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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::fmt::Debug)]
pub struct UpdateMlModelInput {
/// <p>The ID assigned to the <code>MLModel</code> during creation.</p>
pub ml_model_id: ::std::option::Option<::std::string::String>,
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub ml_model_name: ::std::option::Option<::std::string::String>,
/// <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>
pub score_threshold: ::std::option::Option<f32>,
}
impl UpdateMlModelInput {
/// <p>The ID assigned to the <code>MLModel</code> during creation.</p>
pub fn ml_model_id(&self) -> ::std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn ml_model_name(&self) -> ::std::option::Option<&str> {
self.ml_model_name.as_deref()
}
/// <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>
pub fn score_threshold(&self) -> ::std::option::Option<f32> {
self.score_threshold
}
}
impl UpdateMlModelInput {
/// Creates a new builder-style object to manufacture [`UpdateMlModelInput`](crate::operation::update_ml_model::UpdateMlModelInput).
pub fn builder() -> crate::operation::update_ml_model::builders::UpdateMlModelInputBuilder {
crate::operation::update_ml_model::builders::UpdateMlModelInputBuilder::default()
}
}
/// A builder for [`UpdateMlModelInput`](crate::operation::update_ml_model::UpdateMlModelInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default, ::std::fmt::Debug)]
#[non_exhaustive]
pub struct UpdateMlModelInputBuilder {
pub(crate) ml_model_id: ::std::option::Option<::std::string::String>,
pub(crate) ml_model_name: ::std::option::Option<::std::string::String>,
pub(crate) score_threshold: ::std::option::Option<f32>,
}
impl UpdateMlModelInputBuilder {
/// <p>The ID assigned to the <code>MLModel</code> during creation.</p>
/// This field is required.
pub fn ml_model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.ml_model_id = ::std::option::Option::Some(input.into());
self
}
/// <p>The ID assigned to the <code>MLModel</code> during creation.</p>
pub fn set_ml_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.ml_model_id = input;
self
}
/// <p>The ID assigned to the <code>MLModel</code> during creation.</p>
pub fn get_ml_model_id(&self) -> &::std::option::Option<::std::string::String> {
&self.ml_model_id
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn ml_model_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.ml_model_name = ::std::option::Option::Some(input.into());
self
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn set_ml_model_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.ml_model_name = input;
self
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn get_ml_model_name(&self) -> &::std::option::Option<::std::string::String> {
&self.ml_model_name
}
/// <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>
pub fn score_threshold(mut self, input: f32) -> Self {
self.score_threshold = ::std::option::Option::Some(input);
self
}
/// <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>
pub fn set_score_threshold(mut self, input: ::std::option::Option<f32>) -> Self {
self.score_threshold = input;
self
}
/// <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>
pub fn get_score_threshold(&self) -> &::std::option::Option<f32> {
&self.score_threshold
}
/// Consumes the builder and constructs a [`UpdateMlModelInput`](crate::operation::update_ml_model::UpdateMlModelInput).
pub fn build(
self,
) -> ::std::result::Result<crate::operation::update_ml_model::UpdateMlModelInput, ::aws_smithy_types::error::operation::BuildError> {
::std::result::Result::Ok(crate::operation::update_ml_model::UpdateMlModelInput {
ml_model_id: self.ml_model_id,
ml_model_name: self.ml_model_name,
score_threshold: self.score_threshold,
})
}
}