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 v1.26.0 18// protoc v3.12.2 19// source: google/cloud/aiplatform/v1beta1/explanation.proto 20 21package aiplatform 22 23import ( 24 reflect "reflect" 25 sync "sync" 26 27 _ "google.golang.org/genproto/googleapis/api/annotations" 28 protoreflect "google.golang.org/protobuf/reflect/protoreflect" 29 protoimpl "google.golang.org/protobuf/runtime/protoimpl" 30 structpb "google.golang.org/protobuf/types/known/structpb" 31) 32 33const ( 34 // Verify that this generated code is sufficiently up-to-date. 35 _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) 36 // Verify that runtime/protoimpl is sufficiently up-to-date. 37 _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) 38) 39 40// Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1beta1.PredictResponse.predictions]) 41// produced by the Model on a given [instance][google.cloud.aiplatform.v1beta1.ExplainRequest.instances]. 42type Explanation struct { 43 state protoimpl.MessageState 44 sizeCache protoimpl.SizeCache 45 unknownFields protoimpl.UnknownFields 46 47 // Output only. Feature attributions grouped by predicted outputs. 48 // 49 // For Models that predict only one output, such as regression Models that 50 // predict only one score, there is only one attibution that explains the 51 // predicted output. For Models that predict multiple outputs, such as 52 // multiclass Models that predict multiple classes, each element explains one 53 // specific item. [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] can be used to identify which 54 // output this attribution is explaining. 55 // 56 // If users set [ExplanationParameters.top_k][google.cloud.aiplatform.v1beta1.ExplanationParameters.top_k], the attributions are sorted 57 // by [instance_output_value][Attributions.instance_output_value] in 58 // descending order. If [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices] is specified, 59 // the attributions are stored by [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] in the same 60 // order as they appear in the output_indices. 61 Attributions []*Attribution `protobuf:"bytes,1,rep,name=attributions,proto3" json:"attributions,omitempty"` 62} 63 64func (x *Explanation) Reset() { 65 *x = Explanation{} 66 if protoimpl.UnsafeEnabled { 67 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0] 68 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 69 ms.StoreMessageInfo(mi) 70 } 71} 72 73func (x *Explanation) String() string { 74 return protoimpl.X.MessageStringOf(x) 75} 76 77func (*Explanation) ProtoMessage() {} 78 79func (x *Explanation) ProtoReflect() protoreflect.Message { 80 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0] 81 if protoimpl.UnsafeEnabled && x != nil { 82 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 83 if ms.LoadMessageInfo() == nil { 84 ms.StoreMessageInfo(mi) 85 } 86 return ms 87 } 88 return mi.MessageOf(x) 89} 90 91// Deprecated: Use Explanation.ProtoReflect.Descriptor instead. 92func (*Explanation) Descriptor() ([]byte, []int) { 93 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{0} 94} 95 96func (x *Explanation) GetAttributions() []*Attribution { 97 if x != nil { 98 return x.Attributions 99 } 100 return nil 101} 102 103// Aggregated explanation metrics for a Model over a set of instances. 104type ModelExplanation struct { 105 state protoimpl.MessageState 106 sizeCache protoimpl.SizeCache 107 unknownFields protoimpl.UnknownFields 108 109 // Output only. Aggregated attributions explaining the Model's prediction outputs over the 110 // set of instances. The attributions are grouped by outputs. 111 // 112 // For Models that predict only one output, such as regression Models that 113 // predict only one score, there is only one attibution that explains the 114 // predicted output. For Models that predict multiple outputs, such as 115 // multiclass Models that predict multiple classes, each element explains one 116 // specific item. [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] can be used to identify which 117 // output this attribution is explaining. 118 // 119 // The [baselineOutputValue][google.cloud.aiplatform.v1beta1.Attribution.baseline_output_value], 120 // [instanceOutputValue][google.cloud.aiplatform.v1beta1.Attribution.instance_output_value] and 121 // [featureAttributions][google.cloud.aiplatform.v1beta1.Attribution.feature_attributions] fields are 122 // averaged over the test data. 123 // 124 // NOTE: Currently AutoML tabular classification Models produce only one 125 // attribution, which averages attributions over all the classes it predicts. 126 // [Attribution.approximation_error][google.cloud.aiplatform.v1beta1.Attribution.approximation_error] is not populated. 127 MeanAttributions []*Attribution `protobuf:"bytes,1,rep,name=mean_attributions,json=meanAttributions,proto3" json:"mean_attributions,omitempty"` 128} 129 130func (x *ModelExplanation) Reset() { 131 *x = ModelExplanation{} 132 if protoimpl.UnsafeEnabled { 133 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1] 134 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 135 ms.StoreMessageInfo(mi) 136 } 137} 138 139func (x *ModelExplanation) String() string { 140 return protoimpl.X.MessageStringOf(x) 141} 142 143func (*ModelExplanation) ProtoMessage() {} 144 145func (x *ModelExplanation) ProtoReflect() protoreflect.Message { 146 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1] 147 if protoimpl.UnsafeEnabled && x != nil { 148 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 149 if ms.LoadMessageInfo() == nil { 150 ms.StoreMessageInfo(mi) 151 } 152 return ms 153 } 154 return mi.MessageOf(x) 155} 156 157// Deprecated: Use ModelExplanation.ProtoReflect.Descriptor instead. 158func (*ModelExplanation) Descriptor() ([]byte, []int) { 159 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{1} 160} 161 162func (x *ModelExplanation) GetMeanAttributions() []*Attribution { 163 if x != nil { 164 return x.MeanAttributions 165 } 166 return nil 167} 168 169// Attribution that explains a particular prediction output. 170type Attribution struct { 171 state protoimpl.MessageState 172 sizeCache protoimpl.SizeCache 173 unknownFields protoimpl.UnknownFields 174 175 // Output only. Model predicted output if the input instance is constructed from the 176 // baselines of all the features defined in [ExplanationMetadata.inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 177 // The field name of the output is determined by the key in 178 // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 179 // 180 // If the Model's predicted output has multiple dimensions (rank > 1), this is 181 // the value in the output located by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. 