amazonka-ml 1.3.0 → 1.3.1

raw patch · 9 files changed

+242/−242 lines, 9 filesdep ~amazonka-coredep ~amazonka-mldep ~amazonka-testPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

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

API changes (from Hackage documentation)

Files

• README.md +1/−1
• amazonka-ml.cabal +6/−6
• fixture/CreateDataSourceFromS.yaml +0/−0
• fixture/CreateDataSourceFromS3.yaml +0/−0
• fixture/CreateDataSourceFromS3Response.proto +0/−0
• gen/Network/AWS/MachineLearning.hs +3/−3
• gen/Network/AWS/MachineLearning/CreateDataSourceFromS.hs +0/−219
• gen/Network/AWS/MachineLearning/CreateDataSourceFromS3.hs +219/−0
• test/Test/AWS/Gen/MachineLearning.hs +13/−13

@@ -8,7 +8,7 @@  ## Version -`1.3.0`+`1.3.1`   ## Description

@@ -1,5 +1,5 @@ name:                  amazonka-ml-version:               1.3.0+version:               1.3.1 synopsis:              Amazon Machine Learning SDK. homepage:              https://github.com/brendanhay/amazonka bug-reports:           https://github.com/brendanhay/amazonka/issues@@ -44,7 +44,7 @@         , Network.AWS.MachineLearning.CreateBatchPrediction         , Network.AWS.MachineLearning.CreateDataSourceFromRDS         , Network.AWS.MachineLearning.CreateDataSourceFromRedshift-        , Network.AWS.MachineLearning.CreateDataSourceFromS+        , Network.AWS.MachineLearning.CreateDataSourceFromS3         , Network.AWS.MachineLearning.CreateEvaluation         , Network.AWS.MachineLearning.CreateMLModel         , Network.AWS.MachineLearning.CreateRealtimeEndpoint@@ -74,7 +74,7 @@         , Network.AWS.MachineLearning.Types.Sum      build-depends:-          amazonka-core == 1.3.0.*+          amazonka-core == 1.3.1.*         , base          >= 4.7     && < 5  test-suite amazonka-ml-test@@ -94,9 +94,9 @@         , Test.AWS.MachineLearning.Internal      build-depends:-          amazonka-core == 1.3.0.*-        , amazonka-test == 1.3.0.*-        , amazonka-ml == 1.3.0.*+          amazonka-core == 1.3.1.*+        , amazonka-test == 1.3.1.*+        , amazonka-ml == 1.3.1.*         , base         , bytestring         , lens

@@ -67,8 +67,8 @@     -- ** CreateDataSourceFromRedshift     , module Network.AWS.MachineLearning.CreateDataSourceFromRedshift -    -- ** CreateDataSourceFromS-    , module Network.AWS.MachineLearning.CreateDataSourceFromS+    -- ** CreateDataSourceFromS3+    , module Network.AWS.MachineLearning.CreateDataSourceFromS3      -- ** CreateMLModel     , module Network.AWS.MachineLearning.CreateMLModel@@ -334,7 +334,7 @@ import           Network.AWS.MachineLearning.CreateBatchPrediction import           Network.AWS.MachineLearning.CreateDataSourceFromRDS import           Network.AWS.MachineLearning.CreateDataSourceFromRedshift-import           Network.AWS.MachineLearning.CreateDataSourceFromS+import           Network.AWS.MachineLearning.CreateDataSourceFromS3 import           Network.AWS.MachineLearning.CreateEvaluation import           Network.AWS.MachineLearning.CreateMLModel import           Network.AWS.MachineLearning.CreateRealtimeEndpoint

