amazonka-ml 1.4.4 → 1.4.5

raw patch · 34 files changed

+960/−1336 lines, 34 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 +9/−6
• amazonka-ml.cabal +10/−11
• gen/Network/AWS/MachineLearning/AddTags.hs +13/−9
• gen/Network/AWS/MachineLearning/CreateBatchPrediction.hs +23/−21
• gen/Network/AWS/MachineLearning/CreateDataSourceFromRDS.hs +23/−45
• gen/Network/AWS/MachineLearning/CreateDataSourceFromRedshift.hs +26/−46
• gen/Network/AWS/MachineLearning/CreateDataSourceFromS3.hs +23/−30
• gen/Network/AWS/MachineLearning/CreateEvaluation.hs +21/−19
• gen/Network/AWS/MachineLearning/CreateMLModel.hs +29/−50
• gen/Network/AWS/MachineLearning/CreateRealtimeEndpoint.hs +14/−12
• gen/Network/AWS/MachineLearning/DeleteBatchPrediction.hs +15/−11
• gen/Network/AWS/MachineLearning/DeleteDataSource.hs +14/−10
• gen/Network/AWS/MachineLearning/DeleteEvaluation.hs +16/−12
• gen/Network/AWS/MachineLearning/DeleteMLModel.hs +15/−11
• gen/Network/AWS/MachineLearning/DeleteRealtimeEndpoint.hs +15/−11
• gen/Network/AWS/MachineLearning/DeleteTags.hs +14/−10
• gen/Network/AWS/MachineLearning/DescribeBatchPredictions.hs +32/−50
• gen/Network/AWS/MachineLearning/DescribeDataSources.hs +32/−48
• gen/Network/AWS/MachineLearning/DescribeEvaluations.hs +32/−50
• gen/Network/AWS/MachineLearning/DescribeMLModels.hs +32/−52
• gen/Network/AWS/MachineLearning/DescribeTags.hs +12/−8
• gen/Network/AWS/MachineLearning/GetBatchPrediction.hs +41/−43
• gen/Network/AWS/MachineLearning/GetDataSource.hs +48/−58
• gen/Network/AWS/MachineLearning/GetEvaluation.hs +38/−48
• gen/Network/AWS/MachineLearning/GetMLModel.hs +53/−92
• gen/Network/AWS/MachineLearning/Predict.hs +8/−10
• gen/Network/AWS/MachineLearning/Types.hs +15/−3
• gen/Network/AWS/MachineLearning/Types/Product.hs +246/−465
• gen/Network/AWS/MachineLearning/Types/Sum.hs +21/−35
• gen/Network/AWS/MachineLearning/UpdateBatchPrediction.hs +16/−12
• gen/Network/AWS/MachineLearning/UpdateDataSource.hs +16/−12
• gen/Network/AWS/MachineLearning/UpdateEvaluation.hs +16/−12
• gen/Network/AWS/MachineLearning/UpdateMLModel.hs +18/−16
• gen/Network/AWS/MachineLearning/Waiters.hs +4/−8

@@ -8,24 +8,27 @@  ## Version -`1.4.4`+`1.4.5`   ## Description -Definition of the public APIs exposed by Amazon Machine Learning- Documentation is available via [Hackage](http://hackage.haskell.org/package/amazonka-ml) and the [AWS API Reference](https://aws.amazon.com/documentation/).  The types from this library are intended to be used with [amazonka](http://hackage.haskell.org/package/amazonka),-which provides mechanisms for specifying AuthN/AuthZ information and sending requests.+which provides mechanisms for specifying AuthN/AuthZ information, sending requests,+and receiving responses. -Use of lenses is required for constructing and manipulating types.-This is due to the amount of nesting of AWS types and transparency regarding+Lenses are used for constructing and manipulating types,+due to the depth of nesting of AWS types and transparency regarding de/serialisation into more palatable Haskell values. The provided lenses should be compatible with any of the major lens libraries [lens](http://hackage.haskell.org/package/lens) or [lens-family-core](http://hackage.haskell.org/package/lens-family-core).++See [Network.AWS.MachineLearning](http://hackage.haskell.org/package/amazonka-ml/docs/Network-AWS-MachineLearning.html)+or [the AWS documentation](https://aws.amazon.com/documentation/) to get started.+  ## Contribute 

@@ -1,5 +1,5 @@ name:                  amazonka-ml-version:               1.4.4+version:               1.4.5 synopsis:              Amazon Machine Learning SDK. homepage:              https://github.com/brendanhay/amazonka bug-reports:           https://github.com/brendanhay/amazonka/issues@@ -13,20 +13,19 @@ cabal-version:         >= 1.10 extra-source-files:    README.md fixture/*.yaml fixture/*.proto src/.gitkeep description:-    Definition of the public APIs exposed by Amazon Machine Learning-    .     The types from this library are intended to be used with     <http://hackage.haskell.org/package/amazonka amazonka>, which provides-    mechanisms for specifying AuthN/AuthZ information and sending requests.+    mechanisms for specifying AuthN/AuthZ information, sending requests,+    and receiving responses.     .-    Use of lenses is required for constructing and manipulating types.-    This is due to the amount of nesting of AWS types and transparency regarding+    Lenses are used for constructing and manipulating types,+    due to the depth of nesting of AWS types and transparency regarding     de/serialisation into more palatable Haskell values.     The provided lenses should be compatible with any of the major lens libraries     such as <http://hackage.haskell.org/package/lens lens> or     <http://hackage.haskell.org/package/lens-family-core lens-family-core>.     .-    See "Network.AWS.MachineLearning" or <https://aws.amazon.com/documentation/ the AWS Documentation>+    See "Network.AWS.MachineLearning" or <https://aws.amazon.com/documentation/ the AWS documentation>     to get started.  source-repository head@@ -77,7 +76,7 @@         , Network.AWS.MachineLearning.Types.Sum      build-depends:-          amazonka-core == 1.4.4.*+          amazonka-core == 1.4.5.*         , base          >= 4.7     && < 5  test-suite amazonka-ml-test@@ -97,9 +96,9 @@         , Test.AWS.MachineLearning.Internal      build-depends:-          amazonka-core == 1.4.4.*-        , amazonka-test == 1.4.4.*-        , amazonka-ml == 1.4.4.*+          amazonka-core == 1.4.5.*+        , amazonka-test == 1.4.5.*+        , amazonka-ml == 1.4.5.*         , base         , bytestring         , tasty

@@ -18,7 +18,9 @@ -- 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.+-- 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@@ -56,11 +58,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'atTags'+-- * 'atTags' - 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. ----- * 'atResourceId'+-- * 'atResourceId' - The ID of the ML object to tag. For example, @exampleModelId@ . ----- * 'atResourceType'+-- * 'atResourceType' - The type of the ML object to tag. addTags     :: Text -- ^ 'atResourceId'     -> TaggableResourceType -- ^ 'atResourceType'@@ -76,7 +78,7 @@ atTags :: Lens' AddTags [Tag] atTags = lens _atTags (\ s a -> s{_atTags = a}) . _Coerce; --- | The ID of the ML object to tag. For example, 'exampleModelId'.+-- | The ID of the ML object to tag. For example, @exampleModelId@ . atResourceId :: Lens' AddTags Text atResourceId = lens _atResourceId (\ s a -> s{_atResourceId = a}); @@ -123,6 +125,8 @@  -- | Amazon ML returns the following elements. --+--+-- -- /See:/ 'addTagsResponse' smart constructor. data AddTagsResponse = AddTagsResponse'     { _atrsResourceId     :: !(Maybe Text)@@ -134,11 +138,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'atrsResourceId'+-- * 'atrsResourceId' - The ID of the ML object that was tagged. ----- * 'atrsResourceType'+-- * 'atrsResourceType' - The type of the ML object that was tagged. ----- * 'atrsResponseStatus'+-- * 'atrsResponseStatus' - -- | The response status code. addTagsResponse     :: Int -- ^ 'atrsResponseStatus'     -> AddTagsResponse@@ -157,7 +161,7 @@ atrsResourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType) atrsResourceType = lens _atrsResourceType (\ s a -> s{_atrsResourceType = a}); --- | The response status code.+-- | -- | The response status code. atrsResponseStatus :: Lens' AddTagsResponse Int atrsResponseStatus = lens _atrsResponseStatus (\ s a -> s{_atrsResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a 'DataSource'. This operation creates a new 'BatchPrediction', and uses an 'MLModel' and the data files referenced by the 'DataSource' as information sources.+-- Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a @DataSource@ . This operation creates a new @BatchPrediction@ , and uses an @MLModel@ and the data files referenced by the @DataSource@ as information sources. ----- 'CreateBatchPrediction' is an asynchronous operation. In response to 'CreateBatchPrediction', Amazon Machine Learning (Amazon ML) immediately returns and sets the 'BatchPrediction' status to 'PENDING'. After the 'BatchPrediction' completes, Amazon ML sets the status to 'COMPLETED'. ----- You can poll for status updates by using the < GetBatchPrediction> operation and checking the 'Status' parameter of the result. After the 'COMPLETED' status appears, the results are available in the location specified by the 'OutputUri' parameter.+-- @CreateBatchPrediction@ is an asynchronous operation. In response to @CreateBatchPrediction@ , Amazon Machine Learning (Amazon ML) immediately returns and sets the @BatchPrediction@ status to @PENDING@ . After the @BatchPrediction@ completes, Amazon ML sets the status to @COMPLETED@ .+--+-- You can poll for status updates by using the 'GetBatchPrediction' operation and checking the @Status@ parameter of the result. After the @COMPLETED@ status appears, the results are available in the location specified by the @OutputUri@ parameter.+-- module Network.AWS.MachineLearning.CreateBatchPrediction     (     -- * Creating a Request@@ -63,15 +65,15 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cbpBatchPredictionName'+-- * 'cbpBatchPredictionName' - A user-supplied name or description of the @BatchPrediction@ . @BatchPredictionName@ can only use the UTF-8 character set. ----- * 'cbpBatchPredictionId'+-- * 'cbpBatchPredictionId' - A user-supplied ID that uniquely identifies the @BatchPrediction@ . ----- * 'cbpMLModelId'+-- * 'cbpMLModelId' - The ID of the @MLModel@ that will generate predictions for the group of observations. ----- * 'cbpBatchPredictionDataSourceId'+-- * 'cbpBatchPredictionDataSourceId' - The ID of the @DataSource@ that points to the group of observations to predict. ----- * 'cbpOutputURI'+-- * 'cbpOutputURI' - 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> . createBatchPrediction     :: Text -- ^ 'cbpBatchPredictionId'     -> Text -- ^ 'cbpMLModelId'@@ -87,25 +89,23 @@     , _cbpOutputURI = pOutputURI_     } --- | A user-supplied name or description of the 'BatchPrediction'. 'BatchPredictionName' can only use the UTF-8 character set.+-- | A user-supplied name or description of the @BatchPrediction@ . @BatchPredictionName@ can only use the UTF-8 character set. cbpBatchPredictionName :: Lens' CreateBatchPrediction (Maybe Text) cbpBatchPredictionName = lens _cbpBatchPredictionName (\ s a -> s{_cbpBatchPredictionName = a}); --- | A user-supplied ID that uniquely identifies the 'BatchPrediction'.+-- | A user-supplied ID that uniquely identifies the @BatchPrediction@ . cbpBatchPredictionId :: Lens' CreateBatchPrediction Text cbpBatchPredictionId = lens _cbpBatchPredictionId (\ s a -> s{_cbpBatchPredictionId = a}); --- | The ID of the 'MLModel' that will generate predictions for the group of observations.+-- | The ID of the @MLModel@ that will generate predictions for the group of observations. cbpMLModelId :: Lens' CreateBatchPrediction Text cbpMLModelId = lens _cbpMLModelId (\ s a -> s{_cbpMLModelId = a}); --- | The ID of the 'DataSource' that points to the group of observations to predict.+-- | The ID of the @DataSource@ that points to the group of observations to predict. 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: \':\', \'\/\/\', \'\/.\/\', \'\/..\/\'.------ 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>.+-- | 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 cbpOutputURI = lens _cbpOutputURI (\ s a -> s{_cbpOutputURI = a}); @@ -152,10 +152,12 @@ 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'     { _cbprsBatchPredictionId :: !(Maybe Text)@@ -166,9 +168,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cbprsBatchPredictionId'+-- * 'cbprsBatchPredictionId' - A user-supplied ID that uniquely identifies the @BatchPrediction@ . This value is identical to the value of the @BatchPredictionId@ in the request. ----- * 'cbprsResponseStatus'+-- * 'cbprsResponseStatus' - -- | The response status code. createBatchPredictionResponse     :: Int -- ^ 'cbprsResponseStatus'     -> CreateBatchPredictionResponse@@ -178,11 +180,11 @@     , _cbprsResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the 'BatchPrediction'. This value is identical to the value of the 'BatchPredictionId' in the request.+-- | A user-supplied ID that uniquely identifies the @BatchPrediction@ . This value is identical to the value of the @BatchPredictionId@ in the request. cbprsBatchPredictionId :: Lens' CreateBatchPredictionResponse (Maybe Text) cbprsBatchPredictionId = lens _cbprsBatchPredictionId (\ s a -> s{_cbprsBatchPredictionId = a}); --- | The response status code.+-- | -- | The response status code. cbprsResponseStatus :: Lens' CreateBatchPredictionResponse Int cbprsResponseStatus = lens _cbprsResponseStatus (\ s a -> s{_cbprsResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- 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 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.+-- @CreateDataSourceFromRDS@ is an asynchronous operation. In response to @CreateDataSourceFromRDS@ , Amazon Machine Learning (Amazon ML) immediately returns and sets the @DataSource@ status to @PENDING@ . After the @DataSource@ is created and ready for use, Amazon ML sets the @Status@ parameter to @COMPLETED@ . @DataSource@ in the @COMPLETED@ or @PENDING@ state can be used only to perform @>CreateMLModel@ >, @CreateEvaluation@ , or @CreateBatchPrediction@ operations.+--+-- If Amazon ML cannot accept the input source, it sets the @Status@ parameter to @FAILED@ and includes an error message in the @Message@ attribute of the @GetDataSource@ operation response.+-- module Network.AWS.MachineLearning.CreateDataSourceFromRDS     (     -- * Creating a Request@@ -63,15 +65,15 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfrdsDataSourceName'+-- * 'cdsfrdsDataSourceName' - A user-supplied name or description of the @DataSource@ . ----- * 'cdsfrdsComputeStatistics'+-- * 'cdsfrdsComputeStatistics' - 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 DataSourceneeds to be used for @MLModel@ training. ----- * 'cdsfrdsDataSourceId'+-- * 'cdsfrdsDataSourceId' - A user-supplied ID that uniquely identifies the @DataSource@ . Typically, an Amazon Resource Number (ARN) becomes the ID for a @DataSource@ . ----- * 'cdsfrdsRDSData'+-- * 'cdsfrdsRDSData' - The data specification of an Amazon RDS @DataSource@ :     * DatabaseInformation -     * @DatabaseName@ - The name of the Amazon RDS database.    * @InstanceIdentifier @ - A unique identifier for the Amazon RDS database instance.     * DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.     * ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see <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 - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [@SubnetId@ , @SecurityGroupIds@ ] pair for a VPC-based RDS DB instance.     * SelectSqlQuery - A query that is used to retrieve the observation data for the @Datasource@ .     * S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using @SelectSqlQuery@ is stored in this location.     * DataSchemaUri - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @Datasource@ .  Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ ----- * 'cdsfrdsRoleARN'+-- * 'cdsfrdsRoleARN' - The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the @SelectSqlQuery@ query from Amazon RDS to Amazon S3. createDataSourceFromRDS     :: Text -- ^ 'cdsfrdsDataSourceId'     -> RDSDataSpec -- ^ 'cdsfrdsRDSData'@@ -86,49 +88,23 @@     , _cdsfrdsRoleARN = pRoleARN_     } --- | A user-supplied name or description of the 'DataSource'.+-- | A user-supplied name or description of the @DataSource@ . 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 '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 DataSourceneeds to be used for @MLModel@ training. cdsfrdsComputeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool) cdsfrdsComputeStatistics = lens _cdsfrdsComputeStatistics (\ s a -> s{_cdsfrdsComputeStatistics = a}); --- | A user-supplied ID that uniquely identifies the 'DataSource'. Typically, an Amazon Resource Number (ARN) becomes the ID for a 'DataSource'.+-- | A user-supplied ID that uniquely identifies the @DataSource@ . Typically, an Amazon Resource Number (ARN) becomes the ID for a @DataSource@ . cdsfrdsDataSourceId :: Lens' CreateDataSourceFromRDS Text cdsfrdsDataSourceId = lens _cdsfrdsDataSourceId (\ s a -> s{_cdsfrdsDataSourceId = a}); --- | The data specification of an Amazon RDS 'DataSource':------ -   DatabaseInformation -------     -   'DatabaseName' - The name of the Amazon RDS database.---     -   'InstanceIdentifier ' - A unique identifier for the Amazon RDS database instance.--- -   DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.------ -   ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see <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 - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a ['SubnetId', 'SecurityGroupIds'] pair for a VPC-based RDS DB instance.------ -   SelectSqlQuery - A query that is used to retrieve the observation data for the 'Datasource'.------ -   S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using 'SelectSqlQuery' is stored in this location.------ -   DataSchemaUri - The Amazon S3 location of the 'DataSchema'.------ -   DataSchema - A JSON string representing the schema. This is not required if 'DataSchemaUri' is specified.------ -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'Datasource'.------     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'---+-- | The data specification of an Amazon RDS @DataSource@ :     * DatabaseInformation -     * @DatabaseName@ - The name of the Amazon RDS database.    * @InstanceIdentifier @ - A unique identifier for the Amazon RDS database instance.     * DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.     * ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see <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 - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [@SubnetId@ , @SecurityGroupIds@ ] pair for a VPC-based RDS DB instance.     * SelectSqlQuery - A query that is used to retrieve the observation data for the @Datasource@ .     * S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using @SelectSqlQuery@ is stored in this location.     * DataSchemaUri - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @Datasource@ .  Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ 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}); @@ -173,10 +149,12 @@ 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'     { _cdsfrdsrsDataSourceId   :: !(Maybe Text)@@ -187,9 +165,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfrdsrsDataSourceId'+-- * 'cdsfrdsrsDataSourceId' - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the @DataSourceID@ in the request. ----- * 'cdsfrdsrsResponseStatus'+-- * 'cdsfrdsrsResponseStatus' - -- | The response status code. createDataSourceFromRDSResponse     :: Int -- ^ 'cdsfrdsrsResponseStatus'     -> CreateDataSourceFromRDSResponse@@ -199,11 +177,11 @@     , _cdsfrdsrsResponseStatus = 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. cdsfrdsrsDataSourceId :: Lens' CreateDataSourceFromRDSResponse (Maybe Text) cdsfrdsrsDataSourceId = lens _cdsfrdsrsDataSourceId (\ s a -> s{_cdsfrdsrsDataSourceId = a}); --- | The response status code.+-- | -- | The response status code. cdsfrdsrsResponseStatus :: Lens' CreateDataSourceFromRDSResponse Int cdsfrdsrsResponseStatus = lens _cdsfrdsrsResponseStatus (\ s a -> s{_cdsfrdsrsResponseStatus = a}); 

@@ -18,17 +18,19 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- 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.+-- Creates a @DataSource@ from a database hosted on an Amazon Redshift cluster. A @DataSource@ references data that can be used to perform either @CreateMLModel@ , @CreateEvaluation@ , or @CreateBatchPrediction@ operations. ----- 'CreateDataSourceFromRedshift' is an asynchronous operation. In response to 'CreateDataSourceFromRedshift', Amazon Machine Learning (Amazon ML) immediately returns and sets the 'DataSource' status to 'PENDING'. After the 'DataSource' is created and ready for use, Amazon ML sets the 'Status' parameter to 'COMPLETED'. 'DataSource' in 'COMPLETED' or 'PENDING' states can be used to perform only 'CreateMLModel', 'CreateEvaluation', or 'CreateBatchPrediction' operations. ----- If Amazon ML can\'t accept the input source, it sets the 'Status' parameter to 'FAILED' and includes an error message in the 'Message' attribute of the 'GetDataSource' operation response.+-- @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. ----- 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'.+-- 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. ----- 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.+-- 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@ . ----- 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.+-- 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@@ -69,15 +71,15 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfrDataSourceName'+-- * 'cdsfrDataSourceName' - A user-supplied name or description of the @DataSource@ . ----- * 'cdsfrComputeStatistics'+-- * 'cdsfrComputeStatistics' - 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. ----- * 'cdsfrDataSourceId'+-- * 'cdsfrDataSourceId' - A user-supplied ID that uniquely identifies the @DataSource@ . ----- * 'cdsfrDataSpec'+-- * 'cdsfrDataSpec' - The data specification of an Amazon Redshift @DataSource@ :     * DatabaseInformation -     * @DatabaseName@ - The name of the Amazon Redshift database.     * @ClusterIdentifier@ - The unique ID for the Amazon Redshift cluster.     * DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.     * SelectSqlQuery - The query that is used to retrieve the observation data for the @Datasource@ .     * S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the @SelectSqlQuery@ query is stored in this location.     * DataSchemaUri - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @DataSource@ . Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ ----- * 'cdsfrRoleARN'+-- * 'cdsfrRoleARN' - A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:      * A security group to allow Amazon ML to execute the @SelectSqlQuery@ query on an Amazon Redshift cluster     * An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the @S3StagingLocation@ createDataSourceFromRedshift     :: Text -- ^ 'cdsfrDataSourceId'     -> RedshiftDataSpec -- ^ 'cdsfrDataSpec'@@ -92,47 +94,23 @@     , _cdsfrRoleARN = pRoleARN_     } --- | A user-supplied name or description of the 'DataSource'.+-- | A user-supplied name or description of the @DataSource@ . 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}); --- | A user-supplied ID that uniquely identifies the 'DataSource'.+-- | A user-supplied ID that uniquely identifies the @DataSource@ . cdsfrDataSourceId :: Lens' CreateDataSourceFromRedshift Text cdsfrDataSourceId = lens _cdsfrDataSourceId (\ s a -> s{_cdsfrDataSourceId = a}); --- | The data specification of an Amazon Redshift 'DataSource':------ -   DatabaseInformation -------     -   'DatabaseName' - The name of the Amazon Redshift database.---     -   ' ClusterIdentifier' - The unique ID for the Amazon Redshift cluster.--- -   DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.------ -   SelectSqlQuery - The query that is used to retrieve the observation data for the 'Datasource'.------ -   S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the 'SelectSqlQuery' query is stored in this location.------ -   DataSchemaUri - The Amazon S3 location of the 'DataSchema'.------ -   DataSchema - A JSON string representing the schema. This is not required if 'DataSchemaUri' is specified.------ -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'DataSource'.------     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'---+-- | The data specification of an Amazon Redshift @DataSource@ :     * DatabaseInformation -     * @DatabaseName@ - The name of the Amazon Redshift database.     * @ClusterIdentifier@ - The unique ID for the Amazon Redshift cluster.     * DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.     * SelectSqlQuery - The query that is used to retrieve the observation data for the @Datasource@ .     * S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the @SelectSqlQuery@ query is stored in this location.     * DataSchemaUri - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @DataSource@ . Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ cdsfrDataSpec :: Lens' CreateDataSourceFromRedshift RedshiftDataSpec cdsfrDataSpec = lens _cdsfrDataSpec (\ s a -> s{_cdsfrDataSpec = a}); --- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:------ -   A security group to allow Amazon ML to execute the 'SelectSqlQuery' query on an Amazon Redshift cluster------ -   An Amazon S3 bucket policy to grant Amazon ML read\/write permissions on the 'S3StagingLocation'---+-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:      * A security group to allow Amazon ML to execute the @SelectSqlQuery@ query on an Amazon Redshift cluster     * An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the @S3StagingLocation@ cdsfrRoleARN :: Lens' CreateDataSourceFromRedshift Text cdsfrRoleARN = lens _cdsfrRoleARN (\ s a -> s{_cdsfrRoleARN = a}); @@ -177,10 +155,12 @@ 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'     { _cdsfrrsDataSourceId   :: !(Maybe Text)@@ -191,9 +171,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfrrsDataSourceId'+-- * 'cdsfrrsDataSourceId' - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the @DataSourceID@ in the request. ----- * 'cdsfrrsResponseStatus'+-- * 'cdsfrrsResponseStatus' - -- | The response status code. createDataSourceFromRedshiftResponse     :: Int -- ^ 'cdsfrrsResponseStatus'     -> CreateDataSourceFromRedshiftResponse@@ -203,11 +183,11 @@     , _cdsfrrsResponseStatus = 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. cdsfrrsDataSourceId :: Lens' CreateDataSourceFromRedshiftResponse (Maybe Text) cdsfrrsDataSourceId = lens _cdsfrrsDataSourceId (\ s a -> s{_cdsfrrsDataSourceId = a}); --- | The response status code.+-- | -- | The response status code. cdsfrrsResponseStatus :: Lens' CreateDataSourceFromRedshiftResponse Int cdsfrrsResponseStatus = lens _cdsfrrsResponseStatus (\ s a -> s{_cdsfrrsResponseStatus = a}); 