182 // 183 // If there are multiple baselines, their output values are averaged. 184 BaselineOutputValue float64 `protobuf:"fixed64,1,opt,name=baseline_output_value,json=baselineOutputValue,proto3" json:"baseline_output_value,omitempty"` 185 // Output only. Model predicted output on the corresponding [explanation 186 // instance][ExplainRequest.instances]. The field name of the output is 187 // determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 188 // 189 // If the Model predicted output has multiple dimensions, this is the value in 190 // the output located by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. 191 InstanceOutputValue float64 `protobuf:"fixed64,2,opt,name=instance_output_value,json=instanceOutputValue,proto3" json:"instance_output_value,omitempty"` 192 // Output only. Attributions of each explained feature. Features are extracted from 193 // the [prediction instances][google.cloud.aiplatform.v1beta1.ExplainRequest.instances] according to 194 // [explanation metadata for inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 195 // 196 // The value is a struct, whose keys are the name of the feature. The values 197 // are how much the feature in the [instance][google.cloud.aiplatform.v1beta1.ExplainRequest.instances] 198 // contributed to the predicted result. 199 // 200 // The format of the value is determined by the feature's input format: 201 // 202 // * If the feature is a scalar value, the attribution value is a 203 // [floating number][google.protobuf.Value.number_value]. 204 // 205 // * If the feature is an array of scalar values, the attribution value is 206 // an [array][google.protobuf.Value.list_value]. 207 // 208 // * If the feature is a struct, the attribution value is a 209 // [struct][google.protobuf.Value.struct_value]. The keys in the 210 // attribution value struct are the same as the keys in the feature 211 // struct. The formats of the values in the attribution struct are 212 // determined by the formats of the values in the feature struct. 213 // 214 // The [ExplanationMetadata.feature_attributions_schema_uri][google.cloud.aiplatform.v1beta1.ExplanationMetadata.feature_attributions_schema_uri] field, 215 // pointed to by the [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] field of the 216 // [Endpoint.deployed_models][google.cloud.aiplatform.v1beta1.Endpoint.deployed_models] object, points to the schema file that 217 // describes the features and their attribution values (if it is populated). 218 FeatureAttributions *structpb.Value `protobuf:"bytes,3,opt,name=feature_attributions,json=featureAttributions,proto3" json:"feature_attributions,omitempty"` 219 // Output only. The index that locates the explained prediction output. 220 // 221 // If the prediction output is a scalar value, output_index is not populated. 222 // If the prediction output has multiple dimensions, the length of the 223 // output_index list is the same as the number of dimensions of the output. 224 // The i-th element in output_index is the element index of the i-th dimension 225 // of the output vector. Indices start from 0. 226 OutputIndex []int32 `protobuf:"varint,4,rep,packed,name=output_index,json=outputIndex,proto3" json:"output_index,omitempty"` 227 // Output only. The display name of the output identified by [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]. For example, 228 // the predicted class name by a multi-classification Model. 229 // 230 // This field is only populated iff the Model predicts display names as a 231 // separate field along with the explained output. The predicted display name 232 // must has the same shape of the explained output, and can be located using 233 // output_index. 234 OutputDisplayName string `protobuf:"bytes,5,opt,name=output_display_name,json=outputDisplayName,proto3" json:"output_display_name,omitempty"` 235 // Output only. Error of [feature_attributions][google.cloud.aiplatform.v1beta1.Attribution.feature_attributions] caused by approximation used in the 236 // explanation method. Lower value means more precise attributions. 237 // 238 // * For Sampled Shapley 239 // [attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.sampled_shapley_attribution], 240 // increasing [path_count][google.cloud.aiplatform.v1beta1.SampledShapleyAttribution.path_count] might reduce 241 // the error. 242 // * For Integrated Gradients 243 // [attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution], 244 // increasing [step_count][google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution.step_count] might 245 // reduce the error. 246 // * For [XRAI attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution], 247 // increasing 248 // [step_count][google.cloud.aiplatform.v1beta1.XraiAttribution.step_count] might reduce the error. 249 // 250 // See [this introduction](/vertex-ai/docs/explainable-ai/overview) 251 // for more information. 252 ApproximationError float64 `protobuf:"fixed64,6,opt,name=approximation_error,json=approximationError,proto3" json:"approximation_error,omitempty"` 253 // Output only. Name of the explain output. Specified as the key in 254 // [ExplanationMetadata.outputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.outputs]. 255 OutputName string `protobuf:"bytes,7,opt,name=output_name,json=outputName,proto3" json:"output_name,omitempty"` 256} 257 258func (x *Attribution) Reset() { 259 *x = Attribution{} 260 if protoimpl.UnsafeEnabled { 261 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2] 262 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 263 ms.StoreMessageInfo(mi) 264 } 265} 266 267func (x *Attribution) String() string { 268 return protoimpl.X.MessageStringOf(x) 269} 270 271func (*Attribution) ProtoMessage() {} 272 273func (x *Attribution) ProtoReflect() protoreflect.Message { 274 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2] 275 if protoimpl.UnsafeEnabled && x != nil { 276 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 277 if ms.LoadMessageInfo() == nil { 278 ms.StoreMessageInfo(mi) 279 } 280 return ms 281 } 282 return mi.MessageOf(x) 283} 284 285// Deprecated: Use Attribution.ProtoReflect.Descriptor instead. 