Struct google_api_proto::google::cloud::retail::v2beta::Model

pub struct Model {

    pub name: String,
    pub display_name: String,
    pub training_state: i32,
    pub serving_state: i32,
    pub create_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub type: String,
    pub optimization_objective: String,
    pub periodic_tuning_state: i32,
    pub last_tune_time: Option<Timestamp>,
    pub tuning_operation: String,
    pub data_state: i32,
    pub filtering_option: i32,
    pub serving_config_lists: Vec<ServingConfigList>,
    pub model_features_config: Option<ModelFeaturesConfig>,
}

Metadata that describes the training and serving parameters of a [Model][google.cloud.retail.v2beta.Model]. A [Model][google.cloud.retail.v2beta.Model] can be associated with a [ServingConfig][google.cloud.retail.v2beta.ServingConfig] and then queried through the Predict API.

Fields

name: String

Required. The fully qualified resource name of the model.

Format: projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id} catalog_id has char limit of 50. recommendation_model_id has char limit of 40.

display_name: String

Required. The display name of the model.

Should be human readable, used to display Recommendation Models in the Retail Cloud Console Dashboard. UTF-8 encoded string with limit of 1024 characters.

training_state: i32

Optional. The training state that the model is in (e.g. TRAINING or PAUSED).

Since part of the cost of running the service is frequency of training - this can be used to determine when to train model in order to control cost. If not specified: the default value for CreateModel method is TRAINING. The default value for UpdateModel method is to keep the state the same as before.

serving_state: i32

Output only. The serving state of the model: ACTIVE, NOT_ACTIVE.

create_time: Option<Timestamp>

Output only. Timestamp the Recommendation Model was created at.

update_time: Option<Timestamp>

Output only. Timestamp the Recommendation Model was last updated. E.g. if a Recommendation Model was paused - this would be the time the pause was initiated.

type: String

Required. The type of model e.g. home-page.

Currently supported values: recommended-for-you, others-you-may-like, frequently-bought-together, page-optimization, similar-items, buy-it-again, on-sale-items, and recently-viewed(readonly value).

This field together with [optimization_objective][google.cloud.retail.v2beta.Model.optimization_objective] describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimization_objective = ctr), you receive an error 400 if you try to create/update a recommendation with this set of knobs.

optimization_objective: String

Optional. The optimization objective e.g. cvr.

Currently supported values: ctr, cvr, revenue-per-order.

If not specified, we choose default based on model type. Default depends on type of recommendation:

recommended-for-you => ctr

others-you-may-like => ctr

frequently-bought-together => revenue_per_order

This field together with [optimization_objective][google.cloud.retail.v2beta.Model.type] describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = frequently-bought-together and optimization_objective = ctr), you receive an error 400 if you try to create/update a recommendation with this set of knobs.

periodic_tuning_state: i32

Optional. The state of periodic tuning.

The period we use is 3 months - to do a one-off tune earlier use the TuneModel method. Default value is PERIODIC_TUNING_ENABLED.

last_tune_time: Option<Timestamp>

Output only. The timestamp when the latest successful tune finished.

tuning_operation: String

Output only. The tune operation associated with the model.

Can be used to determine if there is an ongoing tune for this recommendation. Empty field implies no tune is goig on.

data_state: i32

Output only. The state of data requirements for this model: DATA_OK and DATA_ERROR.

Recommendation model cannot be trained if the data is in DATA_ERROR state. Recommendation model can have DATA_ERROR state even if serving state is ACTIVE: models were trained successfully before, but cannot be refreshed because model no longer has sufficient data for training.

filtering_option: i32

Optional. If RECOMMENDATIONS_FILTERING_ENABLED, recommendation filtering by attributes is enabled for the model.

serving_config_lists: Vec<ServingConfigList>

Output only. The list of valid serving configs associated with the PageOptimizationConfig.

model_features_config: Option<ModelFeaturesConfig>

Optional. Additional model features config.

Implementations

impl Model

pub fn training_state(&self) -> TrainingState

Returns the enum value of training_state, or the default if the field is set to an invalid enum value.

pub fn set_training_state(&mut self, value: TrainingState)

Sets training_state to the provided enum value.

pub fn serving_state(&self) -> ServingState

Returns the enum value of serving_state, or the default if the field is set to an invalid enum value.

pub fn set_serving_state(&mut self, value: ServingState)

Sets serving_state to the provided enum value.

pub fn periodic_tuning_state(&self) -> PeriodicTuningState

Returns the enum value of periodic_tuning_state, or the default if the field is set to an invalid enum value.

pub fn set_periodic_tuning_state(&mut self, value: PeriodicTuningState)

Sets periodic_tuning_state to the provided enum value.

pub fn data_state(&self) -> DataState

Returns the enum value of data_state, or the default if the field is set to an invalid enum value.

pub fn set_data_state(&mut self, value: DataState)

Sets data_state to the provided enum value.

pub fn filtering_option(&self) -> RecommendationsFilteringOption

Returns the enum value of filtering_option, or the default if the field is set to an invalid enum value.

pub fn set_filtering_option(&mut self, value: RecommendationsFilteringOption)

Sets filtering_option to the provided enum value.

Trait Implementations

impl Clone for Model

fn clone(&self) -> Model

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Model

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

Formats the value using the given formatter.

impl Default for Model

fn default() -> Self

Returns the “default value” for a type.

impl Message for Model

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for Model

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for Model