Struct TrainingRun 

#[non_exhaustive]
pub struct TrainingRun {
    pub training_options: Option<TrainingOptions>,
    pub start_time: Option<Timestamp>,
    pub results: Vec<IterationResult>,
    pub evaluation_metrics: Option<EvaluationMetrics>,
    pub data_split_result: Option<DataSplitResult>,
    pub model_level_global_explanation: Option<GlobalExplanation>,
    pub class_level_global_explanations: Vec<GlobalExplanation>,
    pub vertex_ai_model_id: String,
    pub vertex_ai_model_version: String,
    /* private fields */
}

Information about a single training query run for the model.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

training_options: Option<TrainingOptions>

Output only. Options that were used for this training run, includes user specified and default options that were used.

start_time: Option<Timestamp>

Output only. The start time of this training run.

results: Vec<IterationResult>

Output only. Output of each iteration run, results.size() <= max_iterations.

evaluation_metrics: Option<EvaluationMetrics>

Output only. The evaluation metrics over training/eval data that were computed at the end of training.

data_split_result: Option<DataSplitResult>

Output only. Data split result of the training run. Only set when the input data is actually split.

model_level_global_explanation: Option<GlobalExplanation>

Output only. Global explanation contains the explanation of top features on the model level. Applies to both regression and classification models.

class_level_global_explanations: Vec<GlobalExplanation>

Output only. Global explanation contains the explanation of top features on the class level. Applies to classification models only.

vertex_ai_model_id: String

The model id in the Vertex AI Model Registry for this training run.

vertex_ai_model_version: String

Output only. The model version in the Vertex AI Model Registry for this training run.

Implementations

impl TrainingRun

pub fn new() -> Self

Creates a new default instance.

pub fn set_training_options<T>(self, v: T) -> Self

where T: Into<TrainingOptions>,

Sets the value of training_options.

Example

use google_cloud_bigquery_v2::model::model::training_run::TrainingOptions;
let x = TrainingRun::new().set_training_options(TrainingOptions::default()/* use setters */);

pub fn set_or_clear_training_options<T>(self, v: Option<T>) -> Self

where T: Into<TrainingOptions>,

Sets or clears the value of training_options.

Example

use google_cloud_bigquery_v2::model::model::training_run::TrainingOptions;
let x = TrainingRun::new().set_or_clear_training_options(Some(TrainingOptions::default()/* use setters */));
let x = TrainingRun::new().set_or_clear_training_options(None::<TrainingOptions>);

pub fn set_start_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of start_time.

Example

use wkt::Timestamp;
let x = TrainingRun::new().set_start_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_start_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of start_time.

Example

use wkt::Timestamp;
let x = TrainingRun::new().set_or_clear_start_time(Some(Timestamp::default()/* use setters */));
let x = TrainingRun::new().set_or_clear_start_time(None::<Timestamp>);

pub fn set_results<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<IterationResult>,

Sets the value of results.

Example

use google_cloud_bigquery_v2::model::model::training_run::IterationResult;
let x = TrainingRun::new()
    .set_results([
        IterationResult::default()/* use setters */,
        IterationResult::default()/* use (different) setters */,
    ]);

pub fn set_evaluation_metrics<T>(self, v: T) -> Self

where T: Into<EvaluationMetrics>,

Sets the value of evaluation_metrics.

Example

use google_cloud_bigquery_v2::model::model::EvaluationMetrics;
let x = TrainingRun::new().set_evaluation_metrics(EvaluationMetrics::default()/* use setters */);

pub fn set_or_clear_evaluation_metrics<T>(self, v: Option<T>) -> Self

where T: Into<EvaluationMetrics>,

Sets or clears the value of evaluation_metrics.

Example

use google_cloud_bigquery_v2::model::model::EvaluationMetrics;
let x = TrainingRun::new().set_or_clear_evaluation_metrics(Some(EvaluationMetrics::default()/* use setters */));
let x = TrainingRun::new().set_or_clear_evaluation_metrics(None::<EvaluationMetrics>);

pub fn set_data_split_result<T>(self, v: T) -> Self

where T: Into<DataSplitResult>,

Sets the value of data_split_result.

Example

use google_cloud_bigquery_v2::model::model::DataSplitResult;
let x = TrainingRun::new().set_data_split_result(DataSplitResult::default()/* use setters */);

pub fn set_or_clear_data_split_result<T>(self, v: Option<T>) -> Self

where T: Into<DataSplitResult>,

Sets or clears the value of data_split_result.

Example

use google_cloud_bigquery_v2::model::model::DataSplitResult;
let x = TrainingRun::new().set_or_clear_data_split_result(Some(DataSplitResult::default()/* use setters */));
let x = TrainingRun::new().set_or_clear_data_split_result(None::<DataSplitResult>);

pub fn set_model_level_global_explanation<T>(self, v: T) -> Self

where T: Into<GlobalExplanation>,

Sets the value of model_level_global_explanation.

Example

use google_cloud_bigquery_v2::model::model::GlobalExplanation;
let x = TrainingRun::new().set_model_level_global_explanation(GlobalExplanation::default()/* use setters */);

pub fn set_or_clear_model_level_global_explanation<T>( self, v: Option<T>, ) -> Self

where T: Into<GlobalExplanation>,

Sets or clears the value of model_level_global_explanation.

Example

use google_cloud_bigquery_v2::model::model::GlobalExplanation;
let x = TrainingRun::new().set_or_clear_model_level_global_explanation(Some(GlobalExplanation::default()/* use setters */));
let x = TrainingRun::new().set_or_clear_model_level_global_explanation(None::<GlobalExplanation>);

pub fn set_class_level_global_explanations<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<GlobalExplanation>,

Sets the value of class_level_global_explanations.

Example

use google_cloud_bigquery_v2::model::model::GlobalExplanation;
let x = TrainingRun::new()
    .set_class_level_global_explanations([
        GlobalExplanation::default()/* use setters */,
        GlobalExplanation::default()/* use (different) setters */,
    ]);

pub fn set_vertex_ai_model_id<T: Into<String>>(self, v: T) -> Self

Sets the value of vertex_ai_model_id.

Example

let x = TrainingRun::new().set_vertex_ai_model_id("example");

pub fn set_vertex_ai_model_version<T: Into<String>>(self, v: T) -> Self

Sets the value of vertex_ai_model_version.

Example

let x = TrainingRun::new().set_vertex_ai_model_version("example");

Trait Implementations

impl Clone for TrainingRun

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TrainingRun

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

Formats the value using the given formatter.

impl Default for TrainingRun

fn default() -> Self

Returns the “default value” for a type.

impl Message for TrainingRun

fn typename() -> &'static str

The typename of this message.

impl PartialEq for TrainingRun

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

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for TrainingRun