1// Copyright 2020 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 v1.22.0 18// protoc v3.11.2 19// source: google/cloud/automl/v1/classification.proto 20 21package automl 22 23import ( 24 reflect "reflect" 25 sync "sync" 26 27 proto "github.com/golang/protobuf/proto" 28 _ "google.golang.org/genproto/googleapis/api/annotations" 29 protoreflect "google.golang.org/protobuf/reflect/protoreflect" 30 protoimpl "google.golang.org/protobuf/runtime/protoimpl" 31) 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// This is a compile-time assertion that a sufficiently up-to-date version 41// of the legacy proto package is being used. 42const _ = proto.ProtoPackageIsVersion4 43 44// Type of the classification problem. 45type ClassificationType int32 46 47const ( 48 // An un-set value of this enum. 49 ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0 50 // At most one label is allowed per example. 51 ClassificationType_MULTICLASS ClassificationType = 1 52 // Multiple labels are allowed for one example. 53 ClassificationType_MULTILABEL ClassificationType = 2 54) 55 56// Enum value maps for ClassificationType. 57var ( 58 ClassificationType_name = map[int32]string{ 59 0: "CLASSIFICATION_TYPE_UNSPECIFIED", 60 1: "MULTICLASS", 61 2: "MULTILABEL", 62 } 63 ClassificationType_value = map[string]int32{ 64 "CLASSIFICATION_TYPE_UNSPECIFIED": 0, 65 "MULTICLASS": 1, 66 "MULTILABEL": 2, 67 } 68) 69 70func (x ClassificationType) Enum() *ClassificationType { 71 p := new(ClassificationType) 72 *p = x 73 return p 74} 75 76func (x ClassificationType) String() string { 77 return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x)) 78} 79 80func (ClassificationType) Descriptor() protoreflect.EnumDescriptor { 81 return file_google_cloud_automl_v1_classification_proto_enumTypes[0].Descriptor() 82} 83 84func (ClassificationType) Type() protoreflect.EnumType { 85 return &file_google_cloud_automl_v1_classification_proto_enumTypes[0] 86} 87 88func (x ClassificationType) Number() protoreflect.EnumNumber { 89 return protoreflect.EnumNumber(x) 90} 91 92// Deprecated: Use ClassificationType.Descriptor instead. 93func (ClassificationType) EnumDescriptor() ([]byte, []int) { 94 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{0} 95} 96 97// Contains annotation details specific to classification. 98type ClassificationAnnotation struct { 99 state protoimpl.MessageState 100 sizeCache protoimpl.SizeCache 101 unknownFields protoimpl.UnknownFields 102 103 // Output only. A confidence estimate between 0.0 and 1.0. A higher value 104 // means greater confidence that the annotation is positive. If a user 105 // approves an annotation as negative or positive, the score value remains 106 // unchanged. If a user creates an annotation, the score is 0 for negative or 107 // 1 for positive. 108 Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"` 109} 110 111func (x *ClassificationAnnotation) Reset() { 112 *x = ClassificationAnnotation{} 113 if protoimpl.UnsafeEnabled { 114 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[0] 115 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 116 ms.StoreMessageInfo(mi) 117 } 118} 119 120func (x *ClassificationAnnotation) String() string { 121 return protoimpl.X.MessageStringOf(x) 122} 123 124func (*ClassificationAnnotation) ProtoMessage() {} 125 126func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Message { 127 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[0] 128 if protoimpl.UnsafeEnabled && x != nil { 129 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 130 if ms.LoadMessageInfo() == nil { 131 ms.StoreMessageInfo(mi) 132 } 133 return ms 134 } 135 return mi.MessageOf(x) 136} 137 138// Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead. 139func (*ClassificationAnnotation) Descriptor() ([]byte, []int) { 140 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{0} 141} 142 143func (x *ClassificationAnnotation) GetScore() float32 { 144 if x != nil { 145 return x.Score 146 } 147 return 0 148} 149 150// Model evaluation metrics for classification problems. 151// Note: For Video Classification this metrics only describe quality of the 152// Video Classification predictions of "segment_classification" type. 153type ClassificationEvaluationMetrics struct { 154 state protoimpl.MessageState 155 sizeCache protoimpl.SizeCache 156 unknownFields protoimpl.UnknownFields 157 158 // Output only. The Area Under Precision-Recall Curve metric. Micro-averaged 159 // for the overall evaluation. 160 AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"` 161 // Output only. The Area Under Receiver Operating Characteristic curve metric. 162 // Micro-averaged for the overall evaluation. 