Package com.amazonaws.services.machinelearning.model

Class Evaluation

    • Constructor Detail

      • Evaluation

        public Evaluation()

    • Method Detail

      • setEvaluationId

        public void setEvaluationId​(String evaluationId)

        The ID that is assigned to the Evaluation at creation.

        Parameters:
        evaluationId - The ID that is assigned to the Evaluation at creation.

      • 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.

      • withEvaluationId

        public Evaluation withEvaluationId​(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.

      • setMLModelId

        public void setMLModelId​(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.

      • 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.

      • withMLModelId

        public Evaluation withMLModelId​(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.

      • setEvaluationDataSourceId

        public void setEvaluationDataSourceId​(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.

      • getEvaluationDataSourceId

        public String getEvaluationDataSourceId()

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

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

      • withEvaluationDataSourceId

        public Evaluation withEvaluationDataSourceId​(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.

      • 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.

      • 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.

      • 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.

      • 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.

      • 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.

      • 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.

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

      • 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.

      • 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 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.

      • 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 the Evaluation. The time is expressed in epoch time.

      • 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.

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

      • setName

        public void setName​(String name)

        A user-supplied name or description of the Evaluation.

        Parameters:
        name - A user-supplied name or description of the Evaluation .

      • getName

        public String getName()

        A user-supplied name or description of the Evaluation.

        Returns:
        A user-supplied name or description of the Evaluation.

      • withName

        public Evaluation withName​(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.

      • 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 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.
        See Also:
        EntityStatus

      • 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 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:
        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.
        See Also:
        EntityStatus

      • 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 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:
        EntityStatus

      • 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 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.
        See Also:
        EntityStatus

      • 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 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:
        EntityStatus

      • setPerformanceMetrics

        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.

        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.

      • getPerformanceMetrics

        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.

        Returns:
        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.

      • withPerformanceMetrics

        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.

        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.

      • 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 the MLModel.

      • 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 the MLModel.
        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 class Object
        Returns:
        A string representation of this object.
        See Also:
        Object.toString()

      • hashCode

        public int hashCode()
        Overrides:
        hashCode in class Object