1// Copyright 2021 Google LLC 2// 3// Licensed under the Apache License, Version 2.0 (the "License"); 4// you may not use this file except in compliance with the License. 5// You may obtain a copy of the License at 6// 7// http://www.apache.org/licenses/LICENSE-2.0 8// 9// Unless required by applicable law or agreed to in writing, software 10// distributed under the License is distributed on an "AS IS" BASIS, 11// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12// See the License for the specific language governing permissions and 13// limitations under the License. 14 15// Code generated by protoc-gen-go. DO NOT EDIT. 16// versions: 17// protoc-gen-go v1.25.0-devel 18// protoc v3.13.0 19// source: google/cloud/aiplatform/v1beta1/explanation.proto 20 21package aiplatform 22 23import ( 24 reflect "reflect" 25 sync "sync" 26 27 proto "github.com/golang/protobuf/proto" 28 _ "google.golang.org/genproto/googleapis/api/annotations" 29 protoreflect "google.golang.org/protobuf/reflect/protoreflect" 30 protoimpl "google.golang.org/protobuf/runtime/protoimpl" 31 structpb "google.golang.org/protobuf/types/known/structpb" 32) 33 34const ( 35 // Verify that this generated code is sufficiently up-to-date. 36 _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) 37 // Verify that runtime/protoimpl is sufficiently up-to-date. 38 _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) 39) 40 41// This is a compile-time assertion that a sufficiently up-to-date version 42// of the legacy proto package is being used. 43const _ = proto.ProtoPackageIsVersion4 44 45// Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1beta1.PredictResponse.predictions]) 46// produced by the Model on a given [instance][google.cloud.aiplatform.v1beta1.ExplainRequest.instances]. 47type Explanation struct { 48 state protoimpl.MessageState 49 sizeCache protoimpl.SizeCache 50 unknownFields protoimpl.UnknownFields 51 52 // Output only. Feature attributions grouped by predicted outputs. 53 // 54 // For Models that predict only one output, such as regression Models that 55 // predict only one score, there is only one attibution that explains the 56 // predicted output. For Models that predict multiple outputs, such as 57 // multiclass Models that predict multiple classes, each element explains one 58 // specific item. [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] can be used to identify which 59 // output this attribution is explaining. 60 // 61 // If users set [ExplanationParameters.top_k][google.cloud.aiplatform.v1beta1.ExplanationParameters.top_k], the attributions are sorted 62 // by [instance_output_value][Attributions.instance_output_value] in 63 // descending order. If [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices] is specified, 64 // the attributions are stored by [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] in the same 65 // order as they appear in the output_indices. 66 Attributions []*Attribution `protobuf:"bytes,1,rep,name=attributions,proto3" json:"attributions,omitempty"` 67} 68 69func (x *Explanation) Reset() { 70 *x = Explanation{} 71 if protoimpl.UnsafeEnabled { 72 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0] 73 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 74 ms.StoreMessageInfo(mi) 75 } 76} 77 78func (x *Explanation) String() string { 79 return protoimpl.X.MessageStringOf(x) 80} 81 82func (*Explanation) ProtoMessage() {} 83 84func (x *Explanation) ProtoReflect() protoreflect.Message { 85 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0] 86 if protoimpl.UnsafeEnabled && x != nil { 87 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 88 if ms.LoadMessageInfo() == nil { 89 ms.StoreMessageInfo(mi) 90 } 91 return ms 92 } 93 return mi.MessageOf(x) 94} 95 96// Deprecated: Use Explanation.ProtoReflect.Descriptor instead. 97func (*Explanation) Descriptor() ([]byte, []int) { 98 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{0} 99} 100 101func (x *Explanation) GetAttributions() []*Attribution { 102 if x != nil { 103 return x.Attributions 104 } 105 return nil 106} 107 108// Aggregated explanation metrics for a Model over a set of instances. 109type ModelExplanation struct { 110 state protoimpl.MessageState 111 sizeCache protoimpl.SizeCache 112 unknownFields protoimpl.UnknownFields 113 114 // Output only. Aggregated attributions explaining the Model's prediction outputs over the 115 // set of instances. The attributions are grouped by outputs. 116 // 117 // For Models that predict only one output, such as regression Models that 118 // predict only one score, there is only one attibution that explains the 119 // predicted output. For Models that predict multiple outputs, such as 120 // multiclass Models that predict multiple classes, each element explains one 121 // specific item. [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] can be used to identify which 122 // output this attribution is explaining. 123 // 124 // The [baselineOutputValue][google.cloud.aiplatform.v1beta1.Attribution.baseline_output_value], 125 // [instanceOutputValue][google.cloud.aiplatform.v1beta1.Attribution.instance_output_value] and 126 // [featureAttributions][google.cloud.aiplatform.v1beta1.Attribution.feature_attributions] fields are 127 // averaged over the test data. 128 // 129 // NOTE: Currently AutoML tabular classification Models produce only one 130 // attribution, which averages attributions over all the classes it predicts. 131 // [Attribution.approximation_error][google.cloud.aiplatform.v1beta1.Attribution.approximation_error] is not populated. 132 MeanAttributions []*Attribution `protobuf:"bytes,1,rep,name=mean_attributions,json=meanAttributions,proto3" json:"mean_attributions,omitempty"` 133} 134 135func (x *ModelExplanation) Reset() { 136 *x = ModelExplanation{} 137 if protoimpl.UnsafeEnabled { 138 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1] 139 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 140 ms.StoreMessageInfo(mi) 141 } 142} 143 144func (x *ModelExplanation) String() string { 145 return protoimpl.X.MessageStringOf(x) 146} 147 148func (*ModelExplanation) ProtoMessage() {} 149 150func (x *ModelExplanation) ProtoReflect() protoreflect.Message { 151 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1] 152 if protoimpl.UnsafeEnabled && x != nil { 153 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 154 if ms.LoadMessageInfo() == nil { 155 ms.StoreMessageInfo(mi) 156 } 157 return ms 158 } 159 return mi.MessageOf(x) 160} 161 162// Deprecated: Use ModelExplanation.ProtoReflect.Descriptor instead. 163func (*ModelExplanation) Descriptor() ([]byte, []int) { 164 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{1} 165} 166 167func (x *ModelExplanation) GetMeanAttributions() []*Attribution { 168 if x != nil { 169 return x.MeanAttributions 170 } 171 return nil 172} 173 174// Attribution that explains a particular prediction output. 