{-# 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.CreateMLModel
-- 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 new 'MLModel' using the data files and the recipe as
-- information sources.
--
-- An 'MLModel' is nearly immutable. Users can only update the
-- 'MLModelName' and the 'ScoreThreshold' in an 'MLModel' without creating
-- a new 'MLModel'.
--
-- 'CreateMLModel' is an asynchronous operation. In response to
-- 'CreateMLModel', Amazon Machine Learning (Amazon ML) immediately returns
-- and sets the 'MLModel' status to 'PENDING'. After the 'MLModel' is
-- created and ready for use, Amazon ML sets the status to 'COMPLETED'.
--
-- You can use the GetMLModel operation to check 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.
--
-- /See:/ <http://http://docs.aws.amazon.com/machine-learning/latest/APIReference/API_CreateMLModel.html AWS API Reference> for CreateMLModel.
module Network.AWS.MachineLearning.CreateMLModel
    (
    -- * Creating a Request
      createMLModel
    , CreateMLModel
    -- * Request Lenses
    , cmlmRecipe
    , cmlmRecipeURI
    , cmlmMLModelName
    , cmlmParameters
    , cmlmMLModelId
    , cmlmMLModelType
    , cmlmTrainingDataSourceId

    -- * Destructuring the Response
    , createMLModelResponse
    , CreateMLModelResponse
    -- * Response Lenses
    , cmlmrsMLModelId
    , cmlmrsResponseStatus
    ) 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:/ 'createMLModel' smart constructor.
data CreateMLModel = CreateMLModel'
    { _cmlmRecipe               :: !(Maybe Text)
    , _cmlmRecipeURI            :: !(Maybe Text)
    , _cmlmMLModelName          :: !(Maybe Text)
    , _cmlmParameters           :: !(Maybe (Map Text Text))
    , _cmlmMLModelId            :: !Text
    , _cmlmMLModelType          :: !MLModelType
    , _cmlmTrainingDataSourceId :: !Text
    } deriving (Eq,Read,Show,Data,Typeable,Generic)

-- | Creates a value of 'CreateMLModel' with the minimum fields required to make a request.
--
-- Use one of the following lenses to modify other fields as desired:
--
-- * 'cmlmRecipe'
--
-- * 'cmlmRecipeURI'
--
-- * 'cmlmMLModelName'
--
-- * 'cmlmParameters'
--
-- * 'cmlmMLModelId'
--
-- * 'cmlmMLModelType'
--
-- * 'cmlmTrainingDataSourceId'
createMLModel
    :: Text -- ^ 'cmlmMLModelId'
    -> MLModelType -- ^ 'cmlmMLModelType'
    -> Text -- ^ 'cmlmTrainingDataSourceId'
    -> CreateMLModel
createMLModel pMLModelId_ pMLModelType_ pTrainingDataSourceId_ =
    CreateMLModel'
    { _cmlmRecipe = Nothing
    , _cmlmRecipeURI = Nothing
    , _cmlmMLModelName = Nothing
    , _cmlmParameters = Nothing
    , _cmlmMLModelId = pMLModelId_
    , _cmlmMLModelType = pMLModelType_
    , _cmlmTrainingDataSourceId = pTrainingDataSourceId_
    }

-- | The data recipe for creating 'MLModel'. You must specify either the
-- recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML
-- creates a default.
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.
cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text)
cmlmRecipeURI = lens _cmlmRecipeURI (\ s a -> s{_cmlmRecipeURI = a});

