Struct MlStatistics 

#[non_exhaustive]
pub struct MlStatistics {
    pub max_iterations: i64,
    pub iteration_results: Vec<IterationResult>,
    pub model_type: ModelType,
    pub training_type: TrainingType,
    pub hparam_trials: Vec<HparamTuningTrial>,
    /* private fields */
}

Job statistics specific to a BigQuery ML training job.

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.

max_iterations: i64

Output only. Maximum number of iterations specified as max_iterations in the ‘CREATE MODEL’ query. The actual number of iterations may be less than this number due to early stop.

iteration_results: Vec<IterationResult>

Results for all completed iterations. Empty for hyperparameter tuning jobs.

model_type: ModelType

Output only. The type of the model that is being trained.

training_type: TrainingType

Output only. Training type of the job.

hparam_trials: Vec<HparamTuningTrial>

Output only. Trials of a hyperparameter tuning job sorted by trial_id.

Implementations

impl MlStatistics

pub fn new() -> Self

Creates a new default instance.

pub fn set_max_iterations<T: Into<i64>>(self, v: T) -> Self

Sets the value of max_iterations.

Example

let x = MlStatistics::new().set_max_iterations(42);

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

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

Sets the value of iteration_results.

Example

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

pub fn set_model_type<T: Into<ModelType>>(self, v: T) -> Self

Sets the value of model_type.

Example

use google_cloud_bigquery_v2::model::model::ModelType;
let x0 = MlStatistics::new().set_model_type(ModelType::LinearRegression);
let x1 = MlStatistics::new().set_model_type(ModelType::LogisticRegression);
let x2 = MlStatistics::new().set_model_type(ModelType::Kmeans);

pub fn set_training_type<T: Into<TrainingType>>(self, v: T) -> Self

Sets the value of training_type.

Example

use google_cloud_bigquery_v2::model::ml_statistics::TrainingType;
let x0 = MlStatistics::new().set_training_type(TrainingType::SingleTraining);
let x1 = MlStatistics::new().set_training_type(TrainingType::HparamTuning);

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

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

Sets the value of hparam_trials.

Example

use google_cloud_bigquery_v2::model::model::HparamTuningTrial;
let x = MlStatistics::new()
    .set_hparam_trials([
        HparamTuningTrial::default()/* use setters */,
        HparamTuningTrial::default()/* use (different) setters */,
    ]);

Trait Implementations

impl Clone for MlStatistics

fn clone(&self) -> MlStatistics

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MlStatistics

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

Formats the value using the given formatter.

impl Default for MlStatistics

fn default() -> MlStatistics

Returns the “default value” for a type.

impl Message for MlStatistics

fn typename() -> &'static str

The typename of this message.

impl PartialEq for MlStatistics

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

Equality operator ==.

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

Inequality operator !=.

impl StructuralPartialEq for MlStatistics