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.
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.
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.