// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
/// <p>Represents the output of an <code>UpdateMLModel</code> operation.</p>
/// <p>You can see the updated content by using the <code>GetMLModel</code> operation.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateMlModelOutput {
/// <p>The ID assigned to the <code>MLModel</code> during creation. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
}
impl UpdateMlModelOutput {
/// <p>The ID assigned to the <code>MLModel</code> during creation. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
}
/// See [`UpdateMlModelOutput`](crate::output::UpdateMlModelOutput).
pub mod update_ml_model_output {
/// A builder for [`UpdateMlModelOutput`](crate::output::UpdateMlModelOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID assigned to the <code>MLModel</code> during creation. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>The ID assigned to the <code>MLModel</code> during creation. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn set_ml_model_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.ml_model_id = input;
self
}
/// Consumes the builder and constructs a [`UpdateMlModelOutput`](crate::output::UpdateMlModelOutput).
pub fn build(self) -> crate::output::UpdateMlModelOutput {
crate::output::UpdateMlModelOutput {
ml_model_id: self.ml_model_id,
}
}
}
}
impl UpdateMlModelOutput {
/// Creates a new builder-style object to manufacture [`UpdateMlModelOutput`](crate::output::UpdateMlModelOutput).
pub fn builder() -> crate::output::update_ml_model_output::Builder {
crate::output::update_ml_model_output::Builder::default()
}
}
/// <p>Represents the output of an <code>UpdateEvaluation</code> operation.</p>
/// <p>You can see the updated content by using the <code>GetEvaluation</code> operation.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateEvaluationOutput {
/// <p>The ID assigned to the <code>Evaluation</code> during creation. This value should be identical to the value of the <code>Evaluation</code> in the request.</p>
#[doc(hidden)]
pub evaluation_id: std::option::Option<std::string::String>,
}
impl UpdateEvaluationOutput {
/// <p>The ID assigned to the <code>Evaluation</code> during creation. This value should be identical to the value of the <code>Evaluation</code> in the request.</p>
pub fn evaluation_id(&self) -> std::option::Option<&str> {
self.evaluation_id.as_deref()
}
}
/// See [`UpdateEvaluationOutput`](crate::output::UpdateEvaluationOutput).
pub mod update_evaluation_output {
/// A builder for [`UpdateEvaluationOutput`](crate::output::UpdateEvaluationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) evaluation_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID assigned to the <code>Evaluation</code> during creation. This value should be identical to the value of the <code>Evaluation</code> in the request.</p>
pub fn evaluation_id(mut self, input: impl Into<std::string::String>) -> Self {
self.evaluation_id = Some(input.into());
self
}
/// <p>The ID assigned to the <code>Evaluation</code> during creation. This value should be identical to the value of the <code>Evaluation</code> in the request.</p>
pub fn set_evaluation_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.evaluation_id = input;
self
}
/// Consumes the builder and constructs a [`UpdateEvaluationOutput`](crate::output::UpdateEvaluationOutput).
pub fn build(self) -> crate::output::UpdateEvaluationOutput {
crate::output::UpdateEvaluationOutput {
evaluation_id: self.evaluation_id,
}
}
}
}
impl UpdateEvaluationOutput {
/// Creates a new builder-style object to manufacture [`UpdateEvaluationOutput`](crate::output::UpdateEvaluationOutput).
pub fn builder() -> crate::output::update_evaluation_output::Builder {
crate::output::update_evaluation_output::Builder::default()
}
}
/// <p>Represents the output of an <code>UpdateDataSource</code> operation.</p>
/// <p>You can see the updated content by using the <code>GetBatchPrediction</code> operation.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateDataSourceOutput {
/// <p>The ID assigned to the <code>DataSource</code> during creation. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
}
impl UpdateDataSourceOutput {
/// <p>The ID assigned to the <code>DataSource</code> during creation. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
}
/// See [`UpdateDataSourceOutput`](crate::output::UpdateDataSourceOutput).
pub mod update_data_source_output {
/// A builder for [`UpdateDataSourceOutput`](crate::output::UpdateDataSourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID assigned to the <code>DataSource</code> during creation. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>The ID assigned to the <code>DataSource</code> during creation. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// Consumes the builder and constructs a [`UpdateDataSourceOutput`](crate::output::UpdateDataSourceOutput).
pub fn build(self) -> crate::output::UpdateDataSourceOutput {
crate::output::UpdateDataSourceOutput {
data_source_id: self.data_source_id,
}
}
}
}
impl UpdateDataSourceOutput {
/// Creates a new builder-style object to manufacture [`UpdateDataSourceOutput`](crate::output::UpdateDataSourceOutput).
pub fn builder() -> crate::output::update_data_source_output::Builder {
crate::output::update_data_source_output::Builder::default()
}
}
/// <p>Represents the output of an <code>UpdateBatchPrediction</code> operation.</p>
/// <p>You can see the updated content by using the <code>GetBatchPrediction</code> operation.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct UpdateBatchPredictionOutput {
/// <p>The ID assigned to the <code>BatchPrediction</code> during creation. This value should be identical to the value of the <code>BatchPredictionId</code> in the request.</p>
#[doc(hidden)]
pub batch_prediction_id: std::option::Option<std::string::String>,
}
impl UpdateBatchPredictionOutput {
/// <p>The ID assigned to the <code>BatchPrediction</code> during creation. This value should be identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn batch_prediction_id(&self) -> std::option::Option<&str> {
self.batch_prediction_id.as_deref()
}
}
/// See [`UpdateBatchPredictionOutput`](crate::output::UpdateBatchPredictionOutput).
pub mod update_batch_prediction_output {
/// A builder for [`UpdateBatchPredictionOutput`](crate::output::UpdateBatchPredictionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) batch_prediction_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID assigned to the <code>BatchPrediction</code> during creation. This value should be identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn batch_prediction_id(mut self, input: impl Into<std::string::String>) -> Self {
self.batch_prediction_id = Some(input.into());
self
}
/// <p>The ID assigned to the <code>BatchPrediction</code> during creation. This value should be identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn set_batch_prediction_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.batch_prediction_id = input;
self
}
/// Consumes the builder and constructs a [`UpdateBatchPredictionOutput`](crate::output::UpdateBatchPredictionOutput).
pub fn build(self) -> crate::output::UpdateBatchPredictionOutput {
crate::output::UpdateBatchPredictionOutput {
batch_prediction_id: self.batch_prediction_id,
}
}
}
}
impl UpdateBatchPredictionOutput {
/// Creates a new builder-style object to manufacture [`UpdateBatchPredictionOutput`](crate::output::UpdateBatchPredictionOutput).
pub fn builder() -> crate::output::update_batch_prediction_output::Builder {
crate::output::update_batch_prediction_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct PredictOutput {
/// <p>The output from a <code>Predict</code> operation: </p>
/// <ul>
/// <li> <p> <code>Details</code> - Contains the following attributes: <code>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS</code> <code>DetailsAttributes.ALGORITHM - SGD</code> </p> </li>
/// <li> <p> <code>PredictedLabel</code> - Present for either a <code>BINARY</code> or <code>MULTICLASS</code> <code>MLModel</code> request. </p> </li>
/// <li> <p> <code>PredictedScores</code> - Contains the raw classification score corresponding to each label. </p> </li>
/// <li> <p> <code>PredictedValue</code> - Present for a <code>REGRESSION</code> <code>MLModel</code> request. </p> </li>
/// </ul>
#[doc(hidden)]
pub prediction: std::option::Option<crate::model::Prediction>,
}
impl PredictOutput {
/// <p>The output from a <code>Predict</code> operation: </p>
/// <ul>
/// <li> <p> <code>Details</code> - Contains the following attributes: <code>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS</code> <code>DetailsAttributes.ALGORITHM - SGD</code> </p> </li>
/// <li> <p> <code>PredictedLabel</code> - Present for either a <code>BINARY</code> or <code>MULTICLASS</code> <code>MLModel</code> request. </p> </li>
/// <li> <p> <code>PredictedScores</code> - Contains the raw classification score corresponding to each label. </p> </li>
/// <li> <p> <code>PredictedValue</code> - Present for a <code>REGRESSION</code> <code>MLModel</code> request. </p> </li>
/// </ul>
pub fn prediction(&self) -> std::option::Option<&crate::model::Prediction> {
self.prediction.as_ref()
}
}
/// See [`PredictOutput`](crate::output::PredictOutput).
pub mod predict_output {
/// A builder for [`PredictOutput`](crate::output::PredictOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) prediction: std::option::Option<crate::model::Prediction>,
}
impl Builder {
/// <p>The output from a <code>Predict</code> operation: </p>
/// <ul>
/// <li> <p> <code>Details</code> - Contains the following attributes: <code>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS</code> <code>DetailsAttributes.ALGORITHM - SGD</code> </p> </li>
/// <li> <p> <code>PredictedLabel</code> - Present for either a <code>BINARY</code> or <code>MULTICLASS</code> <code>MLModel</code> request. </p> </li>
/// <li> <p> <code>PredictedScores</code> - Contains the raw classification score corresponding to each label. </p> </li>
/// <li> <p> <code>PredictedValue</code> - Present for a <code>REGRESSION</code> <code>MLModel</code> request. </p> </li>
/// </ul>
pub fn prediction(mut self, input: crate::model::Prediction) -> Self {
self.prediction = Some(input);
self
}
/// <p>The output from a <code>Predict</code> operation: </p>
/// <ul>
/// <li> <p> <code>Details</code> - Contains the following attributes: <code>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS</code> <code>DetailsAttributes.ALGORITHM - SGD</code> </p> </li>
/// <li> <p> <code>PredictedLabel</code> - Present for either a <code>BINARY</code> or <code>MULTICLASS</code> <code>MLModel</code> request. </p> </li>
/// <li> <p> <code>PredictedScores</code> - Contains the raw classification score corresponding to each label. </p> </li>
/// <li> <p> <code>PredictedValue</code> - Present for a <code>REGRESSION</code> <code>MLModel</code> request. </p> </li>
/// </ul>
pub fn set_prediction(
mut self,
input: std::option::Option<crate::model::Prediction>,
) -> Self {
self.prediction = input;
self
}
/// Consumes the builder and constructs a [`PredictOutput`](crate::output::PredictOutput).
pub fn build(self) -> crate::output::PredictOutput {
crate::output::PredictOutput {
prediction: self.prediction,
}
}
}
}
impl PredictOutput {
/// Creates a new builder-style object to manufacture [`PredictOutput`](crate::output::PredictOutput).
