// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_evaluation::_create_evaluation_input::CreateEvaluationInputBuilder;
pub use crate::operation::create_evaluation::_create_evaluation_output::CreateEvaluationOutputBuilder;
impl crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder {
/// Sends a request with this input using the given client.
pub async fn send_with(
self,
client: &crate::Client,
) -> ::std::result::Result<
crate::operation::create_evaluation::CreateEvaluationOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_evaluation::CreateEvaluationError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.create_evaluation();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `CreateEvaluation`.
///
/// <p>Creates a new <code>Evaluation</code> of an <code>MLModel</code>. An <code>MLModel</code> is evaluated on a set of observations associated to a <code>DataSource</code>. Like a <code>DataSource</code> for an <code>MLModel</code>, the <code>DataSource</code> for an <code>Evaluation</code> contains values for the <code>Target Variable</code>. The <code>Evaluation</code> compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the <code>MLModel</code> functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding <code>MLModelType</code>: <code>BINARY</code>, <code>REGRESSION</code> or <code>MULTICLASS</code>.</p>
/// <p><code>CreateEvaluation</code> is an asynchronous operation. In response to <code>CreateEvaluation</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to <code>PENDING</code>. After the <code>Evaluation</code> is created and ready for use, Amazon ML sets the status to <code>COMPLETED</code>.</p>
/// <p>You can use the <code>GetEvaluation</code> operation to check progress of the evaluation during the creation operation.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateEvaluationFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::create_evaluation::CreateEvaluationOutput,
crate::operation::create_evaluation::CreateEvaluationError,
> for CreateEvaluationFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::create_evaluation::CreateEvaluationOutput,
crate::operation::create_evaluation::CreateEvaluationError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl CreateEvaluationFluentBuilder {
/// Creates a new `CreateEvaluationFluentBuilder`.
pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
Self {
handle,
inner: ::std::default::Default::default(),
config_override: ::std::option::Option::None,
}
}
/// Access the CreateEvaluation as a reference.
pub fn as_input(&self) -> &crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder {
&self.inner
}
/// Sends the request and returns the response.
///
/// If an error occurs, an `SdkError` will be returned with additional details that
/// can be matched against.
///
/// By default, any retryable failures will be retried twice. Retry behavior
/// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
/// set when configuring the client.
pub async fn send(
self,
) -> ::std::result::Result<
crate::operation::create_evaluation::CreateEvaluationOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_evaluation::CreateEvaluationError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let input = self
.inner
.build()
.map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
let runtime_plugins = crate::operation::create_evaluation::CreateEvaluation::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::create_evaluation::CreateEvaluation::orchestrate(&runtime_plugins, input).await
}
/// Consumes this builder, creating a customizable operation that can be modified before being sent.
pub fn customize(
self,
) -> crate::client::customize::CustomizableOperation<
crate::operation::create_evaluation::CreateEvaluationOutput,
crate::operation::create_evaluation::CreateEvaluationError,
Self,
> {
crate::client::customize::CustomizableOperation::new(self)
}
pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
self.set_config_override(::std::option::Option::Some(config_override.into()));
self
}
pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
self.config_override = config_override;
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
pub fn evaluation_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.evaluation_id(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
pub fn set_evaluation_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_evaluation_id(input);
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
pub fn get_evaluation_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_evaluation_id()
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
pub fn evaluation_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.evaluation_name(input.into());
self
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
pub fn set_evaluation_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_evaluation_name(input);
self
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
pub fn get_evaluation_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_evaluation_name()
}
/// <p>The ID of the <code>MLModel</code> to evaluate.</p>
/// <p>The schema used in creating the <code>MLModel</code> must match the schema of the <code>DataSource</code> used in the <code>Evaluation</code>.</p>
pub fn ml_model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.ml_model_id(input.into());
self
}
/// <p>The ID of the <code>MLModel</code> to evaluate.</p>
/// <p>The schema used in creating the <code>MLModel</code> must match the schema of the <code>DataSource</code> used in the <code>Evaluation</code>.</p>
pub fn set_ml_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_ml_model_id(input);
self
}
/// <p>The ID of the <code>MLModel</code> to evaluate.</p>
/// <p>The schema used in creating the <code>MLModel</code> must match the schema of the <code>DataSource</code> used in the <code>Evaluation</code>.</p>
pub fn get_ml_model_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_ml_model_id()
}
/// <p>The ID of the <code>DataSource</code> for the evaluation. The schema of the <code>DataSource</code> must match the schema used to create the <code>MLModel</code>.</p>
pub fn evaluation_data_source_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.evaluation_data_source_id(input.into());
self
}
/// <p>The ID of the <code>DataSource</code> for the evaluation. The schema of the <code>DataSource</code> must match the schema used to create the <code>MLModel</code>.</p>
pub fn set_evaluation_data_source_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_evaluation_data_source_id(input);
self
}
/// <p>The ID of the <code>DataSource</code> for the evaluation. The schema of the <code>DataSource</code> must match the schema used to create the <code>MLModel</code>.</p>
pub fn get_evaluation_data_source_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_evaluation_data_source_id()
}
}