// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/automl/v1beta1/model_evaluation.proto

package automl // import "google.golang.org/genproto/googleapis/cloud/automl/v1beta1"

import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import timestamp "github.com/golang/protobuf/ptypes/timestamp"
import _ "google.golang.org/genproto/googleapis/api/annotations"

// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf

// This is a compile-time assertion to ensure that this generated file
// is compatible with the proto package it is being compiled against.
// A compilation error at this line likely means your copy of the
// proto package needs to be updated.
const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package

// Evaluation results of a model.
type ModelEvaluation struct {
	// Output only. Problem type specific evaluation metrics.
	//
	// Types that are valid to be assigned to Metrics:
	//	*ModelEvaluation_ClassificationEvaluationMetrics
	//	*ModelEvaluation_RegressionEvaluationMetrics
	//	*ModelEvaluation_TranslationEvaluationMetrics
	//	*ModelEvaluation_ImageObjectDetectionEvaluationMetrics
	//	*ModelEvaluation_TextSentimentEvaluationMetrics
	//	*ModelEvaluation_TextExtractionEvaluationMetrics
	Metrics isModelEvaluation_Metrics `protobuf_oneof:"metrics"`
	// Output only.
	// Resource name of the model evaluation.
	// Format:
	//
	// `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Output only.
	// The ID of the annotation spec that the model evaluation applies to. The
	// The ID is empty for the overall model evaluation.
	// For Tables classification these are the distinct values of the target
	// column at the moment of the evaluation; for this problem annotation specs
	// in the dataset do not exist.
	// NOTE: Currently there is no way to obtain the display_name of the
	// annotation spec from its ID. To see the display_names, review the model
	// evaluations in the UI.
	AnnotationSpecId string `protobuf:"bytes,2,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. The value of [AnnotationSpec.display_name][google.cloud.automl.v1beta1.AnnotationSpec.display_name] when the model
	// was trained. Because this field returns a value at model training time,
	// for different models trained using the same dataset, the returned value
	// could be different as model owner could update the display_name between
	// any two model training.
	// The display_name is empty for the overall model evaluation.
	DisplayName string `protobuf:"bytes,15,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only.
	// Timestamp when this model evaluation was created.
	CreateTime *timestamp.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Output only.
	// The number of examples used for model evaluation, i.e. for
	// which ground truth from time of model creation is compared against the
	// predicted annotations created by the model.
	// For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is
	// the total number of all examples used for evaluation.
	// Otherwise, this is the count of examples that according to the ground
	// truth were annotated by the
	//
	// [annotation_spec_id][google.cloud.automl.v1beta1.ModelEvaluation.annotation_spec_id].
	EvaluatedExampleCount int32    `protobuf:"varint,6,opt,name=evaluated_example_count,json=evaluatedExampleCount,proto3" json:"evaluated_example_count,omitempty"`
	XXX_NoUnkeyedLiteral  struct{} `json:"-"`
	XXX_unrecognized      []byte   `json:"-"`
	XXX_sizecache         int32    `json:"-"`
}

func (m *ModelEvaluation) Reset()         { *m = ModelEvaluation{} }
func (m *ModelEvaluation) String() string { return proto.CompactTextString(m) }
func (*ModelEvaluation) ProtoMessage()    {}
func (*ModelEvaluation) Descriptor() ([]byte, []int) {
	return fileDescriptor_model_evaluation_b5dd07caabcd50f5, []int{0}
}
func (m *ModelEvaluation) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ModelEvaluation.Unmarshal(m, b)
}
func (m *ModelEvaluation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ModelEvaluation.Marshal(b, m, deterministic)
}
func (dst *ModelEvaluation) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ModelEvaluation.Merge(dst, src)
}
func (m *ModelEvaluation) XXX_Size() int {
	return xxx_messageInfo_ModelEvaluation.Size(m)
}
func (m *ModelEvaluation) XXX_DiscardUnknown() {
	xxx_messageInfo_ModelEvaluation.DiscardUnknown(m)
}