175type Attribution struct { 176 state protoimpl.MessageState 177 sizeCache protoimpl.SizeCache 178 unknownFields protoimpl.UnknownFields 179 180 // Output only. Model predicted output if the input instance is constructed from the 181 // baselines of all the features defined in [ExplanationMetadata.inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 182 // The field name of the output is determined by the key in 183 // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 184 // 185 // If the Model's predicted output has multiple dimensions (rank > 1), this is 186 // the value in the output located by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. 187 // 188 // If there are multiple baselines, their output values are averaged. 189 BaselineOutputValue float64 `protobuf:"fixed64,1,opt,name=baseline_output_value,json=baselineOutputValue,proto3" json:"baseline_output_value,omitempty"` 190 // Output only. Model predicted output on the corresponding [explanation 191 // instance][ExplainRequest.instances]. The field name of the output is 192 // determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 193 // 194 // If the Model predicted output has multiple dimensions, this is the value in 195 // the output located by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. 196 InstanceOutputValue float64 `protobuf:"fixed64,2,opt,name=instance_output_value,json=instanceOutputValue,proto3" json:"instance_output_value,omitempty"` 197 // Output only. Attributions of each explained feature. Features are extracted from 198 // the [prediction instances][google.cloud.aiplatform.v1beta1.ExplainRequest.instances] according to 199 // [explanation metadata for inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 200 // 201 // The value is a struct, whose keys are the name of the feature. The values 202 // are how much the feature in the [instance][google.cloud.aiplatform.v1beta1.ExplainRequest.instances] 203 // contributed to the predicted result. 204 // 205 // The format of the value is determined by the feature's input format: 206 // 207 // * If the feature is a scalar value, the attribution value is a 208 // [floating number][google.protobuf.Value.number_value]. 209 // 210 // * If the feature is an array of scalar values, the attribution value is 211 // an [array][google.protobuf.Value.list_value]. 212 // 213 // * If the feature is a struct, the attribution value is a 214 // [struct][google.protobuf.Value.struct_value]. The keys in the 215 // attribution value struct are the same as the keys in the feature 216 // struct. The formats of the values in the attribution struct are 217 // determined by the formats of the values in the feature struct. 218 // 219 // The [ExplanationMetadata.feature_attributions_schema_uri][google.cloud.aiplatform.v1beta1.ExplanationMetadata.feature_attributions_schema_uri] field, 220 // pointed to by the [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] field of the 221 // [Endpoint.deployed_models][google.cloud.aiplatform.v1beta1.Endpoint.deployed_models] object, points to the schema file that 222 // describes the features and their attribution values (if it is populated). 223 FeatureAttributions *structpb.Value `protobuf:"bytes,3,opt,name=feature_attributions,json=featureAttributions,proto3" json:"feature_attributions,omitempty"` 224 // Output only. The index that locates the explained prediction output. 225 // 226 // If the prediction output is a scalar value, output_index is not populated. 227 // If the prediction output has multiple dimensions, the length of the 228 // output_index list is the same as the number of dimensions of the output. 229 // The i-th element in output_index is the element index of the i-th dimension 230 // of the output vector. Indices start from 0. 231 OutputIndex []int32 `protobuf:"varint,4,rep,packed,name=output_index,json=outputIndex,proto3" json:"output_index,omitempty"` 232 // Output only. The display name of the output identified by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. For example, 233 // the predicted class name by a multi-classification Model. 234 // 235 // This field is only populated iff the Model predicts display names as a 236 // separate field along with the explained output. The predicted display name 237 // must has the same shape of the explained output, and can be located using 238 // output_index. 239 OutputDisplayName string `protobuf:"bytes,5,opt,name=output_display_name,json=outputDisplayName,proto3" json:"output_display_name,omitempty"` 240 // Output only. Error of [feature_attributions][google.cloud.aiplatform.v1beta1.Attribution.feature_attributions] caused by approximation used in the 241 // explanation method. Lower value means more precise attributions. 242 // 243 // * For Sampled Shapley 244 // [attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.sampled_shapley_attribution], 245 // increasing [path_count][google.cloud.aiplatform.v1beta1.SampledShapleyAttribution.path_count] might reduce 246 // the error. 247 // * For Integrated Gradients 248 // [attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution], 249 // increasing [step_count][google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution.step_count] might 250 // reduce the error. 251 // * For [XRAI attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution], 252 // increasing 253 // [step_count][google.cloud.aiplatform.v1beta1.XraiAttribution.step_count] might reduce the error. 254 // 255 // See [this introduction](/ai-platform-unified/docs/explainable-ai/overview) 256 // for more information. 257 ApproximationError float64 `protobuf:"fixed64,6,opt,name=approximation_error,json=approximationError,proto3" json:"approximation_error,omitempty"` 258 // Output only. Name of the explain output. Specified as the key in 259 // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 260 OutputName string `protobuf:"bytes,7,opt,name=output_name,json=outputName,proto3" json:"output_name,omitempty"` 261} 262 263func (x *Attribution) Reset() { 264 *x = Attribution{} 265 if protoimpl.UnsafeEnabled { 266 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2] 267 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 268 ms.StoreMessageInfo(mi) 269 } 270} 271 272func (x *Attribution) String() string { 273 return protoimpl.X.MessageStringOf(x) 274} 275 276func (*Attribution) ProtoMessage() {} 277 278func (x *Attribution) ProtoReflect() protoreflect.Message { 279 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2] 280 if protoimpl.UnsafeEnabled && x != nil { 281 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 282 if ms.LoadMessageInfo() == nil { 283 ms.StoreMessageInfo(mi) 284 } 285 return ms 286 } 287 return mi.MessageOf(x) 288} 289 290// Deprecated: Use Attribution.ProtoReflect.Descriptor instead. 