@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class Evaluation extends Object implements Serializable, Cloneable, StructuredPojo
Represents the output of GetEvaluation
operation.
The content consists of the detailed metadata and data file information and the current status of the
Evaluation
.
Constructor and Description |
---|
Evaluation() |
Modifier and Type | Method and Description |
---|---|
Evaluation |
clone() |
boolean |
equals(Object obj) |
Long |
getComputeTime() |
Date |
getCreatedAt()
The time that the
Evaluation was created. |
String |
getCreatedByIamUser()
The AWS user account that invoked the evaluation.
|
String |
getEvaluationDataSourceId()
The ID of the
DataSource that is used to evaluate the MLModel . |
String |
getEvaluationId()
The ID that is assigned to the
Evaluation at creation. |
Date |
getFinishedAt() |
String |
getInputDataLocationS3()
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
|
Date |
getLastUpdatedAt()
The time of the most recent edit to the
Evaluation . |
String |
getMessage()
A description of the most recent details about evaluating the
MLModel . |
String |
getMLModelId()
The ID of the
MLModel that is the focus of the evaluation. |
String |
getName()
A user-supplied name or description of the
Evaluation . |
PerformanceMetrics |
getPerformanceMetrics()
Measurements of how well the
MLModel performed, using observations referenced by the
DataSource . |
Date |
getStartedAt() |
String |
getStatus()
The status of the evaluation.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setComputeTime(Long computeTime) |
void |
setCreatedAt(Date createdAt)
The time that the
Evaluation was created. |
void |
setCreatedByIamUser(String createdByIamUser)
The AWS user account that invoked the evaluation.
|
void |
setEvaluationDataSourceId(String evaluationDataSourceId)
The ID of the
DataSource that is used to evaluate the MLModel . |
void |
setEvaluationId(String evaluationId)
The ID that is assigned to the
Evaluation at creation. |
void |
setFinishedAt(Date finishedAt) |
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 the
Evaluation . |
void |
setMessage(String message)
A description of the most recent details about evaluating the
MLModel . |
void |
setMLModelId(String mLModelId)
The ID of the
MLModel that is the focus of the evaluation. |
void |
setName(String name)
A user-supplied name or description of the
Evaluation . |
void |
setPerformanceMetrics(PerformanceMetrics performanceMetrics)
Measurements of how well the
MLModel performed, using observations referenced by the
DataSource . |
void |
setStartedAt(Date startedAt) |
void |
setStatus(EntityStatus status)
The status of the evaluation.
|
void |
setStatus(String status)
The status of the evaluation.
|
String |
toString()
Returns a string representation of this object.
|
Evaluation |
withComputeTime(Long computeTime) |
Evaluation |
withCreatedAt(Date createdAt)
The time that the
Evaluation was created. |
Evaluation |
withCreatedByIamUser(String createdByIamUser)
The AWS user account that invoked the evaluation.
|
Evaluation |
withEvaluationDataSourceId(String evaluationDataSourceId)
The ID of the
DataSource that is used to evaluate the MLModel . |
Evaluation |
withEvaluationId(String evaluationId)
The ID that is assigned to the
Evaluation at creation. |
Evaluation |
withFinishedAt(Date finishedAt) |
Evaluation |
withInputDataLocationS3(String inputDataLocationS3)
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
|
Evaluation |
withLastUpdatedAt(Date lastUpdatedAt)
The time of the most recent edit to the
Evaluation . |
Evaluation |
withMessage(String message)
A description of the most recent details about evaluating the
MLModel . |
Evaluation |
withMLModelId(String mLModelId)
The ID of the
MLModel that is the focus of the evaluation. |
Evaluation |
withName(String name)
A user-supplied name or description of the
Evaluation . |
Evaluation |
withPerformanceMetrics(PerformanceMetrics performanceMetrics)
Measurements of how well the
MLModel performed, using observations referenced by the
DataSource . |
Evaluation |
withStartedAt(Date startedAt) |
Evaluation |
withStatus(EntityStatus status)
The status of the evaluation.
|
Evaluation |
withStatus(String status)
The status of the evaluation.
|
public void setEvaluationId(String evaluationId)
The ID that is assigned to the Evaluation
at creation.
evaluationId
- The ID that is assigned to the Evaluation
at creation.public String getEvaluationId()
The ID that is assigned to the Evaluation
at creation.
