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classification.pb.go
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classification.pb.go
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// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.26.0
// protoc v3.12.2
// source: google/cloud/automl/v1/classification.proto
package automl
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Type of the classification problem.
type ClassificationType int32
const (
// An un-set value of this enum.
ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0
// At most one label is allowed per example.
ClassificationType_MULTICLASS ClassificationType = 1
// Multiple labels are allowed for one example.
ClassificationType_MULTILABEL ClassificationType = 2
)
// Enum value maps for ClassificationType.
var (
ClassificationType_name = map[int32]string{
0: "CLASSIFICATION_TYPE_UNSPECIFIED",
1: "MULTICLASS",
2: "MULTILABEL",
}
ClassificationType_value = map[string]int32{
"CLASSIFICATION_TYPE_UNSPECIFIED": 0,
"MULTICLASS": 1,
"MULTILABEL": 2,
}
)
func (x ClassificationType) Enum() *ClassificationType {
p := new(ClassificationType)
*p = x
return p
}
func (x ClassificationType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ClassificationType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_automl_v1_classification_proto_enumTypes[0].Descriptor()
}
func (ClassificationType) Type() protoreflect.EnumType {
return &file_google_cloud_automl_v1_classification_proto_enumTypes[0]
}
func (x ClassificationType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ClassificationType.Descriptor instead.
func (ClassificationType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{0}
}
// Contains annotation details specific to classification.
type ClassificationAnnotation struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. A confidence estimate between 0.0 and 1.0. A higher value
// means greater confidence that the annotation is positive. If a user
// approves an annotation as negative or positive, the score value remains
// unchanged. If a user creates an annotation, the score is 0 for negative or
// 1 for positive.
Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
}
func (x *ClassificationAnnotation) Reset() {
*x = ClassificationAnnotation{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ClassificationAnnotation) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ClassificationAnnotation) ProtoMessage() {}
func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.
func (*ClassificationAnnotation) Descriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{0}
}
func (x *ClassificationAnnotation) GetScore() float32 {
if x != nil {
return x.Score
}
return 0
}
// Model evaluation metrics for classification problems.
// Note: For Video Classification this metrics only describe quality of the
// Video Classification predictions of "segment_classification" type.
type ClassificationEvaluationMetrics struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
// for the overall evaluation.
AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
// Output only. The Area Under Receiver Operating Characteristic curve metric.
// Micro-averaged for the overall evaluation.
AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`
// Output only. The Log Loss metric.
LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
// Output only. Metrics for each confidence_threshold in
// 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
// position_threshold = INT32_MAX_VALUE.
// ROC and precision-recall curves, and other aggregated metrics are derived
// from them. The confidence metrics entries may also be supplied for
// additional values of position_threshold, but from these no aggregated
// metrics are computed.
ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `protobuf:"bytes,3,rep,name=confidence_metrics_entry,json=confidenceMetricsEntry,proto3" json:"confidence_metrics_entry,omitempty"`
// Output only. Confusion matrix of the evaluation.
// Only set for MULTICLASS classification problems where number
// of labels is no more than 10.
// Only set for model level evaluation, not for evaluation per label.
ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
// Output only. The annotation spec ids used for this evaluation.
AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
}
func (x *ClassificationEvaluationMetrics) Reset() {
*x = ClassificationEvaluationMetrics{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ClassificationEvaluationMetrics) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ClassificationEvaluationMetrics) ProtoMessage() {}
func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1}
}
func (x *ClassificationEvaluationMetrics) GetAuPrc() float32 {
if x != nil {
return x.AuPrc
}
return 0
}
func (x *ClassificationEvaluationMetrics) GetAuRoc() float32 {
if x != nil {
return x.AuRoc
}
return 0
}
func (x *ClassificationEvaluationMetrics) GetLogLoss() float32 {
if x != nil {
return x.LogLoss
}
return 0
}
func (x *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry {
if x != nil {
return x.ConfidenceMetricsEntry
}
return nil
}
func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix {
if x != nil {
return x.ConfusionMatrix
}
return nil
}
func (x *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string {
if x != nil {
return x.AnnotationSpecId
}
return nil
}
// Metrics for a single confidence threshold.
type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. Metrics are computed with an assumption that the model
// never returns predictions with score lower than this value.
ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
// Output only. Metrics are computed with an assumption that the model
// always returns at most this many predictions (ordered by their score,
// descendingly), but they all still need to meet the confidence_threshold.
PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`
// Output only. Recall (True Positive Rate) for the given confidence
// threshold.
Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
// Output only. Precision for the given confidence threshold.
Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
// Output only. False Positive Rate for the given confidence threshold.
FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`
// Output only. The harmonic mean of recall and precision.
F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
// Output only. The Recall (True Positive Rate) when only considering the
// label that has the highest prediction score and not below the confidence
// threshold for each example.
RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`
// Output only. The precision when only considering the label that has the
// highest prediction score and not below the confidence threshold for each
// example.
PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`
// Output only. The False Positive Rate when only considering the label that
// has the highest prediction score and not below the confidence threshold
// for each example.
FalsePositiveRateAt1 float32 `protobuf:"fixed32,9,opt,name=false_positive_rate_at1,json=falsePositiveRateAt1,proto3" json:"false_positive_rate_at1,omitempty"`
// 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].
F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`
// Output only. The number of model created labels that match a ground truth
// label.
TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`
// Output only. The number of model created labels that do not match a
// ground truth label.
FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`
// Output only. The number of ground truth labels that are not matched
// by a model created label.
FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`
// Output only. The number of labels that were not created by the model,
// but if they would, they would not match a ground truth label.
TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset() {
*x = ClassificationEvaluationMetrics_ConfidenceMetricsEntry{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage() {}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 0}
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32 {
if x != nil {
return x.ConfidenceThreshold
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32 {
if x != nil {
return x.PositionThreshold
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32 {
if x != nil {
return x.Recall
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32 {
if x != nil {
return x.Precision
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32 {
if x != nil {
return x.FalsePositiveRate
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32 {
if x != nil {
return x.F1Score
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32 {
if x != nil {
return x.RecallAt1
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32 {
if x != nil {
return x.PrecisionAt1
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32 {
if x != nil {
return x.FalsePositiveRateAt1
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32 {
if x != nil {
return x.F1ScoreAt1
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64 {
if x != nil {
return x.TruePositiveCount
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64 {
if x != nil {
return x.FalsePositiveCount
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64 {
if x != nil {
return x.FalseNegativeCount
}
return 0
}
func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64 {
if x != nil {
return x.TrueNegativeCount
}
return 0
}
// Confusion matrix of the model running the classification.
type ClassificationEvaluationMetrics_ConfusionMatrix struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. IDs of the annotation specs used in the confusion matrix.
// For Tables CLASSIFICATION
// [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
// only list of [annotation_spec_display_name-s][] is populated.
AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
// Output only. Display name of the annotation specs used in the confusion
// matrix, as they were at the moment of the evaluation. For Tables
// CLASSIFICATION
// [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
// distinct values of the target column at the moment of the model
// evaluation are populated here.
DisplayName []string `protobuf:"bytes,3,rep,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
// Output only. Rows in the confusion matrix. The number of rows is equal to
// the size of `annotation_spec_id`.
// `row[i].example_count[j]` is the number of examples that have ground
// truth of the `annotation_spec_id[i]` and are predicted as
// `annotation_spec_id[j]` by the model being evaluated.
Row []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"`
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset() {
*x = ClassificationEvaluationMetrics_ConfusionMatrix{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage() {}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 1}
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string {
if x != nil {
return x.AnnotationSpecId
}
return nil
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []string {
if x != nil {
return x.DisplayName
}
return nil
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row {
if x != nil {
return x.Row
}
return nil
}
// Output only. A row in the confusion matrix.
type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. Value of the specific cell in the confusion matrix.
