ML.NET Archives - icodebroker

[C#/ML.NET] k-평균 클러스터링(k-means clustering) 사용하기

■ k-평균 클러스터링(k-means clustering)을 사용하는 방법을 보여준다. ▶ SourceModel.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 소스 모델
    /// </summary>
    public class SourceModel
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 알코올 - Alcohol

        /// <summary>
        /// 알코올
        /// </summary>
        [LoadColumn(0)]
        public float Alcohol { get; set; }

        #endregion
        #region 사과산 - MalicAcid

        /// <summary>
        /// 사과산
        /// </summary>
        [LoadColumn(1)]
        public float MalicAcid { get; set; }

        #endregion
        #region 애쉬 - Ash

        /// <summary>
        /// 애쉬
        /// </summary>
        [LoadColumn(2)]
        public float Ash { get; set; }

        #endregion
        #region 애쉬 알카니티 - AshAlcanity

        /// <summary>
        /// 애쉬 알카니티
        /// </summary>
        [LoadColumn(3)]
        public float AshAlcanity { get; set; }

        #endregion
        #region 마그네슘 - Magnesium

        /// <summary>
        /// 마그네슘
        /// </summary>
        [LoadColumn(4)]
        public float Magnesium { get; set; }

        #endregion
        #region 전체 페놀 - TotalPhenols

        /// <summary>
        /// 전체 페놀
        /// </summary>
        [LoadColumn(5)]
        public float TotalPhenols { get; set; }

        #endregion
        #region 플라보노이드 - Flavanoids

        /// <summary>
        /// 플라보노이드
        /// </summary>
        [LoadColumn(6)]
        public float Flavanoids { get; set; }

        #endregion
        #region 비 플라보노이드 페놀 - NonflavanoidPhenols

        /// <summary>
        /// 비 플라보노이드 페놀
        /// </summary>
        [LoadColumn(7)]
        public float NonflavanoidPhenols { get; set; }

        #endregion
        #region 프로안토시아닌 - Proanthocyanins

        /// <summary>
        /// 프로안토시아닌
        /// </summary>
        [LoadColumn(8)]
        public float Proanthocyanins { get; set; }

        #endregion
        #region 색상 강도 - ColorIntensity

        /// <summary>
        /// 색상 강도
        /// </summary>
        [LoadColumn(9)]
        public float ColorIntensity { get; set; }

        #endregion
        #region 색조 - Hue

        /// <summary>
        /// 색조
        /// </summary>
        [LoadColumn(10)]
        public float Hue { get; set; }

        #endregion
        #region OD280 - OD280

        /// <summary>
        /// OD280
        /// </summary>
        [LoadColumn(11)]
        public float OD280 { get; set; }

        #endregion
        #region 프롤린 - Proline

        /// <summary>
        /// 프롤린
        /// </summary>
        [LoadColumn(12)]
        public float Proline { get; set; }

        #endregion
    }
}

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using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 소스 모델

/// </summary>

public class SourceModel

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 알코올 - Alcohol

/// <summary>

/// 알코올

/// </summary>

[LoadColumn(0)]

public float Alcohol { get; set; }

#endregion

#region 사과산 - MalicAcid

/// <summary>

/// 사과산

/// </summary>

[LoadColumn(1)]

public float MalicAcid { get; set; }

#endregion

#region 애쉬 - Ash

/// <summary>

/// 애쉬

/// </summary>

[LoadColumn(2)]

public float Ash { get; set; }

#endregion

#region 애쉬 알카니티 - AshAlcanity

/// <summary>

/// 애쉬 알카니티

/// </summary>

[LoadColumn(3)]

public float AshAlcanity { get; set; }

#endregion

#region 마그네슘 - Magnesium

/// <summary>

/// 마그네슘

/// </summary>

[LoadColumn(4)]

public float Magnesium { get; set; }

#endregion

#region 전체 페놀 - TotalPhenols

/// <summary>

/// 전체 페놀

/// </summary>

[LoadColumn(5)]

public float TotalPhenols { get; set; }

#endregion

#region 플라보노이드 - Flavanoids

/// <summary>

/// 플라보노이드

/// </summary>

[LoadColumn(6)]

