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 // *ExplanationParameters_Similarity 408 Method isExplanationParameters_Method `protobuf_oneof:"method"` 409 // If populated, returns attributions for top K indices of outputs 410 // (defaults to 1). Only applies to Models that predicts more than one outputs 411 // (e,g, multi-class Models). When set to -1, returns explanations for all 412 // outputs. 413 TopK int32 `protobuf:"varint,4,opt,name=top_k,json=topK,proto3" json:"top_k,omitempty"` 414 // If populated, only returns attributions that have 415 // [output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index] contained in output_indices. It 416 // must be an ndarray of integers, with the same shape of the output it's 417 // explaining. 418 // 419 // If not populated, returns attributions for [top_k][google.cloud.aiplatform.v1beta1.ExplanationParameters.top_k] indices of outputs. 420 // If neither top_k nor output_indeices is populated, returns the argmax 421 // index of the outputs. 422 // 423 // Only applicable to Models that predict multiple outputs (e,g, multi-class 424 // Models that predict multiple classes). 425 OutputIndices *structpb.ListValue `protobuf:"bytes,5,opt,name=output_indices,json=outputIndices,proto3" json:"output_indices,omitempty"` 426} 427 428func (x *ExplanationParameters) Reset() { 429 *x = ExplanationParameters{} 430 if protoimpl.UnsafeEnabled { 431 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 432 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 433 ms.StoreMessageInfo(mi) 434 } 435} 436 437func (x *ExplanationParameters) String() string { 438 return protoimpl.X.MessageStringOf(x) 439} 440 441func (*ExplanationParameters) ProtoMessage() {} 442 443func (x *ExplanationParameters) ProtoReflect() protoreflect.Message { 444 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4] 445 if protoimpl.UnsafeEnabled && x != nil { 446 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 447 if ms.LoadMessageInfo() == nil { 448 ms.StoreMessageInfo(mi) 449 } 450 return ms 451 } 452 return mi.MessageOf(x) 453} 454 455// Deprecated: Use ExplanationParameters.ProtoReflect.Descriptor instead. 456func (*ExplanationParameters) Descriptor() ([]byte, []int) { 457 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{4} 458} 459 460func (m *ExplanationParameters) GetMethod() isExplanationParameters_Method { 461 if m != nil { 462 return m.Method 463 } 464 return nil 465} 466 467func (x *ExplanationParameters) GetSampledShapleyAttribution() *SampledShapleyAttribution { 468 if x, ok := x.GetMethod().(*ExplanationParameters_SampledShapleyAttribution); ok { 469 return x.SampledShapleyAttribution 470 } 471 return nil 472} 473 474func (x *ExplanationParameters) GetIntegratedGradientsAttribution() *IntegratedGradientsAttribution { 475 if x, ok := x.GetMethod().(*ExplanationParameters_IntegratedGradientsAttribution); ok { 476 return x.IntegratedGradientsAttribution 477 } 478 return nil 479} 480 481func (x *ExplanationParameters) GetXraiAttribution() *XraiAttribution { 482 if x, ok := x.GetMethod().(*ExplanationParameters_XraiAttribution); ok { 483 return x.XraiAttribution 484 } 485 return nil 486} 487 488func (x *ExplanationParameters) GetSimilarity() *Similarity { 489 if x, ok := x.GetMethod().(*ExplanationParameters_Similarity); ok { 490 return x.Similarity 491 } 492 return nil 493} 494 495func (x *ExplanationParameters) GetTopK() int32 { 496 if x != nil { 497 return x.TopK 498 } 499 return 0 500} 501 502func (x *ExplanationParameters) GetOutputIndices() *structpb.ListValue { 503 if x != nil { 504 return x.OutputIndices 505 } 506 return nil 507} 508 509type isExplanationParameters_Method interface { 510 isExplanationParameters_Method() 511} 512 513type ExplanationParameters_SampledShapleyAttribution struct { 514 // An attribution method that approximates Shapley values for features that 515 // contribute to the label being predicted. A sampling strategy is used to 516 // approximate the value rather than considering all subsets of features. 517 // Refer to this paper for model details: https://arxiv.org/abs/1306.4265. 518 SampledShapleyAttribution *SampledShapleyAttribution `protobuf:"bytes,1,opt,name=sampled_shapley_attribution,json=sampledShapleyAttribution,proto3,oneof"` 519} 520 521type ExplanationParameters_IntegratedGradientsAttribution struct { 522 // An attribution method that computes Aumann-Shapley values taking 523 // advantage of the model's fully differentiable structure. Refer to this 524 // paper for more details: https://arxiv.org/abs/1703.01365 525 IntegratedGradientsAttribution *IntegratedGradientsAttribution `protobuf:"bytes,2,opt,name=integrated_gradients_attribution,json=integratedGradientsAttribution,proto3,oneof"` 526} 527 528type ExplanationParameters_XraiAttribution struct { 529 // An attribution method that redistributes Integrated Gradients 530 // attribution to segmented regions, taking advantage of the model's fully 531 // differentiable structure. Refer to this paper for 532 // more details: https://arxiv.org/abs/1906.02825 533 // 534 // XRAI currently performs better on natural images, like a picture of a 535 // house or an animal. If the images are taken in artificial environments, 536 // like a lab or manufacturing line, or from diagnostic equipment, like 537 // x-rays or quality-control cameras, use Integrated Gradients instead. 