pub fn builder() -> crate::output::predict_output::Builder {
crate::output::predict_output::Builder::default()
}
}
/// <p>Represents the output of a <code>GetMLModel</code> operation, and provides detailed information about a <code>MLModel</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetMlModelOutput {
/// <p>The MLModel ID, which is same as the <code>MLModelId</code> in the request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
/// <p>The ID of the training <code>DataSource</code>.</p>
#[doc(hidden)]
pub training_data_source_id: std::option::Option<std::string::String>,
/// <p>The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
#[doc(hidden)]
pub created_by_iam_user: std::option::Option<std::string::String>,
/// <p>The time that the <code>MLModel</code> was created. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub created_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li>
/// <li> <p> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The request completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li>
/// </ul>
#[doc(hidden)]
pub status: std::option::Option<crate::model::EntityStatus>,
/// <p>Long integer type that is a 64-bit signed number.</p>
#[doc(hidden)]
pub size_in_bytes: std::option::Option<i64>,
/// <p>The current endpoint of the <code>MLModel</code> </p>
#[doc(hidden)]
pub endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
/// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
/// <p>The following is the current set of training parameters:</p>
/// <ul>
/// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
/// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
/// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
/// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
/// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
/// </ul>
#[doc(hidden)]
pub training_parameters:
std::option::Option<std::collections::HashMap<std::string::String, std::string::String>>,
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
#[doc(hidden)]
pub input_data_location_s3: std::option::Option<std::string::String>,
/// <p>Identifies the <code>MLModel</code> category. The following are the available types: </p>
/// <ul>
/// <li> <p>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</p> </li>
/// <li> <p>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</p> </li>
/// <li> <p>MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li>
/// </ul>
#[doc(hidden)]
pub ml_model_type: std::option::Option<crate::model::MlModelType>,
/// <p>The scoring threshold is used in binary classification <code>MLModel</code> models. It marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the threshold receive a positive result from the MLModel, such as <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as <code>false</code>.</p>
#[doc(hidden)]
pub score_threshold: std::option::Option<f32>,
/// <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub score_threshold_last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>A link to the file that contains logs of the <code>CreateMLModel</code> operation.</p>
#[doc(hidden)]
pub log_uri: std::option::Option<std::string::String>,
/// <p>A description of the most recent details about accessing the <code>MLModel</code>.</p>
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
#[doc(hidden)]
pub compute_time: std::option::Option<i64>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
#[doc(hidden)]
pub finished_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.</p>
#[doc(hidden)]
pub started_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
#[doc(hidden)]
pub recipe: std::option::Option<std::string::String>,
/// <p>The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
#[doc(hidden)]
pub schema: std::option::Option<std::string::String>,
}
impl GetMlModelOutput {
/// <p>The MLModel ID, which is same as the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>The ID of the training <code>DataSource</code>.</p>
pub fn training_data_source_id(&self) -> std::option::Option<&str> {
self.training_data_source_id.as_deref()
}
/// <p>The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(&self) -> std::option::Option<&str> {
self.created_by_iam_user.as_deref()
}
/// <p>The time that the <code>MLModel</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.created_at.as_ref()
}
/// <p>The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.last_updated_at.as_ref()
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li>
/// <li> <p> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The request completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li>
/// </ul>
pub fn status(&self) -> std::option::Option<&crate::model::EntityStatus> {
self.status.as_ref()
}
/// <p>Long integer type that is a 64-bit signed number.</p>
pub fn size_in_bytes(&self) -> std::option::Option<i64> {
self.size_in_bytes
}
/// <p>The current endpoint of the <code>MLModel</code> </p>
pub fn endpoint_info(&self) -> std::option::Option<&crate::model::RealtimeEndpointInfo> {
self.endpoint_info.as_ref()
}
/// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
/// <p>The following is the current set of training parameters:</p>
/// <ul>
/// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
/// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
/// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
/// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
/// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
/// </ul>
pub fn training_parameters(
&self,
) -> std::option::Option<&std::collections::HashMap<std::string::String, std::string::String>>
{
self.training_parameters.as_ref()
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(&self) -> std::option::Option<&str> {
self.input_data_location_s3.as_deref()
}
/// <p>Identifies the <code>MLModel</code> category. The following are the available types: </p>
/// <ul>
/// <li> <p>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</p> </li>
/// <li> <p>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</p> </li>
/// <li> <p>MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li>
/// </ul>
pub fn ml_model_type(&self) -> std::option::Option<&crate::model::MlModelType> {
self.ml_model_type.as_ref()
}
/// <p>The scoring threshold is used in binary classification <code>MLModel</code> models. It marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the threshold receive a positive result from the MLModel, such as <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as <code>false</code>.</p>
pub fn score_threshold(&self) -> std::option::Option<f32> {
self.score_threshold
}
/// <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.</p>
pub fn score_threshold_last_updated_at(
&self,
) -> std::option::Option<&aws_smithy_types::DateTime> {
self.score_threshold_last_updated_at.as_ref()
}
/// <p>A link to the file that contains logs of the <code>CreateMLModel</code> operation.</p>
pub fn log_uri(&self) -> std::option::Option<&str> {
self.log_uri.as_deref()
}
/// <p>A description of the most recent details about accessing the <code>MLModel</code>.</p>
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(&self) -> std::option::Option<i64> {
self.compute_time
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.finished_at.as_ref()
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.started_at.as_ref()
}
/// <p>The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn recipe(&self) -> std::option::Option<&str> {
self.recipe.as_deref()
}
/// <p>The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn schema(&self) -> std::option::Option<&str> {
self.schema.as_deref()
}
}
/// See [`GetMlModelOutput`](crate::output::GetMlModelOutput).
pub mod get_ml_model_output {
/// A builder for [`GetMlModelOutput`](crate::output::GetMlModelOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
pub(crate) training_data_source_id: std::option::Option<std::string::String>,
pub(crate) created_by_iam_user: std::option::Option<std::string::String>,
pub(crate) created_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) status: std::option::Option<crate::model::EntityStatus>,
pub(crate) size_in_bytes: std::option::Option<i64>,
pub(crate) endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
pub(crate) training_parameters: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
pub(crate) input_data_location_s3: std::option::Option<std::string::String>,
pub(crate) ml_model_type: std::option::Option<crate::model::MlModelType>,
pub(crate) score_threshold: std::option::Option<f32>,
pub(crate) score_threshold_last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) log_uri: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) compute_time: std::option::Option<i64>,
pub(crate) finished_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) started_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) recipe: std::option::Option<std::string::String>,
pub(crate) schema: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The MLModel ID, which is same as the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>The MLModel ID, which is same as the <code>MLModelId</code> in the request.</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 of the training <code>DataSource</code>.</p>
pub fn training_data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.training_data_source_id = Some(input.into());
self
}
/// <p>The ID of the training <code>DataSource</code>.</p>
pub fn set_training_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.training_data_source_id = input;
self
}
/// <p>The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(mut self, input: impl Into<std::string::String>) -> Self {
self.created_by_iam_user = Some(input.into());
self
}
/// <p>The AWS user account from which the <code>MLModel</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn set_created_by_iam_user(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.created_by_iam_user = input;
self
}
/// <p>The time that the <code>MLModel</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.created_at = Some(input);
self
}
/// <p>The time that the <code>MLModel</code> was created. The time is expressed in epoch time.</p>
pub fn set_created_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.created_at = input;
self
}
/// <p>The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.last_updated_at = Some(input);
self
}
/// <p>The time of the most recent edit to the <code>MLModel</code>. The time is expressed in epoch time.</p>
pub fn set_last_updated_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.last_updated_at = input;
self
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>A user-supplied name or description of the <code>MLModel</code>.</p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li>
/// <li> <p> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The request completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li>
/// </ul>
pub fn status(mut self, input: crate::model::EntityStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The current status of the <code>MLModel</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to describe a <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The request is processing.</p> </li>
/// <li> <p> <code>FAILED</code> - The request did not run to completion. The ML model isn't usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The request completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>MLModel</code> is marked as deleted. It isn't usable.</p> </li>
/// </ul>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::EntityStatus>,
) -> Self {
self.status = input;
self
}
/// <p>Long integer type that is a 64-bit signed number.</p>
pub fn size_in_bytes(mut self, input: i64) -> Self {
self.size_in_bytes = Some(input);
self
}
/// <p>Long integer type that is a 64-bit signed number.</p>
pub fn set_size_in_bytes(mut self, input: std::option::Option<i64>) -> Self {
self.size_in_bytes = input;
self
}
/// <p>The current endpoint of the <code>MLModel</code> </p>
pub fn endpoint_info(mut self, input: crate::model::RealtimeEndpointInfo) -> Self {
self.endpoint_info = Some(input);
self
}
/// <p>The current endpoint of the <code>MLModel</code> </p>
pub fn set_endpoint_info(
mut self,
input: std::option::Option<crate::model::RealtimeEndpointInfo>,
) -> Self {
self.endpoint_info = input;
self
}
/// Adds a key-value pair to `training_parameters`.
///
/// To override the contents of this collection use [`set_training_parameters`](Self::set_training_parameters).
///
/// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
/// <p>The following is the current set of training parameters:</p>
/// <ul>
/// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
/// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
/// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
/// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
/// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
/// </ul>
pub fn training_parameters(
mut self,
k: impl Into<std::string::String>,
v: impl Into<std::string::String>,
) -> Self {
let mut hash_map = self.training_parameters.unwrap_or_default();
hash_map.insert(k.into(), v.into());
self.training_parameters = Some(hash_map);
self
}
/// <p>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</p>
/// <p>The following is the current set of training parameters:</p>
/// <ul>
/// <li> <p> <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</p> <p> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</p> </li>
/// <li> <p> <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</p> </li>
/// <li> <p> <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</p> </li>
/// <li> <p> <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</p> </li>
/// <li> <p> <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</p> </li>
/// </ul>
pub fn set_training_parameters(
mut self,
input: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
) -> Self {
self.training_parameters = input;
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(mut self, input: impl Into<std::string::String>) -> Self {
self.input_data_location_s3 = Some(input.into());
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn set_input_data_location_s3(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.input_data_location_s3 = input;
self
}
/// <p>Identifies the <code>MLModel</code> category. The following are the available types: </p>
/// <ul>
/// <li> <p>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</p> </li>
/// <li> <p>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</p> </li>
/// <li> <p>MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li>
/// </ul>
pub fn ml_model_type(mut self, input: crate::model::MlModelType) -> Self {
self.ml_model_type = Some(input);
self
}
/// <p>Identifies the <code>MLModel</code> category. The following are the available types: </p>
/// <ul>
/// <li> <p>REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"</p> </li>
/// <li> <p>BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"</p> </li>
/// <li> <p>MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"</p> </li>
/// </ul>
pub fn set_ml_model_type(
mut self,
input: std::option::Option<crate::model::MlModelType>,
) -> Self {
self.ml_model_type = input;
self
}
/// <p>The scoring threshold is used in binary classification <code>MLModel</code> models. It marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the threshold receive a positive result from the MLModel, such as <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, such as <code>false</code>.</p>
pub fn score_threshold(mut self, input: f32) -> Self {
self.score_threshold = Some(input);
self
}
/// <p>The scoring threshold is used in binary classification <code>MLModel</code> models. It marks the boundary between a positive prediction and a negative prediction.</p>
/// <p>Output values greater than or equal to the threshold receive a positive result from the MLModel, such as <code>true</code>. Output values less than the threshold receive a negative response from the MLModel, 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 time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.</p>
pub fn score_threshold_last_updated_at(
mut self,
input: aws_smithy_types::DateTime,
) -> Self {
self.score_threshold_last_updated_at = Some(input);
self
}
/// <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time is expressed in epoch time.</p>
pub fn set_score_threshold_last_updated_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.score_threshold_last_updated_at = input;
self
}
/// <p>A link to the file that contains logs of the <code>CreateMLModel</code> operation.</p>
pub fn log_uri(mut self, input: impl Into<std::string::String>) -> Self {
self.log_uri = Some(input.into());
self
}
/// <p>A link to the file that contains logs of the <code>CreateMLModel</code> operation.</p>
pub fn set_log_uri(mut self, input: std::option::Option<std::string::String>) -> Self {
self.log_uri = input;
self
}
/// <p>A description of the most recent details about accessing the <code>MLModel</code>.</p>
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>A description of the most recent details about accessing the <code>MLModel</code>.</p>
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(mut self, input: i64) -> Self {
self.compute_time = Some(input);
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>MLModel</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
pub fn set_compute_time(mut self, input: std::option::Option<i64>) -> Self {
self.compute_time = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.finished_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn set_finished_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.finished_at = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.started_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>MLModel</code> is in the <code>PENDING</code> state.</p>
pub fn set_started_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.started_at = input;
self
}
/// <p>The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn recipe(mut self, input: impl Into<std::string::String>) -> Self {
self.recipe = Some(input.into());
self
}
/// <p>The recipe to use when training the <code>MLModel</code>. The <code>Recipe</code> provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn set_recipe(mut self, input: std::option::Option<std::string::String>) -> Self {
self.recipe = input;
self
}
/// <p>The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn schema(mut self, input: impl Into<std::string::String>) -> Self {
self.schema = Some(input.into());
self
}
/// <p>The schema used by all of the data files referenced by the <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn set_schema(mut self, input: std::option::Option<std::string::String>) -> Self {
self.schema = input;
self
}
/// Consumes the builder and constructs a [`GetMlModelOutput`](crate::output::GetMlModelOutput).
pub fn build(self) -> crate::output::GetMlModelOutput {
crate::output::GetMlModelOutput {
ml_model_id: self.ml_model_id,
training_data_source_id: self.training_data_source_id,
created_by_iam_user: self.created_by_iam_user,
created_at: self.created_at,
last_updated_at: self.last_updated_at,
name: self.name,
status: self.status,
size_in_bytes: self.size_in_bytes,
endpoint_info: self.endpoint_info,
training_parameters: self.training_parameters,
input_data_location_s3: self.input_data_location_s3,
ml_model_type: self.ml_model_type,
score_threshold: self.score_threshold,
score_threshold_last_updated_at: self.score_threshold_last_updated_at,
log_uri: self.log_uri,
message: self.message,
compute_time: self.compute_time,
finished_at: self.finished_at,
started_at: self.started_at,
recipe: self.recipe,
schema: self.schema,
}
}
}
}
impl GetMlModelOutput {
/// Creates a new builder-style object to manufacture [`GetMlModelOutput`](crate::output::GetMlModelOutput).