@@ -18,15 +18,17 @@ -- 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' has been created and is ready for use, Amazon ML sets the 'Status' parameter to 'COMPLETED'. 'DataSource' in the 'COMPLETED' or 'PENDING' state can be used to perform only 'CreateMLModel', 'CreateEvaluation' or 'CreateBatchPrediction' operations. ----- If Amazon ML can\'t accept the input source, it sets the 'Status' parameter to 'FAILED' and includes an error message in the 'Message' attribute of the 'GetDataSource' operation response.+-- @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. ----- 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'.+-- 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. ----- 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.+-- The observation data used in a @DataSource@ should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the @DataSource@ .+--+-- After the @DataSource@ has been created, it's ready to use in evaluations and batch predictions. If you plan to use the @DataSource@ to train an @MLModel@ , the @DataSource@ also needs a recipe. A recipe describes how each input variable will be used in training an @MLModel@ . Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.+-- module Network.AWS.MachineLearning.CreateDataSourceFromS3     (     -- * Creating a Request@@ -65,13 +67,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfsDataSourceName'+-- * 'cdsfsDataSourceName' - A user-supplied name or description of the @DataSource@ . ----- * 'cdsfsComputeStatistics'+-- * 'cdsfsComputeStatistics' - 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 DataSourceneeds to be used for @MLModel@ training. ----- * 'cdsfsDataSourceId'+-- * 'cdsfsDataSourceId' - A user-supplied identifier that uniquely identifies the @DataSource@ . ----- * 'cdsfsDataSpec'+-- * 'cdsfsDataSpec' - The data specification of a @DataSource@ :     * DataLocationS3 - The Amazon S3 location of the observation data.     * DataSchemaLocationS3 - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @Datasource@ .  Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ createDataSourceFromS3     :: Text -- ^ 'cdsfsDataSourceId'     -> S3DataSpec -- ^ 'cdsfsDataSpec'@@ -84,30 +86,19 @@     , _cdsfsDataSpec = pDataSpec_     } --- | A user-supplied name or description of the 'DataSource'.+-- | A user-supplied name or description of the @DataSource@ . 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 '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 DataSourceneeds to be used for @MLModel@ training. cdsfsComputeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool) cdsfsComputeStatistics = lens _cdsfsComputeStatistics (\ s a -> s{_cdsfsComputeStatistics = a}); --- | A user-supplied identifier that uniquely identifies the 'DataSource'.+-- | A user-supplied identifier that uniquely identifies the @DataSource@ . cdsfsDataSourceId :: Lens' CreateDataSourceFromS3 Text cdsfsDataSourceId = lens _cdsfsDataSourceId (\ s a -> s{_cdsfsDataSourceId = a}); --- | The data specification of a 'DataSource':------ -   DataLocationS3 - The Amazon S3 location of the observation data.------ -   DataSchemaLocationS3 - The Amazon S3 location of the 'DataSchema'.------ -   DataSchema - A JSON string representing the schema. This is not required if 'DataSchemaUri' is specified.------ -   DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the 'Datasource'.------     Sample - ' \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'---+-- | The data specification of a @DataSource@ :     * DataLocationS3 - The Amazon S3 location of the observation data.     * DataSchemaLocationS3 - The Amazon S3 location of the @DataSchema@ .     * DataSchema - A JSON string representing the schema. This is not required if @DataSchemaUri@ is specified.      * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the @Datasource@ .  Sample - @"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"@ cdsfsDataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec cdsfsDataSpec = lens _cdsfsDataSpec (\ s a -> s{_cdsfsDataSpec = a}); @@ -150,10 +141,12 @@ 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'     { _cdsfsrsDataSourceId   :: !(Maybe Text)@@ -164,9 +157,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cdsfsrsDataSourceId'+-- * 'cdsfsrsDataSourceId' - A user-supplied ID that uniquely identifies the @DataSource@ . This value should be identical to the value of the @DataSourceID@ in the request. ----- * 'cdsfsrsResponseStatus'+-- * 'cdsfsrsResponseStatus' - -- | The response status code. createDataSourceFromS3Response     :: Int -- ^ 'cdsfsrsResponseStatus'     -> CreateDataSourceFromS3Response@@ -176,11 +169,11 @@     , _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}); --- | The response status code.+-- | -- | The response status code. cdsfsrsResponseStatus :: Lens' CreateDataSourceFromS3Response Int cdsfsrsResponseStatus = lens _cdsfsrsResponseStatus (\ s a -> s{_cdsfsrsResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- 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.+-- @CreateEvaluation@ is an asynchronous operation. In response to @CreateEvaluation@ , Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to @PENDING@ . After the @Evaluation@ is created and ready for use, Amazon ML sets the status to @COMPLETED@ .+--+-- You can use the @GetEvaluation@ operation to check progress of the evaluation during the creation operation.+-- module Network.AWS.MachineLearning.CreateEvaluation     (     -- * Creating a Request@@ -61,13 +63,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ceEvaluationName'+-- * 'ceEvaluationName' - A user-supplied name or description of the @Evaluation@ . ----- * 'ceEvaluationId'+-- * 'ceEvaluationId' - A user-supplied ID that uniquely identifies the @Evaluation@ . ----- * 'ceMLModelId'+-- * 'ceMLModelId' - The ID of the @MLModel@ to evaluate. The schema used in creating the @MLModel@ must match the schema of the @DataSource@ used in the @Evaluation@ . ----- * 'ceEvaluationDataSourceId'+-- * 'ceEvaluationDataSourceId' - The ID of the @DataSource@ for the evaluation. The schema of the @DataSource@ must match the schema used to create the @MLModel@ . createEvaluation     :: Text -- ^ 'ceEvaluationId'     -> Text -- ^ 'ceMLModelId'@@ -81,21 +83,19 @@     , _ceEvaluationDataSourceId = pEvaluationDataSourceId_     } --- | A user-supplied name or description of the 'Evaluation'.+-- | A user-supplied name or description of the @Evaluation@ . ceEvaluationName :: Lens' CreateEvaluation (Maybe Text) ceEvaluationName = lens _ceEvaluationName (\ s a -> s{_ceEvaluationName = a}); --- | A user-supplied ID that uniquely identifies the 'Evaluation'.+-- | A user-supplied ID that uniquely identifies the @Evaluation@ . ceEvaluationId :: Lens' CreateEvaluation Text ceEvaluationId = lens _ceEvaluationId (\ s a -> s{_ceEvaluationId = a}); --- | The ID of the 'MLModel' to evaluate.------ The schema used in creating the 'MLModel' must match the schema of the 'DataSource' used in the 'Evaluation'.+-- | The ID of the @MLModel@ to evaluate. The schema used in creating the @MLModel@ must match the schema of the @DataSource@ used in the @Evaluation@ . ceMLModelId :: Lens' CreateEvaluation Text ceMLModelId = lens _ceMLModelId (\ s a -> s{_ceMLModelId = a}); --- | The ID of the 'DataSource' for the evaluation. The schema of the 'DataSource' must match the schema used to create the 'MLModel'.+-- | The ID of the @DataSource@ for the evaluation. The schema of the @DataSource@ must match the schema used to create the @MLModel@ . ceEvaluationDataSourceId :: Lens' CreateEvaluation Text ceEvaluationDataSourceId = lens _ceEvaluationDataSourceId (\ s a -> s{_ceEvaluationDataSourceId = a}); @@ -138,10 +138,12 @@ 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 'GetEvcaluation' 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'     { _cersEvaluationId   :: !(Maybe Text)@@ -152,9 +154,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cersEvaluationId'+-- * 'cersEvaluationId' - The user-supplied ID that uniquely identifies the @Evaluation@ . This value should be identical to the value of the @EvaluationId@ in the request. ----- * 'cersResponseStatus'+-- * 'cersResponseStatus' - -- | The response status code. createEvaluationResponse     :: Int -- ^ 'cersResponseStatus'     -> CreateEvaluationResponse@@ -164,11 +166,11 @@     , _cersResponseStatus = pResponseStatus_     } --- | The user-supplied ID that uniquely identifies the 'Evaluation'. This value should be identical to the value of the 'EvaluationId' in the request.+-- | The user-supplied ID that uniquely identifies the @Evaluation@ . This value should be identical to the value of the @EvaluationId@ in the request. cersEvaluationId :: Lens' CreateEvaluationResponse (Maybe Text) cersEvaluationId = lens _cersEvaluationId (\ s a -> s{_cersEvaluationId = a}); --- | The response status code.+-- | -- | The response status code. cersResponseStatus :: Lens' CreateEvaluationResponse Int cersResponseStatus = lens _cersResponseStatus (\ s a -> s{_cersResponseStatus = a}); 

@@ -18,15 +18,17 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a new 'MLModel' using the 'DataSource' 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 update only the 'MLModelName' and the 'ScoreThreshold' in an 'MLModel' without creating a new 'MLModel'. ----- 'CreateMLModel' is an asynchronous operation. In response to 'CreateMLModel', Amazon Machine Learning (Amazon ML) immediately returns and sets the 'MLModel' status to 'PENDING'. After the 'MLModel' has been created and ready is for use, Amazon ML sets the status to 'COMPLETED'.+-- An @MLModel@ is nearly immutable. Users can update only the @MLModelName@ and the @ScoreThreshold@ in an @MLModel@ without creating a new @MLModel@ . ----- You can use the 'GetMLModel' operation to check the progress of the 'MLModel' during the creation operation.+-- @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@ . ----- 'CreateMLModel' requires a 'DataSource' with computed statistics, which can be created by setting 'ComputeStatistics' to 'true' in 'CreateDataSourceFromRDS', 'CreateDataSourceFromS3', or 'CreateDataSourceFromRedshift' operations.+-- You can use the @GetMLModel@ operation to check the progress of the @MLModel@ during the creation operation.+--+-- @CreateMLModel@ requires a @DataSource@ with computed statistics, which can be created by setting @ComputeStatistics@ to @true@ in @CreateDataSourceFromRDS@ , @CreateDataSourceFromS3@ , or @CreateDataSourceFromRedshift@ operations.+-- module Network.AWS.MachineLearning.CreateMLModel     (     -- * Creating a Request@@ -71,19 +73,19 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cmlmRecipe'+-- * 'cmlmRecipe' - 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. ----- * 'cmlmRecipeURI'+-- * 'cmlmRecipeURI' - 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. ----- * 'cmlmMLModelName'+-- * 'cmlmMLModelName' - A user-supplied name or description of the @MLModel@ . ----- * 'cmlmParameters'+-- * 'cmlmParameters' - A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ . We strongly recommend that you shuffle your data.     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. ----- * 'cmlmMLModelId'+-- * 'cmlmMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . ----- * 'cmlmMLModelType'+-- * 'cmlmMLModelType' - The category of supervised learning that this @MLModel@ will address. Choose from the following types:     * Choose @REGRESSION@ if the @MLModel@ will be used to predict a numeric value.    * Choose @BINARY@ if the @MLModel@ result has two possible values.    * Choose @MULTICLASS@ if the @MLModel@ result has a limited number of values.  For more information, see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . ----- * 'cmlmTrainingDataSourceId'+-- * 'cmlmTrainingDataSourceId' - The @DataSource@ that points to the training data. createMLModel     :: Text -- ^ 'cmlmMLModelId'     -> MLModelType -- ^ 'cmlmMLModelType'@@ -100,56 +102,31 @@     , _cmlmTrainingDataSourceId = pTrainingDataSourceId_     } --- | 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.+-- | 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}); --- | A user-supplied name or description of the 'MLModel'.+-- | A user-supplied name or description of the @MLModel@ . 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.------ The following is the current set of training parameters:------ -   'sgd.maxMLModelSizeInBytes' - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.------     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'.------ -   'sgd.maxPasses' - The number of times that the training process traverses the observations to build the 'MLModel'. The value is an integer that ranges from '1' to '10000'. The default value is '10'.------ -   'sgd.shuffleType' - Whether Amazon ML shuffles the training data. Shuffling the data improves a model\'s ability to find the optimal solution for a variety of data types. The valid values are 'auto' and 'none'. The default value is 'none'. We strongly recommend that you shuffle your data.------ -   'sgd.l1RegularizationAmount' - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L1 normalization. This parameter can\'t be used when 'L2' is specified. Use this parameter sparingly.------ -   'sgd.l2RegularizationAmount' - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L2 normalization. This parameter can\'t be used when 'L1' is specified. Use this parameter sparingly.---+-- | A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ . We strongly recommend that you shuffle your data.     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. cmlmParameters :: Lens' CreateMLModel (HashMap Text Text) cmlmParameters = lens _cmlmParameters (\ s a -> s{_cmlmParameters = a}) . _Default . _Map; --- | A user-supplied ID that uniquely identifies the 'MLModel'.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . cmlmMLModelId :: Lens' CreateMLModel Text cmlmMLModelId = lens _cmlmMLModelId (\ s a -> s{_cmlmMLModelId = a}); --- | The category of supervised learning that this 'MLModel' will address. Choose from the following types:------ -   Choose 'REGRESSION' if the 'MLModel' will be used to predict a numeric value.--- -   Choose 'BINARY' if the 'MLModel' result has two possible values.--- -   Choose 'MULTICLASS' if the 'MLModel' result has a limited number of values.------ For more information, see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+-- | The category of supervised learning that this @MLModel@ will address. Choose from the following types:     * Choose @REGRESSION@ if the @MLModel@ will be used to predict a numeric value.    * Choose @BINARY@ if the @MLModel@ result has two possible values.    * Choose @MULTICLASS@ if the @MLModel@ result has a limited number of values.  For more information, see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . cmlmMLModelType :: Lens' CreateMLModel MLModelType cmlmMLModelType = lens _cmlmMLModelType (\ s a -> s{_cmlmMLModelType = a}); --- | The 'DataSource' that points to the training data.+-- | The @DataSource@ that points to the training data. cmlmTrainingDataSourceId :: Lens' CreateMLModel Text cmlmTrainingDataSourceId = lens _cmlmTrainingDataSourceId (\ s a -> s{_cmlmTrainingDataSourceId = a}); @@ -195,10 +172,12 @@ 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'     { _cmlmrsMLModelId      :: !(Maybe Text)@@ -209,9 +188,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'cmlmrsMLModelId'+-- * 'cmlmrsMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. ----- * 'cmlmrsResponseStatus'+-- * 'cmlmrsResponseStatus' - -- | The response status code. createMLModelResponse     :: Int -- ^ 'cmlmrsResponseStatus'     -> CreateMLModelResponse@@ -221,11 +200,11 @@     , _cmlmrsResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the 'MLModel'. This value should be identical to the value of the 'MLModelId' in the request.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text) cmlmrsMLModelId = lens _cmlmrsMLModelId (\ s a -> s{_cmlmrsMLModelId = a}); --- | The response status code.+-- | -- | The response status code. cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int cmlmrsResponseStatus = lens _cmlmrsResponseStatus (\ s a -> s{_cmlmrsResponseStatus = a}); 

@@ -18,7 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Creates a real-time endpoint for the 'MLModel'. The endpoint contains the URI of the 'MLModel'; that is, the location to send real-time prediction requests for the specified 'MLModel'.+-- Creates a real-time endpoint for the @MLModel@ . The endpoint contains the URI of the @MLModel@ ; that is, the location to send real-time prediction requests for the specified @MLModel@ .+--+-- module Network.AWS.MachineLearning.CreateRealtimeEndpoint     (     -- * Creating a Request@@ -52,7 +54,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'creMLModelId'+-- * 'creMLModelId' - The ID assigned to the @MLModel@ during creation. createRealtimeEndpoint     :: Text -- ^ 'creMLModelId'     -> CreateRealtimeEndpoint@@ -61,7 +63,7 @@     { _creMLModelId = pMLModelId_     } --- | The ID assigned to the 'MLModel' during creation.+-- | The ID assigned to the @MLModel@ during creation. creMLModelId :: Lens' CreateRealtimeEndpoint Text creMLModelId = lens _creMLModelId (\ s a -> s{_creMLModelId = a}); @@ -102,12 +104,12 @@ 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'. ----- The endpoint information includes the URI of the 'MLModel'; that is, the location to send online prediction requests for the specified 'MLModel'.+-- The result contains the @MLModelId@ and the endpoint information for the @MLModel@ . --+-- -- /See:/ 'createRealtimeEndpointResponse' smart constructor. data CreateRealtimeEndpointResponse = CreateRealtimeEndpointResponse'     { _crersRealtimeEndpointInfo :: !(Maybe RealtimeEndpointInfo)@@ -119,11 +121,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'crersRealtimeEndpointInfo'+-- * 'crersRealtimeEndpointInfo' - The endpoint information of the @MLModel@ ----- * 'crersMLModelId'+-- * 'crersMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. ----- * 'crersResponseStatus'+-- * 'crersResponseStatus' - -- | The response status code. createRealtimeEndpointResponse     :: Int -- ^ 'crersResponseStatus'     -> CreateRealtimeEndpointResponse@@ -134,15 +136,15 @@     , _crersResponseStatus = pResponseStatus_     } --- | The endpoint information of the 'MLModel'+-- | The endpoint information of the @MLModel@ crersRealtimeEndpointInfo :: Lens' CreateRealtimeEndpointResponse (Maybe RealtimeEndpointInfo) crersRealtimeEndpointInfo = lens _crersRealtimeEndpointInfo (\ s a -> s{_crersRealtimeEndpointInfo = a}); --- | A user-supplied ID that uniquely identifies the 'MLModel'. This value should be identical to the value of the 'MLModelId' in the request.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. crersMLModelId :: Lens' CreateRealtimeEndpointResponse (Maybe Text) crersMLModelId = lens _crersMLModelId (\ s a -> s{_crersMLModelId = a}); --- | The response status code.+-- | -- | The response status code. crersResponseStatus :: Lens' CreateRealtimeEndpointResponse Int crersResponseStatus = lens _crersResponseStatus (\ s a -> s{_crersResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Assigns the DELETED status to a 'BatchPrediction', rendering it unusable.+-- Assigns the DELETED status to a @BatchPrediction@ , rendering it unusable. ----- After using the 'DeleteBatchPrediction' operation, you can use the < GetBatchPrediction> operation to verify that the status of the 'BatchPrediction' changed to DELETED. ----- __Caution:__ The result of the 'DeleteBatchPrediction' operation is irreversible.+-- After using the @DeleteBatchPrediction@ operation, you can use the 'GetBatchPrediction' operation to verify that the status of the @BatchPrediction@ changed to DELETED.+--+-- __Caution:__ The result of the @DeleteBatchPrediction@ operation is irreversible.+-- module Network.AWS.MachineLearning.DeleteBatchPrediction     (     -- * Creating a Request@@ -55,7 +57,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dbpBatchPredictionId'+-- * 'dbpBatchPredictionId' - A user-supplied ID that uniquely identifies the @BatchPrediction@ . deleteBatchPrediction     :: Text -- ^ 'dbpBatchPredictionId'     -> DeleteBatchPrediction@@ -64,7 +66,7 @@     { _dbpBatchPredictionId = pBatchPredictionId_     } --- | A user-supplied ID that uniquely identifies the 'BatchPrediction'.+-- | A user-supplied ID that uniquely identifies the @BatchPrediction@ . dbpBatchPredictionId :: Lens' DeleteBatchPrediction Text dbpBatchPredictionId = lens _dbpBatchPredictionId (\ s a -> s{_dbpBatchPredictionId = a}); @@ -105,10 +107,12 @@ 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'     { _dbprsBatchPredictionId :: !(Maybe Text)@@ -119,9 +123,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dbprsBatchPredictionId'+-- * 'dbprsBatchPredictionId' - A user-supplied ID that uniquely identifies the @BatchPrediction@ . This value should be identical to the value of the @BatchPredictionID@ in the request. ----- * 'dbprsResponseStatus'+-- * 'dbprsResponseStatus' - -- | The response status code. deleteBatchPredictionResponse     :: Int -- ^ 'dbprsResponseStatus'     -> DeleteBatchPredictionResponse@@ -131,11 +135,11 @@     , _dbprsResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the 'BatchPrediction'. This value should be identical to the value of the 'BatchPredictionID' in the request.+-- | A user-supplied ID that uniquely identifies the @BatchPrediction@ . This value should be identical to the value of the @BatchPredictionID@ in the request. dbprsBatchPredictionId :: Lens' DeleteBatchPredictionResponse (Maybe Text) dbprsBatchPredictionId = lens _dbprsBatchPredictionId (\ s a -> s{_dbprsBatchPredictionId = a}); --- | The response status code.+-- | -- | The response status code. dbprsResponseStatus :: Lens' DeleteBatchPredictionResponse Int dbprsResponseStatus = lens _dbprsResponseStatus (\ s a -> s{_dbprsResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Assigns the DELETED status to a 'DataSource', rendering it unusable.+-- Assigns the DELETED status to a @DataSource@ , rendering it unusable. ----- After using the 'DeleteDataSource' operation, you can use the < GetDataSource> operation to verify that the status of the 'DataSource' changed to DELETED. ----- __Caution:__ The results of the 'DeleteDataSource' operation are irreversible.+-- After using the @DeleteDataSource@ operation, you can use the 'GetDataSource' operation to verify that the status of the @DataSource@ changed to DELETED.+--+-- __Caution:__ The results of the @DeleteDataSource@ operation are irreversible.+-- module Network.AWS.MachineLearning.DeleteDataSource     (     -- * Creating a Request@@ -55,7 +57,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ddsDataSourceId'+-- * 'ddsDataSourceId' - A user-supplied ID that uniquely identifies the @DataSource@ . deleteDataSource     :: Text -- ^ 'ddsDataSourceId'     -> DeleteDataSource@@ -64,7 +66,7 @@     { _ddsDataSourceId = pDataSourceId_     } --- | A user-supplied ID that uniquely identifies the 'DataSource'.+-- | A user-supplied ID that uniquely identifies the @DataSource@ . ddsDataSourceId :: Lens' DeleteDataSource Text ddsDataSourceId = lens _ddsDataSourceId (\ s a -> s{_ddsDataSourceId = a}); @@ -102,8 +104,10 @@ 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'     { _ddsrsDataSourceId   :: !(Maybe Text)@@ -114,9 +118,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ddsrsDataSourceId'+-- * 'ddsrsDataSourceId' - A user-supplied ID that uniquely identifies the @DataSource@ . This value should be identical to the value of the @DataSourceID@ in the request. ----- * 'ddsrsResponseStatus'+-- * 'ddsrsResponseStatus' - -- | The response status code. deleteDataSourceResponse     :: Int -- ^ 'ddsrsResponseStatus'     -> DeleteDataSourceResponse@@ -126,11 +130,11 @@     , _ddsrsResponseStatus = 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. ddsrsDataSourceId :: Lens' DeleteDataSourceResponse (Maybe Text) ddsrsDataSourceId = lens _ddsrsDataSourceId (\ s a -> s{_ddsrsDataSourceId = a}); --- | The response status code.+-- | -- | The response status code. ddsrsResponseStatus :: Lens' DeleteDataSourceResponse Int ddsrsResponseStatus = lens _ddsrsResponseStatus (\ s a -> s{_ddsrsResponseStatus = a}); 

@@ -18,13 +18,15 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Assigns the 'DELETED' status to an 'Evaluation', rendering it unusable.+-- Assigns the @DELETED@ status to an @Evaluation@ , rendering it unusable. ----- After invoking the 'DeleteEvaluation' operation, you can use the 'GetEvaluation' operation to verify that the status of the 'Evaluation' changed to 'DELETED'. ----- Caution+-- After invoking the @DeleteEvaluation@ operation, you can use the @GetEvaluation@ operation to verify that the status of the @Evaluation@ changed to @DELETED@ . ----- 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@@ -57,7 +59,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'deEvaluationId'+-- * 'deEvaluationId' - A user-supplied ID that uniquely identifies the @Evaluation@ to delete. deleteEvaluation     :: Text -- ^ 'deEvaluationId'     -> DeleteEvaluation@@ -66,7 +68,7 @@     { _deEvaluationId = pEvaluationId_     } --- | A user-supplied ID that uniquely identifies the 'Evaluation' to delete.+-- | A user-supplied ID that uniquely identifies the @Evaluation@ to delete. deEvaluationId :: Lens' DeleteEvaluation Text deEvaluationId = lens _deEvaluationId (\ s a -> s{_deEvaluationId = a}); @@ -104,10 +106,12 @@ 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'     { _dersEvaluationId   :: !(Maybe Text)@@ -118,9 +122,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dersEvaluationId'+-- * 'dersEvaluationId' - A user-supplied ID that uniquely identifies the @Evaluation@ . This value should be identical to the value of the @EvaluationId@ in the request. ----- * 'dersResponseStatus'+-- * 'dersResponseStatus' - -- | The response status code. deleteEvaluationResponse     :: Int -- ^ 'dersResponseStatus'     -> DeleteEvaluationResponse@@ -130,11 +134,11 @@     , _dersResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the 'Evaluation'. This value should be identical to the value of the 'EvaluationId' in the request.+-- | A user-supplied ID that uniquely identifies the @Evaluation@ . This value should be identical to the value of the @EvaluationId@ in the request. dersEvaluationId :: Lens' DeleteEvaluationResponse (Maybe Text) dersEvaluationId = lens _dersEvaluationId (\ s a -> s{_dersEvaluationId = a}); --- | The response status code.+-- | -- | The response status code. dersResponseStatus :: Lens' DeleteEvaluationResponse Int dersResponseStatus = lens _dersResponseStatus (\ s a -> s{_dersResponseStatus = a}); 

@@ -18,11 +18,13 @@ -- 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. ----- __Caution:__ The result of the 'DeleteMLModel' operation is irreversible.+-- 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     (     -- * Creating a Request@@ -55,7 +57,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dmlmMLModelId'+-- * 'dmlmMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . deleteMLModel     :: Text -- ^ 'dmlmMLModelId'     -> DeleteMLModel@@ -64,7 +66,7 @@     { _dmlmMLModelId = pMLModelId_     } --- | A user-supplied ID that uniquely identifies the 'MLModel'.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . dmlmMLModelId :: Lens' DeleteMLModel Text dmlmMLModelId = lens _dmlmMLModelId (\ s a -> s{_dmlmMLModelId = a}); @@ -101,10 +103,12 @@ 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'     { _dmlmrsMLModelId      :: !(Maybe Text)@@ -115,9 +119,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dmlmrsMLModelId'+-- * 'dmlmrsMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelID@ in the request. ----- * 'dmlmrsResponseStatus'+-- * 'dmlmrsResponseStatus' - -- | The response status code. deleteMLModelResponse     :: Int -- ^ 'dmlmrsResponseStatus'     -> DeleteMLModelResponse@@ -127,11 +131,11 @@     , _dmlmrsResponseStatus = pResponseStatus_     } --- | A user-supplied ID that uniquely identifies the 'MLModel'. This value should be identical to the value of the 'MLModelID' in the request.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelID@ in the request. dmlmrsMLModelId :: Lens' DeleteMLModelResponse (Maybe Text) dmlmrsMLModelId = lens _dmlmrsMLModelId (\ s a -> s{_dmlmrsMLModelId = a}); --- | The response status code.+-- | -- | The response status code. dmlmrsResponseStatus :: Lens' DeleteMLModelResponse Int dmlmrsResponseStatus = lens _dmlmrsResponseStatus (\ s a -> s{_dmlmrsResponseStatus = a}); 