public float Flavanoids { get; set; }

#endregion

#region 비 플라보노이드 페놀 - NonflavanoidPhenols

/// <summary>

/// 비 플라보노이드 페놀

/// </summary>

[LoadColumn(7)]

public float NonflavanoidPhenols { get; set; }

#endregion

#region 프로안토시아닌 - Proanthocyanins

/// <summary>

/// 프로안토시아닌

/// </summary>

[LoadColumn(8)]

public float Proanthocyanins { get; set; }

#endregion

#region 색상 강도 - ColorIntensity

/// <summary>

/// 색상 강도

/// </summary>

[LoadColumn(9)]

public float ColorIntensity { get; set; }

#endregion

#region 색조 - Hue

/// <summary>

/// 색조

/// </summary>

[LoadColumn(10)]

public float Hue { get; set; }

#endregion

#region OD280 - OD280

/// <summary>

/// OD280

/// </summary>

[LoadColumn(11)]

public float OD280 { get; set; }

#endregion

#region 프롤린 - Proline

/// <summary>

/// 프롤린

/// </summary>

[LoadColumn(12)]

public float Proline { get; set; }

#endregion

}

▶ TargetModel.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 타겟 모델
    /// </summary>
    public class TargetModel
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region PredictedClusterID

        /// <summary>
        /// 예측 클러스터 ID
        /// </summary>
        [ColumnName("PredictedLabel")]
        public uint PredictedClusterID;

        #endregion
        #region DistanceArray

        /// <summary>
        /// 거리 배열
        /// </summary>
        [ColumnName("Score")]
        public float[] DistanceArray;

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 타겟 모델

/// </summary>

public class TargetModel

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region PredictedClusterID

/// <summary>

/// 예측 클러스터 ID

/// </summary>

[ColumnName("PredictedLabel")]

public uint PredictedClusterID;

#endregion

#region DistanceArray

/// <summary>

/// 거리 배열

/// </summary>

[ColumnName("Score")]

public float[] DistanceArray;

#endregion

}

▶ Program.cs


using System;
using System.Linq;

using Microsoft.ML;
using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 프로그램
    /// </summary>
    class Program
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region 프로그램 시작하기 - Main()

        /// <summary>
        /// 프로그램 시작하기
        /// </summary>
        private static void Main()
        {
            string dataFilePath = "wine-clustering.csv";

            MLContext mlContext = new MLContext();

            IDataView sourceDataView = mlContext.Data.LoadFromTextFile<SourceModel>(dataFilePath, hasHeader : true, separatorChar : ',');

            string[] columnNameArray = sourceDataView.Schema.Select(column => column.Name).ToArray();

            var data = mlContext.Data.TrainTestSplit(sourceDataView, testFraction : 0.2);

            var pipeline = mlContext.Transforms.Concatenate("Feature", columnNameArray).Append(mlContext.Clustering.Trainers.KMeans("Feature", numberOfClusters : 3));

            var model = pipeline.Fit(data.TrainSet);

            IDataView testDataView = model.Transform(data.TestSet);

            ClusteringMetrics metrics = mlContext.Clustering.Evaluate(testDataView, scoreColumnName : "Score", featureColumnName : "Feature");

            PredictionEngine<SourceModel, TargetModel> predictionEngine = mlContext.Model.CreatePredictionEngine<SourceModel, TargetModel>(model);

            TargetModel target = predictionEngine.Predict
            (
                new SourceModel()
                {
                    Alcohol             = 11,
                    Ash                 = 2.33f,
                    AshAlcanity         = 3.21f,
                    ColorIntensity      = 2.9f,
                    Flavanoids          = 1.54f,
                    Hue                 = 1.55f,
                    Magnesium           = 120,
                    MalicAcid           = 1.44f,
                    NonflavanoidPhenols = 0.3f,
                    OD280               = 2.93f,
                    Proanthocyanins     = 1.72f,
                    TotalPhenols        = 3.86f,
                    Proline             = 1300
                }
            );