pub fn builder() -> crate::output::get_ml_model_output::Builder {
crate::output::get_ml_model_output::Builder::default()
}
}
/// <p>Represents the output of a <code>GetEvaluation</code> operation and describes an <code>Evaluation</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetEvaluationOutput {
/// <p>The evaluation ID which is same as the <code>EvaluationId</code> in the request.</p>
#[doc(hidden)]
pub evaluation_id: std::option::Option<std::string::String>,
/// <p>The ID of the <code>MLModel</code> that was the focus of the evaluation.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
/// <p>The <code>DataSource</code> used for this evaluation.</p>
#[doc(hidden)]
pub evaluation_data_source_id: std::option::Option<std::string::String>,
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
#[doc(hidden)]
pub input_data_location_s3: std::option::Option<std::string::String>,
/// <p>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.</p>
#[doc(hidden)]
pub created_by_iam_user: std::option::Option<std::string::String>,
/// <p>The time that the <code>Evaluation</code> was created. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub created_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The time of the most recent edit to the <code>Evaluation</code>. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>A user-supplied name or description of the <code>Evaluation</code>. </p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>The status of the evaluation. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Language (Amazon ML) submitted a request to evaluate an <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The evaluation is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to evaluate an <code>MLModel</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The evaluation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>Evaluation</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
#[doc(hidden)]
pub status: std::option::Option<crate::model::EntityStatus>,
/// <p>Measurements of how well the <code>MLModel</code> performed using observations referenced by the <code>DataSource</code>. One of the following metric is returned based on the type of the <code>MLModel</code>: </p>
/// <ul>
/// <li> <p>BinaryAUC: A binary <code>MLModel</code> uses the Area Under the Curve (AUC) technique to measure performance. </p> </li>
/// <li> <p>RegressionRMSE: A regression <code>MLModel</code> uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.</p> </li>
/// <li> <p>MulticlassAvgFScore: A multiclass <code>MLModel</code> uses the F1 score technique to measure performance. </p> </li>
/// </ul>
/// <p> For more information about performance metrics, please see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>. </p>
#[doc(hidden)]
pub performance_metrics: std::option::Option<crate::model::PerformanceMetrics>,
/// <p>A link to the file that contains logs of the <code>CreateEvaluation</code> operation.</p>
#[doc(hidden)]
pub log_uri: std::option::Option<std::string::String>,
/// <p>A description of the most recent details about evaluating the <code>MLModel</code>.</p>
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>Evaluation</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>Evaluation</code> is in the <code>COMPLETED</code> state.</p>
#[doc(hidden)]
pub compute_time: std::option::Option<i64>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>Evaluation</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
#[doc(hidden)]
pub finished_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>Evaluation</code> is in the <code>PENDING</code> state.</p>
#[doc(hidden)]
pub started_at: std::option::Option<aws_smithy_types::DateTime>,
}
impl GetEvaluationOutput {
/// <p>The evaluation ID which is same as the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(&self) -> std::option::Option<&str> {
self.evaluation_id.as_deref()
}
/// <p>The ID of the <code>MLModel</code> that was the focus of the evaluation.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>The <code>DataSource</code> used for this evaluation.</p>
pub fn evaluation_data_source_id(&self) -> std::option::Option<&str> {
self.evaluation_data_source_id.as_deref()
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(&self) -> std::option::Option<&str> {
self.input_data_location_s3.as_deref()
}
/// <p>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.</p>
pub fn created_by_iam_user(&self) -> std::option::Option<&str> {
self.created_by_iam_user.as_deref()
}
/// <p>The time that the <code>Evaluation</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.created_at.as_ref()
}
/// <p>The time of the most recent edit to the <code>Evaluation</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.last_updated_at.as_ref()
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>. </p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The status of the evaluation. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Language (Amazon ML) submitted a request to evaluate an <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The evaluation is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to evaluate an <code>MLModel</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The evaluation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>Evaluation</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(&self) -> std::option::Option<&crate::model::EntityStatus> {
self.status.as_ref()
}
/// <p>Measurements of how well the <code>MLModel</code> performed using observations referenced by the <code>DataSource</code>. One of the following metric is returned based on the type of the <code>MLModel</code>: </p>
/// <ul>
/// <li> <p>BinaryAUC: A binary <code>MLModel</code> uses the Area Under the Curve (AUC) technique to measure performance. </p> </li>
/// <li> <p>RegressionRMSE: A regression <code>MLModel</code> uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.</p> </li>
/// <li> <p>MulticlassAvgFScore: A multiclass <code>MLModel</code> uses the F1 score technique to measure performance. </p> </li>
/// </ul>
/// <p> For more information about performance metrics, please see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>. </p>
pub fn performance_metrics(&self) -> std::option::Option<&crate::model::PerformanceMetrics> {
self.performance_metrics.as_ref()
}
/// <p>A link to the file that contains logs of the <code>CreateEvaluation</code> operation.</p>
pub fn log_uri(&self) -> std::option::Option<&str> {
self.log_uri.as_deref()
}
/// <p>A description of the most recent details about evaluating the <code>MLModel</code>.</p>
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>Evaluation</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>Evaluation</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(&self) -> std::option::Option<i64> {
self.compute_time
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>Evaluation</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.finished_at.as_ref()
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>Evaluation</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.started_at.as_ref()
}
}
/// See [`GetEvaluationOutput`](crate::output::GetEvaluationOutput).
pub mod get_evaluation_output {
/// A builder for [`GetEvaluationOutput`](crate::output::GetEvaluationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) evaluation_id: std::option::Option<std::string::String>,
pub(crate) ml_model_id: std::option::Option<std::string::String>,
pub(crate) evaluation_data_source_id: std::option::Option<std::string::String>,
pub(crate) input_data_location_s3: std::option::Option<std::string::String>,
pub(crate) created_by_iam_user: std::option::Option<std::string::String>,
pub(crate) created_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) status: std::option::Option<crate::model::EntityStatus>,
pub(crate) performance_metrics: std::option::Option<crate::model::PerformanceMetrics>,
pub(crate) log_uri: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) compute_time: std::option::Option<i64>,
pub(crate) finished_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) started_at: std::option::Option<aws_smithy_types::DateTime>,
}
impl Builder {
/// <p>The evaluation ID which is same as the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(mut self, input: impl Into<std::string::String>) -> Self {
self.evaluation_id = Some(input.into());
self
}
/// <p>The evaluation ID which is same as the <code>EvaluationId</code> in the request.</p>
pub fn set_evaluation_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.evaluation_id = input;
self
}
/// <p>The ID of the <code>MLModel</code> that was the focus of the evaluation.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>The ID of the <code>MLModel</code> that was the focus of the evaluation.</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 <code>DataSource</code> used for this evaluation.</p>
pub fn evaluation_data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.evaluation_data_source_id = Some(input.into());
self
}
/// <p>The <code>DataSource</code> used for this evaluation.</p>
pub fn set_evaluation_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.evaluation_data_source_id = input;
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(mut self, input: impl Into<std::string::String>) -> Self {
self.input_data_location_s3 = Some(input.into());
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn set_input_data_location_s3(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.input_data_location_s3 = input;
self
}
/// <p>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.</p>
pub fn created_by_iam_user(mut self, input: impl Into<std::string::String>) -> Self {
self.created_by_iam_user = Some(input.into());
self
}
/// <p>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.</p>
pub fn set_created_by_iam_user(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.created_by_iam_user = input;
self
}
/// <p>The time that the <code>Evaluation</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.created_at = Some(input);
self
}
/// <p>The time that the <code>Evaluation</code> was created. The time is expressed in epoch time.</p>
pub fn set_created_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.created_at = input;
self
}
/// <p>The time of the most recent edit to the <code>Evaluation</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.last_updated_at = Some(input);
self
}
/// <p>The time of the most recent edit to the <code>Evaluation</code>. The time is expressed in epoch time.</p>
pub fn set_last_updated_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.last_updated_at = input;
self
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>. </p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>A user-supplied name or description of the <code>Evaluation</code>. </p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>The status of the evaluation. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Language (Amazon ML) submitted a request to evaluate an <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The evaluation is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to evaluate an <code>MLModel</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The evaluation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>Evaluation</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(mut self, input: crate::model::EntityStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The status of the evaluation. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Language (Amazon ML) submitted a request to evaluate an <code>MLModel</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The evaluation is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to evaluate an <code>MLModel</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The evaluation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>Evaluation</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::EntityStatus>,
) -> Self {
self.status = input;
self
}
/// <p>Measurements of how well the <code>MLModel</code> performed using observations referenced by the <code>DataSource</code>. One of the following metric is returned based on the type of the <code>MLModel</code>: </p>
/// <ul>
/// <li> <p>BinaryAUC: A binary <code>MLModel</code> uses the Area Under the Curve (AUC) technique to measure performance. </p> </li>
/// <li> <p>RegressionRMSE: A regression <code>MLModel</code> uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.</p> </li>
/// <li> <p>MulticlassAvgFScore: A multiclass <code>MLModel</code> uses the F1 score technique to measure performance. </p> </li>
/// </ul>
/// <p> For more information about performance metrics, please see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>. </p>
pub fn performance_metrics(mut self, input: crate::model::PerformanceMetrics) -> Self {
self.performance_metrics = Some(input);
self
}
/// <p>Measurements of how well the <code>MLModel</code> performed using observations referenced by the <code>DataSource</code>. One of the following metric is returned based on the type of the <code>MLModel</code>: </p>
/// <ul>
/// <li> <p>BinaryAUC: A binary <code>MLModel</code> uses the Area Under the Curve (AUC) technique to measure performance. </p> </li>
/// <li> <p>RegressionRMSE: A regression <code>MLModel</code> uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.</p> </li>
/// <li> <p>MulticlassAvgFScore: A multiclass <code>MLModel</code> uses the F1 score technique to measure performance. </p> </li>
/// </ul>
/// <p> For more information about performance metrics, please see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>. </p>
pub fn set_performance_metrics(
mut self,
input: std::option::Option<crate::model::PerformanceMetrics>,
) -> Self {
self.performance_metrics = input;
self
}
/// <p>A link to the file that contains logs of the <code>CreateEvaluation</code> operation.</p>
pub fn log_uri(mut self, input: impl Into<std::string::String>) -> Self {
self.log_uri = Some(input.into());
self
}
/// <p>A link to the file that contains logs of the <code>CreateEvaluation</code> operation.</p>
pub fn set_log_uri(mut self, input: std::option::Option<std::string::String>) -> Self {
self.log_uri = input;
self
}
/// <p>A description of the most recent details about evaluating the <code>MLModel</code>.</p>
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>A description of the most recent details about evaluating the <code>MLModel</code>.</p>
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>Evaluation</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>Evaluation</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(mut self, input: i64) -> Self {
self.compute_time = Some(input);
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>Evaluation</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>Evaluation</code> is in the <code>COMPLETED</code> state.</p>
pub fn set_compute_time(mut self, input: std::option::Option<i64>) -> Self {
self.compute_time = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>Evaluation</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.finished_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>Evaluation</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn set_finished_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.finished_at = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>Evaluation</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.started_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>Evaluation</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>Evaluation</code> is in the <code>PENDING</code> state.</p>
pub fn set_started_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.started_at = input;
self
}
/// Consumes the builder and constructs a [`GetEvaluationOutput`](crate::output::GetEvaluationOutput).
pub fn build(self) -> crate::output::GetEvaluationOutput {
crate::output::GetEvaluationOutput {
evaluation_id: self.evaluation_id,
ml_model_id: self.ml_model_id,
evaluation_data_source_id: self.evaluation_data_source_id,
input_data_location_s3: self.input_data_location_s3,
created_by_iam_user: self.created_by_iam_user,
created_at: self.created_at,
last_updated_at: self.last_updated_at,
name: self.name,
status: self.status,
performance_metrics: self.performance_metrics,
log_uri: self.log_uri,
message: self.message,
compute_time: self.compute_time,
finished_at: self.finished_at,
started_at: self.started_at,
}
}
}
}
impl GetEvaluationOutput {
/// Creates a new builder-style object to manufacture [`GetEvaluationOutput`](crate::output::GetEvaluationOutput).