163 AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"` 164 // Output only. The Log Loss metric. 165 LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"` 166 // Output only. Metrics for each confidence_threshold in 167 // 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and 168 // position_threshold = INT32_MAX_VALUE. 169 // ROC and precision-recall curves, and other aggregated metrics are derived 170 // from them. The confidence metrics entries may also be supplied for 171 // additional values of position_threshold, but from these no aggregated 172 // metrics are computed. 173 ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `protobuf:"bytes,3,rep,name=confidence_metrics_entry,json=confidenceMetricsEntry,proto3" json:"confidence_metrics_entry,omitempty"` 174 // Output only. Confusion matrix of the evaluation. 175 // Only set for MULTICLASS classification problems where number 176 // of labels is no more than 10. 177 // Only set for model level evaluation, not for evaluation per label. 178 ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"` 179 // Output only. The annotation spec ids used for this evaluation. 180 AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"` 181} 182 183func (x *ClassificationEvaluationMetrics) Reset() { 184 *x = ClassificationEvaluationMetrics{} 185 if protoimpl.UnsafeEnabled { 186 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[1] 187 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 188 ms.StoreMessageInfo(mi) 189 } 190} 191 192func (x *ClassificationEvaluationMetrics) String() string { 193 return protoimpl.X.MessageStringOf(x) 194} 195 196func (*ClassificationEvaluationMetrics) ProtoMessage() {} 197 198func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Message { 199 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[1] 200 if protoimpl.UnsafeEnabled && x != nil { 201 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 202 if ms.LoadMessageInfo() == nil { 203 ms.StoreMessageInfo(mi) 204 } 205 return ms 206 } 207 return mi.MessageOf(x) 208} 209 210// Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead. 211func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int) { 212 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1} 213} 214 215func (x *ClassificationEvaluationMetrics) GetAuPrc() float32 { 216 if x != nil { 217 return x.AuPrc 218 } 219 return 0 220} 221 222func (x *ClassificationEvaluationMetrics) GetAuRoc() float32 { 223 if x != nil { 224 return x.AuRoc 225 } 226 return 0 227} 228 229func (x *ClassificationEvaluationMetrics) GetLogLoss() float32 { 230 if x != nil { 231 return x.LogLoss 232 } 233 return 0 234} 235 236func (x *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry { 237 if x != nil { 238 return x.ConfidenceMetricsEntry 239 } 240 return nil 241} 242 243func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix { 244 if x != nil { 245 return x.ConfusionMatrix 246 } 247 return nil 248} 249 250func (x *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string { 251 if x != nil { 252 return x.AnnotationSpecId 253 } 254 return nil 255} 256 257// Metrics for a single confidence threshold. 258type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct { 259 state protoimpl.MessageState 260 sizeCache protoimpl.SizeCache 261 unknownFields protoimpl.UnknownFields 262 263 // Output only. Metrics are computed with an assumption that the model 264 // never returns predictions with score lower than this value. 265 ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"` 266 // Output only. Metrics are computed with an assumption that the model 267 // always returns at most this many predictions (ordered by their score, 268 // descendingly), but they all still need to meet the confidence_threshold. 269 PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"` 270 // Output only. Recall (True Positive Rate) for the given confidence 271 // threshold. 272 Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"` 273 // Output only. Precision for the given confidence threshold. 274 Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"` 275 // Output only. False Positive Rate for the given confidence threshold. 276 FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"` 277 // Output only. The harmonic mean of recall and precision. 278 F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"` 279 // Output only. The Recall (True Positive Rate) when only considering the 280 // label that has the highest prediction score and not below the confidence 281 // threshold for each example. 282 RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"` 283 // Output only. The precision when only considering the label that has the 284 // highest prediction score and not below the confidence threshold for each 285 // example. 