Class GetEvaluationResult
- All Implemented Interfaces:
Serializable
,Cloneable
Represents the output of a GetEvaluation operation and describes an
Evaluation
.
- See Also:
-
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionclone()
boolean
The time that theEvaluation
was created.The AWS user account that invoked the evaluation.TheDataSource
used for this evaluation.The evaluation ID which is same as theEvaluationId
in the request.The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).The time of the most recent edit to theBatchPrediction
.A link to the file that contains logs of the CreateEvaluation operation.A description of the most recent details about evaluating theMLModel
.The ID of theMLModel
that was 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) TheDataSource
used for this evaluation.void
setEvaluationId
(String evaluationId) The evaluation ID which is same as theEvaluationId
in the request.void
setInputDataLocationS3
(String inputDataLocationS3) The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).void
setLastUpdatedAt
(Date lastUpdatedAt) The time of the most recent edit to theBatchPrediction
.void
A link to the file that contains logs of the CreateEvaluation operation.void
setMessage
(String message) A description of the most recent details about evaluating theMLModel
.void
setMLModelId
(String mLModelId) The ID of theMLModel
that was 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) TheDataSource
used for this evaluation.withEvaluationId
(String evaluationId) The evaluation ID which is same as theEvaluationId
in the request.withInputDataLocationS3
(String inputDataLocationS3) The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).withLastUpdatedAt
(Date lastUpdatedAt) The time of the most recent edit to theBatchPrediction
.withLogUri
(String logUri) A link to the file that contains logs of the CreateEvaluation operation.withMessage
(String message) A description of the most recent details about evaluating theMLModel
.withMLModelId
(String mLModelId) The ID of theMLModel
that was 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.
-
Constructor Details
-
GetEvaluationResult
public GetEvaluationResult()
-
-
Method Details
-
setEvaluationId
The evaluation ID which is same as the
EvaluationId
in the request.- Parameters:
evaluationId
- The evaluation ID which is same as theEvaluationId
in the request.
-
getEvaluationId
The evaluation ID which is same as the
EvaluationId
in the request.- Returns:
- The evaluation ID which is same as the
EvaluationId
in the request.
-
withEvaluationId
The evaluation ID which is same as the
EvaluationId
in the request.- Parameters:
evaluationId
- The evaluation ID which is same as theEvaluationId
in the request.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setMLModelId
The ID of the
MLModel
that was the focus of the evaluation.- Parameters:
mLModelId
- The ID of theMLModel
that was the focus of the evaluation.
-
getMLModelId
The ID of the
MLModel
that was the focus of the evaluation.- Returns:
- The ID of the
MLModel
that was the focus of the evaluation.
-
withMLModelId
The ID of the
MLModel
that was the focus of the evaluation.- Parameters:
mLModelId
- The ID of theMLModel
that was the focus of the evaluation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setEvaluationDataSourceId
The
DataSource
used for this evaluation.- Parameters:
evaluationDataSourceId
- TheDataSource
used for this evaluation.
-
getEvaluationDataSourceId
The
DataSource
used for this evaluation.- Returns:
- The
DataSource
used for this evaluation.
-
withEvaluationDataSourceId
The
DataSource
used for this evaluation.- Parameters:
evaluationDataSourceId
- TheDataSource
used for this evaluation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setInputDataLocationS3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
- Parameters:
inputDataLocationS3
- The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
-
getInputDataLocationS3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
- Returns:
- The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
-
withInputDataLocationS3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
- Parameters:
inputDataLocationS3
- The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.
-
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.
-
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.
-
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
BatchPrediction
. The time is expressed in epoch time.- Parameters:
lastUpdatedAt
- The time of the most recent edit to theBatchPrediction
. The time is expressed in epoch time.
-
getLastUpdatedAt
The time of the most recent edit to the
BatchPrediction
. The time is expressed in epoch time.- Returns:
- The time of the most recent edit to the
BatchPrediction
. The time is expressed in epoch time.
-
withLastUpdatedAt
The time of the most recent edit to the
BatchPrediction
. The time is expressed in epoch time.- Parameters:
lastUpdatedAt
- The time of the most recent edit to theBatchPrediction
. The time is expressed in epoch time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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 Language (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 Language (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:
-
-
getStatus
The status of the evaluation. This element can have one of the following values:
-
PENDING
- Amazon Machine Language (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 Language (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 Language (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 Language (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 Language (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 Language (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 Language (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 Language (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 metric is returned based on the type of theMLModel
:-
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 metric is returned based on the type of theMLModel
:-
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.
-
-
-
getPerformanceMetrics
Measurements of how well the
MLModel
performed using observations referenced by theDataSource
. One of the following metric is returned based on the type of theMLModel
:-
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 metric is returned based on the type of theMLModel
:-
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 metric is returned based on the type of theMLModel
:-
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 metric is returned based on the type of theMLModel
:-
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.
-
-
setLogUri
A link to the file that contains logs of the CreateEvaluation operation.
- Parameters:
logUri
- A link to the file that contains logs of the CreateEvaluation operation.
-
getLogUri
A link to the file that contains logs of the CreateEvaluation operation.
- Returns:
- A link to the file that contains logs of the CreateEvaluation operation.
-
withLogUri
A link to the file that contains logs of the CreateEvaluation operation.
- Parameters:
logUri
- A link to the file that contains logs of the CreateEvaluation operation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.
-
toString
Returns a string representation of this object; useful for testing and debugging. -
equals
-
hashCode
public int hashCode() -
clone
-