pub fn builder() -> crate::output::get_evaluation_output::Builder {
crate::output::get_evaluation_output::Builder::default()
}
}
/// <p>Represents the output of a <code>GetDataSource</code> operation and describes a <code>DataSource</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetDataSourceOutput {
/// <p>The ID assigned to the <code>DataSource</code> at creation. This value should be identical to the value of the <code>DataSourceId</code> in the request.</p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
#[doc(hidden)]
pub data_location_s3: std::option::Option<std::string::String>,
/// <p>A JSON string that represents the splitting and rearrangement requirement used when this <code>DataSource</code> was created.</p>
#[doc(hidden)]
pub data_rearrangement: std::option::Option<std::string::String>,
/// <p>The AWS user account from which the <code>DataSource</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
#[doc(hidden)]
pub created_by_iam_user: std::option::Option<std::string::String>,
/// <p>The time that the <code>DataSource</code> was created. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub created_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The time of the most recent edit to the <code>DataSource</code>. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The total size of observations in the data files.</p>
#[doc(hidden)]
pub data_size_in_bytes: std::option::Option<i64>,
/// <p>The number of data files referenced by the <code>DataSource</code>.</p>
#[doc(hidden)]
pub number_of_files: std::option::Option<i64>,
/// <p>A user-supplied name or description of the <code>DataSource</code>.</p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>The current status of the <code>DataSource</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon ML submitted a request to create a <code>DataSource</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to create a <code>DataSource</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The creation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>DataSource</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
#[doc(hidden)]
pub status: std::option::Option<crate::model::EntityStatus>,
/// <p>A link to the file containing logs of <code>CreateDataSourceFrom*</code> operations.</p>
#[doc(hidden)]
pub log_uri: std::option::Option<std::string::String>,
/// <p>The user-supplied description of the most recent details about creating the <code>DataSource</code>.</p>
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>Describes the <code>DataSource</code> details specific to Amazon Redshift.</p>
#[doc(hidden)]
pub redshift_metadata: std::option::Option<crate::model::RedshiftMetadata>,
/// <p>The datasource details that are specific to Amazon RDS.</p>
#[doc(hidden)]
pub rds_metadata: std::option::Option<crate::model::RdsMetadata>,
/// <p>The Amazon Resource Name (ARN) of an <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts">AWS IAM Role</a>, such as the following: arn:aws:iam::account:role/rolename. </p>
#[doc(hidden)]
pub role_arn: std::option::Option<std::string::String>,
/// <p> The parameter is <code>true</code> if statistics need to be generated from the observation data. </p>
#[doc(hidden)]
pub compute_statistics: bool,
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>DataSource</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>DataSource</code> is in the <code>COMPLETED</code> state and the <code>ComputeStatistics</code> is set to true.</p>
#[doc(hidden)]
pub compute_time: std::option::Option<i64>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>DataSource</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
#[doc(hidden)]
pub finished_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>DataSource</code> is in the <code>PENDING</code> state.</p>
#[doc(hidden)]
pub started_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The schema used by all of the data files of this <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
#[doc(hidden)]
pub data_source_schema: std::option::Option<std::string::String>,
}
impl GetDataSourceOutput {
/// <p>The ID assigned to the <code>DataSource</code> at creation. This value should be identical to the value of the <code>DataSourceId</code> in the request.</p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn data_location_s3(&self) -> std::option::Option<&str> {
self.data_location_s3.as_deref()
}
/// <p>A JSON string that represents the splitting and rearrangement requirement used when this <code>DataSource</code> was created.</p>
pub fn data_rearrangement(&self) -> std::option::Option<&str> {
self.data_rearrangement.as_deref()
}
/// <p>The AWS user account from which the <code>DataSource</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(&self) -> std::option::Option<&str> {
self.created_by_iam_user.as_deref()
}
/// <p>The time that the <code>DataSource</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.created_at.as_ref()
}
/// <p>The time of the most recent edit to the <code>DataSource</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.last_updated_at.as_ref()
}
/// <p>The total size of observations in the data files.</p>
pub fn data_size_in_bytes(&self) -> std::option::Option<i64> {
self.data_size_in_bytes
}
/// <p>The number of data files referenced by the <code>DataSource</code>.</p>
pub fn number_of_files(&self) -> std::option::Option<i64> {
self.number_of_files
}
/// <p>A user-supplied name or description of the <code>DataSource</code>.</p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The current status of the <code>DataSource</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon ML submitted a request to create a <code>DataSource</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to create a <code>DataSource</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The creation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>DataSource</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(&self) -> std::option::Option<&crate::model::EntityStatus> {
self.status.as_ref()
}
/// <p>A link to the file containing logs of <code>CreateDataSourceFrom*</code> operations.</p>
pub fn log_uri(&self) -> std::option::Option<&str> {
self.log_uri.as_deref()
}
/// <p>The user-supplied description of the most recent details about creating the <code>DataSource</code>.</p>
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>Describes the <code>DataSource</code> details specific to Amazon Redshift.</p>
pub fn redshift_metadata(&self) -> std::option::Option<&crate::model::RedshiftMetadata> {
self.redshift_metadata.as_ref()
}
/// <p>The datasource details that are specific to Amazon RDS.</p>
pub fn rds_metadata(&self) -> std::option::Option<&crate::model::RdsMetadata> {
self.rds_metadata.as_ref()
}
/// <p>The Amazon Resource Name (ARN) of an <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts">AWS IAM Role</a>, such as the following: arn:aws:iam::account:role/rolename. </p>
pub fn role_arn(&self) -> std::option::Option<&str> {
self.role_arn.as_deref()
}
/// <p> The parameter is <code>true</code> if statistics need to be generated from the observation data. </p>
pub fn compute_statistics(&self) -> bool {
self.compute_statistics
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>DataSource</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>DataSource</code> is in the <code>COMPLETED</code> state and the <code>ComputeStatistics</code> is set to true.</p>
pub fn compute_time(&self) -> std::option::Option<i64> {
self.compute_time
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>DataSource</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.finished_at.as_ref()
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>DataSource</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.started_at.as_ref()
}
/// <p>The schema used by all of the data files of this <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn data_source_schema(&self) -> std::option::Option<&str> {
self.data_source_schema.as_deref()
}
}
/// See [`GetDataSourceOutput`](crate::output::GetDataSourceOutput).
pub mod get_data_source_output {
/// A builder for [`GetDataSourceOutput`](crate::output::GetDataSourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
pub(crate) data_location_s3: std::option::Option<std::string::String>,
pub(crate) data_rearrangement: std::option::Option<std::string::String>,
pub(crate) created_by_iam_user: std::option::Option<std::string::String>,
pub(crate) created_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) data_size_in_bytes: std::option::Option<i64>,
pub(crate) number_of_files: std::option::Option<i64>,
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) status: std::option::Option<crate::model::EntityStatus>,
pub(crate) log_uri: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) redshift_metadata: std::option::Option<crate::model::RedshiftMetadata>,
pub(crate) rds_metadata: std::option::Option<crate::model::RdsMetadata>,
pub(crate) role_arn: std::option::Option<std::string::String>,
pub(crate) compute_statistics: std::option::Option<bool>,
pub(crate) compute_time: std::option::Option<i64>,
pub(crate) finished_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) started_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) data_source_schema: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The ID assigned to the <code>DataSource</code> at creation. This value should be identical to the value of the <code>DataSourceId</code> in the request.</p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>The ID assigned to the <code>DataSource</code> at creation. This value should be identical to the value of the <code>DataSourceId</code> in the request.</p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn data_location_s3(mut self, input: impl Into<std::string::String>) -> Self {
self.data_location_s3 = Some(input.into());
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn set_data_location_s3(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_location_s3 = input;
self
}
/// <p>A JSON string that represents the splitting and rearrangement requirement used when this <code>DataSource</code> was created.</p>
pub fn data_rearrangement(mut self, input: impl Into<std::string::String>) -> Self {
self.data_rearrangement = Some(input.into());
self
}
/// <p>A JSON string that represents the splitting and rearrangement requirement used when this <code>DataSource</code> was created.</p>
pub fn set_data_rearrangement(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_rearrangement = input;
self
}
/// <p>The AWS user account from which the <code>DataSource</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(mut self, input: impl Into<std::string::String>) -> Self {
self.created_by_iam_user = Some(input.into());
self
}
/// <p>The AWS user account from which the <code>DataSource</code> was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn set_created_by_iam_user(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.created_by_iam_user = input;
self
}
/// <p>The time that the <code>DataSource</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.created_at = Some(input);
self
}
/// <p>The time that the <code>DataSource</code> was created. The time is expressed in epoch time.</p>
pub fn set_created_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.created_at = input;
self
}
/// <p>The time of the most recent edit to the <code>DataSource</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.last_updated_at = Some(input);
self
}
/// <p>The time of the most recent edit to the <code>DataSource</code>. The time is expressed in epoch time.</p>
pub fn set_last_updated_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.last_updated_at = input;
self
}
/// <p>The total size of observations in the data files.</p>
pub fn data_size_in_bytes(mut self, input: i64) -> Self {
self.data_size_in_bytes = Some(input);
self
}
/// <p>The total size of observations in the data files.</p>
pub fn set_data_size_in_bytes(mut self, input: std::option::Option<i64>) -> Self {
self.data_size_in_bytes = input;
self
}
/// <p>The number of data files referenced by the <code>DataSource</code>.</p>
pub fn number_of_files(mut self, input: i64) -> Self {
self.number_of_files = Some(input);
self
}
/// <p>The number of data files referenced by the <code>DataSource</code>.</p>
pub fn set_number_of_files(mut self, input: std::option::Option<i64>) -> Self {
self.number_of_files = input;
self
}
/// <p>A user-supplied name or description of the <code>DataSource</code>.</p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>A user-supplied name or description of the <code>DataSource</code>.</p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>The current status of the <code>DataSource</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon ML submitted a request to create a <code>DataSource</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to create a <code>DataSource</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The creation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>DataSource</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(mut self, input: crate::model::EntityStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The current status of the <code>DataSource</code>. This element can have one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon ML submitted a request to create a <code>DataSource</code>.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The creation process is underway.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to create a <code>DataSource</code> did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The creation process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>DataSource</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::EntityStatus>,
) -> Self {
self.status = input;
self
}
/// <p>A link to the file containing logs of <code>CreateDataSourceFrom*</code> operations.</p>
pub fn log_uri(mut self, input: impl Into<std::string::String>) -> Self {
self.log_uri = Some(input.into());
self
}
/// <p>A link to the file containing logs of <code>CreateDataSourceFrom*</code> operations.</p>
pub fn set_log_uri(mut self, input: std::option::Option<std::string::String>) -> Self {
self.log_uri = input;
self
}
/// <p>The user-supplied description of the most recent details about creating the <code>DataSource</code>.</p>
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>The user-supplied description of the most recent details about creating the <code>DataSource</code>.</p>
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>Describes the <code>DataSource</code> details specific to Amazon Redshift.</p>
pub fn redshift_metadata(mut self, input: crate::model::RedshiftMetadata) -> Self {
self.redshift_metadata = Some(input);
self
}
/// <p>Describes the <code>DataSource</code> details specific to Amazon Redshift.</p>
pub fn set_redshift_metadata(
mut self,
input: std::option::Option<crate::model::RedshiftMetadata>,
) -> Self {
self.redshift_metadata = input;
self
}
/// <p>The datasource details that are specific to Amazon RDS.</p>
pub fn rds_metadata(mut self, input: crate::model::RdsMetadata) -> Self {
self.rds_metadata = Some(input);
self
}
/// <p>The datasource details that are specific to Amazon RDS.</p>
pub fn set_rds_metadata(
mut self,
input: std::option::Option<crate::model::RdsMetadata>,
) -> Self {
self.rds_metadata = input;
self
}
/// <p>The Amazon Resource Name (ARN) of an <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts">AWS IAM Role</a>, such as the following: arn:aws:iam::account:role/rolename. </p>
pub fn role_arn(mut self, input: impl Into<std::string::String>) -> Self {
self.role_arn = Some(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of an <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-toplevel.html#roles-about-termsandconcepts">AWS IAM Role</a>, such as the following: arn:aws:iam::account:role/rolename. </p>
pub fn set_role_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
self.role_arn = input;
self
}
/// <p> The parameter is <code>true</code> if statistics need to be generated from the observation data. </p>
pub fn compute_statistics(mut self, input: bool) -> Self {
self.compute_statistics = Some(input);
self
}
/// <p> The parameter is <code>true</code> if statistics need to be generated from the observation data. </p>
pub fn set_compute_statistics(mut self, input: std::option::Option<bool>) -> Self {
self.compute_statistics = input;
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>DataSource</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>DataSource</code> is in the <code>COMPLETED</code> state and the <code>ComputeStatistics</code> is set to true.</p>
pub fn compute_time(mut self, input: i64) -> Self {
self.compute_time = Some(input);
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>DataSource</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>DataSource</code> is in the <code>COMPLETED</code> state and the <code>ComputeStatistics</code> is set to true.</p>
pub fn set_compute_time(mut self, input: std::option::Option<i64>) -> Self {
self.compute_time = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>DataSource</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.finished_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>DataSource</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn set_finished_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.finished_at = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>DataSource</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.started_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>DataSource</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>DataSource</code> is in the <code>PENDING</code> state.</p>
pub fn set_started_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.started_at = input;
self
}
/// <p>The schema used by all of the data files of this <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn data_source_schema(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_schema = Some(input.into());
self
}
/// <p>The schema used by all of the data files of this <code>DataSource</code>.</p>
/// <p> <b>Note:</b> This parameter is provided as part of the verbose format.</p>
pub fn set_data_source_schema(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_schema = input;
self
}
/// Consumes the builder and constructs a [`GetDataSourceOutput`](crate::output::GetDataSourceOutput).
