/AddTags.yaml"+ requestDescribeMLModels :: DescribeMLModels -> TestTree requestDescribeMLModels = req     "DescribeMLModels"@@ -327,6 +360,13 @@     machineLearning     (Proxy :: Proxy DeleteDataSource) +responseDescribeTags :: DescribeTagsResponse -> TestTree+responseDescribeTags = res+    "DescribeTagsResponse"+    "fixture/DescribeTagsResponse.proto"+    machineLearning+    (Proxy :: Proxy DescribeTags)+ responseCreateDataSourceFromRedshift :: CreateDataSourceFromRedshiftResponse -> TestTree responseCreateDataSourceFromRedshift = res     "CreateDataSourceFromRedshiftResponse"@@ -348,6 +388,13 @@     machineLearning     (Proxy :: Proxy CreateMLModel) +responseDeleteTags :: DeleteTagsResponse -> TestTree+responseDeleteTags = res+    "DeleteTagsResponse"+    "fixture/DeleteTagsResponse.proto"+    machineLearning+    (Proxy :: Proxy DeleteTags)+ responseDeleteBatchPrediction :: DeleteBatchPredictionResponse -> TestTree responseDeleteBatchPrediction = res     "DeleteBatchPredictionResponse"@@ -473,6 +520,13 @@     "fixture/CreateRealtimeEndpointResponse.proto"     machineLearning     (Proxy :: Proxy CreateRealtimeEndpoint)++responseAddTags :: AddTagsResponse -> TestTree+responseAddTags = res+    "AddTagsResponse"+    "fixture/AddTagsResponse.proto"+    machineLearning+    (Proxy :: Proxy AddTags)  responseDescribeMLModels :: DescribeMLModelsResponse -> TestTree responseDescribeMLModels = res