{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE StrictData #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# OPTIONS_GHC -fno-warn-unused-binds #-}
{-# OPTIONS_GHC -fno-warn-unused-imports #-}
{-# OPTIONS_GHC -fno-warn-unused-matches #-}
-- Derived from AWS service descriptions, licensed under Apache 2.0.
-- |
-- Module : Amazonka.LookoutEquipment.CreateModel
-- Copyright : (c) 2013-2023 Brendan Hay
-- License : Mozilla Public License, v. 2.0.
-- Maintainer : Brendan Hay
-- Stability : auto-generated
-- Portability : non-portable (GHC extensions)
--
-- Creates an ML model for data inference.
--
-- A machine-learning (ML) model is a mathematical model that finds
-- patterns in your data. In Amazon Lookout for Equipment, the model learns
-- the patterns of normal behavior and detects abnormal behavior that could
-- be potential equipment failure (or maintenance events). The models are
-- made by analyzing normal data and abnormalities in machine behavior that
-- have already occurred.
--
-- Your model is trained using a portion of the data from your dataset and
-- uses that data to learn patterns of normal behavior and abnormal
-- patterns that lead to equipment failure. Another portion of the data is
-- used to evaluate the model\'s accuracy.
module Amazonka.LookoutEquipment.CreateModel
( -- * Creating a Request
CreateModel (..),
newCreateModel,
-- * Request Lenses
createModel_dataPreProcessingConfiguration,
createModel_datasetSchema,
createModel_evaluationDataEndTime,
createModel_evaluationDataStartTime,
createModel_labelsInputConfiguration,
createModel_offCondition,
createModel_roleArn,
createModel_serverSideKmsKeyId,
createModel_tags,
createModel_trainingDataEndTime,
createModel_trainingDataStartTime,
createModel_modelName,
createModel_datasetName,
createModel_clientToken,
-- * Destructuring the Response
CreateModelResponse (..),
newCreateModelResponse,
-- * Response Lenses
createModelResponse_modelArn,
createModelResponse_status,
createModelResponse_httpStatus,
)
where
import qualified Amazonka.Core as Core
import qualified Amazonka.Core.Lens.Internal as Lens
import qualified Amazonka.Data as Data
import Amazonka.LookoutEquipment.Types
import qualified Amazonka.Prelude as Prelude
import qualified Amazonka.Request as Request
import qualified Amazonka.Response as Response
-- | /See:/ 'newCreateModel' smart constructor.
data CreateModel = CreateModel'
{ -- | The configuration is the @TargetSamplingRate@, which is the sampling
-- rate of the data after post processing by Amazon Lookout for Equipment.
-- For example, if you provide data that has been collected at a 1 second
-- level and you want the system to resample the data at a 1 minute rate
-- before training, the @TargetSamplingRate@ is 1 minute.
--
-- When providing a value for the @TargetSamplingRate@, you must attach the
-- prefix \"PT\" to the rate you want. The value for a 1 second rate is
-- therefore /PT1S/, the value for a 15 minute rate is /PT15M/, and the
-- value for a 1 hour rate is /PT1H/
dataPreProcessingConfiguration :: Prelude.Maybe DataPreProcessingConfiguration,
-- | The data schema for the ML model being created.
datasetSchema :: Prelude.Maybe DatasetSchema,
-- | Indicates the time reference in the dataset that should be used to end
-- the subset of evaluation data for the ML model.
evaluationDataEndTime :: Prelude.Maybe Data.POSIX,
-- | Indicates the time reference in the dataset that should be used to begin
-- the subset of evaluation data for the ML model.
evaluationDataStartTime :: Prelude.Maybe Data.POSIX,
-- | The input configuration for the labels being used for the ML model
-- that\'s being created.
labelsInputConfiguration :: Prelude.Maybe LabelsInputConfiguration,
-- | Indicates that the asset associated with this sensor has been shut off.