pub fn build(self) -> crate::output::GetDataSourceOutput {
crate::output::GetDataSourceOutput {
data_source_id: self.data_source_id,
data_location_s3: self.data_location_s3,
data_rearrangement: self.data_rearrangement,
created_by_iam_user: self.created_by_iam_user,
created_at: self.created_at,
last_updated_at: self.last_updated_at,
data_size_in_bytes: self.data_size_in_bytes,
number_of_files: self.number_of_files,
name: self.name,
status: self.status,
log_uri: self.log_uri,
message: self.message,
redshift_metadata: self.redshift_metadata,
rds_metadata: self.rds_metadata,
role_arn: self.role_arn,
compute_statistics: self.compute_statistics.unwrap_or_default(),
compute_time: self.compute_time,
finished_at: self.finished_at,
started_at: self.started_at,
data_source_schema: self.data_source_schema,
}
}
}
}
impl GetDataSourceOutput {
/// Creates a new builder-style object to manufacture [`GetDataSourceOutput`](crate::output::GetDataSourceOutput).
pub fn builder() -> crate::output::get_data_source_output::Builder {
crate::output::get_data_source_output::Builder::default()
}
}
/// <p>Represents the output of a <code>GetBatchPrediction</code> operation and describes a <code>BatchPrediction</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct GetBatchPredictionOutput {
/// <p>An ID assigned to the <code>BatchPrediction</code> at creation. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
#[doc(hidden)]
pub batch_prediction_id: std::option::Option<std::string::String>,
/// <p>The ID of the <code>MLModel</code> that generated predictions for the <code>BatchPrediction</code> request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
/// <p>The ID of the <code>DataSource</code> that was used to create the <code>BatchPrediction</code>. </p>
#[doc(hidden)]
pub batch_prediction_data_source_id: std::option::Option<std::string::String>,
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
#[doc(hidden)]
pub input_data_location_s3: std::option::Option<std::string::String>,
/// <p>The AWS user account that invoked the <code>BatchPrediction</code>. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
#[doc(hidden)]
pub created_by_iam_user: std::option::Option<std::string::String>,
/// <p>The time when the <code>BatchPrediction</code> was created. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub created_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The time of the most recent edit to <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
#[doc(hidden)]
pub last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>A user-supplied name or description of the <code>BatchPrediction</code>.</p>
#[doc(hidden)]
pub name: std::option::Option<std::string::String>,
/// <p>The status of the <code>BatchPrediction</code>, which can be one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The batch predictions are in progress.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to perform a batch prediction did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The batch prediction process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>BatchPrediction</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
#[doc(hidden)]
pub status: std::option::Option<crate::model::EntityStatus>,
/// <p>The location of an Amazon S3 bucket or directory to receive the operation results.</p>
#[doc(hidden)]
pub output_uri: std::option::Option<std::string::String>,
/// <p>A link to the file that contains logs of the <code>CreateBatchPrediction</code> operation.</p>
#[doc(hidden)]
pub log_uri: std::option::Option<std::string::String>,
/// <p>A description of the most recent details about processing the batch prediction request.</p>
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>BatchPrediction</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>BatchPrediction</code> is in the <code>COMPLETED</code> state.</p>
#[doc(hidden)]
pub compute_time: std::option::Option<i64>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>BatchPrediction</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
#[doc(hidden)]
pub finished_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>BatchPrediction</code> is in the <code>PENDING</code> state.</p>
#[doc(hidden)]
pub started_at: std::option::Option<aws_smithy_types::DateTime>,
/// <p>The number of total records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
#[doc(hidden)]
pub total_record_count: std::option::Option<i64>,
/// <p>The number of invalid records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
#[doc(hidden)]
pub invalid_record_count: std::option::Option<i64>,
}
impl GetBatchPredictionOutput {
/// <p>An ID assigned to the <code>BatchPrediction</code> at creation. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn batch_prediction_id(&self) -> std::option::Option<&str> {
self.batch_prediction_id.as_deref()
}
/// <p>The ID of the <code>MLModel</code> that generated predictions for the <code>BatchPrediction</code> request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>The ID of the <code>DataSource</code> that was used to create the <code>BatchPrediction</code>. </p>
pub fn batch_prediction_data_source_id(&self) -> std::option::Option<&str> {
self.batch_prediction_data_source_id.as_deref()
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(&self) -> std::option::Option<&str> {
self.input_data_location_s3.as_deref()
}
/// <p>The AWS user account that invoked the <code>BatchPrediction</code>. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(&self) -> std::option::Option<&str> {
self.created_by_iam_user.as_deref()
}
/// <p>The time when the <code>BatchPrediction</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.created_at.as_ref()
}
/// <p>The time of the most recent edit to <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.last_updated_at.as_ref()
}
/// <p>A user-supplied name or description of the <code>BatchPrediction</code>.</p>
pub fn name(&self) -> std::option::Option<&str> {
self.name.as_deref()
}
/// <p>The status of the <code>BatchPrediction</code>, which can be one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The batch predictions are in progress.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to perform a batch prediction did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The batch prediction process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>BatchPrediction</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(&self) -> std::option::Option<&crate::model::EntityStatus> {
self.status.as_ref()
}
/// <p>The location of an Amazon S3 bucket or directory to receive the operation results.</p>
pub fn output_uri(&self) -> std::option::Option<&str> {
self.output_uri.as_deref()
}
/// <p>A link to the file that contains logs of the <code>CreateBatchPrediction</code> operation.</p>
pub fn log_uri(&self) -> std::option::Option<&str> {
self.log_uri.as_deref()
}
/// <p>A description of the most recent details about processing the batch prediction request.</p>
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>BatchPrediction</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>BatchPrediction</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(&self) -> std::option::Option<i64> {
self.compute_time
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>BatchPrediction</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.finished_at.as_ref()
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>BatchPrediction</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(&self) -> std::option::Option<&aws_smithy_types::DateTime> {
self.started_at.as_ref()
}
/// <p>The number of total records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn total_record_count(&self) -> std::option::Option<i64> {
self.total_record_count
}
/// <p>The number of invalid records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn invalid_record_count(&self) -> std::option::Option<i64> {
self.invalid_record_count
}
}
/// See [`GetBatchPredictionOutput`](crate::output::GetBatchPredictionOutput).
pub mod get_batch_prediction_output {
/// A builder for [`GetBatchPredictionOutput`](crate::output::GetBatchPredictionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) batch_prediction_id: std::option::Option<std::string::String>,
pub(crate) ml_model_id: std::option::Option<std::string::String>,
pub(crate) batch_prediction_data_source_id: std::option::Option<std::string::String>,
pub(crate) input_data_location_s3: std::option::Option<std::string::String>,
pub(crate) created_by_iam_user: std::option::Option<std::string::String>,
pub(crate) created_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) last_updated_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) name: std::option::Option<std::string::String>,
pub(crate) status: std::option::Option<crate::model::EntityStatus>,
pub(crate) output_uri: std::option::Option<std::string::String>,
pub(crate) log_uri: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) compute_time: std::option::Option<i64>,
pub(crate) finished_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) started_at: std::option::Option<aws_smithy_types::DateTime>,
pub(crate) total_record_count: std::option::Option<i64>,
pub(crate) invalid_record_count: std::option::Option<i64>,
}
impl Builder {
/// <p>An ID assigned to the <code>BatchPrediction</code> at creation. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn batch_prediction_id(mut self, input: impl Into<std::string::String>) -> Self {
self.batch_prediction_id = Some(input.into());
self
}
/// <p>An ID assigned to the <code>BatchPrediction</code> at creation. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn set_batch_prediction_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.batch_prediction_id = input;
self
}
/// <p>The ID of the <code>MLModel</code> that generated predictions for the <code>BatchPrediction</code> request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>The ID of the <code>MLModel</code> that generated predictions for the <code>BatchPrediction</code> request.</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 of the <code>DataSource</code> that was used to create the <code>BatchPrediction</code>. </p>
pub fn batch_prediction_data_source_id(
mut self,
input: impl Into<std::string::String>,
) -> Self {
self.batch_prediction_data_source_id = Some(input.into());
self
}
/// <p>The ID of the <code>DataSource</code> that was used to create the <code>BatchPrediction</code>. </p>
pub fn set_batch_prediction_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.batch_prediction_data_source_id = input;
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn input_data_location_s3(mut self, input: impl Into<std::string::String>) -> Self {
self.input_data_location_s3 = Some(input.into());
self
}
/// <p>The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).</p>
pub fn set_input_data_location_s3(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.input_data_location_s3 = input;
self
}
/// <p>The AWS user account that invoked the <code>BatchPrediction</code>. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn created_by_iam_user(mut self, input: impl Into<std::string::String>) -> Self {
self.created_by_iam_user = Some(input.into());
self
}
/// <p>The AWS user account that invoked the <code>BatchPrediction</code>. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>
pub fn set_created_by_iam_user(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.created_by_iam_user = input;
self
}
/// <p>The time when the <code>BatchPrediction</code> was created. The time is expressed in epoch time.</p>
pub fn created_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.created_at = Some(input);
self
}
/// <p>The time when the <code>BatchPrediction</code> was created. The time is expressed in epoch time.</p>
pub fn set_created_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.created_at = input;
self
}
/// <p>The time of the most recent edit to <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
pub fn last_updated_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.last_updated_at = Some(input);
self
}
/// <p>The time of the most recent edit to <code>BatchPrediction</code>. The time is expressed in epoch time.</p>
pub fn set_last_updated_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.last_updated_at = input;
self
}
/// <p>A user-supplied name or description of the <code>BatchPrediction</code>.</p>
pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
self.name = Some(input.into());
self
}
/// <p>A user-supplied name or description of the <code>BatchPrediction</code>.</p>
pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.name = input;
self
}
/// <p>The status of the <code>BatchPrediction</code>, which can be one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The batch predictions are in progress.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to perform a batch prediction did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The batch prediction process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>BatchPrediction</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn status(mut self, input: crate::model::EntityStatus) -> Self {
self.status = Some(input);
self
}
/// <p>The status of the <code>BatchPrediction</code>, which can be one of the following values:</p>
/// <ul>
/// <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.</p> </li>
/// <li> <p> <code>INPROGRESS</code> - The batch predictions are in progress.</p> </li>
/// <li> <p> <code>FAILED</code> - The request to perform a batch prediction did not run to completion. It is not usable.</p> </li>
/// <li> <p> <code>COMPLETED</code> - The batch prediction process completed successfully.</p> </li>
/// <li> <p> <code>DELETED</code> - The <code>BatchPrediction</code> is marked as deleted. It is not usable.</p> </li>
/// </ul>
pub fn set_status(
mut self,
input: std::option::Option<crate::model::EntityStatus>,
) -> Self {
self.status = input;
self
}
/// <p>The location of an Amazon S3 bucket or directory to receive the operation results.</p>
pub fn output_uri(mut self, input: impl Into<std::string::String>) -> Self {
self.output_uri = Some(input.into());
self
}
/// <p>The location of an Amazon S3 bucket or directory to receive the operation results.</p>
pub fn set_output_uri(mut self, input: std::option::Option<std::string::String>) -> Self {
self.output_uri = input;
self
}
/// <p>A link to the file that contains logs of the <code>CreateBatchPrediction</code> operation.</p>
pub fn log_uri(mut self, input: impl Into<std::string::String>) -> Self {
self.log_uri = Some(input.into());
self
}
/// <p>A link to the file that contains logs of the <code>CreateBatchPrediction</code> operation.</p>
pub fn set_log_uri(mut self, input: std::option::Option<std::string::String>) -> Self {
self.log_uri = input;
self
}
/// <p>A description of the most recent details about processing the batch prediction request.</p>
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>A description of the most recent details about processing the batch prediction request.</p>
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>BatchPrediction</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>BatchPrediction</code> is in the <code>COMPLETED</code> state.</p>
pub fn compute_time(mut self, input: i64) -> Self {
self.compute_time = Some(input);
self
}
/// <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the <code>BatchPrediction</code>, normalized and scaled on computation resources. <code>ComputeTime</code> is only available if the <code>BatchPrediction</code> is in the <code>COMPLETED</code> state.</p>
pub fn set_compute_time(mut self, input: std::option::Option<i64>) -> Self {
self.compute_time = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>BatchPrediction</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn finished_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.finished_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is only available when the <code>BatchPrediction</code> is in the <code>COMPLETED</code> or <code>FAILED</code> state.</p>
pub fn set_finished_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.finished_at = input;
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>BatchPrediction</code> is in the <code>PENDING</code> state.</p>
pub fn started_at(mut self, input: aws_smithy_types::DateTime) -> Self {
self.started_at = Some(input);
self
}
/// <p>The epoch time when Amazon Machine Learning marked the <code>BatchPrediction</code> as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the <code>BatchPrediction</code> is in the <code>PENDING</code> state.</p>
pub fn set_started_at(
mut self,
input: std::option::Option<aws_smithy_types::DateTime>,
) -> Self {
self.started_at = input;
self
}
/// <p>The number of total records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn total_record_count(mut self, input: i64) -> Self {
self.total_record_count = Some(input);
self
}
/// <p>The number of total records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn set_total_record_count(mut self, input: std::option::Option<i64>) -> Self {
self.total_record_count = input;
self
}
/// <p>The number of invalid records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn invalid_record_count(mut self, input: i64) -> Self {
self.invalid_record_count = Some(input);
self
}
/// <p>The number of invalid records that Amazon Machine Learning saw while processing the <code>BatchPrediction</code>.</p>
pub fn set_invalid_record_count(mut self, input: std::option::Option<i64>) -> Self {
self.invalid_record_count = input;
self
}
/// Consumes the builder and constructs a [`GetBatchPredictionOutput`](crate::output::GetBatchPredictionOutput).