538 XraiAttribution *XraiAttribution `protobuf:"bytes,3,opt,name=xrai_attribution,json=xraiAttribution,proto3,oneof"` 539} 540 541type ExplanationParameters_Similarity struct { 542 // Similarity explainability that returns the nearest neighbors from the 543 // provided dataset. 544 Similarity *Similarity `protobuf:"bytes,7,opt,name=similarity,proto3,oneof"` 545} 546 547func (*ExplanationParameters_SampledShapleyAttribution) isExplanationParameters_Method() {} 548 549func (*ExplanationParameters_IntegratedGradientsAttribution) isExplanationParameters_Method() {} 550 551func (*ExplanationParameters_XraiAttribution) isExplanationParameters_Method() {} 552 553func (*ExplanationParameters_Similarity) isExplanationParameters_Method() {} 554 555// An attribution method that approximates Shapley values for features that 556// contribute to the label being predicted. A sampling strategy is used to 557// approximate the value rather than considering all subsets of features. 558type SampledShapleyAttribution struct { 559 state protoimpl.MessageState 560 sizeCache protoimpl.SizeCache 561 unknownFields protoimpl.UnknownFields 562 563 // Required. The number of feature permutations to consider when approximating the 564 // Shapley values. 565 // 566 // Valid range of its value is [1, 50], inclusively. 567 PathCount int32 `protobuf:"varint,1,opt,name=path_count,json=pathCount,proto3" json:"path_count,omitempty"` 568} 569 570func (x *SampledShapleyAttribution) Reset() { 571 *x = SampledShapleyAttribution{} 572 if protoimpl.UnsafeEnabled { 573 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 574 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 575 ms.StoreMessageInfo(mi) 576 } 577} 578 579func (x *SampledShapleyAttribution) String() string { 580 return protoimpl.X.MessageStringOf(x) 581} 582 583func (*SampledShapleyAttribution) ProtoMessage() {} 584 585func (x *SampledShapleyAttribution) ProtoReflect() protoreflect.Message { 586 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5] 587 if protoimpl.UnsafeEnabled && x != nil { 588 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 589 if ms.LoadMessageInfo() == nil { 590 ms.StoreMessageInfo(mi) 591 } 592 return ms 593 } 594 return mi.MessageOf(x) 595} 596 597// Deprecated: Use SampledShapleyAttribution.ProtoReflect.Descriptor instead. 598func (*SampledShapleyAttribution) Descriptor() ([]byte, []int) { 599 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{5} 600} 601 602func (x *SampledShapleyAttribution) GetPathCount() int32 { 603 if x != nil { 604 return x.PathCount 605 } 606 return 0 607} 608 609// An attribution method that computes the Aumann-Shapley value taking advantage 610// of the model's fully differentiable structure. Refer to this paper for 611// more details: https://arxiv.org/abs/1703.01365 612type IntegratedGradientsAttribution struct { 613 state protoimpl.MessageState 614 sizeCache protoimpl.SizeCache 615 unknownFields protoimpl.UnknownFields 616 617 // Required. The number of steps for approximating the path integral. 618 // A good value to start is 50 and gradually increase until the 619 // sum to diff property is within the desired error range. 620 // 621 // Valid range of its value is [1, 100], inclusively. 622 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 623 // Config for SmoothGrad approximation of gradients. 624 // 625 // When enabled, the gradients are approximated by averaging the gradients 626 // from noisy samples in the vicinity of the inputs. Adding 627 // noise can help improve the computed gradients. Refer to this paper for more 628 // details: https://arxiv.org/pdf/1706.03825.pdf 629 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 630} 631 632func (x *IntegratedGradientsAttribution) Reset() { 633 *x = IntegratedGradientsAttribution{} 634 if protoimpl.UnsafeEnabled { 635 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 636 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 637 ms.StoreMessageInfo(mi) 638 } 639} 640 641func (x *IntegratedGradientsAttribution) String() string { 642 return protoimpl.X.MessageStringOf(x) 643} 644 645func (*IntegratedGradientsAttribution) ProtoMessage() {} 646 647func (x *IntegratedGradientsAttribution) ProtoReflect() protoreflect.Message { 648 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6] 649 if protoimpl.UnsafeEnabled && x != nil { 650 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 651 if ms.LoadMessageInfo() == nil { 652 ms.StoreMessageInfo(mi) 653 } 654 return ms 655 } 656 return mi.MessageOf(x) 657} 658 659// Deprecated: Use IntegratedGradientsAttribution.ProtoReflect.Descriptor instead. 660func (*IntegratedGradientsAttribution) Descriptor() ([]byte, []int) { 661 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{6} 662} 663 664func (x *IntegratedGradientsAttribution) GetStepCount() int32 { 665 if x != nil { 666 return x.StepCount 667 } 668 return 0 669} 670 671func (x *IntegratedGradientsAttribution) GetSmoothGradConfig() *SmoothGradConfig { 672 if x != nil { 673 return x.SmoothGradConfig 674 } 675 return nil 676} 677 678// An explanation method that redistributes Integrated Gradients 679// attributions to segmented regions, taking advantage of the model's fully 680// differentiable structure. Refer to this paper for more details: 681// https://arxiv.org/abs/1906.02825 682// 683// Supported only by image Models. 