var xxx_messageInfo_ModelEvaluation proto.InternalMessageInfo

type isModelEvaluation_Metrics interface {
	isModelEvaluation_Metrics()
}

type ModelEvaluation_ClassificationEvaluationMetrics struct {
	ClassificationEvaluationMetrics *ClassificationEvaluationMetrics `protobuf:"bytes,8,opt,name=classification_evaluation_metrics,json=classificationEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_RegressionEvaluationMetrics struct {
	RegressionEvaluationMetrics *RegressionEvaluationMetrics `protobuf:"bytes,24,opt,name=regression_evaluation_metrics,json=regressionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TranslationEvaluationMetrics struct {
	TranslationEvaluationMetrics *TranslationEvaluationMetrics `protobuf:"bytes,9,opt,name=translation_evaluation_metrics,json=translationEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics struct {
	ImageObjectDetectionEvaluationMetrics *ImageObjectDetectionEvaluationMetrics `protobuf:"bytes,12,opt,name=image_object_detection_evaluation_metrics,json=imageObjectDetectionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextSentimentEvaluationMetrics struct {
	TextSentimentEvaluationMetrics *TextSentimentEvaluationMetrics `protobuf:"bytes,11,opt,name=text_sentiment_evaluation_metrics,json=textSentimentEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextExtractionEvaluationMetrics struct {
	TextExtractionEvaluationMetrics *TextExtractionEvaluationMetrics `protobuf:"bytes,13,opt,name=text_extraction_evaluation_metrics,json=textExtractionEvaluationMetrics,proto3,oneof"`
}

func (*ModelEvaluation_ClassificationEvaluationMetrics) isModelEvaluation_Metrics() {}

func (*ModelEvaluation_RegressionEvaluationMetrics) isModelEvaluation_Metrics() {}

func (*ModelEvaluation_TranslationEvaluationMetrics) isModelEvaluation_Metrics() {}

func (*ModelEvaluation_ImageObjectDetectionEvaluationMetrics) isModelEvaluation_Metrics() {}

func (*ModelEvaluation_TextSentimentEvaluationMetrics) isModelEvaluation_Metrics() {}

func (*ModelEvaluation_TextExtractionEvaluationMetrics) isModelEvaluation_Metrics() {}

func (m *ModelEvaluation) GetMetrics() isModelEvaluation_Metrics {
	if m != nil {
		return m.Metrics
	}
	return nil
}

func (m *ModelEvaluation) GetClassificationEvaluationMetrics() *ClassificationEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_ClassificationEvaluationMetrics); ok {
		return x.ClassificationEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetRegressionEvaluationMetrics() *RegressionEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_RegressionEvaluationMetrics); ok {
		return x.RegressionEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetTranslationEvaluationMetrics() *TranslationEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_TranslationEvaluationMetrics); ok {
		return x.TranslationEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetImageObjectDetectionEvaluationMetrics() *ImageObjectDetectionEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_ImageObjectDetectionEvaluationMetrics); ok {
		return x.ImageObjectDetectionEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetTextSentimentEvaluationMetrics() *TextSentimentEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_TextSentimentEvaluationMetrics); ok {
		return x.TextSentimentEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetTextExtractionEvaluationMetrics() *TextExtractionEvaluationMetrics {
	if x, ok := m.GetMetrics().(*ModelEvaluation_TextExtractionEvaluationMetrics); ok {
		return x.TextExtractionEvaluationMetrics
	}
	return nil
}

func (m *ModelEvaluation) GetName() string {
	if m != nil {
		return m.Name
	}
	return ""
}

func (m *ModelEvaluation) GetAnnotationSpecId() string {
	if m != nil {
		return m.AnnotationSpecId
	}
	return ""
}

func (m *ModelEvaluation) GetDisplayName() string {
	if m != nil {
		return m.DisplayName
	}
	return ""
}

func (m *ModelEvaluation) GetCreateTime() *timestamp.Timestamp {
	if m != nil {
		return m.CreateTime
	}
	return nil
}

func (m *ModelEvaluation) GetEvaluatedExampleCount() int32 {
	if m != nil {
		return m.EvaluatedExampleCount
	}
	return 0
}

// XXX_OneofFuncs is for the internal use of the proto package.
func (*ModelEvaluation) XXX_OneofFuncs() (func(msg proto.Message, b *proto.Buffer) error, func(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error), func(msg proto.Message) (n int), []interface{}) {
	return _ModelEvaluation_OneofMarshaler, _ModelEvaluation_OneofUnmarshaler, _ModelEvaluation_OneofSizer, []interface{}{
		(*ModelEvaluation_ClassificationEvaluationMetrics)(nil),
		(*ModelEvaluation_RegressionEvaluationMetrics)(nil),
		(*ModelEvaluation_TranslationEvaluationMetrics)(nil),
		(*ModelEvaluation_ImageObjectDetectionEvaluationMetrics)(nil),
		(*ModelEvaluation_TextSentimentEvaluationMetrics)(nil),
		(*ModelEvaluation_TextExtractionEvaluationMetrics)(nil),
	}
}

