blob: 4e79dc52094ca786ebe319572e4bdeb6b584be94 [file] [edit]
// 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
//
// 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.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.33.0
// protoc v4.25.3
// source: google/cloud/aiplatform/v1/model_monitoring.proto
package aiplatformpb
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// The storage format of the predictions generated BatchPrediction job.
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat int32
const (
// Should not be set.
ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PREDICTION_FORMAT_UNSPECIFIED ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 0
// Predictions are in JSONL files.
ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_JSONL ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 2
// Predictions are in BigQuery.
ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_BIGQUERY ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 3
)
// Enum value maps for ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat.
var (
ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat_name = map[int32]string{
0: "PREDICTION_FORMAT_UNSPECIFIED",
2: "JSONL",
3: "BIGQUERY",
}
ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat_value = map[string]int32{
"PREDICTION_FORMAT_UNSPECIFIED": 0,
"JSONL": 2,
"BIGQUERY": 3,
}
)
func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Enum() *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat {
p := new(ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat)
*p = x
return p
}
func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_enumTypes[0].Descriptor()
}
func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_model_monitoring_proto_enumTypes[0]
}
func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat.Descriptor instead.
func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 3, 0, 0}
}
// The objective configuration for model monitoring, including the information
// needed to detect anomalies for one particular model.
type ModelMonitoringObjectiveConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Training dataset for models. This field has to be set only if
// TrainingPredictionSkewDetectionConfig is specified.
TrainingDataset *ModelMonitoringObjectiveConfig_TrainingDataset `protobuf:"bytes,1,opt,name=training_dataset,json=trainingDataset,proto3" json:"training_dataset,omitempty"`
// The config for skew between training data and prediction data.
TrainingPredictionSkewDetectionConfig *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig `protobuf:"bytes,2,opt,name=training_prediction_skew_detection_config,json=trainingPredictionSkewDetectionConfig,proto3" json:"training_prediction_skew_detection_config,omitempty"`
// The config for drift of prediction data.
PredictionDriftDetectionConfig *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig `protobuf:"bytes,3,opt,name=prediction_drift_detection_config,json=predictionDriftDetectionConfig,proto3" json:"prediction_drift_detection_config,omitempty"`
// The config for integrating with Vertex Explainable AI.
ExplanationConfig *ModelMonitoringObjectiveConfig_ExplanationConfig `protobuf:"bytes,5,opt,name=explanation_config,json=explanationConfig,proto3" json:"explanation_config,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig) Reset() {
*x = ModelMonitoringObjectiveConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0}
}
func (x *ModelMonitoringObjectiveConfig) GetTrainingDataset() *ModelMonitoringObjectiveConfig_TrainingDataset {
if x != nil {
return x.TrainingDataset
}
return nil
}
func (x *ModelMonitoringObjectiveConfig) GetTrainingPredictionSkewDetectionConfig() *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig {
if x != nil {
return x.TrainingPredictionSkewDetectionConfig
}
return nil
}
func (x *ModelMonitoringObjectiveConfig) GetPredictionDriftDetectionConfig() *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig {
if x != nil {
return x.PredictionDriftDetectionConfig
}
return nil
}
func (x *ModelMonitoringObjectiveConfig) GetExplanationConfig() *ModelMonitoringObjectiveConfig_ExplanationConfig {
if x != nil {
return x.ExplanationConfig
}
return nil
}
// The alert config for model monitoring.
type ModelMonitoringAlertConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Types that are assignable to Alert:
//
// *ModelMonitoringAlertConfig_EmailAlertConfig_
Alert isModelMonitoringAlertConfig_Alert `protobuf_oneof:"alert"`
// Dump the anomalies to Cloud Logging. The anomalies will be put to json
// payload encoded from proto
// [google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry][].
// This can be further sinked to Pub/Sub or any other services supported
// by Cloud Logging.
EnableLogging bool `protobuf:"varint,2,opt,name=enable_logging,json=enableLogging,proto3" json:"enable_logging,omitempty"`
// Resource names of the NotificationChannels to send alert.