684type XraiAttribution struct { 685 state protoimpl.MessageState 686 sizeCache protoimpl.SizeCache 687 unknownFields protoimpl.UnknownFields 688 689 // Required. The number of steps for approximating the path integral. 690 // A good value to start is 50 and gradually increase until the 691 // sum to diff property is met within the desired error range. 692 // 693 // Valid range of its value is [1, 100], inclusively. 694 StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"` 695 // Config for SmoothGrad approximation of gradients. 696 // 697 // When enabled, the gradients are approximated by averaging the gradients 698 // from noisy samples in the vicinity of the inputs. Adding 699 // noise can help improve the computed gradients. Refer to this paper for more 700 // details: https://arxiv.org/pdf/1706.03825.pdf 701 SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"` 702} 703 704func (x *XraiAttribution) Reset() { 705 *x = XraiAttribution{} 706 if protoimpl.UnsafeEnabled { 707 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 708 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 709 ms.StoreMessageInfo(mi) 710 } 711} 712 713func (x *XraiAttribution) String() string { 714 return protoimpl.X.MessageStringOf(x) 715} 716 717func (*XraiAttribution) ProtoMessage() {} 718 719func (x *XraiAttribution) ProtoReflect() protoreflect.Message { 720 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7] 721 if protoimpl.UnsafeEnabled && x != nil { 722 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 723 if ms.LoadMessageInfo() == nil { 724 ms.StoreMessageInfo(mi) 725 } 726 return ms 727 } 728 return mi.MessageOf(x) 729} 730 731// Deprecated: Use XraiAttribution.ProtoReflect.Descriptor instead. 732func (*XraiAttribution) Descriptor() ([]byte, []int) { 733 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{7} 734} 735 736func (x *XraiAttribution) GetStepCount() int32 { 737 if x != nil { 738 return x.StepCount 739 } 740 return 0 741} 742 743func (x *XraiAttribution) GetSmoothGradConfig() *SmoothGradConfig { 744 if x != nil { 745 return x.SmoothGradConfig 746 } 747 return nil 748} 749 750// Config for SmoothGrad approximation of gradients. 751// 752// When enabled, the gradients are approximated by averaging the gradients from 753// noisy samples in the vicinity of the inputs. Adding noise can help improve 754// the computed gradients. Refer to this paper for more details: 755// https://arxiv.org/pdf/1706.03825.pdf 756type SmoothGradConfig struct { 757 state protoimpl.MessageState 758 sizeCache protoimpl.SizeCache 759 unknownFields protoimpl.UnknownFields 760 761 // Represents the standard deviation of the gaussian kernel 762 // that will be used to add noise to the interpolated inputs 763 // prior to computing gradients. 764 // 765 // Types that are assignable to GradientNoiseSigma: 766 // *SmoothGradConfig_NoiseSigma 767 // *SmoothGradConfig_FeatureNoiseSigma 768 GradientNoiseSigma isSmoothGradConfig_GradientNoiseSigma `protobuf_oneof:"GradientNoiseSigma"` 769 // The number of gradient samples to use for 770 // approximation. The higher this number, the more accurate the gradient 771 // is, but the runtime complexity increases by this factor as well. 772 // Valid range of its value is [1, 50]. Defaults to 3. 773 NoisySampleCount int32 `protobuf:"varint,3,opt,name=noisy_sample_count,json=noisySampleCount,proto3" json:"noisy_sample_count,omitempty"` 774} 775 776func (x *SmoothGradConfig) Reset() { 777 *x = SmoothGradConfig{} 778 if protoimpl.UnsafeEnabled { 779 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 780 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 781 ms.StoreMessageInfo(mi) 782 } 783} 784 785func (x *SmoothGradConfig) String() string { 786 return protoimpl.X.MessageStringOf(x) 787} 788 789func (*SmoothGradConfig) ProtoMessage() {} 790 791func (x *SmoothGradConfig) ProtoReflect() protoreflect.Message { 792 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8] 793 if protoimpl.UnsafeEnabled && x != nil { 794 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 795 if ms.LoadMessageInfo() == nil { 796 ms.StoreMessageInfo(mi) 797 } 798 return ms 799 } 800 return mi.MessageOf(x) 801} 802 803// Deprecated: Use SmoothGradConfig.ProtoReflect.Descriptor instead. 