| // 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 v1.34.1 |
| // protoc v4.25.3 |
| // source: google/cloud/aiplatform/v1/explanation.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" |
| structpb "google.golang.org/protobuf/types/known/structpb" |
| ) |
| |
| 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 format of the input example instances. |
| type Examples_ExampleGcsSource_DataFormat int32 |
| |
| const ( |
| // Format unspecified, used when unset. |
| Examples_ExampleGcsSource_DATA_FORMAT_UNSPECIFIED Examples_ExampleGcsSource_DataFormat = 0 |
| // Examples are stored in JSONL files. |
| Examples_ExampleGcsSource_JSONL Examples_ExampleGcsSource_DataFormat = 1 |
| ) |
| |
| // Enum value maps for Examples_ExampleGcsSource_DataFormat. |
| var ( |
| Examples_ExampleGcsSource_DataFormat_name = map[int32]string{ |
| 0: "DATA_FORMAT_UNSPECIFIED", |
| 1: "JSONL", |
| } |
| Examples_ExampleGcsSource_DataFormat_value = map[string]int32{ |
| "DATA_FORMAT_UNSPECIFIED": 0, |
| "JSONL": 1, |
| } |
| ) |
| |
| func (x Examples_ExampleGcsSource_DataFormat) Enum() *Examples_ExampleGcsSource_DataFormat { |
| p := new(Examples_ExampleGcsSource_DataFormat) |
| *p = x |
| return p |
| } |
| |
| func (x Examples_ExampleGcsSource_DataFormat) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Examples_ExampleGcsSource_DataFormat) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[0].Descriptor() |
| } |
| |
| func (Examples_ExampleGcsSource_DataFormat) Type() protoreflect.EnumType { |
| return &file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[0] |
| } |
| |
| func (x Examples_ExampleGcsSource_DataFormat) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Examples_ExampleGcsSource_DataFormat.Descriptor instead. |
| func (Examples_ExampleGcsSource_DataFormat) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{12, 0, 0} |
| } |
| |
| // Preset option controlling parameters for query speed-precision trade-off |
| type Presets_Query int32 |
| |
| const ( |
| // More precise neighbors as a trade-off against slower response. |
| Presets_PRECISE Presets_Query = 0 |
| // Faster response as a trade-off against less precise neighbors. |
| Presets_FAST Presets_Query = 1 |
| ) |
| |
| // Enum value maps for Presets_Query. |
| var ( |
| Presets_Query_name = map[int32]string{ |
| 0: "PRECISE", |
| 1: "FAST", |
| } |
| Presets_Query_value = map[string]int32{ |
| "PRECISE": 0, |
| "FAST": 1, |
| } |
| ) |
| |
| func (x Presets_Query) Enum() *Presets_Query { |
| p := new(Presets_Query) |
| *p = x |
| return p |
| } |
| |
| func (x Presets_Query) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Presets_Query) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[1].Descriptor() |
| } |
| |
| func (Presets_Query) Type() protoreflect.EnumType { |
| return &file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[1] |
| } |
| |
| func (x Presets_Query) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Presets_Query.Descriptor instead. |
| func (Presets_Query) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{13, 0} |
| } |
| |
| // Preset option controlling parameters for different modalities |
| type Presets_Modality int32 |
| |
| const ( |
| // Should not be set. Added as a recommended best practice for enums |
| Presets_MODALITY_UNSPECIFIED Presets_Modality = 0 |
| // IMAGE modality |
| Presets_IMAGE Presets_Modality = 1 |
| // TEXT modality |
| Presets_TEXT Presets_Modality = 2 |
| // TABULAR modality |
| Presets_TABULAR Presets_Modality = 3 |
| ) |
| |
| // Enum value maps for Presets_Modality. |
| var ( |
| Presets_Modality_name = map[int32]string{ |
| 0: "MODALITY_UNSPECIFIED", |
| 1: "IMAGE", |
| 2: "TEXT", |
| 3: "TABULAR", |
| } |
| Presets_Modality_value = map[string]int32{ |
| "MODALITY_UNSPECIFIED": 0, |
| "IMAGE": 1, |
| "TEXT": 2, |
| "TABULAR": 3, |
| } |
| ) |
| |
| func (x Presets_Modality) Enum() *Presets_Modality { |
| p := new(Presets_Modality) |
| *p = x |
| return p |
| } |
| |
| func (x Presets_Modality) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (Presets_Modality) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[2].Descriptor() |
| } |
| |
| func (Presets_Modality) Type() protoreflect.EnumType { |
| return &file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[2] |
| } |
| |
| func (x Presets_Modality) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use Presets_Modality.Descriptor instead. |
| func (Presets_Modality) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{13, 1} |
| } |
| |
| // Data format enum. |
| type ExamplesOverride_DataFormat int32 |
| |
| const ( |
| // Unspecified format. Must not be used. |
| ExamplesOverride_DATA_FORMAT_UNSPECIFIED ExamplesOverride_DataFormat = 0 |
| // Provided data is a set of model inputs. |
| ExamplesOverride_INSTANCES ExamplesOverride_DataFormat = 1 |
| // Provided data is a set of embeddings. |
| ExamplesOverride_EMBEDDINGS ExamplesOverride_DataFormat = 2 |
| ) |
| |
| // Enum value maps for ExamplesOverride_DataFormat. |
| var ( |
| ExamplesOverride_DataFormat_name = map[int32]string{ |
| 0: "DATA_FORMAT_UNSPECIFIED", |
| 1: "INSTANCES", |
| 2: "EMBEDDINGS", |
| } |
| ExamplesOverride_DataFormat_value = map[string]int32{ |
| "DATA_FORMAT_UNSPECIFIED": 0, |
| "INSTANCES": 1, |
| "EMBEDDINGS": 2, |
| } |
| ) |
| |
| func (x ExamplesOverride_DataFormat) Enum() *ExamplesOverride_DataFormat { |
| p := new(ExamplesOverride_DataFormat) |
| *p = x |
| return p |
| } |
| |
| func (x ExamplesOverride_DataFormat) String() string { |
| return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) |
| } |
| |
| func (ExamplesOverride_DataFormat) Descriptor() protoreflect.EnumDescriptor { |
| return file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[3].Descriptor() |
| } |
| |
| func (ExamplesOverride_DataFormat) Type() protoreflect.EnumType { |
| return &file_google_cloud_aiplatform_v1_explanation_proto_enumTypes[3] |
| } |
| |
| func (x ExamplesOverride_DataFormat) Number() protoreflect.EnumNumber { |
| return protoreflect.EnumNumber(x) |
| } |
| |
| // Deprecated: Use ExamplesOverride_DataFormat.Descriptor instead. |
| func (ExamplesOverride_DataFormat) EnumDescriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{16, 0} |
| } |
| |
| // Explanation of a prediction (provided in |
| // [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions]) |
| // produced by the Model on a given |
| // [instance][google.cloud.aiplatform.v1.ExplainRequest.instances]. |
| type Explanation struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. Feature attributions grouped by predicted outputs. |
| // |
| // For Models that predict only one output, such as regression Models that |
| // predict only one score, there is only one attibution that explains the |
| // predicted output. For Models that predict multiple outputs, such as |
| // multiclass Models that predict multiple classes, each element explains one |
| // specific item. |
| // [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] |
| // can be used to identify which output this attribution is explaining. |
| // |
| // By default, we provide Shapley values for the predicted class. However, |
| // you can configure the explanation request to generate Shapley values for |
| // any other classes too. For example, if a model predicts a probability of |
| // `0.4` for approving a loan application, the model's decision is to reject |
| // the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default |
| // Shapley values would be computed for rejection decision and not approval, |
| // even though the latter might be the positive class. |
| // |
| // If users set |
| // [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k], |
| // the attributions are sorted by |
| // [instance_output_value][Attributions.instance_output_value] in descending |
| // order. If |
| // [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices] |
| // is specified, the attributions are stored by |
| // [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] |
| // in the same order as they appear in the output_indices. |
| Attributions []*Attribution `protobuf:"bytes,1,rep,name=attributions,proto3" json:"attributions,omitempty"` |
| // Output only. List of the nearest neighbors for example-based explanations. |
| // |
| // For models deployed with the examples explanations feature enabled, the |
| // attributions field is empty and instead the neighbors field is populated. |
| Neighbors []*Neighbor `protobuf:"bytes,2,rep,name=neighbors,proto3" json:"neighbors,omitempty"` |
| } |
| |
| func (x *Explanation) Reset() { |
| *x = Explanation{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Explanation) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Explanation) ProtoMessage() {} |
| |