-- As long as this condition is met, Lookout for Equipment will not use
-- data from this asset for training, evaluation, or inference.
offCondition :: Prelude.Maybe Prelude.Text,
-- | The Amazon Resource Name (ARN) of a role with permission to access the
-- data source being used to create the ML model.
roleArn :: Prelude.Maybe Prelude.Text,
-- | Provides the identifier of the KMS key used to encrypt model data by
-- Amazon Lookout for Equipment.
serverSideKmsKeyId :: Prelude.Maybe Prelude.Text,
-- | Any tags associated with the ML model being created.
tags :: Prelude.Maybe [Tag],
-- | Indicates the time reference in the dataset that should be used to end
-- the subset of training data for the ML model.
trainingDataEndTime :: Prelude.Maybe Data.POSIX,
-- | Indicates the time reference in the dataset that should be used to begin
-- the subset of training data for the ML model.
trainingDataStartTime :: Prelude.Maybe Data.POSIX,
-- | The name for the ML model to be created.
modelName :: Prelude.Text,
-- | The name of the dataset for the ML model being created.
datasetName :: Prelude.Text,
-- | A unique identifier for the request. If you do not set the client
-- request token, Amazon Lookout for Equipment generates one.
clientToken :: Prelude.Text
}
deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)
-- |
-- Create a value of 'CreateModel' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'dataPreProcessingConfiguration', 'createModel_dataPreProcessingConfiguration' - The configuration is the @TargetSamplingRate@, which is the sampling
-- rate of the data after post processing by Amazon Lookout for Equipment.
-- For example, if you provide data that has been collected at a 1 second
-- level and you want the system to resample the data at a 1 minute rate
-- before training, the @TargetSamplingRate@ is 1 minute.
--
-- When providing a value for the @TargetSamplingRate@, you must attach the
-- prefix \"PT\" to the rate you want. The value for a 1 second rate is
-- therefore /PT1S/, the value for a 15 minute rate is /PT15M/, and the
-- value for a 1 hour rate is /PT1H/
--
-- 'datasetSchema', 'createModel_datasetSchema' - The data schema for the ML model being created.
--
-- 'evaluationDataEndTime', 'createModel_evaluationDataEndTime' - Indicates the time reference in the dataset that should be used to end
-- the subset of evaluation data for the ML model.
--
-- 'evaluationDataStartTime', 'createModel_evaluationDataStartTime' - Indicates the time reference in the dataset that should be used to begin
-- the subset of evaluation data for the ML model.
--
-- 'labelsInputConfiguration', 'createModel_labelsInputConfiguration' - The input configuration for the labels being used for the ML model
-- that\'s being created.
--
-- 'offCondition', 'createModel_offCondition' - Indicates that the asset associated with this sensor has been shut off.
-- As long as this condition is met, Lookout for Equipment will not use
-- data from this asset for training, evaluation, or inference.
--
-- 'roleArn', 'createModel_roleArn' - The Amazon Resource Name (ARN) of a role with permission to access the
-- data source being used to create the ML model.
--
-- 'serverSideKmsKeyId', 'createModel_serverSideKmsKeyId' - Provides the identifier of the KMS key used to encrypt model data by
-- Amazon Lookout for Equipment.
--
-- 'tags', 'createModel_tags' - Any tags associated with the ML model being created.
--
-- 'trainingDataEndTime', 'createModel_trainingDataEndTime' - Indicates the time reference in the dataset that should be used to end
-- the subset of training data for the ML model.
--
-- 'trainingDataStartTime', 'createModel_trainingDataStartTime' - Indicates the time reference in the dataset that should be used to begin
-- the subset of training data for the ML model.
--
-- 'modelName', 'createModel_modelName' - The name for the ML model to be created.
--
-- 'datasetName', 'createModel_datasetName' - The name of the dataset for the ML model being created.
--
-- 'clientToken', 'createModel_clientToken' - A unique identifier for the request. If you do not set the client
-- request token, Amazon Lookout for Equipment generates one.
newCreateModel ::
-- | 'modelName'
Prelude.Text ->
-- | 'datasetName'
Prelude.Text ->
-- | 'clientToken'
Prelude.Text ->
CreateModel
newCreateModel
pModelName_
pDatasetName_
pClientToken_ =
CreateModel'
{ dataPreProcessingConfiguration =
Prelude.Nothing,
datasetSchema = Prelude.Nothing,
evaluationDataEndTime = Prelude.Nothing,
evaluationDataStartTime = Prelude.Nothing,
labelsInputConfiguration = Prelude.Nothing,
offCondition = Prelude.Nothing,
roleArn = Prelude.Nothing,
serverSideKmsKeyId = Prelude.Nothing,
tags = Prelude.Nothing,
trainingDataEndTime = Prelude.Nothing,
trainingDataStartTime = Prelude.Nothing,
modelName = pModelName_,
datasetName = pDatasetName_,
clientToken = pClientToken_
}
-- | The configuration is the @TargetSamplingRate@, which is the sampling
-- rate of the data after post processing by Amazon Lookout for Equipment.