286func (*Attribution) Descriptor() ([]byte, []int) { 287 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{2} 288} 289 290func (x *Attribution) GetBaselineOutputValue() float64 { 291 if x != nil { 292 return x.BaselineOutputValue 293 } 294 return 0 295} 296 297func (x *Attribution) GetInstanceOutputValue() float64 { 298 if x != nil { 299 return x.InstanceOutputValue 300 } 301 return 0 302} 303 304func (x *Attribution) GetFeatureAttributions() *structpb.Value { 305 if x != nil { 306 return x.FeatureAttributions 307 } 308 return nil 309} 310 311func (x *Attribution) GetOutputIndex() []int32 { 312 if x != nil { 313 return x.OutputIndex 314 } 315 return nil 316} 317 318func (x *Attribution) GetOutputDisplayName() string { 319 if x != nil { 320 return x.OutputDisplayName 321 } 322 return "" 323} 324 325func (x *Attribution) GetApproximationError() float64 { 326 if x != nil { 327 return x.ApproximationError 328 } 329 return 0 330} 331 332func (x *Attribution) GetOutputName() string { 333 if x != nil { 334 return x.OutputName 335 } 336 return "" 337} 338 339// Specification of Model explanation. 340type ExplanationSpec struct { 341 state protoimpl.MessageState 342 sizeCache protoimpl.SizeCache 343 unknownFields protoimpl.UnknownFields 344 345 // Required. Parameters that configure explaining of the Model's predictions. 346 Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` 347 // Required. Metadata describing the Model's input and output for explanation. 348 Metadata *ExplanationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` 349} 350 351func (x *ExplanationSpec) Reset() { 352 *x = ExplanationSpec{} 353 if protoimpl.UnsafeEnabled { 354 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3] 355 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 356 ms.StoreMessageInfo(mi) 357 } 358} 359 360func (x *ExplanationSpec) String() string { 361 return protoimpl.X.MessageStringOf(x) 362} 363 364func (*ExplanationSpec) ProtoMessage() {} 365 366func (x *ExplanationSpec) ProtoReflect() protoreflect.Message { 367 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3] 368 if protoimpl.UnsafeEnabled && x != nil { 369 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 370 if ms.LoadMessageInfo() == nil { 371 ms.StoreMessageInfo(mi) 372 } 373 return ms 374 } 375 return mi.MessageOf(x) 376} 377 378// Deprecated: Use ExplanationSpec.ProtoReflect.Descriptor instead. 379func (*ExplanationSpec) Descriptor() ([]byte, []int) { 380 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{3} 381} 382 383func (x *ExplanationSpec) GetParameters() *ExplanationParameters { 384 if x != nil { 385 return x.Parameters 386 } 387 return nil 388} 389 390func (x *ExplanationSpec) GetMetadata() *ExplanationMetadata { 391 if x != nil { 392 return x.Metadata 393 } 394 return nil 395} 396 397// Parameters to configure explaining for Model's predictions. 398type ExplanationParameters struct { 399 state protoimpl.MessageState 400 sizeCache protoimpl.SizeCache 401 unknownFields protoimpl.UnknownFields 402 403 // Types that are assignable to Method: 404 // *ExplanationParameters_SampledShapleyAttribution 405 // *ExplanationParameters_IntegratedGradientsAttribution 406 // *ExplanationParameters_XraiAttribution 407 Method isExplanationParameters_Method `protobuf_oneof:"method"` 408 // If populated, returns attributions for top K indices of outputs 409 // (defaults to 1). Only applies to Models that predicts more than one outputs 410 // (e,g, multi-class Models). When set to -1, returns explanations for all 411 // outputs. 412 TopK int32 `protobuf:"varint,4,opt,name=top_k,json=topK,proto3" json:"top_k,omitempty"` 413 // If populated, only returns attributions that have 414 // [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] contained in output_indices. It 415 // must be an ndarray of integers, with the same shape of the output it's 416 // explaining. 417 // 418 // If not populated, returns attributions for [top_k][google.cloud.aiplatform.v1beta1.ExplanationParameters.top_k] indices of outputs. 419 // If neither top_k nor output_indeices is populated, returns the argmax 420 // index of the outputs. 421 // 422 // Only applicable to Models that predict multiple outputs (e,g, multi-class 423 // Models that predict multiple classes). 424 OutputIndices *structpb.ListValue `protobuf:"bytes,5,opt,name=output_indices,json=outputIndices,proto3" json:"output_indices,omitempty"` 425} 426 427func (x *ExplanationParameters) Reset() { 428 *x = ExplanationParameters{} 429 if protoimpl.UnsafeEnabled { 430 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 431 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 432 ms.StoreMessageInfo(mi) 433 } 434} 435 436func (x *ExplanationParameters) String() string { 437 return protoimpl.X.MessageStringOf(x) 438} 439 440func (*ExplanationParameters) ProtoMessage() {} 441 442func (x *ExplanationParameters) ProtoReflect() protoreflect.Message { 443 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 444 if protoimpl.UnsafeEnabled && x != nil { 445 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 446 if ms.LoadMessageInfo() == nil { 447 ms.StoreMessageInfo(mi) 448 } 449 return ms 450 } 451 return mi.MessageOf(x) 452} 453 454// Deprecated: Use ExplanationParameters.ProtoReflect.Descriptor instead. 455func (*ExplanationParameters) Descriptor() ([]byte, []int) { 456 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{4} 457} 458 459func (m *ExplanationParameters) GetMethod() isExplanationParameters_Method { 460 if m != nil { 461 return m.Method 462 } 463 return nil 464} 465 466func (x *ExplanationParameters) GetSampledShapleyAttribution() *SampledShapleyAttribution { 467 if x, ok := x.GetMethod().(*ExplanationParameters_SampledShapleyAttribution); ok { 468 return x.SampledShapleyAttribution 469 } 470 return nil 471} 472 473func (x *ExplanationParameters) GetIntegratedGradientsAttribution() *IntegratedGradientsAttribution { 474 if x, ok := x.GetMethod().(*ExplanationParameters_IntegratedGradientsAttribution); ok { 475 return x.IntegratedGradientsAttribution 476 } 477 return nil 478} 479 480func (x *ExplanationParameters) GetXraiAttribution() *XraiAttribution { 481 if x, ok := x.GetMethod().(*ExplanationParameters_XraiAttribution); ok { 482 return x.XraiAttribution 483 } 484 return nil 485} 486 487func (x *ExplanationParameters) GetTopK() int32 { 488 if x != nil { 489 return x.TopK 490 } 491 return 0 492} 493 494func (x *ExplanationParameters) GetOutputIndices() *structpb.ListValue { 495 if x != nil { 496 return x.OutputIndices 497 } 498 return nil 499} 500 501type isExplanationParameters_Method interface { 502 isExplanationParameters_Method() 503} 504 505type ExplanationParameters_SampledShapleyAttribution struct { 506 // An attribution method that approximates Shapley values for features that 507 // contribute to the label being predicted. A sampling strategy is used to 508 // approximate the value rather than considering all subsets of features. 