            Console.WriteLine($"예측 클러스터 ID : {target.PredictedClusterID}");
        }

        #endregion
    }
}

using System;

using System.Linq;

using Microsoft.ML;

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 프로그램

/// </summary>

class Program

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region 프로그램 시작하기 - Main()

/// <summary>

/// 프로그램 시작하기

/// </summary>

private static void Main()

{

string dataFilePath = "wine-clustering.csv";

MLContext mlContext = new MLContext();

IDataView sourceDataView = mlContext.Data.LoadFromTextFile<SourceModel>(dataFilePath, hasHeader : true, separatorChar : ',');

string[] columnNameArray = sourceDataView.Schema.Select(column => column.Name).ToArray();

var data = mlContext.Data.TrainTestSplit(sourceDataView, testFraction : 0.2);

var pipeline = mlContext.Transforms.Concatenate("Feature", columnNameArray).Append(mlContext.Clustering.Trainers.KMeans("Feature", numberOfClusters : 3));

var model = pipeline.Fit(data.TrainSet);

IDataView testDataView = model.Transform(data.TestSet);

ClusteringMetrics metrics = mlContext.Clustering.Evaluate(testDataView, scoreColumnName : "Score", featureColumnName : "Feature");

PredictionEngine<SourceModel, TargetModel> predictionEngine = mlContext.Model.CreatePredictionEngine<SourceModel, TargetModel>(model);

TargetModel target = predictionEngine.Predict

(

new SourceModel()

{

Alcohol = 11,

Ash = 2.33f,

AshAlcanity = 3.21f,

ColorIntensity = 2.9f,

Flavanoids = 1.54f,

Hue = 1.55f,

Magnesium = 120,

MalicAcid = 1.44f,

NonflavanoidPhenols = 0.3f,

OD280 = 2.93f,

Proanthocyanins = 1.72f,

TotalPhenols = 3.86f,

Proline = 1300

}

);

Console.WriteLine($"예측 클러스터 ID : {target.PredictedClusterID}");

}

#endregion

}

TestProject.zip

[C#/ML.NET] Microsoft.ML.DataView 누겟 설치하기

■ Microsoft.ML.DataView 누겟을 설치하는 방법을 보여준다. 1. Visual Studio를 실행한다. 2. [도구] / [NuGet 패키지 관리자] / [패키지 관리자 콘솔] 메뉴를 실행한다.

[C#/ML.NET] Microsoft.ML.CpuMath 누겟 설치하기

■ Microsoft.ML.CpuMath 누겟을 설치하는 방법을 보여준다. 1. Visual Studio를 실행한다. 2. [도구] / [NuGet 패키지 관리자] / [패키지 관리자 콘솔] 메뉴를 실행한다.

[C#/ML.NET] Microsoft.ML 누겟 설치하기

■ Microsoft.ML 누겟을 설치하는 방법을 보여준다. 1. Visual Studio를 실행한다. 2. [도구] / [NuGet 패키지 관리자] / [패키지 관리자 콘솔] 메뉴를 실행한다.

[C#/ML.NET/.NETCORE] 모델 빌더를 사용해 택시 요금 예측하기

■ 모델 빌더를 사용해 택시 요금을 예측하는 방법을 보여준다. [TestLibrary 프로젝트] ▶ ModelInput.cs


using Microsoft.ML.Data;

namespace TestLibrary
{
    /// <summary>
    /// 모델 입력
    /// </summary>
    public class ModelInput
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 공급자 ID - VendorID

        /// <summary>
        /// 공급자 ID
        /// </summary>
        [ColumnName("vendor_id"), LoadColumn(0)]
        public string VendorID { get; set; }

        #endregion
        #region 요금 코드 - RateCode

        /// <summary>
        /// 요금 코드
        /// </summary>
        [ColumnName("rate_code"), LoadColumn(1)]
        public float RateCode { get; set; }

        #endregion
        #region 승객 수 - PassengerCount

        /// <summary>
        /// 승객 수
        /// </summary>
        [ColumnName("passenger_count"), LoadColumn(2)]
        public float PassengerCount { get; set; }

