| // Copyright 2024 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/v1beta1/model_monitor.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" |
| timestamppb "google.golang.org/protobuf/types/known/timestamppb" |
| ) |
| |
| 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) |
| ) |
| |
| // Vertex AI Model Monitoring Service serves as a central hub for the analysis |
| // and visualization of data quality and performance related to models. |
| // ModelMonitor stands as a top level resource for overseeing your model |
| // monitoring tasks. |
| type ModelMonitor struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Optional default monitoring objective, it can be overridden in the |
| // ModelMonitoringJob objective spec. |
| // |
| // Types that are assignable to DefaultObjective: |
| // |
| // *ModelMonitor_TabularObjective |
| DefaultObjective isModelMonitor_DefaultObjective `protobuf_oneof:"default_objective"` |
| // Immutable. Resource name of the ModelMonitor. Format: |
| // `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| // The display name of the ModelMonitor. |
| // The name can be up to 128 characters long and can consist of any UTF-8. |
| DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"` |
| // The entity that is subject to analysis. |
| // Currently only models in Vertex AI Model Registry are supported. If you |
| // want to analyze the model which is outside the Vertex AI, you could |
| // register a model in Vertex AI Model Registry using just a display name. |
| ModelMonitoringTarget *ModelMonitor_ModelMonitoringTarget `protobuf:"bytes,3,opt,name=model_monitoring_target,json=modelMonitoringTarget,proto3" json:"model_monitoring_target,omitempty"` |
| // Optional training dataset used to train the model. |
| // It can serve as a reference dataset to identify changes in production. |
| TrainingDataset *ModelMonitoringInput `protobuf:"bytes,10,opt,name=training_dataset,json=trainingDataset,proto3" json:"training_dataset,omitempty"` |
| // Optional default notification spec, it can be overridden in the |
| // ModelMonitoringJob notification spec. |
| NotificationSpec *ModelMonitoringNotificationSpec `protobuf:"bytes,12,opt,name=notification_spec,json=notificationSpec,proto3" json:"notification_spec,omitempty"` |
| // Optional default monitoring metrics/logs export spec, it can be overridden |
| // in the ModelMonitoringJob output spec. |
| // If not specified, a default Google Cloud Storage bucket will be created |
| // under your project. |
| OutputSpec *ModelMonitoringOutputSpec `protobuf:"bytes,13,opt,name=output_spec,json=outputSpec,proto3" json:"output_spec,omitempty"` |
| // Optional model explanation spec. It is used for feature attribution |
| // monitoring. |
| ExplanationSpec *ExplanationSpec `protobuf:"bytes,16,opt,name=explanation_spec,json=explanationSpec,proto3" json:"explanation_spec,omitempty"` |
| // Monitoring Schema is to specify the model's features, prediction outputs |
| // and ground truth properties. It is used to extract pertinent data from the |
| // dataset and to process features based on their properties. |
| // Make sure that the schema aligns with your dataset, if it does not, we will |
| // be unable to extract data from the dataset. |
| // It is required for most models, but optional for Vertex AI AutoML Tables |
| // unless the schem information is not available. |
| ModelMonitoringSchema *ModelMonitoringSchema `protobuf:"bytes,9,opt,name=model_monitoring_schema,json=modelMonitoringSchema,proto3" json:"model_monitoring_schema,omitempty"` |
| // Output only. Timestamp when this ModelMonitor was created. |
| CreateTime *timestamppb.Timestamp `protobuf:"bytes,6,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"` |
| // Output only. Timestamp when this ModelMonitor was updated most recently. |
| UpdateTime *timestamppb.Timestamp `protobuf:"bytes,7,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"` |
| } |
| |
| func (x *ModelMonitor) Reset() { |
| *x = ModelMonitor{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelMonitor) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelMonitor) ProtoMessage() {} |
| |
| func (x *ModelMonitor) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_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 ModelMonitor.ProtoReflect.Descriptor instead. |