509 // Refer to this paper for model details: https://arxiv.org/abs/1306.4265. 510 SampledShapleyAttribution *SampledShapleyAttribution `protobuf:"bytes,1,opt,name=sampled_shapley_attribution,json=sampledShapleyAttribution,proto3,oneof"` 511} 512 513type ExplanationParameters_IntegratedGradientsAttribution struct { 514 // An attribution method that computes Aumann-Shapley values taking 515 // advantage of the model's fully differentiable structure. Refer to this 516 // paper for more details: https://arxiv.org/abs/1703.01365 517 IntegratedGradientsAttribution *IntegratedGradientsAttribution `protobuf:"bytes,2,opt,name=integrated_gradients_attribution,json=integratedGradientsAttribution,proto3,oneof"` 518} 519 520type ExplanationParameters_XraiAttribution struct { 521 // An attribution method that redistributes Integrated Gradients 522 // attribution to segmented regions, taking advantage of the model's fully 523 // differentiable structure. Refer to this paper for 524 // more details: https://arxiv.org/abs/1906.02825 525 // 526 // XRAI currently performs better on natural images, like a picture of a 527 // house or an animal. If the images are taken in artificial environments, 528 // like a lab or manufacturing line, or from diagnostic equipment, like 529 // x-rays or quality-control cameras, use Integrated Gradients instead. 530 XraiAttribution *XraiAttribution `protobuf:"bytes,3,opt,name=xrai_attribution,json=xraiAttribution,proto3,oneof"` 531} 532 533func (*ExplanationParameters_SampledShapleyAttribution) isExplanationParameters_Method() {} 534 535func (*ExplanationParameters_IntegratedGradientsAttribution) isExplanationParameters_Method() {} 536 537func (*ExplanationParameters_XraiAttribution) isExplanationParameters_Method() {} 538 539// An attribution method that approximates Shapley values for features that 540// contribute to the label being predicted. A sampling strategy is used to 541// approximate the value rather than considering all subsets of features. 542type SampledShapleyAttribution struct { 543 state protoimpl.MessageState 544 sizeCache protoimpl.SizeCache 545 unknownFields protoimpl.UnknownFields 546 547 // Required. The number of feature permutations to consider when approximating the 548 // Shapley values. 549 // 550 // Valid range of its value is [1, 50], inclusively. 551 PathCount int32 `protobuf:"varint,1,opt,name=path_count,json=pathCount,proto3" json:"path_count,omitempty"` 552} 553 554func (x *SampledShapleyAttribution) Reset() { 555 *x = SampledShapleyAttribution{} 556 if protoimpl.UnsafeEnabled { 557 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 558 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 559 ms.StoreMessageInfo(mi) 560 } 561} 562 563func (x *SampledShapleyAttribution) String() string { 564 return protoimpl.X.MessageStringOf(x) 565} 566 567func (*SampledShapleyAttribution) ProtoMessage() {} 568 569func (x *SampledShapleyAttribution) ProtoReflect() protoreflect.Message { 570 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 571 if protoimpl.UnsafeEnabled && x != nil { 572 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 573 if ms.LoadMessageInfo() == nil { 574 ms.StoreMessageInfo(mi) 575 } 576 return ms 577 } 578 return mi.MessageOf(x) 579} 580 581// Deprecated: Use SampledShapleyAttribution.ProtoReflect.Descriptor instead. 582func (*SampledShapleyAttribution) Descriptor() ([]byte, []int) { 583 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{5} 584} 585 586func (x *SampledShapleyAttribution) GetPathCount() int32 { 587 if x != nil { 588 return x.PathCount 589 } 590 return 0 591} 592 593// An attribution method that computes the Aumann-Shapley value taking advantage 594// of the model's fully differentiable structure. Refer to this paper for 595// more details: https://arxiv.org/abs/1703.01365 596type IntegratedGradientsAttribution struct { 597 state protoimpl.MessageState 598 sizeCache protoimpl.SizeCache 599 unknownFields protoimpl.UnknownFields 600 601 // Required. The number of steps for approximating the path integral. 602 // A good value to start is 50 and gradually increase until the 603 // sum to diff property is within the desired error range. 604 // 605 // Valid range of its value is [1, 100], inclusively. 606 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 607 // Config for SmoothGrad approximation of gradients. 608 // 609 // When enabled, the gradients are approximated by averaging the gradients 610 // from noisy samples in the vicinity of the inputs. Adding 611 // noise can help improve the computed gradients. Refer to this paper for more 612 // details: https://arxiv.org/pdf/1706.03825.pdf 613 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 614} 615 616func (x *IntegratedGradientsAttribution) Reset() { 617 *x = IntegratedGradientsAttribution{} 618 if protoimpl.UnsafeEnabled { 619 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 620 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 621 ms.StoreMessageInfo(mi) 622 } 623} 624 625func (x *IntegratedGradientsAttribution) String() string { 626 return protoimpl.X.MessageStringOf(x) 627} 628 629func (*IntegratedGradientsAttribution) ProtoMessage() {} 630 631func (x *IntegratedGradientsAttribution) ProtoReflect() protoreflect.Message { 632 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 633 if protoimpl.UnsafeEnabled && x != nil { 634 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 635 if ms.LoadMessageInfo() == nil { 636 ms.StoreMessageInfo(mi) 637 } 638 return ms 639 } 640 return mi.MessageOf(x) 641} 642 643// Deprecated: Use IntegratedGradientsAttribution.ProtoReflect.Descriptor instead. 644func (*IntegratedGradientsAttribution) Descriptor() ([]byte, []int) { 645 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{6} 646} 647 648func (x *IntegratedGradientsAttribution) GetStepCount() int32 { 649 if x != nil { 650 return x.StepCount 651 } 652 return 0 653} 654 655func (x *IntegratedGradientsAttribution) GetSmoothGradConfig() *SmoothGradConfig { 656 if x != nil { 657 return x.SmoothGradConfig 658 } 659 return nil 660} 661 662// An explanation method that redistributes Integrated Gradients 663// attributions to segmented regions, taking advantage of the model's fully 664// differentiable structure. Refer to this paper for more details: 665// https://arxiv.org/abs/1906.02825 666// 667// Supported only by image Models. 668type XraiAttribution struct { 669 state protoimpl.MessageState 670 sizeCache protoimpl.SizeCache 671 unknownFields protoimpl.UnknownFields 672 673 // Required. The number of steps for approximating the path integral. 