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

package automl

import (
	fmt "fmt"
	math "math"

	proto "github.com/golang/protobuf/proto"
	_ "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.ProtoPackageIsVersion3 // please upgrade the proto package

// Contains annotation details specific to text sentiment.
type TextSentimentAnnotation struct {
	// Output only. The sentiment with the semantic, as given to the
	// [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData] when populating the dataset from which the model used
	// for the prediction had been trained.
	// The sentiment values are between 0 and
	// Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
	// with higher value meaning more positive sentiment. They are completely
	// relative, i.e. 0 means least positive sentiment and sentiment_max means
	// the most positive from the sentiments present in the train data. Therefore
	//  e.g. if train data had only negative sentiment, then sentiment_max, would
	// be still negative (although least negative).
	// The sentiment shouldn't be confused with "score" or "magnitude"
	// from the previous Natural Language Sentiment Analysis API.
	Sentiment            int32    `protobuf:"varint,1,opt,name=sentiment,proto3" json:"sentiment,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *TextSentimentAnnotation) Reset()         { *m = TextSentimentAnnotation{} }
func (m *TextSentimentAnnotation) String() string { return proto.CompactTextString(m) }
func (*TextSentimentAnnotation) ProtoMessage()    {}
func (*TextSentimentAnnotation) Descriptor() ([]byte, []int) {
	return fileDescriptor_2c3765cde9ff2509, []int{0}
}

func (m *TextSentimentAnnotation) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TextSentimentAnnotation.Unmarshal(m, b)
}
func (m *TextSentimentAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TextSentimentAnnotation.Marshal(b, m, deterministic)
}
func (m *TextSentimentAnnotation) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TextSentimentAnnotation.Merge(m, src)
}
func (m *TextSentimentAnnotation) XXX_Size() int {
	return xxx_messageInfo_TextSentimentAnnotation.Size(m)
}
func (m *TextSentimentAnnotation) XXX_DiscardUnknown() {
	xxx_messageInfo_TextSentimentAnnotation.DiscardUnknown(m)
}

var xxx_messageInfo_TextSentimentAnnotation proto.InternalMessageInfo

func (m *TextSentimentAnnotation) GetSentiment() int32 {
	if m != nil {
		return m.Sentiment
	}
	return 0
}

// Model evaluation metrics for text sentiment problems.
type TextSentimentEvaluationMetrics struct {
	// Output only. Precision.
	Precision float32 `protobuf:"fixed32,1,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. Recall.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,3,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// Output only. Mean absolute error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanAbsoluteError float32 `protobuf:"fixed32,4,opt,name=mean_absolute_error,json=meanAbsoluteError,proto3" json:"mean_absolute_error,omitempty"`
	// Output only. Mean squared error. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	MeanSquaredError float32 `protobuf:"fixed32,5,opt,name=mean_squared_error,json=meanSquaredError,proto3" json:"mean_squared_error,omitempty"`
	// Output only. Linear weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	LinearKappa float32 `protobuf:"fixed32,6,opt,name=linear_kappa,json=linearKappa,proto3" json:"linear_kappa,omitempty"`
	// Output only. Quadratic weighted kappa. Only set for the overall model
	// evaluation, not for evaluation of a single annotation spec.
	QuadraticKappa float32 `protobuf:"fixed32,7,opt,name=quadratic_kappa,json=quadraticKappa,proto3" json:"quadratic_kappa,omitempty"`
	// Output only. Confusion matrix of the evaluation.
	// Only set for the overall model evaluation, not for evaluation of a single
	// annotation spec.
	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,8,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
	// Output only. The annotation spec ids used for this evaluation.
	// Deprecated, remove after Boq Migration and use then
	// TextSentimentModelMetadata.annotation_spec_count for count, and list
	// all model evaluations to see the exact annotation_spec_ids that were
	// used.
	AnnotationSpecId     []string `protobuf:"bytes,9,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *TextSentimentEvaluationMetrics) Reset()         { *m = TextSentimentEvaluationMetrics{} }
func (m *TextSentimentEvaluationMetrics) String() string { return proto.CompactTextString(m) }
func (*TextSentimentEvaluationMetrics) ProtoMessage()    {}
func (*TextSentimentEvaluationMetrics) Descriptor() ([]byte, []int) {
	return fileDescriptor_2c3765cde9ff2509, []int{1}
}

func (m *TextSentimentEvaluationMetrics) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_TextSentimentEvaluationMetrics.Unmarshal(m, b)
}
func (m *TextSentimentEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_TextSentimentEvaluationMetrics.Marshal(b, m, deterministic)
}
func (m *TextSentimentEvaluationMetrics) XXX_Merge(src proto.Message) {
	xxx_messageInfo_TextSentimentEvaluationMetrics.Merge(m, src)
}
func (m *TextSentimentEvaluationMetrics) XXX_Size() int {
	return xxx_messageInfo_TextSentimentEvaluationMetrics.Size(m)
}
func (m *TextSentimentEvaluationMetrics) XXX_DiscardUnknown() {
	xxx_messageInfo_TextSentimentEvaluationMetrics.DiscardUnknown(m)
}

var xxx_messageInfo_TextSentimentEvaluationMetrics proto.InternalMessageInfo

func (m *TextSentimentEvaluationMetrics) GetPrecision() float32 {
	if m != nil {
		return m.Precision
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetRecall() float32 {
	if m != nil {
		return m.Recall
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetF1Score() float32 {
	if m != nil {
		return m.F1Score
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetMeanAbsoluteError() float32 {
	if m != nil {
		return m.MeanAbsoluteError
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetMeanSquaredError() float32 {
	if m != nil {
		return m.MeanSquaredError
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetLinearKappa() float32 {
	if m != nil {
		return m.LinearKappa
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetQuadraticKappa() float32 {
	if m != nil {
		return m.QuadraticKappa
	}
	return 0
}

func (m *TextSentimentEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix {
	if m != nil {
		return m.ConfusionMatrix
	}
	return nil
}

func (m *TextSentimentEvaluationMetrics) GetAnnotationSpecId() []string {
	if m != nil {
		return m.AnnotationSpecId
	}
	return nil
}

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

func init() {
	proto.RegisterFile("google/cloud/automl/v1beta1/text_sentiment.proto", fileDescriptor_2c3765cde9ff2509)
}

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