291func (*Attribution) Descriptor() ([]byte, []int) { 292 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{2} 293} 294 295func (x *Attribution) GetBaselineOutputValue() float64 { 296 if x != nil { 297 return x.BaselineOutputValue 298 } 299 return 0 300} 301 302func (x *Attribution) GetInstanceOutputValue() float64 { 303 if x != nil { 304 return x.InstanceOutputValue 305 } 306 return 0 307} 308 309func (x *Attribution) GetFeatureAttributions() *structpb.Value { 310 if x != nil { 311 return x.FeatureAttributions 312 } 313 return nil 314} 315 316func (x *Attribution) GetOutputIndex() []int32 { 317 if x != nil { 318 return x.OutputIndex 319 } 320 return nil 321} 322 323func (x *Attribution) GetOutputDisplayName() string { 324 if x != nil { 325 return x.OutputDisplayName 326 } 327 return "" 328} 329 330func (x *Attribution) GetApproximationError() float64 { 331 if x != nil { 332 return x.ApproximationError 333 } 334 return 0 335} 336 337func (x *Attribution) GetOutputName() string { 338 if x != nil { 339 return x.OutputName 340 } 341 return "" 342} 343 344// Specification of Model explanation. 345type ExplanationSpec struct { 346 state protoimpl.MessageState 347 sizeCache protoimpl.SizeCache 348 unknownFields protoimpl.UnknownFields 349 350 // Required. Parameters that configure explaining of the Model's predictions. 351 Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` 352 // Required. Metadata describing the Model's input and output for explanation. 353 Metadata *ExplanationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` 354} 355 356func (x *ExplanationSpec) Reset() { 357 *x = ExplanationSpec{} 358 if protoimpl.UnsafeEnabled { 359 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3] 360 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 361 ms.StoreMessageInfo(mi) 362 } 363} 364 365func (x *ExplanationSpec) String() string { 366 return protoimpl.X.MessageStringOf(x) 367} 368 369func (*ExplanationSpec) ProtoMessage() {} 370 371func (x *ExplanationSpec) ProtoReflect() protoreflect.Message { 372 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3] 373 if protoimpl.UnsafeEnabled && x != nil { 374 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 375 if ms.LoadMessageInfo() == nil { 376 ms.StoreMessageInfo(mi) 377 } 378 return ms 379 } 380 return mi.MessageOf(x) 381} 382 383// Deprecated: Use ExplanationSpec.ProtoReflect.Descriptor instead. 384func (*ExplanationSpec) Descriptor() ([]byte, []int) { 385 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{3} 386} 387 388func (x *ExplanationSpec) GetParameters() *ExplanationParameters { 389 if x != nil { 390 return x.Parameters 391 } 392 return nil 393} 394 395func (x *ExplanationSpec) GetMetadata() *ExplanationMetadata { 396 if x != nil { 397 return x.Metadata 398 } 399 return nil 400} 401 402// Parameters to configure explaining for Model's predictions. 403type ExplanationParameters struct { 404 state protoimpl.MessageState 405 sizeCache protoimpl.SizeCache 406 unknownFields protoimpl.UnknownFields 407 408 // Types that are assignable to Method: 409 // *ExplanationParameters_SampledShapleyAttribution 410 // *ExplanationParameters_IntegratedGradientsAttribution 411 // *ExplanationParameters_XraiAttribution 412 Method isExplanationParameters_Method `protobuf_oneof:"method"` 413 // If populated, returns attributions for top K indices of outputs 414 // (defaults to 1). Only applies to Models that predicts more than one outputs 415 // (e,g, multi-class Models). When set to -1, returns explanations for all 416 // outputs. 417 TopK int32 `protobuf:"varint,4,opt,name=top_k,json=topK,proto3" json:"top_k,omitempty"` 418 // If populated, only returns attributions that have 419 // [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] contained in output_indices. It 420 // must be an ndarray of integers, with the same shape of the output it's 421 // explaining. 422 // 423 // If not populated, returns attributions for [top_k][google.cloud.aiplatform.v1beta1.ExplanationParameters.top_k] indices of outputs. 424 // If neither top_k nor output_indeices is populated, returns the argmax 425 // index of the outputs. 426 // 427 // Only applicable to Models that predict multiple outputs (e,g, multi-class 428 // Models that predict multiple classes). 429 OutputIndices *structpb.ListValue `protobuf:"bytes,5,opt,name=output_indices,json=outputIndices,proto3" json:"output_indices,omitempty"` 430} 431 432func (x *ExplanationParameters) Reset() { 433 *x = ExplanationParameters{} 434 if protoimpl.UnsafeEnabled { 435 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 436 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 437 ms.StoreMessageInfo(mi) 438 } 439} 440 441func (x *ExplanationParameters) String() string { 442 return protoimpl.X.MessageStringOf(x) 443} 444 445func (*ExplanationParameters) ProtoMessage() {} 446 447func (x *ExplanationParameters) ProtoReflect() protoreflect.Message { 448 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 449 if protoimpl.UnsafeEnabled && x != nil { 450 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 451 if ms.LoadMessageInfo() == nil { 452 ms.StoreMessageInfo(mi) 453 } 454 return ms 455 } 456 return mi.MessageOf(x) 457} 458 459// Deprecated: Use ExplanationParameters.ProtoReflect.Descriptor instead. 460func (*ExplanationParameters) Descriptor() ([]byte, []int) { 461 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{4} 462} 463 464func (m *ExplanationParameters) GetMethod() isExplanationParameters_Method { 465 if m != nil { 466 return m.Method 467 } 468 return nil 469} 470 471func (x *ExplanationParameters) GetSampledShapleyAttribution() *SampledShapleyAttribution { 472 if x, ok := x.GetMethod().(*ExplanationParameters_SampledShapleyAttribution); ok { 473 return x.SampledShapleyAttribution 474 } 475 return nil 476} 477 478func (x *ExplanationParameters) GetIntegratedGradientsAttribution() *IntegratedGradientsAttribution { 479 if x, ok := x.GetMethod().(*ExplanationParameters_IntegratedGradientsAttribution); ok { 480 return x.IntegratedGradientsAttribution 481 } 482 return nil 483} 484 485func (x *ExplanationParameters) GetXraiAttribution() *XraiAttribution { 486 if x, ok := x.GetMethod().(*ExplanationParameters_XraiAttribution); ok { 487 return x.XraiAttribution 488 } 489 return nil 490} 491 492func (x *ExplanationParameters) GetTopK() int32 { 493 if x != nil { 494 return x.TopK 495 } 496 return 0 497} 498 499func (x *ExplanationParameters) GetOutputIndices() *structpb.ListValue { 500 if x != nil { 501 return x.OutputIndices 502 } 503 return nil 504} 505 506type isExplanationParameters_Method interface { 507 isExplanationParameters_Method() 508} 509 510type ExplanationParameters_SampledShapleyAttribution struct { 511 // An attribution method that approximates Shapley values for features that 512 // contribute to the label being predicted. A sampling strategy is used to 513 // approximate the value rather than considering all subsets of features. 