| func (x *Explanation) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 Explanation.ProtoReflect.Descriptor instead. |
| func (*Explanation) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (x *Explanation) GetAttributions() []*Attribution { |
| if x != nil { |
| return x.Attributions |
| } |
| return nil |
| } |
| |
| func (x *Explanation) GetNeighbors() []*Neighbor { |
| if x != nil { |
| return x.Neighbors |
| } |
| return nil |
| } |
| |
| // Aggregated explanation metrics for a Model over a set of instances. |
| type ModelExplanation struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. Aggregated attributions explaining the Model's prediction |
| // outputs over the set of instances. The attributions are grouped by outputs. |
| // |
| // For Models that predict only one output, such as regression Models that |
| // predict only one score, there is only one attibution that explains the |
| // predicted output. For Models that predict multiple outputs, such as |
| // multiclass Models that predict multiple classes, each element explains one |
| // specific item. |
| // [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] |
| // can be used to identify which output this attribution is explaining. |
| // |
| // The |
| // [baselineOutputValue][google.cloud.aiplatform.v1.Attribution.baseline_output_value], |
| // [instanceOutputValue][google.cloud.aiplatform.v1.Attribution.instance_output_value] |
| // and |
| // [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] |
| // fields are averaged over the test data. |
| // |
| // NOTE: Currently AutoML tabular classification Models produce only one |
| // attribution, which averages attributions over all the classes it predicts. |
| // [Attribution.approximation_error][google.cloud.aiplatform.v1.Attribution.approximation_error] |
| // is not populated. |
| MeanAttributions []*Attribution `protobuf:"bytes,1,rep,name=mean_attributions,json=meanAttributions,proto3" json:"mean_attributions,omitempty"` |
| } |
| |
| func (x *ModelExplanation) Reset() { |
| *x = ModelExplanation{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelExplanation) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelExplanation) ProtoMessage() {} |
| |
| func (x *ModelExplanation) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 ModelExplanation.ProtoReflect.Descriptor instead. |
| func (*ModelExplanation) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (x *ModelExplanation) GetMeanAttributions() []*Attribution { |
| if x != nil { |
| return x.MeanAttributions |
| } |
| return nil |
| } |
| |
| // Attribution that explains a particular prediction output. |
| type Attribution struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. Model predicted output if the input instance is constructed |
| // from the baselines of all the features defined in |
| // [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs]. |
| // The field name of the output is determined by the key in |
| // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs]. |
| // |
| // If the Model's predicted output has multiple dimensions (rank > 1), this is |
| // the value in the output located by |
| // [output_index][google.cloud.aiplatform.v1.Attribution.output_index]. |
| // |
| // If there are multiple baselines, their output values are averaged. |
| BaselineOutputValue float64 `protobuf:"fixed64,1,opt,name=baseline_output_value,json=baselineOutputValue,proto3" json:"baseline_output_value,omitempty"` |
| // Output only. Model predicted output on the corresponding [explanation |
| // instance][ExplainRequest.instances]. The field name of the output is |
| // determined by the key in |
| // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs]. |
| // |
| // If the Model predicted output has multiple dimensions, this is the value in |
| // the output located by |
| // [output_index][google.cloud.aiplatform.v1.Attribution.output_index]. |
| InstanceOutputValue float64 `protobuf:"fixed64,2,opt,name=instance_output_value,json=instanceOutputValue,proto3" json:"instance_output_value,omitempty"` |
| // Output only. Attributions of each explained feature. Features are extracted |
| // from the [prediction |
| // instances][google.cloud.aiplatform.v1.ExplainRequest.instances] according |
| // to [explanation metadata for |
| // inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs]. |
| // |
| // The value is a struct, whose keys are the name of the feature. The values |
| // are how much the feature in the |
| // [instance][google.cloud.aiplatform.v1.ExplainRequest.instances] contributed |
| // to the predicted result. |
| // |
| // The format of the value is determined by the feature's input format: |
| // |
| // - If the feature is a scalar value, the attribution value is a |
| // [floating number][google.protobuf.Value.number_value]. |
| // |
| // - If the feature is an array of scalar values, the attribution value is |
| // an [array][google.protobuf.Value.list_value]. |
| // |
| // - If the feature is a struct, the attribution value is a |
| // [struct][google.protobuf.Value.struct_value]. The keys in the |
| // attribution value struct are the same as the keys in the feature |
| // struct. The formats of the values in the attribution struct are |
| // determined by the formats of the values in the feature struct. |
| // |
| // The |
| // [ExplanationMetadata.feature_attributions_schema_uri][google.cloud.aiplatform.v1.ExplanationMetadata.feature_attributions_schema_uri] |
| // field, pointed to by the |
| // [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] field of the |
| // [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models] |
| // object, points to the schema file that describes the features and their |
| // attribution values (if it is populated). |
| FeatureAttributions *structpb.Value `protobuf:"bytes,3,opt,name=feature_attributions,json=featureAttributions,proto3" json:"feature_attributions,omitempty"` |
| // Output only. The index that locates the explained prediction output. |
| // |
| // If the prediction output is a scalar value, output_index is not populated. |
| // If the prediction output has multiple dimensions, the length of the |
| // output_index list is the same as the number of dimensions of the output. |
| // The i-th element in output_index is the element index of the i-th dimension |
| // of the output vector. Indices start from 0. |
| OutputIndex []int32 `protobuf:"varint,4,rep,packed,name=output_index,json=outputIndex,proto3" json:"output_index,omitempty"` |
| // Output only. The display name of the output identified by |
| // [output_index][google.cloud.aiplatform.v1.Attribution.output_index]. For |
| // example, the predicted class name by a multi-classification Model. |
| // |
| // This field is only populated iff the Model predicts display names as a |
| // separate field along with the explained output. The predicted display name |
| // must has the same shape of the explained output, and can be located using |
| // output_index. |
| OutputDisplayName string `protobuf:"bytes,5,opt,name=output_display_name,json=outputDisplayName,proto3" json:"output_display_name,omitempty"` |
| // Output only. Error of |
| // [feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] |
| // caused by approximation used in the explanation method. Lower value means |
| // more precise attributions. |
| // |
| // * For Sampled Shapley |
| // [attribution][google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution], |
| // increasing |
| // [path_count][google.cloud.aiplatform.v1.SampledShapleyAttribution.path_count] |
| // might reduce the error. |
| // * For Integrated Gradients |
| // [attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution], |
| // increasing |
| // [step_count][google.cloud.aiplatform.v1.IntegratedGradientsAttribution.step_count] |
| // might reduce the error. |
| // * For [XRAI |
| // attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution], |
| // increasing |
| // [step_count][google.cloud.aiplatform.v1.XraiAttribution.step_count] might |
| // reduce the error. |
| // |
| // See [this introduction](/vertex-ai/docs/explainable-ai/overview) |
| // for more information. |
| ApproximationError float64 `protobuf:"fixed64,6,opt,name=approximation_error,json=approximationError,proto3" json:"approximation_error,omitempty"` |
| // Output only. Name of the explain output. Specified as the key in |
| // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs]. |
| OutputName string `protobuf:"bytes,7,opt,name=output_name,json=outputName,proto3" json:"output_name,omitempty"` |
| } |
| |
| func (x *Attribution) Reset() { |
| *x = Attribution{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Attribution) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Attribution) ProtoMessage() {} |
| |
| func (x *Attribution) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 Attribution.ProtoReflect.Descriptor instead. |