| func (*ModelMonitor) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (m *ModelMonitor) GetDefaultObjective() isModelMonitor_DefaultObjective { |
| if m != nil { |
| return m.DefaultObjective |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetTabularObjective() *ModelMonitoringObjectiveSpec_TabularObjective { |
| if x, ok := x.GetDefaultObjective().(*ModelMonitor_TabularObjective); ok { |
| return x.TabularObjective |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| func (x *ModelMonitor) GetDisplayName() string { |
| if x != nil { |
| return x.DisplayName |
| } |
| return "" |
| } |
| |
| func (x *ModelMonitor) GetModelMonitoringTarget() *ModelMonitor_ModelMonitoringTarget { |
| if x != nil { |
| return x.ModelMonitoringTarget |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetTrainingDataset() *ModelMonitoringInput { |
| if x != nil { |
| return x.TrainingDataset |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetNotificationSpec() *ModelMonitoringNotificationSpec { |
| if x != nil { |
| return x.NotificationSpec |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetOutputSpec() *ModelMonitoringOutputSpec { |
| if x != nil { |
| return x.OutputSpec |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetExplanationSpec() *ExplanationSpec { |
| if x != nil { |
| return x.ExplanationSpec |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetModelMonitoringSchema() *ModelMonitoringSchema { |
| if x != nil { |
| return x.ModelMonitoringSchema |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetCreateTime() *timestamppb.Timestamp { |
| if x != nil { |
| return x.CreateTime |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor) GetUpdateTime() *timestamppb.Timestamp { |
| if x != nil { |
| return x.UpdateTime |
| } |
| return nil |
| } |
| |
| type isModelMonitor_DefaultObjective interface { |
| isModelMonitor_DefaultObjective() |
| } |
| |
| type ModelMonitor_TabularObjective struct { |
| // Optional default tabular model monitoring objective. |
| TabularObjective *ModelMonitoringObjectiveSpec_TabularObjective `protobuf:"bytes,11,opt,name=tabular_objective,json=tabularObjective,proto3,oneof"` |
| } |
| |
| func (*ModelMonitor_TabularObjective) isModelMonitor_DefaultObjective() {} |
| |
| // The Model Monitoring Schema definition. |
| type ModelMonitoringSchema struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Feature names of the model. Vertex AI will try to match the features from |
| // your dataset as follows: |
| // - For 'csv' files, the header names are required, and we will extract the |
| // corresponding feature values when the header names align with the |
| // feature names. |
| // - For 'jsonl' files, we will extract the corresponding feature values if |
| // the key names match the feature names. |
| // Note: Nested features are not supported, so please ensure your features |
| // are flattened. Ensure the feature values are scalar or an array of |
| // scalars. |
| // - For 'bigquery' dataset, we will extract the corresponding feature values |
| // if the column names match the feature names. |
| // Note: The column type can be a scalar or an array of scalars. STRUCT or |
| // JSON types are not supported. You may use SQL queries to select or |
| // aggregate the relevant features from your original table. However, |
| // ensure that the 'schema' of the query results meets our requirements. |
| // - For the Vertex AI Endpoint Request Response Logging table or Vertex AI |
| // Batch Prediction Job results. If the |
| // [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type] |
| // is an array, ensure that the sequence in |
| // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields] |
| // matches the order of features in the prediction instance. We will match |
| // the feature with the array in the order specified in [feature_fields]. |
| FeatureFields []*ModelMonitoringSchema_FieldSchema `protobuf:"bytes,1,rep,name=feature_fields,json=featureFields,proto3" json:"feature_fields,omitempty"` |
| // Prediction output names of the model. The requirements are the same as the |
| // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]. |
| // For AutoML Tables, the prediction output name presented in schema will be: |