@@ -18,7 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Deletes a real time endpoint of an 'MLModel'.+-- Deletes a real time endpoint of an @MLModel@ .+--+-- module Network.AWS.MachineLearning.DeleteRealtimeEndpoint     (     -- * Creating a Request@@ -52,7 +54,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dreMLModelId'+-- * 'dreMLModelId' - The ID assigned to the @MLModel@ during creation. deleteRealtimeEndpoint     :: Text -- ^ 'dreMLModelId'     -> DeleteRealtimeEndpoint@@ -61,7 +63,7 @@     { _dreMLModelId = pMLModelId_     } --- | The ID assigned to the 'MLModel' during creation.+-- | The ID assigned to the @MLModel@ during creation. dreMLModelId :: Lens' DeleteRealtimeEndpoint Text dreMLModelId = lens _dreMLModelId (\ s a -> s{_dreMLModelId = a}); @@ -102,10 +104,12 @@ 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'. --+-- The result contains the @MLModelId@ and the endpoint information for the @MLModel@ .+--+-- -- /See:/ 'deleteRealtimeEndpointResponse' smart constructor. data DeleteRealtimeEndpointResponse = DeleteRealtimeEndpointResponse'     { _drersRealtimeEndpointInfo :: !(Maybe RealtimeEndpointInfo)@@ -117,11 +121,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'drersRealtimeEndpointInfo'+-- * 'drersRealtimeEndpointInfo' - The endpoint information of the @MLModel@ ----- * 'drersMLModelId'+-- * 'drersMLModelId' - A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. ----- * 'drersResponseStatus'+-- * 'drersResponseStatus' - -- | The response status code. deleteRealtimeEndpointResponse     :: Int -- ^ 'drersResponseStatus'     -> DeleteRealtimeEndpointResponse@@ -132,15 +136,15 @@     , _drersResponseStatus = pResponseStatus_     } --- | The endpoint information of the 'MLModel'+-- | The endpoint information of the @MLModel@ drersRealtimeEndpointInfo :: Lens' DeleteRealtimeEndpointResponse (Maybe RealtimeEndpointInfo) drersRealtimeEndpointInfo = lens _drersRealtimeEndpointInfo (\ s a -> s{_drersRealtimeEndpointInfo = a}); --- | A user-supplied ID that uniquely identifies the 'MLModel'. This value should be identical to the value of the 'MLModelId' in the request.+-- | A user-supplied ID that uniquely identifies the @MLModel@ . This value should be identical to the value of the @MLModelId@ in the request. drersMLModelId :: Lens' DeleteRealtimeEndpointResponse (Maybe Text) drersMLModelId = lens _drersMLModelId (\ s a -> s{_drersMLModelId = a}); --- | The response status code.+-- | -- | The response status code. drersResponseStatus :: Lens' DeleteRealtimeEndpointResponse Int drersResponseStatus = lens _drersResponseStatus (\ s a -> s{_drersResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- 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.+-- 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.+--+-- If you specify a tag that doesn't exist, Amazon ML ignores it.+-- module Network.AWS.MachineLearning.DeleteTags     (     -- * Creating a Request@@ -58,11 +60,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dTagKeys'+-- * 'dTagKeys' - One or more tags to delete. ----- * 'dResourceId'+-- * 'dResourceId' - The ID of the tagged ML object. For example, @exampleModelId@ . ----- * 'dResourceType'+-- * 'dResourceType' - The type of the tagged ML object. deleteTags     :: Text -- ^ 'dResourceId'     -> TaggableResourceType -- ^ 'dResourceType'@@ -78,7 +80,7 @@ dTagKeys :: Lens' DeleteTags [Text] dTagKeys = lens _dTagKeys (\ s a -> s{_dTagKeys = a}) . _Coerce; --- | The ID of the tagged ML object. For example, 'exampleModelId'.+-- | The ID of the tagged ML object. For example, @exampleModelId@ . dResourceId :: Lens' DeleteTags Text dResourceId = lens _dResourceId (\ s a -> s{_dResourceId = a}); @@ -125,6 +127,8 @@  -- | Amazon ML returns the following elements. --+--+-- -- /See:/ 'deleteTagsResponse' smart constructor. data DeleteTagsResponse = DeleteTagsResponse'     { _drsResourceId     :: !(Maybe Text)@@ -136,11 +140,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'drsResourceId'+-- * 'drsResourceId' - The ID of the ML object from which tags were deleted. ----- * 'drsResourceType'+-- * 'drsResourceType' - The type of the ML object from which tags were deleted. ----- * 'drsResponseStatus'+-- * 'drsResponseStatus' - -- | The response status code. deleteTagsResponse     :: Int -- ^ 'drsResponseStatus'     -> DeleteTagsResponse@@ -159,7 +163,7 @@ drsResourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType) drsResourceType = lens _drsResourceType (\ s a -> s{_drsResourceType = a}); --- | The response status code.+-- | -- | The response status code. drsResponseStatus :: Lens' DeleteTagsResponse Int drsResponseStatus = lens _drsResponseStatus (\ s a -> s{_drsResponseStatus = a}); 

@@ -18,8 +18,10 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a list of 'BatchPrediction' operations that match the search criteria in the request.+-- Returns a list of @BatchPrediction@ operations that match the search criteria in the request. --+--+-- -- This operation returns paginated results. module Network.AWS.MachineLearning.DescribeBatchPredictions     (@@ -75,27 +77,27 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dbpEQ'+-- * 'dbpEQ' - The equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . ----- * 'dbpGE'+-- * 'dbpGE' - The greater than or equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . ----- * 'dbpPrefix'+-- * 'dbpPrefix' - A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, a @Batch Prediction@ operation could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @BatchPrediction@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday ----- * 'dbpGT'+-- * 'dbpGT' - The greater than operator. The @BatchPrediction@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . ----- * 'dbpNE'+-- * 'dbpNE' - The not equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . ----- * 'dbpNextToken'+-- * 'dbpNextToken' - An ID of the page in the paginated results. ----- * 'dbpSortOrder'+-- * 'dbpSortOrder' - A two-value parameter that determines the sequence of the resulting list of @MLModel@ s.     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . ----- * 'dbpLimit'+-- * 'dbpLimit' - 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@ . ----- * 'dbpLT'+-- * 'dbpLT' - The less than operator. The @BatchPrediction@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . ----- * 'dbpFilterVariable'+-- * 'dbpFilterVariable' - Use one of the following variables to filter a list of @BatchPrediction@ :     * @CreatedAt@ - Sets the search criteria to the @BatchPrediction@ creation date.    * @Status@ - Sets the search criteria to the @BatchPrediction@ status.    * @Name@ - Sets the search criteria to the contents of the @BatchPrediction@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked the @BatchPrediction@ creation.    * @MLModelId@ - Sets the search criteria to the @MLModel@ used in the @BatchPrediction@ .    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in the @BatchPrediction@ .    * @DataURI@ - Sets the search criteria to the data file(s) used in the @BatchPrediction@ . The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. ----- * 'dbpLE'+-- * 'dbpLE' - The less than or equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . describeBatchPredictions     :: DescribeBatchPredictions describeBatchPredictions =@@ -113,32 +115,23 @@     , _dbpLE = Nothing     } --- | The equal to operator. The 'BatchPrediction' results will have 'FilterVariable' values that exactly match the value specified with 'EQ'.+-- | The equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . dbpEQ :: Lens' DescribeBatchPredictions (Maybe Text) dbpEQ = lens _dbpEQ (\ s a -> s{_dbpEQ = a}); --- | The greater than or equal to operator. The 'BatchPrediction' results will have 'FilterVariable' values that are greater than or equal to the value specified with 'GE'.+-- | The greater than or equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . dbpGE :: Lens' DescribeBatchPredictions (Maybe Text) dbpGE = lens _dbpGE (\ s a -> s{_dbpGE = a}); --- | A string that is found at the beginning of a variable, such as 'Name' or 'Id'.------ For example, a 'Batch Prediction' operation could have the 'Name' '2014-09-09-HolidayGiftMailer'. To search for this 'BatchPrediction', select 'Name' for the 'FilterVariable' and any of the following strings for the 'Prefix':------ -   2014-09------ -   2014-09-09------ -   2014-09-09-Holiday---+-- | A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, a @Batch Prediction@ operation could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @BatchPrediction@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday dbpPrefix :: Lens' DescribeBatchPredictions (Maybe Text) dbpPrefix = lens _dbpPrefix (\ s a -> s{_dbpPrefix = a}); --- | The greater than operator. The 'BatchPrediction' results will have 'FilterVariable' values that are greater than the value specified with 'GT'.+-- | The greater than operator. The @BatchPrediction@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . dbpGT :: Lens' DescribeBatchPredictions (Maybe Text) dbpGT = lens _dbpGT (\ s a -> s{_dbpGT = a}); --- | The not equal to operator. The 'BatchPrediction' results will have 'FilterVariable' values not equal to the value specified with 'NE'.+-- | The not equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . dbpNE :: Lens' DescribeBatchPredictions (Maybe Text) dbpNE = lens _dbpNE (\ s a -> s{_dbpNE = a}); @@ -146,36 +139,23 @@ dbpNextToken :: Lens' DescribeBatchPredictions (Maybe Text) dbpNextToken = lens _dbpNextToken (\ s a -> s{_dbpNextToken = a}); --- | A two-value parameter that determines the sequence of the resulting list of 'MLModel's.------ -   'asc' - Arranges the list in ascending order (A-Z, 0-9).--- -   'dsc' - Arranges the list in descending order (Z-A, 9-0).------ Results are sorted by 'FilterVariable'.+-- | A two-value parameter that determines the sequence of the resulting list of @MLModel@ s.     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . 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; --- | The less than operator. The 'BatchPrediction' results will have 'FilterVariable' values that are less than the value specified with 'LT'.+-- | The less than operator. The @BatchPrediction@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . dbpLT :: Lens' DescribeBatchPredictions (Maybe Text) dbpLT = lens _dbpLT (\ s a -> s{_dbpLT = a}); --- | Use one of the following variables to filter a list of 'BatchPrediction':------ -   'CreatedAt' - Sets the search criteria to the 'BatchPrediction' creation date.--- -   'Status' - Sets the search criteria to the 'BatchPrediction' status.--- -   'Name' - Sets the search criteria to the contents of the 'BatchPrediction' ____ 'Name'.--- -   'IAMUser' - Sets the search criteria to the user account that invoked the 'BatchPrediction' creation.--- -   'MLModelId' - Sets the search criteria to the 'MLModel' used in the 'BatchPrediction'.--- -   'DataSourceId' - Sets the search criteria to the 'DataSource' used in the 'BatchPrediction'.--- -   'DataURI' - Sets the search criteria to the data file(s) used in the 'BatchPrediction'. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.+-- | Use one of the following variables to filter a list of @BatchPrediction@ :     * @CreatedAt@ - Sets the search criteria to the @BatchPrediction@ creation date.    * @Status@ - Sets the search criteria to the @BatchPrediction@ status.    * @Name@ - Sets the search criteria to the contents of the @BatchPrediction@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked the @BatchPrediction@ creation.    * @MLModelId@ - Sets the search criteria to the @MLModel@ used in the @BatchPrediction@ .    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in the @BatchPrediction@ .    * @DataURI@ - Sets the search criteria to the data file(s) used in the @BatchPrediction@ . The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. dbpFilterVariable :: Lens' DescribeBatchPredictions (Maybe BatchPredictionFilterVariable) dbpFilterVariable = lens _dbpFilterVariable (\ s a -> s{_dbpFilterVariable = a}); --- | The less than or equal to operator. The 'BatchPrediction' results will have 'FilterVariable' values that are less than or equal to the value specified with 'LE'.+-- | The less than or equal to operator. The @BatchPrediction@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . dbpLE :: Lens' DescribeBatchPredictions (Maybe Text) dbpLE = lens _dbpLE (\ s a -> s{_dbpLE = a}); @@ -230,8 +210,10 @@ 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'     { _dbpsrsResults        :: !(Maybe [BatchPrediction])@@ -243,11 +225,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dbpsrsResults'+-- * 'dbpsrsResults' - A list of @BatchPrediction@ objects that meet the search criteria. ----- * 'dbpsrsNextToken'+-- * 'dbpsrsNextToken' - The ID of the next page in the paginated results that indicates at least one more page follows. ----- * 'dbpsrsResponseStatus'+-- * 'dbpsrsResponseStatus' - -- | The response status code. describeBatchPredictionsResponse     :: Int -- ^ 'dbpsrsResponseStatus'     -> DescribeBatchPredictionsResponse@@ -258,7 +240,7 @@     , _dbpsrsResponseStatus = pResponseStatus_     } --- | A list of 'BatchPrediction' objects that meet the search criteria.+-- | A list of @BatchPrediction@ objects that meet the search criteria. dbpsrsResults :: Lens' DescribeBatchPredictionsResponse [BatchPrediction] dbpsrsResults = lens _dbpsrsResults (\ s a -> s{_dbpsrsResults = a}) . _Default . _Coerce; @@ -266,7 +248,7 @@ dbpsrsNextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text) dbpsrsNextToken = lens _dbpsrsNextToken (\ s a -> s{_dbpsrsNextToken = a}); --- | The response status code.+-- | -- | The response status code. dbpsrsResponseStatus :: Lens' DescribeBatchPredictionsResponse Int dbpsrsResponseStatus = lens _dbpsrsResponseStatus (\ s a -> s{_dbpsrsResponseStatus = a}); 

@@ -18,8 +18,10 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a list of 'DataSource' that match the search criteria in the request.+-- Returns a list of @DataSource@ that match the search criteria in the request. --+--+-- -- This operation returns paginated results. module Network.AWS.MachineLearning.DescribeDataSources     (@@ -75,27 +77,27 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ddsEQ'+-- * 'ddsEQ' - The equal to operator. The @DataSource@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . ----- * 'ddsGE'+-- * 'ddsGE' - The greater than or equal to operator. The @DataSource@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . ----- * 'ddsPrefix'+-- * 'ddsPrefix' - A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, a @DataSource@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @DataSource@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday ----- * 'ddsGT'+-- * 'ddsGT' - The greater than operator. The @DataSource@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . ----- * 'ddsNE'+-- * 'ddsNE' - The not equal to operator. The @DataSource@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . ----- * 'ddsNextToken'+-- * 'ddsNextToken' - The ID of the page in the paginated results. ----- * 'ddsSortOrder'+-- * 'ddsSortOrder' - A two-value parameter that determines the sequence of the resulting list of @DataSource@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . ----- * 'ddsLimit'+-- * 'ddsLimit' - The maximum number of @DataSource@ to include in the result. ----- * 'ddsLT'+-- * 'ddsLT' - The less than operator. The @DataSource@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . ----- * 'ddsFilterVariable'+-- * 'ddsFilterVariable' - Use one of the following variables to filter a list of @DataSource@ :     * @CreatedAt@ - Sets the search criteria to @DataSource@ creation dates.    * @Status@ - Sets the search criteria to @DataSource@ statuses.    * @Name@ - Sets the search criteria to the contents of @DataSource@ ____ @Name@ .    * @DataUri@ - Sets the search criteria to the URI of data files used to create the @DataSource@ . The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.    * @IAMUser@ - Sets the search criteria to the user account that invoked the @DataSource@ creation. ----- * 'ddsLE'+-- * 'ddsLE' - The less than or equal to operator. The @DataSource@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . describeDataSources     :: DescribeDataSources describeDataSources =@@ -113,32 +115,23 @@     , _ddsLE = Nothing     } --- | The equal to operator. The 'DataSource' results will have 'FilterVariable' values that exactly match the value specified with 'EQ'.+-- | The equal to operator. The @DataSource@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . ddsEQ :: Lens' DescribeDataSources (Maybe Text) ddsEQ = lens _ddsEQ (\ s a -> s{_ddsEQ = a}); --- | The greater than or equal to operator. The 'DataSource' results will have 'FilterVariable' values that are greater than or equal to the value specified with 'GE'.+-- | The greater than or equal to operator. The @DataSource@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . ddsGE :: Lens' DescribeDataSources (Maybe Text) ddsGE = lens _ddsGE (\ s a -> s{_ddsGE = a}); --- | A string that is found at the beginning of a variable, such as 'Name' or 'Id'.------ For example, a 'DataSource' could have the 'Name' '2014-09-09-HolidayGiftMailer'. To search for this 'DataSource', select 'Name' for the 'FilterVariable' and any of the following strings for the 'Prefix':------ -   2014-09------ -   2014-09-09------ -   2014-09-09-Holiday---+-- | A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, a @DataSource@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @DataSource@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday ddsPrefix :: Lens' DescribeDataSources (Maybe Text) ddsPrefix = lens _ddsPrefix (\ s a -> s{_ddsPrefix = a}); --- | The greater than operator. The 'DataSource' results will have 'FilterVariable' values that are greater than the value specified with 'GT'.+-- | The greater than operator. The @DataSource@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . ddsGT :: Lens' DescribeDataSources (Maybe Text) ddsGT = lens _ddsGT (\ s a -> s{_ddsGT = a}); --- | The not equal to operator. The 'DataSource' results will have 'FilterVariable' values not equal to the value specified with 'NE'.+-- | The not equal to operator. The @DataSource@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . ddsNE :: Lens' DescribeDataSources (Maybe Text) ddsNE = lens _ddsNE (\ s a -> s{_ddsNE = a}); @@ -146,34 +139,23 @@ ddsNextToken :: Lens' DescribeDataSources (Maybe Text) ddsNextToken = lens _ddsNextToken (\ s a -> s{_ddsNextToken = a}); --- | A two-value parameter that determines the sequence of the resulting list of 'DataSource'.------ -   'asc' - Arranges the list in ascending order (A-Z, 0-9).--- -   'dsc' - Arranges the list in descending order (Z-A, 9-0).------ Results are sorted by 'FilterVariable'.+-- | A two-value parameter that determines the sequence of the resulting list of @DataSource@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . ddsSortOrder :: Lens' DescribeDataSources (Maybe SortOrder) ddsSortOrder = lens _ddsSortOrder (\ s a -> s{_ddsSortOrder = a}); --- | The maximum number of 'DataSource' to include in the result.+-- | The maximum number of @DataSource@ to include in the result. ddsLimit :: Lens' DescribeDataSources (Maybe Natural) ddsLimit = lens _ddsLimit (\ s a -> s{_ddsLimit = a}) . mapping _Nat; --- | The less than operator. The 'DataSource' results will have 'FilterVariable' values that are less than the value specified with 'LT'.+-- | The less than operator. The @DataSource@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . ddsLT :: Lens' DescribeDataSources (Maybe Text) ddsLT = lens _ddsLT (\ s a -> s{_ddsLT = a}); --- | Use one of the following variables to filter a list of 'DataSource':------ -   'CreatedAt' - Sets the search criteria to 'DataSource' creation dates.--- -   'Status' - Sets the search criteria to 'DataSource' statuses.--- -   'Name' - Sets the search criteria to the contents of 'DataSource' ____ 'Name'.--- -   'DataUri' - Sets the search criteria to the URI of data files used to create the 'DataSource'. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.--- -   'IAMUser' - Sets the search criteria to the user account that invoked the 'DataSource' creation.+-- | Use one of the following variables to filter a list of @DataSource@ :     * @CreatedAt@ - Sets the search criteria to @DataSource@ creation dates.    * @Status@ - Sets the search criteria to @DataSource@ statuses.    * @Name@ - Sets the search criteria to the contents of @DataSource@ ____ @Name@ .    * @DataUri@ - Sets the search criteria to the URI of data files used to create the @DataSource@ . The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.    * @IAMUser@ - Sets the search criteria to the user account that invoked the @DataSource@ creation. ddsFilterVariable :: Lens' DescribeDataSources (Maybe DataSourceFilterVariable) ddsFilterVariable = lens _ddsFilterVariable (\ s a -> s{_ddsFilterVariable = a}); --- | The less than or equal to operator. The 'DataSource' results will have 'FilterVariable' values that are less than or equal to the value specified with 'LE'.+-- | The less than or equal to operator. The @DataSource@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . ddsLE :: Lens' DescribeDataSources (Maybe Text) ddsLE = lens _ddsLE (\ s a -> s{_ddsLE = a}); @@ -228,8 +210,10 @@ instance ToQuery DescribeDataSources where         toQuery = const mempty --- | Represents the query results from a < DescribeDataSources> operation. The content is essentially a list of 'DataSource'.+-- | Represents the query results from a 'DescribeDataSources' operation. The content is essentially a list of @DataSource@ . --+--+-- -- /See:/ 'describeDataSourcesResponse' smart constructor. data DescribeDataSourcesResponse = DescribeDataSourcesResponse'     { _ddssrsResults        :: !(Maybe [DataSource])@@ -241,11 +225,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ddssrsResults'+-- * 'ddssrsResults' - A list of @DataSource@ that meet the search criteria. ----- * 'ddssrsNextToken'+-- * 'ddssrsNextToken' - An ID of the next page in the paginated results that indicates at least one more page follows. ----- * 'ddssrsResponseStatus'+-- * 'ddssrsResponseStatus' - -- | The response status code. describeDataSourcesResponse     :: Int -- ^ 'ddssrsResponseStatus'     -> DescribeDataSourcesResponse@@ -256,7 +240,7 @@     , _ddssrsResponseStatus = pResponseStatus_     } --- | A list of 'DataSource' that meet the search criteria.+-- | A list of @DataSource@ that meet the search criteria. ddssrsResults :: Lens' DescribeDataSourcesResponse [DataSource] ddssrsResults = lens _ddssrsResults (\ s a -> s{_ddssrsResults = a}) . _Default . _Coerce; @@ -264,7 +248,7 @@ ddssrsNextToken :: Lens' DescribeDataSourcesResponse (Maybe Text) ddssrsNextToken = lens _ddssrsNextToken (\ s a -> s{_ddssrsNextToken = a}); --- | The response status code.+-- | -- | The response status code. ddssrsResponseStatus :: Lens' DescribeDataSourcesResponse Int ddssrsResponseStatus = lens _ddssrsResponseStatus (\ s a -> s{_ddssrsResponseStatus = a}); 