| func (*Attribution) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{2} |
| } |
| |
| func (x *Attribution) GetBaselineOutputValue() float64 { |
| if x != nil { |
| return x.BaselineOutputValue |
| } |
| return 0 |
| } |
| |
| func (x *Attribution) GetInstanceOutputValue() float64 { |
| if x != nil { |
| return x.InstanceOutputValue |
| } |
| return 0 |
| } |
| |
| func (x *Attribution) GetFeatureAttributions() *structpb.Value { |
| if x != nil { |
| return x.FeatureAttributions |
| } |
| return nil |
| } |
| |
| func (x *Attribution) GetOutputIndex() []int32 { |
| if x != nil { |
| return x.OutputIndex |
| } |
| return nil |
| } |
| |
| func (x *Attribution) GetOutputDisplayName() string { |
| if x != nil { |
| return x.OutputDisplayName |
| } |
| return "" |
| } |
| |
| func (x *Attribution) GetApproximationError() float64 { |
| if x != nil { |
| return x.ApproximationError |
| } |
| return 0 |
| } |
| |
| func (x *Attribution) GetOutputName() string { |
| if x != nil { |
| return x.OutputName |
| } |
| return "" |
| } |
| |
| // Neighbors for example-based explanations. |
| type Neighbor struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Output only. The neighbor id. |
| NeighborId string `protobuf:"bytes,1,opt,name=neighbor_id,json=neighborId,proto3" json:"neighbor_id,omitempty"` |
| // Output only. The neighbor distance. |
| NeighborDistance float64 `protobuf:"fixed64,2,opt,name=neighbor_distance,json=neighborDistance,proto3" json:"neighbor_distance,omitempty"` |
| } |
| |
| func (x *Neighbor) Reset() { |
| *x = Neighbor{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[3] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Neighbor) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Neighbor) ProtoMessage() {} |
| |
| func (x *Neighbor) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 Neighbor.ProtoReflect.Descriptor instead. |
| func (*Neighbor) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{3} |
| } |
| |
| func (x *Neighbor) GetNeighborId() string { |
| if x != nil { |
| return x.NeighborId |
| } |
| return "" |
| } |
| |
| func (x *Neighbor) GetNeighborDistance() float64 { |
| if x != nil { |
| return x.NeighborDistance |
| } |
| return 0 |
| } |
| |
| // Specification of Model explanation. |
| type ExplanationSpec struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. Parameters that configure explaining of the Model's predictions. |
| Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` |
| // Optional. Metadata describing the Model's input and output for explanation. |
| Metadata *ExplanationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` |
| } |
| |
| func (x *ExplanationSpec) Reset() { |
| *x = ExplanationSpec{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExplanationSpec) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExplanationSpec) ProtoMessage() {} |
| |
| func (x *ExplanationSpec) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 ExplanationSpec.ProtoReflect.Descriptor instead. |
| func (*ExplanationSpec) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{4} |
| } |
| |
| func (x *ExplanationSpec) GetParameters() *ExplanationParameters { |
| if x != nil { |
| return x.Parameters |
| } |
| return nil |
| } |
| |
| func (x *ExplanationSpec) GetMetadata() *ExplanationMetadata { |
| if x != nil { |
| return x.Metadata |
| } |
| return nil |
| } |
| |
| // Parameters to configure explaining for Model's predictions. |
| type ExplanationParameters struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Types that are assignable to Method: |
| // |
| // *ExplanationParameters_SampledShapleyAttribution |
| // *ExplanationParameters_IntegratedGradientsAttribution |
| // *ExplanationParameters_XraiAttribution |
| // *ExplanationParameters_Examples |
| Method isExplanationParameters_Method `protobuf_oneof:"method"` |
| // If populated, returns attributions for top K indices of outputs |
| // (defaults to 1). Only applies to Models that predicts more than one outputs |
| // (e,g, multi-class Models). When set to -1, returns explanations for all |
| // outputs. |
| TopK int32 `protobuf:"varint,4,opt,name=top_k,json=topK,proto3" json:"top_k,omitempty"` |
| // If populated, only returns attributions that have |
| // [output_index][google.cloud.aiplatform.v1.Attribution.output_index] |
| // contained in output_indices. It must be an ndarray of integers, with the |
| // same shape of the output it's explaining. |
| // |
| // If not populated, returns attributions for |
| // [top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k] indices of |
| // outputs. If neither top_k nor output_indices is populated, returns the |
| // argmax index of the outputs. |
| // |
| // Only applicable to Models that predict multiple outputs (e,g, multi-class |
| // Models that predict multiple classes). |
| OutputIndices *structpb.ListValue `protobuf:"bytes,5,opt,name=output_indices,json=outputIndices,proto3" json:"output_indices,omitempty"` |
| } |
| |
| func (x *ExplanationParameters) Reset() { |
| *x = ExplanationParameters{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExplanationParameters) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExplanationParameters) ProtoMessage() {} |
| |
| func (x *ExplanationParameters) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 ExplanationParameters.ProtoReflect.Descriptor instead. |
| func (*ExplanationParameters) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{5} |
| } |
| |
| func (m *ExplanationParameters) GetMethod() isExplanationParameters_Method { |
| if m != nil { |
| return m.Method |
| } |
| return nil |
| } |
| |
| func (x *ExplanationParameters) GetSampledShapleyAttribution() *SampledShapleyAttribution { |
| if x, ok := x.GetMethod().(*ExplanationParameters_SampledShapleyAttribution); ok { |
| return x.SampledShapleyAttribution |
| } |
| return nil |
| } |
| |
| func (x *ExplanationParameters) GetIntegratedGradientsAttribution() *IntegratedGradientsAttribution { |
| if x, ok := x.GetMethod().(*ExplanationParameters_IntegratedGradientsAttribution); ok { |
| return x.IntegratedGradientsAttribution |
| } |
| return nil |
| } |
| |
| func (x *ExplanationParameters) GetXraiAttribution() *XraiAttribution { |
| if x, ok := x.GetMethod().(*ExplanationParameters_XraiAttribution); ok { |
| return x.XraiAttribution |
| } |
| return nil |
| } |
| |
| func (x *ExplanationParameters) GetExamples() *Examples { |
| if x, ok := x.GetMethod().(*ExplanationParameters_Examples); ok { |
| return x.Examples |
| } |
| return nil |
| } |
| |
| func (x *ExplanationParameters) GetTopK() int32 { |
| if x != nil { |
| return x.TopK |
| } |
| return 0 |
| } |
| |
| func (x *ExplanationParameters) GetOutputIndices() *structpb.ListValue { |
| if x != nil { |
| return x.OutputIndices |
| } |
| return nil |
| } |
| |
| type isExplanationParameters_Method interface { |
| isExplanationParameters_Method() |
| } |
| |
| type ExplanationParameters_SampledShapleyAttribution struct { |
| // An attribution method that approximates Shapley values for features that |
| // contribute to the label being predicted. A sampling strategy is used to |
| // approximate the value rather than considering all subsets of features. |
| // Refer to this paper for model details: https://arxiv.org/abs/1306.4265. |
| SampledShapleyAttribution *SampledShapleyAttribution `protobuf:"bytes,1,opt,name=sampled_shapley_attribution,json=sampledShapleyAttribution,proto3,oneof"` |
| } |
| |
| type ExplanationParameters_IntegratedGradientsAttribution struct { |
| // An attribution method that computes Aumann-Shapley values taking |
| // advantage of the model's fully differentiable structure. Refer to this |
| // paper for more details: https://arxiv.org/abs/1703.01365 |
| IntegratedGradientsAttribution *IntegratedGradientsAttribution `protobuf:"bytes,2,opt,name=integrated_gradients_attribution,json=integratedGradientsAttribution,proto3,oneof"` |
| } |
| |
| type ExplanationParameters_XraiAttribution struct { |
| // An attribution method that redistributes Integrated Gradients |
| // attribution to segmented regions, taking advantage of the model's fully |
| // differentiable structure. Refer to this paper for |
| // more details: https://arxiv.org/abs/1906.02825 |
| // |
| // XRAI currently performs better on natural images, like a picture of a |
| // house or an animal. If the images are taken in artificial environments, |
| // like a lab or manufacturing line, or from diagnostic equipment, like |
| // x-rays or quality-control cameras, use Integrated Gradients instead. |
| XraiAttribution *XraiAttribution `protobuf:"bytes,3,opt,name=xrai_attribution,json=xraiAttribution,proto3,oneof"` |
| } |
| |
| type ExplanationParameters_Examples struct { |