674 // A good value to start is 50 and gradually increase until the 675 // sum to diff property is met within the desired error range. 676 // 677 // Valid range of its value is [1, 100], inclusively. 678 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 679 // Config for SmoothGrad approximation of gradients. 680 // 681 // When enabled, the gradients are approximated by averaging the gradients 682 // from noisy samples in the vicinity of the inputs. Adding 683 // noise can help improve the computed gradients. Refer to this paper for more 684 // details: https://arxiv.org/pdf/1706.03825.pdf 685 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 686} 687 688func (x *XraiAttribution) Reset() { 689 *x = XraiAttribution{} 690 if protoimpl.UnsafeEnabled { 691 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 692 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 693 ms.StoreMessageInfo(mi) 694 } 695} 696 697func (x *XraiAttribution) String() string { 698 return protoimpl.X.MessageStringOf(x) 699} 700 701func (*XraiAttribution) ProtoMessage() {} 702 703func (x *XraiAttribution) ProtoReflect() protoreflect.Message { 704 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 705 if protoimpl.UnsafeEnabled && x != nil { 706 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 707 if ms.LoadMessageInfo() == nil { 708 ms.StoreMessageInfo(mi) 709 } 710 return ms 711 } 712 return mi.MessageOf(x) 713} 714 715// Deprecated: Use XraiAttribution.ProtoReflect.Descriptor instead. 716func (*XraiAttribution) Descriptor() ([]byte, []int) { 717 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{7} 718} 719 720func (x *XraiAttribution) GetStepCount() int32 { 721 if x != nil { 722 return x.StepCount 723 } 724 return 0 725} 726 727func (x *XraiAttribution) GetSmoothGradConfig() *SmoothGradConfig { 728 if x != nil { 729 return x.SmoothGradConfig 730 } 731 return nil 732} 733 734// Config for SmoothGrad approximation of gradients. 735// 736// When enabled, the gradients are approximated by averaging the gradients from 737// noisy samples in the vicinity of the inputs. Adding noise can help improve 738// the computed gradients. Refer to this paper for more details: 739// https://arxiv.org/pdf/1706.03825.pdf 740type SmoothGradConfig struct { 741 state protoimpl.MessageState 742 sizeCache protoimpl.SizeCache 743 unknownFields protoimpl.UnknownFields 744 745 // Represents the standard deviation of the gaussian kernel 746 // that will be used to add noise to the interpolated inputs 747 // prior to computing gradients. 748 // 749 // Types that are assignable to GradientNoiseSigma: 750 // *SmoothGradConfig_NoiseSigma 751 // *SmoothGradConfig_FeatureNoiseSigma 752 GradientNoiseSigma isSmoothGradConfig_GradientNoiseSigma `protobuf_oneof:"GradientNoiseSigma"` 753 // The number of gradient samples to use for 754 // approximation. The higher this number, the more accurate the gradient 755 // is, but the runtime complexity increases by this factor as well. 756 // Valid range of its value is [1, 50]. Defaults to 3. 757 NoisySampleCount int32 `protobuf:"varint,3,opt,name=noisy_sample_count,json=noisySampleCount,proto3" json:"noisy_sample_count,omitempty"` 758} 759 760func (x *SmoothGradConfig) Reset() { 761 *x = SmoothGradConfig{} 762 if protoimpl.UnsafeEnabled { 763 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 764 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 765 ms.StoreMessageInfo(mi) 766 } 767} 768 769func (x *SmoothGradConfig) String() string { 770 return protoimpl.X.MessageStringOf(x) 771} 772 773func (*SmoothGradConfig) ProtoMessage() {} 774 775func (x *SmoothGradConfig) ProtoReflect() protoreflect.Message { 776 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 777 if protoimpl.UnsafeEnabled && x != nil { 778 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 779 if ms.LoadMessageInfo() == nil { 780 ms.StoreMessageInfo(mi) 781 } 782 return ms 783 } 784 return mi.MessageOf(x) 785} 786 787// Deprecated: Use SmoothGradConfig.ProtoReflect.Descriptor instead. 788func (*SmoothGradConfig) Descriptor() ([]byte, []int) { 789 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{8} 790} 791 792func (m *SmoothGradConfig) GetGradientNoiseSigma() isSmoothGradConfig_GradientNoiseSigma { 793 if m != nil { 794 return m.GradientNoiseSigma 795 } 796 return nil 797} 798 799func (x *SmoothGradConfig) GetNoiseSigma() float32 { 800 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_NoiseSigma); ok { 801 return x.NoiseSigma 802 } 803 return 0 804} 805 806func (x *SmoothGradConfig) GetFeatureNoiseSigma() *FeatureNoiseSigma { 807 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_FeatureNoiseSigma); ok { 808 return x.FeatureNoiseSigma 809 } 810 return nil 811} 812 813func (x *SmoothGradConfig) GetNoisySampleCount() int32 { 814 if x != nil { 815 return x.NoisySampleCount 816 } 817 return 0 818} 819 820type isSmoothGradConfig_GradientNoiseSigma interface { 821 isSmoothGradConfig_GradientNoiseSigma() 822} 823 824type SmoothGradConfig_NoiseSigma struct { 825 // This is a single float value and will be used to add noise to all the 826 // features. Use this field when all features are normalized to have the 827 // same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where 828 // features are normalized to have 0-mean and 1-variance. Learn more about 829 // [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). 830 // 831 // For best results the recommended value is about 10% - 20% of the standard 832 // deviation of the input feature. Refer to section 3.2 of the SmoothGrad 833 // paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. 834 // 835 // If the distribution is different per feature, set 836 // [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma] instead 837 // for each feature. 838 NoiseSigma float32 `protobuf:"fixed32,1,opt,name=noise_sigma,json=noiseSigma,proto3,oneof"` 839} 840 841type SmoothGradConfig_FeatureNoiseSigma struct { 842 // This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but 843 // provides additional flexibility. A separate noise sigma can be provided 844 // for each feature, which is useful if their distributions are different. 845 // No noise is added to features that are not set. If this field is unset, 846 // [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] will be used for all 847 // features. 848 FeatureNoiseSigma *FeatureNoiseSigma `protobuf:"bytes,2,opt,name=feature_noise_sigma,json=featureNoiseSigma,proto3,oneof"` 849} 850 851func (*SmoothGradConfig_NoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 852 853func (*SmoothGradConfig_FeatureNoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 854 855// Noise sigma by features. Noise sigma represents the standard deviation of the 856// gaussian kernel that will be used to add noise to interpolated inputs prior 857// to computing gradients. 