Class Evaluation
- All Implemented Interfaces:
Serializable
,Cloneable
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information and
the current status of the Evaluation
.
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionclone()
boolean
The time that theEvaluation
was created.The AWS user account that invoked the evaluation.The ID of theDataSource
that is used to evaluate theMLModel
.The ID that is assigned to theEvaluation
at creation.The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.The time of the most recent edit to theEvaluation
.A description of the most recent details about evaluating theMLModel
.The ID of theMLModel
that is the focus of the evaluation.getName()
A user-supplied name or description of theEvaluation
.Measurements of how well theMLModel
performed, using observations referenced by theDataSource
.The status of the evaluation.int
hashCode()
void
setCreatedAt
(Date createdAt) The time that theEvaluation
was created.void
setCreatedByIamUser
(String createdByIamUser) The AWS user account that invoked the evaluation.void
setEvaluationDataSourceId
(String evaluationDataSourceId) The ID of theDataSource
that is used to evaluate theMLModel
.void
setEvaluationId
(String evaluationId) The ID that is assigned to theEvaluation
at creation.void
setInputDataLocationS3
(String inputDataLocationS3) The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.void
setLastUpdatedAt
(Date lastUpdatedAt) The time of the most recent edit to theEvaluation
.void
setMessage
(String message) A description of the most recent details about evaluating theMLModel
.void
setMLModelId
(String mLModelId) The ID of theMLModel
that is the focus of the evaluation.void
A user-supplied name or description of theEvaluation
.void
setPerformanceMetrics
(PerformanceMetrics performanceMetrics) Measurements of how well theMLModel
performed, using observations referenced by theDataSource
.void
setStatus
(EntityStatus status) The status of the evaluation.void
The status of the evaluation.toString()
Returns a string representation of this object; useful for testing and debugging.withCreatedAt
(Date createdAt) The time that theEvaluation
was created.withCreatedByIamUser
(String createdByIamUser) The AWS user account that invoked the evaluation.withEvaluationDataSourceId
(String evaluationDataSourceId) The ID of theDataSource
that is used to evaluate theMLModel
.withEvaluationId
(String evaluationId) The ID that is assigned to theEvaluation
at creation.withInputDataLocationS3
(String inputDataLocationS3) The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.withLastUpdatedAt
(Date lastUpdatedAt) The time of the most recent edit to theEvaluation
.withMessage
(String message) A description of the most recent details about evaluating theMLModel
.withMLModelId
(String mLModelId) The ID of theMLModel
that is the focus of the evaluation.A user-supplied name or description of theEvaluation
.withPerformanceMetrics
(PerformanceMetrics performanceMetrics) Measurements of how well theMLModel
performed, using observations referenced by theDataSource
.withStatus
(EntityStatus status) The status of the evaluation.withStatus
(String status) The status of the evaluation.
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Constructor Details
-
Evaluation
public Evaluation()
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Method Details
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setEvaluationId
The ID that is assigned to the
Evaluation
at creation.- Parameters:
evaluationId
- The ID that is assigned to theEvaluation
at creation.
-
getEvaluationId
The ID that is assigned to the
Evaluation
at creation.- Returns:
- The ID that is assigned to the
Evaluation
at creation.
-
withEvaluationId
The ID that is assigned to the
Evaluation
at creation.- Parameters:
evaluationId
- The ID that is assigned to theEvaluation
at creation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setMLModelId
The ID of the
MLModel
that is the focus of the evaluation.- Parameters:
mLModelId
- The ID of theMLModel
that is the focus of the evaluation.
-
getMLModelId
The ID of the
MLModel
that is the focus of the evaluation.- Returns:
- The ID of the
MLModel
that is the focus of the evaluation.
-
withMLModelId
The ID of the
MLModel
that is the focus of the evaluation.- Parameters:
mLModelId
- The ID of theMLModel
that is the focus of the evaluation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setEvaluationDataSourceId
The ID of the
DataSource
that is used to evaluate theMLModel
.- Parameters:
evaluationDataSourceId
- The ID of theDataSource
that is used to evaluate theMLModel
.
-
getEvaluationDataSourceId
The ID of the
DataSource
that is used to evaluate theMLModel
.- Returns:
- The ID of the
DataSource
that is used to evaluate theMLModel
.
