# frozen_string_literal: true
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module Retail
module V2
# Metadata that describes the training and serving parameters of a
# {::Google::Cloud::Retail::V2::Model Model}. A
# {::Google::Cloud::Retail::V2::Model Model} can be associated with a
# {::Google::Cloud::Retail::V2::ServingConfig ServingConfig} and then queried
# through the Predict API.
# @!attribute [rw] name
# @return [::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.
# @!attribute [rw] display_name
# @return [::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.
# @!attribute [rw] training_state
# @return [::Google::Cloud::Retail::V2::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.
# @!attribute [r] serving_state
# @return [::Google::Cloud::Retail::V2::Model::ServingState]
# Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
# @!attribute [r] create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Timestamp the Recommendation Model was created at.
# @!attribute [r] update_time
# @return [::Google::Protobuf::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.
# @!attribute [rw] type
# @return [::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
# {::Google::Cloud::Retail::V2::Model#optimization_objective 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.
# @!attribute [rw] optimization_objective
# @return [::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
# {::Google::Cloud::Retail::V2::Model#type 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.
# @!attribute [rw] periodic_tuning_state
# @return [::Google::Cloud::Retail::V2::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`.
# @!attribute [r] last_tune_time
# @return [::Google::Protobuf::Timestamp]
# Output only. The timestamp when the latest successful tune finished.
# @!attribute [r] tuning_operation
# @return [::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.
# @!attribute [r] data_state
# @return [::Google::Cloud::Retail::V2::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.
# @!attribute [rw] filtering_option
# @return [::Google::Cloud::Retail::V2::RecommendationsFilteringOption]
# Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering
# by attributes is enabled for the model.
# @!attribute [r] serving_config_lists
# @return [::Array<::Google::Cloud::Retail::V2::Model::ServingConfigList>]
# Output only. The list of valid serving configs associated with the
# PageOptimizationConfig.
class Model
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Represents an ordered combination of valid serving configs, which
# can be used for `PAGE_OPTIMIZATION` recommendations.
# @!attribute [rw] serving_config_ids
# @return [::Array<::String>]
# Optional. A set of valid serving configs that may be used for
# `PAGE_OPTIMIZATION`.
class ServingConfigList
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# The serving state of the model.
module ServingState
# Unspecified serving state.
SERVING_STATE_UNSPECIFIED = 0
# The model is not serving.
INACTIVE = 1
# The model is serving and can be queried.
ACTIVE = 2
# The model is trained on tuned hyperparameters and can be
# queried.
TUNED = 3
end
# The training state of the model.
module TrainingState
# Unspecified training state.
TRAINING_STATE_UNSPECIFIED = 0
# The model training is paused.
PAUSED = 1
# The model is training.
TRAINING = 2
end
# 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.
module PeriodicTuningState
# Unspecified default value, should never be explicitly set.
PERIODIC_TUNING_STATE_UNSPECIFIED = 0
# The model has periodic tuning disabled. Tuning
# can be reenabled by calling the `EnableModelPeriodicTuning`
# method or by calling the `TuneModel` method.
PERIODIC_TUNING_DISABLED = 1
# 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.
ALL_TUNING_DISABLED = 3
# The model has periodic tuning enabled. Tuning
# can be disabled by calling the `DisableModelPeriodicTuning`
# method.
PERIODIC_TUNING_ENABLED = 2
end
# Describes whether this model have sufficient training data
# to be continuously trained.
module DataState
# Unspecified default value, should never be explicitly set.
DATA_STATE_UNSPECIFIED = 0
# The model has sufficient training data.
DATA_OK = 1
# The model does not have sufficient training data. Error
# messages can be queried via Stackdriver.
DATA_ERROR = 2
end
end
end
end
end
end