@@ -1,219 +0,0 @@-{-# LANGUAGE DeriveDataTypeable #-}-{-# LANGUAGE DeriveGeneric      #-}-{-# LANGUAGE OverloadedStrings  #-}-{-# LANGUAGE RecordWildCards    #-}-{-# LANGUAGE TypeFamilies       #-}--{-# OPTIONS_GHC -fno-warn-unused-imports #-}-{-# OPTIONS_GHC -fno-warn-unused-binds   #-}-{-# OPTIONS_GHC -fno-warn-unused-matches #-}---- Derived from AWS service descriptions, licensed under Apache 2.0.---- |--- Module      : Network.AWS.MachineLearning.CreateDataSourceFromS--- Copyright   : (c) 2013-2015 Brendan Hay--- License     : Mozilla Public License, v. 2.0.--- Maintainer  : Brendan Hay <brendan.g.hay@gmail.com>--- Stability   : auto-generated--- Portability : non-portable (GHC extensions)------ Creates a 'DataSource' object. A 'DataSource' references data that can--- be used to perform CreateMLModel, CreateEvaluation, or--- CreateBatchPrediction operations.------ 'CreateDataSourceFromS3' is an asynchronous operation. In response to--- 'CreateDataSourceFromS3', Amazon Machine Learning (Amazon ML)--- immediately returns and sets the 'DataSource' status to 'PENDING'. After--- the 'DataSource' is created and ready for use, Amazon ML sets the--- 'Status' parameter to 'COMPLETED'. 'DataSource' in 'COMPLETED' or--- 'PENDING' status can only be used to perform CreateMLModel,--- CreateEvaluation or CreateBatchPrediction operations.------ 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.------ The observation data used in a 'DataSource' should be ready to use; that--- is, it should have a consistent structure, and missing data values--- should be kept to a minimum. The observation data must reside in one or--- more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket,--- along with a schema that describes the data items by name and type. The--- same schema must be used for all of the data files referenced by the--- 'DataSource'.------ After the 'DataSource' has been created, it\'s ready to use in--- evaluations and batch predictions. If you plan to use the 'DataSource'--- to train an 'MLModel', the 'DataSource' requires another item: a recipe.--- A recipe describes the observation variables that participate in--- training an 'MLModel'. A recipe describes how each input variable will--- be used in training. Will the variable be included or excluded from--- training? Will the variable be manipulated, for example, combined with--- another variable, or split apart into word combinations? The recipe--- provides answers to these questions. For more information, see the--- <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.------ /See:/ <http://http://docs.aws.amazon.com/machine-learning/latest/APIReference/API_CreateDataSourceFromS.html AWS API Reference> for CreateDataSourceFromS.-module Network.AWS.MachineLearning.CreateDataSourceFromS-    (-    -- * Creating a Request-      createDataSourceFromS-    , CreateDataSourceFromS-    -- * Request Lenses-    , cdsfsDataSourceName-    , cdsfsComputeStatistics-    , cdsfsDataSourceId-    , cdsfsDataSpec--    -- * Destructuring the Response-    , createDataSourceFromSResponse-    , CreateDataSourceFromSResponse-    -- * Response Lenses-    , cdsfsrsDataSourceId-    , cdsfsrsResponseStatus-    ) where--import           Network.AWS.MachineLearning.Types-import           Network.AWS.MachineLearning.Types.Product-import           Network.AWS.Prelude-import           Network.AWS.Request-import           Network.AWS.Response---- | /See:/ 'createDataSourceFromS' smart constructor.-data CreateDataSourceFromS = CreateDataSourceFromS'-    { _cdsfsDataSourceName    :: !(Maybe Text)-    , _cdsfsComputeStatistics :: !(Maybe Bool)-    , _cdsfsDataSourceId      :: !Text-    , _cdsfsDataSpec          :: !S3DataSpec-    } deriving (Eq,Read,Show,Data,Typeable,Generic)---- | Creates a value of 'CreateDataSourceFromS' with the minimum fields required to make a request.------ Use one of the following lenses to modify other fields as desired:------ * 'cdsfsDataSourceName'------ * 'cdsfsComputeStatistics'------ * 'cdsfsDataSourceId'------ * 'cdsfsDataSpec'-createDataSourceFromS-    :: Text -- ^ 'cdsfsDataSourceId'-    -> S3DataSpec -- ^ 'cdsfsDataSpec'-    -> CreateDataSourceFromS-createDataSourceFromS pDataSourceId_ pDataSpec_ =-    CreateDataSourceFromS'-    { _cdsfsDataSourceName = Nothing-    , _cdsfsComputeStatistics = Nothing-    , _cdsfsDataSourceId = pDataSourceId_-    , _cdsfsDataSpec = pDataSpec_-    }---- | A user-supplied name or description of the 'DataSource'.