| // Example-based explanations that returns the nearest neighbors from the |
| // provided dataset. |
| Examples *Examples `protobuf:"bytes,7,opt,name=examples,proto3,oneof"` |
| } |
| |
| func (*ExplanationParameters_SampledShapleyAttribution) isExplanationParameters_Method() {} |
| |
| func (*ExplanationParameters_IntegratedGradientsAttribution) isExplanationParameters_Method() {} |
| |
| func (*ExplanationParameters_XraiAttribution) isExplanationParameters_Method() {} |
| |
| func (*ExplanationParameters_Examples) isExplanationParameters_Method() {} |
| |
| // An attribution method that approximates Shapley values for features that |
| // contribute to the label being predicted. A sampling strategy is used to |
| // approximate the value rather than considering all subsets of features. |
| type SampledShapleyAttribution struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The number of feature permutations to consider when approximating |
| // the Shapley values. |
| // |
| // Valid range of its value is [1, 50], inclusively. |
| PathCount int32 `protobuf:"varint,1,opt,name=path_count,json=pathCount,proto3" json:"path_count,omitempty"` |
| } |
| |
| func (x *SampledShapleyAttribution) Reset() { |
| *x = SampledShapleyAttribution{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[6] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *SampledShapleyAttribution) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*SampledShapleyAttribution) ProtoMessage() {} |
| |
| func (x *SampledShapleyAttribution) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 SampledShapleyAttribution.ProtoReflect.Descriptor instead. |
| func (*SampledShapleyAttribution) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{6} |
| } |
| |
| func (x *SampledShapleyAttribution) GetPathCount() int32 { |
| if x != nil { |
| return x.PathCount |
| } |
| return 0 |
| } |
| |
| // An attribution method that computes the Aumann-Shapley value taking advantage |
| // of the model's fully differentiable structure. Refer to this paper for |
| // more details: https://arxiv.org/abs/1703.01365 |
| type IntegratedGradientsAttribution struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The number of steps for approximating the path integral. |
| // A good value to start is 50 and gradually increase until the |
| // sum to diff property is within the desired error range. |
| // |
| // Valid range of its value is [1, 100], inclusively. |
| StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` |
| // Config for SmoothGrad approximation of gradients. |
| // |
| // When enabled, the gradients are approximated by averaging the gradients |
| // from noisy samples in the vicinity of the inputs. Adding |
| // noise can help improve the computed gradients. Refer to this paper for more |
| // details: https://arxiv.org/pdf/1706.03825.pdf |
| SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` |
| // Config for IG with blur baseline. |
| // |
| // When enabled, a linear path from the maximally blurred image to the input |
| // image is created. Using a blurred baseline instead of zero (black image) is |
| // motivated by the BlurIG approach explained here: |
| // https://arxiv.org/abs/2004.03383 |
| BlurBaselineConfig *BlurBaselineConfig `protobuf:"bytes,3,opt,name=blur_baseline_config,json=blurBaselineConfig,proto3" json:"blur_baseline_config,omitempty"` |
| } |
| |
| func (x *IntegratedGradientsAttribution) Reset() { |
| *x = IntegratedGradientsAttribution{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[7] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *IntegratedGradientsAttribution) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*IntegratedGradientsAttribution) ProtoMessage() {} |
| |
| func (x *IntegratedGradientsAttribution) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 IntegratedGradientsAttribution.ProtoReflect.Descriptor instead. |
| func (*IntegratedGradientsAttribution) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{7} |
| } |
| |
| func (x *IntegratedGradientsAttribution) GetStepCount() int32 { |
| if x != nil { |
| return x.StepCount |
| } |
| return 0 |
| } |
| |
| func (x *IntegratedGradientsAttribution) GetSmoothGradConfig() *SmoothGradConfig { |
| if x != nil { |
| return x.SmoothGradConfig |
| } |
| return nil |
| } |
| |
| func (x *IntegratedGradientsAttribution) GetBlurBaselineConfig() *BlurBaselineConfig { |
| if x != nil { |
| return x.BlurBaselineConfig |
| } |
| return nil |
| } |
| |
| // An explanation method that redistributes Integrated Gradients |
| // attributions to segmented regions, taking advantage of the model's fully |
| // differentiable structure. Refer to this paper for more details: |
| // https://arxiv.org/abs/1906.02825 |
| // |
| // Supported only by image Models. |
| type XraiAttribution struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The number of steps for approximating the path integral. |
| // A good value to start is 50 and gradually increase until the |
| // sum to diff property is met within the desired error range. |
| // |
| // Valid range of its value is [1, 100], inclusively. |
| StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` |
| // Config for SmoothGrad approximation of gradients. |
| // |
| // When enabled, the gradients are approximated by averaging the gradients |
| // from noisy samples in the vicinity of the inputs. Adding |
| // noise can help improve the computed gradients. Refer to this paper for more |
| // details: https://arxiv.org/pdf/1706.03825.pdf |
| SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` |
| // Config for XRAI with blur baseline. |
| // |
| // When enabled, a linear path from the maximally blurred image to the input |
| // image is created. Using a blurred baseline instead of zero (black image) is |
| // motivated by the BlurIG approach explained here: |
| // https://arxiv.org/abs/2004.03383 |
| BlurBaselineConfig *BlurBaselineConfig `protobuf:"bytes,3,opt,name=blur_baseline_config,json=blurBaselineConfig,proto3" json:"blur_baseline_config,omitempty"` |
| } |
| |
| func (x *XraiAttribution) Reset() { |
| *x = XraiAttribution{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *XraiAttribution) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*XraiAttribution) ProtoMessage() {} |
| |
| func (x *XraiAttribution) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8] |
| 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 XraiAttribution.ProtoReflect.Descriptor instead. |
| func (*XraiAttribution) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{8} |
| } |
| |
| func (x *XraiAttribution) GetStepCount() int32 { |
| if x != nil { |
| return x.StepCount |
| } |
| return 0 |
| } |
| |
| func (x *XraiAttribution) GetSmoothGradConfig() *SmoothGradConfig { |
| if x != nil { |
| return x.SmoothGradConfig |
| } |
| return nil |
| } |
| |
| func (x *XraiAttribution) GetBlurBaselineConfig() *BlurBaselineConfig { |
| if x != nil { |
| return x.BlurBaselineConfig |
| } |
| return nil |
| } |
| |
| // Config for SmoothGrad approximation of gradients. |
| // |
| // When enabled, the gradients are approximated by averaging the gradients from |
| // noisy samples in the vicinity of the inputs. Adding noise can help improve |
| // the computed gradients. Refer to this paper for more details: |
| // https://arxiv.org/pdf/1706.03825.pdf |
| type SmoothGradConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Represents the standard deviation of the gaussian kernel |
| // that will be used to add noise to the interpolated inputs |
| // prior to computing gradients. |
| // |
| // Types that are assignable to GradientNoiseSigma: |
| // |
| // *SmoothGradConfig_NoiseSigma |
| // *SmoothGradConfig_FeatureNoiseSigma |
| GradientNoiseSigma isSmoothGradConfig_GradientNoiseSigma `protobuf_oneof:"GradientNoiseSigma"` |
| // The number of gradient samples to use for |
| // approximation. The higher this number, the more accurate the gradient |
| // is, but the runtime complexity increases by this factor as well. |
| // Valid range of its value is [1, 50]. Defaults to 3. |
| NoisySampleCount int32 `protobuf:"varint,3,opt,name=noisy_sample_count,json=noisySampleCount,proto3" json:"noisy_sample_count,omitempty"` |
| } |
| |
| func (x *SmoothGradConfig) Reset() { |
| *x = SmoothGradConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *SmoothGradConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*SmoothGradConfig) ProtoMessage() {} |
| |
| func (x *SmoothGradConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9] |
| 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 SmoothGradConfig.ProtoReflect.Descriptor instead. |
| func (*SmoothGradConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{9} |
| } |
| |
| func (m *SmoothGradConfig) GetGradientNoiseSigma() isSmoothGradConfig_GradientNoiseSigma { |