-- | 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.l1RegularizationAmount' - Coefficient regularization L1 norm.
--     It controls overfitting the data by penalizing large coefficients.
--     This tends to drive coefficients to zero, resulting in sparse
--     feature set. If you use this parameter, start by specifying a small
--     value such as 1.0E-08.
--
--     The value is a double that ranges from 0 to MAX_DOUBLE. The default
--     is not to use L1 normalization. The parameter cannot be used when
--     'L2' is specified. Use this parameter sparingly.
--
-- -   'sgd.l2RegularizationAmount' - Coefficient regularization L2 norm.
--     It controls overfitting the data by penalizing large coefficients.
--     This tends to drive coefficients to small, nonzero values. If you
--     use this parameter, start by specifying a small value such as
--     1.0E-08.
--
--     The valuseis a double that ranges from 0 to MAX_DOUBLE. The default
--     is not to use L2 normalization. This cannot be used when 'L1' is
--     specified. Use this parameter sparingly.
--
-- -   'sgd.maxPasses' - Number of times that the training process
--     traverses the observations to build the 'MLModel'. The value is an
--     integer that ranges from 1 to 10000. The default value is 10.
--
-- -   'sgd.maxMLModelSizeInBytes' - 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.
--
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'.
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>.
cmlmMLModelType :: Lens' CreateMLModel MLModelType
cmlmMLModelType = lens _cmlmMLModelType (\ s a -> s{_cmlmMLModelType = a});

-- | The 'DataSource' that points to the training data.
cmlmTrainingDataSourceId :: Lens' CreateMLModel Text
cmlmTrainingDataSourceId = lens _cmlmTrainingDataSourceId (\ s a -> s{_cmlmTrainingDataSourceId = a});

instance AWSRequest CreateMLModel where
        type Rs CreateMLModel = CreateMLModelResponse
        request = postJSON machineLearning
        response
          = receiveJSON
              (\ s h x ->
                 CreateMLModelResponse' <$>
                   (x .?> "MLModelId") <*> (pure (fromEnum s)))

instance ToHeaders CreateMLModel where
        toHeaders
          = const
              (mconcat
                 ["X-Amz-Target" =#
                    ("AmazonML_20141212.CreateMLModel" :: ByteString),
                  "Content-Type" =#
                    ("application/x-amz-json-1.1" :: ByteString)])

instance ToJSON CreateMLModel where
        toJSON CreateMLModel'{..}
          = object
              (catMaybes
                 [("Recipe" .=) <$> _cmlmRecipe,
                  ("RecipeUri" .=) <$> _cmlmRecipeURI,
                  ("MLModelName" .=) <$> _cmlmMLModelName,
                  ("Parameters" .=) <$> _cmlmParameters,
                  Just ("MLModelId" .= _cmlmMLModelId),
                  Just ("MLModelType" .= _cmlmMLModelType),
                  Just
                    ("TrainingDataSourceId" .=
                       _cmlmTrainingDataSourceId)])

instance ToPath CreateMLModel where
        toPath = const "/"

instance ToQuery CreateMLModel where
        toQuery = const mempty

-- | Represents the output of a CreateMLModel operation, and is an
-- acknowledgement that Amazon ML received the request.
--
-- The CreateMLModel operation is asynchronous. You can poll for status
-- updates by using the GetMLModel operation and checking the 'Status'
-- parameter.
--
-- /See:/ 'createMLModelResponse' smart constructor.
data CreateMLModelResponse = CreateMLModelResponse'
    { _cmlmrsMLModelId      :: !(Maybe Text)
    , _cmlmrsResponseStatus :: !Int
    } deriving (Eq,Read,Show,Data,Typeable,Generic)

-- | Creates a value of 'CreateMLModelResponse' with the minimum fields required to make a request.
--
-- Use one of the following lenses to modify other fields as desired:
--
-- * 'cmlmrsMLModelId'
--
-- * 'cmlmrsResponseStatus'
createMLModelResponse
    :: Int -- ^ 'cmlmrsResponseStatus'
    -> CreateMLModelResponse
createMLModelResponse pResponseStatus_ =
    CreateMLModelResponse'
    { _cmlmrsMLModelId = Nothing
    , _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.
cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text)
cmlmrsMLModelId = lens _cmlmrsMLModelId (\ s a -> s{_cmlmrsMLModelId = a});

-- | The response status code.
cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int
cmlmrsResponseStatus = lens _cmlmrsResponseStatus (\ s a -> s{_cmlmrsResponseStatus = a});