514 // Refer to this paper for model details: https://arxiv.org/abs/1306.4265. 515 SampledShapleyAttribution *SampledShapleyAttribution `protobuf:"bytes,1,opt,name=sampled_shapley_attribution,json=sampledShapleyAttribution,proto3,oneof"` 516} 517 518type ExplanationParameters_IntegratedGradientsAttribution struct { 519 // An attribution method that computes Aumann-Shapley values taking 520 // advantage of the model's fully differentiable structure. Refer to this 521 // paper for more details: https://arxiv.org/abs/1703.01365 522 IntegratedGradientsAttribution *IntegratedGradientsAttribution `protobuf:"bytes,2,opt,name=integrated_gradients_attribution,json=integratedGradientsAttribution,proto3,oneof"` 523} 524 525type ExplanationParameters_XraiAttribution struct { 526 // An attribution method that redistributes Integrated Gradients 527 // attribution to segmented regions, taking advantage of the model's fully 528 // differentiable structure. Refer to this paper for 529 // more details: https://arxiv.org/abs/1906.02825 530 // 531 // XRAI currently performs better on natural images, like a picture of a 532 // house or an animal. If the images are taken in artificial environments, 533 // like a lab or manufacturing line, or from diagnostic equipment, like 534 // x-rays or quality-control cameras, use Integrated Gradients instead. 535 XraiAttribution *XraiAttribution `protobuf:"bytes,3,opt,name=xrai_attribution,json=xraiAttribution,proto3,oneof"` 536} 537 538func (*ExplanationParameters_SampledShapleyAttribution) isExplanationParameters_Method() {} 539 540func (*ExplanationParameters_IntegratedGradientsAttribution) isExplanationParameters_Method() {} 541 542func (*ExplanationParameters_XraiAttribution) isExplanationParameters_Method() {} 543 544// An attribution method that approximates Shapley values for features that 545// contribute to the label being predicted. A sampling strategy is used to 546// approximate the value rather than considering all subsets of features. 547type SampledShapleyAttribution struct { 548 state protoimpl.MessageState 549 sizeCache protoimpl.SizeCache 550 unknownFields protoimpl.UnknownFields 551 552 // Required. The number of feature permutations to consider when approximating the 553 // Shapley values. 554 // 555 // Valid range of its value is [1, 50], inclusively. 556 PathCount int32 `protobuf:"varint,1,opt,name=path_count,json=pathCount,proto3" json:"path_count,omitempty"` 557} 558 559func (x *SampledShapleyAttribution) Reset() { 560 *x = SampledShapleyAttribution{} 561 if protoimpl.UnsafeEnabled { 562 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 563 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 564 ms.StoreMessageInfo(mi) 565 } 566} 567 568func (x *SampledShapleyAttribution) String() string { 569 return protoimpl.X.MessageStringOf(x) 570} 571 572func (*SampledShapleyAttribution) ProtoMessage() {} 573 574func (x *SampledShapleyAttribution) ProtoReflect() protoreflect.Message { 575 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 576 if protoimpl.UnsafeEnabled && x != nil { 577 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 578 if ms.LoadMessageInfo() == nil { 579 ms.StoreMessageInfo(mi) 580 } 581 return ms 582 } 583 return mi.MessageOf(x) 584} 585 586// Deprecated: Use SampledShapleyAttribution.ProtoReflect.Descriptor instead. 587func (*SampledShapleyAttribution) Descriptor() ([]byte, []int) { 588 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{5} 589} 590 591func (x *SampledShapleyAttribution) GetPathCount() int32 { 592 if x != nil { 593 return x.PathCount 594 } 595 return 0 596} 597 598// An attribution method that computes the Aumann-Shapley value taking advantage 599// of the model's fully differentiable structure. Refer to this paper for 600// more details: https://arxiv.org/abs/1703.01365 601type IntegratedGradientsAttribution struct { 602 state protoimpl.MessageState 603 sizeCache protoimpl.SizeCache 604 unknownFields protoimpl.UnknownFields 605 606 // Required. The number of steps for approximating the path integral. 607 // A good value to start is 50 and gradually increase until the 608 // sum to diff property is within the desired error range. 609 // 610 // Valid range of its value is [1, 100], inclusively. 611 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 612 // Config for SmoothGrad approximation of gradients. 613 // 614 // When enabled, the gradients are approximated by averaging the gradients 615 // from noisy samples in the vicinity of the inputs. Adding 616 // noise can help improve the computed gradients. Refer to this paper for more 617 // details: https://arxiv.org/pdf/1706.03825.pdf 618 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 619} 620 621func (x *IntegratedGradientsAttribution) Reset() { 622 *x = IntegratedGradientsAttribution{} 623 if protoimpl.UnsafeEnabled { 624 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 625 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 626 ms.StoreMessageInfo(mi) 627 } 628} 629 630func (x *IntegratedGradientsAttribution) String() string { 631 return protoimpl.X.MessageStringOf(x) 632} 633 634func (*IntegratedGradientsAttribution) ProtoMessage() {} 635 636func (x *IntegratedGradientsAttribution) ProtoReflect() protoreflect.Message { 637 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 638 if protoimpl.UnsafeEnabled && x != nil { 639 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 640 if ms.LoadMessageInfo() == nil { 641 ms.StoreMessageInfo(mi) 642 } 643 return ms 644 } 645 return mi.MessageOf(x) 646} 647 648// Deprecated: Use IntegratedGradientsAttribution.ProtoReflect.Descriptor instead. 649func (*IntegratedGradientsAttribution) Descriptor() ([]byte, []int) { 650 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{6} 651} 652 653func (x *IntegratedGradientsAttribution) GetStepCount() int32 { 654 if x != nil { 655 return x.StepCount 656 } 657 return 0 658} 659 660func (x *IntegratedGradientsAttribution) GetSmoothGradConfig() *SmoothGradConfig { 661 if x != nil { 662 return x.SmoothGradConfig 663 } 664 return nil 665} 666 667// An explanation method that redistributes Integrated Gradients 668// attributions to segmented regions, taking advantage of the model's fully 669// differentiable structure. Refer to this paper for more details: 670// https://arxiv.org/abs/1906.02825 671// 672// Supported only by image Models. 