kpt-grpc-demo/google/cloud/aiplatform/v1/feature_monitoring_stats.proto

// 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
//
//     http://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.

syntax = "proto3";

package google.cloud.aiplatform.v1;

import "google/protobuf/timestamp.proto";

option csharp_namespace = "Google.Cloud.AIPlatform.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/aiplatform/v1;aiplatform";
option java_multiple_files = true;
option java_outer_classname = "FeatureMonitoringStatsProto";
option java_package = "com.google.cloud.aiplatform.v1";
option php_namespace = "Google\\Cloud\\AIPlatform\\V1";
option ruby_package = "Google::Cloud::AIPlatform::V1";

// Stats and Anomaly generated at specific timestamp for specific Feature.
// The start_time and end_time are used to define the time range of the dataset
// that current stats belongs to, e.g. prediction traffic is bucketed into
// prediction datasets by time window. If the Dataset is not defined by time
// window, start_time = end_time. Timestamp of the stats and anomalies always
// refers to end_time. Raw stats and anomalies are stored in stats_uri or
// anomaly_uri in the tensorflow defined protos. Field data_stats contains
// almost identical information with the raw stats in Vertex AI
// defined proto, for UI to display.
message FeatureStatsAnomaly {
  // Feature importance score, only populated when cross-feature monitoring is
  // enabled. For now only used to represent feature attribution score within
  // range [0, 1] for
  // [ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW] and
  // [ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT].
  double score = 1;

  // Path of the stats file for current feature values in Cloud Storage bucket.
  // Format: gs://<bucket_name>/<object_name>/stats.
  // Example: gs://monitoring_bucket/feature_name/stats.
  // Stats are stored as binary format with Protobuf message
  // [tensorflow.metadata.v0.FeatureNameStatistics](https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/statistics.proto).
  string stats_uri = 3;

  // Path of the anomaly file for current feature values in Cloud Storage
  // bucket.
  // Format: gs://<bucket_name>/<object_name>/anomalies.
  // Example: gs://monitoring_bucket/feature_name/anomalies.
  // Stats are stored as binary format with Protobuf message
  // Anoamlies are stored as binary format with Protobuf message
  // [tensorflow.metadata.v0.AnomalyInfo]
  // (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
  string anomaly_uri = 4;

  // Deviation from the current stats to baseline stats.
  //   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.
  double distribution_deviation = 5;

  // This is the threshold used when detecting anomalies.
  // The threshold can be changed by user, so this one might be different from
  // [ThresholdConfig.value][google.cloud.aiplatform.v1.ThresholdConfig.value].
  double anomaly_detection_threshold = 9;

  // The start timestamp of window where stats were generated.
  // For objectives where time window doesn't make sense (e.g. Featurestore
  // Snapshot Monitoring), start_time is only used to indicate the monitoring
  // intervals, so it always equals to (end_time - monitoring_interval).
  google.protobuf.Timestamp start_time = 7;

  // The end timestamp of window where stats were generated.
  // For objectives where time window doesn't make sense (e.g. Featurestore
  // Snapshot Monitoring), end_time indicates the timestamp of the data used to
  // generate stats (e.g. timestamp we take snapshots for feature values).
  google.protobuf.Timestamp end_time = 8;
}