-- For example, if you provide data that has been collected at a 1 second
-- level and you want the system to resample the data at a 1 minute rate
-- before training, the @TargetSamplingRate@ is 1 minute.
--
-- When providing a value for the @TargetSamplingRate@, you must attach the
-- prefix \"PT\" to the rate you want. The value for a 1 second rate is
-- therefore /PT1S/, the value for a 15 minute rate is /PT15M/, and the
-- value for a 1 hour rate is /PT1H/
createModel_dataPreProcessingConfiguration :: Lens.Lens' CreateModel (Prelude.Maybe DataPreProcessingConfiguration)
createModel_dataPreProcessingConfiguration = Lens.lens (\CreateModel' {dataPreProcessingConfiguration} -> dataPreProcessingConfiguration) (\s@CreateModel' {} a -> s {dataPreProcessingConfiguration = a} :: CreateModel)
-- | The data schema for the ML model being created.
createModel_datasetSchema :: Lens.Lens' CreateModel (Prelude.Maybe DatasetSchema)
createModel_datasetSchema = Lens.lens (\CreateModel' {datasetSchema} -> datasetSchema) (\s@CreateModel' {} a -> s {datasetSchema = a} :: CreateModel)
-- | Indicates the time reference in the dataset that should be used to end
-- the subset of evaluation data for the ML model.
createModel_evaluationDataEndTime :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.UTCTime)
createModel_evaluationDataEndTime = Lens.lens (\CreateModel' {evaluationDataEndTime} -> evaluationDataEndTime) (\s@CreateModel' {} a -> s {evaluationDataEndTime = a} :: CreateModel) Prelude.. Lens.mapping Data._Time
-- | Indicates the time reference in the dataset that should be used to begin
-- the subset of evaluation data for the ML model.
createModel_evaluationDataStartTime :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.UTCTime)
createModel_evaluationDataStartTime = Lens.lens (\CreateModel' {evaluationDataStartTime} -> evaluationDataStartTime) (\s@CreateModel' {} a -> s {evaluationDataStartTime = a} :: CreateModel) Prelude.. Lens.mapping Data._Time
-- | The input configuration for the labels being used for the ML model
-- that\'s being created.
createModel_labelsInputConfiguration :: Lens.Lens' CreateModel (Prelude.Maybe LabelsInputConfiguration)
createModel_labelsInputConfiguration = Lens.lens (\CreateModel' {labelsInputConfiguration} -> labelsInputConfiguration) (\s@CreateModel' {} a -> s {labelsInputConfiguration = a} :: CreateModel)
-- | Indicates that the asset associated with this sensor has been shut off.
-- As long as this condition is met, Lookout for Equipment will not use
-- data from this asset for training, evaluation, or inference.
createModel_offCondition :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.Text)
createModel_offCondition = Lens.lens (\CreateModel' {offCondition} -> offCondition) (\s@CreateModel' {} a -> s {offCondition = a} :: CreateModel)
-- | The Amazon Resource Name (ARN) of a role with permission to access the
-- data source being used to create the ML model.
createModel_roleArn :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.Text)
createModel_roleArn = Lens.lens (\CreateModel' {roleArn} -> roleArn) (\s@CreateModel' {} a -> s {roleArn = a} :: CreateModel)
-- | Provides the identifier of the KMS key used to encrypt model data by
-- Amazon Lookout for Equipment.
createModel_serverSideKmsKeyId :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.Text)
createModel_serverSideKmsKeyId = Lens.lens (\CreateModel' {serverSideKmsKeyId} -> serverSideKmsKeyId) (\s@CreateModel' {} a -> s {serverSideKmsKeyId = a} :: CreateModel)
-- | Any tags associated with the ML model being created.