@@ -18,8 +18,10 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a list of 'DescribeEvaluations' that match the search criteria in the request.+-- Returns a list of @DescribeEvaluations@ that match the search criteria in the request. --+--+-- -- This operation returns paginated results. module Network.AWS.MachineLearning.DescribeEvaluations     (@@ -75,27 +77,27 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'deEQ'+-- * 'deEQ' - The equal to operator. The @Evaluation@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . ----- * 'deGE'+-- * 'deGE' - The greater than or equal to operator. The @Evaluation@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . ----- * 'dePrefix'+-- * 'dePrefix' - A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, an @Evaluation@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @Evaluation@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday ----- * 'deGT'+-- * 'deGT' - The greater than operator. The @Evaluation@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . ----- * 'deNE'+-- * 'deNE' - The not equal to operator. The @Evaluation@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . ----- * 'deNextToken'+-- * 'deNextToken' - The ID of the page in the paginated results. ----- * 'deSortOrder'+-- * 'deSortOrder' - A two-value parameter that determines the sequence of the resulting list of @Evaluation@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . ----- * 'deLimit'+-- * 'deLimit' - The maximum number of @Evaluation@ to include in the result. ----- * 'deLT'+-- * 'deLT' - The less than operator. The @Evaluation@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . ----- * 'deFilterVariable'+-- * 'deFilterVariable' - Use one of the following variable to filter a list of @Evaluation@ objects:     * @CreatedAt@ - Sets the search criteria to the @Evaluation@ creation date.    * @Status@ - Sets the search criteria to the @Evaluation@ status.    * @Name@ - Sets the search criteria to the contents of @Evaluation@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked an @Evaluation@ .    * @MLModelId@ - Sets the search criteria to the @MLModel@ that was evaluated.    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in @Evaluation@ .    * @DataUri@ - Sets the search criteria to the data file(s) used in @Evaluation@ . The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. ----- * 'deLE'+-- * 'deLE' - The less than or equal to operator. The @Evaluation@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . describeEvaluations     :: DescribeEvaluations describeEvaluations =@@ -113,32 +115,23 @@     , _deLE = Nothing     } --- | The equal to operator. The 'Evaluation' results will have 'FilterVariable' values that exactly match the value specified with 'EQ'.+-- | The equal to operator. The @Evaluation@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . deEQ :: Lens' DescribeEvaluations (Maybe Text) deEQ = lens _deEQ (\ s a -> s{_deEQ = a}); --- | The greater than or equal to operator. The 'Evaluation' results will have 'FilterVariable' values that are greater than or equal to the value specified with 'GE'.+-- | The greater than or equal to operator. The @Evaluation@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . deGE :: Lens' DescribeEvaluations (Maybe Text) deGE = lens _deGE (\ s a -> s{_deGE = a}); --- | A string that is found at the beginning of a variable, such as 'Name' or 'Id'.------ For example, an 'Evaluation' could have the 'Name' '2014-09-09-HolidayGiftMailer'. To search for this 'Evaluation', select 'Name' for the 'FilterVariable' and any of the following strings for the 'Prefix':------ -   2014-09------ -   2014-09-09------ -   2014-09-09-Holiday---+-- | A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, an @Evaluation@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @Evaluation@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday dePrefix :: Lens' DescribeEvaluations (Maybe Text) dePrefix = lens _dePrefix (\ s a -> s{_dePrefix = a}); --- | The greater than operator. The 'Evaluation' results will have 'FilterVariable' values that are greater than the value specified with 'GT'.+-- | The greater than operator. The @Evaluation@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . deGT :: Lens' DescribeEvaluations (Maybe Text) deGT = lens _deGT (\ s a -> s{_deGT = a}); --- | The not equal to operator. The 'Evaluation' results will have 'FilterVariable' values not equal to the value specified with 'NE'.+-- | The not equal to operator. The @Evaluation@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . deNE :: Lens' DescribeEvaluations (Maybe Text) deNE = lens _deNE (\ s a -> s{_deNE = a}); @@ -146,36 +139,23 @@ deNextToken :: Lens' DescribeEvaluations (Maybe Text) deNextToken = lens _deNextToken (\ s a -> s{_deNextToken = a}); --- | A two-value parameter that determines the sequence of the resulting list of 'Evaluation'.------ -   'asc' - Arranges the list in ascending order (A-Z, 0-9).--- -   'dsc' - Arranges the list in descending order (Z-A, 9-0).------ Results are sorted by 'FilterVariable'.+-- | A two-value parameter that determines the sequence of the resulting list of @Evaluation@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . deSortOrder :: Lens' DescribeEvaluations (Maybe SortOrder) deSortOrder = lens _deSortOrder (\ s a -> s{_deSortOrder = a}); --- | The maximum number of 'Evaluation' to include in the result.+-- | The maximum number of @Evaluation@ to include in the result. deLimit :: Lens' DescribeEvaluations (Maybe Natural) deLimit = lens _deLimit (\ s a -> s{_deLimit = a}) . mapping _Nat; --- | The less than operator. The 'Evaluation' results will have 'FilterVariable' values that are less than the value specified with 'LT'.+-- | The less than operator. The @Evaluation@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . deLT :: Lens' DescribeEvaluations (Maybe Text) deLT = lens _deLT (\ s a -> s{_deLT = a}); --- | Use one of the following variable to filter a list of 'Evaluation' objects:------ -   'CreatedAt' - Sets the search criteria to the 'Evaluation' creation date.--- -   'Status' - Sets the search criteria to the 'Evaluation' status.--- -   'Name' - Sets the search criteria to the contents of 'Evaluation' ____ 'Name'.--- -   'IAMUser' - Sets the search criteria to the user account that invoked an 'Evaluation'.--- -   'MLModelId' - Sets the search criteria to the 'MLModel' that was evaluated.--- -   'DataSourceId' - Sets the search criteria to the 'DataSource' used in 'Evaluation'.--- -   'DataUri' - Sets the search criteria to the data file(s) used in 'Evaluation'. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.+-- | Use one of the following variable to filter a list of @Evaluation@ objects:     * @CreatedAt@ - Sets the search criteria to the @Evaluation@ creation date.    * @Status@ - Sets the search criteria to the @Evaluation@ status.    * @Name@ - Sets the search criteria to the contents of @Evaluation@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked an @Evaluation@ .    * @MLModelId@ - Sets the search criteria to the @MLModel@ that was evaluated.    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in @Evaluation@ .    * @DataUri@ - Sets the search criteria to the data file(s) used in @Evaluation@ . The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. deFilterVariable :: Lens' DescribeEvaluations (Maybe EvaluationFilterVariable) deFilterVariable = lens _deFilterVariable (\ s a -> s{_deFilterVariable = a}); --- | The less than or equal to operator. The 'Evaluation' results will have 'FilterVariable' values that are less than or equal to the value specified with 'LE'.+-- | The less than or equal to operator. The @Evaluation@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . deLE :: Lens' DescribeEvaluations (Maybe Text) deLE = lens _deLE (\ s a -> s{_deLE = a}); @@ -230,8 +210,10 @@ 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'     { _desrsResults        :: !(Maybe [Evaluation])@@ -243,11 +225,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'desrsResults'+-- * 'desrsResults' - A list of @Evaluation@ that meet the search criteria. ----- * 'desrsNextToken'+-- * 'desrsNextToken' - The ID of the next page in the paginated results that indicates at least one more page follows. ----- * 'desrsResponseStatus'+-- * 'desrsResponseStatus' - -- | The response status code. describeEvaluationsResponse     :: Int -- ^ 'desrsResponseStatus'     -> DescribeEvaluationsResponse@@ -258,7 +240,7 @@     , _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; @@ -266,7 +248,7 @@ desrsNextToken :: Lens' DescribeEvaluationsResponse (Maybe Text) desrsNextToken = lens _desrsNextToken (\ s a -> s{_desrsNextToken = a}); --- | The response status code.+-- | -- | The response status code. desrsResponseStatus :: Lens' DescribeEvaluationsResponse Int desrsResponseStatus = lens _desrsResponseStatus (\ s a -> s{_desrsResponseStatus = a}); 

@@ -18,8 +18,10 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a list of 'MLModel' that match the search criteria in the request.+-- Returns a list of @MLModel@ that match the search criteria in the request. --+--+-- -- This operation returns paginated results. module Network.AWS.MachineLearning.DescribeMLModels     (@@ -75,27 +77,27 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dmlmEQ'+-- * 'dmlmEQ' - The equal to operator. The @MLModel@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . ----- * 'dmlmGE'+-- * 'dmlmGE' - The greater than or equal to operator. The @MLModel@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . ----- * 'dmlmPrefix'+-- * 'dmlmPrefix' - A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, an @MLModel@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @MLModel@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday ----- * 'dmlmGT'+-- * 'dmlmGT' - The greater than operator. The @MLModel@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . ----- * 'dmlmNE'+-- * 'dmlmNE' - The not equal to operator. The @MLModel@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . ----- * 'dmlmNextToken'+-- * 'dmlmNextToken' - The ID of the page in the paginated results. ----- * 'dmlmSortOrder'+-- * 'dmlmSortOrder' - A two-value parameter that determines the sequence of the resulting list of @MLModel@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . ----- * 'dmlmLimit'+-- * 'dmlmLimit' - 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@ . ----- * 'dmlmLT'+-- * 'dmlmLT' - The less than operator. The @MLModel@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . ----- * 'dmlmFilterVariable'+-- * 'dmlmFilterVariable' - Use one of the following variables to filter a list of @MLModel@ :     * @CreatedAt@ - Sets the search criteria to @MLModel@ creation date.    * @Status@ - Sets the search criteria to @MLModel@ status.    * @Name@ - Sets the search criteria to the contents of @MLModel@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked the @MLModel@ creation.    * @TrainingDataSourceId@ - Sets the search criteria to the @DataSource@ used to train one or more @MLModel@ .    * @RealtimeEndpointStatus@ - Sets the search criteria to the @MLModel@ real-time endpoint status.    * @MLModelType@ - Sets the search criteria to @MLModel@ type: binary, regression, or multi-class.    * @Algorithm@ - Sets the search criteria to the algorithm that the @MLModel@ uses.    * @TrainingDataURI@ - Sets the search criteria to the data file(s) used in training a @MLModel@ . The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. ----- * 'dmlmLE'+-- * 'dmlmLE' - The less than or equal to operator. The @MLModel@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . describeMLModels     :: DescribeMLModels describeMLModels =@@ -113,32 +115,23 @@     , _dmlmLE = Nothing     } --- | The equal to operator. The 'MLModel' results will have 'FilterVariable' values that exactly match the value specified with 'EQ'.+-- | The equal to operator. The @MLModel@ results will have @FilterVariable@ values that exactly match the value specified with @EQ@ . dmlmEQ :: Lens' DescribeMLModels (Maybe Text) dmlmEQ = lens _dmlmEQ (\ s a -> s{_dmlmEQ = a}); --- | The greater than or equal to operator. The 'MLModel' results will have 'FilterVariable' values that are greater than or equal to the value specified with 'GE'.+-- | The greater than or equal to operator. The @MLModel@ results will have @FilterVariable@ values that are greater than or equal to the value specified with @GE@ . dmlmGE :: Lens' DescribeMLModels (Maybe Text) dmlmGE = lens _dmlmGE (\ s a -> s{_dmlmGE = a}); --- | A string that is found at the beginning of a variable, such as 'Name' or 'Id'.------ For example, an 'MLModel' could have the 'Name' '2014-09-09-HolidayGiftMailer'. To search for this 'MLModel', select 'Name' for the 'FilterVariable' and any of the following strings for the 'Prefix':------ -   2014-09------ -   2014-09-09------ -   2014-09-09-Holiday---+-- | A string that is found at the beginning of a variable, such as @Name@ or @Id@ . For example, an @MLModel@ could have the @Name@ @2014-09-09-HolidayGiftMailer@ . To search for this @MLModel@ , select @Name@ for the @FilterVariable@ and any of the following strings for the @Prefix@ :      * 2014-09     * 2014-09-09     * 2014-09-09-Holiday dmlmPrefix :: Lens' DescribeMLModels (Maybe Text) dmlmPrefix = lens _dmlmPrefix (\ s a -> s{_dmlmPrefix = a}); --- | The greater than operator. The 'MLModel' results will have 'FilterVariable' values that are greater than the value specified with 'GT'.+-- | The greater than operator. The @MLModel@ results will have @FilterVariable@ values that are greater than the value specified with @GT@ . dmlmGT :: Lens' DescribeMLModels (Maybe Text) dmlmGT = lens _dmlmGT (\ s a -> s{_dmlmGT = a}); --- | The not equal to operator. The 'MLModel' results will have 'FilterVariable' values not equal to the value specified with 'NE'.+-- | The not equal to operator. The @MLModel@ results will have @FilterVariable@ values not equal to the value specified with @NE@ . dmlmNE :: Lens' DescribeMLModels (Maybe Text) dmlmNE = lens _dmlmNE (\ s a -> s{_dmlmNE = a}); @@ -146,38 +139,23 @@ dmlmNextToken :: Lens' DescribeMLModels (Maybe Text) dmlmNextToken = lens _dmlmNextToken (\ s a -> s{_dmlmNextToken = a}); --- | A two-value parameter that determines the sequence of the resulting list of 'MLModel'.------ -   'asc' - Arranges the list in ascending order (A-Z, 0-9).--- -   'dsc' - Arranges the list in descending order (Z-A, 9-0).------ Results are sorted by 'FilterVariable'.+-- | A two-value parameter that determines the sequence of the resulting list of @MLModel@ .     * @asc@ - Arranges the list in ascending order (A-Z, 0-9).    * @dsc@ - Arranges the list in descending order (Z-A, 9-0). Results are sorted by @FilterVariable@ . 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; --- | The less than operator. The 'MLModel' results will have 'FilterVariable' values that are less than the value specified with 'LT'.+-- | The less than operator. The @MLModel@ results will have @FilterVariable@ values that are less than the value specified with @LT@ . dmlmLT :: Lens' DescribeMLModels (Maybe Text) dmlmLT = lens _dmlmLT (\ s a -> s{_dmlmLT = a}); --- | Use one of the following variables to filter a list of 'MLModel':------ -   'CreatedAt' - Sets the search criteria to 'MLModel' creation date.--- -   'Status' - Sets the search criteria to 'MLModel' status.--- -   'Name' - Sets the search criteria to the contents of 'MLModel' ____ 'Name'.--- -   'IAMUser' - Sets the search criteria to the user account that invoked the 'MLModel' creation.--- -   'TrainingDataSourceId' - Sets the search criteria to the 'DataSource' used to train one or more 'MLModel'.--- -   'RealtimeEndpointStatus' - Sets the search criteria to the 'MLModel' real-time endpoint status.--- -   'MLModelType' - Sets the search criteria to 'MLModel' type: binary, regression, or multi-class.--- -   'Algorithm' - Sets the search criteria to the algorithm that the 'MLModel' uses.--- -   'TrainingDataURI' - Sets the search criteria to the data file(s) used in training a 'MLModel'. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.+-- | Use one of the following variables to filter a list of @MLModel@ :     * @CreatedAt@ - Sets the search criteria to @MLModel@ creation date.    * @Status@ - Sets the search criteria to @MLModel@ status.    * @Name@ - Sets the search criteria to the contents of @MLModel@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked the @MLModel@ creation.    * @TrainingDataSourceId@ - Sets the search criteria to the @DataSource@ used to train one or more @MLModel@ .    * @RealtimeEndpointStatus@ - Sets the search criteria to the @MLModel@ real-time endpoint status.    * @MLModelType@ - Sets the search criteria to @MLModel@ type: binary, regression, or multi-class.    * @Algorithm@ - Sets the search criteria to the algorithm that the @MLModel@ uses.    * @TrainingDataURI@ - Sets the search criteria to the data file(s) used in training a @MLModel@ . The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. dmlmFilterVariable :: Lens' DescribeMLModels (Maybe MLModelFilterVariable) dmlmFilterVariable = lens _dmlmFilterVariable (\ s a -> s{_dmlmFilterVariable = a}); --- | The less than or equal to operator. The 'MLModel' results will have 'FilterVariable' values that are less than or equal to the value specified with 'LE'.+-- | The less than or equal to operator. The @MLModel@ results will have @FilterVariable@ values that are less than or equal to the value specified with @LE@ . dmlmLE :: Lens' DescribeMLModels (Maybe Text) dmlmLE = lens _dmlmLE (\ s a -> s{_dmlmLE = a}); @@ -230,8 +208,10 @@ 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'     { _dmlmsrsResults        :: !(Maybe [MLModel])@@ -243,11 +223,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dmlmsrsResults'+-- * 'dmlmsrsResults' - A list of @MLModel@ that meet the search criteria. ----- * 'dmlmsrsNextToken'+-- * 'dmlmsrsNextToken' - The ID of the next page in the paginated results that indicates at least one more page follows. ----- * 'dmlmsrsResponseStatus'+-- * 'dmlmsrsResponseStatus' - -- | The response status code. describeMLModelsResponse     :: Int -- ^ 'dmlmsrsResponseStatus'     -> DescribeMLModelsResponse@@ -258,7 +238,7 @@     , _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; @@ -266,7 +246,7 @@ dmlmsrsNextToken :: Lens' DescribeMLModelsResponse (Maybe Text) dmlmsrsNextToken = lens _dmlmsrsNextToken (\ s a -> s{_dmlmsrsNextToken = a}); --- | The response status code.+-- | -- | The response status code. dmlmsrsResponseStatus :: Lens' DescribeMLModelsResponse Int dmlmsrsResponseStatus = lens _dmlmsrsResponseStatus (\ s a -> s{_dmlmsrsResponseStatus = a}); 

@@ -19,6 +19,8 @@ -- 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@@ -55,9 +57,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dtResourceId'+-- * 'dtResourceId' - The ID of the ML object. For example, @exampleModelId@ . ----- * 'dtResourceType'+-- * 'dtResourceType' - The type of the ML object. describeTags     :: Text -- ^ 'dtResourceId'     -> TaggableResourceType -- ^ 'dtResourceType'@@ -68,7 +70,7 @@     , _dtResourceType = pResourceType_     } --- | The ID of the ML object. For example, 'exampleModelId'.+-- | The ID of the ML object. For example, @exampleModelId@ . dtResourceId :: Lens' DescribeTags Text dtResourceId = lens _dtResourceId (\ s a -> s{_dtResourceId = a}); @@ -115,6 +117,8 @@  -- | Amazon ML returns the following elements. --+--+-- -- /See:/ 'describeTagsResponse' smart constructor. data DescribeTagsResponse = DescribeTagsResponse'     { _dtrsResourceId     :: !(Maybe Text)@@ -127,13 +131,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dtrsResourceId'+-- * 'dtrsResourceId' - The ID of the tagged ML object. ----- * 'dtrsResourceType'+-- * 'dtrsResourceType' - The type of the tagged ML object. ----- * 'dtrsTags'+-- * 'dtrsTags' - A list of tags associated with the ML object. ----- * 'dtrsResponseStatus'+-- * 'dtrsResponseStatus' - -- | The response status code. describeTagsResponse     :: Int -- ^ 'dtrsResponseStatus'     -> DescribeTagsResponse@@ -157,7 +161,7 @@ dtrsTags :: Lens' DescribeTagsResponse [Tag] dtrsTags = lens _dtrsTags (\ s a -> s{_dtrsTags = a}) . _Default . _Coerce; --- | The response status code.+-- | -- | The response status code. dtrsResponseStatus :: Lens' DescribeTagsResponse Int dtrsResponseStatus = lens _dtrsResponseStatus (\ s a -> s{_dtrsResponseStatus = a}); 

@@ -18,7 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a 'BatchPrediction' that includes detailed metadata, status, and data file information for a 'Batch Prediction' request.+-- Returns a @BatchPrediction@ that includes detailed metadata, status, and data file information for a @Batch Prediction@ request.+--+-- module Network.AWS.MachineLearning.GetBatchPrediction     (     -- * Creating a Request@@ -67,7 +69,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gbpBatchPredictionId'+-- * 'gbpBatchPredictionId' - An ID assigned to the @BatchPrediction@ at creation. getBatchPrediction     :: Text -- ^ 'gbpBatchPredictionId'     -> GetBatchPrediction@@ -76,7 +78,7 @@     { _gbpBatchPredictionId = pBatchPredictionId_     } --- | An ID assigned to the 'BatchPrediction' at creation.+-- | An ID assigned to the @BatchPrediction@ at creation. gbpBatchPredictionId :: Lens' GetBatchPrediction Text gbpBatchPredictionId = lens _gbpBatchPredictionId (\ s a -> s{_gbpBatchPredictionId = a}); @@ -133,8 +135,10 @@ 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)@@ -161,41 +165,41 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gbprsStatus'+-- * 'gbprsStatus' - The status of the @BatchPrediction@ , which can be one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.    * @INPROGRESS@ - The batch predictions are in progress.    * @FAILED@ - The request to perform a batch prediction did not run to completion. It is not usable.    * @COMPLETED@ - The batch prediction process completed successfully.    * @DELETED@ - The @BatchPrediction@ is marked as deleted. It is not usable. ----- * 'gbprsLastUpdatedAt'+-- * 'gbprsLastUpdatedAt' - The time of the most recent edit to @BatchPrediction@ . The time is expressed in epoch time. ----- * 'gbprsCreatedAt'+-- * 'gbprsCreatedAt' - The time when the @BatchPrediction@ was created. The time is expressed in epoch time. ----- * 'gbprsComputeTime'+-- * 'gbprsComputeTime' - 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. ----- * 'gbprsInputDataLocationS3'+-- * 'gbprsInputDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'gbprsMLModelId'+-- * 'gbprsMLModelId' - The ID of the @MLModel@ that generated predictions for the @BatchPrediction@ request. ----- * 'gbprsBatchPredictionDataSourceId'+-- * 'gbprsBatchPredictionDataSourceId' - The ID of the @DataSource@ that was used to create the @BatchPrediction@ . ----- * 'gbprsTotalRecordCount'+-- * 'gbprsTotalRecordCount' - The number of total records that Amazon Machine Learning saw while processing the @BatchPrediction@ . ----- * 'gbprsStartedAt'+-- * 'gbprsStartedAt' - The epoch time when Amazon Machine Learning marked the @BatchPrediction@ as @INPROGRESS@ . @StartedAt@ isn't available if the @BatchPrediction@ is in the @PENDING@ state. ----- * 'gbprsBatchPredictionId'+-- * 'gbprsBatchPredictionId' - An ID assigned to the @BatchPrediction@ at creation. This value should be identical to the value of the @BatchPredictionID@ in the request. ----- * 'gbprsFinishedAt'+-- * 'gbprsFinishedAt' - 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. ----- * 'gbprsInvalidRecordCount'+-- * 'gbprsInvalidRecordCount' - The number of invalid records that Amazon Machine Learning saw while processing the @BatchPrediction@ . ----- * 'gbprsCreatedByIAMUser'+-- * 'gbprsCreatedByIAMUser' - 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. ----- * 'gbprsName'+-- * 'gbprsName' - A user-supplied name or description of the @BatchPrediction@ . ----- * 'gbprsLogURI'+-- * 'gbprsLogURI' - A link to the file that contains logs of the @CreateBatchPrediction@ operation. ----- * 'gbprsMessage'+-- * 'gbprsMessage' - A description of the most recent details about processing the batch prediction request. ----- * 'gbprsOutputURI'+-- * 'gbprsOutputURI' - The location of an Amazon S3 bucket or directory to receive the operation results. ----- * 'gbprsResponseStatus'+-- * 'gbprsResponseStatus' - -- | The response status code. getBatchPredictionResponse     :: Int -- ^ 'gbprsResponseStatus'     -> GetBatchPredictionResponse@@ -221,25 +225,19 @@     , _gbprsResponseStatus = pResponseStatus_     } --- | The status of the 'BatchPrediction', which can be one of the following values:------ -   'PENDING' - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.--- -   'INPROGRESS' - The batch predictions are in progress.--- -   'FAILED' - The request to perform a batch prediction did not run to completion. It is not usable.--- -   'COMPLETED' - The batch prediction process completed successfully.--- -   'DELETED' - The 'BatchPrediction' is marked as deleted. It is not usable.+-- | The status of the @BatchPrediction@ , which can be one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.    * @INPROGRESS@ - The batch predictions are in progress.    * @FAILED@ - The request to perform a batch prediction did not run to completion. It is not usable.    * @COMPLETED@ - The batch prediction process completed successfully.    * @DELETED@ - The @BatchPrediction@ is marked as deleted. It is not usable. gbprsStatus :: Lens' GetBatchPredictionResponse (Maybe EntityStatus) gbprsStatus = lens _gbprsStatus (\ s a -> s{_gbprsStatus = a}); --- | The time of the most recent edit to 'BatchPrediction'. The time is expressed in epoch time.+-- | The time of the most recent edit to @BatchPrediction@ . The time is expressed in epoch time. gbprsLastUpdatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime) gbprsLastUpdatedAt = lens _gbprsLastUpdatedAt (\ s a -> s{_gbprsLastUpdatedAt = a}) . mapping _Time; --- | The time when the 'BatchPrediction' was created. The time is expressed in epoch time.+-- | The time when the @BatchPrediction@ was created. The time is expressed in epoch time. 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.+-- | 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}); @@ -247,43 +245,43 @@ gbprsInputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsInputDataLocationS3 = lens _gbprsInputDataLocationS3 (\ s a -> s{_gbprsInputDataLocationS3 = a}); --- | The ID of the 'MLModel' that generated predictions for the 'BatchPrediction' request.+-- | The ID of the @MLModel@ that generated predictions for the @BatchPrediction@ request. gbprsMLModelId :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsMLModelId = lens _gbprsMLModelId (\ s a -> s{_gbprsMLModelId = a}); --- | The ID of the 'DataSource' that was used to create the 'BatchPrediction'.+-- | The ID of the @DataSource@ that was used to create the @BatchPrediction@ . 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'.+-- | 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.+-- | 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.+-- | 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.+-- | 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'.+-- | 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.+-- | 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}); --- | A user-supplied name or description of the 'BatchPrediction'.+-- | A user-supplied name or description of the @BatchPrediction@ . 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}); @@ -295,7 +293,7 @@ gbprsOutputURI :: Lens' GetBatchPredictionResponse (Maybe Text) gbprsOutputURI = lens _gbprsOutputURI (\ s a -> s{_gbprsOutputURI = a}); --- | The response status code.+-- | -- | The response status code. gbprsResponseStatus :: Lens' GetBatchPredictionResponse Int gbprsResponseStatus = lens _gbprsResponseStatus (\ s a -> s{_gbprsResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns a 'DataSource' that includes metadata and data file information, as well as the current status of the 'DataSource'.+-- Returns a @DataSource@ that includes metadata and data file information, as well as the current status of the @DataSource@ . ----- 'GetDataSource' provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.+--+-- @GetDataSource@ provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.+-- module Network.AWS.MachineLearning.GetDataSource     (     -- * Creating a Request@@ -74,9 +76,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gdsVerbose'+-- * 'gdsVerbose' - Specifies whether the @GetDataSource@ operation should return @DataSourceSchema@ . If true, @DataSourceSchema@ is returned. If false, @DataSourceSchema@ is not returned. ----- * 'gdsDataSourceId'+-- * 'gdsDataSourceId' - The ID assigned to the @DataSource@ at creation. getDataSource     :: Text -- ^ 'gdsDataSourceId'     -> GetDataSource@@ -86,15 +88,11 @@     , _gdsDataSourceId = pDataSourceId_     } --- | Specifies whether the 'GetDataSource' operation should return 'DataSourceSchema'.------ If true, 'DataSourceSchema' is returned.------ If false, 'DataSourceSchema' is not returned.+-- | Specifies whether the @GetDataSource@ operation should return @DataSourceSchema@ . If true, @DataSourceSchema@ is returned. If false, @DataSourceSchema@ is not returned. gdsVerbose :: Lens' GetDataSource (Maybe Bool) gdsVerbose = lens _gdsVerbose (\ s a -> s{_gdsVerbose = a}); --- | The ID assigned to the 'DataSource' at creation.+-- | The ID assigned to the @DataSource@ at creation. gdsDataSourceId :: Lens' GetDataSource Text gdsDataSourceId = lens _gdsDataSourceId (\ s a -> s{_gdsDataSourceId = a}); @@ -152,8 +150,10 @@ 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'     { _gdsrsStatus            :: !(Maybe EntityStatus)@@ -183,47 +183,47 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gdsrsStatus'+-- * 'gdsrsStatus' - The current status of the @DataSource@ . This element can have one of the following values:     * @PENDING@ - Amazon ML submitted a request to create a @DataSource@ .    * @INPROGRESS@ - The creation process is underway.    * @FAILED@ - The request to create a @DataSource@ did not run to completion. It is not usable.    * @COMPLETED@ - The creation process completed successfully.    * @DELETED@ - The @DataSource@ is marked as deleted. It is not usable. ----- * 'gdsrsNumberOfFiles'+-- * 'gdsrsNumberOfFiles' - The number of data files referenced by the @DataSource@ . ----- * 'gdsrsLastUpdatedAt'+-- * 'gdsrsLastUpdatedAt' - The time of the most recent edit to the @DataSource@ . The time is expressed in epoch time. ----- * 'gdsrsCreatedAt'+-- * 'gdsrsCreatedAt' - The time that the @DataSource@ was created. The time is expressed in epoch time. ----- * 'gdsrsComputeTime'+-- * 'gdsrsComputeTime' - 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. ----- * 'gdsrsDataSourceId'+-- * 'gdsrsDataSourceId' - The ID assigned to the @DataSource@ at creation. This value should be identical to the value of the @DataSourceId@ in the request. ----- * 'gdsrsRDSMetadata'+-- * 'gdsrsRDSMetadata' - Undocumented member. ----- * 'gdsrsDataSizeInBytes'+-- * 'gdsrsDataSizeInBytes' - The total size of observations in the data files. ----- * 'gdsrsDataSourceSchema'+-- * 'gdsrsDataSourceSchema' - The schema used by all of the data files of this @DataSource@ . ----- * 'gdsrsStartedAt'+-- * 'gdsrsStartedAt' - The epoch time when Amazon Machine Learning marked the @DataSource@ as @INPROGRESS@ . @StartedAt@ isn't available if the @DataSource@ is in the @PENDING@ state. ----- * 'gdsrsFinishedAt'+-- * 'gdsrsFinishedAt' - 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. ----- * 'gdsrsCreatedByIAMUser'+-- * 'gdsrsCreatedByIAMUser' - 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. ----- * 'gdsrsName'+-- * 'gdsrsName' - A user-supplied name or description of the @DataSource@ . ----- * 'gdsrsLogURI'+-- * 'gdsrsLogURI' - A link to the file containing logs of @CreateDataSourceFrom*@ operations. ----- * 'gdsrsDataLocationS3'+-- * 'gdsrsDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'gdsrsComputeStatistics'+-- * 'gdsrsComputeStatistics' - The parameter is @true@ if statistics need to be generated from the observation data. ----- * 'gdsrsMessage'+-- * 'gdsrsMessage' - The user-supplied description of the most recent details about creating the @DataSource@ . ----- * 'gdsrsRedshiftMetadata'+-- * 'gdsrsRedshiftMetadata' - Undocumented member. ----- * 'gdsrsDataRearrangement'+-- * 'gdsrsDataRearrangement' - A JSON string that represents the splitting and rearrangement requirement used when this @DataSource@ was created. ----- * 'gdsrsRoleARN'+-- * 'gdsrsRoleARN' - Undocumented member. ----- * 'gdsrsResponseStatus'+-- * 'gdsrsResponseStatus' - -- | The response status code. getDataSourceResponse     :: Int -- ^ 'gdsrsResponseStatus'     -> GetDataSourceResponse@@ -252,33 +252,27 @@     , _gdsrsResponseStatus = pResponseStatus_     } --- | The current status of the 'DataSource'. This element can have one of the following values:------ -   'PENDING' - Amazon ML submitted a request to create a 'DataSource'.--- -   'INPROGRESS' - The creation process is underway.--- -   'FAILED' - The request to create a 'DataSource' did not run to completion. It is not usable.--- -   'COMPLETED' - The creation process completed successfully.--- -   'DELETED' - The 'DataSource' is marked as deleted. It is not usable.+-- | The current status of the @DataSource@ . This element can have one of the following values:     * @PENDING@ - Amazon ML submitted a request to create a @DataSource@ .    * @INPROGRESS@ - The creation process is underway.    * @FAILED@ - The request to create a @DataSource@ did not run to completion. It is not usable.    * @COMPLETED@ - The creation process completed successfully.    * @DELETED@ - The @DataSource@ is marked as deleted. It is not usable. gdsrsStatus :: Lens' GetDataSourceResponse (Maybe EntityStatus) gdsrsStatus = lens _gdsrsStatus (\ s a -> s{_gdsrsStatus = a}); --- | The number of data files referenced by the 'DataSource'.+-- | The number of data files referenced by the @DataSource@ . gdsrsNumberOfFiles :: Lens' GetDataSourceResponse (Maybe Integer) gdsrsNumberOfFiles = lens _gdsrsNumberOfFiles (\ s a -> s{_gdsrsNumberOfFiles = a}); --- | The time of the most recent edit to the 'DataSource'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @DataSource@ . The time is expressed in epoch time. gdsrsLastUpdatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime) gdsrsLastUpdatedAt = lens _gdsrsLastUpdatedAt (\ s a -> s{_gdsrsLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'DataSource' was created. The time is expressed in epoch time.+-- | The time that the @DataSource@ was created. The time is expressed in epoch time. 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.+-- | 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.+-- | 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}); @@ -290,31 +284,27 @@ gdsrsDataSizeInBytes :: Lens' GetDataSourceResponse (Maybe Integer) gdsrsDataSizeInBytes = lens _gdsrsDataSizeInBytes (\ s a -> s{_gdsrsDataSizeInBytes = a}); --- | The schema used by all of the data files of this 'DataSource'.------ Note------ This parameter is provided as part of the verbose format.+-- | The schema used by all of the data files of this @DataSource@ . 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.+-- | 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.+-- | 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.+-- | 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}); --- | A user-supplied name or description of the 'DataSource'.+-- | A user-supplied name or description of the @DataSource@ . gdsrsName :: Lens' GetDataSourceResponse (Maybe Text) gdsrsName = lens _gdsrsName (\ s a -> s{_gdsrsName = a}); --- | A link to the file containing logs of 'CreateDataSourceFrom*' operations.+-- | A link to the file containing logs of @CreateDataSourceFrom*@ operations. gdsrsLogURI :: Lens' GetDataSourceResponse (Maybe Text) gdsrsLogURI = lens _gdsrsLogURI (\ s a -> s{_gdsrsLogURI = a}); @@ -322,11 +312,11 @@ gdsrsDataLocationS3 :: Lens' GetDataSourceResponse (Maybe Text) gdsrsDataLocationS3 = lens _gdsrsDataLocationS3 (\ s a -> s{_gdsrsDataLocationS3 = a}); --- | The parameter is 'true' if statistics need to be generated from the observation data.+-- | The parameter is @true@ if statistics need to be generated from the observation data. gdsrsComputeStatistics :: Lens' GetDataSourceResponse (Maybe Bool) gdsrsComputeStatistics = lens _gdsrsComputeStatistics (\ s a -> s{_gdsrsComputeStatistics = a}); --- | The user-supplied 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}); @@ -334,7 +324,7 @@ gdsrsRedshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata) gdsrsRedshiftMetadata = lens _gdsrsRedshiftMetadata (\ s a -> s{_gdsrsRedshiftMetadata = a}); --- | A JSON string that represents the splitting and rearrangement requirement used when this 'DataSource' was created.+-- | 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}); @@ -342,7 +332,7 @@ gdsrsRoleARN :: Lens' GetDataSourceResponse (Maybe Text) gdsrsRoleARN = lens _gdsrsRoleARN (\ s a -> s{_gdsrsRoleARN = a}); --- | The response status code.+-- | -- | The response status code. gdsrsResponseStatus :: Lens' GetDataSourceResponse Int gdsrsResponseStatus = lens _gdsrsResponseStatus (\ s a -> s{_gdsrsResponseStatus = a}); 