858type FeatureNoiseSigma struct { 859 state protoimpl.MessageState 860 sizeCache protoimpl.SizeCache 861 unknownFields protoimpl.UnknownFields 862 863 // Noise sigma per feature. No noise is added to features that are not set. 864 NoiseSigma []*FeatureNoiseSigma_NoiseSigmaForFeature `protobuf:"bytes,1,rep,name=noise_sigma,json=noiseSigma,proto3" json:"noise_sigma,omitempty"` 865} 866 867func (x *FeatureNoiseSigma) Reset() { 868 *x = FeatureNoiseSigma{} 869 if protoimpl.UnsafeEnabled { 870 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 871 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 872 ms.StoreMessageInfo(mi) 873 } 874} 875 876func (x *FeatureNoiseSigma) String() string { 877 return protoimpl.X.MessageStringOf(x) 878} 879 880func (*FeatureNoiseSigma) ProtoMessage() {} 881 882func (x *FeatureNoiseSigma) ProtoReflect() protoreflect.Message { 883 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 884 if protoimpl.UnsafeEnabled && x != nil { 885 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 886 if ms.LoadMessageInfo() == nil { 887 ms.StoreMessageInfo(mi) 888 } 889 return ms 890 } 891 return mi.MessageOf(x) 892} 893 894// Deprecated: Use FeatureNoiseSigma.ProtoReflect.Descriptor instead. 895func (*FeatureNoiseSigma) Descriptor() ([]byte, []int) { 896 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9} 897} 898 899func (x *FeatureNoiseSigma) GetNoiseSigma() []*FeatureNoiseSigma_NoiseSigmaForFeature { 900 if x != nil { 901 return x.NoiseSigma 902 } 903 return nil 904} 905 906// The [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] entries that can be overridden at [online 907// explanation][PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 908type ExplanationSpecOverride struct { 909 state protoimpl.MessageState 910 sizeCache protoimpl.SizeCache 911 unknownFields protoimpl.UnknownFields 912 913 // The parameters to be overridden. Note that the 914 // [method][google.cloud.aiplatform.v1beta1.ExplanationParameters.method] cannot be changed. If not specified, 915 // no parameter is overridden. 916 Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` 917 // The metadata to be overridden. If not specified, no metadata is overridden. 918 Metadata *ExplanationMetadataOverride `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` 919} 920 921func (x *ExplanationSpecOverride) Reset() { 922 *x = ExplanationSpecOverride{} 923 if protoimpl.UnsafeEnabled { 924 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 925 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 926 ms.StoreMessageInfo(mi) 927 } 928} 929 930func (x *ExplanationSpecOverride) String() string { 931 return protoimpl.X.MessageStringOf(x) 932} 933 934func (*ExplanationSpecOverride) ProtoMessage() {} 935 936func (x *ExplanationSpecOverride) ProtoReflect() protoreflect.Message { 937 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 938 if protoimpl.UnsafeEnabled && x != nil { 939 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 940 if ms.LoadMessageInfo() == nil { 941 ms.StoreMessageInfo(mi) 942 } 943 return ms 944 } 945 return mi.MessageOf(x) 946} 947 948// Deprecated: Use ExplanationSpecOverride.ProtoReflect.Descriptor instead. 949func (*ExplanationSpecOverride) Descriptor() ([]byte, []int) { 950 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{10} 951} 952 953func (x *ExplanationSpecOverride) GetParameters() *ExplanationParameters { 954 if x != nil { 955 return x.Parameters 956 } 957 return nil 958} 959 960func (x *ExplanationSpecOverride) GetMetadata() *ExplanationMetadataOverride { 961 if x != nil { 962 return x.Metadata 963 } 964 return nil 965} 966 967// The [ExplanationMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata] entries that can be overridden at 968// [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 969type ExplanationMetadataOverride struct { 970 state protoimpl.MessageState 971 sizeCache protoimpl.SizeCache 972 unknownFields protoimpl.UnknownFields 973 974 // Required. Overrides the [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs] of the features. 975 // The key is the name of the feature to be overridden. The keys specified 976 // here must exist in the input metadata to be overridden. If a feature is 977 // not specified here, the corresponding feature's input metadata is not 978 // overridden. 979 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"` 980} 981 982func (x *ExplanationMetadataOverride) Reset() { 983 *x = ExplanationMetadataOverride{} 984 if protoimpl.UnsafeEnabled { 985 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 986 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 987 ms.StoreMessageInfo(mi) 988 } 989} 990 991func (x *ExplanationMetadataOverride) String() string { 992 return protoimpl.X.MessageStringOf(x) 993} 994 995func (*ExplanationMetadataOverride) ProtoMessage() {} 996 997func (x *ExplanationMetadataOverride) ProtoReflect() protoreflect.Message { 998 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 999 if protoimpl.UnsafeEnabled && x != nil { 1000 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1001 if ms.LoadMessageInfo() == nil { 1002 ms.StoreMessageInfo(mi) 1003 } 1004 return ms 1005 } 1006 return mi.MessageOf(x) 1007} 1008 1009// Deprecated: Use ExplanationMetadataOverride.ProtoReflect.Descriptor instead. 1010func (*ExplanationMetadataOverride) Descriptor() ([]byte, []int) { 1011 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{11} 1012} 1013 1014func (x *ExplanationMetadataOverride) GetInputs() map[string]*ExplanationMetadataOverride_InputMetadataOverride { 1015 if x != nil { 1016 return x.Inputs 1017 } 1018 return nil 1019} 1020 1021// Noise sigma for a single feature. 1022type FeatureNoiseSigma_NoiseSigmaForFeature struct { 1023 state protoimpl.MessageState 1024 sizeCache protoimpl.SizeCache 1025 unknownFields protoimpl.UnknownFields 1026 1027 // The name of the input feature for which noise sigma is provided. The 1028 // features are defined in 1029 // [explanation metadata inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 1030 Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` 1031 // This represents the standard deviation of the Gaussian kernel that will 1032 // be used to add noise to the feature prior to computing gradients. Similar 1033 // to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] but represents the 1034 // noise added to the current feature. Defaults to 0.1. 