func _ModelEvaluation_OneofMarshaler(msg proto.Message, b *proto.Buffer) error {
	m := msg.(*ModelEvaluation)
	// metrics
	switch x := m.Metrics.(type) {
	case *ModelEvaluation_ClassificationEvaluationMetrics:
		b.EncodeVarint(8<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.ClassificationEvaluationMetrics); err != nil {
			return err
		}
	case *ModelEvaluation_RegressionEvaluationMetrics:
		b.EncodeVarint(24<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.RegressionEvaluationMetrics); err != nil {
			return err
		}
	case *ModelEvaluation_TranslationEvaluationMetrics:
		b.EncodeVarint(9<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.TranslationEvaluationMetrics); err != nil {
			return err
		}
	case *ModelEvaluation_ImageObjectDetectionEvaluationMetrics:
		b.EncodeVarint(12<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.ImageObjectDetectionEvaluationMetrics); err != nil {
			return err
		}
	case *ModelEvaluation_TextSentimentEvaluationMetrics:
		b.EncodeVarint(11<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.TextSentimentEvaluationMetrics); err != nil {
			return err
		}
	case *ModelEvaluation_TextExtractionEvaluationMetrics:
		b.EncodeVarint(13<<3 | proto.WireBytes)
		if err := b.EncodeMessage(x.TextExtractionEvaluationMetrics); err != nil {
			return err
		}
	case nil:
	default:
		return fmt.Errorf("ModelEvaluation.Metrics has unexpected type %T", x)
	}
	return nil
}

func _ModelEvaluation_OneofUnmarshaler(msg proto.Message, tag, wire int, b *proto.Buffer) (bool, error) {
	m := msg.(*ModelEvaluation)
	switch tag {
	case 8: // metrics.classification_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(ClassificationEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_ClassificationEvaluationMetrics{msg}
		return true, err
	case 24: // metrics.regression_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(RegressionEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_RegressionEvaluationMetrics{msg}
		return true, err
	case 9: // metrics.translation_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(TranslationEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_TranslationEvaluationMetrics{msg}
		return true, err
	case 12: // metrics.image_object_detection_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(ImageObjectDetectionEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_ImageObjectDetectionEvaluationMetrics{msg}
		return true, err
	case 11: // metrics.text_sentiment_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(TextSentimentEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_TextSentimentEvaluationMetrics{msg}
		return true, err
	case 13: // metrics.text_extraction_evaluation_metrics
		if wire != proto.WireBytes {
			return true, proto.ErrInternalBadWireType
		}
		msg := new(TextExtractionEvaluationMetrics)
		err := b.DecodeMessage(msg)
		m.Metrics = &ModelEvaluation_TextExtractionEvaluationMetrics{msg}
		return true, err
	default:
		return false, nil
	}
}

func _ModelEvaluation_OneofSizer(msg proto.Message) (n int) {
	m := msg.(*ModelEvaluation)
	// metrics
	switch x := m.Metrics.(type) {
	case *ModelEvaluation_ClassificationEvaluationMetrics:
		s := proto.Size(x.ClassificationEvaluationMetrics)
		n += 1 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case *ModelEvaluation_RegressionEvaluationMetrics:
		s := proto.Size(x.RegressionEvaluationMetrics)
		n += 2 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case *ModelEvaluation_TranslationEvaluationMetrics:
		s := proto.Size(x.TranslationEvaluationMetrics)
		n += 1 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case *ModelEvaluation_ImageObjectDetectionEvaluationMetrics:
		s := proto.Size(x.ImageObjectDetectionEvaluationMetrics)
		n += 1 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case *ModelEvaluation_TextSentimentEvaluationMetrics:
		s := proto.Size(x.TextSentimentEvaluationMetrics)
		n += 1 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case *ModelEvaluation_TextExtractionEvaluationMetrics:
		s := proto.Size(x.TextExtractionEvaluationMetrics)
		n += 1 // tag and wire
		n += proto.SizeVarint(uint64(s))
		n += s
	case nil:
	default:
		panic(fmt.Sprintf("proto: unexpected type %T in oneof", x))
	}
	return n
}

func init() {
	proto.RegisterType((*ModelEvaluation)(nil), "google.cloud.automl.v1beta1.ModelEvaluation")
}

func init() {
	proto.RegisterFile("google/cloud/automl/v1beta1/model_evaluation.proto", fileDescriptor_model_evaluation_b5dd07caabcd50f5)
}

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