        #endregion
        #region 이동 시간 (단위 : 초) - TripTimeInSeconds

        /// <summary>
        /// 이동 시간 (단위 : 초)
        /// </summary>
        [ColumnName("trip_time_in_secs"), LoadColumn(3)]
        public float TripTimeInSeconds { get; set; }

        #endregion
        #region 이동 거리 - TripDistance

        /// <summary>
        /// 이동 거리
        /// </summary>
        [ColumnName("trip_distance"), LoadColumn(4)]
        public float TripDistance { get; set; }

        #endregion
        #region 지불 타입 - PaymentType

        /// <summary>
        /// 지불 타입
        /// </summary>
        [ColumnName("payment_type"), LoadColumn(5)]
        public string PaymentType { get; set; }

        #endregion
        #region 요금 - FareAmount

        /// <summary>
        /// 요금
        /// </summary>
        [ColumnName("fare_amount"), LoadColumn(6)]
        public float FareAmount { get; set; }

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestLibrary

{

/// <summary>

/// 모델 입력

/// </summary>

public class ModelInput

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 공급자 ID - VendorID

/// <summary>

/// 공급자 ID

/// </summary>

[ColumnName("vendor_id"), LoadColumn(0)]

public string VendorID { get; set; }

#endregion

#region 요금 코드 - RateCode

/// <summary>

/// 요금 코드

/// </summary>

[ColumnName("rate_code"), LoadColumn(1)]

public float RateCode { get; set; }

#endregion

#region 승객 수 - PassengerCount

/// <summary>

/// 승객 수

/// </summary>

[ColumnName("passenger_count"), LoadColumn(2)]

public float PassengerCount { get; set; }

#endregion

#region 이동 시간 (단위 : 초) - TripTimeInSeconds

/// <summary>

/// 이동 시간 (단위 : 초)

/// </summary>

[ColumnName("trip_time_in_secs"), LoadColumn(3)]

public float TripTimeInSeconds { get; set; }

#endregion

#region 이동 거리 - TripDistance

/// <summary>

/// 이동 거리

/// </summary>

[ColumnName("trip_distance"), LoadColumn(4)]

public float TripDistance { get; set; }

#endregion

#region 지불 타입 - PaymentType

/// <summary>

/// 지불 타입

/// </summary>

[ColumnName("payment_type"), LoadColumn(5)]

public string PaymentType { get; set; }

#endregion

#region 요금 - FareAmount

/// <summary>

/// 요금

/// </summary>

[ColumnName("fare_amount"), LoadColumn(6)]

public float FareAmount { get; set; }

#endregion

}

▶ ModelOutput.cs


namespace TestLibrary
{
    /// <summary>
    /// 모델 입력
    /// </summary>
    public class ModelOutput
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 점수 - Score

        /// <summary>
        /// 점수
        /// </summary>
        public float Score { get; set; }

        #endregion
    }
}

namespace TestLibrary

{

/// <summary>

/// 모델 입력

/// </summary>

public class ModelOutput

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 점수 - Score

/// <summary>

/// 점수

/// </summary>

public float Score { get; set; }

#endregion

}

▶ ConsumeModel.cs


using Microsoft.ML;
using System;

namespace TestLibrary
{
    /// <summary>
    /// 모델 소비
    /// </summary>
    public class ConsumeModel
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Field
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region Field

        /// <summary>
        /// 예측 엔진 LAZY
        /// </summary>
        private static Lazy<PredictionEngine<ModelInput, ModelOutput>> _predictionEngineLazy = new Lazy<PredictionEngine<ModelInput, ModelOutput>>(CreatePredictionEngine);

        #endregion

        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Public

        #region 예측 엔진 생성하기 - CreatePredictionEngine()

        /// <summary>
        /// 예측 엔진 생성하기
        /// </summary>
        /// <returns>예측 엔진</returns>
        public static PredictionEngine<ModelInput, ModelOutput> CreatePredictionEngine()
        {
            MLContext context = new MLContext();

            string filePath = @"WEIGHT\MLModel.zip";

            ITransformer mlModel = context.Model.Load(filePath, out var modelInputSchema);