// Must be of the format
// `projects/<project_id_or_number>/notificationChannels/<channel_id>`
NotificationChannels []string `protobuf:"bytes,3,rep,name=notification_channels,json=notificationChannels,proto3" json:"notification_channels,omitempty"`
}
func (x *ModelMonitoringAlertConfig) Reset() {
*x = ModelMonitoringAlertConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringAlertConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringAlertConfig) ProtoMessage() {}
func (x *ModelMonitoringAlertConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringAlertConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringAlertConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{1}
}
func (m *ModelMonitoringAlertConfig) GetAlert() isModelMonitoringAlertConfig_Alert {
if m != nil {
return m.Alert
}
return nil
}
func (x *ModelMonitoringAlertConfig) GetEmailAlertConfig() *ModelMonitoringAlertConfig_EmailAlertConfig {
if x, ok := x.GetAlert().(*ModelMonitoringAlertConfig_EmailAlertConfig_); ok {
return x.EmailAlertConfig
}
return nil
}
func (x *ModelMonitoringAlertConfig) GetEnableLogging() bool {
if x != nil {
return x.EnableLogging
}
return false
}
func (x *ModelMonitoringAlertConfig) GetNotificationChannels() []string {
if x != nil {
return x.NotificationChannels
}
return nil
}
type isModelMonitoringAlertConfig_Alert interface {
isModelMonitoringAlertConfig_Alert()
}
type ModelMonitoringAlertConfig_EmailAlertConfig_ struct {
// Email alert config.
EmailAlertConfig *ModelMonitoringAlertConfig_EmailAlertConfig `protobuf:"bytes,1,opt,name=email_alert_config,json=emailAlertConfig,proto3,oneof"`
}
func (*ModelMonitoringAlertConfig_EmailAlertConfig_) isModelMonitoringAlertConfig_Alert() {}
// The config for feature monitoring threshold.
type ThresholdConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Types that are assignable to Threshold:
//
// *ThresholdConfig_Value
Threshold isThresholdConfig_Threshold `protobuf_oneof:"threshold"`
}
func (x *ThresholdConfig) Reset() {
*x = ThresholdConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ThresholdConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ThresholdConfig) ProtoMessage() {}
func (x *ThresholdConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ThresholdConfig.ProtoReflect.Descriptor instead.
func (*ThresholdConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{2}
}
func (m *ThresholdConfig) GetThreshold() isThresholdConfig_Threshold {
if m != nil {
return m.Threshold
}
return nil
}
func (x *ThresholdConfig) GetValue() float64 {
if x, ok := x.GetThreshold().(*ThresholdConfig_Value); ok {
return x.Value
}
return 0
}
type isThresholdConfig_Threshold interface {
isThresholdConfig_Threshold()
}
type ThresholdConfig_Value struct {
// Specify a threshold value that can trigger the alert.
// If this threshold config is for feature distribution distance:
// 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.
Value float64 `protobuf:"fixed64,1,opt,name=value,proto3,oneof"`
}
func (*ThresholdConfig_Value) isThresholdConfig_Threshold() {}
// Sampling Strategy for logging, can be for both training and prediction
// dataset.
type SamplingStrategy struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Random sample config. Will support more sampling strategies later.
RandomSampleConfig *SamplingStrategy_RandomSampleConfig `protobuf:"bytes,1,opt,name=random_sample_config,json=randomSampleConfig,proto3" json:"random_sample_config,omitempty"`
}
func (x *SamplingStrategy) Reset() {
*x = SamplingStrategy{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *SamplingStrategy) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*SamplingStrategy) ProtoMessage() {}
func (x *SamplingStrategy) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use SamplingStrategy.ProtoReflect.Descriptor instead.
func (*SamplingStrategy) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{3}
}
func (x *SamplingStrategy) GetRandomSampleConfig() *SamplingStrategy_RandomSampleConfig {
if x != nil {
return x.RandomSampleConfig
}
return nil
}
// Training Dataset information.
type ModelMonitoringObjectiveConfig_TrainingDataset struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Types that are assignable to DataSource:
//
// *ModelMonitoringObjectiveConfig_TrainingDataset_Dataset
// *ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource
// *ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource
DataSource isModelMonitoringObjectiveConfig_TrainingDataset_DataSource `protobuf_oneof:"data_source"`
// Data format of the dataset, only applicable if the input is from
// Google Cloud Storage.
