/**
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0.
*/
#pragma once
#include Represents the output of The
* content consists of the detailed metadata and data file information and the
* current status of the GetEvaluation
operation. Evaluation
.See Also:
AWS
* API Reference
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID that is assigned to the Evaluation
at creation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The ID of the DataSource
that is used to evaluate the
* MLModel
.
The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline const Aws::String& GetInputDataLocationS3() const{ return m_inputDataLocationS3; } /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline bool InputDataLocationS3HasBeenSet() const { return m_inputDataLocationS3HasBeenSet; } /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline void SetInputDataLocationS3(const Aws::String& value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3 = value; } /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline void SetInputDataLocationS3(Aws::String&& value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3 = std::move(value); } /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline void SetInputDataLocationS3(const char* value) { m_inputDataLocationS3HasBeenSet = true; m_inputDataLocationS3.assign(value); } /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline Evaluation& WithInputDataLocationS3(const Aws::String& value) { SetInputDataLocationS3(value); return *this;} /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline Evaluation& WithInputDataLocationS3(Aws::String&& value) { SetInputDataLocationS3(std::move(value)); return *this;} /** *The location and name of the data in Amazon Simple Storage Server (Amazon S3) * that is used in the evaluation.
*/ inline Evaluation& WithInputDataLocationS3(const char* value) { SetInputDataLocationS3(value); return *this;} /** *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.
*/ inline const Aws::String& GetCreatedByIamUser() const{ return m_createdByIamUser; } /** *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.
*/ inline bool CreatedByIamUserHasBeenSet() const { return m_createdByIamUserHasBeenSet; } /** *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.
*/ inline void SetCreatedByIamUser(const Aws::String& value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser = value; } /** *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.
*/ inline void SetCreatedByIamUser(Aws::String&& value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser = std::move(value); } /** *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.
*/ inline void SetCreatedByIamUser(const char* value) { m_createdByIamUserHasBeenSet = true; m_createdByIamUser.assign(value); } /** *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.
*/ inline Evaluation& WithCreatedByIamUser(const Aws::String& value) { SetCreatedByIamUser(value); return *this;} /** *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.
*/ inline Evaluation& WithCreatedByIamUser(Aws::String&& value) { SetCreatedByIamUser(std::move(value)); return *this;} /** *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.
*/ inline Evaluation& WithCreatedByIamUser(const char* value) { SetCreatedByIamUser(value); return *this;} /** *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.
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.
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.
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.
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.
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.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the Evaluation
.
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.
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.
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.
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 Amazon Machine * Learning Developer Guide.
*/ inline const PerformanceMetrics& GetPerformanceMetrics() const{ return m_performanceMetrics; } /** *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 Amazon Machine * Learning Developer Guide.
*/ inline bool PerformanceMetricsHasBeenSet() const { return m_performanceMetricsHasBeenSet; } /** *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 Amazon Machine * Learning Developer Guide.
*/ inline void SetPerformanceMetrics(const PerformanceMetrics& value) { m_performanceMetricsHasBeenSet = true; m_performanceMetrics = value; } /** *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 Amazon Machine * Learning Developer Guide.
*/ inline void SetPerformanceMetrics(PerformanceMetrics&& value) { m_performanceMetricsHasBeenSet = true; m_performanceMetrics = std::move(value); } /** *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 Amazon Machine * Learning Developer Guide.
*/ inline Evaluation& WithPerformanceMetrics(const PerformanceMetrics& value) { SetPerformanceMetrics(value); return *this;} /** *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 Amazon Machine * Learning Developer Guide.
*/ inline Evaluation& WithPerformanceMetrics(PerformanceMetrics&& value) { SetPerformanceMetrics(std::move(value)); return *this;} /** *A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.
A description of the most recent details about evaluating the
* MLModel
.