| // `predicted_{target_column}`, the `target_column` is the one you specified |
| // when you train the model. |
| // For Prediction output drift analysis: |
| // - AutoML Classification, the distribution of the argmax label will be |
| // analyzed. |
| // - AutoML Regression, the distribution of the value will be analyzed. |
| PredictionFields []*ModelMonitoringSchema_FieldSchema `protobuf:"bytes,2,rep,name=prediction_fields,json=predictionFields,proto3" json:"prediction_fields,omitempty"` |
| // Target /ground truth names of the model. |
| GroundTruthFields []*ModelMonitoringSchema_FieldSchema `protobuf:"bytes,3,rep,name=ground_truth_fields,json=groundTruthFields,proto3" json:"ground_truth_fields,omitempty"` |
| } |
| |
| func (x *ModelMonitoringSchema) Reset() { |
| *x = ModelMonitoringSchema{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelMonitoringSchema) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelMonitoringSchema) ProtoMessage() {} |
| |
| func (x *ModelMonitoringSchema) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_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 ModelMonitoringSchema.ProtoReflect.Descriptor instead. |
| func (*ModelMonitoringSchema) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (x *ModelMonitoringSchema) GetFeatureFields() []*ModelMonitoringSchema_FieldSchema { |
| if x != nil { |
| return x.FeatureFields |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitoringSchema) GetPredictionFields() []*ModelMonitoringSchema_FieldSchema { |
| if x != nil { |
| return x.PredictionFields |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitoringSchema) GetGroundTruthFields() []*ModelMonitoringSchema_FieldSchema { |
| if x != nil { |
| return x.GroundTruthFields |
| } |
| return nil |
| } |
| |
| // The monitoring target refers to the entity that is subject to analysis. |
| // e.g. Vertex AI Model version. |
| type ModelMonitor_ModelMonitoringTarget struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Types that are assignable to Source: |
| // |
| // *ModelMonitor_ModelMonitoringTarget_VertexModel |
| Source isModelMonitor_ModelMonitoringTarget_Source `protobuf_oneof:"source"` |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget) Reset() { |
| *x = ModelMonitor_ModelMonitoringTarget{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelMonitor_ModelMonitoringTarget) ProtoMessage() {} |
| |
| func (x *ModelMonitor_ModelMonitoringTarget) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_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 ModelMonitor_ModelMonitoringTarget.ProtoReflect.Descriptor instead. |
| func (*ModelMonitor_ModelMonitoringTarget) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP(), []int{0, 0} |
| } |
| |
| func (m *ModelMonitor_ModelMonitoringTarget) GetSource() isModelMonitor_ModelMonitoringTarget_Source { |
| if m != nil { |
| return m.Source |
| } |
| return nil |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget) GetVertexModel() *ModelMonitor_ModelMonitoringTarget_VertexModelSource { |
| if x, ok := x.GetSource().(*ModelMonitor_ModelMonitoringTarget_VertexModel); ok { |
| return x.VertexModel |
| } |
| return nil |
| } |
| |
| type isModelMonitor_ModelMonitoringTarget_Source interface { |
| isModelMonitor_ModelMonitoringTarget_Source() |
| } |
| |
| type ModelMonitor_ModelMonitoringTarget_VertexModel struct { |
| // Model in Vertex AI Model Registry. |
| VertexModel *ModelMonitor_ModelMonitoringTarget_VertexModelSource `protobuf:"bytes,1,opt,name=vertex_model,json=vertexModel,proto3,oneof"` |
| } |
| |
| func (*ModelMonitor_ModelMonitoringTarget_VertexModel) isModelMonitor_ModelMonitoringTarget_Source() { |
| } |
| |
| // Model in Vertex AI Model Registry. |
| type ModelMonitor_ModelMonitoringTarget_VertexModelSource struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Model resource name. Format: |
| // projects/{project}/locations/{location}/models/{model}. |
| Model string `protobuf:"bytes,1,opt,name=model,proto3" json:"model,omitempty"` |
| // Model version id. |
| ModelVersionId string `protobuf:"bytes,2,opt,name=model_version_id,json=modelVersionId,proto3" json:"model_version_id,omitempty"` |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget_VertexModelSource) Reset() { |