673type XraiAttribution struct { 674 state protoimpl.MessageState 675 sizeCache protoimpl.SizeCache 676 unknownFields protoimpl.UnknownFields 677 678 // Required. The number of steps for approximating the path integral. 679 // A good value to start is 50 and gradually increase until the 680 // sum to diff property is met within the desired error range. 681 // 682 // Valid range of its value is [1, 100], inclusively. 683 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 684 // Config for SmoothGrad approximation of gradients. 685 // 686 // When enabled, the gradients are approximated by averaging the gradients 687 // from noisy samples in the vicinity of the inputs. Adding 688 // noise can help improve the computed gradients. Refer to this paper for more 689 // details: https://arxiv.org/pdf/1706.03825.pdf 690 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 691} 692 693func (x *XraiAttribution) Reset() { 694 *x = XraiAttribution{} 695 if protoimpl.UnsafeEnabled { 696 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 697 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 698 ms.StoreMessageInfo(mi) 699 } 700} 701 702func (x *XraiAttribution) String() string { 703 return protoimpl.X.MessageStringOf(x) 704} 705 706func (*XraiAttribution) ProtoMessage() {} 707 708func (x *XraiAttribution) ProtoReflect() protoreflect.Message { 709 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 710 if protoimpl.UnsafeEnabled && x != nil { 711 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 712 if ms.LoadMessageInfo() == nil { 713 ms.StoreMessageInfo(mi) 714 } 715 return ms 716 } 717 return mi.MessageOf(x) 718} 719 720// Deprecated: Use XraiAttribution.ProtoReflect.Descriptor instead. 721func (*XraiAttribution) Descriptor() ([]byte, []int) { 722 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{7} 723} 724 725func (x *XraiAttribution) GetStepCount() int32 { 726 if x != nil { 727 return x.StepCount 728 } 729 return 0 730} 731 732func (x *XraiAttribution) GetSmoothGradConfig() *SmoothGradConfig { 733 if x != nil { 734 return x.SmoothGradConfig 735 } 736 return nil 737} 738 739// Config for SmoothGrad approximation of gradients. 740// 741// When enabled, the gradients are approximated by averaging the gradients from 742// noisy samples in the vicinity of the inputs. Adding noise can help improve 743// the computed gradients. Refer to this paper for more details: 744// https://arxiv.org/pdf/1706.03825.pdf 745type SmoothGradConfig struct { 746 state protoimpl.MessageState 747 sizeCache protoimpl.SizeCache 748 unknownFields protoimpl.UnknownFields 749 750 // Represents the standard deviation of the gaussian kernel 751 // that will be used to add noise to the interpolated inputs 752 // prior to computing gradients. 753 // 754 // Types that are assignable to GradientNoiseSigma: 755 // *SmoothGradConfig_NoiseSigma 756 // *SmoothGradConfig_FeatureNoiseSigma 757 GradientNoiseSigma isSmoothGradConfig_GradientNoiseSigma `protobuf_oneof:"GradientNoiseSigma"` 758 // The number of gradient samples to use for 759 // approximation. The higher this number, the more accurate the gradient 760 // is, but the runtime complexity increases by this factor as well. 761 // Valid range of its value is [1, 50]. Defaults to 3. 762 NoisySampleCount int32 `protobuf:"varint,3,opt,name=noisy_sample_count,json=noisySampleCount,proto3" json:"noisy_sample_count,omitempty"` 763} 764 765func (x *SmoothGradConfig) Reset() { 766 *x = SmoothGradConfig{} 767 if protoimpl.UnsafeEnabled { 768 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 769 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 770 ms.StoreMessageInfo(mi) 771 } 772} 773 774func (x *SmoothGradConfig) String() string { 775 return protoimpl.X.MessageStringOf(x) 776} 777 778func (*SmoothGradConfig) ProtoMessage() {} 779 780func (x *SmoothGradConfig) ProtoReflect() protoreflect.Message { 781 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 782 if protoimpl.UnsafeEnabled && x != nil { 783 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 784 if ms.LoadMessageInfo() == nil { 785 ms.StoreMessageInfo(mi) 786 } 787 return ms 788 } 789 return mi.MessageOf(x) 790} 791 792// Deprecated: Use SmoothGradConfig.ProtoReflect.Descriptor instead. 793func (*SmoothGradConfig) Descriptor() ([]byte, []int) { 794 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{8} 795} 796 797func (m *SmoothGradConfig) GetGradientNoiseSigma() isSmoothGradConfig_GradientNoiseSigma { 798 if m != nil { 799 return m.GradientNoiseSigma 800 } 801 return nil 802} 803 804func (x *SmoothGradConfig) GetNoiseSigma() float32 { 805 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_NoiseSigma); ok { 806 return x.NoiseSigma 807 } 808 return 0 809} 810 811func (x *SmoothGradConfig) GetFeatureNoiseSigma() *FeatureNoiseSigma { 812 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_FeatureNoiseSigma); ok { 813 return x.FeatureNoiseSigma 814 } 815 return nil 816} 817 818func (x *SmoothGradConfig) GetNoisySampleCount() int32 { 819 if x != nil { 820 return x.NoisySampleCount 821 } 822 return 0 823} 824 825type isSmoothGradConfig_GradientNoiseSigma interface { 826 isSmoothGradConfig_GradientNoiseSigma() 827} 828 829type SmoothGradConfig_NoiseSigma struct { 830 // This is a single float value and will be used to add noise to all the 831 // features. Use this field when all features are normalized to have the 832 // same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where 833 // features are normalized to have 0-mean and 1-variance. For more details 834 // about normalization: 835 // https://tinyurl.com/dgc-normalization. 836 // 837 // For best results the recommended value is about 10% - 20% of the standard 838 // deviation of the input feature. Refer to section 3.2 of the SmoothGrad 839 // paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. 840 // 841 // If the distribution is different per feature, set 842 // [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma] instead 843 // for each feature. 844 NoiseSigma float32 `protobuf:"fixed32,1,opt,name=noise_sigma,json=noiseSigma,proto3,oneof"` 845} 846 847type SmoothGradConfig_FeatureNoiseSigma struct { 848 // This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but 849 // provides additional flexibility. A separate noise sigma can be provided 850 // for each feature, which is useful if their distributions are different. 851 // No noise is added to features that are not set. If this field is unset, 852 // [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] will be used for all 853 // features. 854 FeatureNoiseSigma *FeatureNoiseSigma `protobuf:"bytes,2,opt,name=feature_noise_sigma,json=featureNoiseSigma,proto3,oneof"` 855} 856 857func (*SmoothGradConfig_NoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 858 859func (*SmoothGradConfig_FeatureNoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 860 861// Noise sigma by features. Noise sigma represents the standard deviation of the 862// gaussian kernel that will be used to add noise to interpolated inputs prior 863// to computing gradients. 