// The possible formats are:
//
// "tf-record"
// The source file is a TFRecord file.
//
// "csv"
// The source file is a CSV file.
// "jsonl"
// The source file is a JSONL file.
DataFormat string `protobuf:"bytes,2,opt,name=data_format,json=dataFormat,proto3" json:"data_format,omitempty"`
// The target field name the model is to predict.
// This field will be excluded when doing Predict and (or) Explain for the
// training data.
TargetField string `protobuf:"bytes,6,opt,name=target_field,json=targetField,proto3" json:"target_field,omitempty"`
// Strategy to sample data from Training Dataset.
// If not set, we process the whole dataset.
LoggingSamplingStrategy *SamplingStrategy `protobuf:"bytes,7,opt,name=logging_sampling_strategy,json=loggingSamplingStrategy,proto3" json:"logging_sampling_strategy,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) Reset() {
*x = ModelMonitoringObjectiveConfig_TrainingDataset{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig_TrainingDataset) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_TrainingDataset.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_TrainingDataset) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 0}
}
func (m *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataSource() isModelMonitoringObjectiveConfig_TrainingDataset_DataSource {
if m != nil {
return m.DataSource
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataset() string {
if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset); ok {
return x.Dataset
}
return ""
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetGcsSource() *GcsSource {
if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource); ok {
return x.GcsSource
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetBigquerySource() *BigQuerySource {
if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource); ok {
return x.BigquerySource
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataFormat() string {
if x != nil {
return x.DataFormat
}
return ""
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetTargetField() string {
if x != nil {
return x.TargetField
}
return ""
}
func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetLoggingSamplingStrategy() *SamplingStrategy {
if x != nil {
return x.LoggingSamplingStrategy
}
return nil
}
type isModelMonitoringObjectiveConfig_TrainingDataset_DataSource interface {
isModelMonitoringObjectiveConfig_TrainingDataset_DataSource()
}
type ModelMonitoringObjectiveConfig_TrainingDataset_Dataset struct {
// The resource name of the Dataset used to train this Model.
Dataset string `protobuf:"bytes,3,opt,name=dataset,proto3,oneof"`
}
type ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource struct {
// The Google Cloud Storage uri of the unmanaged Dataset used to train
// this Model.
GcsSource *GcsSource `protobuf:"bytes,4,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}
type ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource struct {
// The BigQuery table of the unmanaged Dataset used to train this
// Model.
BigquerySource *BigQuerySource `protobuf:"bytes,5,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}
func (*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}
func (*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}
func (*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}
// The config for Training & Prediction data skew detection. It specifies the
// training dataset sources and the skew detection parameters.
type ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Key is the feature name and value is the threshold. If a feature needs to
// be monitored for skew, a value threshold must be configured for that
// feature. The threshold here is against feature distribution distance
// between the training and prediction feature.
SkewThresholds map[string]*ThresholdConfig `protobuf:"bytes,1,rep,name=skew_thresholds,json=skewThresholds,proto3" json:"skew_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Key is the feature name and value is the threshold. The threshold here is
// against attribution score distance between the training and prediction
// feature.
AttributionScoreSkewThresholds map[string]*ThresholdConfig `protobuf:"bytes,2,rep,name=attribution_score_skew_thresholds,json=attributionScoreSkewThresholds,proto3" json:"attribution_score_skew_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Skew anomaly detection threshold used by all features.
// When the per-feature thresholds are not set, this field can be used to
// specify a threshold for all features.
DefaultSkewThreshold *ThresholdConfig `protobuf:"bytes,6,opt,name=default_skew_threshold,json=defaultSkewThreshold,proto3" json:"default_skew_threshold,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) Reset() {
*x = ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[5]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[5]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 1}
}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetSkewThresholds() map[string]*ThresholdConfig {
if x != nil {
return x.SkewThresholds
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetAttributionScoreSkewThresholds() map[string]*ThresholdConfig {
if x != nil {
return x.AttributionScoreSkewThresholds
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetDefaultSkewThreshold() *ThresholdConfig {
if x != nil {
return x.DefaultSkewThreshold
}
return nil
}
// The config for Prediction data drift detection.
type ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Key is the feature name and value is the threshold. If a feature needs to
// be monitored for drift, a value threshold must be configured for that
// feature. The threshold here is against feature distribution distance
// between different time windws.