createModel_tags :: Lens.Lens' CreateModel (Prelude.Maybe [Tag])
createModel_tags = Lens.lens (\CreateModel' {tags} -> tags) (\s@CreateModel' {} a -> s {tags = a} :: CreateModel) Prelude.. Lens.mapping Lens.coerced
-- | Indicates the time reference in the dataset that should be used to end
-- the subset of training data for the ML model.
createModel_trainingDataEndTime :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.UTCTime)
createModel_trainingDataEndTime = Lens.lens (\CreateModel' {trainingDataEndTime} -> trainingDataEndTime) (\s@CreateModel' {} a -> s {trainingDataEndTime = a} :: CreateModel) Prelude.. Lens.mapping Data._Time
-- | Indicates the time reference in the dataset that should be used to begin
-- the subset of training data for the ML model.
createModel_trainingDataStartTime :: Lens.Lens' CreateModel (Prelude.Maybe Prelude.UTCTime)
createModel_trainingDataStartTime = Lens.lens (\CreateModel' {trainingDataStartTime} -> trainingDataStartTime) (\s@CreateModel' {} a -> s {trainingDataStartTime = a} :: CreateModel) Prelude.. Lens.mapping Data._Time
-- | The name for the ML model to be created.
createModel_modelName :: Lens.Lens' CreateModel Prelude.Text
createModel_modelName = Lens.lens (\CreateModel' {modelName} -> modelName) (\s@CreateModel' {} a -> s {modelName = a} :: CreateModel)
-- | The name of the dataset for the ML model being created.
createModel_datasetName :: Lens.Lens' CreateModel Prelude.Text
createModel_datasetName = Lens.lens (\CreateModel' {datasetName} -> datasetName) (\s@CreateModel' {} a -> s {datasetName = a} :: CreateModel)
-- | A unique identifier for the request. If you do not set the client
-- request token, Amazon Lookout for Equipment generates one.
createModel_clientToken :: Lens.Lens' CreateModel Prelude.Text
createModel_clientToken = Lens.lens (\CreateModel' {clientToken} -> clientToken) (\s@CreateModel' {} a -> s {clientToken = a} :: CreateModel)
instance Core.AWSRequest CreateModel where
type AWSResponse CreateModel = CreateModelResponse
request overrides =
Request.postJSON (overrides defaultService)
response =
Response.receiveJSON
( \s h x ->
CreateModelResponse'
Prelude.<$> (x Data..?> "ModelArn")
Prelude.<*> (x Data..?> "Status")
Prelude.<*> (Prelude.pure (Prelude.fromEnum s))
)
instance Prelude.Hashable CreateModel where
hashWithSalt _salt CreateModel' {..} =
_salt
`Prelude.hashWithSalt` dataPreProcessingConfiguration
`Prelude.hashWithSalt` datasetSchema
`Prelude.hashWithSalt` evaluationDataEndTime
`Prelude.hashWithSalt` evaluationDataStartTime
`Prelude.hashWithSalt` labelsInputConfiguration
`Prelude.hashWithSalt` offCondition
`Prelude.hashWithSalt` roleArn
`Prelude.hashWithSalt` serverSideKmsKeyId
`Prelude.hashWithSalt` tags
`Prelude.hashWithSalt` trainingDataEndTime
`Prelude.hashWithSalt` trainingDataStartTime
`Prelude.hashWithSalt` modelName
`Prelude.hashWithSalt` datasetName
`Prelude.hashWithSalt` clientToken
instance Prelude.NFData CreateModel where
rnf CreateModel' {..} =
Prelude.rnf dataPreProcessingConfiguration
`Prelude.seq` Prelude.rnf datasetSchema
`Prelude.seq` Prelude.rnf evaluationDataEndTime
`Prelude.seq` Prelude.rnf evaluationDataStartTime
`Prelude.seq` Prelude.rnf labelsInputConfiguration
`Prelude.seq` Prelude.rnf offCondition
`Prelude.seq` Prelude.rnf roleArn
`Prelude.seq` Prelude.rnf serverSideKmsKeyId
`Prelude.seq` Prelude.rnf tags
`Prelude.seq` Prelude.rnf trainingDataEndTime
`Prelude.seq` Prelude.rnf trainingDataStartTime
`Prelude.seq` Prelude.rnf modelName
`Prelude.seq` Prelude.rnf datasetName