| if m != nil { |
| return m.GradientNoiseSigma |
| } |
| return nil |
| } |
| |
| func (x *SmoothGradConfig) GetNoiseSigma() float32 { |
| if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_NoiseSigma); ok { |
| return x.NoiseSigma |
| } |
| return 0 |
| } |
| |
| func (x *SmoothGradConfig) GetFeatureNoiseSigma() *FeatureNoiseSigma { |
| if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_FeatureNoiseSigma); ok { |
| return x.FeatureNoiseSigma |
| } |
| return nil |
| } |
| |
| func (x *SmoothGradConfig) GetNoisySampleCount() int32 { |
| if x != nil { |
| return x.NoisySampleCount |
| } |
| return 0 |
| } |
| |
| type isSmoothGradConfig_GradientNoiseSigma interface { |
| isSmoothGradConfig_GradientNoiseSigma() |
| } |
| |
| type SmoothGradConfig_NoiseSigma struct { |
| // This is a single float value and will be used to add noise to all the |
| // features. Use this field when all features are normalized to have the |
| // same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where |
| // features are normalized to have 0-mean and 1-variance. Learn more about |
| // [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). |
| // |
| // For best results the recommended value is about 10% - 20% of the standard |
| // deviation of the input feature. Refer to section 3.2 of the SmoothGrad |
| // paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. |
| // |
| // If the distribution is different per feature, set |
| // [feature_noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma] |
| // instead for each feature. |
| NoiseSigma float32 `protobuf:"fixed32,1,opt,name=noise_sigma,json=noiseSigma,proto3,oneof"` |
| } |
| |
| type SmoothGradConfig_FeatureNoiseSigma struct { |
| // This is similar to |
| // [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma], |
| // but provides additional flexibility. A separate noise sigma can be |
| // provided for each feature, which is useful if their distributions are |
| // different. No noise is added to features that are not set. If this field |
| // is unset, |
| // [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] |
| // will be used for all features. |
| FeatureNoiseSigma *FeatureNoiseSigma `protobuf:"bytes,2,opt,name=feature_noise_sigma,json=featureNoiseSigma,proto3,oneof"` |
| } |
| |
| func (*SmoothGradConfig_NoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} |
| |
| func (*SmoothGradConfig_FeatureNoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} |
| |
| // Noise sigma by features. Noise sigma represents the standard deviation of the |
| // gaussian kernel that will be used to add noise to interpolated inputs prior |
| // to computing gradients. |
| type FeatureNoiseSigma struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Noise sigma per feature. No noise is added to features that are not set. |
| NoiseSigma []*FeatureNoiseSigma_NoiseSigmaForFeature `protobuf:"bytes,1,rep,name=noise_sigma,json=noiseSigma,proto3" json:"noise_sigma,omitempty"` |
| } |
| |
| func (x *FeatureNoiseSigma) Reset() { |
| *x = FeatureNoiseSigma{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *FeatureNoiseSigma) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*FeatureNoiseSigma) ProtoMessage() {} |
| |
| func (x *FeatureNoiseSigma) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10] |
| 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 FeatureNoiseSigma.ProtoReflect.Descriptor instead. |
| func (*FeatureNoiseSigma) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{10} |
| } |
| |
| func (x *FeatureNoiseSigma) GetNoiseSigma() []*FeatureNoiseSigma_NoiseSigmaForFeature { |
| if x != nil { |
| return x.NoiseSigma |
| } |
| return nil |
| } |
| |
| // Config for blur baseline. |
| // |
| // When enabled, a linear path from the maximally blurred image to the input |
| // image is created. Using a blurred baseline instead of zero (black image) is |
| // motivated by the BlurIG approach explained here: |
| // https://arxiv.org/abs/2004.03383 |
| type BlurBaselineConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The standard deviation of the blur kernel for the blurred baseline. The |
| // same blurring parameter is used for both the height and the width |
| // dimension. If not set, the method defaults to the zero (i.e. black for |
| // images) baseline. |
| MaxBlurSigma float32 `protobuf:"fixed32,1,opt,name=max_blur_sigma,json=maxBlurSigma,proto3" json:"max_blur_sigma,omitempty"` |
| } |
| |
| func (x *BlurBaselineConfig) Reset() { |
| *x = BlurBaselineConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *BlurBaselineConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*BlurBaselineConfig) ProtoMessage() {} |
| |
| func (x *BlurBaselineConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11] |
| 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 BlurBaselineConfig.ProtoReflect.Descriptor instead. |
| func (*BlurBaselineConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{11} |
| } |
| |
| func (x *BlurBaselineConfig) GetMaxBlurSigma() float32 { |
| if x != nil { |
| return x.MaxBlurSigma |
| } |
| return 0 |
| } |
| |
| // Example-based explainability that returns the nearest neighbors from the |
| // provided dataset. |
| type Examples struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Types that are assignable to Source: |
| // |
| // *Examples_ExampleGcsSource_ |
| Source isExamples_Source `protobuf_oneof:"source"` |
| // Types that are assignable to Config: |
| // |
| // *Examples_NearestNeighborSearchConfig |
| // *Examples_Presets |
| Config isExamples_Config `protobuf_oneof:"config"` |
| // The number of neighbors to return when querying for examples. |
| NeighborCount int32 `protobuf:"varint,3,opt,name=neighbor_count,json=neighborCount,proto3" json:"neighbor_count,omitempty"` |
| } |
| |
| func (x *Examples) Reset() { |
| *x = Examples{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Examples) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Examples) ProtoMessage() {} |
| |
| func (x *Examples) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 Examples.ProtoReflect.Descriptor instead. |
| func (*Examples) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{12} |
| } |
| |
| func (m *Examples) GetSource() isExamples_Source { |
| if m != nil { |
| return m.Source |
| } |
| return nil |
| } |
| |
| func (x *Examples) GetExampleGcsSource() *Examples_ExampleGcsSource { |
| if x, ok := x.GetSource().(*Examples_ExampleGcsSource_); ok { |
| return x.ExampleGcsSource |
| } |
| return nil |
| } |
| |
| func (m *Examples) GetConfig() isExamples_Config { |
| if m != nil { |
| return m.Config |
| } |
| return nil |
| } |
| |
| func (x *Examples) GetNearestNeighborSearchConfig() *structpb.Value { |
| if x, ok := x.GetConfig().(*Examples_NearestNeighborSearchConfig); ok { |
| return x.NearestNeighborSearchConfig |
| } |
| return nil |
| } |
| |
| func (x *Examples) GetPresets() *Presets { |
| if x, ok := x.GetConfig().(*Examples_Presets); ok { |
| return x.Presets |
| } |
| return nil |
| } |
| |
| func (x *Examples) GetNeighborCount() int32 { |
| if x != nil { |
| return x.NeighborCount |
| } |
| return 0 |
| } |
| |
| type isExamples_Source interface { |
| isExamples_Source() |
| } |
| |
| type Examples_ExampleGcsSource_ struct { |
| // The Cloud Storage input instances. |
| ExampleGcsSource *Examples_ExampleGcsSource `protobuf:"bytes,5,opt,name=example_gcs_source,json=exampleGcsSource,proto3,oneof"` |
| } |
| |
| func (*Examples_ExampleGcsSource_) isExamples_Source() {} |
| |
| type isExamples_Config interface { |
| isExamples_Config() |
| } |
| |
| type Examples_NearestNeighborSearchConfig struct { |
| // The full configuration for the generated index, the semantics are the |
| // same as [metadata][google.cloud.aiplatform.v1.Index.metadata] and should |
| // match |
| // [NearestNeighborSearchConfig](https://cloud.google.com/vertex-ai/docs/explainable-ai/configuring-explanations-example-based#nearest-neighbor-search-config). |
| NearestNeighborSearchConfig *structpb.Value `protobuf:"bytes,2,opt,name=nearest_neighbor_search_config,json=nearestNeighborSearchConfig,proto3,oneof"` |
| } |
| |
| type Examples_Presets struct { |
| // Simplified preset configuration, which automatically sets configuration |
| // values based on the desired query speed-precision trade-off and modality. |
| Presets *Presets `protobuf:"bytes,4,opt,name=presets,proto3,oneof"` |
| } |
| |
| func (*Examples_NearestNeighborSearchConfig) isExamples_Config() {} |
| |
| func (*Examples_Presets) isExamples_Config() {} |
| |
| // Preset configuration for example-based explanations |
| type Presets struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Preset option controlling parameters for speed-precision trade-off when |
| // querying for examples. If omitted, defaults to `PRECISE`. |