864type FeatureNoiseSigma struct { 865 state protoimpl.MessageState 866 sizeCache protoimpl.SizeCache 867 unknownFields protoimpl.UnknownFields 868 869 // Noise sigma per feature. No noise is added to features that are not set. 870 NoiseSigma []*FeatureNoiseSigma_NoiseSigmaForFeature `protobuf:"bytes,1,rep,name=noise_sigma,json=noiseSigma,proto3" json:"noise_sigma,omitempty"` 871} 872 873func (x *FeatureNoiseSigma) Reset() { 874 *x = FeatureNoiseSigma{} 875 if protoimpl.UnsafeEnabled { 876 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 877 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 878 ms.StoreMessageInfo(mi) 879 } 880} 881 882func (x *FeatureNoiseSigma) String() string { 883 return protoimpl.X.MessageStringOf(x) 884} 885 886func (*FeatureNoiseSigma) ProtoMessage() {} 887 888func (x *FeatureNoiseSigma) ProtoReflect() protoreflect.Message { 889 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 890 if protoimpl.UnsafeEnabled && x != nil { 891 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 892 if ms.LoadMessageInfo() == nil { 893 ms.StoreMessageInfo(mi) 894 } 895 return ms 896 } 897 return mi.MessageOf(x) 898} 899 900// Deprecated: Use FeatureNoiseSigma.ProtoReflect.Descriptor instead. 901func (*FeatureNoiseSigma) Descriptor() ([]byte, []int) { 902 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9} 903} 904 905func (x *FeatureNoiseSigma) GetNoiseSigma() []*FeatureNoiseSigma_NoiseSigmaForFeature { 906 if x != nil { 907 return x.NoiseSigma 908 } 909 return nil 910} 911 912// The [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] entries that can be overridden at [online 913// explanation][PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 914type ExplanationSpecOverride struct { 915 state protoimpl.MessageState 916 sizeCache protoimpl.SizeCache 917 unknownFields protoimpl.UnknownFields 918 919 // The parameters to be overridden. Note that the 920 // [method][google.cloud.aiplatform.v1beta1.ExplanationParameters.method] cannot be changed. If not specified, 921 // no parameter is overridden. 922 Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` 923 // The metadata to be overridden. If not specified, no metadata is overridden. 924 Metadata *ExplanationMetadataOverride `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` 925} 926 927func (x *ExplanationSpecOverride) Reset() { 928 *x = ExplanationSpecOverride{} 929 if protoimpl.UnsafeEnabled { 930 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 931 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 932 ms.StoreMessageInfo(mi) 933 } 934} 935 936func (x *ExplanationSpecOverride) String() string { 937 return protoimpl.X.MessageStringOf(x) 938} 939 940func (*ExplanationSpecOverride) ProtoMessage() {} 941 942func (x *ExplanationSpecOverride) ProtoReflect() protoreflect.Message { 943 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 944 if protoimpl.UnsafeEnabled && x != nil { 945 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 946 if ms.LoadMessageInfo() == nil { 947 ms.StoreMessageInfo(mi) 948 } 949 return ms 950 } 951 return mi.MessageOf(x) 952} 953 954// Deprecated: Use ExplanationSpecOverride.ProtoReflect.Descriptor instead. 955func (*ExplanationSpecOverride) Descriptor() ([]byte, []int) { 956 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{10} 957} 958 959func (x *ExplanationSpecOverride) GetParameters() *ExplanationParameters { 960 if x != nil { 961 return x.Parameters 962 } 963 return nil 964} 965 966func (x *ExplanationSpecOverride) GetMetadata() *ExplanationMetadataOverride { 967 if x != nil { 968 return x.Metadata 969 } 970 return nil 971} 972 973// The [ExplanationMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata] entries that can be overridden at 974// [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 975type ExplanationMetadataOverride struct { 976 state protoimpl.MessageState 977 sizeCache protoimpl.SizeCache 978 unknownFields protoimpl.UnknownFields 979 980 // Required. Overrides the [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs] of the features. 981 // The key is the name of the feature to be overridden. The keys specified 982 // here must exist in the input metadata to be overridden. If a feature is 983 // not specified here, the corresponding feature's input metadata is not 984 // overridden. 985 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"` 986} 987 988func (x *ExplanationMetadataOverride) Reset() { 989 *x = ExplanationMetadataOverride{} 990 if protoimpl.UnsafeEnabled { 991 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 992 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 993 ms.StoreMessageInfo(mi) 994 } 995} 996 997func (x *ExplanationMetadataOverride) String() string { 998 return protoimpl.X.MessageStringOf(x) 999} 1000 1001func (*ExplanationMetadataOverride) ProtoMessage() {} 1002 1003func (x *ExplanationMetadataOverride) ProtoReflect() protoreflect.Message { 1004 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 1005 if protoimpl.UnsafeEnabled && x != nil { 1006 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1007 if ms.LoadMessageInfo() == nil { 1008 ms.StoreMessageInfo(mi) 1009 } 1010 return ms 1011 } 1012 return mi.MessageOf(x) 1013} 1014 1015// Deprecated: Use ExplanationMetadataOverride.ProtoReflect.Descriptor instead. 1016func (*ExplanationMetadataOverride) Descriptor() ([]byte, []int) { 1017 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{11} 1018} 1019 1020func (x *ExplanationMetadataOverride) GetInputs() map[string]*ExplanationMetadataOverride_InputMetadataOverride { 1021 if x != nil { 1022 return x.Inputs 1023 } 1024 return nil 1025} 1026 1027// Noise sigma for a single feature. 1028type FeatureNoiseSigma_NoiseSigmaForFeature struct { 1029 state protoimpl.MessageState 1030 sizeCache protoimpl.SizeCache 1031 unknownFields protoimpl.UnknownFields 1032 1033 // The name of the input feature for which noise sigma is provided. The 1034 // features are defined in 1035 // [explanation metadata inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 1036 Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` 1037 // This represents the standard deviation of the Gaussian kernel that will 1038 // be used to add noise to the feature prior to computing gradients. Similar 1039 // to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] but represents the 1040 // noise added to the current feature. Defaults to 0.1. 