286 PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"` 287 // Output only. The False Positive Rate when only considering the label that 288 // has the highest prediction score and not below the confidence threshold 289 // for each example. 290 FalsePositiveRateAt1 float32 `protobuf:"fixed32,9,opt,name=false_positive_rate_at1,json=falsePositiveRateAt1,proto3" json:"false_positive_rate_at1,omitempty"` 291 // Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1]. 292 F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"` 293 // Output only. The number of model created labels that match a ground truth 294 // label. 295 TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"` 296 // Output only. The number of model created labels that do not match a 297 // ground truth label. 298 FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"` 299 // Output only. The number of ground truth labels that are not matched 300 // by a model created label. 301 FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"` 302 // Output only. The number of labels that were not created by the model, 303 // but if they would, they would not match a ground truth label. 304 TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"` 305} 306 307func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset() { 308 *x = ClassificationEvaluationMetrics_ConfidenceMetricsEntry{} 309 if protoimpl.UnsafeEnabled { 310 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[2] 311 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 312 ms.StoreMessageInfo(mi) 313 } 314} 315 316func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string { 317 return protoimpl.X.MessageStringOf(x) 318} 319 320func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage() {} 321 322func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message { 323 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[2] 324 if protoimpl.UnsafeEnabled && x != nil { 325 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 326 if ms.LoadMessageInfo() == nil { 327 ms.StoreMessageInfo(mi) 328 } 329 return ms 330 } 331 return mi.MessageOf(x) 332} 333 334// Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead. 335func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int) { 336 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 0} 337} 338 339func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32 { 340 if x != nil { 341 return x.ConfidenceThreshold 342 } 343 return 0 344} 345 346func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32 { 347 if x != nil { 348 return x.PositionThreshold 349 } 350 return 0 351} 352 353func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32 { 354 if x != nil { 355 return x.Recall 356 } 357 return 0 358} 359 360func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32 { 361 if x != nil { 362 return x.Precision 363 } 364 return 0 365} 366 367func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32 { 368 if x != nil { 369 return x.FalsePositiveRate 370 } 371 return 0 372} 373 374func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32 { 375 if x != nil { 376 return x.F1Score 377 } 378 return 0 379} 380 381func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32 { 382 if x != nil { 383 return x.RecallAt1 384 } 385 return 0 386} 387 388func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32 { 389 if x != nil { 390 return x.PrecisionAt1 391 } 392 return 0 393} 394 395func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32 { 396 if x != nil { 397 return x.FalsePositiveRateAt1 398 } 399 return 0 400} 401 402func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32 { 403 if x != nil { 404 return x.F1ScoreAt1 405 } 406 return 0 407} 408 409func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64 { 410 if x != nil { 411 return x.TruePositiveCount 412 } 413 return 0 414} 415 416func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64 { 417 if x != nil { 418 return x.FalsePositiveCount 419 } 420 return 0 421} 422 423func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64 { 424 if x != nil { 425 return x.FalseNegativeCount 426 } 427 return 0 428} 429 430func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64 { 431 if x != nil { 432 return x.TrueNegativeCount 433 } 434 return 0 435} 436 437// Confusion matrix of the model running the classification. 438type ClassificationEvaluationMetrics_ConfusionMatrix struct { 439 state protoimpl.MessageState 440 sizeCache protoimpl.SizeCache 441 unknownFields protoimpl.UnknownFields 442 443 // Output only. IDs of the annotation specs used in the confusion matrix. 