| Query *Presets_Query `protobuf:"varint,1,opt,name=query,proto3,enum=google.cloud.aiplatform.v1.Presets_Query,oneof" json:"query,omitempty"` |
| // The modality of the uploaded model, which automatically configures the |
| // distance measurement and feature normalization for the underlying example |
| // index and queries. If your model does not precisely fit one of these types, |
| // it is okay to choose the closest type. |
| Modality Presets_Modality `protobuf:"varint,2,opt,name=modality,proto3,enum=google.cloud.aiplatform.v1.Presets_Modality" json:"modality,omitempty"` |
| } |
| |
| func (x *Presets) Reset() { |
| *x = Presets{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Presets) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Presets) ProtoMessage() {} |
| |
| func (x *Presets) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 Presets.ProtoReflect.Descriptor instead. |
| func (*Presets) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{13} |
| } |
| |
| func (x *Presets) GetQuery() Presets_Query { |
| if x != nil && x.Query != nil { |
| return *x.Query |
| } |
| return Presets_PRECISE |
| } |
| |
| func (x *Presets) GetModality() Presets_Modality { |
| if x != nil { |
| return x.Modality |
| } |
| return Presets_MODALITY_UNSPECIFIED |
| } |
| |
| // The [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] entries |
| // that can be overridden at [online |
| // explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time. |
| type ExplanationSpecOverride struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The parameters to be overridden. Note that the |
| // attribution method cannot be changed. If not specified, |
| // no parameter is overridden. |
| Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` |
| // The metadata to be overridden. If not specified, no metadata is overridden. |
| Metadata *ExplanationMetadataOverride `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` |
| // The example-based explanations parameter overrides. |
| ExamplesOverride *ExamplesOverride `protobuf:"bytes,3,opt,name=examples_override,json=examplesOverride,proto3" json:"examples_override,omitempty"` |
| } |
| |
| func (x *ExplanationSpecOverride) Reset() { |
| *x = ExplanationSpecOverride{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[14] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExplanationSpecOverride) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExplanationSpecOverride) ProtoMessage() {} |
| |
| func (x *ExplanationSpecOverride) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_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 ExplanationSpecOverride.ProtoReflect.Descriptor instead. |
| func (*ExplanationSpecOverride) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{14} |
| } |
| |
| func (x *ExplanationSpecOverride) GetParameters() *ExplanationParameters { |
| if x != nil { |
| return x.Parameters |
| } |
| return nil |
| } |
| |
| func (x *ExplanationSpecOverride) GetMetadata() *ExplanationMetadataOverride { |
| if x != nil { |
| return x.Metadata |
| } |
| return nil |
| } |
| |
| func (x *ExplanationSpecOverride) GetExamplesOverride() *ExamplesOverride { |
| if x != nil { |
| return x.ExamplesOverride |
| } |
| return nil |
| } |
| |
| // The [ExplanationMetadata][google.cloud.aiplatform.v1.ExplanationMetadata] |
| // entries that can be overridden at [online |
| // explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time. |
| type ExplanationMetadataOverride struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. Overrides the [input |
| // metadata][google.cloud.aiplatform.v1.ExplanationMetadata.inputs] of the |
| // features. The key is the name of the feature to be overridden. The keys |
| // specified here must exist in the input metadata to be overridden. If a |
| // feature is not specified here, the corresponding feature's input metadata |
| // is not overridden. |
| Inputs map[string]*ExplanationMetadataOverride_InputMetadataOverride `protobuf:"bytes,1,rep,name=inputs,proto3" json:"inputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` |
| } |
| |
| func (x *ExplanationMetadataOverride) Reset() { |
| *x = ExplanationMetadataOverride{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[15] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExplanationMetadataOverride) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExplanationMetadataOverride) ProtoMessage() {} |
| |
| func (x *ExplanationMetadataOverride) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[15] |
| 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 ExplanationMetadataOverride.ProtoReflect.Descriptor instead. |
| func (*ExplanationMetadataOverride) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{15} |
| } |
| |
| func (x *ExplanationMetadataOverride) GetInputs() map[string]*ExplanationMetadataOverride_InputMetadataOverride { |
| if x != nil { |
| return x.Inputs |
| } |
| return nil |
| } |
| |
| // Overrides for example-based explanations. |
| type ExamplesOverride struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The number of neighbors to return. |
| NeighborCount int32 `protobuf:"varint,1,opt,name=neighbor_count,json=neighborCount,proto3" json:"neighbor_count,omitempty"` |
| // The number of neighbors to return that have the same crowding tag. |
| CrowdingCount int32 `protobuf:"varint,2,opt,name=crowding_count,json=crowdingCount,proto3" json:"crowding_count,omitempty"` |
| // Restrict the resulting nearest neighbors to respect these constraints. |
| Restrictions []*ExamplesRestrictionsNamespace `protobuf:"bytes,3,rep,name=restrictions,proto3" json:"restrictions,omitempty"` |
| // If true, return the embeddings instead of neighbors. |
| ReturnEmbeddings bool `protobuf:"varint,4,opt,name=return_embeddings,json=returnEmbeddings,proto3" json:"return_embeddings,omitempty"` |
| // The format of the data being provided with each call. |
| DataFormat ExamplesOverride_DataFormat `protobuf:"varint,5,opt,name=data_format,json=dataFormat,proto3,enum=google.cloud.aiplatform.v1.ExamplesOverride_DataFormat" json:"data_format,omitempty"` |
| } |
| |
| func (x *ExamplesOverride) Reset() { |
| *x = ExamplesOverride{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[16] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExamplesOverride) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExamplesOverride) ProtoMessage() {} |
| |
| func (x *ExamplesOverride) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[16] |
| 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 ExamplesOverride.ProtoReflect.Descriptor instead. |
| func (*ExamplesOverride) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{16} |
| } |
| |
| func (x *ExamplesOverride) GetNeighborCount() int32 { |
| if x != nil { |
| return x.NeighborCount |
| } |
| return 0 |
| } |
| |
| func (x *ExamplesOverride) GetCrowdingCount() int32 { |
| if x != nil { |
| return x.CrowdingCount |
| } |
| return 0 |
| } |
| |
| func (x *ExamplesOverride) GetRestrictions() []*ExamplesRestrictionsNamespace { |
| if x != nil { |
| return x.Restrictions |
| } |
| return nil |
| } |
| |
| func (x *ExamplesOverride) GetReturnEmbeddings() bool { |
| if x != nil { |
| return x.ReturnEmbeddings |
| } |
| return false |
| } |
| |
| func (x *ExamplesOverride) GetDataFormat() ExamplesOverride_DataFormat { |
| if x != nil { |
| return x.DataFormat |
| } |
| return ExamplesOverride_DATA_FORMAT_UNSPECIFIED |
| } |
| |
| // Restrictions namespace for example-based explanations overrides. |
| type ExamplesRestrictionsNamespace struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The namespace name. |
| NamespaceName string `protobuf:"bytes,1,opt,name=namespace_name,json=namespaceName,proto3" json:"namespace_name,omitempty"` |
| // The list of allowed tags. |
| Allow []string `protobuf:"bytes,2,rep,name=allow,proto3" json:"allow,omitempty"` |
| // The list of deny tags. |
| Deny []string `protobuf:"bytes,3,rep,name=deny,proto3" json:"deny,omitempty"` |
| } |
| |
| func (x *ExamplesRestrictionsNamespace) Reset() { |
| *x = ExamplesRestrictionsNamespace{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[17] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExamplesRestrictionsNamespace) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExamplesRestrictionsNamespace) ProtoMessage() {} |
| |
| func (x *ExamplesRestrictionsNamespace) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[17] |
| 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 ExamplesRestrictionsNamespace.ProtoReflect.Descriptor instead. |
| func (*ExamplesRestrictionsNamespace) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{17} |
| } |
| |
| func (x *ExamplesRestrictionsNamespace) GetNamespaceName() string { |
| if x != nil { |
| return x.NamespaceName |
| } |
| return "" |
| } |
| |