pub fn build(self) -> crate::output::GetBatchPredictionOutput {
crate::output::GetBatchPredictionOutput {
batch_prediction_id: self.batch_prediction_id,
ml_model_id: self.ml_model_id,
batch_prediction_data_source_id: self.batch_prediction_data_source_id,
input_data_location_s3: self.input_data_location_s3,
created_by_iam_user: self.created_by_iam_user,
created_at: self.created_at,
last_updated_at: self.last_updated_at,
name: self.name,
status: self.status,
output_uri: self.output_uri,
log_uri: self.log_uri,
message: self.message,
compute_time: self.compute_time,
finished_at: self.finished_at,
started_at: self.started_at,
total_record_count: self.total_record_count,
invalid_record_count: self.invalid_record_count,
}
}
}
}
impl GetBatchPredictionOutput {
/// Creates a new builder-style object to manufacture [`GetBatchPredictionOutput`](crate::output::GetBatchPredictionOutput).
pub fn builder() -> crate::output::get_batch_prediction_output::Builder {
crate::output::get_batch_prediction_output::Builder::default()
}
}
/// <p>Amazon ML returns the following elements.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeTagsOutput {
/// <p>The ID of the tagged ML object.</p>
#[doc(hidden)]
pub resource_id: std::option::Option<std::string::String>,
/// <p>The type of the tagged ML object.</p>
#[doc(hidden)]
pub resource_type: std::option::Option<crate::model::TaggableResourceType>,
/// <p>A list of tags associated with the ML object.</p>
#[doc(hidden)]
pub tags: std::option::Option<std::vec::Vec<crate::model::Tag>>,
}
impl DescribeTagsOutput {
/// <p>The ID of the tagged ML object.</p>
pub fn resource_id(&self) -> std::option::Option<&str> {
self.resource_id.as_deref()
}
/// <p>The type of the tagged ML object.</p>
pub fn resource_type(&self) -> std::option::Option<&crate::model::TaggableResourceType> {
self.resource_type.as_ref()
}
/// <p>A list of tags associated with the ML object.</p>
pub fn tags(&self) -> std::option::Option<&[crate::model::Tag]> {
self.tags.as_deref()
}
}
/// See [`DescribeTagsOutput`](crate::output::DescribeTagsOutput).
pub mod describe_tags_output {
/// A builder for [`DescribeTagsOutput`](crate::output::DescribeTagsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) resource_id: std::option::Option<std::string::String>,
pub(crate) resource_type: std::option::Option<crate::model::TaggableResourceType>,
pub(crate) tags: std::option::Option<std::vec::Vec<crate::model::Tag>>,
}
impl Builder {
/// <p>The ID of the tagged ML object.</p>
pub fn resource_id(mut self, input: impl Into<std::string::String>) -> Self {
self.resource_id = Some(input.into());
self
}
/// <p>The ID of the tagged ML object.</p>
pub fn set_resource_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.resource_id = input;
self
}
/// <p>The type of the tagged ML object.</p>
pub fn resource_type(mut self, input: crate::model::TaggableResourceType) -> Self {
self.resource_type = Some(input);
self
}
/// <p>The type of the tagged ML object.</p>
pub fn set_resource_type(
mut self,
input: std::option::Option<crate::model::TaggableResourceType>,
) -> Self {
self.resource_type = input;
self
}
/// Appends an item to `tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p>A list of tags associated with the ML object.</p>
pub fn tags(mut self, input: crate::model::Tag) -> Self {
let mut v = self.tags.unwrap_or_default();
v.push(input);
self.tags = Some(v);
self
}
/// <p>A list of tags associated with the ML object.</p>
pub fn set_tags(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Tag>>,
) -> Self {
self.tags = input;
self
}
/// Consumes the builder and constructs a [`DescribeTagsOutput`](crate::output::DescribeTagsOutput).
pub fn build(self) -> crate::output::DescribeTagsOutput {
crate::output::DescribeTagsOutput {
resource_id: self.resource_id,
resource_type: self.resource_type,
tags: self.tags,
}
}
}
}
impl DescribeTagsOutput {
/// Creates a new builder-style object to manufacture [`DescribeTagsOutput`](crate::output::DescribeTagsOutput).
pub fn builder() -> crate::output::describe_tags_output::Builder {
crate::output::describe_tags_output::Builder::default()
}
}
/// <p>Represents the output of a <code>DescribeMLModels</code> operation. The content is essentially a list of <code>MLModel</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeMlModelsOutput {
/// <p>A list of <code>MLModel</code> that meet the search criteria.</p>
#[doc(hidden)]
pub results: std::option::Option<std::vec::Vec<crate::model::MlModel>>,
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeMlModelsOutput {
/// <p>A list of <code>MLModel</code> that meet the search criteria.</p>
pub fn results(&self) -> std::option::Option<&[crate::model::MlModel]> {
self.results.as_deref()
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeMlModelsOutput`](crate::output::DescribeMlModelsOutput).
pub mod describe_ml_models_output {
/// A builder for [`DescribeMlModelsOutput`](crate::output::DescribeMlModelsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) results: std::option::Option<std::vec::Vec<crate::model::MlModel>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `results`.
///
/// To override the contents of this collection use [`set_results`](Self::set_results).
///
/// <p>A list of <code>MLModel</code> that meet the search criteria.</p>
pub fn results(mut self, input: crate::model::MlModel) -> Self {
let mut v = self.results.unwrap_or_default();
v.push(input);
self.results = Some(v);
self
}
/// <p>A list of <code>MLModel</code> that meet the search criteria.</p>
pub fn set_results(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::MlModel>>,
) -> Self {
self.results = input;
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeMlModelsOutput`](crate::output::DescribeMlModelsOutput).
pub fn build(self) -> crate::output::DescribeMlModelsOutput {
crate::output::DescribeMlModelsOutput {
results: self.results,
next_token: self.next_token,
}
}
}
}
impl DescribeMlModelsOutput {
/// Creates a new builder-style object to manufacture [`DescribeMlModelsOutput`](crate::output::DescribeMlModelsOutput).
pub fn builder() -> crate::output::describe_ml_models_output::Builder {
crate::output::describe_ml_models_output::Builder::default()
}
}
/// <p>Represents the query results from a <code>DescribeEvaluations</code> operation. The content is essentially a list of <code>Evaluation</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeEvaluationsOutput {
/// <p>A list of <code>Evaluation</code> that meet the search criteria. </p>
#[doc(hidden)]
pub results: std::option::Option<std::vec::Vec<crate::model::Evaluation>>,
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeEvaluationsOutput {
/// <p>A list of <code>Evaluation</code> that meet the search criteria. </p>
pub fn results(&self) -> std::option::Option<&[crate::model::Evaluation]> {
self.results.as_deref()
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeEvaluationsOutput`](crate::output::DescribeEvaluationsOutput).
pub mod describe_evaluations_output {
/// A builder for [`DescribeEvaluationsOutput`](crate::output::DescribeEvaluationsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) results: std::option::Option<std::vec::Vec<crate::model::Evaluation>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `results`.
///
/// To override the contents of this collection use [`set_results`](Self::set_results).
///
/// <p>A list of <code>Evaluation</code> that meet the search criteria. </p>
pub fn results(mut self, input: crate::model::Evaluation) -> Self {
let mut v = self.results.unwrap_or_default();
v.push(input);
self.results = Some(v);
self
}
/// <p>A list of <code>Evaluation</code> that meet the search criteria. </p>
pub fn set_results(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::Evaluation>>,
) -> Self {
self.results = input;
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeEvaluationsOutput`](crate::output::DescribeEvaluationsOutput).
pub fn build(self) -> crate::output::DescribeEvaluationsOutput {
crate::output::DescribeEvaluationsOutput {
results: self.results,
next_token: self.next_token,
}
}
}
}
impl DescribeEvaluationsOutput {
/// Creates a new builder-style object to manufacture [`DescribeEvaluationsOutput`](crate::output::DescribeEvaluationsOutput).
pub fn builder() -> crate::output::describe_evaluations_output::Builder {
crate::output::describe_evaluations_output::Builder::default()
}
}
/// <p>Represents the query results from a <code>DescribeDataSources</code> operation. The content is essentially a list of <code>DataSource</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeDataSourcesOutput {
/// <p>A list of <code>DataSource</code> that meet the search criteria. </p>
#[doc(hidden)]
pub results: std::option::Option<std::vec::Vec<crate::model::DataSource>>,
/// <p>An ID of the next page in the paginated results that indicates at least one more page follows.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeDataSourcesOutput {
/// <p>A list of <code>DataSource</code> that meet the search criteria. </p>
pub fn results(&self) -> std::option::Option<&[crate::model::DataSource]> {
self.results.as_deref()
}
/// <p>An ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeDataSourcesOutput`](crate::output::DescribeDataSourcesOutput).
pub mod describe_data_sources_output {
/// A builder for [`DescribeDataSourcesOutput`](crate::output::DescribeDataSourcesOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) results: std::option::Option<std::vec::Vec<crate::model::DataSource>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `results`.
///
/// To override the contents of this collection use [`set_results`](Self::set_results).
///
/// <p>A list of <code>DataSource</code> that meet the search criteria. </p>
pub fn results(mut self, input: crate::model::DataSource) -> Self {
let mut v = self.results.unwrap_or_default();
v.push(input);
self.results = Some(v);
self
}
/// <p>A list of <code>DataSource</code> that meet the search criteria. </p>
pub fn set_results(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::DataSource>>,
) -> Self {
self.results = input;
self
}
/// <p>An ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>An ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeDataSourcesOutput`](crate::output::DescribeDataSourcesOutput).
pub fn build(self) -> crate::output::DescribeDataSourcesOutput {
crate::output::DescribeDataSourcesOutput {
results: self.results,
next_token: self.next_token,
}
}
}
}
impl DescribeDataSourcesOutput {
/// Creates a new builder-style object to manufacture [`DescribeDataSourcesOutput`](crate::output::DescribeDataSourcesOutput).
pub fn builder() -> crate::output::describe_data_sources_output::Builder {
crate::output::describe_data_sources_output::Builder::default()
}
}
/// <p>Represents the output of a <code>DescribeBatchPredictions</code> operation. The content is essentially a list of <code>BatchPrediction</code>s.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DescribeBatchPredictionsOutput {
/// <p>A list of <code>BatchPrediction</code> objects that meet the search criteria. </p>
#[doc(hidden)]
pub results: std::option::Option<std::vec::Vec<crate::model::BatchPrediction>>,
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
#[doc(hidden)]
pub next_token: std::option::Option<std::string::String>,
}
impl DescribeBatchPredictionsOutput {
/// <p>A list of <code>BatchPrediction</code> objects that meet the search criteria. </p>
pub fn results(&self) -> std::option::Option<&[crate::model::BatchPrediction]> {
self.results.as_deref()
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(&self) -> std::option::Option<&str> {
self.next_token.as_deref()
}
}
/// See [`DescribeBatchPredictionsOutput`](crate::output::DescribeBatchPredictionsOutput).
pub mod describe_batch_predictions_output {
/// A builder for [`DescribeBatchPredictionsOutput`](crate::output::DescribeBatchPredictionsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) results: std::option::Option<std::vec::Vec<crate::model::BatchPrediction>>,
pub(crate) next_token: std::option::Option<std::string::String>,
}
impl Builder {
/// Appends an item to `results`.
