@Generated(value="software.amazon.awssdk:codegen") public final class Evaluation extends Object implements SdkPojo, Serializable, ToCopyableBuilder<Evaluation.Builder,Evaluation>
Represents the output of GetEvaluation
operation.
The content consists of the detailed metadata and data file information and the current status of the
Evaluation
.
Modifier and Type | Class and Description |
---|---|
static interface |
Evaluation.Builder |
Modifier and Type | Method and Description |
---|---|
static Evaluation.Builder |
builder() |
Long |
computeTime()
Returns the value of the ComputeTime property for this object.
|
Instant |
createdAt()
The time that the
Evaluation was created. |
String |
createdByIamUser()
The AWS user account that invoked the evaluation.
|
boolean |
equals(Object obj) |
String |
evaluationDataSourceId()
The ID of the
DataSource that is used to evaluate the MLModel . |
String |
evaluationId()
The ID that is assigned to the
Evaluation at creation. |
Instant |
finishedAt()
Returns the value of the FinishedAt property for this object.
|
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
int |
hashCode() |
String |
inputDataLocationS3()
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
|
Instant |
lastUpdatedAt()
The time of the most recent edit to the
Evaluation . |
String |
message()
A description of the most recent details about evaluating the
MLModel . |
String |
mlModelId()
The ID of the
MLModel that is the focus of the evaluation. |
String |
name()
A user-supplied name or description of the
Evaluation . |
PerformanceMetrics |
performanceMetrics()
Measurements of how well the
MLModel performed, using observations referenced by the
DataSource . |
List<SdkField<?>> |
sdkFields() |
static Class<? extends Evaluation.Builder> |
serializableBuilderClass() |
Instant |
startedAt()
Returns the value of the StartedAt property for this object.
|
EntityStatus |
status()
The status of the evaluation.
|
String |
statusAsString()
The status of the evaluation.
|
Evaluation.Builder |
toBuilder()
Take this object and create a builder that contains all of the current property values of this object.
|
String |
toString() |
copy
public String evaluationId()
The ID that is assigned to the Evaluation
at creation.
Evaluation
at creation.public String mlModelId()
The ID of the MLModel
that is the focus of the evaluation.
MLModel
that is the focus of the evaluation.public String evaluationDataSourceId()
The ID of the DataSource
that is used to evaluate the MLModel
.
DataSource
that is used to evaluate the MLModel
.public String inputDataLocationS3()
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
public String 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.
public Instant createdAt()
The time that the Evaluation
was created. The time is expressed in epoch time.
Evaluation
was created. The time is expressed in epoch time.public Instant lastUpdatedAt()
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
Evaluation
. The time is expressed in epoch time.public String name()
A user-supplied name or description of the Evaluation
.
Evaluation
.public EntityStatus 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 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.
If the service returns an enum value that is not available in the current SDK version, status
will
return EntityStatus.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available from
statusAsString()
.
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.EntityStatus
public String statusAsString()
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.
If the service returns an enum value that is not available in the current SDK version, status
will
return EntityStatus.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available from
statusAsString()
.
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.EntityStatus
public PerformanceMetrics 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.
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.
public String message()
A description of the most recent details about evaluating the MLModel
.
MLModel
.public Long computeTime()
public Instant finishedAt()
public Instant startedAt()
public Evaluation.Builder toBuilder()
ToCopyableBuilder
toBuilder
in interface ToCopyableBuilder<Evaluation.Builder,Evaluation>
public static Evaluation.Builder builder()
public static Class<? extends Evaluation.Builder> serializableBuilderClass()
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