| func (x *ExamplesRestrictionsNamespace) GetAllow() []string { |
| if x != nil { |
| return x.Allow |
| } |
| return nil |
| } |
| |
| func (x *ExamplesRestrictionsNamespace) GetDeny() []string { |
| if x != nil { |
| return x.Deny |
| } |
| return nil |
| } |
| |
| // Noise sigma for a single feature. |
| type FeatureNoiseSigma_NoiseSigmaForFeature struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The name of the input feature for which noise sigma is provided. The |
| // features are defined in |
| // [explanation metadata |
| // inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs]. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| // This represents the standard deviation of the Gaussian kernel that will |
| // be used to add noise to the feature prior to computing gradients. Similar |
| // to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] |
| // but represents the noise added to the current feature. Defaults to 0.1. |
| Sigma float32 `protobuf:"fixed32,2,opt,name=sigma,proto3" json:"sigma,omitempty"` |
| } |
| |
| func (x *FeatureNoiseSigma_NoiseSigmaForFeature) Reset() { |
| *x = FeatureNoiseSigma_NoiseSigmaForFeature{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[18] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *FeatureNoiseSigma_NoiseSigmaForFeature) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*FeatureNoiseSigma_NoiseSigmaForFeature) ProtoMessage() {} |
| |
| func (x *FeatureNoiseSigma_NoiseSigmaForFeature) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[18] |
| 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 FeatureNoiseSigma_NoiseSigmaForFeature.ProtoReflect.Descriptor instead. |
| func (*FeatureNoiseSigma_NoiseSigmaForFeature) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{10, 0} |
| } |
| |
| func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetSigma() float32 { |
| if x != nil { |
| return x.Sigma |
| } |
| return 0 |
| } |
| |
| // The Cloud Storage input instances. |
| type Examples_ExampleGcsSource struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The format in which instances are given, if not specified, assume it's |
| // JSONL format. Currently only JSONL format is supported. |
| DataFormat Examples_ExampleGcsSource_DataFormat `protobuf:"varint,1,opt,name=data_format,json=dataFormat,proto3,enum=google.cloud.aiplatform.v1.Examples_ExampleGcsSource_DataFormat" json:"data_format,omitempty"` |
| // The Cloud Storage location for the input instances. |
| GcsSource *GcsSource `protobuf:"bytes,2,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"` |
| } |
| |
| func (x *Examples_ExampleGcsSource) Reset() { |
| *x = Examples_ExampleGcsSource{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[19] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *Examples_ExampleGcsSource) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*Examples_ExampleGcsSource) ProtoMessage() {} |
| |
| func (x *Examples_ExampleGcsSource) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[19] |
| 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 Examples_ExampleGcsSource.ProtoReflect.Descriptor instead. |
| func (*Examples_ExampleGcsSource) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{12, 0} |
| } |
| |
| func (x *Examples_ExampleGcsSource) GetDataFormat() Examples_ExampleGcsSource_DataFormat { |
| if x != nil { |
| return x.DataFormat |
| } |
| return Examples_ExampleGcsSource_DATA_FORMAT_UNSPECIFIED |
| } |
| |
| func (x *Examples_ExampleGcsSource) GetGcsSource() *GcsSource { |
| if x != nil { |
| return x.GcsSource |
| } |
| return nil |
| } |
| |
| // The [input |
| // metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] |
| // entries to be overridden. |
| type ExplanationMetadataOverride_InputMetadataOverride struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Baseline inputs for this feature. |
| // |
| // This overrides the `input_baseline` field of the |
| // [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] |
| // object of the corresponding feature's input metadata. If it's not |
| // specified, the original baselines are not overridden. |
| InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"` |
| } |
| |
| func (x *ExplanationMetadataOverride_InputMetadataOverride) Reset() { |
| *x = ExplanationMetadataOverride_InputMetadataOverride{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[20] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ExplanationMetadataOverride_InputMetadataOverride) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ExplanationMetadataOverride_InputMetadataOverride) ProtoMessage() {} |
| |
| func (x *ExplanationMetadataOverride_InputMetadataOverride) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[20] |
| 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 ExplanationMetadataOverride_InputMetadataOverride.ProtoReflect.Descriptor instead. |
| func (*ExplanationMetadataOverride_InputMetadataOverride) Descriptor() ([]byte, []int) { |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{15, 0} |
| } |
| |
| func (x *ExplanationMetadataOverride_InputMetadataOverride) GetInputBaselines() []*structpb.Value { |
| if x != nil { |
| return x.InputBaselines |
| } |
| return nil |
| } |
| |
| var File_google_cloud_aiplatform_v1_explanation_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_aiplatform_v1_explanation_proto_rawDesc = []byte{ |
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| 0x67, 0x6c, 0x65, 0x5c, 0x43, 0x6c, 0x6f, 0x75, 0x64, 0x5c, 0x41, 0x49, 0x50, 0x6c, 0x61, 0x74, |
| 0x66, 0x6f, 0x72, 0x6d, 0x5c, 0x56, 0x31, 0xea, 0x02, 0x1d, 0x47, 0x6f, 0x6f, 0x67, 0x6c, 0x65, |
| 0x3a, 0x3a, 0x43, 0x6c, 0x6f, 0x75, 0x64, 0x3a, 0x3a, 0x41, 0x49, 0x50, 0x6c, 0x61, 0x74, 0x66, |
| 0x6f, 0x72, 0x6d, 0x3a, 0x3a, 0x56, 0x31, 0x62, 0x06, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x33, |
| } |
| |
| var ( |
| file_google_cloud_aiplatform_v1_explanation_proto_rawDescOnce sync.Once |
| file_google_cloud_aiplatform_v1_explanation_proto_rawDescData = file_google_cloud_aiplatform_v1_explanation_proto_rawDesc |
| ) |
| |
| func file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP() []byte { |
| file_google_cloud_aiplatform_v1_explanation_proto_rawDescOnce.Do(func() { |
| file_google_cloud_aiplatform_v1_explanation_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1_explanation_proto_rawDescData) |
| }) |
| return file_google_cloud_aiplatform_v1_explanation_proto_rawDescData |
| } |
| |
| var file_google_cloud_aiplatform_v1_explanation_proto_enumTypes = make([]protoimpl.EnumInfo, 4) |
| var file_google_cloud_aiplatform_v1_explanation_proto_msgTypes = make([]protoimpl.MessageInfo, 22) |
| var file_google_cloud_aiplatform_v1_explanation_proto_goTypes = []interface{}{ |
| (Examples_ExampleGcsSource_DataFormat)(0), // 0: google.cloud.aiplatform.v1.Examples.ExampleGcsSource.DataFormat |
| (Presets_Query)(0), // 1: google.cloud.aiplatform.v1.Presets.Query |
| (Presets_Modality)(0), // 2: google.cloud.aiplatform.v1.Presets.Modality |
| (ExamplesOverride_DataFormat)(0), // 3: google.cloud.aiplatform.v1.ExamplesOverride.DataFormat |
| (*Explanation)(nil), // 4: google.cloud.aiplatform.v1.Explanation |
| (*ModelExplanation)(nil), // 5: google.cloud.aiplatform.v1.ModelExplanation |
| (*Attribution)(nil), // 6: google.cloud.aiplatform.v1.Attribution |
| (*Neighbor)(nil), // 7: google.cloud.aiplatform.v1.Neighbor |
| (*ExplanationSpec)(nil), // 8: google.cloud.aiplatform.v1.ExplanationSpec |
| (*ExplanationParameters)(nil), // 9: google.cloud.aiplatform.v1.ExplanationParameters |
| (*SampledShapleyAttribution)(nil), // 10: google.cloud.aiplatform.v1.SampledShapleyAttribution |
| (*IntegratedGradientsAttribution)(nil), // 11: google.cloud.aiplatform.v1.IntegratedGradientsAttribution |
| (*XraiAttribution)(nil), // 12: google.cloud.aiplatform.v1.XraiAttribution |
| (*SmoothGradConfig)(nil), // 13: google.cloud.aiplatform.v1.SmoothGradConfig |
| (*FeatureNoiseSigma)(nil), // 14: google.cloud.aiplatform.v1.FeatureNoiseSigma |
| (*BlurBaselineConfig)(nil), // 15: google.cloud.aiplatform.v1.BlurBaselineConfig |
| (*Examples)(nil), // 16: google.cloud.aiplatform.v1.Examples |
| (*Presets)(nil), // 17: google.cloud.aiplatform.v1.Presets |
| (*ExplanationSpecOverride)(nil), // 18: google.cloud.aiplatform.v1.ExplanationSpecOverride |
| (*ExplanationMetadataOverride)(nil), // 19: google.cloud.aiplatform.v1.ExplanationMetadataOverride |
| (*ExamplesOverride)(nil), // 20: google.cloud.aiplatform.v1.ExamplesOverride |
| (*ExamplesRestrictionsNamespace)(nil), // 21: google.cloud.aiplatform.v1.ExamplesRestrictionsNamespace |
| (*FeatureNoiseSigma_NoiseSigmaForFeature)(nil), // 22: google.cloud.aiplatform.v1.FeatureNoiseSigma.NoiseSigmaForFeature |
| (*Examples_ExampleGcsSource)(nil), // 23: google.cloud.aiplatform.v1.Examples.ExampleGcsSource |