///
/// To override the contents of this collection use [`set_results`](Self::set_results).
///
/// <p>A list of <code>BatchPrediction</code> objects that meet the search criteria. </p>
pub fn results(mut self, input: crate::model::BatchPrediction) -> Self {
let mut v = self.results.unwrap_or_default();
v.push(input);
self.results = Some(v);
self
}
/// <p>A list of <code>BatchPrediction</code> objects that meet the search criteria. </p>
pub fn set_results(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::BatchPrediction>>,
) -> Self {
self.results = input;
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn next_token(mut self, input: impl Into<std::string::String>) -> Self {
self.next_token = Some(input.into());
self
}
/// <p>The ID of the next page in the paginated results that indicates at least one more page follows.</p>
pub fn set_next_token(mut self, input: std::option::Option<std::string::String>) -> Self {
self.next_token = input;
self
}
/// Consumes the builder and constructs a [`DescribeBatchPredictionsOutput`](crate::output::DescribeBatchPredictionsOutput).
pub fn build(self) -> crate::output::DescribeBatchPredictionsOutput {
crate::output::DescribeBatchPredictionsOutput {
results: self.results,
next_token: self.next_token,
}
}
}
}
impl DescribeBatchPredictionsOutput {
/// Creates a new builder-style object to manufacture [`DescribeBatchPredictionsOutput`](crate::output::DescribeBatchPredictionsOutput).
pub fn builder() -> crate::output::describe_batch_predictions_output::Builder {
crate::output::describe_batch_predictions_output::Builder::default()
}
}
/// <p>Amazon ML returns the following elements.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteTagsOutput {
/// <p>The ID of the ML object from which tags were deleted.</p>
#[doc(hidden)]
pub resource_id: std::option::Option<std::string::String>,
/// <p>The type of the ML object from which tags were deleted.</p>
#[doc(hidden)]
pub resource_type: std::option::Option<crate::model::TaggableResourceType>,
}
impl DeleteTagsOutput {
/// <p>The ID of the ML object from which tags were deleted.</p>
pub fn resource_id(&self) -> std::option::Option<&str> {
self.resource_id.as_deref()
}
/// <p>The type of the ML object from which tags were deleted.</p>
pub fn resource_type(&self) -> std::option::Option<&crate::model::TaggableResourceType> {
self.resource_type.as_ref()
}
}
/// See [`DeleteTagsOutput`](crate::output::DeleteTagsOutput).
pub mod delete_tags_output {
/// A builder for [`DeleteTagsOutput`](crate::output::DeleteTagsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) resource_id: std::option::Option<std::string::String>,
pub(crate) resource_type: std::option::Option<crate::model::TaggableResourceType>,
}
impl Builder {
/// <p>The ID of the ML object from which tags were deleted.</p>
pub fn resource_id(mut self, input: impl Into<std::string::String>) -> Self {
self.resource_id = Some(input.into());
self
}
/// <p>The ID of the ML object from which tags were deleted.</p>
pub fn set_resource_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.resource_id = input;
self
}
/// <p>The type of the ML object from which tags were deleted.</p>
pub fn resource_type(mut self, input: crate::model::TaggableResourceType) -> Self {
self.resource_type = Some(input);
self
}
/// <p>The type of the ML object from which tags were deleted.</p>
pub fn set_resource_type(
mut self,
input: std::option::Option<crate::model::TaggableResourceType>,
) -> Self {
self.resource_type = input;
self
}
/// Consumes the builder and constructs a [`DeleteTagsOutput`](crate::output::DeleteTagsOutput).
pub fn build(self) -> crate::output::DeleteTagsOutput {
crate::output::DeleteTagsOutput {
resource_id: self.resource_id,
resource_type: self.resource_type,
}
}
}
}
impl DeleteTagsOutput {
/// Creates a new builder-style object to manufacture [`DeleteTagsOutput`](crate::output::DeleteTagsOutput).
pub fn builder() -> crate::output::delete_tags_output::Builder {
crate::output::delete_tags_output::Builder::default()
}
}
/// <p>Represents the output of an <code>DeleteRealtimeEndpoint</code> operation.</p>
/// <p>The result contains the <code>MLModelId</code> and the endpoint information for the <code>MLModel</code>. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteRealtimeEndpointOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
/// <p>The endpoint information of the <code>MLModel</code> </p>
#[doc(hidden)]
pub realtime_endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
}
impl DeleteRealtimeEndpointOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>The endpoint information of the <code>MLModel</code> </p>
pub fn realtime_endpoint_info(
&self,
) -> std::option::Option<&crate::model::RealtimeEndpointInfo> {
self.realtime_endpoint_info.as_ref()
}
}
/// See [`DeleteRealtimeEndpointOutput`](crate::output::DeleteRealtimeEndpointOutput).
pub mod delete_realtime_endpoint_output {
/// A builder for [`DeleteRealtimeEndpointOutput`](crate::output::DeleteRealtimeEndpointOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
pub(crate) realtime_endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</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 endpoint information of the <code>MLModel</code> </p>
pub fn realtime_endpoint_info(mut self, input: crate::model::RealtimeEndpointInfo) -> Self {
self.realtime_endpoint_info = Some(input);
self
}
/// <p>The endpoint information of the <code>MLModel</code> </p>
pub fn set_realtime_endpoint_info(
mut self,
input: std::option::Option<crate::model::RealtimeEndpointInfo>,
) -> Self {
self.realtime_endpoint_info = input;
self
}
/// Consumes the builder and constructs a [`DeleteRealtimeEndpointOutput`](crate::output::DeleteRealtimeEndpointOutput).
pub fn build(self) -> crate::output::DeleteRealtimeEndpointOutput {
crate::output::DeleteRealtimeEndpointOutput {
ml_model_id: self.ml_model_id,
realtime_endpoint_info: self.realtime_endpoint_info,
}
}
}
}
impl DeleteRealtimeEndpointOutput {
/// Creates a new builder-style object to manufacture [`DeleteRealtimeEndpointOutput`](crate::output::DeleteRealtimeEndpointOutput).
pub fn builder() -> crate::output::delete_realtime_endpoint_output::Builder {
crate::output::delete_realtime_endpoint_output::Builder::default()
}
}
/// <p>Represents the output of a <code>DeleteMLModel</code> operation.</p>
/// <p>You can use the <code>GetMLModel</code> operation and check the value of the <code>Status</code> parameter to see whether an <code>MLModel</code> is marked as <code>DELETED</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteMlModelOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
}
impl DeleteMlModelOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
}
/// See [`DeleteMlModelOutput`](crate::output::DeleteMlModelOutput).
pub mod delete_ml_model_output {
/// A builder for [`DeleteMlModelOutput`](crate::output::DeleteMlModelOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelID</code> in the request.</p>
pub fn set_ml_model_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.ml_model_id = input;
self
}
/// Consumes the builder and constructs a [`DeleteMlModelOutput`](crate::output::DeleteMlModelOutput).
pub fn build(self) -> crate::output::DeleteMlModelOutput {
crate::output::DeleteMlModelOutput {
ml_model_id: self.ml_model_id,
}
}
}
}
impl DeleteMlModelOutput {
/// Creates a new builder-style object to manufacture [`DeleteMlModelOutput`](crate::output::DeleteMlModelOutput).
pub fn builder() -> crate::output::delete_ml_model_output::Builder {
crate::output::delete_ml_model_output::Builder::default()
}
}
/// <p> Represents the output of a <code>DeleteEvaluation</code> operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.</p>
/// <p>You can use the <code>GetEvaluation</code> operation and check the value of the <code>Status</code> parameter to see whether an <code>Evaluation</code> is marked as <code>DELETED</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteEvaluationOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
#[doc(hidden)]
pub evaluation_id: std::option::Option<std::string::String>,
}
impl DeleteEvaluationOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(&self) -> std::option::Option<&str> {
self.evaluation_id.as_deref()
}
}
/// See [`DeleteEvaluationOutput`](crate::output::DeleteEvaluationOutput).
pub mod delete_evaluation_output {
/// A builder for [`DeleteEvaluationOutput`](crate::output::DeleteEvaluationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) evaluation_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(mut self, input: impl Into<std::string::String>) -> Self {
self.evaluation_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn set_evaluation_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.evaluation_id = input;
self
}
/// Consumes the builder and constructs a [`DeleteEvaluationOutput`](crate::output::DeleteEvaluationOutput).
pub fn build(self) -> crate::output::DeleteEvaluationOutput {
crate::output::DeleteEvaluationOutput {
evaluation_id: self.evaluation_id,
}
}
}
}
impl DeleteEvaluationOutput {
/// Creates a new builder-style object to manufacture [`DeleteEvaluationOutput`](crate::output::DeleteEvaluationOutput).
pub fn builder() -> crate::output::delete_evaluation_output::Builder {
crate::output::delete_evaluation_output::Builder::default()
}
}
/// <p> Represents the output of a <code>DeleteDataSource</code> operation.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteDataSourceOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
}
impl DeleteDataSourceOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
}
/// See [`DeleteDataSourceOutput`](crate::output::DeleteDataSourceOutput).
pub mod delete_data_source_output {
/// A builder for [`DeleteDataSourceOutput`](crate::output::DeleteDataSourceOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request.</p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// Consumes the builder and constructs a [`DeleteDataSourceOutput`](crate::output::DeleteDataSourceOutput).
pub fn build(self) -> crate::output::DeleteDataSourceOutput {
crate::output::DeleteDataSourceOutput {
data_source_id: self.data_source_id,
}
}
}
}
impl DeleteDataSourceOutput {
/// Creates a new builder-style object to manufacture [`DeleteDataSourceOutput`](crate::output::DeleteDataSourceOutput).
pub fn builder() -> crate::output::delete_data_source_output::Builder {
crate::output::delete_data_source_output::Builder::default()
}
}
/// <p> Represents the output of a <code>DeleteBatchPrediction</code> operation.</p>
/// <p>You can use the <code>GetBatchPrediction</code> operation and check the value of the <code>Status</code> parameter to see whether a <code>BatchPrediction</code> is marked as <code>DELETED</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteBatchPredictionOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
#[doc(hidden)]
pub batch_prediction_id: std::option::Option<std::string::String>,
}
impl DeleteBatchPredictionOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn batch_prediction_id(&self) -> std::option::Option<&str> {
self.batch_prediction_id.as_deref()
}
}
/// See [`DeleteBatchPredictionOutput`](crate::output::DeleteBatchPredictionOutput).
pub mod delete_batch_prediction_output {
/// A builder for [`DeleteBatchPredictionOutput`](crate::output::DeleteBatchPredictionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) batch_prediction_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn batch_prediction_id(mut self, input: impl Into<std::string::String>) -> Self {
self.batch_prediction_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value should be identical to the value of the <code>BatchPredictionID</code> in the request.</p>
pub fn set_batch_prediction_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.batch_prediction_id = input;
self
}
/// Consumes the builder and constructs a [`DeleteBatchPredictionOutput`](crate::output::DeleteBatchPredictionOutput).
pub fn build(self) -> crate::output::DeleteBatchPredictionOutput {
crate::output::DeleteBatchPredictionOutput {
batch_prediction_id: self.batch_prediction_id,
}
}
}
}
impl DeleteBatchPredictionOutput {
/// Creates a new builder-style object to manufacture [`DeleteBatchPredictionOutput`](crate::output::DeleteBatchPredictionOutput).
pub fn builder() -> crate::output::delete_batch_prediction_output::Builder {
crate::output::delete_batch_prediction_output::Builder::default()
}
}
/// <p>Represents the output of an <code>CreateRealtimeEndpoint</code> operation.</p>
/// <p>The result contains the <code>MLModelId</code> and the endpoint information for the <code>MLModel</code>.</p>
/// <p> <b>Note:</b> The endpoint information includes the URI of the <code>MLModel</code>; that is, the location to send online prediction requests for the specified <code>MLModel</code>.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateRealtimeEndpointOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
/// <p>The endpoint information of the <code>MLModel</code> </p>
#[doc(hidden)]
pub realtime_endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
}
impl CreateRealtimeEndpointOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
/// <p>The endpoint information of the <code>MLModel</code> </p>
pub fn realtime_endpoint_info(
&self,
) -> std::option::Option<&crate::model::RealtimeEndpointInfo> {
self.realtime_endpoint_info.as_ref()
}
}
/// See [`CreateRealtimeEndpointOutput`](crate::output::CreateRealtimeEndpointOutput).
