aws_sdk_machinelearning::types::builders

Struct EvaluationBuilder

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#[non_exhaustive]
pub struct EvaluationBuilder { /* private fields */ }
Expand description

A builder for Evaluation.

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impl EvaluationBuilder

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pub fn evaluation_id(self, input: impl Into<String>) -> Self

The ID that is assigned to the Evaluation at creation.

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pub fn set_evaluation_id(self, input: Option<String>) -> Self

The ID that is assigned to the Evaluation at creation.

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pub fn get_evaluation_id(&self) -> &Option<String>

The ID that is assigned to the Evaluation at creation.

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pub fn ml_model_id(self, input: impl Into<String>) -> Self

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

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pub fn set_ml_model_id(self, input: Option<String>) -> Self

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

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pub fn get_ml_model_id(&self) -> &Option<String>

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

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pub fn evaluation_data_source_id(self, input: impl Into<String>) -> Self

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

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pub fn set_evaluation_data_source_id(self, input: Option<String>) -> Self

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

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pub fn get_evaluation_data_source_id(&self) -> &Option<String>

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

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pub fn input_data_location_s3(self, input: impl Into<String>) -> Self

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

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pub fn set_input_data_location_s3(self, input: Option<String>) -> Self

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

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pub fn get_input_data_location_s3(&self) -> &Option<String>

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

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pub fn created_by_iam_user(self, input: impl Into<String>) -> Self

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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pub fn set_created_by_iam_user(self, input: Option<String>) -> Self

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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pub fn get_created_by_iam_user(&self) -> &Option<String>

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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pub fn created_at(self, input: DateTime) -> Self

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

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pub fn set_created_at(self, input: Option<DateTime>) -> Self

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

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pub fn get_created_at(&self) -> &Option<DateTime>

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

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pub fn last_updated_at(self, input: DateTime) -> Self

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

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pub fn set_last_updated_at(self, input: Option<DateTime>) -> Self

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

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pub fn get_last_updated_at(&self) -> &Option<DateTime>

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

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pub fn name(self, input: impl Into<String>) -> Self

A user-supplied name or description of the Evaluation.

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pub fn set_name(self, input: Option<String>) -> Self

A user-supplied name or description of the Evaluation.

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pub fn get_name(&self) -> &Option<String>

A user-supplied name or description of the Evaluation.

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pub fn status(self, input: EntityStatus) -> Self

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.

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pub fn set_status(self, input: Option<EntityStatus>) -> Self

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.

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pub fn get_status(&self) -> &Option<EntityStatus>

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.

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pub fn performance_metrics(self, input: PerformanceMetrics) -> Self

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.

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pub fn set_performance_metrics(self, input: Option<PerformanceMetrics>) -> Self

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.

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pub fn get_performance_metrics(&self) -> &Option<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.

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pub fn message(self, input: impl Into<String>) -> Self

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

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pub fn set_message(self, input: Option<String>) -> Self

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

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pub fn get_message(&self) -> &Option<String>

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

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pub fn compute_time(self, input: i64) -> Self

Long integer type that is a 64-bit signed number.

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pub fn set_compute_time(self, input: Option<i64>) -> Self

Long integer type that is a 64-bit signed number.

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pub fn get_compute_time(&self) -> &Option<i64>

Long integer type that is a 64-bit signed number.

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pub fn finished_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

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pub fn set_finished_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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pub fn get_finished_at(&self) -> &Option<DateTime>

A timestamp represented in epoch time.

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pub fn started_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

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pub fn set_started_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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pub fn get_started_at(&self) -> &Option<DateTime>

A timestamp represented in epoch time.

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pub fn build(self) -> Evaluation

Consumes the builder and constructs a Evaluation.

Trait Implementations§

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impl Clone for EvaluationBuilder

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fn clone(&self) -> EvaluationBuilder

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for EvaluationBuilder

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl Default for EvaluationBuilder

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fn default() -> EvaluationBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for EvaluationBuilder

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fn eq(&self, other: &EvaluationBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for EvaluationBuilder

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