444 // For Tables CLASSIFICATION 445 // 446 // [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type] 447 // only list of [annotation_spec_display_name-s][] is populated. 448 AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"` 449 // Output only. Display name of the annotation specs used in the confusion 450 // matrix, as they were at the moment of the evaluation. For Tables 451 // CLASSIFICATION 452 // 453 // [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type], 454 // distinct values of the target column at the moment of the model 455 // evaluation are populated here. 456 DisplayName []string `protobuf:"bytes,3,rep,name=display_name,json=displayName,proto3" json:"display_name,omitempty"` 457 // Output only. Rows in the confusion matrix. The number of rows is equal to 458 // the size of `annotation_spec_id`. 459 // `row[i].example_count[j]` is the number of examples that have ground 460 // truth of the `annotation_spec_id[i]` and are predicted as 461 // `annotation_spec_id[j]` by the model being evaluated. 462 Row []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"` 463} 464 465func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset() { 466 *x = ClassificationEvaluationMetrics_ConfusionMatrix{} 467 if protoimpl.UnsafeEnabled { 468 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[3] 469 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 470 ms.StoreMessageInfo(mi) 471 } 472} 473 474func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string { 475 return protoimpl.X.MessageStringOf(x) 476} 477 478func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage() {} 479 480func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message { 481 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[3] 482 if protoimpl.UnsafeEnabled && x != nil { 483 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 484 if ms.LoadMessageInfo() == nil { 485 ms.StoreMessageInfo(mi) 486 } 487 return ms 488 } 489 return mi.MessageOf(x) 490} 491 492// Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead. 493func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int) { 494 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 1} 495} 496 497func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string { 498 if x != nil { 499 return x.AnnotationSpecId 500 } 501 return nil 502} 503 504func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []string { 505 if x != nil { 506 return x.DisplayName 507 } 508 return nil 509} 510 511func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row { 512 if x != nil { 513 return x.Row 514 } 515 return nil 516} 517 518// Output only. A row in the confusion matrix. 519type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct { 520 state protoimpl.MessageState 521 sizeCache protoimpl.SizeCache 522 unknownFields protoimpl.UnknownFields 523 524 // Output only. Value of the specific cell in the confusion matrix. 525 // The number of values each row has (i.e. the length of the row) is equal 526 // to the length of the `annotation_spec_id` field or, if that one is not 527 // populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field. 528 ExampleCount []int32 `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"` 529} 530 531func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset() { 532 *x = ClassificationEvaluationMetrics_ConfusionMatrix_Row{} 533 if protoimpl.UnsafeEnabled { 534 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[4] 535 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 536 ms.StoreMessageInfo(mi) 537 } 538} 539 540func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string { 541 return protoimpl.X.MessageStringOf(x) 542} 543 544func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage() {} 545 546func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message { 547 mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[4] 548 if protoimpl.UnsafeEnabled && x != nil { 549 ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) 550 if ms.LoadMessageInfo() == nil { 551 ms.StoreMessageInfo(mi) 552 } 553 return ms 554 } 555 return mi.MessageOf(x) 556} 557 558// Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead. 559func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int) { 560 return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 1, 0} 561} 562 563func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32 { 564 if x != nil { 565 return x.ExampleCount 566 } 567 return nil 568} 569 570var 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686 687func file_google_cloud_automl_v1_classification_proto_rawDescGZIP() []byte { 688 file_google_cloud_automl_v1_classification_proto_rawDescOnce.Do(func() { 