// The number of values each row has (i.e. the length of the row) is equal
// to the length of the `annotation_spec_id` field or, if that one is not
// populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
ExampleCount []int32 `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset() {
*x = ClassificationEvaluationMetrics_ConfusionMatrix_Row{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage() {}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_automl_v1_classification_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.
func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int) {
return file_google_cloud_automl_v1_classification_proto_rawDescGZIP(), []int{1, 1, 0}
}
func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32 {
if x != nil {
return x.ExampleCount
}
return nil
}
var File_google_cloud_automl_v1_classification_proto protoreflect.FileDescriptor
var file_google_cloud_automl_v1_classification_proto_rawDesc = []byte{
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0x74, 0x6f, 0x33,
}
var (
file_google_cloud_automl_v1_classification_proto_rawDescOnce sync.Once
file_google_cloud_automl_v1_classification_proto_rawDescData = file_google_cloud_automl_v1_classification_proto_rawDesc
)
func file_google_cloud_automl_v1_classification_proto_rawDescGZIP() []byte {
file_google_cloud_automl_v1_classification_proto_rawDescOnce.Do(func() {
file_google_cloud_automl_v1_classification_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1_classification_proto_rawDescData)
})
return file_google_cloud_automl_v1_classification_proto_rawDescData
}
var file_google_cloud_automl_v1_classification_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_cloud_automl_v1_classification_proto_msgTypes = make([]protoimpl.MessageInfo, 5)
var file_google_cloud_automl_v1_classification_proto_goTypes = []interface{}{
(ClassificationType)(0), // 0: google.cloud.automl.v1.ClassificationType
(*ClassificationAnnotation)(nil), // 1: google.cloud.automl.v1.ClassificationAnnotation
(*ClassificationEvaluationMetrics)(nil), // 2: google.cloud.automl.v1.ClassificationEvaluationMetrics
(*ClassificationEvaluationMetrics_ConfidenceMetricsEntry)(nil), // 3: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
(*ClassificationEvaluationMetrics_ConfusionMatrix)(nil), // 4: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix
(*ClassificationEvaluationMetrics_ConfusionMatrix_Row)(nil), // 5: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
}
var file_google_cloud_automl_v1_classification_proto_depIdxs = []int32{
3, // 0: google.cloud.automl.v1.ClassificationEvaluationMetrics.confidence_metrics_entry:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
4, // 1: google.cloud.automl.v1.ClassificationEvaluationMetrics.confusion_matrix:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix
5, // 2: google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.row:type_name -> google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
3, // [3:3] is the sub-list for method output_type
3, // [3:3] is the sub-list for method input_type
3, // [3:3] is the sub-list for extension type_name
3, // [3:3] is the sub-list for extension extendee
0, // [0:3] is the sub-list for field type_name
}
func init() { file_google_cloud_automl_v1_classification_proto_init() }
func file_google_cloud_automl_v1_classification_proto_init() {
if File_google_cloud_automl_v1_classification_proto != nil {
return
}
if !protoimpl.UnsafeEnabled {
file_google_cloud_automl_v1_classification_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ClassificationAnnotation); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_automl_v1_classification_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ClassificationEvaluationMetrics); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_automl_v1_classification_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ClassificationEvaluationMetrics_ConfidenceMetricsEntry); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_automl_v1_classification_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ClassificationEvaluationMetrics_ConfusionMatrix); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_automl_v1_classification_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ClassificationEvaluationMetrics_ConfusionMatrix_Row); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_cloud_automl_v1_classification_proto_rawDesc,
NumEnums: 1,
NumMessages: 5,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_cloud_automl_v1_classification_proto_goTypes,
DependencyIndexes: file_google_cloud_automl_v1_classification_proto_depIdxs,
EnumInfos: file_google_cloud_automl_v1_classification_proto_enumTypes,
MessageInfos: file_google_cloud_automl_v1_classification_proto_msgTypes,
}.Build()
File_google_cloud_automl_v1_classification_proto = out.File
file_google_cloud_automl_v1_classification_proto_rawDesc = nil
file_google_cloud_automl_v1_classification_proto_goTypes = nil
file_google_cloud_automl_v1_classification_proto_depIdxs = nil
}