-cdsfsDataSourceName :: Lens' CreateDataSourceFromS (Maybe Text)-cdsfsDataSourceName = lens _cdsfsDataSourceName (\ s a -> s{_cdsfsDataSourceName = a});---- | The compute statistics for a 'DataSource'. The statistics are generated--- from the observation data referenced by a 'DataSource'. Amazon ML uses--- the statistics internally during an 'MLModel' training. This parameter--- must be set to 'true' if the ''DataSource'' needs to be used for--- 'MLModel' training-cdsfsComputeStatistics :: Lens' CreateDataSourceFromS (Maybe Bool)-cdsfsComputeStatistics = lens _cdsfsComputeStatistics (\ s a -> s{_cdsfsComputeStatistics = a});---- | A user-supplied identifier that uniquely identifies the 'DataSource'.-cdsfsDataSourceId :: Lens' CreateDataSourceFromS Text-cdsfsDataSourceId = lens _cdsfsDataSourceId (\ s a -> s{_cdsfsDataSourceId = a});---- | The data specification of a 'DataSource':------ -   DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location---     of the observation data.------ -   DataSchemaLocationS3 - Amazon S3 location of the 'DataSchema'.------ -   DataSchema - A JSON string representing the schema. This is not---     required if 'DataSchemaUri' is specified.------ -   DataRearrangement - A JSON string representing the splitting---     requirement of a 'Datasource'.------     Sample ----     ' \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'----cdsfsDataSpec :: Lens' CreateDataSourceFromS S3DataSpec-cdsfsDataSpec = lens _cdsfsDataSpec (\ s a -> s{_cdsfsDataSpec = a});--instance AWSRequest CreateDataSourceFromS where-        type Rs CreateDataSourceFromS =-             CreateDataSourceFromSResponse-        request = postJSON machineLearning-        response-          = receiveJSON-              (\ s h x ->-                 CreateDataSourceFromSResponse' <$>-                   (x .?> "DataSourceId") <*> (pure (fromEnum s)))--instance ToHeaders CreateDataSourceFromS where-        toHeaders-          = const-              (mconcat-                 ["X-Amz-Target" =#-                    ("AmazonML_20141212.CreateDataSourceFromS" ::-                       ByteString),-                  "Content-Type" =#-                    ("application/x-amz-json-1.1" :: ByteString)])--instance ToJSON CreateDataSourceFromS where-        toJSON CreateDataSourceFromS'{..}-          = object-              (catMaybes-                 [("DataSourceName" .=) <$> _cdsfsDataSourceName,-                  ("ComputeStatistics" .=) <$> _cdsfsComputeStatistics,-                  Just ("DataSourceId" .= _cdsfsDataSourceId),-                  Just ("DataSpec" .= _cdsfsDataSpec)])--instance ToPath CreateDataSourceFromS where-        toPath = const "/"--instance ToQuery CreateDataSourceFromS where-        toQuery = const mempty---- | Represents the output of a CreateDataSourceFromS3 operation, and is an--- acknowledgement that Amazon ML received the request.------ The CreateDataSourceFromS3 operation is asynchronous. You can poll for--- updates by using the GetBatchPrediction operation and checking the--- 'Status' parameter.------ /See:/ 'createDataSourceFromSResponse' smart constructor.-data CreateDataSourceFromSResponse = CreateDataSourceFromSResponse'-    { _cdsfsrsDataSourceId   :: !(Maybe Text)-    , _cdsfsrsResponseStatus :: !Int-    } deriving (Eq,Read,Show,Data,Typeable,Generic)---- | Creates a value of 'CreateDataSourceFromSResponse' with the minimum fields required to make a request.------ Use one of the following lenses to modify other fields as desired:------ * 'cdsfsrsDataSourceId'------ * 'cdsfsrsResponseStatus'-createDataSourceFromSResponse-    :: Int -- ^ 'cdsfsrsResponseStatus'-    -> CreateDataSourceFromSResponse-createDataSourceFromSResponse pResponseStatus_ =-    CreateDataSourceFromSResponse'-    { _cdsfsrsDataSourceId = Nothing-    , _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.-cdsfsrsDataSourceId :: Lens' CreateDataSourceFromSResponse (Maybe Text)-cdsfsrsDataSourceId = lens _cdsfsrsDataSourceId (\ s a -> s{_cdsfsrsDataSourceId = a});---- | The response status code.-cdsfsrsResponseStatus :: Lens' CreateDataSourceFromSResponse Int-cdsfsrsResponseStatus = lens _cdsfsrsResponseStatus (\ s a -> s{_cdsfsrsResponseStatus = a});