`Prelude.seq` Prelude.rnf clientToken
instance Data.ToHeaders CreateModel where
toHeaders =
Prelude.const
( Prelude.mconcat
[ "X-Amz-Target"
Data.=# ( "AWSLookoutEquipmentFrontendService.CreateModel" ::
Prelude.ByteString
),
"Content-Type"
Data.=# ( "application/x-amz-json-1.0" ::
Prelude.ByteString
)
]
)
instance Data.ToJSON CreateModel where
toJSON CreateModel' {..} =
Data.object
( Prelude.catMaybes
[ ("DataPreProcessingConfiguration" Data..=)
Prelude.<$> dataPreProcessingConfiguration,
("DatasetSchema" Data..=) Prelude.<$> datasetSchema,
("EvaluationDataEndTime" Data..=)
Prelude.<$> evaluationDataEndTime,
("EvaluationDataStartTime" Data..=)
Prelude.<$> evaluationDataStartTime,
("LabelsInputConfiguration" Data..=)
Prelude.<$> labelsInputConfiguration,
("OffCondition" Data..=) Prelude.<$> offCondition,
("RoleArn" Data..=) Prelude.<$> roleArn,
("ServerSideKmsKeyId" Data..=)
Prelude.<$> serverSideKmsKeyId,
("Tags" Data..=) Prelude.<$> tags,
("TrainingDataEndTime" Data..=)
Prelude.<$> trainingDataEndTime,
("TrainingDataStartTime" Data..=)
Prelude.<$> trainingDataStartTime,
Prelude.Just ("ModelName" Data..= modelName),
Prelude.Just ("DatasetName" Data..= datasetName),
Prelude.Just ("ClientToken" Data..= clientToken)
]
)
instance Data.ToPath CreateModel where
toPath = Prelude.const "/"
instance Data.ToQuery CreateModel where
toQuery = Prelude.const Prelude.mempty
-- | /See:/ 'newCreateModelResponse' smart constructor.
data CreateModelResponse = CreateModelResponse'
{ -- | The Amazon Resource Name (ARN) of the model being created.
modelArn :: Prelude.Maybe Prelude.Text,
-- | Indicates the status of the @CreateModel@ operation.
status :: Prelude.Maybe ModelStatus,
-- | The response's http status code.
httpStatus :: Prelude.Int
}
deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)
-- |
-- Create a value of 'CreateModelResponse' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'modelArn', 'createModelResponse_modelArn' - The Amazon Resource Name (ARN) of the model being created.
--
-- 'status', 'createModelResponse_status' - Indicates the status of the @CreateModel@ operation.
--
-- 'httpStatus', 'createModelResponse_httpStatus' - The response's http status code.
newCreateModelResponse ::
-- | 'httpStatus'
Prelude.Int ->
CreateModelResponse
newCreateModelResponse pHttpStatus_ =
CreateModelResponse'
{ modelArn = Prelude.Nothing,
status = Prelude.Nothing,
httpStatus = pHttpStatus_
}
-- | The Amazon Resource Name (ARN) of the model being created.
createModelResponse_modelArn :: Lens.Lens' CreateModelResponse (Prelude.Maybe Prelude.Text)
createModelResponse_modelArn = Lens.lens (\CreateModelResponse' {modelArn} -> modelArn) (\s@CreateModelResponse' {} a -> s {modelArn = a} :: CreateModelResponse)
-- | Indicates the status of the @CreateModel@ operation.
createModelResponse_status :: Lens.Lens' CreateModelResponse (Prelude.Maybe ModelStatus)
createModelResponse_status = Lens.lens (\CreateModelResponse' {status} -> status) (\s@CreateModelResponse' {} a -> s {status = a} :: CreateModelResponse)
-- | The response's http status code.
createModelResponse_httpStatus :: Lens.Lens' CreateModelResponse Prelude.Int
createModelResponse_httpStatus = Lens.lens (\CreateModelResponse' {httpStatus} -> httpStatus) (\s@CreateModelResponse' {} a -> s {httpStatus = a} :: CreateModelResponse)
instance Prelude.NFData CreateModelResponse where
rnf CreateModelResponse' {..} =
Prelude.rnf modelArn
`Prelude.seq` Prelude.rnf status
`Prelude.seq` Prelude.rnf httpStatus