DriftThresholds map[string]*ThresholdConfig `protobuf:"bytes,1,rep,name=drift_thresholds,json=driftThresholds,proto3" json:"drift_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Key is the feature name and value is the threshold. The threshold here is
// against attribution score distance between different time windows.
AttributionScoreDriftThresholds map[string]*ThresholdConfig `protobuf:"bytes,2,rep,name=attribution_score_drift_thresholds,json=attributionScoreDriftThresholds,proto3" json:"attribution_score_drift_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Drift anomaly detection threshold used by all features.
// When the per-feature thresholds are not set, this field can be used to
// specify a threshold for all features.
DefaultDriftThreshold *ThresholdConfig `protobuf:"bytes,5,opt,name=default_drift_threshold,json=defaultDriftThreshold,proto3" json:"default_drift_threshold,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) Reset() {
*x = ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[6]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[6]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 2}
}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetDriftThresholds() map[string]*ThresholdConfig {
if x != nil {
return x.DriftThresholds
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetAttributionScoreDriftThresholds() map[string]*ThresholdConfig {
if x != nil {
return x.AttributionScoreDriftThresholds
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetDefaultDriftThreshold() *ThresholdConfig {
if x != nil {
return x.DefaultDriftThreshold
}
return nil
}
// The config for integrating with Vertex Explainable AI. Only applicable if
// the Model has explanation_spec populated.
type ModelMonitoringObjectiveConfig_ExplanationConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// If want to analyze the Vertex Explainable AI feature attribute scores or
// not. If set to true, Vertex AI will log the feature attributions from
// explain response and do the skew/drift detection for them.
EnableFeatureAttributes bool `protobuf:"varint,1,opt,name=enable_feature_attributes,json=enableFeatureAttributes,proto3" json:"enable_feature_attributes,omitempty"`
// Predictions generated by the BatchPredictionJob using baseline dataset.
ExplanationBaseline *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline `protobuf:"bytes,2,opt,name=explanation_baseline,json=explanationBaseline,proto3" json:"explanation_baseline,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) Reset() {
*x = ModelMonitoringObjectiveConfig_ExplanationConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[7]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig_ExplanationConfig) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[7]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_ExplanationConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 3}
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) GetEnableFeatureAttributes() bool {
if x != nil {
return x.EnableFeatureAttributes
}
return false
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) GetExplanationBaseline() *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline {
if x != nil {
return x.ExplanationBaseline
}
return nil
}
// Output from
// [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob] for
// Model Monitoring baseline dataset, which can be used to generate baseline
// attribution scores.
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The configuration specifying of BatchExplain job output. This can be
// used to generate the baseline of feature attribution scores.
//
// Types that are assignable to Destination:
//
// *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs
// *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery
Destination isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination `protobuf_oneof:"destination"`
// The storage format of the predictions generated BatchPrediction job.
PredictionFormat ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat `protobuf:"varint,1,opt,name=prediction_format,json=predictionFormat,proto3,enum=google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat" json:"prediction_format,omitempty"`
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) Reset() {
*x = ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[12]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) ProtoMessage() {}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[12]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{0, 3, 0}
}
func (m *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetDestination() isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination {
if m != nil {
return m.Destination
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetGcs() *GcsDestination {
if x, ok := x.GetDestination().(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs); ok {
return x.Gcs
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetBigquery() *BigQueryDestination {
if x, ok := x.GetDestination().(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery); ok {
return x.Bigquery
}
return nil
}
func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetPredictionFormat() ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat {
if x != nil {
return x.PredictionFormat
}
return ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PREDICTION_FORMAT_UNSPECIFIED
}
type isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination interface {
isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination()
}
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs struct {
// Cloud Storage location for BatchExplain output.
Gcs *GcsDestination `protobuf:"bytes,2,opt,name=gcs,proto3,oneof"`
}
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery struct {
// BigQuery location for BatchExplain output.