@@ -0,0 +1,219 @@+{-# LANGUAGE DeriveDataTypeable #-}+{-# LANGUAGE DeriveGeneric      #-}+{-# LANGUAGE OverloadedStrings  #-}+{-# LANGUAGE RecordWildCards    #-}+{-# LANGUAGE TypeFamilies       #-}++{-# OPTIONS_GHC -fno-warn-unused-imports #-}+{-# OPTIONS_GHC -fno-warn-unused-binds   #-}+{-# OPTIONS_GHC -fno-warn-unused-matches #-}++-- Derived from AWS service descriptions, licensed under Apache 2.0.++-- |+-- Module      : Network.AWS.MachineLearning.CreateDataSourceFromS3+-- Copyright   : (c) 2013-2015 Brendan Hay+-- License     : Mozilla Public License, v. 2.0.+-- Maintainer  : Brendan Hay <brendan.g.hay@gmail.com>+-- Stability   : auto-generated+-- Portability : non-portable (GHC extensions)+--+-- Creates a 'DataSource' object. A 'DataSource' references data that can+-- be used to perform CreateMLModel, CreateEvaluation, or+-- CreateBatchPrediction operations.+--+-- 'CreateDataSourceFromS3' is an asynchronous operation. In response to+-- 'CreateDataSourceFromS3', Amazon Machine Learning (Amazon ML)+-- immediately returns and sets the 'DataSource' status to 'PENDING'. After+-- the 'DataSource' is created and ready for use, Amazon ML sets the+-- 'Status' parameter to 'COMPLETED'. 'DataSource' in 'COMPLETED' or+-- 'PENDING' status can only be used to perform CreateMLModel,+-- CreateEvaluation or CreateBatchPrediction operations.+--+-- 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.+--+-- The observation data used in a 'DataSource' should be ready to use; that+-- is, it should have a consistent structure, and missing data values+-- should be kept to a minimum. The observation data must reside in one or+-- more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket,+-- along with a schema that describes the data items by name and type. The+-- same schema must be used for all of the data files referenced by the+-- 'DataSource'.+--+-- After the 'DataSource' has been created, it\'s ready to use in+-- evaluations and batch predictions. If you plan to use the 'DataSource'+-- to train an 'MLModel', the 'DataSource' requires another item: a recipe.+-- A recipe describes the observation variables that participate in+-- training an 'MLModel'. A recipe describes how each input variable will+-- be used in training. Will the variable be included or excluded from+-- training? Will the variable be manipulated, for example, combined with+-- another variable, or split apart into word combinations? The recipe+-- provides answers to these questions. For more information, see the+-- <http://docs.aws.amazon.com/machine-learning/latest/dg Amazon Machine Learning Developer Guide>.+--+-- /See:/ <http://http://docs.aws.amazon.com/machine-learning/latest/APIReference/API_CreateDataSourceFromS3.html AWS API Reference> for CreateDataSourceFromS3.+module Network.AWS.MachineLearning.CreateDataSourceFromS3+    (+    -- * Creating a Request+      createDataSourceFromS3+    , CreateDataSourceFromS3+    -- * Request Lenses+    , cdsfsDataSourceName+    , cdsfsComputeStatistics+    , cdsfsDataSourceId+    , cdsfsDataSpec++    -- * Destructuring the Response+    , createDataSourceFromS3Response+    , CreateDataSourceFromS3Response+    -- * Response Lenses+    , cdsfsrsDataSourceId+    , cdsfsrsResponseStatus+    ) where++import           Network.AWS.MachineLearning.Types+import           Network.AWS.MachineLearning.Types.Product+import           Network.AWS.Prelude+import           Network.AWS.Request+import           Network.AWS.Response++-- | /See:/ 'createDataSourceFromS3' smart constructor.+data CreateDataSourceFromS3 = CreateDataSourceFromS3'+    { _cdsfsDataSourceName    :: !(Maybe Text)+    , _cdsfsComputeStatistics :: !