-
withEvaluationDataSourceId
The ID of the
DataSource
that is used to evaluate theMLModel
.- Parameters:
evaluationDataSourceId
- The ID of theDataSource
that is used to evaluate theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setInputDataLocationS3
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
- Parameters:
inputDataLocationS3
- The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
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getInputDataLocationS3
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
- Returns:
- The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
-
withInputDataLocationS3
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
- Parameters:
inputDataLocationS3
- The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setCreatedByIamUser
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.
- Parameters:
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.
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getCreatedByIamUser
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.
- Returns:
- 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.
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withCreatedByIamUser
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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setCreatedAt
The time that the
Evaluation
was created. The time is expressed in epoch time.- Parameters:
createdAt
- The time that theEvaluation
was created. The time is expressed in epoch time.
-
getCreatedAt
The time that the
Evaluation
was created. The time is expressed in epoch time.- Returns:
- The time that the
Evaluation
was created. The time is expressed in epoch time.
-
withCreatedAt
The time that the
Evaluation
was created. The time is expressed in epoch time.- Parameters:
createdAt
- The time that theEvaluation
was created. The time is expressed in epoch time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setLastUpdatedAt
The time of the most recent edit to the
Evaluation
. The time is expressed in epoch time.- Parameters:
lastUpdatedAt
- The time of the most recent edit to theEvaluation
. The time is expressed in epoch time.
-
getLastUpdatedAt
The time of the most recent edit to the
Evaluation
. The time is expressed in epoch time.- Returns:
- The time of the most recent edit to the
Evaluation
. The time is expressed in epoch time.
-
withLastUpdatedAt
The time of the most recent edit to the
Evaluation
. The time is expressed in epoch time.- Parameters:
lastUpdatedAt
- The time of the most recent edit to theEvaluation
. The time is expressed in epoch time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setName
A user-supplied name or description of the
Evaluation
.- Parameters:
name
- A user-supplied name or description of theEvaluation
.
-
getName
A user-supplied name or description of the
Evaluation
.- Returns:
- A user-supplied name or description of the
Evaluation
.
-
withName
A user-supplied name or description of the
Evaluation
.- Parameters:
name
- A user-supplied name or description of theEvaluation
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setStatus
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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
- Parameters:
status
- 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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
-
- See Also:
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getStatus
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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
- Returns:
- 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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
-
- See Also:
-
-
withStatus
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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
- Parameters:
status
- 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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
-
setStatus
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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
- Parameters:
status
- 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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
-
- See Also:
-
-
withStatus
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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
- Parameters:
status
- 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 anMLModel
. -
INPROGRESS
- The evaluation is underway. -
FAILED
- The request to evaluate anMLModel
did not run to completion. It is not usable. -
COMPLETED
- The evaluation process completed successfully. -
DELETED
- TheEvaluation
is marked as deleted. It is not usable.
-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
-
setPerformanceMetrics
Measurements of how well the
MLModel
performed, using observations referenced by theDataSource
. 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.
- Parameters:
performanceMetrics
- Measurements of how well theMLModel
performed, using observations referenced by theDataSource
. 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.
-
-
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getPerformanceMetrics
Measurements of how well the
MLModel
performed, using observations referenced by theDataSource
. 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.
- Returns:
- Measurements of how well the
MLModel
performed, using observations referenced by theDataSource
. 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.
-
-
-
withPerformanceMetrics
Measurements of how well the
MLModel
performed, using observations referenced by theDataSource
. 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.
- Parameters:
performanceMetrics
- Measurements of how well theMLModel
performed, using observations referenced by theDataSource
. 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.
-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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setMessage
A description of the most recent details about evaluating the
MLModel
.- Parameters:
message
- A description of the most recent details about evaluating theMLModel
.
-
getMessage
A description of the most recent details about evaluating the
MLModel
.- Returns:
- A description of the most recent details about evaluating the
MLModel
.
-
withMessage
A description of the most recent details about evaluating the
MLModel
.- Parameters:
message
- A description of the most recent details about evaluating theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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toString
Returns a string representation of this object; useful for testing and debugging. -
equals
-
hashCode
public int hashCode() -
clone
-