Interface GetEvaluationResponse.Builder
- All Superinterfaces:
AwsResponse.Builder
,Buildable
,CopyableBuilder<GetEvaluationResponse.Builder,
,GetEvaluationResponse> MachineLearningResponse.Builder
,SdkBuilder<GetEvaluationResponse.Builder,
,GetEvaluationResponse> SdkPojo
,SdkResponse.Builder
- Enclosing class:
GetEvaluationResponse
-
Method Summary
Modifier and TypeMethodDescriptioncomputeTime
(Long computeTime) The approximate CPU time in milliseconds that Amazon Machine Learning spent processing theEvaluation
, normalized and scaled on computation resources.The time that theEvaluation
was created.createdByIamUser
(String createdByIamUser) The AWS user account that invoked the evaluation.evaluationDataSourceId
(String evaluationDataSourceId) TheDataSource
used for this evaluation.evaluationId
(String evaluationId) The evaluation ID which is same as theEvaluationId
in the request.finishedAt
(Instant finishedAt) The epoch time when Amazon Machine Learning marked theEvaluation
asCOMPLETED
orFAILED
.inputDataLocationS3
(String inputDataLocationS3) The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).lastUpdatedAt
(Instant lastUpdatedAt) The time of the most recent edit to theEvaluation
.A link to the file that contains logs of theCreateEvaluation
operation.A description of the most recent details about evaluating theMLModel
.The ID of theMLModel
that was the focus of the evaluation.A user-supplied name or description of theEvaluation
.default GetEvaluationResponse.Builder
performanceMetrics
(Consumer<PerformanceMetrics.Builder> performanceMetrics) Measurements of how well theMLModel
performed using observations referenced by theDataSource
.performanceMetrics
(PerformanceMetrics performanceMetrics) Measurements of how well theMLModel
performed using observations referenced by theDataSource
.The epoch time when Amazon Machine Learning marked theEvaluation
asINPROGRESS
.The status of the evaluation.status
(EntityStatus status) The status of the evaluation.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.services.machinelearning.model.MachineLearningResponse.Builder
build, responseMetadata, responseMetadata
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
Methods inherited from interface software.amazon.awssdk.core.SdkResponse.Builder
sdkHttpResponse, sdkHttpResponse
-
Method Details
-
evaluationId
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.
-
mlModelId
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.
-
evaluationDataSourceId
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.
-
inputDataLocationS3
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.
-
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.
- 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.
-
createdAt
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.
-
lastUpdatedAt
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.
-
name
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.
-
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.
- 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:
-
-
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.
- 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:
-
-
performanceMetrics
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.
-
-
performanceMetrics
default GetEvaluationResponse.Builder performanceMetrics(Consumer<PerformanceMetrics.Builder> performanceMetrics) 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.
This is a convenience method that creates an instance of thePerformanceMetrics.Builder
avoiding the need to create one manually viaPerformanceMetrics.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toperformanceMetrics(PerformanceMetrics)
.- Parameters:
performanceMetrics
- a consumer that will call methods onPerformanceMetrics.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
-
logUri
A link to the file that contains logs of the
CreateEvaluation
operation.- Parameters:
logUri
- A link to the file that contains logs of theCreateEvaluation
operation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
message
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.
-
computeTime
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the
Evaluation
, normalized and scaled on computation resources.ComputeTime
is only available if theEvaluation
is in theCOMPLETED
state.- Parameters:
computeTime
- The approximate CPU time in milliseconds that Amazon Machine Learning spent processing theEvaluation
, normalized and scaled on computation resources.ComputeTime
is only available if theEvaluation
is in theCOMPLETED
state.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
finishedAt
The epoch time when Amazon Machine Learning marked the
Evaluation
asCOMPLETED
orFAILED
.FinishedAt
is only available when theEvaluation
is in theCOMPLETED
orFAILED
state.- Parameters:
finishedAt
- The epoch time when Amazon Machine Learning marked theEvaluation
asCOMPLETED
orFAILED
.FinishedAt
is only available when theEvaluation
is in theCOMPLETED
orFAILED
state.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
startedAt
The epoch time when Amazon Machine Learning marked the
Evaluation
asINPROGRESS
.StartedAt
isn't available if theEvaluation
is in thePENDING
state.- Parameters:
startedAt
- The epoch time when Amazon Machine Learning marked theEvaluation
asINPROGRESS
.StartedAt
isn't available if theEvaluation
is in thePENDING
state.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-