(Maybe Bool)+    , _cdsfsDataSourceId      :: !Text+    , _cdsfsDataSpec          :: !S3DataSpec+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'CreateDataSourceFromS3' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'cdsfsDataSourceName'+--+-- * 'cdsfsComputeStatistics'+--+-- * 'cdsfsDataSourceId'+--+-- * 'cdsfsDataSpec'+createDataSourceFromS3+    :: Text -- ^ 'cdsfsDataSourceId'+    -> S3DataSpec -- ^ 'cdsfsDataSpec'+    -> CreateDataSourceFromS3+createDataSourceFromS3 pDataSourceId_ pDataSpec_ =+    CreateDataSourceFromS3'+    { _cdsfsDataSourceName = Nothing+    , _cdsfsComputeStatistics = Nothing+    , _cdsfsDataSourceId = pDataSourceId_+    , _cdsfsDataSpec = pDataSpec_+    }++-- | 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 an 'MLModel' training. This parameter+-- must be set to 'true' if the ''DataSource'' needs to be used for+-- 'MLModel' training+cdsfsComputeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool)+cdsfsComputeStatistics = lens _cdsfsComputeStatistics (\ s a -> s{_cdsfsComputeStatistics = a});++-- | 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 - Amazon Simple Storage Service (Amazon S3) location+--     of the observation data.+--+-- -   DataSchemaLocationS3 - Amazon S3 location of the 'DataSchema'.+--+-- -   DataSchema - A JSON string representing the schema. This is not+--     required if 'DataSchemaUri' is specified.+--+-- -   DataRearrangement - A JSON string representing the splitting+--     requirement of a 'Datasource'.+--+--     Sample -+--     ' \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"'+--+cdsfsDataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec+cdsfsDataSpec = lens _cdsfsDataSpec (\ s a -> s{_cdsfsDataSpec = a});++instance AWSRequest CreateDataSourceFromS3 where+        type Rs CreateDataSourceFromS3 =+             CreateDataSourceFromS3Response+        request = postJSON machineLearning+        response+          = receiveJSON+              (\ s h x ->+                 CreateDataSourceFromS3Response' <$>+                   (x .?> "DataSourceId") <*> (pure (fromEnum s)))++instance ToHeaders CreateDataSourceFromS3 where+        toHeaders+          = const+              (mconcat+                 ["X-Amz-Target" =#+                    ("AmazonML_20141212.CreateDataSourceFromS3" ::+                       ByteString),+                  "Content-Type" =#+                    ("application/x-amz-json-1.1" :: ByteString)])++instance ToJSON CreateDataSourceFromS3 where+        toJSON CreateDataSourceFromS3'{..}+          = object+              (catMaybes+                 [("DataSourceName" .=) <$> _cdsfsDataSourceName,+                  ("ComputeStatistics" .=) <$> _cdsfsComputeStatistics,+                  Just ("DataSourceId" .= _cdsfsDataSourceId),+                  Just ("DataSpec" .= _cdsfsDataSpec)])++instance ToPath CreateDataSourceFromS3 where+        toPath = const "/"++instance ToQuery CreateDataSourceFromS3 where+        toQuery = const mempty++-- | Represents the output of a CreateDataSourceFromS3 operation, and is an+-- acknowledgement that Amazon ML received the request.+--+-- The CreateDataSourceFromS3 operation is asynchronous. You can poll for+-- updates by using the GetBatchPrediction operation and checking the+-- 'Status' parameter.+--+-- /See:/ 'createDataSourceFromS3Response' smart constructor.+data CreateDataSourceFromS3Response = CreateDataSourceFromS3Response'+    { _cdsfsrsDataSourceId   :: !(Maybe Text)+    , _cdsfsrsResponseStatus :: !Int+    } deriving (Eq,Read,Show,Data,Typeable,Generic)++-- | Creates a value of 'CreateDataSourceFromS3Response' with the minimum fields required to make a request.+--+-- Use one of the following lenses to modify other fields as desired:+--+-- * 'cdsfsrsDataSourceId'+--+-- * 'cdsfsrsResponseStatus'+createDataSourceFromS3Response+    :: Int -- ^ 'cdsfsrsResponseStatus'+    -> CreateDataSourceFromS3Response+createDataSourceFromS3Response pResponseStatus_ =+    CreateDataSourceFromS3Response'+    { _cdsfsrsDataSourceId = Nothing+    , _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.+cdsfsrsDataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text)+cdsfsrsDataSourceId = lens _cdsfsrsDataSourceId (\ s a -> s{_cdsfsrsDataSourceId = a});++-- | The response status code.+cdsfsrsResponseStatus :: Lens' CreateDataSourceFromS3Response Int+cdsfsrsResponseStatus = lens _cdsfsrsResponseStatus (\ s a -> s{_cdsfsrsResponseStatus = a});