1041 Sigma float32 `protobuf:"fixed32,2,opt,name=sigma,proto3" json:"sigma,omitempty"` 1042} 1043 1044func (x *FeatureNoiseSigma_NoiseSigmaForFeature) Reset() { 1045 *x = FeatureNoiseSigma_NoiseSigmaForFeature{} 1046 if protoimpl.UnsafeEnabled { 1047 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1048 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1049 ms.StoreMessageInfo(mi) 1050 } 1051} 1052 1053func (x *FeatureNoiseSigma_NoiseSigmaForFeature) String() string { 1054 return protoimpl.X.MessageStringOf(x) 1055} 1056 1057func (*FeatureNoiseSigma_NoiseSigmaForFeature) ProtoMessage() {} 1058 1059func (x *FeatureNoiseSigma_NoiseSigmaForFeature) ProtoReflect() protoreflect.Message { 1060 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1061 if protoimpl.UnsafeEnabled && x != nil { 1062 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1063 if ms.LoadMessageInfo() == nil { 1064 ms.StoreMessageInfo(mi) 1065 } 1066 return ms 1067 } 1068 return mi.MessageOf(x) 1069} 1070 1071// Deprecated: Use FeatureNoiseSigma_NoiseSigmaForFeature.ProtoReflect.Descriptor instead. 1072func (*FeatureNoiseSigma_NoiseSigmaForFeature) Descriptor() ([]byte, []int) { 1073 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9, 0} 1074} 1075 1076func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetName() string { 1077 if x != nil { 1078 return x.Name 1079 } 1080 return "" 1081} 1082 1083func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetSigma() float32 { 1084 if x != nil { 1085 return x.Sigma 1086 } 1087 return 0 1088} 1089 1090// The [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] entries to be 1091// overridden. 1092type ExplanationMetadataOverride_InputMetadataOverride struct { 1093 state protoimpl.MessageState 1094 sizeCache protoimpl.SizeCache 1095 unknownFields protoimpl.UnknownFields 1096 1097 // Baseline inputs for this feature. 1098 // 1099 // This overrides the `input_baseline` field of the 1100 // [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] 1101 // object of the corresponding feature's input metadata. If it's not 1102 // specified, the original baselines are not overridden. 1103 InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"` 1104} 1105 1106func (x *ExplanationMetadataOverride_InputMetadataOverride) Reset() { 1107 *x = ExplanationMetadataOverride_InputMetadataOverride{} 1108 if protoimpl.UnsafeEnabled { 1109 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1110 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1111 ms.StoreMessageInfo(mi) 1112 } 1113} 1114 1115func (x *ExplanationMetadataOverride_InputMetadataOverride) String() string { 1116 return protoimpl.X.MessageStringOf(x) 1117} 1118 1119func (*ExplanationMetadataOverride_InputMetadataOverride) ProtoMessage() {} 1120 1121func (x *ExplanationMetadataOverride_InputMetadataOverride) ProtoReflect() protoreflect.Message { 1122 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1123 if protoimpl.UnsafeEnabled && x != nil { 1124 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1125 if ms.LoadMessageInfo() == nil { 1126 ms.StoreMessageInfo(mi) 1127 } 1128 return ms 1129 } 1130 return mi.MessageOf(x) 1131} 1132 1133// Deprecated: Use ExplanationMetadataOverride_InputMetadataOverride.ProtoReflect.Descriptor instead. 1134func (*ExplanationMetadataOverride_InputMetadataOverride) Descriptor() ([]byte, []int) { 1135 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{11, 0} 1136} 1137 1138func (x *ExplanationMetadataOverride_InputMetadataOverride) GetInputBaselines() []*structpb.Value { 1139 if x != nil { 1140 return x.InputBaselines 1141 } 1142 return nil 1143} 1144 1145var File_google_cloud_aiplatform_v1beta1_explanation_proto protoreflect.FileDescriptor 1146 1147var file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = []byte{ 1148 0x0a, 0x31, 0x67, 0x6f, 0x6f, 0x67, 0x6c, 0x65, 0x2f, 0x63, 0x6c, 0x6f, 0x75, 0x64, 0x2f, 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file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData) 1352 }) 1353 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData 1354} 1355 1356var file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes = make([]protoimpl.MessageInfo, 15) 1357var file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes = []interface{}{ 1358 (*Explanation)(nil), // 0: google.cloud.aiplatform.v1beta1.Explanation 1359 (*ModelExplanation)(nil), // 1: google.cloud.aiplatform.v1beta1.ModelExplanation 1360 (*Attribution)(nil), // 2: google.cloud.aiplatform.v1beta1.Attribution 1361 (*ExplanationSpec)(nil), // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec 1362 (*ExplanationParameters)(nil), // 4: google.cloud.aiplatform.v1beta1.ExplanationParameters 1363 (*SampledShapleyAttribution)(nil), // 5: google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1364 (*IntegratedGradientsAttribution)(nil), // 6: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1365 (*XraiAttribution)(nil), // 7: google.cloud.aiplatform.v1beta1.XraiAttribution 1366 (*SmoothGradConfig)(nil), // 8: google.cloud.aiplatform.v1beta1.SmoothGradConfig 1367 (*FeatureNoiseSigma)(nil), // 9: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1368 (*ExplanationSpecOverride)(nil), // 10: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride 1369 (*ExplanationMetadataOverride)(nil), // 11: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1370 (*FeatureNoiseSigma_NoiseSigmaForFeature)(nil), // 12: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1371 (*ExplanationMetadataOverride_InputMetadataOverride)(nil), // 13: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1372 nil, // 14: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1373 (*structpb.Value)(nil), // 15: google.protobuf.Value 1374 (*ExplanationMetadata)(nil), // 16: google.cloud.aiplatform.v1beta1.ExplanationMetadata 1375 (*structpb.ListValue)(nil), // 17: google.protobuf.ListValue 1376} 1377var file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = []int32{ 1378 2, // 0: google.cloud.aiplatform.v1beta1.Explanation.attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1379 2, // 1: google.cloud.aiplatform.v1beta1.ModelExplanation.mean_attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1380 15, // 2: google.cloud.aiplatform.v1beta1.Attribution.feature_attributions:type_name -> google.protobuf.Value 1381 4, // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1382 16, // 4: google.cloud.aiplatform.v1beta1.ExplanationSpec.