            PredictionEngine<ModelInput, ModelOutput> predictionEngine = context.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

            return predictionEngine;
        }

        #endregion
        #region 예측하기 - Predict(input)

        /// <summary>
        /// 예측하기
        /// </summary>
        /// <param name="input">모델 입력</param>
        /// <returns>모델 출력</returns>
        public static ModelOutput Predict(ModelInput input)
        {
            ModelOutput result = _predictionEngineLazy.Value.Predict(input);

            return result;
        }

        #endregion
    }
}

using Microsoft.ML;

using System;

namespace TestLibrary

{

/// <summary>

/// 모델 소비

/// </summary>

public class ConsumeModel

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Field

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region Field

/// <summary>

/// 예측 엔진 LAZY

/// </summary>

private static Lazy<PredictionEngine<ModelInput, ModelOutput>> _predictionEngineLazy = new Lazy<PredictionEngine<ModelInput, ModelOutput>>(CreatePredictionEngine);

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Public

#region 예측 엔진 생성하기 - CreatePredictionEngine()

/// <summary>

/// 예측 엔진 생성하기

/// </summary>

/// <returns>예측 엔진</returns>

public static PredictionEngine<ModelInput, ModelOutput> CreatePredictionEngine()

{

MLContext context = new MLContext();

string filePath = @"WEIGHT\MLModel.zip";

ITransformer mlModel = context.Model.Load(filePath, out var modelInputSchema);

PredictionEngine<ModelInput, ModelOutput> predictionEngine = context.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

return predictionEngine;

}

#endregion

#region 예측하기 - Predict(input)

/// <summary>

/// 예측하기

/// </summary>

/// <param name="input">모델 입력</param>

/// <returns>모델 출력</returns>

public static ModelOutput Predict(ModelInput input)

{

ModelOutput result = _predictionEngineLazy.Value.Predict(input);

return result;

}

#endregion

}

[TestProtect 프로젝트]

[C#/COMMON/ML.NET/.NET5] ONNX와 YOLOv5를 사용해 이미지 객체 인식하기

■ ONNX와 YOLOv5를 사용해 이미지 객체를 인식하는 방법을 보여준다. [소스 이미지] [결과 이미지] ▶ RectangleExtension.cs


using System.Drawing;

namespace TestProject
{
    /// <summary>
    /// 사각형 확장
    /// </summary>
    public static class RectangleExtension
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Public

        #region 영역 구하기 - GetArea(rectangle)

        /// <summary>
        /// 영역 구하기
        /// </summary>
        /// <param name="rectangle">사각형</param>
        /// <returns>영역</returns>
        public static float GetArea(this RectangleF rectangle)
        {
            return rectangle.Width * rectangle.Height;
        }

        #endregion
    }
}

using System.Drawing;

namespace TestProject

{

/// <summary>

/// 사각형 확장

/// </summary>

public static class RectangleExtension

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Public

#region 영역 구하기 - GetArea(rectangle)

/// <summary>

/// 영역 구하기

/// </summary>

/// <param name="rectangle">사각형</param>

/// <returns>영역</returns>

public static float GetArea(this RectangleF rectangle)

{

return rectangle.Width * rectangle.Height;

}

#endregion

}

▶ YOLOModel.cs


using System.Collections.Generic;

namespace TestProject
{
    /// <summary>
    /// YOLO 모델
    /// </summary>
    public abstract class YOLOModel
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 너비 - Width

        /// <summary>
        /// 너비
        /// </summary>
        public abstract int Width { get; set; }

        #endregion
        #region 높이 - Height

        /// <summary>
        /// 높이
        /// </summary>
        public abstract int Height { get; set; }

        #endregion
        #region 깊이 - Depth

        /// <summary>
        /// 깊이
        /// </summary>
        public abstract int Depth { get; set; }

        #endregion
        #region 차원 - Dimension

        /// <summary>
        /// 차원
        /// </summary>
        public abstract int Dimension { get; set; }

        #endregion
        #region 스트라이드 배열 - StrideArray

        /// <summary>
        /// 스트라이드 배열
        /// </summary>
        public abstract float[] StrideArray { get; set; }