804func (*SmoothGradConfig) Descriptor() ([]byte, []int) { 805 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{8} 806} 807 808func (m *SmoothGradConfig) GetGradientNoiseSigma() isSmoothGradConfig_GradientNoiseSigma { 809 if m != nil { 810 return m.GradientNoiseSigma 811 } 812 return nil 813} 814 815func (x *SmoothGradConfig) GetNoiseSigma() float32 { 816 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_NoiseSigma); ok { 817 return x.NoiseSigma 818 } 819 return 0 820} 821 822func (x *SmoothGradConfig) GetFeatureNoiseSigma() *FeatureNoiseSigma { 823 if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_FeatureNoiseSigma); ok { 824 return x.FeatureNoiseSigma 825 } 826 return nil 827} 828 829func (x *SmoothGradConfig) GetNoisySampleCount() int32 { 830 if x != nil { 831 return x.NoisySampleCount 832 } 833 return 0 834} 835 836type isSmoothGradConfig_GradientNoiseSigma interface { 837 isSmoothGradConfig_GradientNoiseSigma() 838} 839 840type SmoothGradConfig_NoiseSigma struct { 841 // This is a single float value and will be used to add noise to all the 842 // features. Use this field when all features are normalized to have the 843 // same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where 844 // features are normalized to have 0-mean and 1-variance. Learn more about 845 // [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). 846 // 847 // For best results the recommended value is about 10% - 20% of the standard 848 // deviation of the input feature. Refer to section 3.2 of the SmoothGrad 849 // paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. 850 // 851 // If the distribution is different per feature, set 852 // [feature_noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma] instead 853 // for each feature. 854 NoiseSigma float32 `protobuf:"fixed32,1,opt,name=noise_sigma,json=noiseSigma,proto3,oneof"` 855} 856 857type SmoothGradConfig_FeatureNoiseSigma struct { 858 // This is similar to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma], but 859 // provides additional flexibility. A separate noise sigma can be provided 860 // for each feature, which is useful if their distributions are different. 861 // No noise is added to features that are not set. If this field is unset, 862 // [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] will be used for all 863 // features. 864 FeatureNoiseSigma *FeatureNoiseSigma `protobuf:"bytes,2,opt,name=feature_noise_sigma,json=featureNoiseSigma,proto3,oneof"` 865} 866 867func (*SmoothGradConfig_NoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 868 869func (*SmoothGradConfig_FeatureNoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {} 870 871// Noise sigma by features. Noise sigma represents the standard deviation of the 872// gaussian kernel that will be used to add noise to interpolated inputs prior 873// to computing gradients. 874type FeatureNoiseSigma struct { 875 state protoimpl.MessageState 876 sizeCache protoimpl.SizeCache 877 unknownFields protoimpl.UnknownFields 878 879 // Noise sigma per feature. No noise is added to features that are not set. 880 NoiseSigma []*FeatureNoiseSigma_NoiseSigmaForFeature `protobuf:"bytes,1,rep,name=noise_sigma,json=noiseSigma,proto3" json:"noise_sigma,omitempty"` 881} 882 883func (x *FeatureNoiseSigma) Reset() { 884 *x = FeatureNoiseSigma{} 885 if protoimpl.UnsafeEnabled { 886 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 887 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 888 ms.StoreMessageInfo(mi) 889 } 890} 891 892func (x *FeatureNoiseSigma) String() string { 893 return protoimpl.X.MessageStringOf(x) 894} 895 896func (*FeatureNoiseSigma) ProtoMessage() {} 897 898func (x *FeatureNoiseSigma) ProtoReflect() protoreflect.Message { 899 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9] 900 if protoimpl.UnsafeEnabled && x != nil { 901 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 902 if ms.LoadMessageInfo() == nil { 903 ms.StoreMessageInfo(mi) 904 } 905 return ms 906 } 907 return mi.MessageOf(x) 908} 909 910// Deprecated: Use FeatureNoiseSigma.ProtoReflect.Descriptor instead. 911func (*FeatureNoiseSigma) Descriptor() ([]byte, []int) { 912 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9} 913} 914 915func (x *FeatureNoiseSigma) GetNoiseSigma() []*FeatureNoiseSigma_NoiseSigmaForFeature { 916 if x != nil { 917 return x.NoiseSigma 918 } 919 return nil 920} 921 922// Similarity explainability that returns the nearest neighbors from the 923// provided dataset. 924type Similarity struct { 925 state protoimpl.MessageState 926 sizeCache protoimpl.SizeCache 927 unknownFields protoimpl.UnknownFields 928 929 // The Cloud Storage location for the input instances. 930 GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"` 931 // The configuration for the generated index, the semantics are the same as 932 // [metadata][google.cloud.aiplatform.v1beta1.Index.metadata] and should match NearestNeighborSearchConfig. 933 NearestNeighborSearchConfig *structpb.Value `protobuf:"bytes,2,opt,name=nearest_neighbor_search_config,json=nearestNeighborSearchConfig,proto3" json:"nearest_neighbor_search_config,omitempty"` 934} 935 936func (x *Similarity) Reset() { 937 *x = Similarity{} 938 if protoimpl.UnsafeEnabled { 939 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 940 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 941 ms.StoreMessageInfo(mi) 942 } 943} 944 945func (x *Similarity) String() string { 946 return protoimpl.X.MessageStringOf(x) 947} 948 949func (*Similarity) ProtoMessage() {} 950 951func (x *Similarity) ProtoReflect() protoreflect.Message { 952 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10] 953 if protoimpl.UnsafeEnabled && x != nil { 954 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 955 if ms.LoadMessageInfo() == nil { 956 ms.StoreMessageInfo(mi) 957 } 958 return ms 959 } 960 return mi.MessageOf(x) 961} 962 963// Deprecated: Use Similarity.ProtoReflect.Descriptor instead. 