Evaluation
at creation.public Evaluation withEvaluationId(String evaluationId)
The ID that is assigned to the Evaluation
at creation.
evaluationId
- The ID that is assigned to the Evaluation
at creation.public void setMLModelId(String mLModelId)
The ID of the MLModel
that is the focus of the evaluation.
mLModelId
- The ID of the MLModel
that is the focus of the evaluation.public String getMLModelId()
The ID of the MLModel
that is the focus of the evaluation.
MLModel
that is the focus of the evaluation.public Evaluation withMLModelId(String mLModelId)
The ID of the MLModel
that is the focus of the evaluation.
mLModelId
- The ID of the MLModel
that is the focus of the evaluation.public void setEvaluationDataSourceId(String evaluationDataSourceId)
The ID of the DataSource
that is used to evaluate the MLModel
.
evaluationDataSourceId
- The ID of the DataSource
that is used to evaluate the MLModel
.public String getEvaluationDataSourceId()
The ID of the DataSource
that is used to evaluate the MLModel
.
DataSource
that is used to evaluate the MLModel
.public Evaluation withEvaluationDataSourceId(String evaluationDataSourceId)
The ID of the DataSource
that is used to evaluate the MLModel
.
evaluationDataSourceId
- The ID of the DataSource
that is used to evaluate the MLModel
.public void setInputDataLocationS3(String inputDataLocationS3)
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
inputDataLocationS3
- The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the
evaluation.public String getInputDataLocationS3()
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
public Evaluation withInputDataLocationS3(String inputDataLocationS3)
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
inputDataLocationS3
- The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the
evaluation.public void setCreatedByIamUser(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.
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 String 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.
public Evaluation withCreatedByIamUser(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.
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 void setCreatedAt(Date createdAt)
The time that the Evaluation
was created. The time is expressed in epoch time.
createdAt
- The time that the Evaluation
was created. The time is expressed in epoch time.public Date getCreatedAt()
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 Evaluation withCreatedAt(Date createdAt)
The time that the Evaluation
was created. The time is expressed in epoch time.
createdAt
- The time that the Evaluation
was created. The time is expressed in epoch time.public void setLastUpdatedAt(Date lastUpdatedAt)
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
lastUpdatedAt
- The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.public Date getLastUpdatedAt()
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 Evaluation withLastUpdatedAt(Date lastUpdatedAt)
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
lastUpdatedAt
- The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.public void setName(String name)
A user-supplied name or description of the Evaluation
.
name
- A user-supplied name or description of the Evaluation
.public String getName()
A user-supplied name or description of the Evaluation
.
Evaluation
.public Evaluation withName(String name)
A user-supplied name or description of the Evaluation
.
name
- A user-supplied name or description of the Evaluation
.public void setStatus(String 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.
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.
EntityStatus
public String 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 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.
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 Evaluation withStatus(String 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.
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.
EntityStatus
public void setStatus(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.
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.
EntityStatus
public Evaluation withStatus(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.
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.
EntityStatus
public void setPerformanceMetrics(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.
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.
public PerformanceMetrics getPerformanceMetrics()
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 Evaluation withPerformanceMetrics(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.
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.
public void setMessage(String message)
A description of the most recent details about evaluating the MLModel
.
message
- A description of the most recent details about evaluating the MLModel
.public String getMessage()
A description of the most recent details about evaluating the MLModel
.
MLModel
.public Evaluation withMessage(String message)
A description of the most recent details about evaluating the MLModel
.
message
- A description of the most recent details about evaluating the MLModel
.public void setComputeTime(Long computeTime)
computeTime
- public Long getComputeTime()
public Evaluation withComputeTime(Long computeTime)
computeTime
- public void setFinishedAt(Date finishedAt)
finishedAt
- public Date getFinishedAt()
public Evaluation withFinishedAt(Date finishedAt)
finishedAt
- public void setStartedAt(Date startedAt)
startedAt
- public Date getStartedAt()
public Evaluation withStartedAt(Date startedAt)
startedAt
- public String toString()
toString
in class Object
Object.toString()
public Evaluation clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.