Class Model (1.23.0) 

Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.

Attributes
Name Description
name str
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 str
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 google.cloud.retail_v2beta.types.Model.TrainingState
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 google.cloud.retail_v2beta.types.Model.ServingState
Output only. The serving state of the model: ACTIVE, NOT_ACTIVE.
create_time google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp the Recommendation Model was created at.
update_time google.protobuf.timestamp_pb2.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_ str
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 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 str
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 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 google.cloud.retail_v2beta.types.Model.PeriodicTuningState
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 google.protobuf.timestamp_pb2.Timestamp
Output only. The timestamp when the latest successful tune finished.
tuning_operation str
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 google.cloud.retail_v2beta.types.Model.DataState
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 google.cloud.retail_v2beta.types.RecommendationsFilteringOption
Optional. If RECOMMENDATIONS_FILTERING_ENABLED, recommendation filtering by attributes is enabled for the model.
serving_config_lists MutableSequence[google.cloud.retail_v2beta.types.Model.ServingConfigList]
Output only. The list of valid serving configs associated with the PageOptimizationConfig.
model_features_config google.cloud.retail_v2beta.types.Model.ModelFeaturesConfig
Optional. Additional model features config.

Classes

ContextProductsType

ContextProductsType(value)

Use single or multiple context products for recommendations.

DataState

DataState(value)

Describes whether this model have sufficient training data to be continuously trained.

FrequentlyBoughtTogetherFeaturesConfig

FrequentlyBoughtTogetherFeaturesConfig(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

Additional configs for the frequently-bought-together model type.

ModelFeaturesConfig

ModelFeaturesConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Additional model features config.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PeriodicTuningState

PeriodicTuningState(value)

Describes whether periodic tuning is enabled for this model or not. Periodic tuning is scheduled at most every three months. You can start a tuning process manually by using the TuneModel method, which starts a tuning process immediately and resets the quarterly schedule. Enabling or disabling periodic tuning does not affect any current tuning processes.

ServingConfigList

ServingConfigList(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Represents an ordered combination of valid serving configs, which can be used for PAGE_OPTIMIZATION recommendations.

ServingState

ServingState(value)

The serving state of the model.

TrainingState

TrainingState(value)

The training state of the model.