@@ -37,8 +37,8 @@ --         , testCreateDataSourceFromRedshift $ --             createDataSourceFromRedshift -----         , testCreateDataSourceFromS $---             createDataSourceFromS+--         , testCreateDataSourceFromS3 $+--             createDataSourceFromS3 -- --         , testCreateMLModel $ --             createMLModel@@ -115,8 +115,8 @@ --         , testCreateDataSourceFromRedshiftResponse $ --             createDataSourceFromRedshiftResponse -----         , testCreateDataSourceFromSResponse $---             createDataSourceFromSResponse+--         , testCreateDataSourceFromS3Response $+--             createDataSourceFromS3Response -- --         , testCreateMLModelResponse $ --             createMLModelResponse@@ -201,10 +201,10 @@     "CreateDataSourceFromRedshift"     "fixture/CreateDataSourceFromRedshift.yaml" -testCreateDataSourceFromS :: CreateDataSourceFromS -> TestTree-testCreateDataSourceFromS = req-    "CreateDataSourceFromS"-    "fixture/CreateDataSourceFromS.yaml"+testCreateDataSourceFromS3 :: CreateDataSourceFromS3 -> TestTree+testCreateDataSourceFromS3 = req+    "CreateDataSourceFromS3"+    "fixture/CreateDataSourceFromS3.yaml"  testCreateMLModel :: CreateMLModel -> TestTree testCreateMLModel = req@@ -334,12 +334,12 @@     machineLearning     (Proxy :: Proxy CreateDataSourceFromRedshift) -testCreateDataSourceFromSResponse :: CreateDataSourceFromSResponse -> TestTree-testCreateDataSourceFromSResponse = res-    "CreateDataSourceFromSResponse"-    "fixture/CreateDataSourceFromSResponse.proto"+testCreateDataSourceFromS3Response :: CreateDataSourceFromS3Response -> TestTree+testCreateDataSourceFromS3Response = res+    "CreateDataSourceFromS3Response"+    "fixture/CreateDataSourceFromS3Response.proto"     machineLearning-    (Proxy :: Proxy CreateDataSourceFromS)+    (Proxy :: Proxy CreateDataSourceFromS3)  testCreateMLModelResponse :: CreateMLModelResponse -> TestTree testCreateMLModelResponse = res