@@ -18,7 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns an 'Evaluation' that includes metadata as well as the current status of the 'Evaluation'.+-- Returns an @Evaluation@ that includes metadata as well as the current status of the @Evaluation@ .+--+-- module Network.AWS.MachineLearning.GetEvaluation     (     -- * Creating a Request@@ -65,7 +67,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'geEvaluationId'+-- * 'geEvaluationId' - The ID of the @Evaluation@ to retrieve. The evaluation of each @MLModel@ is recorded and cataloged. The ID provides the means to access the information. getEvaluation     :: Text -- ^ 'geEvaluationId'     -> GetEvaluation@@ -74,7 +76,7 @@     { _geEvaluationId = pEvaluationId_     } --- | The ID of the 'Evaluation' to retrieve. The evaluation of each 'MLModel' is recorded and cataloged. The ID provides the means to access the information.+-- | The ID of the @Evaluation@ to retrieve. The evaluation of each @MLModel@ is recorded and cataloged. The ID provides the means to access the information. geEvaluationId :: Lens' GetEvaluation Text geEvaluationId = lens _geEvaluationId (\ s a -> s{_geEvaluationId = a}); @@ -126,8 +128,10 @@ 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'     { _gersStatus                 :: !(Maybe EntityStatus)@@ -152,37 +156,37 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gersStatus'+-- * 'gersStatus' - The status of the evaluation. This element can have one of the following values:     * @PENDING@ - Amazon Machine Language (Amazon ML) submitted a request to evaluate an @MLModel@ .    * @INPROGRESS@ - The evaluation is underway.    * @FAILED@ - The request to evaluate an @MLModel@ did not run to completion. It is not usable.    * @COMPLETED@ - The evaluation process completed successfully.    * @DELETED@ - The @Evaluation@ is marked as deleted. It is not usable. ----- * 'gersPerformanceMetrics'+-- * 'gersPerformanceMetrics' - Measurements of how well the @MLModel@ performed using observations referenced by the @DataSource@ . One of the following metric is returned based on the type of the @MLModel@ :      * BinaryAUC: A binary @MLModel@ uses the Area Under the Curve (AUC) technique to measure performance.      * RegressionRMSE: A regression @MLModel@ uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.     * MulticlassAvgFScore: A multiclass @MLModel@ uses the F1 score technique to measure performance.  For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . ----- * 'gersLastUpdatedAt'+-- * 'gersLastUpdatedAt' - The time of the most recent edit to the @Evaluation@ . The time is expressed in epoch time. ----- * 'gersCreatedAt'+-- * 'gersCreatedAt' - The time that the @Evaluation@ was created. The time is expressed in epoch time. ----- * 'gersComputeTime'+-- * 'gersComputeTime' - 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. ----- * 'gersInputDataLocationS3'+-- * 'gersInputDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'gersMLModelId'+-- * 'gersMLModelId' - The ID of the @MLModel@ that was the focus of the evaluation. ----- * 'gersStartedAt'+-- * 'gersStartedAt' - The epoch time when Amazon Machine Learning marked the @Evaluation@ as @INPROGRESS@ . @StartedAt@ isn't available if the @Evaluation@ is in the @PENDING@ state. ----- * 'gersFinishedAt'+-- * 'gersFinishedAt' - 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. ----- * 'gersCreatedByIAMUser'+-- * 'gersCreatedByIAMUser' - 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. ----- * 'gersName'+-- * 'gersName' - A user-supplied name or description of the @Evaluation@ . ----- * 'gersLogURI'+-- * 'gersLogURI' - A link to the file that contains logs of the @CreateEvaluation@ operation. ----- * 'gersEvaluationId'+-- * 'gersEvaluationId' - The evaluation ID which is same as the @EvaluationId@ in the request. ----- * 'gersMessage'+-- * 'gersMessage' - A description of the most recent details about evaluating the @MLModel@ . ----- * 'gersEvaluationDataSourceId'+-- * 'gersEvaluationDataSourceId' - The @DataSource@ used for this evaluation. ----- * 'gersResponseStatus'+-- * 'gersResponseStatus' - -- | The response status code. getEvaluationResponse     :: Int -- ^ 'gersResponseStatus'     -> GetEvaluationResponse@@ -206,37 +210,23 @@     , _gersResponseStatus = pResponseStatus_     } --- | The status of the evaluation. This element can have one of the following values:------ -   'PENDING' - Amazon Machine Language (Amazon ML) submitted a request to evaluate an 'MLModel'.--- -   'INPROGRESS' - The evaluation is underway.--- -   'FAILED' - The request to evaluate an 'MLModel' did not run to completion. It is not usable.--- -   'COMPLETED' - The evaluation process completed successfully.--- -   'DELETED' - The 'Evaluation' is marked as deleted. It is not usable.+-- | The status of the evaluation. This element can have one of the following values:     * @PENDING@ - Amazon Machine Language (Amazon ML) submitted a request to evaluate an @MLModel@ .    * @INPROGRESS@ - The evaluation is underway.    * @FAILED@ - The request to evaluate an @MLModel@ did not run to completion. It is not usable.    * @COMPLETED@ - The evaluation process completed successfully.    * @DELETED@ - The @Evaluation@ is marked as deleted. It is not usable. gersStatus :: Lens' GetEvaluationResponse (Maybe EntityStatus) gersStatus = lens _gersStatus (\ s a -> s{_gersStatus = a}); --- | Measurements of how well the 'MLModel' performed using observations referenced by the 'DataSource'. One of the following metric is returned based on the type of the 'MLModel':------ -   BinaryAUC: A binary 'MLModel' uses the Area Under the Curve (AUC) technique to measure performance.------ -   RegressionRMSE: A regression 'MLModel' uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.------ -   MulticlassAvgFScore: A multiclass 'MLModel' uses the F1 score technique to measure performance.------ For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+-- | Measurements of how well the @MLModel@ performed using observations referenced by the @DataSource@ . One of the following metric is returned based on the type of the @MLModel@ :      * BinaryAUC: A binary @MLModel@ uses the Area Under the Curve (AUC) technique to measure performance.      * RegressionRMSE: A regression @MLModel@ uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.     * MulticlassAvgFScore: A multiclass @MLModel@ uses the F1 score technique to measure performance.  For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . gersPerformanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics) gersPerformanceMetrics = lens _gersPerformanceMetrics (\ s a -> s{_gersPerformanceMetrics = a}); --- | The time of the most recent edit to the 'Evaluation'. 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; --- | The time that the 'Evaluation' was created. The time is expressed in epoch time.+-- | The time that the @Evaluation@ was created. The time is expressed in epoch time. 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.+-- | 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}); @@ -244,15 +234,15 @@ gersInputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text) gersInputDataLocationS3 = lens _gersInputDataLocationS3 (\ s a -> s{_gersInputDataLocationS3 = a}); --- | The ID of the 'MLModel' that was the focus of the evaluation.+-- | The ID of the @MLModel@ that was the focus of the evaluation. 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.+-- | 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.+-- | 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; @@ -260,27 +250,27 @@ gersCreatedByIAMUser :: Lens' GetEvaluationResponse (Maybe Text) gersCreatedByIAMUser = lens _gersCreatedByIAMUser (\ s a -> s{_gersCreatedByIAMUser = a}); --- | A user-supplied name or description of the 'Evaluation'.+-- | A user-supplied name or description of the @Evaluation@ . 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}); --- | The evaluation ID which is same as the 'EvaluationId' in the request.+-- | The evaluation ID which is same as the @EvaluationId@ in the request. gersEvaluationId :: Lens' GetEvaluationResponse (Maybe Text) gersEvaluationId = lens _gersEvaluationId (\ s a -> s{_gersEvaluationId = a}); --- | A description of the most recent details about evaluating the 'MLModel'.+-- | A description of the most recent details about evaluating the @MLModel@ . gersMessage :: Lens' GetEvaluationResponse (Maybe Text) gersMessage = lens _gersMessage (\ s a -> s{_gersMessage = a}); --- | The 'DataSource' used for this evaluation.+-- | The @DataSource@ used for this evaluation. gersEvaluationDataSourceId :: Lens' GetEvaluationResponse (Maybe Text) gersEvaluationDataSourceId = lens _gersEvaluationDataSourceId (\ s a -> s{_gersEvaluationDataSourceId = a}); --- | The response status code.+-- | -- | The response status code. gersResponseStatus :: Lens' GetEvaluationResponse Int gersResponseStatus = lens _gersResponseStatus (\ s a -> s{_gersResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Returns an 'MLModel' that includes detailed metadata, data source information, and 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.+--+-- @GetMLModel@ provides results in normal or verbose format.+-- module Network.AWS.MachineLearning.GetMLModel     (     -- * Creating a Request@@ -75,9 +77,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gmlmVerbose'+-- * 'gmlmVerbose' - Specifies whether the @GetMLModel@ operation should return @Recipe@ . If true, @Recipe@ is returned. If false, @Recipe@ is not returned. ----- * 'gmlmMLModelId'+-- * 'gmlmMLModelId' - The ID assigned to the @MLModel@ at creation. getMLModel     :: Text -- ^ 'gmlmMLModelId'     -> GetMLModel@@ -87,15 +89,11 @@     , _gmlmMLModelId = pMLModelId_     } --- | Specifies whether the 'GetMLModel' operation should return 'Recipe'.------ If true, 'Recipe' is returned.------ If false, 'Recipe' is not returned.+-- | Specifies whether the @GetMLModel@ operation should return @Recipe@ . If true, @Recipe@ is returned. If false, @Recipe@ is not returned. gmlmVerbose :: Lens' GetMLModel (Maybe Bool) gmlmVerbose = lens _gmlmVerbose (\ s a -> s{_gmlmVerbose = a}); --- | The ID assigned to the 'MLModel' at creation.+-- | The ID assigned to the @MLModel@ at creation. gmlmMLModelId :: Lens' GetMLModel Text gmlmMLModelId = lens _gmlmMLModelId (\ s a -> s{_gmlmMLModelId = a}); @@ -154,8 +152,10 @@ 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'     { _gmlmrsStatus                      :: !(Maybe EntityStatus)@@ -186,49 +186,49 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'gmlmrsStatus'+-- * 'gmlmrsStatus' - The current status of the @MLModel@ . This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to describe a @MLModel@ .    * @INPROGRESS@ - The request is processing.    * @FAILED@ - The request did not run to completion. The ML model isn't usable.    * @COMPLETED@ - The request completed successfully.    * @DELETED@ - The @MLModel@ is marked as deleted. It isn't usable. ----- * 'gmlmrsLastUpdatedAt'+-- * 'gmlmrsLastUpdatedAt' - The time of the most recent edit to the @MLModel@ . The time is expressed in epoch time. ----- * 'gmlmrsTrainingParameters'+-- * 'gmlmrsTrainingParameters' - A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ . We strongly recommend that you shuffle your data.     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. ----- * 'gmlmrsScoreThresholdLastUpdatedAt'+-- * 'gmlmrsScoreThresholdLastUpdatedAt' - The time of the most recent edit to the @ScoreThreshold@ . The time is expressed in epoch time. ----- * 'gmlmrsCreatedAt'+-- * 'gmlmrsCreatedAt' - The time that the @MLModel@ was created. The time is expressed in epoch time. ----- * 'gmlmrsComputeTime'+-- * 'gmlmrsComputeTime' - 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. ----- * 'gmlmrsRecipe'+-- * 'gmlmrsRecipe' - 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. ----- * 'gmlmrsInputDataLocationS3'+-- * 'gmlmrsInputDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'gmlmrsMLModelId'+-- * 'gmlmrsMLModelId' - The MLModel ID, which is same as the @MLModelId@ in the request. ----- * 'gmlmrsSizeInBytes'+-- * 'gmlmrsSizeInBytes' - Undocumented member. ----- * 'gmlmrsSchema'+-- * 'gmlmrsSchema' - The schema used by all of the data files referenced by the @DataSource@ . ----- * 'gmlmrsStartedAt'+-- * 'gmlmrsStartedAt' - The epoch time when Amazon Machine Learning marked the @MLModel@ as @INPROGRESS@ . @StartedAt@ isn't available if the @MLModel@ is in the @PENDING@ state. ----- * 'gmlmrsScoreThreshold'+-- * 'gmlmrsScoreThreshold' - 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@ . ----- * 'gmlmrsFinishedAt'+-- * 'gmlmrsFinishedAt' - 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. ----- * 'gmlmrsCreatedByIAMUser'+-- * 'gmlmrsCreatedByIAMUser' - 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. ----- * 'gmlmrsName'+-- * 'gmlmrsName' - A user-supplied name or description of the @MLModel@ . ----- * 'gmlmrsLogURI'+-- * 'gmlmrsLogURI' - A link to the file that contains logs of the @CreateMLModel@ operation. ----- * 'gmlmrsEndpointInfo'+-- * 'gmlmrsEndpointInfo' - The current endpoint of the @MLModel@ ----- * 'gmlmrsTrainingDataSourceId'+-- * 'gmlmrsTrainingDataSourceId' - The ID of the training @DataSource@ . ----- * 'gmlmrsMessage'+-- * 'gmlmrsMessage' - A description of the most recent details about accessing the @MLModel@ . ----- * 'gmlmrsMLModelType'+-- * 'gmlmrsMLModelType' - Identifies the @MLModel@ category. The following are the available types:      * REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"    * BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"    * MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" ----- * 'gmlmrsResponseStatus'+-- * 'gmlmrsResponseStatus' - -- | The response status code. getMLModelResponse     :: Int -- ^ 'gmlmrsResponseStatus'     -> GetMLModelResponse@@ -258,60 +258,31 @@     , _gmlmrsResponseStatus = pResponseStatus_     } --- | The current status of the 'MLModel'. This element can have one of the following values:------ -   'PENDING' - Amazon Machine Learning (Amazon ML) submitted a request to describe a 'MLModel'.--- -   'INPROGRESS' - The request is processing.--- -   'FAILED' - The request did not run to completion. The ML model isn\'t usable.--- -   'COMPLETED' - The request completed successfully.--- -   'DELETED' - The 'MLModel' is marked as deleted. It isn\'t usable.+-- | The current status of the @MLModel@ . This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to describe a @MLModel@ .    * @INPROGRESS@ - The request is processing.    * @FAILED@ - The request did not run to completion. The ML model isn't usable.    * @COMPLETED@ - The request completed successfully.    * @DELETED@ - The @MLModel@ is marked as deleted. It isn't usable. gmlmrsStatus :: Lens' GetMLModelResponse (Maybe EntityStatus) gmlmrsStatus = lens _gmlmrsStatus (\ s a -> s{_gmlmrsStatus = a}); --- | The time of the most recent edit to the 'MLModel'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @MLModel@ . The time is expressed in epoch time. 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.------ The following is the current set of training parameters:------ -   'sgd.maxMLModelSizeInBytes' - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.------     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'.------ -   'sgd.maxPasses' - The number of times that the training process traverses the observations to build the 'MLModel'. The value is an integer that ranges from '1' to '10000'. The default value is '10'.------ -   'sgd.shuffleType' - Whether Amazon ML shuffles the training data. Shuffling data improves a model\'s ability to find the optimal solution for a variety of data types. The valid values are 'auto' and 'none'. The default value is 'none'. We strongly recommend that you shuffle your data.------ -   'sgd.l1RegularizationAmount' - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L1 normalization. This parameter can\'t be used when 'L2' is specified. Use this parameter sparingly.------ -   'sgd.l2RegularizationAmount' - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L2 normalization. This parameter can\'t be used when 'L1' is specified. Use this parameter sparingly.---+-- | A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ . We strongly recommend that you shuffle your data.     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. gmlmrsTrainingParameters :: Lens' GetMLModelResponse (HashMap Text Text) gmlmrsTrainingParameters = lens _gmlmrsTrainingParameters (\ s a -> s{_gmlmrsTrainingParameters = a}) . _Default . _Map; --- | The time of the most recent edit to the 'ScoreThreshold'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @ScoreThreshold@ . The time is expressed in epoch time. gmlmrsScoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) gmlmrsScoreThresholdLastUpdatedAt = lens _gmlmrsScoreThresholdLastUpdatedAt (\ s a -> s{_gmlmrsScoreThresholdLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'MLModel' was created. The time is expressed in epoch time.+-- | The time that the @MLModel@ was created. The time is expressed in epoch time. gmlmrsCreatedAt :: Lens' GetMLModelResponse (Maybe UTCTime) gmlmrsCreatedAt = lens _gmlmrsCreatedAt (\ s a -> s{_gmlmrsCreatedAt = a}) . mapping _Time; --- | 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.+-- | 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------ This parameter is provided as part of the verbose format.+-- | 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. gmlmrsRecipe :: Lens' GetMLModelResponse (Maybe Text) gmlmrsRecipe = lens _gmlmrsRecipe (\ s a -> s{_gmlmrsRecipe = a}); @@ -319,7 +290,7 @@ 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}); @@ -327,61 +298,51 @@ gmlmrsSizeInBytes :: Lens' GetMLModelResponse (Maybe Integer) gmlmrsSizeInBytes = lens _gmlmrsSizeInBytes (\ s a -> s{_gmlmrsSizeInBytes = a}); --- | The schema used by all of the data files referenced by the 'DataSource'.------ Note------ This parameter is provided as part of the verbose format.+-- | The schema used by all of the data files referenced by the @DataSource@ . gmlmrsSchema :: Lens' GetMLModelResponse (Maybe Text) gmlmrsSchema = lens _gmlmrsSchema (\ s a -> s{_gmlmrsSchema = a}); --- | The epoch time when Amazon Machine Learning marked the 'MLModel' as 'INPROGRESS'. 'StartedAt' isn\'t available if the 'MLModel' is in the 'PENDING' state.+-- | 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'.+-- | 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.+-- | 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.+-- | 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}); --- | A user-supplied name or description of the 'MLModel'.+-- | A user-supplied name or description of the @MLModel@ . gmlmrsName :: Lens' GetMLModelResponse (Maybe Text) gmlmrsName = lens _gmlmrsName (\ s a -> s{_gmlmrsName = a}); --- | A link to the file that contains logs of the 'CreateMLModel' operation.+-- | A link to the file that contains logs of the @CreateMLModel@ operation. gmlmrsLogURI :: Lens' GetMLModelResponse (Maybe Text) gmlmrsLogURI = lens _gmlmrsLogURI (\ s a -> s{_gmlmrsLogURI = a}); --- | The current endpoint of the 'MLModel'+-- | The current endpoint of the @MLModel@ gmlmrsEndpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo) gmlmrsEndpointInfo = lens _gmlmrsEndpointInfo (\ s a -> s{_gmlmrsEndpointInfo = a}); --- | The ID of the training 'DataSource'.+-- | The ID of the training @DataSource@ . gmlmrsTrainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text) gmlmrsTrainingDataSourceId = lens _gmlmrsTrainingDataSourceId (\ s a -> s{_gmlmrsTrainingDataSourceId = a}); --- | 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 price should a house be listed at?\"--- -   BINARY -- Produces one of two possible results. For example, \"Is this an e-commerce website?\"--- -   MULTICLASS -- Produces one of several possible results. For example, \"Is this a HIGH, LOW or MEDIUM risk trade?\"+-- | Identifies the @MLModel@ category. The following are the available types:      * REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"    * BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"    * MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" gmlmrsMLModelType :: Lens' GetMLModelResponse (Maybe MLModelType) gmlmrsMLModelType = lens _gmlmrsMLModelType (\ s a -> s{_gmlmrsMLModelType = a}); --- | The response status code.+-- | -- | The response status code. gmlmrsResponseStatus :: Lens' GetMLModelResponse Int gmlmrsResponseStatus = lens _gmlmrsResponseStatus (\ s a -> s{_gmlmrsResponseStatus = a}); 