Bigquery *BigQueryDestination `protobuf:"bytes,3,opt,name=bigquery,proto3,oneof"`
}
func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs) isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination() {
}
func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery) isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination() {
}
// The config for email alert.
type ModelMonitoringAlertConfig_EmailAlertConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The email addresses to send the alert.
UserEmails []string `protobuf:"bytes,1,rep,name=user_emails,json=userEmails,proto3" json:"user_emails,omitempty"`
}
func (x *ModelMonitoringAlertConfig_EmailAlertConfig) Reset() {
*x = ModelMonitoringAlertConfig_EmailAlertConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[13]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelMonitoringAlertConfig_EmailAlertConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelMonitoringAlertConfig_EmailAlertConfig) ProtoMessage() {}
func (x *ModelMonitoringAlertConfig_EmailAlertConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[13]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelMonitoringAlertConfig_EmailAlertConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringAlertConfig_EmailAlertConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{1, 0}
}
func (x *ModelMonitoringAlertConfig_EmailAlertConfig) GetUserEmails() []string {
if x != nil {
return x.UserEmails
}
return nil
}
// Requests are randomly selected.
type SamplingStrategy_RandomSampleConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Sample rate (0, 1]
SampleRate float64 `protobuf:"fixed64,1,opt,name=sample_rate,json=sampleRate,proto3" json:"sample_rate,omitempty"`
}
func (x *SamplingStrategy_RandomSampleConfig) Reset() {
*x = SamplingStrategy_RandomSampleConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[14]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *SamplingStrategy_RandomSampleConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*SamplingStrategy_RandomSampleConfig) ProtoMessage() {}
func (x *SamplingStrategy_RandomSampleConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[14]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use SamplingStrategy_RandomSampleConfig.ProtoReflect.Descriptor instead.
func (*SamplingStrategy_RandomSampleConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP(), []int{3, 0}
}
func (x *SamplingStrategy_RandomSampleConfig) GetSampleRate() float64 {
if x != nil {
return x.SampleRate
}
return 0
}
var File_google_cloud_aiplatform_v1_model_monitoring_proto protoreflect.FileDescriptor
var file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDesc = []byte{
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}
var (
file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescOnce sync.Once
file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescData = file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDesc
)
func file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescGZIP() []byte {
file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescOnce.Do(func() {
file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescData)
})
return file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDescData
}
var file_google_cloud_aiplatform_v1_model_monitoring_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes = make([]protoimpl.MessageInfo, 15)
var file_google_cloud_aiplatform_v1_model_monitoring_proto_goTypes = []interface{}{
(ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat)(0), // 0: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat
(*ModelMonitoringObjectiveConfig)(nil), // 1: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig
(*ModelMonitoringAlertConfig)(nil), // 2: google.cloud.aiplatform.v1.ModelMonitoringAlertConfig
(*ThresholdConfig)(nil), // 3: google.cloud.aiplatform.v1.ThresholdConfig
(*SamplingStrategy)(nil), // 4: google.cloud.aiplatform.v1.SamplingStrategy
(*ModelMonitoringObjectiveConfig_TrainingDataset)(nil), // 5: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingDataset
(*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig)(nil), // 6: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
(*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig)(nil), // 7: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig
(*ModelMonitoringObjectiveConfig_ExplanationConfig)(nil), // 8: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig
nil, // 9: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry
nil, // 10: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry
nil, // 11: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry
nil, // 12: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry
(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline)(nil), // 13: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline
(*ModelMonitoringAlertConfig_EmailAlertConfig)(nil), // 14: google.cloud.aiplatform.v1.ModelMonitoringAlertConfig.EmailAlertConfig
(*SamplingStrategy_RandomSampleConfig)(nil), // 15: google.cloud.aiplatform.v1.SamplingStrategy.RandomSampleConfig
(*GcsSource)(nil), // 16: google.cloud.aiplatform.v1.GcsSource
(*BigQuerySource)(nil), // 17: google.cloud.aiplatform.v1.BigQuerySource
(*GcsDestination)(nil), // 18: google.cloud.aiplatform.v1.GcsDestination
(*BigQueryDestination)(nil), // 19: google.cloud.aiplatform.v1.BigQueryDestination
}
var file_google_cloud_aiplatform_v1_model_monitoring_proto_depIdxs = []int32{
5, // 0: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.training_dataset:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingDataset
6, // 1: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.training_prediction_skew_detection_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
7, // 2: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.prediction_drift_detection_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig
8, // 3: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.explanation_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig
14, // 4: google.cloud.aiplatform.v1.ModelMonitoringAlertConfig.email_alert_config:type_name -> google.cloud.aiplatform.v1.ModelMonitoringAlertConfig.EmailAlertConfig
15, // 5: google.cloud.aiplatform.v1.SamplingStrategy.random_sample_config:type_name -> google.cloud.aiplatform.v1.SamplingStrategy.RandomSampleConfig
16, // 6: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingDataset.gcs_source:type_name -> google.cloud.aiplatform.v1.GcsSource
17, // 7: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingDataset.bigquery_source:type_name -> google.cloud.aiplatform.v1.BigQuerySource
4, // 8: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingDataset.logging_sampling_strategy:type_name -> google.cloud.aiplatform.v1.SamplingStrategy
9, // 9: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.skew_thresholds:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry
10, // 10: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.attribution_score_skew_thresholds:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry
3, // 11: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.default_skew_threshold:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
11, // 12: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.drift_thresholds:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry
12, // 13: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.attribution_score_drift_thresholds:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry
3, // 14: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.default_drift_threshold:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
13, // 15: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.explanation_baseline:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline
3, // 16: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
3, // 17: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
3, // 18: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
3, // 19: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1.ThresholdConfig
18, // 20: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.gcs:type_name -> google.cloud.aiplatform.v1.GcsDestination
19, // 21: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.bigquery:type_name -> google.cloud.aiplatform.v1.BigQueryDestination
0, // 22: google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.prediction_format:type_name -> google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat
23, // [23:23] is the sub-list for method output_type
23, // [23:23] is the sub-list for method input_type
23, // [23:23] is the sub-list for extension type_name
23, // [23:23] is the sub-list for extension extendee
0, // [0:23] is the sub-list for field type_name
}
func init() { file_google_cloud_aiplatform_v1_model_monitoring_proto_init() }
func file_google_cloud_aiplatform_v1_model_monitoring_proto_init() {
if File_google_cloud_aiplatform_v1_model_monitoring_proto != nil {
return
}
file_google_cloud_aiplatform_v1_io_proto_init()
if !protoimpl.UnsafeEnabled {
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringAlertConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ThresholdConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SamplingStrategy); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig_TrainingDataset); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig_ExplanationConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelMonitoringAlertConfig_EmailAlertConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[14].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SamplingStrategy_RandomSampleConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[1].OneofWrappers = []interface{}{
(*ModelMonitoringAlertConfig_EmailAlertConfig_)(nil),
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[2].OneofWrappers = []interface{}{
(*ThresholdConfig_Value)(nil),
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[4].OneofWrappers = []interface{}{
(*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset)(nil),
(*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource)(nil),
(*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource)(nil),
}
file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes[12].OneofWrappers = []interface{}{
(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs)(nil),
(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery)(nil),
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDesc,
NumEnums: 1,
NumMessages: 15,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_cloud_aiplatform_v1_model_monitoring_proto_goTypes,
DependencyIndexes: file_google_cloud_aiplatform_v1_model_monitoring_proto_depIdxs,
EnumInfos: file_google_cloud_aiplatform_v1_model_monitoring_proto_enumTypes,
MessageInfos: file_google_cloud_aiplatform_v1_model_monitoring_proto_msgTypes,
}.Build()
File_google_cloud_aiplatform_v1_model_monitoring_proto = out.File
file_google_cloud_aiplatform_v1_model_monitoring_proto_rawDesc = nil
file_google_cloud_aiplatform_v1_model_monitoring_proto_goTypes = nil
file_google_cloud_aiplatform_v1_model_monitoring_proto_depIdxs = nil
}