Class Model (1.19.0)

Version 1.19.0

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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.

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

Attributes
Name	Description
page_optimization_config	google.cloud.retail_v2alpha.types.Model.PageOptimizationConfig Optional. The page optimization config. This field is a member of oneof_ training_config.
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_v2alpha.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_v2alpha.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_v2alpha.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_v2alpha.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_v2alpha.types.RecommendationsFilteringOption Optional. If RECOMMENDATIONS_FILTERING_ENABLED, recommendation filtering by attributes is enabled for the model.
serving_config_lists	MutableSequence[google.cloud.retail_v2alpha.types.Model.ServingConfigList] Output only. The list of valid serving configs associated with the PageOptimizationConfig.

Classes

DataState

DataState(value)

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

Values: DATA_STATE_UNSPECIFIED (0): Unspecified default value, should never be explicitly set. DATA_OK (1): The model has sufficient training data. DATA_ERROR (2): The model does not have sufficient training data. Error messages can be queried via Stackdriver.

PageOptimizationConfig

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

The PageOptimizationConfig for model training.

This determines how many panels to optimize for, and which serving configs to consider for each panel. The purpose of this model is to optimize which ServingConfig to show on which panels in way that optimizes the visitors shopping journey.

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.

Values: PERIODIC_TUNING_STATE_UNSPECIFIED (0): Unspecified default value, should never be explicitly set. PERIODIC_TUNING_DISABLED (1): The model has periodic tuning disabled. Tuning can be reenabled by calling the EnableModelPeriodicTuning method or by calling the TuneModel method. ALL_TUNING_DISABLED (3): The model cannot be tuned with periodic tuning OR the TuneModel method. Hide the options in customer UI and reject any requests through the backend self serve API. PERIODIC_TUNING_ENABLED (2): The model has periodic tuning enabled. Tuning can be disabled by calling the DisableModelPeriodicTuning method.

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.

Values: SERVING_STATE_UNSPECIFIED (0): Unspecified serving state. INACTIVE (1): The model is not serving. ACTIVE (2): The model is serving and can be queried. TUNED (3): The model is trained on tuned hyperparameters and can be queried.

TrainingState

TrainingState(value)

The training state of the model.

Values: TRAINING_STATE_UNSPECIFIED (0): Unspecified training state. PAUSED (1): The model training is paused. TRAINING (2): The model is training.