| *x = ModelMonitor_ModelMonitoringTarget_VertexModelSource{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[3] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget_VertexModelSource) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelMonitor_ModelMonitoringTarget_VertexModelSource) ProtoMessage() {} |
| |
| func (x *ModelMonitor_ModelMonitoringTarget_VertexModelSource) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_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 ModelMonitor_ModelMonitoringTarget_VertexModelSource.ProtoReflect.Descriptor instead. |
| func (*ModelMonitor_ModelMonitoringTarget_VertexModelSource) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP(), []int{0, 0, 0} |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget_VertexModelSource) GetModel() string { |
| if x != nil { |
| return x.Model |
| } |
| return "" |
| } |
| |
| func (x *ModelMonitor_ModelMonitoringTarget_VertexModelSource) GetModelVersionId() string { |
| if x != nil { |
| return x.ModelVersionId |
| } |
| return "" |
| } |
| |
| // Schema field definition. |
| type ModelMonitoringSchema_FieldSchema struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Field name. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| // Supported data types are: |
| // `float` |
| // `integer` |
| // `boolean` |
| // `string` |
| // `categorical` |
| DataType string `protobuf:"bytes,2,opt,name=data_type,json=dataType,proto3" json:"data_type,omitempty"` |
| // Describes if the schema field is an array of given data type. |
| Repeated bool `protobuf:"varint,3,opt,name=repeated,proto3" json:"repeated,omitempty"` |
| } |
| |
| func (x *ModelMonitoringSchema_FieldSchema) Reset() { |
| *x = ModelMonitoringSchema_FieldSchema{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[4] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelMonitoringSchema_FieldSchema) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelMonitoringSchema_FieldSchema) ProtoMessage() {} |
| |
| func (x *ModelMonitoringSchema_FieldSchema) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1beta1_model_monitor_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 ModelMonitoringSchema_FieldSchema.ProtoReflect.Descriptor instead. |
| func (*ModelMonitoringSchema_FieldSchema) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP(), []int{1, 0} |
| } |
| |
| func (x *ModelMonitoringSchema_FieldSchema) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| func (x *ModelMonitoringSchema_FieldSchema) GetDataType() string { |
| if x != nil { |
| return x.DataType |
| } |
| return "" |
| } |
| |
| func (x *ModelMonitoringSchema_FieldSchema) GetRepeated() bool { |
| if x != nil { |
| return x.Repeated |
| } |
| return false |
| } |
| |
| var File_google_cloud_aiplatform_v1beta1_model_monitor_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDesc = []byte{ |
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| } |
| |
| var ( |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescOnce sync.Once |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescData = file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDesc |
| ) |
| |
| func file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescGZIP() []byte { |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescOnce.Do(func() { |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescData) |
| }) |
| return file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDescData |
| } |
| |
| var file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes = make([]protoimpl.MessageInfo, 5) |
| var file_google_cloud_aiplatform_v1beta1_model_monitor_proto_goTypes = []interface{}{ |
| (*ModelMonitor)(nil), // 0: google.cloud.aiplatform.v1beta1.ModelMonitor |
| (*ModelMonitoringSchema)(nil), // 1: google.cloud.aiplatform.v1beta1.ModelMonitoringSchema |
| (*ModelMonitor_ModelMonitoringTarget)(nil), // 2: google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget |
| (*ModelMonitor_ModelMonitoringTarget_VertexModelSource)(nil), // 3: google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget.VertexModelSource |
| (*ModelMonitoringSchema_FieldSchema)(nil), // 4: google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema |
| (*ModelMonitoringObjectiveSpec_TabularObjective)(nil), // 5: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective |
| (*ModelMonitoringInput)(nil), // 6: google.cloud.aiplatform.v1beta1.ModelMonitoringInput |
| (*ModelMonitoringNotificationSpec)(nil), // 7: google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec |
| (*ModelMonitoringOutputSpec)(nil), // 8: google.cloud.aiplatform.v1beta1.ModelMonitoringOutputSpec |
| (*ExplanationSpec)(nil), // 9: google.cloud.aiplatform.v1beta1.ExplanationSpec |
| (*timestamppb.Timestamp)(nil), // 10: google.protobuf.Timestamp |
| } |
| var file_google_cloud_aiplatform_v1beta1_model_monitor_proto_depIdxs = []int32{ |
| 5, // 0: google.cloud.aiplatform.v1beta1.ModelMonitor.tabular_objective:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveSpec.TabularObjective |
| 2, // 1: google.cloud.aiplatform.v1beta1.ModelMonitor.model_monitoring_target:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget |
| 6, // 2: google.cloud.aiplatform.v1beta1.ModelMonitor.training_dataset:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringInput |
| 7, // 3: google.cloud.aiplatform.v1beta1.ModelMonitor.notification_spec:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringNotificationSpec |
| 8, // 4: google.cloud.aiplatform.v1beta1.ModelMonitor.output_spec:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringOutputSpec |
| 9, // 5: google.cloud.aiplatform.v1beta1.ModelMonitor.explanation_spec:type_name -> google.cloud.aiplatform.v1beta1.ExplanationSpec |
| 1, // 6: google.cloud.aiplatform.v1beta1.ModelMonitor.model_monitoring_schema:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringSchema |
| 10, // 7: google.cloud.aiplatform.v1beta1.ModelMonitor.create_time:type_name -> google.protobuf.Timestamp |
| 10, // 8: google.cloud.aiplatform.v1beta1.ModelMonitor.update_time:type_name -> google.protobuf.Timestamp |
| 4, // 9: google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema |
| 4, // 10: google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.prediction_fields:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema |
| 4, // 11: google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.ground_truth_fields:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema |
| 3, // 12: google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget.vertex_model:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitor.ModelMonitoringTarget.VertexModelSource |
| 13, // [13:13] is the sub-list for method output_type |
| 13, // [13:13] is the sub-list for method input_type |
| 13, // [13:13] is the sub-list for extension type_name |
| 13, // [13:13] is the sub-list for extension extendee |
| 0, // [0:13] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_aiplatform_v1beta1_model_monitor_proto_init() } |
| func file_google_cloud_aiplatform_v1beta1_model_monitor_proto_init() { |
| if File_google_cloud_aiplatform_v1beta1_model_monitor_proto != nil { |
| return |
| } |
| file_google_cloud_aiplatform_v1beta1_explanation_proto_init() |
| file_google_cloud_aiplatform_v1beta1_model_monitoring_spec_proto_init() |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitor); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitoringSchema); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitor_ModelMonitoringTarget); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitor_ModelMonitoringTarget_VertexModelSource); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelMonitoringSchema_FieldSchema); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[0].OneofWrappers = []interface{}{ |
| (*ModelMonitor_TabularObjective)(nil), |
| } |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes[2].OneofWrappers = []interface{}{ |
| (*ModelMonitor_ModelMonitoringTarget_VertexModel)(nil), |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDesc, |
| NumEnums: 0, |
| NumMessages: 5, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_aiplatform_v1beta1_model_monitor_proto_goTypes, |
| DependencyIndexes: file_google_cloud_aiplatform_v1beta1_model_monitor_proto_depIdxs, |
| MessageInfos: file_google_cloud_aiplatform_v1beta1_model_monitor_proto_msgTypes, |
| }.Build() |
| File_google_cloud_aiplatform_v1beta1_model_monitor_proto = out.File |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_rawDesc = nil |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_goTypes = nil |
| file_google_cloud_aiplatform_v1beta1_model_monitor_proto_depIdxs = nil |
| } |