964func (*Similarity) Descriptor() ([]byte, []int) { 965 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{10} 966} 967 968func (x *Similarity) GetGcsSource() *GcsSource { 969 if x != nil { 970 return x.GcsSource 971 } 972 return nil 973} 974 975func (x *Similarity) GetNearestNeighborSearchConfig() *structpb.Value { 976 if x != nil { 977 return x.NearestNeighborSearchConfig 978 } 979 return nil 980} 981 982// The [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] entries that can be overridden at 983// [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 984type ExplanationSpecOverride struct { 985 state protoimpl.MessageState 986 sizeCache protoimpl.SizeCache 987 unknownFields protoimpl.UnknownFields 988 989 // The parameters to be overridden. Note that the 990 // [method][google.cloud.aiplatform.v1beta1.ExplanationParameters.method] cannot be changed. If not specified, 991 // no parameter is overridden. 992 Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"` 993 // The metadata to be overridden. If not specified, no metadata is overridden. 994 Metadata *ExplanationMetadataOverride `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` 995} 996 997func (x *ExplanationSpecOverride) Reset() { 998 *x = ExplanationSpecOverride{} 999 if protoimpl.UnsafeEnabled { 1000 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 1001 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1002 ms.StoreMessageInfo(mi) 1003 } 1004} 1005 1006func (x *ExplanationSpecOverride) String() string { 1007 return protoimpl.X.MessageStringOf(x) 1008} 1009 1010func (*ExplanationSpecOverride) ProtoMessage() {} 1011 1012func (x *ExplanationSpecOverride) ProtoReflect() protoreflect.Message { 1013 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11] 1014 if protoimpl.UnsafeEnabled && x != nil { 1015 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1016 if ms.LoadMessageInfo() == nil { 1017 ms.StoreMessageInfo(mi) 1018 } 1019 return ms 1020 } 1021 return mi.MessageOf(x) 1022} 1023 1024// Deprecated: Use ExplanationSpecOverride.ProtoReflect.Descriptor instead. 1025func (*ExplanationSpecOverride) Descriptor() ([]byte, []int) { 1026 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{11} 1027} 1028 1029func (x *ExplanationSpecOverride) GetParameters() *ExplanationParameters { 1030 if x != nil { 1031 return x.Parameters 1032 } 1033 return nil 1034} 1035 1036func (x *ExplanationSpecOverride) GetMetadata() *ExplanationMetadataOverride { 1037 if x != nil { 1038 return x.Metadata 1039 } 1040 return nil 1041} 1042 1043// The [ExplanationMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata] entries that can be overridden at 1044// [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time. 1045type ExplanationMetadataOverride struct { 1046 state protoimpl.MessageState 1047 sizeCache protoimpl.SizeCache 1048 unknownFields protoimpl.UnknownFields 1049 1050 // Required. Overrides the [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs] of the features. 1051 // The key is the name of the feature to be overridden. The keys specified 1052 // here must exist in the input metadata to be overridden. If a feature is 1053 // not specified here, the corresponding feature's input metadata is not 1054 // overridden. 1055 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"` 1056} 1057 1058func (x *ExplanationMetadataOverride) Reset() { 1059 *x = ExplanationMetadataOverride{} 1060 if protoimpl.UnsafeEnabled { 1061 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1062 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1063 ms.StoreMessageInfo(mi) 1064 } 1065} 1066 1067func (x *ExplanationMetadataOverride) String() string { 1068 return protoimpl.X.MessageStringOf(x) 1069} 1070 1071func (*ExplanationMetadataOverride) ProtoMessage() {} 1072 1073func (x *ExplanationMetadataOverride) ProtoReflect() protoreflect.Message { 1074 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12] 1075 if protoimpl.UnsafeEnabled && x != nil { 1076 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1077 if ms.LoadMessageInfo() == nil { 1078 ms.StoreMessageInfo(mi) 1079 } 1080 return ms 1081 } 1082 return mi.MessageOf(x) 1083} 1084 1085// Deprecated: Use ExplanationMetadataOverride.ProtoReflect.Descriptor instead. 1086func (*ExplanationMetadataOverride) Descriptor() ([]byte, []int) { 1087 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{12} 1088} 1089 1090func (x *ExplanationMetadataOverride) GetInputs() map[string]*ExplanationMetadataOverride_InputMetadataOverride { 1091 if x != nil { 1092 return x.Inputs 1093 } 1094 return nil 1095} 1096 1097// Noise sigma for a single feature. 1098type FeatureNoiseSigma_NoiseSigmaForFeature struct { 1099 state protoimpl.MessageState 1100 sizeCache protoimpl.SizeCache 1101 unknownFields protoimpl.UnknownFields 1102 1103 // The name of the input feature for which noise sigma is provided. The 1104 // features are defined in 1105 // [explanation metadata inputs][google.cloud.aiplatform.v1beta1.ExplanationMetadata.inputs]. 