1035 Sigma float32 `protobuf:"fixed32,2,opt,name=sigma,proto3" json:"sigma,omitempty"` 1036} 1037 1038func (x *FeatureNoiseSigma_NoiseSigmaForFeature) Reset() { 1039 *x = FeatureNoiseSigma_NoiseSigmaForFeature{} 1040 if protoimpl.UnsafeEnabled { 1041 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1042 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1043 ms.StoreMessageInfo(mi) 1044 } 1045} 1046 1047func (x *FeatureNoiseSigma_NoiseSigmaForFeature) String() string { 1048 return protoimpl.X.MessageStringOf(x) 1049} 1050 1051func (*FeatureNoiseSigma_NoiseSigmaForFeature) ProtoMessage() {} 1052 1053func (x *FeatureNoiseSigma_NoiseSigmaForFeature) ProtoReflect() protoreflect.Message { 1054 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1055 if protoimpl.UnsafeEnabled && x != nil { 1056 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1057 if ms.LoadMessageInfo() == nil { 1058 ms.StoreMessageInfo(mi) 1059 } 1060 return ms 1061 } 1062 return mi.MessageOf(x) 1063} 1064 1065// Deprecated: Use FeatureNoiseSigma_NoiseSigmaForFeature.ProtoReflect.Descriptor instead. 1066func (*FeatureNoiseSigma_NoiseSigmaForFeature) Descriptor() ([]byte, []int) { 1067 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9, 0} 1068} 1069 1070func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetName() string { 1071 if x != nil { 1072 return x.Name 1073 } 1074 return "" 1075} 1076 1077func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetSigma() float32 { 1078 if x != nil { 1079 return x.Sigma 1080 } 1081 return 0 1082} 1083 1084// The [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] entries to be 1085// overridden. 1086type ExplanationMetadataOverride_InputMetadataOverride struct { 1087 state protoimpl.MessageState 1088 sizeCache protoimpl.SizeCache 1089 unknownFields protoimpl.UnknownFields 1090 1091 // Baseline inputs for this feature. 1092 // 1093 // This overrides the `input_baseline` field of the 1094 // [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] 1095 // object of the corresponding feature's input metadata. If it's not 1096 // specified, the original baselines are not overridden. 1097 InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"` 1098} 1099 1100func (x *ExplanationMetadataOverride_InputMetadataOverride) Reset() { 1101 *x = ExplanationMetadataOverride_InputMetadataOverride{} 1102 if protoimpl.UnsafeEnabled { 1103 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1104 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1105 ms.StoreMessageInfo(mi) 1106 } 1107} 1108 1109func (x *ExplanationMetadataOverride_InputMetadataOverride) String() string { 1110 return protoimpl.X.MessageStringOf(x) 1111} 1112 1113func (*ExplanationMetadataOverride_InputMetadataOverride) ProtoMessage() {} 1114 1115func (x *ExplanationMetadataOverride_InputMetadataOverride) ProtoReflect() protoreflect.Message { 1116 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1117 if protoimpl.UnsafeEnabled && x != nil { 1118 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1119 if ms.LoadMessageInfo() == nil { 1120 ms.StoreMessageInfo(mi) 1121 } 1122 return ms 1123 } 1124 return mi.MessageOf(x) 1125} 1126 1127// Deprecated: Use ExplanationMetadataOverride_InputMetadataOverride.ProtoReflect.Descriptor instead. 1128func (*ExplanationMetadataOverride_InputMetadataOverride) Descriptor() ([]byte, []int) { 1129 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{11, 0} 1130} 1131 1132func (x *ExplanationMetadataOverride_InputMetadataOverride) GetInputBaselines() []*structpb.Value { 1133 if x != nil { 1134 return x.InputBaselines 1135 } 1136 return nil 1137} 1138 1139var File_google_cloud_aiplatform_v1beta1_explanation_proto protoreflect.FileDescriptor 1140 1141var file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = []byte{ 1142 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_goTypes = []interface{}{ 1359 (*Explanation)(nil), // 0: google.cloud.aiplatform.v1beta1.Explanation 1360 (*ModelExplanation)(nil), // 1: google.cloud.aiplatform.v1beta1.ModelExplanation 1361 (*Attribution)(nil), // 2: google.cloud.aiplatform.v1beta1.Attribution 1362 (*ExplanationSpec)(nil), // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec 1363 (*ExplanationParameters)(nil), // 4: google.cloud.aiplatform.v1beta1.ExplanationParameters 1364 (*SampledShapleyAttribution)(nil), // 5: google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1365 (*IntegratedGradientsAttribution)(nil), // 6: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1366 (*XraiAttribution)(nil), // 7: google.cloud.aiplatform.v1beta1.XraiAttribution 1367 (*SmoothGradConfig)(nil), // 8: google.cloud.aiplatform.v1beta1.SmoothGradConfig 1368 (*FeatureNoiseSigma)(nil), // 9: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1369 (*ExplanationSpecOverride)(nil), // 10: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride 1370 (*ExplanationMetadataOverride)(nil), // 11: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1371 (*FeatureNoiseSigma_NoiseSigmaForFeature)(nil), // 12: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1372 (*ExplanationMetadataOverride_InputMetadataOverride)(nil), // 13: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1373 nil, // 14: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1374 (*structpb.Value)(nil), // 15: google.protobuf.Value 1375 (*ExplanationMetadata)(nil), // 16: google.cloud.aiplatform.v1beta1.ExplanationMetadata 1376 (*structpb.ListValue)(nil), // 17: google.protobuf.ListValue 1377} 1378var file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = []int32{ 1379 2, // 0: google.cloud.aiplatform.v1beta1.Explanation.attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1380 2, // 1: google.cloud.aiplatform.v1beta1.ModelExplanation.mean_attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1381 15, // 2: google.cloud.aiplatform.v1beta1.Attribution.feature_attributions:type_name -> google.protobuf.Value 1382 4, // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1383 16, // 4: google.cloud.aiplatform.v1beta1.ExplanationSpec.