# frozen_string_literal: true # Copyright 2022 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 AIPlatform module V1 # Configuration of how features in Featurestore are monitored. # @!attribute [rw] snapshot_analysis # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::SnapshotAnalysis] # The config for Snapshot Analysis Based Feature Monitoring. # @!attribute [rw] import_features_analysis # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ImportFeaturesAnalysis] # The config for ImportFeatures Analysis Based Feature Monitoring. # @!attribute [rw] numerical_threshold_config # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ThresholdConfig] # Threshold for numerical features of anomaly detection. # This is shared by all objectives of Featurestore Monitoring for numerical # features (i.e. Features with type # ({::Google::Cloud::AIPlatform::V1::Feature::ValueType Feature.ValueType}) DOUBLE # or INT64). # @!attribute [rw] categorical_threshold_config # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ThresholdConfig] # Threshold for categorical features of anomaly detection. # This is shared by all types of Featurestore Monitoring for categorical # features (i.e. Features with type # ({::Google::Cloud::AIPlatform::V1::Feature::ValueType Feature.ValueType}) BOOL or # STRING). class FeaturestoreMonitoringConfig include ::Google::Protobuf::MessageExts extend ::Google::Protobuf::MessageExts::ClassMethods # Configuration of the Featurestore's Snapshot Analysis Based Monitoring. # This type of analysis generates statistics for each Feature based on a # snapshot of the latest feature value of each entities every # monitoring_interval. # @!attribute [rw] disabled # @return [::Boolean] # The monitoring schedule for snapshot analysis. # For EntityType-level config: # unset / disabled = true indicates disabled by # default for Features under it; otherwise by default enable snapshot # analysis monitoring with monitoring_interval for Features under it. # Feature-level config: # disabled = true indicates disabled regardless of the EntityType-level # config; unset monitoring_interval indicates going with EntityType-level # config; otherwise run snapshot analysis monitoring with # monitoring_interval regardless of the EntityType-level config. # Explicitly Disable the snapshot analysis based monitoring. # @!attribute [rw] monitoring_interval_days # @return [::Integer] # Configuration of the snapshot analysis based monitoring pipeline # running interval. The value indicates number of days. # @!attribute [rw] staleness_days # @return [::Integer] # Customized export features time window for snapshot analysis. Unit is one # day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is # 4000 days. class SnapshotAnalysis include ::Google::Protobuf::MessageExts extend ::Google::Protobuf::MessageExts::ClassMethods end # Configuration of the Featurestore's ImportFeature Analysis Based # Monitoring. This type of analysis generates statistics for values of each # Feature imported by every # {::Google::Cloud::AIPlatform::V1::FeaturestoreService::Client#import_feature_values ImportFeatureValues} # operation. # @!attribute [rw] state # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ImportFeaturesAnalysis::State] # Whether to enable / disable / inherite default hebavior for import # features analysis. # @!attribute [rw] anomaly_detection_baseline # @return [::Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ImportFeaturesAnalysis::Baseline] # The baseline used to do anomaly detection for the statistics generated by # import features analysis. class ImportFeaturesAnalysis include ::Google::Protobuf::MessageExts extend ::Google::Protobuf::MessageExts::ClassMethods # The state defines whether to enable ImportFeature analysis. module State # Should not be used. STATE_UNSPECIFIED = 0 # The default behavior of whether to enable the monitoring. # EntityType-level config: disabled. # Feature-level config: inherited from the configuration of EntityType # this Feature belongs to. DEFAULT = 1 # Explicitly enables import features analysis. # EntityType-level config: by default enables import features analysis # for all Features under it. Feature-level config: enables import # features analysis regardless of the EntityType-level config. ENABLED = 2 # Explicitly disables import features analysis. # EntityType-level config: by default disables import features analysis # for all Features under it. Feature-level config: disables import # features analysis regardless of the EntityType-level config. DISABLED = 3 end # Defines the baseline to do anomaly detection for feature values imported # by each # {::Google::Cloud::AIPlatform::V1::FeaturestoreService::Client#import_feature_values ImportFeatureValues} # operation. module Baseline # Should not be used. BASELINE_UNSPECIFIED = 0 # Choose the later one statistics generated by either most recent # snapshot analysis or previous import features analysis. If non of them # exists, skip anomaly detection and only generate a statistics. LATEST_STATS = 1 # Use the statistics generated by the most recent snapshot analysis if # exists. MOST_RECENT_SNAPSHOT_STATS = 2 # Use the statistics generated by the previous import features analysis # if exists. PREVIOUS_IMPORT_FEATURES_STATS = 3 end end # The config for Featurestore Monitoring threshold. # @!attribute [rw] value # @return [::Float] # Specify a threshold value that can trigger the alert. # 1. For categorical feature, the distribution distance is calculated by # L-inifinity norm. # 2. For numerical feature, the distribution distance is calculated by # Jensen–Shannon divergence. Each feature must have a non-zero threshold # if they need to be monitored. Otherwise no alert will be triggered for # that feature. class ThresholdConfig include ::Google::Protobuf::MessageExts extend ::Google::Protobuf::MessageExts::ClassMethods end end end end end end