1106 Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` 1107 // This represents the standard deviation of the Gaussian kernel that will 1108 // be used to add noise to the feature prior to computing gradients. Similar 1109 // to [noise_sigma][google.cloud.aiplatform.v1beta1.SmoothGradConfig.noise_sigma] but represents the 1110 // noise added to the current feature. Defaults to 0.1. 1111 Sigma float32 `protobuf:"fixed32,2,opt,name=sigma,proto3" json:"sigma,omitempty"` 1112} 1113 1114func (x *FeatureNoiseSigma_NoiseSigmaForFeature) Reset() { 1115 *x = FeatureNoiseSigma_NoiseSigmaForFeature{} 1116 if protoimpl.UnsafeEnabled { 1117 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1118 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1119 ms.StoreMessageInfo(mi) 1120 } 1121} 1122 1123func (x *FeatureNoiseSigma_NoiseSigmaForFeature) String() string { 1124 return protoimpl.X.MessageStringOf(x) 1125} 1126 1127func (*FeatureNoiseSigma_NoiseSigmaForFeature) ProtoMessage() {} 1128 1129func (x *FeatureNoiseSigma_NoiseSigmaForFeature) ProtoReflect() protoreflect.Message { 1130 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13] 1131 if protoimpl.UnsafeEnabled && x != nil { 1132 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1133 if ms.LoadMessageInfo() == nil { 1134 ms.StoreMessageInfo(mi) 1135 } 1136 return ms 1137 } 1138 return mi.MessageOf(x) 1139} 1140 1141// Deprecated: Use FeatureNoiseSigma_NoiseSigmaForFeature.ProtoReflect.Descriptor instead. 1142func (*FeatureNoiseSigma_NoiseSigmaForFeature) Descriptor() ([]byte, []int) { 1143 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{9, 0} 1144} 1145 1146func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetName() string { 1147 if x != nil { 1148 return x.Name 1149 } 1150 return "" 1151} 1152 1153func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetSigma() float32 { 1154 if x != nil { 1155 return x.Sigma 1156 } 1157 return 0 1158} 1159 1160// The [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] entries to be 1161// overridden. 1162type ExplanationMetadataOverride_InputMetadataOverride struct { 1163 state protoimpl.MessageState 1164 sizeCache protoimpl.SizeCache 1165 unknownFields protoimpl.UnknownFields 1166 1167 // Baseline inputs for this feature. 1168 // 1169 // This overrides the `input_baseline` field of the 1170 // [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] 1171 // object of the corresponding feature's input metadata. If it's not 1172 // specified, the original baselines are not overridden. 1173 InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"` 1174} 1175 1176func (x *ExplanationMetadataOverride_InputMetadataOverride) Reset() { 1177 *x = ExplanationMetadataOverride_InputMetadataOverride{} 1178 if protoimpl.UnsafeEnabled { 1179 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[14] 1180 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1181 ms.StoreMessageInfo(mi) 1182 } 1183} 1184 1185func (x *ExplanationMetadataOverride_InputMetadataOverride) String() string { 1186 return protoimpl.X.MessageStringOf(x) 1187} 1188 1189func (*ExplanationMetadataOverride_InputMetadataOverride) ProtoMessage() {} 1190 1191func (x *ExplanationMetadataOverride_InputMetadataOverride) ProtoReflect() protoreflect.Message { 1192 mi := &file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[14] 1193 if protoimpl.UnsafeEnabled && x != nil { 1194 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 1195 if ms.LoadMessageInfo() == nil { 1196 ms.StoreMessageInfo(mi) 1197 } 1198 return ms 1199 } 1200 return mi.MessageOf(x) 1201} 1202 1203// Deprecated: Use ExplanationMetadataOverride_InputMetadataOverride.ProtoReflect.Descriptor instead. 1204func (*ExplanationMetadataOverride_InputMetadataOverride) Descriptor() ([]byte, []int) { 1205 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP(), []int{12, 0} 1206} 1207 1208func (x *ExplanationMetadataOverride_InputMetadataOverride) GetInputBaselines() []*structpb.Value { 1209 if x != nil { 1210 return x.InputBaselines 1211 } 1212 return nil 1213} 1214 1215var File_google_cloud_aiplatform_v1beta1_explanation_proto protoreflect.FileDescriptor 1216 1217var file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = []byte{ 1218 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 = file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc 1440) 1441 1442func file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescGZIP() []byte { 1443 file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescOnce.Do(func() { 1444 file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData) 1445 }) 1446 return file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDescData 1447} 1448 1449var file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes = make([]protoimpl.MessageInfo, 16) 1450var file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes = []interface{}{ 1451 (*Explanation)(nil), // 0: google.cloud.aiplatform.v1beta1.Explanation 1452 (*ModelExplanation)(nil), // 1: google.cloud.aiplatform.v1beta1.ModelExplanation 1453 (*Attribution)(nil), // 2: google.cloud.aiplatform.v1beta1.Attribution 1454 (*ExplanationSpec)(nil), // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec 1455 (*ExplanationParameters)(nil), // 4: google.cloud.aiplatform.v1beta1.ExplanationParameters 