Interface Evaluation.Builder

All Superinterfaces:
Buildable, CopyableBuilder<Evaluation.Builder,Evaluation>, SdkBuilder<Evaluation.Builder,Evaluation>, SdkPojo
Enclosing class:
Evaluation
public static interface Evaluation.Builder extends SdkPojo, CopyableBuilder<Evaluation.Builder,Evaluation>

  • Method Details

    • evaluationId

      Evaluation.Builder evaluationId(String evaluationId)

      The ID that is assigned to the Evaluation at creation.

      Parameters:
      evaluationId - The ID that is assigned to the Evaluation at creation.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • mlModelId

      Evaluation.Builder mlModelId(String mlModelId)

      The ID of the MLModel that is the focus of the evaluation.

      Parameters:
      mlModelId - The ID of the MLModel that is the focus of the evaluation.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • evaluationDataSourceId

      Evaluation.Builder evaluationDataSourceId(String evaluationDataSourceId)

      The ID of the DataSource that is used to evaluate the MLModel.

      Parameters:
      evaluationDataSourceId - The ID of the DataSource that is used to evaluate the MLModel.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • inputDataLocationS3

      Evaluation.Builder inputDataLocationS3(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.

    • createdByIamUser

      Evaluation.Builder createdByIamUser(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.

    • createdAt

      Evaluation.Builder createdAt(Instant createdAt)

      The time that the Evaluation was created. The time is expressed in epoch time.

      Parameters:
      createdAt - The time that the Evaluation 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

      Evaluation.Builder lastUpdatedAt(Instant 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 the Evaluation. The time is expressed in epoch time.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • name

      Evaluation.Builder name(String name)

      A user-supplied name or description of the Evaluation.

      Parameters:
      name - A user-supplied name or description of the Evaluation.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • status

      Evaluation.Builder status(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.

      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 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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • status

      Evaluation.Builder status(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.

      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 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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • performanceMetrics

      Evaluation.Builder performanceMetrics(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.

      Parameters:
      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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • performanceMetrics

      default Evaluation.Builder performanceMetrics(Consumer<PerformanceMetrics.Builder> 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.

      This is a convenience method that creates an instance of the PerformanceMetrics.Builder avoiding the need to create one manually via PerformanceMetrics.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to performanceMetrics(PerformanceMetrics).

      Parameters:
      performanceMetrics - a consumer that will call methods on PerformanceMetrics.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • message

      Evaluation.Builder message(String message)

      A description of the most recent details about evaluating the MLModel.

      Parameters:
      message - A description of the most recent details about evaluating the MLModel.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • computeTime

      Evaluation.Builder computeTime(Long computeTime)
      Sets the value of the ComputeTime property for this object.
      Parameters:
      computeTime - The new value for the ComputeTime property for this object.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • finishedAt

      Evaluation.Builder finishedAt(Instant finishedAt)
      Sets the value of the FinishedAt property for this object.
      Parameters:
      finishedAt - The new value for the FinishedAt property for this object.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • startedAt

      Evaluation.Builder startedAt(Instant startedAt)
      Sets the value of the StartedAt property for this object.
      Parameters:
      startedAt - The new value for the StartedAt property for this object.
      Returns:
      Returns a reference to this object so that method calls can be chained together.