@@ -18,11 +18,9 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Generates a prediction for the observation using the specified 'ML Model'.+-- Generates a prediction for the observation using the specified @ML Model@ . ----- Note ----- Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested. module Network.AWS.MachineLearning.Predict     (     -- * Creating a Request@@ -59,11 +57,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'pMLModelId'+-- * 'pMLModelId' - A unique identifier of the @MLModel@ . ----- * 'pRecord'+-- * 'pRecord' - Undocumented member. ----- * 'pPredictEndpoint'+-- * 'pPredictEndpoint' - Undocumented member. predict     :: Text -- ^ 'pMLModelId'     -> Text -- ^ 'pPredictEndpoint'@@ -75,7 +73,7 @@     , _pPredictEndpoint = pPredictEndpoint_     } --- | A unique identifier of the 'MLModel'.+-- | A unique identifier of the @MLModel@ . pMLModelId :: Lens' Predict Text pMLModelId = lens _pMLModelId (\ s a -> s{_pMLModelId = a}); @@ -133,9 +131,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'prsPrediction'+-- * 'prsPrediction' - Undocumented member. ----- * 'prsResponseStatus'+-- * 'prsResponseStatus' - -- | The response status code. predictResponse     :: Int -- ^ 'prsResponseStatus'     -> PredictResponse@@ -149,7 +147,7 @@ prsPrediction :: Lens' PredictResponse (Maybe Prediction) prsPrediction = lens _prsPrediction (\ s a -> s{_prsPrediction = a}); --- | The response status code.+-- | -- | The response status code. prsResponseStatus :: Lens' PredictResponse Int prsResponseStatus = lens _prsResponseStatus (\ s a -> s{_prsResponseStatus = a}); 

@@ -250,7 +250,7 @@ import           Network.AWS.Prelude import           Network.AWS.Sign.V4 --- | API version '2014-12-12' of the Amazon Machine Learning SDK configuration.+-- | API version @2014-12-12@ of the Amazon Machine Learning SDK configuration. machineLearning :: Service machineLearning =     Service@@ -289,16 +289,22 @@ _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 =     _ServiceError . hasStatus 500 . hasCode "InternalServerException"  -- | An error on the client occurred. Typically, the cause is an invalid input value.+--+-- _InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError _InvalidInputException =     _ServiceError . hasStatus 400 . hasCode "InvalidInputException"  -- | A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.+--+-- _IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError _IdempotentParameterMismatchException =     _ServiceError .@@ -309,17 +315,23 @@ _TagLimitExceededException =     _ServiceError . hasCode "TagLimitExceededException" --- | The exception is thrown when a predict request is made to an unmounted 'MLModel'.+-- | The exception is thrown when a predict request is made to an unmounted @MLModel@ .+--+-- _PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError _PredictorNotMountedException =     _ServiceError . hasStatus 400 . hasCode "PredictorNotMountedException"  -- | A specified resource cannot be located.+--+-- _ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError _ResourceNotFoundException =     _ServiceError . hasStatus 404 . hasCode "ResourceNotFoundException" --- | The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as 'DataSource'.+-- | The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as @DataSource@ .+--+-- _LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError _LimitExceededException =     _ServiceError . hasStatus 417 . hasCode "LimitExceededException"