1456 (*SampledShapleyAttribution)(nil), // 5: google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1457 (*IntegratedGradientsAttribution)(nil), // 6: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1458 (*XraiAttribution)(nil), // 7: google.cloud.aiplatform.v1beta1.XraiAttribution 1459 (*SmoothGradConfig)(nil), // 8: google.cloud.aiplatform.v1beta1.SmoothGradConfig 1460 (*FeatureNoiseSigma)(nil), // 9: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1461 (*Similarity)(nil), // 10: google.cloud.aiplatform.v1beta1.Similarity 1462 (*ExplanationSpecOverride)(nil), // 11: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride 1463 (*ExplanationMetadataOverride)(nil), // 12: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1464 (*FeatureNoiseSigma_NoiseSigmaForFeature)(nil), // 13: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1465 (*ExplanationMetadataOverride_InputMetadataOverride)(nil), // 14: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1466 nil, // 15: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1467 (*structpb.Value)(nil), // 16: google.protobuf.Value 1468 (*ExplanationMetadata)(nil), // 17: google.cloud.aiplatform.v1beta1.ExplanationMetadata 1469 (*structpb.ListValue)(nil), // 18: google.protobuf.ListValue 1470 (*GcsSource)(nil), // 19: google.cloud.aiplatform.v1beta1.GcsSource 1471} 1472var file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = []int32{ 1473 2, // 0: google.cloud.aiplatform.v1beta1.Explanation.attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1474 2, // 1: google.cloud.aiplatform.v1beta1.ModelExplanation.mean_attributions:type_name -> google.cloud.aiplatform.v1beta1.Attribution 1475 16, // 2: google.cloud.aiplatform.v1beta1.Attribution.feature_attributions:type_name -> google.protobuf.Value 1476 4, // 3: google.cloud.aiplatform.v1beta1.ExplanationSpec.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1477 17, // 4: google.cloud.aiplatform.v1beta1.ExplanationSpec.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadata 1478 5, // 5: google.cloud.aiplatform.v1beta1.ExplanationParameters.sampled_shapley_attribution:type_name -> google.cloud.aiplatform.v1beta1.SampledShapleyAttribution 1479 6, // 6: google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution:type_name -> google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution 1480 7, // 7: google.cloud.aiplatform.v1beta1.ExplanationParameters.xrai_attribution:type_name -> google.cloud.aiplatform.v1beta1.XraiAttribution 1481 10, // 8: google.cloud.aiplatform.v1beta1.ExplanationParameters.similarity:type_name -> google.cloud.aiplatform.v1beta1.Similarity 1482 18, // 9: google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices:type_name -> google.protobuf.ListValue 1483 8, // 10: google.cloud.aiplatform.v1beta1.IntegratedGradientsAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1484 8, // 11: google.cloud.aiplatform.v1beta1.XraiAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1beta1.SmoothGradConfig 1485 9, // 12: google.cloud.aiplatform.v1beta1.SmoothGradConfig.feature_noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma 1486 13, // 13: google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.noise_sigma:type_name -> google.cloud.aiplatform.v1beta1.FeatureNoiseSigma.NoiseSigmaForFeature 1487 19, // 14: google.cloud.aiplatform.v1beta1.Similarity.gcs_source:type_name -> google.cloud.aiplatform.v1beta1.GcsSource 1488 16, // 15: google.cloud.aiplatform.v1beta1.Similarity.nearest_neighbor_search_config:type_name -> google.protobuf.Value 1489 4, // 16: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.parameters:type_name -> google.cloud.aiplatform.v1beta1.ExplanationParameters 1490 12, // 17: google.cloud.aiplatform.v1beta1.ExplanationSpecOverride.metadata:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride 1491 15, // 18: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.inputs:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry 1492 16, // 19: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride.input_baselines:type_name -> google.protobuf.Value 1493 14, // 20: google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ExplanationMetadataOverride.InputMetadataOverride 1494 21, // [21:21] is the sub-list for method output_type 1495 21, // [21:21] is the sub-list for method input_type 1496 21, // [21:21] is the sub-list for extension type_name 1497 21, // [21:21] is the sub-list for extension extendee 1498 0, // [0:21] is the sub-list for field type_name 1499} 1500 1501func init() { file_google_cloud_aiplatform_v1beta1_explanation_proto_init() } 1502func file_google_cloud_aiplatform_v1beta1_explanation_proto_init() { 1503 if File_google_cloud_aiplatform_v1beta1_explanation_proto != nil { 1504 return 1505 } 1506 file_google_cloud_aiplatform_v1beta1_explanation_metadata_proto_init() 1507 file_google_cloud_aiplatform_v1beta1_io_proto_init() 1508 if !protoimpl.UnsafeEnabled { 1509 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { 1510 switch v := v.(*Explanation); i { 1511 case 0: 1512 return &v.state 1513 case 1: 1514 return &v.sizeCache 1515 case 2: 1516 return &v.unknownFields 1517 default: 1518 return nil 1519 } 1520 } 1521 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { 1522 switch v := v.