Class Evaluation
- java.lang.Object
-
- com.amazonaws.services.machinelearning.model.Evaluation
-
- All Implemented Interfaces:
Serializable
,Cloneable
public class Evaluation extends Object implements 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:
- Serialized Form
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Constructor Summary
Constructors Constructor Description Evaluation()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Evaluation
clone()
boolean
equals(Object obj)
Date
getCreatedAt()
The time that theEvaluation
was created.String
getCreatedByIamUser()
The AWS user account that invoked the evaluation.String
getEvaluationDataSourceId()
The ID of theDataSource
that is used to evaluate theMLModel
.String
getEvaluationId()
The ID that is assigned to theEvaluation
at creation.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 theEvaluation
.String
getMessage()
A description of the most recent details about evaluating theMLModel
.String
getMLModelId()
The ID of theMLModel
that is the focus of the evaluation.String
getName()
A user-supplied name or description of theEvaluation
.PerformanceMetrics
getPerformanceMetrics()
Measurements of how well theMLModel
performed, using observations referenced by theDataSource
.String
getStatus()
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
setName(String name)
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
setStatus(String status)
The status of the evaluation.String
toString()
Returns a string representation of this object; useful for testing and debugging.Evaluation
withCreatedAt(Date createdAt)
The time that theEvaluation
was created.Evaluation
withCreatedByIamUser(String createdByIamUser)
The AWS user account that invoked the evaluation.Evaluation
withEvaluationDataSourceId(String evaluationDataSourceId)
The ID of theDataSource
that is used to evaluate theMLModel
.Evaluation
withEvaluationId(String evaluationId)
The ID that is assigned to theEvaluation
at creation.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 theEvaluation
.Evaluation
withMessage(String message)
A description of the most recent details about evaluating theMLModel
.Evaluation
withMLModelId(String mLModelId)
The ID of theMLModel
that is the focus of the evaluation.Evaluation
withName(String name)
A user-supplied name or description of theEvaluation
.Evaluation
withPerformanceMetrics(PerformanceMetrics performanceMetrics)
Measurements of how well theMLModel
performed, using observations referenced by theDataSource
.Evaluation
withStatus(EntityStatus status)
The status of the evaluation.Evaluation
withStatus(String status)
The status of the evaluation.
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Method Detail
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setEvaluationId
public void setEvaluationId(String evaluationId)
The ID that is assigned to the
Evaluation
at creation.- Parameters:
evaluationId
- The ID that is assigned to theEvaluation
at creation.
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getEvaluationId
public String getEvaluationId()
The ID that is assigned to the
Evaluation
at creation.- Returns:
- The ID that is assigned to the
Evaluation
at creation.
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withEvaluationId
public Evaluation withEvaluationId(String evaluationId)
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.
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setMLModelId
public void setMLModelId(String mLModelId)
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.
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getMLModelId
public String 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.
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withMLModelId
public Evaluation withMLModelId(String mLModelId)
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.
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setEvaluationDataSourceId
public void setEvaluationDataSourceId(String evaluationDataSourceId)
The ID of the
DataSource
that is used to evaluate theMLModel
.- Parameters:
evaluationDataSourceId
- The ID of theDataSource
that is used to evaluate theMLModel
.
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getEvaluationDataSourceId
public String getEvaluationDataSourceId()
The ID of the
DataSource
that is used to evaluate theMLModel
.- Returns:
- The ID of the
DataSource
that is used to evaluate theMLModel
.
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withEvaluationDataSourceId
public Evaluation withEvaluationDataSourceId(String evaluationDataSourceId)
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.
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setInputDataLocationS3
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.
- 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
public String 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.
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withInputDataLocationS3
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.
- 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
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.
- 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
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.
- 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
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.
- 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
public void setCreatedAt(Date 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.
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getCreatedAt
public Date 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.
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withCreatedAt
public Evaluation withCreatedAt(Date 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.
-
setLastUpdatedAt
public void setLastUpdatedAt(Date 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.
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getLastUpdatedAt
public Date 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.
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withLastUpdatedAt
public Evaluation withLastUpdatedAt(Date 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.
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setName
public void setName(String name)
A user-supplied name or description of the
Evaluation
.- Parameters:
name
- A user-supplied name or description of theEvaluation
.
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getName
public String getName()
A user-supplied name or description of the
Evaluation
.- Returns:
- A user-supplied name or description of the
Evaluation
.
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withName
public Evaluation withName(String 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.
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setStatus
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 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:
EntityStatus
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getStatus
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 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:
EntityStatus
-
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withStatus
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 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:
EntityStatus
-
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setStatus
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 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:
EntityStatus
-
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withStatus
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 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:
EntityStatus
-
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setPerformanceMetrics
public void setPerformanceMetrics(PerformanceMetrics performanceMetrics)
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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-
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getPerformanceMetrics
public PerformanceMetrics 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.
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withPerformanceMetrics
public Evaluation withPerformanceMetrics(PerformanceMetrics performanceMetrics)
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
public void setMessage(String message)
A description of the most recent details about evaluating the
MLModel
.- Parameters:
message
- A description of the most recent details about evaluating theMLModel
.
-
getMessage
public String 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
public Evaluation withMessage(String 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.
-
toString
public String toString()
Returns a string representation of this object; useful for testing and debugging.- Overrides:
toString
in classObject
- Returns:
- A string representation of this object.
- See Also:
Object.toString()
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clone
public Evaluation clone()
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