@@ -21,10 +21,12 @@ import           Network.AWS.MachineLearning.Types.Sum import           Network.AWS.Prelude --- | Represents the output of a '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)@@ -49,37 +51,37 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'bpStatus'+-- * 'bpStatus' - The status of the @BatchPrediction@ . This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.    * @INPROGRESS@ - The process is underway.    * @FAILED@ - The request to perform a batch prediction did not run to completion. It is not usable.    * @COMPLETED@ - The batch prediction process completed successfully.    * @DELETED@ - The @BatchPrediction@ is marked as deleted. It is not usable. ----- * 'bpLastUpdatedAt'+-- * 'bpLastUpdatedAt' - The time of the most recent edit to the @BatchPrediction@ . The time is expressed in epoch time. ----- * 'bpCreatedAt'+-- * 'bpCreatedAt' - The time that the @BatchPrediction@ was created. The time is expressed in epoch time. ----- * 'bpComputeTime'+-- * 'bpComputeTime' - Undocumented member. ----- * 'bpInputDataLocationS3'+-- * 'bpInputDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'bpMLModelId'+-- * 'bpMLModelId' - The ID of the @MLModel@ that generated predictions for the @BatchPrediction@ request. ----- * 'bpBatchPredictionDataSourceId'+-- * 'bpBatchPredictionDataSourceId' - The ID of the @DataSource@ that points to the group of observations to predict. ----- * 'bpTotalRecordCount'+-- * 'bpTotalRecordCount' - Undocumented member. ----- * 'bpStartedAt'+-- * 'bpStartedAt' - Undocumented member. ----- * 'bpBatchPredictionId'+-- * 'bpBatchPredictionId' - The ID assigned to the @BatchPrediction@ at creation. This value should be identical to the value of the @BatchPredictionID@ in the request. ----- * 'bpFinishedAt'+-- * 'bpFinishedAt' - Undocumented member. ----- * 'bpInvalidRecordCount'+-- * 'bpInvalidRecordCount' - Undocumented member. ----- * 'bpCreatedByIAMUser'+-- * 'bpCreatedByIAMUser' - The AWS user account that invoked the @BatchPrediction@ . The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. ----- * 'bpName'+-- * 'bpName' - A user-supplied name or description of the @BatchPrediction@ . ----- * 'bpMessage'+-- * 'bpMessage' - A description of the most recent details about processing the batch prediction request. ----- * 'bpOutputURI'+-- * 'bpOutputURI' - The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the @s3 key@ portion of the @outputURI@ field: ':', '//', '/./', '/../'. batchPrediction     :: BatchPrediction batchPrediction =@@ -102,21 +104,15 @@     , _bpOutputURI = Nothing     } --- | The status of the 'BatchPrediction'. This element can have one of the following values:------ -   'PENDING' - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.--- -   'INPROGRESS' - The process is underway.--- -   'FAILED' - The request to perform a batch prediction did not run to completion. It is not usable.--- -   'COMPLETED' - The batch prediction process completed successfully.--- -   'DELETED' - The 'BatchPrediction' is marked as deleted. It is not usable.+-- | The status of the @BatchPrediction@ . This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.    * @INPROGRESS@ - The process is underway.    * @FAILED@ - The request to perform a batch prediction did not run to completion. It is not usable.    * @COMPLETED@ - The batch prediction process completed successfully.    * @DELETED@ - The @BatchPrediction@ is marked as deleted. It is not usable. bpStatus :: Lens' BatchPrediction (Maybe EntityStatus) bpStatus = lens _bpStatus (\ s a -> s{_bpStatus = 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 @BatchPrediction@ . The time is expressed in epoch time. bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime) bpLastUpdatedAt = lens _bpLastUpdatedAt (\ s a -> s{_bpLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'BatchPrediction' was created. The time is expressed in epoch time.+-- | The time that the @BatchPrediction@ was created. The time is expressed in epoch time. bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) bpCreatedAt = lens _bpCreatedAt (\ s a -> s{_bpCreatedAt = a}) . mapping _Time; @@ -128,11 +124,11 @@ bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) bpInputDataLocationS3 = lens _bpInputDataLocationS3 (\ s a -> s{_bpInputDataLocationS3 = a}); --- | The ID of the 'MLModel' that generated predictions for the 'BatchPrediction' request.+-- | The ID of the @MLModel@ that generated predictions for the @BatchPrediction@ request. bpMLModelId :: Lens' BatchPrediction (Maybe Text) bpMLModelId = lens _bpMLModelId (\ s a -> s{_bpMLModelId = a}); --- | The ID of the 'DataSource' that points to the group of observations to predict.+-- | The ID of the @DataSource@ that points to the group of observations to predict. bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) bpBatchPredictionDataSourceId = lens _bpBatchPredictionDataSourceId (\ s a -> s{_bpBatchPredictionDataSourceId = a}); @@ -144,7 +140,7 @@ 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.+-- | 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}); @@ -156,11 +152,11 @@ 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.+-- | 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}); --- | A user-supplied name or description of the 'BatchPrediction'.+-- | A user-supplied name or description of the @BatchPrediction@ . bpName :: Lens' BatchPrediction (Maybe Text) bpName = lens _bpName (\ s a -> s{_bpName = a}); @@ -168,7 +164,7 @@ 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}); @@ -197,10 +193,12 @@  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'. --+-- The content consists of the detailed metadata and data file information and the current status of the @DataSource@ .+--+-- -- /See:/ 'dataSource' smart constructor. data DataSource = DataSource'     { _dsStatus            :: !(Maybe EntityStatus)@@ -227,41 +225,41 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'dsStatus'+-- * 'dsStatus' - The current status of the @DataSource@ . This element can have one of the following values:      * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a @DataSource@ .    * INPROGRESS - The creation process is underway.    * FAILED - The request to create a @DataSource@ did not run to completion. It is not usable.    * COMPLETED - The creation process completed successfully.    * DELETED - The @DataSource@ is marked as deleted. It is not usable. ----- * 'dsNumberOfFiles'+-- * 'dsNumberOfFiles' - The number of data files referenced by the @DataSource@ . ----- * 'dsLastUpdatedAt'+-- * 'dsLastUpdatedAt' - The time of the most recent edit to the @BatchPrediction@ . The time is expressed in epoch time. ----- * 'dsCreatedAt'+-- * 'dsCreatedAt' - The time that the @DataSource@ was created. The time is expressed in epoch time. ----- * 'dsComputeTime'+-- * 'dsComputeTime' - Undocumented member. ----- * 'dsDataSourceId'+-- * 'dsDataSourceId' - The ID that is assigned to the @DataSource@ during creation. ----- * 'dsRDSMetadata'+-- * 'dsRDSMetadata' - Undocumented member. ----- * 'dsDataSizeInBytes'+-- * 'dsDataSizeInBytes' - The total number of observations contained in the data files that the @DataSource@ references. ----- * 'dsStartedAt'+-- * 'dsStartedAt' - Undocumented member. ----- * 'dsFinishedAt'+-- * 'dsFinishedAt' - Undocumented member. ----- * 'dsCreatedByIAMUser'+-- * 'dsCreatedByIAMUser' - The AWS user account from which the @DataSource@ was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. ----- * 'dsName'+-- * 'dsName' - A user-supplied name or description of the @DataSource@ . ----- * 'dsDataLocationS3'+-- * 'dsDataLocationS3' - The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a @DataSource@ . ----- * 'dsComputeStatistics'+-- * 'dsComputeStatistics' - The parameter is @true@ if statistics need to be generated from the observation data. ----- * 'dsMessage'+-- * 'dsMessage' - A description of the most recent details about creating the @DataSource@ . ----- * 'dsRedshiftMetadata'+-- * 'dsRedshiftMetadata' - Undocumented member. ----- * 'dsDataRearrangement'+-- * 'dsDataRearrangement' - A JSON string that represents the splitting and rearrangement requirement used when this @DataSource@ was created. ----- * 'dsRoleARN'+-- * 'dsRoleARN' - Undocumented member. dataSource     :: DataSource dataSource =@@ -286,25 +284,19 @@     , _dsRoleARN = Nothing     } --- | The current status of the 'DataSource'. This element can have one of the following values:------ -   PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a 'DataSource'.--- -   INPROGRESS - The creation process is underway.--- -   FAILED - The request to create a 'DataSource' did not run to completion. It is not usable.--- -   COMPLETED - The creation process completed successfully.--- -   DELETED - The 'DataSource' is marked as deleted. It is not usable.+-- | The current status of the @DataSource@ . This element can have one of the following values:      * PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a @DataSource@ .    * INPROGRESS - The creation process is underway.    * FAILED - The request to create a @DataSource@ did not run to completion. It is not usable.    * COMPLETED - The creation process completed successfully.    * DELETED - The @DataSource@ is marked as deleted. It is not usable. dsStatus :: Lens' DataSource (Maybe EntityStatus) dsStatus = lens _dsStatus (\ s a -> s{_dsStatus = a}); --- | The number of data files referenced by the 'DataSource'.+-- | The number of data files referenced by the @DataSource@ . dsNumberOfFiles :: Lens' DataSource (Maybe Integer) dsNumberOfFiles = lens _dsNumberOfFiles (\ s a -> s{_dsNumberOfFiles = 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 @BatchPrediction@ . The time is expressed in epoch time. dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime) dsLastUpdatedAt = lens _dsLastUpdatedAt (\ s a -> s{_dsLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'DataSource' was created. The time is expressed in epoch time.+-- | The time that the @DataSource@ was created. The time is expressed in epoch time. dsCreatedAt :: Lens' DataSource (Maybe UTCTime) dsCreatedAt = lens _dsCreatedAt (\ s a -> s{_dsCreatedAt = a}) . mapping _Time; @@ -312,7 +304,7 @@ dsComputeTime :: Lens' DataSource (Maybe Integer) dsComputeTime = lens _dsComputeTime (\ s a -> s{_dsComputeTime = a}); --- | The ID that is assigned to the 'DataSource' during creation.+-- | The ID that is assigned to the @DataSource@ during creation. dsDataSourceId :: Lens' DataSource (Maybe Text) dsDataSourceId = lens _dsDataSourceId (\ s a -> s{_dsDataSourceId = a}); @@ -320,7 +312,7 @@ dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata) dsRDSMetadata = lens _dsRDSMetadata (\ s a -> s{_dsRDSMetadata = a}); --- | The total number of observations contained in the data files that the 'DataSource' references.+-- | The total number of observations contained in the data files that the @DataSource@ references. dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) dsDataSizeInBytes = lens _dsDataSizeInBytes (\ s a -> s{_dsDataSizeInBytes = a}); @@ -332,23 +324,23 @@ 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.+-- | 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}); --- | A user-supplied name or description of the 'DataSource'.+-- | A user-supplied name or description of the @DataSource@ . dsName :: Lens' DataSource (Maybe Text) dsName = lens _dsName (\ s a -> s{_dsName = a}); --- | The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a 'DataSource'.+-- | The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a @DataSource@ . dsDataLocationS3 :: Lens' DataSource (Maybe Text) dsDataLocationS3 = lens _dsDataLocationS3 (\ s a -> s{_dsDataLocationS3 = a}); --- | The parameter is 'true' if statistics need to be generated from the observation data.+-- | The parameter is @true@ if statistics need to be generated from the observation data. dsComputeStatistics :: Lens' DataSource (Maybe Bool) dsComputeStatistics = lens _dsComputeStatistics (\ s a -> s{_dsComputeStatistics = a}); --- | A description of the most recent details about creating the 'DataSource'.+-- | A description of the most recent details about creating the @DataSource@ . dsMessage :: Lens' DataSource (Maybe Text) dsMessage = lens _dsMessage (\ s a -> s{_dsMessage = a}); @@ -356,7 +348,7 @@ dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata) dsRedshiftMetadata = lens _dsRedshiftMetadata (\ s a -> s{_dsRedshiftMetadata = a}); --- | A JSON string that represents the splitting and rearrangement requirement used when this 'DataSource' was created.+-- | 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}); @@ -391,10 +383,12 @@  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'. --+-- The content consists of the detailed metadata and data file information and the current status of the @Evaluation@ .+--+-- -- /See:/ 'evaluation' smart constructor. data Evaluation = Evaluation'     { _eStatus                 :: !(Maybe EntityStatus)@@ -417,33 +411,33 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'eStatus'+-- * 'eStatus' - The status of the evaluation. This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an @MLModel@ .    * @INPROGRESS@ - The evaluation is underway.    * @FAILED@ - The request to evaluate an @MLModel@ did not run to completion. It is not usable.    * @COMPLETED@ - The evaluation process completed successfully.    * @DELETED@ - The @Evaluation@ is marked as deleted. It is not usable. ----- * 'ePerformanceMetrics'+-- * 'ePerformanceMetrics' - Measurements of how well the @MLModel@ performed, using observations referenced by the @DataSource@ . One of the following metrics is returned, based on the type of the @MLModel@ :      * BinaryAUC: A binary @MLModel@ uses the Area Under the Curve (AUC) technique to measure performance.      * RegressionRMSE: A regression @MLModel@ uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.     * MulticlassAvgFScore: A multiclass @MLModel@ uses the F1 score technique to measure performance.  For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . ----- * 'eLastUpdatedAt'+-- * 'eLastUpdatedAt' - The time of the most recent edit to the @Evaluation@ . The time is expressed in epoch time. ----- * 'eCreatedAt'+-- * 'eCreatedAt' - The time that the @Evaluation@ was created. The time is expressed in epoch time. ----- * 'eComputeTime'+-- * 'eComputeTime' - Undocumented member. ----- * 'eInputDataLocationS3'+-- * 'eInputDataLocationS3' - The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. ----- * 'eMLModelId'+-- * 'eMLModelId' - The ID of the @MLModel@ that is the focus of the evaluation. ----- * 'eStartedAt'+-- * 'eStartedAt' - Undocumented member. ----- * 'eFinishedAt'+-- * 'eFinishedAt' - Undocumented member. ----- * 'eCreatedByIAMUser'+-- * 'eCreatedByIAMUser' - The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. ----- * 'eName'+-- * 'eName' - A user-supplied name or description of the @Evaluation@ . ----- * 'eEvaluationId'+-- * 'eEvaluationId' - The ID that is assigned to the @Evaluation@ at creation. ----- * 'eMessage'+-- * 'eMessage' - A description of the most recent details about evaluating the @MLModel@ . ----- * 'eEvaluationDataSourceId'+-- * 'eEvaluationDataSourceId' - The ID of the @DataSource@ that is used to evaluate the @MLModel@ . evaluation     :: Evaluation evaluation =@@ -464,33 +458,19 @@     , _eEvaluationDataSourceId = Nothing     } --- | The status of the evaluation. This element can have one of the following values:------ -   'PENDING' - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an 'MLModel'.--- -   'INPROGRESS' - The evaluation is underway.--- -   'FAILED' - The request to evaluate an 'MLModel' did not run to completion. It is not usable.--- -   'COMPLETED' - The evaluation process completed successfully.--- -   'DELETED' - The 'Evaluation' is marked as deleted. It is not usable.+-- | The status of the evaluation. This element can have one of the following values:     * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an @MLModel@ .    * @INPROGRESS@ - The evaluation is underway.    * @FAILED@ - The request to evaluate an @MLModel@ did not run to completion. It is not usable.    * @COMPLETED@ - The evaluation process completed successfully.    * @DELETED@ - The @Evaluation@ is marked as deleted. It is not usable. 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':------ -   BinaryAUC: A binary 'MLModel' uses the Area Under the Curve (AUC) technique to measure performance.------ -   RegressionRMSE: A regression 'MLModel' uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.------ -   MulticlassAvgFScore: A multiclass 'MLModel' uses the F1 score technique to measure performance.------ For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+-- | Measurements of how well the @MLModel@ performed, using observations referenced by the @DataSource@ . One of the following metrics is returned, based on the type of the @MLModel@ :      * BinaryAUC: A binary @MLModel@ uses the Area Under the Curve (AUC) technique to measure performance.      * RegressionRMSE: A regression @MLModel@ uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.     * MulticlassAvgFScore: A multiclass @MLModel@ uses the F1 score technique to measure performance.  For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> . ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics) ePerformanceMetrics = lens _ePerformanceMetrics (\ s a -> s{_ePerformanceMetrics = a}); --- | The time of the most recent edit to the 'Evaluation'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @Evaluation@ . The time is expressed in epoch time. eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime) eLastUpdatedAt = lens _eLastUpdatedAt (\ s a -> s{_eLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'Evaluation' was created. The time is expressed in epoch time.+-- | The time that the @Evaluation@ was created. The time is expressed in epoch time. eCreatedAt :: Lens' Evaluation (Maybe UTCTime) eCreatedAt = lens _eCreatedAt (\ s a -> s{_eCreatedAt = a}) . mapping _Time; @@ -502,7 +482,7 @@ eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) eInputDataLocationS3 = lens _eInputDataLocationS3 (\ s a -> s{_eInputDataLocationS3 = a}); --- | The ID of the 'MLModel' that is the focus of the evaluation.+-- | The ID of the @MLModel@ that is the focus of the evaluation. eMLModelId :: Lens' Evaluation (Maybe Text) eMLModelId = lens _eMLModelId (\ s a -> s{_eMLModelId = a}); @@ -518,19 +498,19 @@ eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) eCreatedByIAMUser = lens _eCreatedByIAMUser (\ s a -> s{_eCreatedByIAMUser = a}); --- | A user-supplied name or description of the 'Evaluation'.+-- | A user-supplied name or description of the @Evaluation@ . eName :: Lens' Evaluation (Maybe Text) eName = lens _eName (\ s a -> s{_eName = a}); --- | The ID that is assigned to the 'Evaluation' at creation.+-- | The ID that is assigned to the @Evaluation@ at creation. eEvaluationId :: Lens' Evaluation (Maybe Text) eEvaluationId = lens _eEvaluationId (\ s a -> s{_eEvaluationId = a}); --- | A description of the most recent details about evaluating the 'MLModel'.+-- | A description of the most recent details about evaluating the @MLModel@ . eMessage :: Lens' Evaluation (Maybe Text) eMessage = lens _eMessage (\ s a -> s{_eMessage = a}); --- | The ID of the 'DataSource' that is used to evaluate the 'MLModel'.+-- | The ID of the @DataSource@ that is used to evaluate the @MLModel@ . eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text) eEvaluationDataSourceId = lens _eEvaluationDataSourceId (\ s a -> s{_eEvaluationDataSourceId = a}); @@ -557,10 +537,12 @@  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'. --+-- The content consists of the detailed metadata and the current status of the @MLModel@ .+--+-- -- /See:/ 'mLModel' smart constructor. data MLModel = MLModel'     { _mlmStatus                      :: !(Maybe EntityStatus)@@ -588,43 +570,43 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'mlmStatus'+-- * 'mlmStatus' - The current status of an @MLModel@ . This element can have one of the following values:      * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to create an @MLModel@ .    * @INPROGRESS@ - The creation process is underway.    * @FAILED@ - The request to create an @MLModel@ didn't run to completion. The model isn't usable.    * @COMPLETED@ - The creation process completed successfully.    * @DELETED@ - The @MLModel@ is marked as deleted. It isn't usable. ----- * 'mlmLastUpdatedAt'+-- * 'mlmLastUpdatedAt' - The time of the most recent edit to the @MLModel@ . The time is expressed in epoch time. ----- * 'mlmTrainingParameters'+-- * 'mlmTrainingParameters' - A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ .     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. ----- * 'mlmScoreThresholdLastUpdatedAt'+-- * 'mlmScoreThresholdLastUpdatedAt' - The time of the most recent edit to the @ScoreThreshold@ . The time is expressed in epoch time. ----- * 'mlmCreatedAt'+-- * 'mlmCreatedAt' - The time that the @MLModel@ was created. The time is expressed in epoch time. ----- * 'mlmComputeTime'+-- * 'mlmComputeTime' - Undocumented member. ----- * 'mlmInputDataLocationS3'+-- * 'mlmInputDataLocationS3' - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). ----- * 'mlmMLModelId'+-- * 'mlmMLModelId' - The ID assigned to the @MLModel@ at creation. ----- * 'mlmSizeInBytes'+-- * 'mlmSizeInBytes' - Undocumented member. ----- * 'mlmStartedAt'+-- * 'mlmStartedAt' - Undocumented member. ----- * 'mlmScoreThreshold'+-- * 'mlmScoreThreshold' - Undocumented member. ----- * 'mlmFinishedAt'+-- * 'mlmFinishedAt' - Undocumented member. ----- * 'mlmAlgorithm'+-- * 'mlmAlgorithm' - The algorithm used to train the @MLModel@ . The following algorithm is supported:     * @SGD@ -- Stochastic gradient descent. The goal of @SGD@ is to minimize the gradient of the loss function. ----- * 'mlmCreatedByIAMUser'+-- * 'mlmCreatedByIAMUser' - The AWS user account from which the @MLModel@ was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. ----- * 'mlmName'+-- * 'mlmName' - A user-supplied name or description of the @MLModel@ . ----- * 'mlmEndpointInfo'+-- * 'mlmEndpointInfo' - The current endpoint of the @MLModel@ . ----- * 'mlmTrainingDataSourceId'+-- * 'mlmTrainingDataSourceId' - The ID of the training @DataSource@ . The @CreateMLModel@ operation uses the @TrainingDataSourceId@ . ----- * 'mlmMessage'+-- * 'mlmMessage' - A description of the most recent details about accessing the @MLModel@ . ----- * 'mlmMLModelType'+-- * 'mlmMLModelType' - Identifies the @MLModel@ category. The following are the available types:     * @REGRESSION@ - Produces a numeric result. For example, "What price should a house be listed at?"    * @BINARY@ - Produces one of two possible results. For example, "Is this a child-friendly web site?".    * @MULTICLASS@ - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". mLModel     :: MLModel mLModel =@@ -650,48 +632,23 @@     , _mlmMLModelType = Nothing     } --- | The current status of an 'MLModel'. This element can have one of the following values:------ -   'PENDING' - Amazon Machine Learning (Amazon ML) submitted a request to create an 'MLModel'.--- -   'INPROGRESS' - The creation process is underway.--- -   'FAILED' - The request to create an 'MLModel' didn\'t run to completion. The model isn\'t usable.--- -   'COMPLETED' - The creation process completed successfully.--- -   'DELETED' - The 'MLModel' is marked as deleted. It isn\'t usable.+-- | The current status of an @MLModel@ . This element can have one of the following values:      * @PENDING@ - Amazon Machine Learning (Amazon ML) submitted a request to create an @MLModel@ .    * @INPROGRESS@ - The creation process is underway.    * @FAILED@ - The request to create an @MLModel@ didn't run to completion. The model isn't usable.    * @COMPLETED@ - The creation process completed successfully.    * @DELETED@ - The @MLModel@ is marked as deleted. It isn't usable. mlmStatus :: Lens' MLModel (Maybe EntityStatus) mlmStatus = lens _mlmStatus (\ s a -> s{_mlmStatus = a}); --- | The time of the most recent edit to the 'MLModel'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @MLModel@ . The time is expressed in epoch time. 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.------ The following is the current set of training parameters:------ -   'sgd.maxMLModelSizeInBytes' - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.------     The value is an integer that ranges from '100000' to '2147483648'. The default value is '33554432'.------ -   'sgd.maxPasses' - The number of times that the training process traverses the observations to build the 'MLModel'. The value is an integer that ranges from '1' to '10000'. The default value is '10'.------ -   'sgd.shuffleType' - Whether Amazon ML shuffles the training data. Shuffling the data improves a model\'s ability to find the optimal solution for a variety of data types. The valid values are 'auto' and 'none'. The default value is 'none'.------ -   'sgd.l1RegularizationAmount' - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L1 normalization. This parameter can\'t be used when 'L2' is specified. Use this parameter sparingly.------ -   'sgd.l2RegularizationAmount' - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as '1.0E-08'.------     The value is a double that ranges from '0' to 'MAX_DOUBLE'. The default is to not use L2 normalization. This parameter can\'t be used when 'L1' is specified. Use this parameter sparingly.---+-- | A list of the training parameters in the @MLModel@ . The list is implemented as a map of key-value pairs. The following is the current set of training parameters:      * @sgd.maxMLModelSizeInBytes@ - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from @100000@ to @2147483648@ . The default value is @33554432@ .     * @sgd.maxPasses@ - The number of times that the training process traverses the observations to build the @MLModel@ . The value is an integer that ranges from @1@ to @10000@ . The default value is @10@ .     * @sgd.shuffleType@ - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are @auto@ and @none@ . The default value is @none@ .     * @sgd.l1RegularizationAmount@ - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L1 normalization. This parameter can't be used when @L2@ is specified. Use this parameter sparingly.     * @sgd.l2RegularizationAmount@ - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as @1.0E-08@ . The value is a double that ranges from @0@ to @MAX_DOUBLE@ . The default is to not use L2 normalization. This parameter can't be used when @L1@ is specified. Use this parameter sparingly. mlmTrainingParameters :: Lens' MLModel (HashMap Text Text) mlmTrainingParameters = lens _mlmTrainingParameters (\ s a -> s{_mlmTrainingParameters = a}) . _Default . _Map; --- | The time of the most recent edit to the 'ScoreThreshold'. The time is expressed in epoch time.+-- | The time of the most recent edit to the @ScoreThreshold@ . The time is expressed in epoch time. mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) mlmScoreThresholdLastUpdatedAt = lens _mlmScoreThresholdLastUpdatedAt (\ s a -> s{_mlmScoreThresholdLastUpdatedAt = a}) . mapping _Time; --- | The time that the 'MLModel' was created. The time is expressed in epoch time.+-- | The time that the @MLModel@ was created. The time is expressed in epoch time. mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) mlmCreatedAt = lens _mlmCreatedAt (\ s a -> s{_mlmCreatedAt = a}) . mapping _Time; @@ -703,7 +660,7 @@ mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) mlmInputDataLocationS3 = lens _mlmInputDataLocationS3 (\ s a -> s{_mlmInputDataLocationS3 = a}); --- | The ID assigned to the 'MLModel' at creation.+-- | The ID assigned to the @MLModel@ at creation. mlmMLModelId :: Lens' MLModel (Maybe Text) mlmMLModelId = lens _mlmMLModelId (\ s a -> s{_mlmMLModelId = a}); @@ -723,39 +680,31 @@ 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.+-- | 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. mlmAlgorithm :: Lens' MLModel (Maybe Algorithm) mlmAlgorithm = lens _mlmAlgorithm (\ s a -> s{_mlmAlgorithm = a}); --- | 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.+-- | 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. mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text) mlmCreatedByIAMUser = lens _mlmCreatedByIAMUser (\ s a -> s{_mlmCreatedByIAMUser = a}); --- | A user-supplied name or description of the 'MLModel'.+-- | A user-supplied name or description of the @MLModel@ . mlmName :: Lens' MLModel (Maybe Text) mlmName = lens _mlmName (\ s a -> s{_mlmName = a}); --- | The current endpoint of the 'MLModel'.+-- | The current endpoint of the @MLModel@ . 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}); --- | A description of the most recent details about accessing the 'MLModel'.+-- | A description of the most recent details about accessing the @MLModel@ . mlmMessage :: Lens' MLModel (Maybe Text) mlmMessage = lens _mlmMessage (\ s a -> s{_mlmMessage = a}); --- | Identifies the 'MLModel' category. The following are the available types:------ -   'REGRESSION' - Produces a numeric result. For example, \"What price should a house be listed at?\"--- -   'BINARY' - Produces one of two possible results. For example, \"Is this a child-friendly web site?\".--- -   'MULTICLASS' - Produces one of several possible results. For example, \"Is this a HIGH-, LOW-, or MEDIUM---     ----     risk trade?\".+-- | Identifies the @MLModel@ category. The following are the available types:     * @REGRESSION@ - Produces a numeric result. For example, "What price should a house be listed at?"    * @BINARY@ - Produces one of two possible results. For example, "Is this a child-friendly web site?".    * @MULTICLASS@ - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". mlmMLModelType :: Lens' MLModel (Maybe MLModelType) mlmMLModelType = lens _mlmMLModelType (\ s a -> s{_mlmMLModelType = a}); @@ -787,16 +736,20 @@  instance NFData MLModel --- | Measurements of how well the 'MLModel' performed on known observations. One of the following metrics is returned, based on the type of the 'MLModel':+-- | Measurements of how well the @MLModel@ performed on known observations. One of the following metrics is returned, based on the type of the @MLModel@ : ----- -   BinaryAUC: The binary 'MLModel' uses the Area Under the Curve (AUC) technique to measure performance. ----- -   RegressionRMSE: The regression 'MLModel' uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.+--     * BinaryAUC: The binary @MLModel@ uses the Area Under the Curve (AUC) technique to measure performance. ----- -   MulticlassAvgFScore: The multiclass 'MLModel' uses the F1 score technique to measure performance.+--     * RegressionRMSE: The regression @MLModel@ uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. ----- For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+--     * MulticlassAvgFScore: The multiclass @MLModel@ uses the F1 score technique to measure performance. --+--+--+-- For more information about performance metrics, please see the <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide> .+--+-- -- /See:/ 'performanceMetrics' smart constructor. newtype PerformanceMetrics = PerformanceMetrics'     { _pmProperties :: Maybe (Map Text Text)@@ -806,7 +759,7 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'pmProperties'+-- * 'pmProperties' - Undocumented member. performanceMetrics     :: PerformanceMetrics performanceMetrics =@@ -829,17 +782,20 @@  instance NFData PerformanceMetrics --- | The output from a 'Predict' operation:+-- | The output from a @Predict@ operation: ----- -   'Details' - Contains the following attributes: 'DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS' 'DetailsAttributes.ALGORITHM - SGD' ----- -   'PredictedLabel' - Present for either a 'BINARY' or 'MULTICLASS' 'MLModel' request.+--     * @Details@ - Contains the following attributes: @DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS@ @DetailsAttributes.ALGORITHM - SGD@ ----- -   'PredictedScores' - Contains the raw classification score corresponding to each label.+--     * @PredictedLabel@ - Present for either a @BINARY@ or @MULTICLASS@ @MLModel@ request. ----- -   'PredictedValue' - Present for a 'REGRESSION' 'MLModel' request.+--     * @PredictedScores@ - Contains the raw classification score corresponding to each label. --+--     * @PredictedValue@ - Present for a @REGRESSION@ @MLModel@ request. --+--+--+-- -- /See:/ 'prediction' smart constructor. data Prediction = Prediction'     { _pPredictedValue  :: !(Maybe Double)@@ -852,13 +808,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'pPredictedValue'+-- * 'pPredictedValue' - The prediction value for @REGRESSION@ @MLModel@ . ----- * 'pPredictedLabel'+-- * 'pPredictedLabel' - The prediction label for either a @BINARY@ or @MULTICLASS@ @MLModel@ . ----- * 'pPredictedScores'+-- * 'pPredictedScores' - Undocumented member. ----- * 'pDetails'+-- * 'pDetails' - Undocumented member. prediction     :: Prediction prediction =@@ -869,11 +825,11 @@     , _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}); @@ -898,8 +854,10 @@  instance NFData Prediction --- | The data specification of an Amazon Relational Database Service (Amazon RDS) 'DataSource'.+-- | The data specification of an Amazon Relational Database Service (Amazon RDS) @DataSource@ . --+--+-- -- /See:/ 'rdsDataSpec' smart constructor. data RDSDataSpec = RDSDataSpec'     { _rdsdsDataSchemaURI       :: !(Maybe Text)@@ -919,27 +877,27 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rdsdsDataSchemaURI'+-- * 'rdsdsDataSchemaURI' - The Amazon S3 location of the @DataSchema@ . ----- * 'rdsdsDataSchema'+-- * 'rdsdsDataSchema' - A JSON string that represents the schema for an Amazon RDS @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . A @DataSchema@ is not required if you specify a @DataSchemaUri@  Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } ----- * 'rdsdsDataRearrangement'+-- * 'rdsdsDataRearrangement' - A JSON string that represents the splitting and rearrangement processing to be applied to a @DataSource@ . If the @DataRearrangement@ parameter is not provided, all of the input data is used to create the @Datasource@ . There are multiple parameters that control what data is used to create a datasource:     * __@percentBegin@ __  Use @percentBegin@ to indicate the beginning of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@percentEnd@ __  Use @percentEnd@ to indicate the end of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@complement@ __  The @complement@ parameter instructs Amazon ML to use the data that is not included in the range of @percentBegin@ to @percentEnd@ to create a datasource. The @complement@ parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for @percentBegin@ and @percentEnd@ , along with the @complement@ parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: @{"splitting":{"percentBegin":0, "percentEnd":25}}@  Datasource for training: @{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}@      * __@strategy@ __  To change how Amazon ML splits the data for a datasource, use the @strategy@ parameter. The default value for the @strategy@ parameter is @sequential@ , meaning that Amazon ML takes all of the data records between the @percentBegin@ and @percentEnd@ parameters for the datasource, in the order that the records appear in the input data. The following two @DataRearrangement@ lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}@  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the @strategy@ parameter to @random@ and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between @percentBegin@ and @percentEnd@ . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two @DataRearrangement@ lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}@ ----- * 'rdsdsDatabaseInformation'+-- * 'rdsdsDatabaseInformation' - Describes the @DatabaseName@ and @InstanceIdentifier@ of an Amazon RDS database. ----- * 'rdsdsSelectSqlQuery'+-- * 'rdsdsSelectSqlQuery' - The query that is used to retrieve the observation data for the @DataSource@ . ----- * 'rdsdsDatabaseCredentials'+-- * 'rdsdsDatabaseCredentials' - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database. ----- * 'rdsdsS3StagingLocation'+-- * 'rdsdsS3StagingLocation' - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using @SelectSqlQuery@ is stored in this location. ----- * 'rdsdsResourceRole'+-- * 'rdsdsResourceRole' - The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. ----- * 'rdsdsServiceRole'+-- * 'rdsdsServiceRole' - The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. ----- * 'rdsdsSubnetId'+-- * 'rdsdsSubnetId' - The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3. ----- * 'rdsdsSecurityGroupIds'+-- * 'rdsdsSecurityGroupIds' - The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. rdsDataSpec     :: RDSDatabase -- ^ 'rdsdsDatabaseInformation'     -> Text -- ^ 'rdsdsSelectSqlQuery'@@ -964,86 +922,23 @@     , _rdsdsSecurityGroupIds = mempty     } --- | The Amazon S3 location of the 'DataSchema'.+-- | The Amazon S3 location of the @DataSchema@ . rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text) rdsdsDataSchemaURI = lens _rdsdsDataSchemaURI (\ s a -> s{_rdsdsDataSchemaURI = a}); --- | A JSON string that represents the schema for an Amazon RDS 'DataSource'. The 'DataSchema' defines the structure of the observation data in the data file(s) referenced in the 'DataSource'.------ A 'DataSchema' is not required if you specify a 'DataSchemaUri'------ Define your 'DataSchema' as a series of key-value pairs. 'attributes' and 'excludedVariableNames' have an array of key-value pairs for their value. Use the following format to define your 'DataSchema'.------ { \"version\": \"1.0\",------ \"recordAnnotationFieldName\": \"F1\",------ \"recordWeightFieldName\": \"F2\",------ \"targetFieldName\": \"F3\",------ \"dataFormat\": \"CSV\",------ \"dataFileContainsHeader\": true,------ \"attributes\": [------ { \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],------ \"excludedVariableNames\": [ \"F6\" ] }+-- | A JSON string that represents the schema for an Amazon RDS @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . A @DataSchema@ is not required if you specify a @DataSchemaUri@  Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) rdsdsDataSchema = lens _rdsdsDataSchema (\ s a -> s{_rdsdsDataSchema = a}); --- | 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\"}}'---+-- | 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"}}@ rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text) rdsdsDataRearrangement = lens _rdsdsDataRearrangement (\ s a -> s{_rdsdsDataRearrangement = a}); --- | Describes the 'DatabaseName' and 'InstanceIdentifier' of 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}); --- | The query that is used to retrieve the observation data for the 'DataSource'.+-- | The query that is used to retrieve the observation data for the @DataSource@ . rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text rdsdsSelectSqlQuery = lens _rdsdsSelectSqlQuery (\ s a -> s{_rdsdsSelectSqlQuery = a}); @@ -1051,7 +946,7 @@ rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials rdsdsDatabaseCredentials = lens _rdsdsDatabaseCredentials (\ s a -> s{_rdsdsDatabaseCredentials = a}); --- | The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using 'SelectSqlQuery' is stored in this location.+-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using @SelectSqlQuery@ is stored in this location. rdsdsS3StagingLocation :: Lens' RDSDataSpec Text rdsdsS3StagingLocation = lens _rdsdsS3StagingLocation (\ s a -> s{_rdsdsS3StagingLocation = a}); @@ -1096,6 +991,8 @@  -- | The database details of an Amazon RDS database. --+--+-- -- /See:/ 'rdsDatabase' smart constructor. data RDSDatabase = RDSDatabase'     { _rdsdInstanceIdentifier :: !Text@@ -1106,9 +1003,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rdsdInstanceIdentifier'+-- * 'rdsdInstanceIdentifier' - The ID of an RDS DB instance. ----- * 'rdsdDatabaseName'+-- * 'rdsdDatabaseName' - Undocumented member. rdsDatabase     :: Text -- ^ 'rdsdInstanceIdentifier'     -> Text -- ^ 'rdsdDatabaseName'@@ -1149,6 +1046,8 @@  -- | The database credentials to connect to a database on an RDS DB instance. --+--+-- -- /See:/ 'rdsDatabaseCredentials' smart constructor. data RDSDatabaseCredentials = RDSDatabaseCredentials'     { _rdsdcUsername :: !Text@@ -1159,9 +1058,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rdsdcUsername'+-- * 'rdsdcUsername' - Undocumented member. ----- * 'rdsdcPassword'+-- * 'rdsdcPassword' - Undocumented member. rdsDatabaseCredentials     :: Text -- ^ 'rdsdcUsername'     -> Text -- ^ 'rdsdcPassword'@@ -1193,6 +1092,8 @@  -- | The datasource details that are specific to Amazon RDS. --+--+-- -- /See:/ 'rdsMetadata' smart constructor. data RDSMetadata = RDSMetadata'     { _rmSelectSqlQuery   :: !(Maybe Text)@@ -1207,17 +1108,17 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rmSelectSqlQuery'+-- * 'rmSelectSqlQuery' - The SQL query that is supplied during 'CreateDataSourceFromRDS' . Returns only if @Verbose@ is true in @GetDataSourceInput@ . ----- * 'rmDataPipelineId'+-- * 'rmDataPipelineId' - The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console. ----- * 'rmDatabase'+-- * 'rmDatabase' - The database details required to connect to an Amazon RDS. ----- * 'rmDatabaseUserName'+-- * 'rmDatabaseUserName' - Undocumented member. ----- * 'rmResourceRole'+-- * 'rmResourceRole' - The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. ----- * 'rmServiceRole'+-- * 'rmServiceRole' - The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html Role templates> for data pipelines. rdsMetadata     :: RDSMetadata rdsMetadata =@@ -1230,7 +1131,7 @@     , _rmServiceRole = Nothing     } --- | The SQL query that is supplied during < CreateDataSourceFromRDS>. Returns only if 'Verbose' is true in 'GetDataSourceInput'.+-- | The SQL query that is supplied during 'CreateDataSourceFromRDS' . Returns only if @Verbose@ is true in @GetDataSourceInput@ . rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text) rmSelectSqlQuery = lens _rmSelectSqlQuery (\ s a -> s{_rmSelectSqlQuery = a}); @@ -1269,8 +1170,10 @@  instance NFData RDSMetadata --- | Describes the real-time endpoint information for an 'MLModel'.+-- | Describes the real-time endpoint information for an @MLModel@ . --+--+-- -- /See:/ 'realtimeEndpointInfo' smart constructor. data RealtimeEndpointInfo = RealtimeEndpointInfo'     { _reiCreatedAt             :: !(Maybe POSIX)@@ -1283,13 +1186,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'reiCreatedAt'+-- * 'reiCreatedAt' - The time that the request to create the real-time endpoint for the @MLModel@ was received. The time is expressed in epoch time. ----- * 'reiEndpointURL'+-- * 'reiEndpointURL' - The URI that specifies where to send real-time prediction requests for the @MLModel@ . ----- * 'reiEndpointStatus'+-- * 'reiEndpointStatus' - The current status of the real-time endpoint for the @MLModel@ . This element can have one of the following values:      * @NONE@ - Endpoint does not exist or was previously deleted.    * @READY@ - Endpoint is ready to be used for real-time predictions.    * @UPDATING@ - Updating/creating the endpoint. ----- * 'reiPeakRequestsPerSecond'+-- * 'reiPeakRequestsPerSecond' - The maximum processing rate for the real-time endpoint for @MLModel@ , measured in incoming requests per second. realtimeEndpointInfo     :: RealtimeEndpointInfo realtimeEndpointInfo =@@ -1300,27 +1203,19 @@     , _reiPeakRequestsPerSecond = Nothing     } --- | The time that the request to create the real-time endpoint for the 'MLModel' was received. The time is expressed in epoch time.+-- | The time that the request to create the real-time endpoint for the @MLModel@ was received. The time is expressed in epoch time. reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime) reiCreatedAt = lens _reiCreatedAt (\ s a -> s{_reiCreatedAt = a}) . mapping _Time; --- | The URI that specifies where to send real-time prediction requests for the 'MLModel'.------ Note------ The application must wait until the real-time endpoint is ready before using this URI.+-- | The URI that specifies where to send real-time prediction requests for the @MLModel@ . reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text) reiEndpointURL = lens _reiEndpointURL (\ s a -> s{_reiEndpointURL = a}); --- | 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.+-- | 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. reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) reiEndpointStatus = lens _reiEndpointStatus (\ s a -> s{_reiEndpointStatus = a}); --- | The maximum processing rate for the real-time endpoint for 'MLModel', measured in incoming requests per second.+-- | The maximum processing rate for the real-time endpoint for @MLModel@ , measured in incoming requests per second. reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int) reiPeakRequestsPerSecond = lens _reiPeakRequestsPerSecond (\ s a -> s{_reiPeakRequestsPerSecond = a}); @@ -1337,8 +1232,10 @@  instance NFData RealtimeEndpointInfo --- | Describes the data specification of an Amazon Redshift 'DataSource'.+-- | Describes the data specification of an Amazon Redshift @DataSource@ . --+--+-- -- /See:/ 'redshiftDataSpec' smart constructor. data RedshiftDataSpec = RedshiftDataSpec'     { _rDataSchemaURI       :: !(Maybe Text)@@ -1354,19 +1251,19 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rDataSchemaURI'+-- * 'rDataSchemaURI' - Describes the schema location for an Amazon Redshift @DataSource@ . ----- * 'rDataSchema'+-- * 'rDataSchema' - A JSON string that represents the schema for an Amazon Redshift @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . A @DataSchema@ is not required if you specify a @DataSchemaUri@ . Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } ----- * 'rDataRearrangement'+-- * 'rDataRearrangement' - A JSON string that represents the splitting and rearrangement processing to be applied to a @DataSource@ . If the @DataRearrangement@ parameter is not provided, all of the input data is used to create the @Datasource@ . There are multiple parameters that control what data is used to create a datasource:     * __@percentBegin@ __  Use @percentBegin@ to indicate the beginning of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@percentEnd@ __  Use @percentEnd@ to indicate the end of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@complement@ __  The @complement@ parameter instructs Amazon ML to use the data that is not included in the range of @percentBegin@ to @percentEnd@ to create a datasource. The @complement@ parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for @percentBegin@ and @percentEnd@ , along with the @complement@ parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: @{"splitting":{"percentBegin":0, "percentEnd":25}}@  Datasource for training: @{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}@      * __@strategy@ __  To change how Amazon ML splits the data for a datasource, use the @strategy@ parameter. The default value for the @strategy@ parameter is @sequential@ , meaning that Amazon ML takes all of the data records between the @percentBegin@ and @percentEnd@ parameters for the datasource, in the order that the records appear in the input data. The following two @DataRearrangement@ lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}@  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the @strategy@ parameter to @random@ and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between @percentBegin@ and @percentEnd@ . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two @DataRearrangement@ lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}@ ----- * 'rDatabaseInformation'+-- * 'rDatabaseInformation' - Describes the @DatabaseName@ and @ClusterIdentifier@ for an Amazon Redshift @DataSource@ . ----- * 'rSelectSqlQuery'+-- * 'rSelectSqlQuery' - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift @DataSource@ . ----- * 'rDatabaseCredentials'+-- * 'rDatabaseCredentials' - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database. ----- * 'rS3StagingLocation'+-- * 'rS3StagingLocation' - Describes an Amazon S3 location to store the result set of the @SelectSqlQuery@ query. redshiftDataSpec     :: RedshiftDatabase -- ^ 'rDatabaseInformation'     -> Text -- ^ 'rSelectSqlQuery'@@ -1384,86 +1281,23 @@     , _rS3StagingLocation = pS3StagingLocation_     } --- | Describes the schema location for an Amazon Redshift 'DataSource'.+-- | Describes the schema location for an Amazon Redshift @DataSource@ . rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text) rDataSchemaURI = lens _rDataSchemaURI (\ s a -> s{_rDataSchemaURI = a}); --- | A JSON string that represents the schema for an Amazon Redshift 'DataSource'. The 'DataSchema' defines the structure of the observation data in the data file(s) referenced in the 'DataSource'.------ A 'DataSchema' is not required if you specify a 'DataSchemaUri'.------ Define your 'DataSchema' as a series of key-value pairs. 'attributes' and 'excludedVariableNames' have an array of key-value pairs for their value. Use the following format to define your 'DataSchema'.------ { \"version\": \"1.0\",------ \"recordAnnotationFieldName\": \"F1\",------ \"recordWeightFieldName\": \"F2\",------ \"targetFieldName\": \"F3\",------ \"dataFormat\": \"CSV\",------ \"dataFileContainsHeader\": true,------ \"attributes\": [------ { \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],------ \"excludedVariableNames\": [ \"F6\" ] }+-- | A JSON string that represents the schema for an Amazon Redshift @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . A @DataSchema@ is not required if you specify a @DataSchemaUri@ . Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) rDataSchema = lens _rDataSchema (\ s a -> s{_rDataSchema = a}); --- | 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\"}}'---+-- | 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}); --- | Describes the 'DatabaseName' and 'ClusterIdentifier' for an Amazon Redshift 'DataSource'.+-- | Describes the @DatabaseName@ and @ClusterIdentifier@ for an Amazon Redshift @DataSource@ . rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase rDatabaseInformation = lens _rDatabaseInformation (\ s a -> s{_rDatabaseInformation = a}); --- | Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift 'DataSource'.+-- | Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift @DataSource@ . rSelectSqlQuery :: Lens' RedshiftDataSpec Text rSelectSqlQuery = lens _rSelectSqlQuery (\ s a -> s{_rSelectSqlQuery = a}); @@ -1471,7 +1305,7 @@ rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials rDatabaseCredentials = lens _rDatabaseCredentials (\ s a -> s{_rDatabaseCredentials = a}); --- | Describes an Amazon S3 location to store the result set of the 'SelectSqlQuery' query.+-- | Describes an Amazon S3 location to store the result set of the @SelectSqlQuery@ query. rS3StagingLocation :: Lens' RedshiftDataSpec Text rS3StagingLocation = lens _rS3StagingLocation (\ s a -> s{_rS3StagingLocation = a}); @@ -1495,6 +1329,8 @@  -- | Describes the database details required to connect to an Amazon Redshift database. --+--+-- -- /See:/ 'redshiftDatabase' smart constructor. data RedshiftDatabase = RedshiftDatabase'     { _rdDatabaseName      :: !Text@@ -1505,9 +1341,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rdDatabaseName'+-- * 'rdDatabaseName' - Undocumented member. ----- * 'rdClusterIdentifier'+-- * 'rdClusterIdentifier' - Undocumented member. redshiftDatabase     :: Text -- ^ 'rdDatabaseName'     -> Text -- ^ 'rdClusterIdentifier'@@ -1546,6 +1382,8 @@  -- | Describes the database credentials for connecting to a database on an Amazon Redshift cluster. --+--+-- -- /See:/ 'redshiftDatabaseCredentials' smart constructor. data RedshiftDatabaseCredentials = RedshiftDatabaseCredentials'     { _rdcUsername :: !Text@@ -1556,9 +1394,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'rdcUsername'+-- * 'rdcUsername' - Undocumented member. ----- * 'rdcPassword'+-- * 'rdcPassword' - Undocumented member. redshiftDatabaseCredentials     :: Text -- ^ 'rdcUsername'     -> Text -- ^ 'rdcPassword'@@ -1588,8 +1426,10 @@                  [Just ("Username" .= _rdcUsername),                   Just ("Password" .= _rdcPassword)]) --- | Describes the 'DataSource' details specific to Amazon Redshift.+-- | Describes the @DataSource@ details specific to Amazon Redshift. --+--+-- -- /See:/ 'redshiftMetadata' smart constructor. data RedshiftMetadata = RedshiftMetadata'     { _redSelectSqlQuery   :: !(Maybe Text)@@ -1601,11 +1441,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'redSelectSqlQuery'+-- * 'redSelectSqlQuery' - The SQL query that is specified during 'CreateDataSourceFromRedshift' . Returns only if @Verbose@ is true in GetDataSourceInput. ----- * 'redRedshiftDatabase'+-- * 'redRedshiftDatabase' - Undocumented member. ----- * 'redDatabaseUserName'+-- * 'redDatabaseUserName' - Undocumented member. redshiftMetadata     :: RedshiftMetadata redshiftMetadata =@@ -1615,7 +1455,7 @@     , _redDatabaseUserName = Nothing     } --- | The SQL query that is specified during < CreateDataSourceFromRedshift>. Returns only if 'Verbose' is true in GetDataSourceInput.+-- | The SQL query that is specified during 'CreateDataSourceFromRedshift' . Returns only if @Verbose@ is true in GetDataSourceInput. redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text) redSelectSqlQuery = lens _redSelectSqlQuery (\ s a -> s{_redSelectSqlQuery = a}); @@ -1640,8 +1480,10 @@  instance NFData RedshiftMetadata --- | Describes the data specification of a 'DataSource'.+-- | Describes the data specification of a @DataSource@ . --+--+-- -- /See:/ 's3DataSpec' smart constructor. data S3DataSpec = S3DataSpec'     { _sdsDataSchema           :: !(Maybe Text)@@ -1654,13 +1496,13 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'sdsDataSchema'+-- * 'sdsDataSchema' - A JSON string that represents the schema for an Amazon S3 @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . You must provide either the @DataSchema@ or the @DataSchemaLocationS3@ . Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } ----- * 'sdsDataSchemaLocationS3'+-- * 'sdsDataSchemaLocationS3' - Describes the schema location in Amazon S3. You must provide either the @DataSchema@ or the @DataSchemaLocationS3@ . ----- * 'sdsDataRearrangement'+-- * 'sdsDataRearrangement' - A JSON string that represents the splitting and rearrangement processing to be applied to a @DataSource@ . If the @DataRearrangement@ parameter is not provided, all of the input data is used to create the @Datasource@ . There are multiple parameters that control what data is used to create a datasource:     * __@percentBegin@ __  Use @percentBegin@ to indicate the beginning of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@percentEnd@ __  Use @percentEnd@ to indicate the end of the range of the data used to create the Datasource. If you do not include @percentBegin@ and @percentEnd@ , Amazon ML includes all of the data when creating the datasource.     * __@complement@ __  The @complement@ parameter instructs Amazon ML to use the data that is not included in the range of @percentBegin@ to @percentEnd@ to create a datasource. The @complement@ parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for @percentBegin@ and @percentEnd@ , along with the @complement@ parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: @{"splitting":{"percentBegin":0, "percentEnd":25}}@  Datasource for training: @{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}@      * __@strategy@ __  To change how Amazon ML splits the data for a datasource, use the @strategy@ parameter. The default value for the @strategy@ parameter is @sequential@ , meaning that Amazon ML takes all of the data records between the @percentBegin@ and @percentEnd@ parameters for the datasource, in the order that the records appear in the input data. The following two @DataRearrangement@ lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}@  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the @strategy@ parameter to @random@ and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between @percentBegin@ and @percentEnd@ . Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records. The following two @DataRearrangement@ lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}@  Datasource for training: @{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}@ ----- * 'sdsDataLocationS3'+-- * 'sdsDataLocationS3' - The location of the data file(s) used by a @DataSource@ . The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. s3DataSpec     :: Text -- ^ 'sdsDataLocationS3'     -> S3DataSpec@@ -1672,82 +1514,19 @@     , _sdsDataLocationS3 = pDataLocationS3_     } --- | A JSON string that represents the schema for an Amazon S3 'DataSource'. The 'DataSchema' defines the structure of the observation data in the data file(s) referenced in the 'DataSource'.------ You must provide either the 'DataSchema' or the 'DataSchemaLocationS3'.------ Define your 'DataSchema' as a series of key-value pairs. 'attributes' and 'excludedVariableNames' have an array of key-value pairs for their value. Use the following format to define your 'DataSchema'.------ { \"version\": \"1.0\",------ \"recordAnnotationFieldName\": \"F1\",------ \"recordWeightFieldName\": \"F2\",------ \"targetFieldName\": \"F3\",------ \"dataFormat\": \"CSV\",------ \"dataFileContainsHeader\": true,------ \"attributes\": [------ { \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],------ \"excludedVariableNames\": [ \"F6\" ] }+-- | A JSON string that represents the schema for an Amazon S3 @DataSource@ . The @DataSchema@ defines the structure of the observation data in the data file(s) referenced in the @DataSource@ . You must provide either the @DataSchema@ or the @DataSchemaLocationS3@ . Define your @DataSchema@ as a series of key-value pairs. @attributes@ and @excludedVariableNames@ have an array of key-value pairs for their value. Use the following format to define your @DataSchema@ . { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] } sdsDataSchema :: Lens' S3DataSpec (Maybe Text) sdsDataSchema = lens _sdsDataSchema (\ s a -> s{_sdsDataSchema = a}); --- | Describes the schema location in Amazon S3. You must provide either the 'DataSchema' or the 'DataSchemaLocationS3'.+-- | 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}); --- | 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\"}}'---+-- | 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}); --- | The location of the data file(s) used by a 'DataSource'. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.+-- | The location of the data file(s) used by a @DataSource@ . The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. sdsDataLocationS3 :: Lens' S3DataSpec Text sdsDataLocationS3 = lens _sdsDataLocationS3 (\ s a -> s{_sdsDataLocationS3 = a}); @@ -1767,6 +1546,8 @@  -- | 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)@@ -1777,9 +1558,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'tagValue'+-- * 'tagValue' - An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. ----- * 'tagKey'+-- * 'tagKey' - A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. tag     :: Tag tag =@@ -1788,11 +1569,11 @@     , _tagKey = Nothing     } --- | An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., \/, =, +, -, %, and \'.+-- | 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 \'.+-- | 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}); 