(*ModelExplanation); i { 1523 case 0: 1524 return &v.state 1525 case 1: 1526 return &v.sizeCache 1527 case 2: 1528 return &v.unknownFields 1529 default: 1530 return nil 1531 } 1532 } 1533 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { 1534 switch v := v.(*Attribution); i { 1535 case 0: 1536 return &v.state 1537 case 1: 1538 return &v.sizeCache 1539 case 2: 1540 return &v.unknownFields 1541 default: 1542 return nil 1543 } 1544 } 1545 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { 1546 switch v := v.(*ExplanationSpec); i { 1547 case 0: 1548 return &v.state 1549 case 1: 1550 return &v.sizeCache 1551 case 2: 1552 return &v.unknownFields 1553 default: 1554 return nil 1555 } 1556 } 1557 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { 1558 switch v := v.(*ExplanationParameters); i { 1559 case 0: 1560 return &v.state 1561 case 1: 1562 return &v.sizeCache 1563 case 2: 1564 return &v.unknownFields 1565 default: 1566 return nil 1567 } 1568 } 1569 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { 1570 switch v := v.(*SampledShapleyAttribution); i { 1571 case 0: 1572 return &v.state 1573 case 1: 1574 return &v.sizeCache 1575 case 2: 1576 return &v.unknownFields 1577 default: 1578 return nil 1579 } 1580 } 1581 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { 1582 switch v := v.(*IntegratedGradientsAttribution); i { 1583 case 0: 1584 return &v.state 1585 case 1: 1586 return &v.sizeCache 1587 case 2: 1588 return &v.unknownFields 1589 default: 1590 return nil 1591 } 1592 } 1593 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { 1594 switch v := v.(*XraiAttribution); i { 1595 case 0: 1596 return &v.state 1597 case 1: 1598 return &v.sizeCache 1599 case 2: 1600 return &v.unknownFields 1601 default: 1602 return nil 1603 } 1604 } 1605 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} { 1606 switch v := v.(*SmoothGradConfig); i { 1607 case 0: 1608 return &v.state 1609 case 1: 1610 return &v.sizeCache 1611 case 2: 1612 return &v.unknownFields 1613 default: 1614 return nil 1615 } 1616 } 1617 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} { 1618 switch v := v.(*FeatureNoiseSigma); i { 1619 case 0: 1620 return &v.state 1621 case 1: 1622 return &v.sizeCache 1623 case 2: 1624 return &v.unknownFields 1625 default: 1626 return nil 1627 } 1628 } 1629 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} { 1630 switch v := v.(*Similarity); i { 1631 case 0: 1632 return &v.state 1633 case 1: 1634 return &v.sizeCache 1635 case 2: 1636 return &v.unknownFields 1637 default: 1638 return nil 1639 } 1640 } 1641 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} { 1642 switch v := v.(*ExplanationSpecOverride); i { 1643 case 0: 1644 return &v.state 1645 case 1: 1646 return &v.sizeCache 1647 case 2: 1648 return &v.unknownFields 1649 default: 1650 return nil 1651 } 1652 } 1653 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} { 1654 switch v := v.(*ExplanationMetadataOverride); i { 1655 case 0: 1656 return &v.state 1657 case 1: 1658 return &v.sizeCache 1659 case 2: 1660 return &v.unknownFields 1661 default: 1662 return nil 1663 } 1664 } 1665 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} { 1666 switch v := v.(*FeatureNoiseSigma_NoiseSigmaForFeature); i { 1667 case 0: 1668 return &v.state 1669 case 1: 1670 return &v.sizeCache 1671 case 2: 1672 return &v.unknownFields 1673 default: 1674 return nil 1675 } 1676 } 1677 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[14].Exporter = func(v interface{}, i int) interface{} { 1678 switch v := v.(*ExplanationMetadataOverride_InputMetadataOverride); i { 1679 case 0: 1680 return &v.state 1681 case 1: 1682 return &v.sizeCache 1683 case 2: 1684 return &v.unknownFields 1685 default: 1686 return nil 1687 } 1688 } 1689 } 1690 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[4].OneofWrappers = []interface{}{ 1691 (*ExplanationParameters_SampledShapleyAttribution)(nil), 1692 (*ExplanationParameters_IntegratedGradientsAttribution)(nil), 1693 (*ExplanationParameters_XraiAttribution)(nil), 1694 (*ExplanationParameters_Similarity)(nil), 1695 } 1696 file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes[8].OneofWrappers = []interface{}{ 1697 (*SmoothGradConfig_NoiseSigma)(nil), 1698 (*SmoothGradConfig_FeatureNoiseSigma)(nil), 1699 } 1700 type x struct{} 1701 out := protoimpl.TypeBuilder{ 1702 File: protoimpl.DescBuilder{ 1703 GoPackagePath: reflect.TypeOf(x{}).PkgPath(), 1704 RawDescriptor: file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc, 1705 NumEnums: 0, 1706 NumMessages: 16, 1707 NumExtensions: 0, 1708 NumServices: 0, 1709 }, 1710 GoTypes: file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes, 1711 DependencyIndexes: file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs, 1712 MessageInfos: file_google_cloud_aiplatform_v1beta1_explanation_proto_msgTypes, 1713 }.Build() 1714 File_google_cloud_aiplatform_v1beta1_explanation_proto = out.File 1715 file_google_cloud_aiplatform_v1beta1_explanation_proto_rawDesc = nil 1716 file_google_cloud_aiplatform_v1beta1_explanation_proto_goTypes = nil 1717 file_google_cloud_aiplatform_v1beta1_explanation_proto_depIdxs = nil 1718} 1719