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadata 1384 5, // 5: google.cloud.aiplatform.v1beta1.ExplanationParameters.sampled_shapley_attribution:type_name -> google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1385 6, // 6: google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution:type_name -> google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1386 7, // 7: google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution:type_name -> google.cloud.aiplatform.v1beta1.XraiAttribution 1387 17, // 8: google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices:type_name -> google.protobuf.ListValue 1388 8, // 9: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1389 8, // 10: google.cloud.aiplatform.v1beta1.XraiAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1390 9, // 11: google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1391 12, // 12: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1392 4, // 13: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1393 11, // 14: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1394 14, // 15: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.inputs:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1395 15, // 16: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride.input_baselines:type_name -> google.protobuf.Value 1396 13, // 17: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1397 18, // [18:18] is the sub-list for method output_type 1398 18, // [18:18] is the sub-list for method input_type 1399 18, // [18:18] is the sub-list for extension type_name 1400 18, // [18:18] is the sub-list for extension extendee 1401 0, // [0:18] is the sub-list for field type_name 1402} 1403 1404func init() { file_google_cloud_aiplatform_v1beta1_explanation_proto_init() } 1405func file_google_cloud_aiplatform_v1beta1_explanation_proto_init() { 1406 if File_google_cloud_aiplatform_v1beta1_explanation_proto != nil { 1407 return 1408 } 1409 file_google_cloud_aiplatform_v1beta1_explanation_metadata_proto_init() 1410 file_google_cloud_aiplatform_v1beta1_io_proto_init() 1411 if !protoimpl.UnsafeEnabled { 1412 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { 1413 switch v := v.(*Explanation); i { 1414 case 0: 1415 return &v.state 1416 case 1: 1417 return &v.sizeCache 1418 case 2: 1419 return &v.unknownFields 1420 default: 1421 return nil 1422 } 1423 } 1424 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { 1425 switch v := v.(*ModelExplanation); i { 1426 case 0: 1427 return &v.state 1428 case 1: 1429 return &v.sizeCache 1430 case 2: 1431 return &v.unknownFields 1432 default: 1433 return nil 1434 } 1435 } 1436 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { 1437 switch v := v.(*Attribution); i { 1438 case 0: 1439 return &v.state 1440 case 1: 1441 return &v.sizeCache 1442 case 2: 1443 return &v.unknownFields 1444 default: 1445 return nil 1446 } 1447 } 1448 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { 1449 switch v := v.(*ExplanationSpec); i { 1450 case 0: 1451 return &v.state 1452 case 1: 1453 return &v.sizeCache 1454 case 2: 1455 return &v.unknownFields 1456 default: 1457 return nil 1458 } 1459 } 1460 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { 1461 switch v := v.(*ExplanationParameters); i { 1462 case 0: 1463 return &v.state 1464 case 1: 1465 return &v.sizeCache 1466 case 2: 1467 return &v.unknownFields 1468 default: 1469 return nil 1470 } 1471 } 1472 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { 1473 switch v := v.(*SampledShapleyAttribution); i { 1474 case 0: 1475 return &v.state 1476 case 1: 1477 return &v.sizeCache 1478 case 2: 1479 return &v.unknownFields 1480 default: 1481 return nil 1482 } 1483 } 1484 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { 1485 switch v := v.(*IntegratedGradientsAttribution); i { 1486 case 0: 1487 return &v.state 1488 case 1: 1489 return &v.sizeCache 1490 case 2: 1491 return &v.unknownFields 1492 default: 1493 return nil 1494 } 1495 } 1496 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { 1497 switch v := v.(*XraiAttribution); i { 1498 case 0: 1499 return &v.state 1500 case 1: 1501 return &v.sizeCache 1502 case 2: 1503 return &v.unknownFields 1504 default: 1505 return nil 1506 } 1507 } 1508 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} { 1509 switch v := v.(*SmoothGradConfig); i { 1510 case 0: 1511 return &v.state 1512 case 1: 1513 return &v.sizeCache 1514 case 2: 1515 return &v.unknownFields 1516 default: 1517 return nil 1518 } 1519 } 1520 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} { 1521 switch v := v.(*FeatureNoiseSigma); i { 1522 case 0: 1523 return &v.state 1524 case 1: 1525 return &v.sizeCache 1526 case 2: 1527 return &v.unknownFields 1528 default: 1529 return nil 1530 } 1531 } 1532 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} { 1533 switch v := v.(*ExplanationSpecOverride); i { 1534 case 0: 1535 return &v.state 1536 case 1: 1537 return &v.sizeCache 1538 case 2: 1539 return &v.unknownFields 1540 default: 1541 return nil 1542 } 1543 } 1544 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} { 1545 switch v := v.(*ExplanationMetadataOverride); i { 1546 case 0: 1547 return &v.state 1548 case 1: 1549 return &v.sizeCache 1550 case 2: 1551 return &v.unknownFields 1552 default: 1553 return nil 1554 } 1555 } 1556 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} { 1557 switch v := v.(*FeatureNoiseSigma_NoiseSigmaForFeature); i { 1558 case 0: 1559 return &v.state 1560 case 1: 1561 return &v.sizeCache 1562 case 2: 1563 return &v.unknownFields 1564 default: 1565 return nil 1566 } 1567 } 1568 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} { 1569 switch v := v.(*ExplanationMetadataOverride_InputMetadataOverride); i { 1570 case 0: 1571 return &v.state 1572 case 1: 1573 return &v.sizeCache 1574 case 2: 1575 return &v.unknownFields 1576 default: 1577 return nil 1578 } 1579 } 1580 } 1581 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].OneofWrappers = []interface{}{ 1582 (*ExplanationParameters_SampledShapleyAttribution)(nil), 1583 (*ExplanationParameters_IntegratedGradientsAttribution)(nil), 1584 (*ExplanationParameters_XraiAttribution)(nil), 1585 } 1586 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].OneofWrappers = []interface{}{ 1587 (*SmoothGradConfig_NoiseSigma)(nil), 1588 (*SmoothGradConfig_FeatureNoiseSigma)(nil), 1589 } 1590 type x struct{} 1591 out := protoimpl.TypeBuilder{ 1592 File: protoimpl.DescBuilder{ 1593 GoPackagePath: reflect.TypeOf(x{}).PkgPath(), 1594 RawDescriptor: file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc, 1595 NumEnums: 0, 1596 NumMessages: 15, 1597 NumExtensions: 0, 1598 NumServices: 0, 1599 }, 1600 GoTypes: file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes, 1601 DependencyIndexes: file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs, 1602 MessageInfos: file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes, 1603 }.Build() 1604 File_google_cloud_aiplatform_v1beta1_explanation_proto = out.File 1605 file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = nil 1606 file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes = nil 1607 file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = nil 1608} 1609