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadata 1383 5, // 5: google.cloud.aiplatform.v1beta1.ExplanationParameters.sampled_shapley_attribution:type_name -> google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1384 6, // 6: google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution:type_name -> google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1385 7, // 7: google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution:type_name -> google.cloud.aiplatform.v1beta1.XraiAttribution 1386 17, // 8: google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices:type_name -> google.protobuf.ListValue 1387 8, // 9: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1388 8, // 10: google.cloud.aiplatform.v1beta1.XraiAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1389 9, // 11: google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1390 12, // 12: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1391 4, // 13: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1392 11, // 14: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1393 14, // 15: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.inputs:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1394 15, // 16: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride.input_baselines:type_name -> google.protobuf.Value 1395 13, // 17: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1396 18, // [18:18] is the sub-list for method output_type 1397 18, // [18:18] is the sub-list for method input_type 1398 18, // [18:18] is the sub-list for extension type_name 1399 18, // [18:18] is the sub-list for extension extendee 1400 0, // [0:18] is the sub-list for field type_name 1401} 1402 1403func init() { file_google_cloud_aiplatform_v1beta1_explanation_proto_init() } 1404func file_google_cloud_aiplatform_v1beta1_explanation_proto_init() { 1405 if File_google_cloud_aiplatform_v1beta1_explanation_proto != nil { 1406 return 1407 } 1408 file_google_cloud_aiplatform_v1beta1_explanation_metadata_proto_init() 1409 file_google_cloud_aiplatform_v1beta1_io_proto_init() 1410 if !protoimpl.UnsafeEnabled { 1411 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { 1412 switch v := v.(*Explanation); i { 1413 case 0: 1414 return &v.state 1415 case 1: 1416 return &v.sizeCache 1417 case 2: 1418 return &v.unknownFields 1419 default: 1420 return nil 1421 } 1422 } 1423 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { 1424 switch v := v.(*ModelExplanation); i { 1425 case 0: 1426 return &v.state 1427 case 1: 1428 return &v.sizeCache 1429 case 2: 1430 return &v.unknownFields 1431 default: 1432 return nil 1433 } 1434 } 1435 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { 1436 switch v := v.(*Attribution); i { 1437 case 0: 1438 return &v.state 1439 case 1: 1440 return &v.sizeCache 1441 case 2: 1442 return &v.unknownFields 1443 default: 1444 return nil 1445 } 1446 } 1447 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { 1448 switch v := v.(*ExplanationSpec); i { 1449 case 0: 1450 return &v.state 1451 case 1: 1452 return &v.sizeCache 1453 case 2: 1454 return &v.unknownFields 1455 default: 1456 return nil 1457 } 1458 } 1459 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { 1460 switch v := v.(*ExplanationParameters); i { 1461 case 0: 1462 return &v.state 1463 case 1: 1464 return &v.sizeCache 1465 case 2: 1466 return &v.unknownFields 1467 default: 1468 return nil 1469 } 1470 } 1471 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { 1472 switch v := v.(*SampledShapleyAttribution); i { 1473 case 0: 1474 return &v.state 1475 case 1: 1476 return &v.sizeCache 1477 case 2: 1478 return &v.unknownFields 1479 default: 1480 return nil 1481 } 1482 } 1483 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { 1484 switch v := v.(*IntegratedGradientsAttribution); i { 1485 case 0: 1486 return &v.state 1487 case 1: 1488 return &v.sizeCache 1489 case 2: 1490 return &v.unknownFields 1491 default: 1492 return nil 1493 } 1494 } 1495 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { 1496 switch v := v.(*XraiAttribution); i { 1497 case 0: 1498 return &v.state 1499 case 1: 1500 return &v.sizeCache 1501 case 2: 1502 return &v.unknownFields 1503 default: 1504 return nil 1505 } 1506 } 1507 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} { 1508 switch v := v.(*SmoothGradConfig); i { 1509 case 0: 1510 return &v.state 1511 case 1: 1512 return &v.sizeCache 1513 case 2: 1514 return &v.unknownFields 1515 default: 1516 return nil 1517 } 1518 } 1519 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} { 1520 switch v := v.(*FeatureNoiseSigma); i { 1521 case 0: 1522 return &v.state 1523 case 1: 1524 return &v.sizeCache 1525 case 2: 1526 return &v.unknownFields 1527 default: 1528 return nil 1529 } 1530 } 1531 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} { 1532 switch v := v.(*ExplanationSpecOverride); i { 1533 case 0: 1534 return &v.state 1535 case 1: 1536 return &v.sizeCache 1537 case 2: 1538 return &v.unknownFields 1539 default: 1540 return nil 1541 } 1542 } 1543 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} { 1544 switch v := v.(*ExplanationMetadataOverride); i { 1545 case 0: 1546 return &v.state 1547 case 1: 1548 return &v.sizeCache 1549 case 2: 1550 return &v.unknownFields 1551 default: 1552 return nil 1553 } 1554 } 1555 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} { 1556 switch v := v.(*FeatureNoiseSigma_NoiseSigmaForFeature); i { 1557 case 0: 1558 return &v.state 1559 case 1: 1560 return &v.sizeCache 1561 case 2: 1562 return &v.unknownFields 1563 default: 1564 return nil 1565 } 1566 } 1567 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} { 1568 switch v := v.(*ExplanationMetadataOverride_InputMetadataOverride); i { 1569 case 0: 1570 return &v.state 1571 case 1: 1572 return &v.sizeCache 1573 case 2: 1574 return &v.unknownFields 1575 default: 1576 return nil 1577 } 1578 } 1579 } 1580 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].OneofWrappers = []interface{}{ 1581 (*ExplanationParameters_SampledShapleyAttribution)(nil), 1582 (*ExplanationParameters_IntegratedGradientsAttribution)(nil), 1583 (*ExplanationParameters_XraiAttribution)(nil), 1584 } 1585 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].OneofWrappers = []interface{}{ 1586 (*SmoothGradConfig_NoiseSigma)(nil), 1587 (*SmoothGradConfig_FeatureNoiseSigma)(nil), 1588 } 1589 type x struct{} 1590 out := protoimpl.TypeBuilder{ 1591 File: protoimpl.DescBuilder{ 1592 GoPackagePath: reflect.TypeOf(x{}).PkgPath(), 1593 RawDescriptor: file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc, 1594 NumEnums: 0, 1595 NumMessages: 15, 1596 NumExtensions: 0, 1597 NumServices: 0, 1598 }, 1599 GoTypes: file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes, 1600 DependencyIndexes: file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs, 1601 MessageInfos: file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes, 1602 }.Build() 1603 File_google_cloud_aiplatform_v1beta1_explanation_proto = out.File 1604 file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = nil 1605 file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes = nil 1606 file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = nil 1607} 1608