| (*ExplanationMetadataOverride_InputMetadataOverride)(nil), // 24: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride |
| nil, // 25: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry |
| (*structpb.Value)(nil), // 26: google.protobuf.Value |
| (*ExplanationMetadata)(nil), // 27: google.cloud.aiplatform.v1.ExplanationMetadata |
| (*structpb.ListValue)(nil), // 28: google.protobuf.ListValue |
| (*GcsSource)(nil), // 29: google.cloud.aiplatform.v1.GcsSource |
| } |
| var file_google_cloud_aiplatform_v1_explanation_proto_depIdxs = []int32{ |
| 6, // 0: google.cloud.aiplatform.v1.Explanation.attributions:type_name -> google.cloud.aiplatform.v1.Attribution |
| 7, // 1: google.cloud.aiplatform.v1.Explanation.neighbors:type_name -> google.cloud.aiplatform.v1.Neighbor |
| 6, // 2: google.cloud.aiplatform.v1.ModelExplanation.mean_attributions:type_name -> google.cloud.aiplatform.v1.Attribution |
| 26, // 3: google.cloud.aiplatform.v1.Attribution.feature_attributions:type_name -> google.protobuf.Value |
| 9, // 4: google.cloud.aiplatform.v1.ExplanationSpec.parameters:type_name -> google.cloud.aiplatform.v1.ExplanationParameters |
| 27, // 5: google.cloud.aiplatform.v1.ExplanationSpec.metadata:type_name -> google.cloud.aiplatform.v1.ExplanationMetadata |
| 10, // 6: google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution:type_name -> google.cloud.aiplatform.v1.SampledShapleyAttribution |
| 11, // 7: google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution:type_name -> google.cloud.aiplatform.v1.IntegratedGradientsAttribution |
| 12, // 8: google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution:type_name -> google.cloud.aiplatform.v1.XraiAttribution |
| 16, // 9: google.cloud.aiplatform.v1.ExplanationParameters.examples:type_name -> google.cloud.aiplatform.v1.Examples |
| 28, // 10: google.cloud.aiplatform.v1.ExplanationParameters.output_indices:type_name -> google.protobuf.ListValue |
| 13, // 11: google.cloud.aiplatform.v1.IntegratedGradientsAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1.SmoothGradConfig |
| 15, // 12: google.cloud.aiplatform.v1.IntegratedGradientsAttribution.blur_baseline_config:type_name -> google.cloud.aiplatform.v1.BlurBaselineConfig |
| 13, // 13: google.cloud.aiplatform.v1.XraiAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1.SmoothGradConfig |
| 15, // 14: google.cloud.aiplatform.v1.XraiAttribution.blur_baseline_config:type_name -> google.cloud.aiplatform.v1.BlurBaselineConfig |
| 14, // 15: google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma:type_name -> google.cloud.aiplatform.v1.FeatureNoiseSigma |
| 22, // 16: google.cloud.aiplatform.v1.FeatureNoiseSigma.noise_sigma:type_name -> google.cloud.aiplatform.v1.FeatureNoiseSigma.NoiseSigmaForFeature |
| 23, // 17: google.cloud.aiplatform.v1.Examples.example_gcs_source:type_name -> google.cloud.aiplatform.v1.Examples.ExampleGcsSource |
| 26, // 18: google.cloud.aiplatform.v1.Examples.nearest_neighbor_search_config:type_name -> google.protobuf.Value |
| 17, // 19: google.cloud.aiplatform.v1.Examples.presets:type_name -> google.cloud.aiplatform.v1.Presets |
| 1, // 20: google.cloud.aiplatform.v1.Presets.query:type_name -> google.cloud.aiplatform.v1.Presets.Query |
| 2, // 21: google.cloud.aiplatform.v1.Presets.modality:type_name -> google.cloud.aiplatform.v1.Presets.Modality |
| 9, // 22: google.cloud.aiplatform.v1.ExplanationSpecOverride.parameters:type_name -> google.cloud.aiplatform.v1.ExplanationParameters |
| 19, // 23: google.cloud.aiplatform.v1.ExplanationSpecOverride.metadata:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride |
| 20, // 24: google.cloud.aiplatform.v1.ExplanationSpecOverride.examples_override:type_name -> google.cloud.aiplatform.v1.ExamplesOverride |
| 25, // 25: google.cloud.aiplatform.v1.ExplanationMetadataOverride.inputs:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry |
| 21, // 26: google.cloud.aiplatform.v1.ExamplesOverride.restrictions:type_name -> google.cloud.aiplatform.v1.ExamplesRestrictionsNamespace |
| 3, // 27: google.cloud.aiplatform.v1.ExamplesOverride.data_format:type_name -> google.cloud.aiplatform.v1.ExamplesOverride.DataFormat |
| 0, // 28: google.cloud.aiplatform.v1.Examples.ExampleGcsSource.data_format:type_name -> google.cloud.aiplatform.v1.Examples.ExampleGcsSource.DataFormat |
| 29, // 29: google.cloud.aiplatform.v1.Examples.ExampleGcsSource.gcs_source:type_name -> google.cloud.aiplatform.v1.GcsSource |
| 26, // 30: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride.input_baselines:type_name -> google.protobuf.Value |
| 24, // 31: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry.value:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride |
| 32, // [32:32] is the sub-list for method output_type |
| 32, // [32:32] is the sub-list for method input_type |
| 32, // [32:32] is the sub-list for extension type_name |
| 32, // [32:32] is the sub-list for extension extendee |
| 0, // [0:32] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_aiplatform_v1_explanation_proto_init() } |
| func file_google_cloud_aiplatform_v1_explanation_proto_init() { |
| if File_google_cloud_aiplatform_v1_explanation_proto != nil { |
| return |
| } |
| file_google_cloud_aiplatform_v1_explanation_metadata_proto_init() |
| file_google_cloud_aiplatform_v1_io_proto_init() |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Explanation); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelExplanation); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Attribution); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Neighbor); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExplanationSpec); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExplanationParameters); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*SampledShapleyAttribution); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*IntegratedGradientsAttribution); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*XraiAttribution); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*SmoothGradConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*FeatureNoiseSigma); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*BlurBaselineConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Examples); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Presets); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[14].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExplanationSpecOverride); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[15].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExplanationMetadataOverride); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[16].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExamplesOverride); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[17].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExamplesRestrictionsNamespace); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[18].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*FeatureNoiseSigma_NoiseSigmaForFeature); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[19].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*Examples_ExampleGcsSource); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[20].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ExplanationMetadataOverride_InputMetadataOverride); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5].OneofWrappers = []interface{}{ |
| (*ExplanationParameters_SampledShapleyAttribution)(nil), |
| (*ExplanationParameters_IntegratedGradientsAttribution)(nil), |
| (*ExplanationParameters_XraiAttribution)(nil), |
| (*ExplanationParameters_Examples)(nil), |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9].OneofWrappers = []interface{}{ |
| (*SmoothGradConfig_NoiseSigma)(nil), |
| (*SmoothGradConfig_FeatureNoiseSigma)(nil), |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12].OneofWrappers = []interface{}{ |
| (*Examples_ExampleGcsSource_)(nil), |
| (*Examples_NearestNeighborSearchConfig)(nil), |
| (*Examples_Presets)(nil), |
| } |
| file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13].OneofWrappers = []interface{}{} |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_aiplatform_v1_explanation_proto_rawDesc, |
| NumEnums: 4, |
| NumMessages: 22, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_aiplatform_v1_explanation_proto_goTypes, |
| DependencyIndexes: file_google_cloud_aiplatform_v1_explanation_proto_depIdxs, |
| EnumInfos: file_google_cloud_aiplatform_v1_explanation_proto_enumTypes, |
| MessageInfos: file_google_cloud_aiplatform_v1_explanation_proto_msgTypes, |
| }.Build() |
| File_google_cloud_aiplatform_v1_explanation_proto = out.File |
| file_google_cloud_aiplatform_v1_explanation_proto_rawDesc = nil |
| file_google_cloud_aiplatform_v1_explanation_proto_goTypes = nil |
| file_google_cloud_aiplatform_v1_explanation_proto_depIdxs = nil |
| } |