@@ -19,10 +19,11 @@  import           Network.AWS.Prelude --- | The function used to train an '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)@@ -46,15 +47,11 @@ instance FromJSON Algorithm where     parseJSON = parseJSONText "Algorithm" --- | A list of the variables to use in searching or filtering 'BatchPrediction'.+-- | A list of the variables to use in searching or filtering @BatchPrediction@ . ----- -   'CreatedAt' - Sets the search criteria to 'BatchPrediction' creation date.--- -   'Status' - Sets the search criteria to 'BatchPrediction' status.--- -   'Name' - Sets the search criteria to the contents of 'BatchPrediction' ____ 'Name'.--- -   'IAMUser' - Sets the search criteria to the user account that invoked the 'BatchPrediction' creation.--- -   'MLModelId' - Sets the search criteria to the 'MLModel' used in the 'BatchPrediction'.--- -   'DataSourceId' - Sets the search criteria to the 'DataSource' used in the 'BatchPrediction'.--- -   'DataURI' - Sets the search criteria to the data file(s) used in the 'BatchPrediction'. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.+--+--     * @CreatedAt@ - Sets the search criteria to @BatchPrediction@ creation date.    * @Status@ - Sets the search criteria to @BatchPrediction@ status.    * @Name@ - Sets the search criteria to the contents of @BatchPrediction@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked the @BatchPrediction@ creation.    * @MLModelId@ - Sets the search criteria to the @MLModel@ used in the @BatchPrediction@ .    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in the @BatchPrediction@ .    * @DataURI@ - Sets the search criteria to the data file(s) used in the @BatchPrediction@ . The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.+-- data BatchPredictionFilterVariable     = BatchCreatedAt     | BatchDataSourceId@@ -99,17 +96,11 @@ instance ToJSON BatchPredictionFilterVariable where     toJSON = toJSONText --- | A list of the variables to use in searching or filtering 'DataSource'.+-- | A list of the variables to use in searching or filtering @DataSource@ . ----- -   'CreatedAt' - Sets the search criteria to 'DataSource' creation date.--- -   'Status' - Sets the search criteria to 'DataSource' status.--- -   'Name' - Sets the search criteria to the contents of 'DataSource' ____ 'Name'.--- -   'DataUri' - Sets the search criteria to the URI of data files used to create the 'DataSource'. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.--- -   'IAMUser' - Sets the search criteria to the user account that invoked the 'DataSource' creation. ----- Note+--     * @CreatedAt@ - Sets the search criteria to @DataSource@ creation date.    * @Status@ - Sets the search criteria to @DataSource@ status.    * @Name@ - Sets the search criteria to the contents of @DataSource@ ____ @Name@ .    * @DataUri@ - Sets the search criteria to the URI of data files used to create the @DataSource@ . The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.    * @IAMUser@ - Sets the search criteria to the user account that invoked the @DataSource@ creation. ----- The variable names should match the variable names in the 'DataSource'. data DataSourceFilterVariable     = DataCreatedAt     | DataDATALOCATIONS3@@ -148,7 +139,7 @@ instance ToJSON DataSourceFilterVariable where     toJSON = toJSONText --- | Contains the key values of 'DetailsMap': 'PredictiveModelType' - Indicates the type of the 'MLModel'. 'Algorithm' - Indicates the algorithm that 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@@ -177,11 +168,9 @@  -- | Object status with the following possible values: ----- -   'PENDING'--- -   'INPROGRESS'--- -   'FAILED'--- -   'COMPLETED'--- -   'DELETED'+--+--     * @PENDING@     * @INPROGRESS@     * @FAILED@     * @COMPLETED@     * @DELETED@+-- data EntityStatus     = ESCompleted     | ESDeleted@@ -217,15 +206,11 @@ instance FromJSON EntityStatus where     parseJSON = parseJSONText "EntityStatus" --- | A list of the variables to use in searching or filtering 'Evaluation'.+-- | A list of the variables to use in searching or filtering @Evaluation@ . ----- -   'CreatedAt' - Sets the search criteria to 'Evaluation' creation date.--- -   'Status' - Sets the search criteria to 'Evaluation' status.--- -   'Name' - Sets the search criteria to the contents of 'Evaluation' ____ 'Name'.--- -   'IAMUser' - Sets the search criteria to the user account that invoked an evaluation.--- -   'MLModelId' - Sets the search criteria to the 'Predictor' that was evaluated.--- -   'DataSourceId' - Sets the search criteria to the 'DataSource' used in evaluation.--- -   'DataUri' - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.+--+--     * @CreatedAt@ - Sets the search criteria to @Evaluation@ creation date.    * @Status@ - Sets the search criteria to @Evaluation@ status.    * @Name@ - Sets the search criteria to the contents of @Evaluation@ ____ @Name@ .    * @IAMUser@ - Sets the search criteria to the user account that invoked an evaluation.    * @MLModelId@ - Sets the search criteria to the @Predictor@ that was evaluated.    * @DataSourceId@ - Sets the search criteria to the @DataSource@ used in evaluation.    * @DataUri@ - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.+-- data EvaluationFilterVariable     = EvalCreatedAt     | EvalDataSourceId@@ -386,8 +371,9 @@  -- | The sort order specified in a listing condition. Possible values include the following: ----- -   'asc' - Present the information in ascending order (from A-Z).--- -   'dsc' - Present the information in descending order (from Z-A).+--+--     * @asc@ - Present the information in ascending order (from A-Z).    * @dsc@ - Present the information in descending order (from Z-A).+-- data SortOrder     = Asc     | Dsc

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Updates the 'BatchPredictionName' of a 'BatchPrediction'.+-- 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@@ -55,9 +57,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ubpBatchPredictionId'+-- * 'ubpBatchPredictionId' - The ID assigned to the @BatchPrediction@ during creation. ----- * 'ubpBatchPredictionName'+-- * 'ubpBatchPredictionName' - A new user-supplied name or description of the @BatchPrediction@ . updateBatchPrediction     :: Text -- ^ 'ubpBatchPredictionId'     -> Text -- ^ 'ubpBatchPredictionName'@@ -68,11 +70,11 @@     , _ubpBatchPredictionName = pBatchPredictionName_     } --- | The ID assigned to the 'BatchPrediction' during creation.+-- | The ID assigned to the @BatchPrediction@ during creation. ubpBatchPredictionId :: Lens' UpdateBatchPrediction Text ubpBatchPredictionId = lens _ubpBatchPredictionId (\ s a -> s{_ubpBatchPredictionId = a}); --- | A new user-supplied name or description of the 'BatchPrediction'.+-- | A new user-supplied name or description of the @BatchPrediction@ . ubpBatchPredictionName :: Lens' UpdateBatchPrediction Text ubpBatchPredictionName = lens _ubpBatchPredictionName (\ s a -> s{_ubpBatchPredictionName = a}); @@ -114,10 +116,12 @@ 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'     { _ubprsBatchPredictionId :: !(Maybe Text)@@ -128,9 +132,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ubprsBatchPredictionId'+-- * 'ubprsBatchPredictionId' - The ID assigned to the @BatchPrediction@ during creation. This value should be identical to the value of the @BatchPredictionId@ in the request. ----- * 'ubprsResponseStatus'+-- * 'ubprsResponseStatus' - -- | The response status code. updateBatchPredictionResponse     :: Int -- ^ 'ubprsResponseStatus'     -> UpdateBatchPredictionResponse@@ -140,11 +144,11 @@     , _ubprsResponseStatus = pResponseStatus_     } --- | The ID assigned to the 'BatchPrediction' during creation. This value should be identical to the value of the 'BatchPredictionId' in the request.+-- | The ID assigned to the @BatchPrediction@ during creation. This value should be identical to the value of the @BatchPredictionId@ in the request. ubprsBatchPredictionId :: Lens' UpdateBatchPredictionResponse (Maybe Text) ubprsBatchPredictionId = lens _ubprsBatchPredictionId (\ s a -> s{_ubprsBatchPredictionId = a}); --- | The response status code.+-- | -- | The response status code. ubprsResponseStatus :: Lens' UpdateBatchPredictionResponse Int ubprsResponseStatus = lens _ubprsResponseStatus (\ s a -> s{_ubprsResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Updates the 'DataSourceName' of a 'DataSource'.+-- 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@@ -55,9 +57,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'udsDataSourceId'+-- * 'udsDataSourceId' - The ID assigned to the @DataSource@ during creation. ----- * 'udsDataSourceName'+-- * 'udsDataSourceName' - A new user-supplied name or description of the @DataSource@ that will replace the current description. updateDataSource     :: Text -- ^ 'udsDataSourceId'     -> Text -- ^ 'udsDataSourceName'@@ -68,11 +70,11 @@     , _udsDataSourceName = pDataSourceName_     } --- | The ID assigned to the 'DataSource' during creation.+-- | The ID assigned to the @DataSource@ during creation. udsDataSourceId :: Lens' UpdateDataSource Text udsDataSourceId = lens _udsDataSourceId (\ s a -> s{_udsDataSourceId = a}); --- | A new user-supplied name or description of the 'DataSource' that will replace the current description.+-- | A new user-supplied name or description of the @DataSource@ that will replace the current description. udsDataSourceName :: Lens' UpdateDataSource Text udsDataSourceName = lens _udsDataSourceName (\ s a -> s{_udsDataSourceName = a}); @@ -111,10 +113,12 @@ 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'     { _udsrsDataSourceId   :: !(Maybe Text)@@ -125,9 +129,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'udsrsDataSourceId'+-- * 'udsrsDataSourceId' - The ID assigned to the @DataSource@ during creation. This value should be identical to the value of the @DataSourceID@ in the request. ----- * 'udsrsResponseStatus'+-- * 'udsrsResponseStatus' - -- | The response status code. updateDataSourceResponse     :: Int -- ^ 'udsrsResponseStatus'     -> UpdateDataSourceResponse@@ -137,11 +141,11 @@     , _udsrsResponseStatus = pResponseStatus_     } --- | The ID assigned to the 'DataSource' during creation. This value should be identical to the value of the 'DataSourceID' in the request.+-- | The ID assigned to the @DataSource@ during creation. This value should be identical to the value of the @DataSourceID@ in the request. udsrsDataSourceId :: Lens' UpdateDataSourceResponse (Maybe Text) udsrsDataSourceId = lens _udsrsDataSourceId (\ s a -> s{_udsrsDataSourceId = a}); --- | The response status code.+-- | -- | The response status code. udsrsResponseStatus :: Lens' UpdateDataSourceResponse Int udsrsResponseStatus = lens _udsrsResponseStatus (\ s a -> s{_udsrsResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Updates the 'EvaluationName' of an 'Evaluation'.+-- 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@@ -55,9 +57,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'ueEvaluationId'+-- * 'ueEvaluationId' - The ID assigned to the @Evaluation@ during creation. ----- * 'ueEvaluationName'+-- * 'ueEvaluationName' - A new user-supplied name or description of the @Evaluation@ that will replace the current content. updateEvaluation     :: Text -- ^ 'ueEvaluationId'     -> Text -- ^ 'ueEvaluationName'@@ -68,11 +70,11 @@     , _ueEvaluationName = pEvaluationName_     } --- | The ID assigned to the 'Evaluation' during creation.+-- | The ID assigned to the @Evaluation@ during creation. ueEvaluationId :: Lens' UpdateEvaluation Text ueEvaluationId = lens _ueEvaluationId (\ s a -> s{_ueEvaluationId = a}); --- | A new user-supplied name or description of the 'Evaluation' that will replace the current content.+-- | A new user-supplied name or description of the @Evaluation@ that will replace the current content. ueEvaluationName :: Lens' UpdateEvaluation Text ueEvaluationName = lens _ueEvaluationName (\ s a -> s{_ueEvaluationName = a}); @@ -111,10 +113,12 @@ 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'     { _uersEvaluationId   :: !(Maybe Text)@@ -125,9 +129,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'uersEvaluationId'+-- * 'uersEvaluationId' - The ID assigned to the @Evaluation@ during creation. This value should be identical to the value of the @Evaluation@ in the request. ----- * 'uersResponseStatus'+-- * 'uersResponseStatus' - -- | The response status code. updateEvaluationResponse     :: Int -- ^ 'uersResponseStatus'     -> UpdateEvaluationResponse@@ -137,11 +141,11 @@     , _uersResponseStatus = pResponseStatus_     } --- | The ID assigned to the 'Evaluation' during creation. This value should be identical to the value of the 'Evaluation' in the request.+-- | The ID assigned to the @Evaluation@ during creation. This value should be identical to the value of the @Evaluation@ in the request. uersEvaluationId :: Lens' UpdateEvaluationResponse (Maybe Text) uersEvaluationId = lens _uersEvaluationId (\ s a -> s{_uersEvaluationId = a}); --- | The response status code.+-- | -- | The response status code. uersResponseStatus :: Lens' UpdateEvaluationResponse Int uersResponseStatus = lens _uersResponseStatus (\ s a -> s{_uersResponseStatus = a}); 

@@ -18,9 +18,11 @@ -- Stability   : auto-generated -- Portability : non-portable (GHC extensions) ----- Updates the 'MLModelName' and the 'ScoreThreshold' of an 'MLModel'.+-- 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@@ -57,11 +59,11 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'umlmMLModelName'+-- * 'umlmMLModelName' - A user-supplied name or description of the @MLModel@ . ----- * 'umlmScoreThreshold'+-- * 'umlmScoreThreshold' - The @ScoreThreshold@ used in binary classification @MLModel@ that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the @ScoreThreshold@ receive a positive result from the @MLModel@ , such as @true@ . Output values less than the @ScoreThreshold@ receive a negative response from the @MLModel@ , such as @false@ . ----- * 'umlmMLModelId'+-- * 'umlmMLModelId' - The ID assigned to the @MLModel@ during creation. updateMLModel     :: Text -- ^ 'umlmMLModelId'     -> UpdateMLModel@@ -72,17 +74,15 @@     , _umlmMLModelId = pMLModelId_     } --- | A user-supplied name or description of the 'MLModel'.+-- | A user-supplied name or description of the @MLModel@ . umlmMLModelName :: Lens' UpdateMLModel (Maybe Text) umlmMLModelName = lens _umlmMLModelName (\ s a -> s{_umlmMLModelName = a}); --- | The 'ScoreThreshold' used in binary classification 'MLModel' that marks the boundary between a positive prediction and a negative prediction.------ Output values greater than or equal to the 'ScoreThreshold' receive a positive result from the 'MLModel', such as 'true'. Output values less than the 'ScoreThreshold' receive a negative response from the 'MLModel', such as 'false'.+-- | The @ScoreThreshold@ used in binary classification @MLModel@ that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the @ScoreThreshold@ receive a positive result from the @MLModel@ , such as @true@ . Output values less than the @ScoreThreshold@ receive a negative response from the @MLModel@ , such as @false@ . umlmScoreThreshold :: Lens' UpdateMLModel (Maybe Double) umlmScoreThreshold = lens _umlmScoreThreshold (\ s a -> s{_umlmScoreThreshold = a}); --- | The ID assigned to the 'MLModel' during creation.+-- | The ID assigned to the @MLModel@ during creation. umlmMLModelId :: Lens' UpdateMLModel Text umlmMLModelId = lens _umlmMLModelId (\ s a -> s{_umlmMLModelId = a}); @@ -122,10 +122,12 @@ 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'     { _umlmrsMLModelId      :: !(Maybe Text)@@ -136,9 +138,9 @@ -- -- Use one of the following lenses to modify other fields as desired: ----- * 'umlmrsMLModelId'+-- * 'umlmrsMLModelId' - The ID assigned to the @MLModel@ during creation. This value should be identical to the value of the @MLModelID@ in the request. ----- * 'umlmrsResponseStatus'+-- * 'umlmrsResponseStatus' - -- | The response status code. updateMLModelResponse     :: Int -- ^ 'umlmrsResponseStatus'     -> UpdateMLModelResponse@@ -148,11 +150,11 @@     , _umlmrsResponseStatus = pResponseStatus_     } --- | The ID assigned to the 'MLModel' during creation. This value should be identical to the value of the 'MLModelID' in the request.+-- | The ID assigned to the @MLModel@ during creation. This value should be identical to the value of the @MLModelID@ in the request. umlmrsMLModelId :: Lens' UpdateMLModelResponse (Maybe Text) umlmrsMLModelId = lens _umlmrsMLModelId (\ s a -> s{_umlmrsMLModelId = a}); --- | The response status code.+-- | -- | The response status code. umlmrsResponseStatus :: Lens' UpdateMLModelResponse Int umlmrsResponseStatus = lens _umlmrsResponseStatus (\ s a -> s{_umlmrsResponseStatus = a}); 

@@ -24,8 +24,7 @@ import           Network.AWS.Prelude import           Network.AWS.Waiter --- | Polls 'Network.AWS.MachineLearning.DescribeMLModels' every 30 seconds until a--- successful state is reached. An error is returned after 60 failed checks.+-- | Polls 'Network.AWS.MachineLearning.DescribeMLModels' every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. mLModelAvailable :: Wait DescribeMLModels mLModelAvailable =     Wait@@ -44,8 +43,7 @@                               mlmStatus . _Just . to toTextCI)]     } --- | Polls 'Network.AWS.MachineLearning.DescribeBatchPredictions' every 30 seconds until a--- successful state is reached. An error is returned after 60 failed checks.+-- | Polls 'Network.AWS.MachineLearning.DescribeBatchPredictions' every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. batchPredictionAvailable :: Wait DescribeBatchPredictions batchPredictionAvailable =     Wait@@ -64,8 +62,7 @@                               bpStatus . _Just . to toTextCI)]     } --- | Polls 'Network.AWS.MachineLearning.DescribeDataSources' every 30 seconds until a--- successful state is reached. An error is returned after 60 failed checks.+-- | Polls 'Network.AWS.MachineLearning.DescribeDataSources' every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. dataSourceAvailable :: Wait DescribeDataSources dataSourceAvailable =     Wait@@ -84,8 +81,7 @@                               dsStatus . _Just . to toTextCI)]     } --- | Polls 'Network.AWS.MachineLearning.DescribeEvaluations' every 30 seconds until a--- successful state is reached. An error is returned after 60 failed checks.+-- | Polls 'Network.AWS.MachineLearning.DescribeEvaluations' every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. evaluationAvailable :: Wait DescribeEvaluations evaluationAvailable =     Wait