aws_sdk_machinelearning/operation/create_evaluation/
builders.rs

1// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
2pub use crate::operation::create_evaluation::_create_evaluation_output::CreateEvaluationOutputBuilder;
3
4pub use crate::operation::create_evaluation::_create_evaluation_input::CreateEvaluationInputBuilder;
5
6impl crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder {
7    /// Sends a request with this input using the given client.
8    pub async fn send_with(
9        self,
10        client: &crate::Client,
11    ) -> ::std::result::Result<
12        crate::operation::create_evaluation::CreateEvaluationOutput,
13        ::aws_smithy_runtime_api::client::result::SdkError<
14            crate::operation::create_evaluation::CreateEvaluationError,
15            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
16        >,
17    > {
18        let mut fluent_builder = client.create_evaluation();
19        fluent_builder.inner = self;
20        fluent_builder.send().await
21    }
22}
23/// Fluent builder constructing a request to `CreateEvaluation`.
24///
25/// <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>
26/// <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>
27/// <p>You can use the <code>GetEvaluation</code> operation to check progress of the evaluation during the creation operation.</p>
28#[derive(::std::clone::Clone, ::std::fmt::Debug)]
29pub struct CreateEvaluationFluentBuilder {
30    handle: ::std::sync::Arc<crate::client::Handle>,
31    inner: crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder,
32    config_override: ::std::option::Option<crate::config::Builder>,
33}
34impl
35    crate::client::customize::internal::CustomizableSend<
36        crate::operation::create_evaluation::CreateEvaluationOutput,
37        crate::operation::create_evaluation::CreateEvaluationError,
38    > for CreateEvaluationFluentBuilder
39{
40    fn send(
41        self,
42        config_override: crate::config::Builder,
43    ) -> crate::client::customize::internal::BoxFuture<
44        crate::client::customize::internal::SendResult<
45            crate::operation::create_evaluation::CreateEvaluationOutput,
46            crate::operation::create_evaluation::CreateEvaluationError,
47        >,
48    > {
49        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
50    }
51}
52impl CreateEvaluationFluentBuilder {
53    /// Creates a new `CreateEvaluationFluentBuilder`.
54    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
55        Self {
56            handle,
57            inner: ::std::default::Default::default(),
58            config_override: ::std::option::Option::None,
59        }
60    }
61    /// Access the CreateEvaluation as a reference.
62    pub fn as_input(&self) -> &crate::operation::create_evaluation::builders::CreateEvaluationInputBuilder {
63        &self.inner
64    }
65    /// Sends the request and returns the response.
66    ///
67    /// If an error occurs, an `SdkError` will be returned with additional details that
68    /// can be matched against.
69    ///
70    /// By default, any retryable failures will be retried twice. Retry behavior
71    /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
72    /// set when configuring the client.
73    pub async fn send(
74        self,
75    ) -> ::std::result::Result<
76        crate::operation::create_evaluation::CreateEvaluationOutput,
77        ::aws_smithy_runtime_api::client::result::SdkError<
78            crate::operation::create_evaluation::CreateEvaluationError,
79            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
80        >,
81    > {
82        let input = self
83            .inner
84            .build()
85            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
86        let runtime_plugins = crate::operation::create_evaluation::CreateEvaluation::operation_runtime_plugins(
87            self.handle.runtime_plugins.clone(),
88            &self.handle.conf,
89            self.config_override,
90        );
91        crate::operation::create_evaluation::CreateEvaluation::orchestrate(&runtime_plugins, input).await
92    }
93
94    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
95    pub fn customize(
96        self,
97    ) -> crate::client::customize::CustomizableOperation<
98        crate::operation::create_evaluation::CreateEvaluationOutput,
99        crate::operation::create_evaluation::CreateEvaluationError,
100        Self,
101    > {
102        crate::client::customize::CustomizableOperation::new(self)
103    }
104    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
105        self.set_config_override(::std::option::Option::Some(config_override.into()));
106        self
107    }
108
109    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
110        self.config_override = config_override;
111        self
112    }
113    /// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
114    pub fn evaluation_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
115        self.inner = self.inner.evaluation_id(input.into());
116        self
117    }
118    /// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
119    pub fn set_evaluation_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
120        self.inner = self.inner.set_evaluation_id(input);
121        self
122    }
123    /// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</p>
124    pub fn get_evaluation_id(&self) -> &::std::option::Option<::std::string::String> {
125        self.inner.get_evaluation_id()
126    }
127    /// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
128    pub fn evaluation_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
129        self.inner = self.inner.evaluation_name(input.into());
130        self
131    }
132    /// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
133    pub fn set_evaluation_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
134        self.inner = self.inner.set_evaluation_name(input);
135        self
136    }
137    /// <p>A user-supplied name or description of the <code>Evaluation</code>.</p>
138    pub fn get_evaluation_name(&self) -> &::std::option::Option<::std::string::String> {
139        self.inner.get_evaluation_name()
140    }
141    /// <p>The ID of the <code>MLModel</code> to evaluate.</p>
142    /// <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>
143    pub fn ml_model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
144        self.inner = self.inner.ml_model_id(input.into());
145        self
146    }
147    /// <p>The ID of the <code>MLModel</code> to evaluate.</p>
148    /// <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>
149    pub fn set_ml_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
150        self.inner = self.inner.set_ml_model_id(input);
151        self
152    }
153    /// <p>The ID of the <code>MLModel</code> to evaluate.</p>
154    /// <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>
155    pub fn get_ml_model_id(&self) -> &::std::option::Option<::std::string::String> {
156        self.inner.get_ml_model_id()
157    }
158    /// <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>
159    pub fn evaluation_data_source_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
160        self.inner = self.inner.evaluation_data_source_id(input.into());
161        self
162    }
163    /// <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>
164    pub fn set_evaluation_data_source_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
165        self.inner = self.inner.set_evaluation_data_source_id(input);
166        self
167    }
168    /// <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>
169    pub fn get_evaluation_data_source_id(&self) -> &::std::option::Option<::std::string::String> {
170        self.inner.get_evaluation_data_source_id()
171    }
172}
aws_sdk_machinelearning/operation/create_evaluation/builders.rs

aws_sdk_machinelearning/operation/create_evaluation/
builders.rs
