Struct TrainCustomModelResponse 

#[non_exhaustive]
pub struct TrainCustomModelResponse {
    pub error_samples: Vec<Status>,
    pub error_config: Option<ImportErrorConfig>,
    pub model_status: String,
    pub metrics: HashMap<String, f64>,
    pub model_name: String,
    /* private fields */
}

Available on crate feature search-tuning-service only.

Response of the TrainCustomModelRequest. This message is returned by the google.longrunning.Operations.response field.

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.

error_samples: Vec<Status>

A sample of errors encountered while processing the data.

error_config: Option<ImportErrorConfig>

Echoes the destination for the complete errors in the request if set.

model_status: String

The trained model status. Possible values are:

• bad-data: The training data quality is bad.
• no-improvement: Tuning didn’t improve performance. Won’t deploy.
• in-progress: Model training job creation is in progress.
• training: Model is actively training.
• evaluating: The model is evaluating trained metrics.
• indexing: The model trained metrics are indexing.
• ready: The model is ready for serving.

metrics: HashMap<String, f64>

The metrics of the trained model.

model_name: String

Fully qualified name of the CustomTuningModel.

Implementations

impl TrainCustomModelResponse

pub fn new() -> Self

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

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

Sets the value of error_samples.

Example

use rpc::model::Status;
let x = TrainCustomModelResponse::new()
    .set_error_samples([
        Status::default()/* use setters */,
        Status::default()/* use (different) setters */,
    ]);

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

where T: Into<ImportErrorConfig>,

Sets the value of error_config.

Example

use google_cloud_discoveryengine_v1::model::ImportErrorConfig;
let x = TrainCustomModelResponse::new().set_error_config(ImportErrorConfig::default()/* use setters */);

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

where T: Into<ImportErrorConfig>,

Sets or clears the value of error_config.

Example

use google_cloud_discoveryengine_v1::model::ImportErrorConfig;
let x = TrainCustomModelResponse::new().set_or_clear_error_config(Some(ImportErrorConfig::default()/* use setters */));
let x = TrainCustomModelResponse::new().set_or_clear_error_config(None::<ImportErrorConfig>);

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

Sets the value of model_status.

Example

let x = TrainCustomModelResponse::new().set_model_status("example");

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

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<f64>,

Sets the value of metrics.

Example

let x = TrainCustomModelResponse::new().set_metrics([
    ("key0", 123.5),
    ("key1", 456.5),
]);

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

Sets the value of model_name.

Example

let x = TrainCustomModelResponse::new().set_model_name("example");

Trait Implementations

impl Clone for TrainCustomModelResponse

fn clone(&self) -> TrainCustomModelResponse

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainCustomModelResponse

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

Formats the value using the given formatter.

impl Default for TrainCustomModelResponse

fn default() -> TrainCustomModelResponse

Returns the “default value” for a type.

impl Message for TrainCustomModelResponse

fn typename() -> &'static str

The typename of this message.

impl PartialEq for TrainCustomModelResponse

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for TrainCustomModelResponse