pub mod create_realtime_endpoint_output {
/// A builder for [`CreateRealtimeEndpointOutput`](crate::output::CreateRealtimeEndpointOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
pub(crate) realtime_endpoint_info: std::option::Option<crate::model::RealtimeEndpointInfo>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request.</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 endpoint information of the <code>MLModel</code> </p>
pub fn realtime_endpoint_info(mut self, input: crate::model::RealtimeEndpointInfo) -> Self {
self.realtime_endpoint_info = Some(input);
self
}
/// <p>The endpoint information of the <code>MLModel</code> </p>
pub fn set_realtime_endpoint_info(
mut self,
input: std::option::Option<crate::model::RealtimeEndpointInfo>,
) -> Self {
self.realtime_endpoint_info = input;
self
}
/// Consumes the builder and constructs a [`CreateRealtimeEndpointOutput`](crate::output::CreateRealtimeEndpointOutput).
pub fn build(self) -> crate::output::CreateRealtimeEndpointOutput {
crate::output::CreateRealtimeEndpointOutput {
ml_model_id: self.ml_model_id,
realtime_endpoint_info: self.realtime_endpoint_info,
}
}
}
}
impl CreateRealtimeEndpointOutput {
/// Creates a new builder-style object to manufacture [`CreateRealtimeEndpointOutput`](crate::output::CreateRealtimeEndpointOutput).
pub fn builder() -> crate::output::create_realtime_endpoint_output::Builder {
crate::output::create_realtime_endpoint_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateMLModel</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p>The <code>CreateMLModel</code> operation is asynchronous. You can poll for status updates by using the <code>GetMLModel</code> operation and checking the <code>Status</code> parameter. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateMlModelOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request. </p>
#[doc(hidden)]
pub ml_model_id: std::option::Option<std::string::String>,
}
impl CreateMlModelOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request. </p>
pub fn ml_model_id(&self) -> std::option::Option<&str> {
self.ml_model_id.as_deref()
}
}
/// See [`CreateMlModelOutput`](crate::output::CreateMlModelOutput).
pub mod create_ml_model_output {
/// A builder for [`CreateMlModelOutput`](crate::output::CreateMlModelOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) ml_model_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request. </p>
pub fn ml_model_id(mut self, input: impl Into<std::string::String>) -> Self {
self.ml_model_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>. This value should be identical to the value of the <code>MLModelId</code> in the request. </p>
pub fn set_ml_model_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.ml_model_id = input;
self
}
/// Consumes the builder and constructs a [`CreateMlModelOutput`](crate::output::CreateMlModelOutput).
pub fn build(self) -> crate::output::CreateMlModelOutput {
crate::output::CreateMlModelOutput {
ml_model_id: self.ml_model_id,
}
}
}
}
impl CreateMlModelOutput {
/// Creates a new builder-style object to manufacture [`CreateMlModelOutput`](crate::output::CreateMlModelOutput).
pub fn builder() -> crate::output::create_ml_model_output::Builder {
crate::output::create_ml_model_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateEvaluation</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p> <code>CreateEvaluation</code> operation is asynchronous. You can poll for status updates by using the <code>GetEvcaluation</code> operation and checking the <code>Status</code> parameter. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateEvaluationOutput {
/// <p>The user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
#[doc(hidden)]
pub evaluation_id: std::option::Option<std::string::String>,
}
impl CreateEvaluationOutput {
/// <p>The user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(&self) -> std::option::Option<&str> {
self.evaluation_id.as_deref()
}
}
/// See [`CreateEvaluationOutput`](crate::output::CreateEvaluationOutput).
pub mod create_evaluation_output {
/// A builder for [`CreateEvaluationOutput`](crate::output::CreateEvaluationOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) evaluation_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn evaluation_id(mut self, input: impl Into<std::string::String>) -> Self {
self.evaluation_id = Some(input.into());
self
}
/// <p>The user-supplied ID that uniquely identifies the <code>Evaluation</code>. This value should be identical to the value of the <code>EvaluationId</code> in the request.</p>
pub fn set_evaluation_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.evaluation_id = input;
self
}
/// Consumes the builder and constructs a [`CreateEvaluationOutput`](crate::output::CreateEvaluationOutput).
pub fn build(self) -> crate::output::CreateEvaluationOutput {
crate::output::CreateEvaluationOutput {
evaluation_id: self.evaluation_id,
}
}
}
}
impl CreateEvaluationOutput {
/// Creates a new builder-style object to manufacture [`CreateEvaluationOutput`](crate::output::CreateEvaluationOutput).
pub fn builder() -> crate::output::create_evaluation_output::Builder {
crate::output::create_evaluation_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateDataSourceFromS3</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p>The <code>CreateDataSourceFromS3</code> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateDataSourceFromS3Output {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
}
impl CreateDataSourceFromS3Output {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
}
/// See [`CreateDataSourceFromS3Output`](crate::output::CreateDataSourceFromS3Output).
pub mod create_data_source_from_s3_output {
/// A builder for [`CreateDataSourceFromS3Output`](crate::output::CreateDataSourceFromS3Output).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// Consumes the builder and constructs a [`CreateDataSourceFromS3Output`](crate::output::CreateDataSourceFromS3Output).
pub fn build(self) -> crate::output::CreateDataSourceFromS3Output {
crate::output::CreateDataSourceFromS3Output {
data_source_id: self.data_source_id,
}
}
}
}
impl CreateDataSourceFromS3Output {
/// Creates a new builder-style object to manufacture [`CreateDataSourceFromS3Output`](crate::output::CreateDataSourceFromS3Output).
pub fn builder() -> crate::output::create_data_source_from_s3_output::Builder {
crate::output::create_data_source_from_s3_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateDataSourceFromRedshift</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p>The <code>CreateDataSourceFromRedshift</code> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateDataSourceFromRedshiftOutput {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
}
impl CreateDataSourceFromRedshiftOutput {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
}
/// See [`CreateDataSourceFromRedshiftOutput`](crate::output::CreateDataSourceFromRedshiftOutput).
pub mod create_data_source_from_redshift_output {
/// A builder for [`CreateDataSourceFromRedshiftOutput`](crate::output::CreateDataSourceFromRedshiftOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// Consumes the builder and constructs a [`CreateDataSourceFromRedshiftOutput`](crate::output::CreateDataSourceFromRedshiftOutput).
pub fn build(self) -> crate::output::CreateDataSourceFromRedshiftOutput {
crate::output::CreateDataSourceFromRedshiftOutput {
data_source_id: self.data_source_id,
}
}
}
}
impl CreateDataSourceFromRedshiftOutput {
/// Creates a new builder-style object to manufacture [`CreateDataSourceFromRedshiftOutput`](crate::output::CreateDataSourceFromRedshiftOutput).
pub fn builder() -> crate::output::create_data_source_from_redshift_output::Builder {
crate::output::create_data_source_from_redshift_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateDataSourceFromRDS</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p>The <code>CreateDataSourceFromRDS</code>> operation is asynchronous. You can poll for updates by using the <code>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter. You can inspect the <code>Message</code> when <code>Status</code> shows up as <code>FAILED</code>. You can also check the progress of the copy operation by going to the <code>DataPipeline</code> console and looking up the pipeline using the <code>pipelineId </code> from the describe call.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateDataSourceFromRdsOutput {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
#[doc(hidden)]
pub data_source_id: std::option::Option<std::string::String>,
}
impl CreateDataSourceFromRdsOutput {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(&self) -> std::option::Option<&str> {
self.data_source_id.as_deref()
}
}
/// See [`CreateDataSourceFromRdsOutput`](crate::output::CreateDataSourceFromRdsOutput).
pub mod create_data_source_from_rds_output {
/// A builder for [`CreateDataSourceFromRdsOutput`](crate::output::CreateDataSourceFromRdsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) data_source_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn data_source_id(mut self, input: impl Into<std::string::String>) -> Self {
self.data_source_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the <code>DataSourceID</code> in the request. </p>
pub fn set_data_source_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.data_source_id = input;
self
}
/// Consumes the builder and constructs a [`CreateDataSourceFromRdsOutput`](crate::output::CreateDataSourceFromRdsOutput).
pub fn build(self) -> crate::output::CreateDataSourceFromRdsOutput {
crate::output::CreateDataSourceFromRdsOutput {
data_source_id: self.data_source_id,
}
}
}
}
impl CreateDataSourceFromRdsOutput {
/// Creates a new builder-style object to manufacture [`CreateDataSourceFromRdsOutput`](crate::output::CreateDataSourceFromRdsOutput).
pub fn builder() -> crate::output::create_data_source_from_rds_output::Builder {
crate::output::create_data_source_from_rds_output::Builder::default()
}
}
/// <p> Represents the output of a <code>CreateBatchPrediction</code> operation, and is an acknowledgement that Amazon ML received the request.</p>
/// <p>The <code>CreateBatchPrediction</code> operation is asynchronous. You can poll for status updates by using the <code>>GetBatchPrediction</code> operation and checking the <code>Status</code> parameter of the result. </p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateBatchPredictionOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value is identical to the value of the <code>BatchPredictionId</code> in the request.</p>
#[doc(hidden)]
pub batch_prediction_id: std::option::Option<std::string::String>,
}
impl CreateBatchPredictionOutput {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value is identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn batch_prediction_id(&self) -> std::option::Option<&str> {
self.batch_prediction_id.as_deref()
}
}
/// See [`CreateBatchPredictionOutput`](crate::output::CreateBatchPredictionOutput).
pub mod create_batch_prediction_output {
/// A builder for [`CreateBatchPredictionOutput`](crate::output::CreateBatchPredictionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) batch_prediction_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value is identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn batch_prediction_id(mut self, input: impl Into<std::string::String>) -> Self {
self.batch_prediction_id = Some(input.into());
self
}
/// <p>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>. This value is identical to the value of the <code>BatchPredictionId</code> in the request.</p>
pub fn set_batch_prediction_id(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.batch_prediction_id = input;
self
}
/// Consumes the builder and constructs a [`CreateBatchPredictionOutput`](crate::output::CreateBatchPredictionOutput).
pub fn build(self) -> crate::output::CreateBatchPredictionOutput {
crate::output::CreateBatchPredictionOutput {
batch_prediction_id: self.batch_prediction_id,
}
}
}
}
impl CreateBatchPredictionOutput {
/// Creates a new builder-style object to manufacture [`CreateBatchPredictionOutput`](crate::output::CreateBatchPredictionOutput).
pub fn builder() -> crate::output::create_batch_prediction_output::Builder {
crate::output::create_batch_prediction_output::Builder::default()
}
}
/// <p>Amazon ML returns the following elements.</p>
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct AddTagsOutput {
/// <p>The ID of the ML object that was tagged.</p>
#[doc(hidden)]
pub resource_id: std::option::Option<std::string::String>,
/// <p>The type of the ML object that was tagged.</p>
#[doc(hidden)]
pub resource_type: std::option::Option<crate::model::TaggableResourceType>,
}
impl AddTagsOutput {
/// <p>The ID of the ML object that was tagged.</p>
pub fn resource_id(&self) -> std::option::Option<&str> {
self.resource_id.as_deref()
}
/// <p>The type of the ML object that was tagged.</p>
pub fn resource_type(&self) -> std::option::Option<&crate::model::TaggableResourceType> {
self.resource_type.as_ref()
}
}
/// See [`AddTagsOutput`](crate::output::AddTagsOutput).
pub mod add_tags_output {
/// A builder for [`AddTagsOutput`](crate::output::AddTagsOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) resource_id: std::option::Option<std::string::String>,
pub(crate) resource_type: std::option::Option<crate::model::TaggableResourceType>,
}
impl Builder {
/// <p>The ID of the ML object that was tagged.</p>
pub fn resource_id(mut self, input: impl Into<std::string::String>) -> Self {
self.resource_id = Some(input.into());
self
}
/// <p>The ID of the ML object that was tagged.</p>
pub fn set_resource_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.resource_id = input;
self
}
/// <p>The type of the ML object that was tagged.</p>
pub fn resource_type(mut self, input: crate::model::TaggableResourceType) -> Self {
self.resource_type = Some(input);
self
}
/// <p>The type of the ML object that was tagged.</p>
pub fn set_resource_type(
mut self,
input: std::option::Option<crate::model::TaggableResourceType>,
) -> Self {
self.resource_type = input;
self
}
/// Consumes the builder and constructs a [`AddTagsOutput`](crate::output::AddTagsOutput).
pub fn build(self) -> crate::output::AddTagsOutput {
crate::output::AddTagsOutput {
resource_id: self.resource_id,
resource_type: self.resource_type,
}
}
}
}
impl AddTagsOutput {
/// Creates a new builder-style object to manufacture [`AddTagsOutput`](crate::output::AddTagsOutput).
pub fn builder() -> crate::output::add_tags_output::Builder {
crate::output::add_tags_output::Builder::default()
}
}