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_v2.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_v2.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_v2.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_v2.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_v2.types.RecommendationsFilteringOption
Optional. If
RECOMMENDATIONS_FILTERING_ENABLED
,
recommendation filtering by attributes is enabled for the
model.serving_config_lists
MutableSequence[ google.cloud.retail_v2.types.Model.ServingConfigList
]
Output only. The list of valid serving configs associated with the PageOptimizationConfig.
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.