689 file_google_cloud_automl_v1_classification_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1_classification_proto_rawDescData) 690 }) 691 return file_google_cloud_automl_v1_classification_proto_rawDescData 692} 693 694var file_google_cloud_automl_v1_classification_proto_enumTypes = make([]protoimpl.EnumInfo, 1) 695var file_google_cloud_automl_v1_classification_proto_msgTypes = make([]protoimpl.MessageInfo, 5) 696var file_google_cloud_automl_v1_classification_proto_goTypes = []interface{}{ 697 (ClassificationType)(0), // 0: google.cloud.automl.v1.ClassificationType 698 (*ClassificationAnnotation)(nil), // 1: google.cloud.automl.v1.ClassificationAnnotation 699 (*ClassificationEvaluationMetrics)(nil), // 2: google.cloud.automl.v1.ClassificationEvaluationMetrics 700 (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry)(nil), // 3: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry 701 (*ClassificationEvaluationMetrics_ConfusionMatrix)(nil), // 4: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix 702 (*ClassificationEvaluationMetrics_ConfusionMatrix_Row)(nil), // 5: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row 703} 704var file_google_cloud_automl_v1_classification_proto_depIdxs = []int32{ 705 3, // 0: google.cloud.automl.v1.ClassificationEvaluationMetrics.confidence_metrics_entry:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry 706 4, // 1: google.cloud.automl.v1.ClassificationEvaluationMetrics.confusion_matrix:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix 707 5, // 2: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.row:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row 708 3, // [3:3] is the sub-list for method output_type 709 3, // [3:3] is the sub-list for method input_type 710 3, // [3:3] is the sub-list for extension type_name 711 3, // [3:3] is the sub-list for extension extendee 712 0, // [0:3] is the sub-list for field type_name 713} 714 715func init() { file_google_cloud_automl_v1_classification_proto_init() } 716func file_google_cloud_automl_v1_classification_proto_init() { 717 if File_google_cloud_automl_v1_classification_proto != nil { 718 return 719 } 720 if !protoimpl.UnsafeEnabled { 721 file_google_cloud_automl_v1_classification_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { 722 switch v := v.(*ClassificationAnnotation); i { 723 case 0: 724 return &v.state 725 case 1: 726 return &v.sizeCache 727 case 2: 728 return &v.unknownFields 729 default: 730 return nil 731 } 732 } 733 file_google_cloud_automl_v1_classification_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { 734 switch v := v.(*ClassificationEvaluationMetrics); i { 735 case 0: 736 return &v.state 737 case 1: 738 return &v.sizeCache 739 case 2: 740 return &v.unknownFields 741 default: 742 return nil 743 } 744 } 745 file_google_cloud_automl_v1_classification_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { 746 switch v := v.(*ClassificationEvaluationMetrics_ConfidenceMetricsEntry); i { 747 case 0: 748 return &v.state 749 case 1: 750 return &v.sizeCache 751 case 2: 752 return &v.unknownFields 753 default: 754 return nil 755 } 756 } 757 file_google_cloud_automl_v1_classification_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { 758 switch v := v.(*ClassificationEvaluationMetrics_ConfusionMatrix); i { 759 case 0: 760 return &v.state 761 case 1: 762 return &v.sizeCache 763 case 2: 764 return &v.unknownFields 765 default: 766 return nil 767 } 768 } 769 file_google_cloud_automl_v1_classification_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { 770 switch v := v.(*ClassificationEvaluationMetrics_ConfusionMatrix_Row); i { 771 case 0: 772 return &v.state 773 case 1: 774 return &v.sizeCache 775 case 2: 776 return &v.unknownFields 777 default: 778 return nil 779 } 780 } 781 } 782 type x struct{} 783 out := protoimpl.TypeBuilder{ 784 File: protoimpl.DescBuilder{ 785 GoPackagePath: reflect.TypeOf(x{}).PkgPath(), 786 RawDescriptor: file_google_cloud_automl_v1_classification_proto_rawDesc, 787 NumEnums: 1, 788 NumMessages: 5, 789 NumExtensions: 0, 790 NumServices: 0, 791 }, 792 GoTypes: file_google_cloud_automl_v1_classification_proto_goTypes, 793 DependencyIndexes: file_google_cloud_automl_v1_classification_proto_depIdxs, 794 EnumInfos: file_google_cloud_automl_v1_classification_proto_enumTypes, 795 MessageInfos: file_google_cloud_automl_v1_classification_proto_msgTypes, 796 }.Build() 797 File_google_cloud_automl_v1_classification_proto = out.File 798 file_google_cloud_automl_v1_classification_proto_rawDesc = nil 799 file_google_cloud_automl_v1_classification_proto_goTypes = nil 800 file_google_cloud_automl_v1_classification_proto_depIdxs = nil 801} 802