        #endregion
        #region 앵커 배열 - AnchorArray

        /// <summary>
        /// 앵커 배열
        /// </summary>
        public abstract float[][][] AnchorArray { get; set; }

        #endregion
        #region 도형 배열 - ShapeArray

        /// <summary>
        /// 도형 배열
        /// </summary>
        public abstract int[] ShapeArray { get; set; }

        #endregion
        #region 신뢰도 - Confidence

        /// <summary>
        /// 신뢰도
        /// </summary>
        public abstract float Confidence { get; set; }

        #endregion
        #region 다중 신뢰도 - MultipleConfidence

        /// <summary>
        /// 다중 신뢰도
        /// </summary>
        public abstract float MultipleConfidence { get; set; }

        #endregion
        #region 오버랩 - Overlap

        /// <summary>
        /// 오버랩
        /// </summary>
        public abstract float Overlap { get; set; }

        #endregion
        #region 출력 배열 - OutputArray

        /// <summary>
        /// 출력 배열
        /// </summary>
        public abstract string[] OutputArray { get; set; }

        #endregion
        #region 레이블 리스트 - LabelList

        /// <summary>
        /// 레이블 리스트
        /// </summary>
        public abstract List<YOLOLabel> LabelList { get; set; }

        #endregion
        #region 탐지 사용 여부 - UseDetect

        /// <summary>
        /// 탐지 사용 여부
        /// </summary>
        public abstract bool UseDetect { get; set; }

        #endregion
    }
}

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using System.Collections.Generic;

namespace TestProject

{

/// <summary>

/// YOLO 모델

/// </summary>

public abstract class YOLOModel

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 너비 - Width

/// <summary>

/// 너비

/// </summary>

public abstract int Width { get; set; }

#endregion

#region 높이 - Height

/// <summary>

/// 높이

/// </summary>

public abstract int Height { get; set; }

#endregion

#region 깊이 - Depth

/// <summary>

/// 깊이

/// </summary>

public abstract int Depth { get; set; }

#endregion

#region 차원 - Dimension

/// <summary>

/// 차원

/// </summary>

public abstract int Dimension { get; set; }

#endregion

#region 스트라이드 배열 - StrideArray

/// <summary>

/// 스트라이드 배열

/// </summary>

public abstract float[] StrideArray { get; set; }

#endregion

#region 앵커 배열 - AnchorArray

/// <summary>

/// 앵커 배열

/// </summary>

public abstract float[][][] AnchorArray { get; set; }

#endregion

#region 도형 배열 - ShapeArray

/// <summary>

/// 도형 배열

/// </summary>

public abstract int[] ShapeArray { get; set; }

#endregion

#region 신뢰도 - Confidence

/// <summary>

/// 신뢰도

/// </summary>

public abstract float Confidence { get; set; }

#endregion

#region 다중 신뢰도 - MultipleConfidence

/// <summary>

/// 다중 신뢰도

/// </summary>

public abstract float MultipleConfidence { get; set; }

#endregion

#region 오버랩 - Overlap

/// <summary>

/// 오버랩

/// </summary>

public abstract float Overlap { get; set; }

#endregion

#region 출력 배열 - OutputArray

/// <summary>

/// 출력 배열

/// </summary>

public abstract string[] OutputArray { get; set; }

#endregion

#region 레이블 리스트 - LabelList

/// <summary>

/// 레이블 리스트

/// </summary>

public abstract List<YOLOLabel> LabelList { get; set; }

#endregion

#region 탐지 사용 여부 - UseDetect

/// <summary>

/// 탐지 사용 여부

/// </summary>

public abstract bool UseDetect { get; set; }

#endregion

}

▶ YOLOCOCOP5Model.cs


using System.Collections.Generic;

namespace TestProject
{
    /// <summary>
    /// YOLO COCO P5 모델
    /// </summary>
    public class YOLOCOCOP5Model : YOLOModel
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 너비 - Width

        /// <summary>
        /// 너비
        /// </summary>
        public override int Width { get; set; } = 640;

        #endregion
        #region 높이 - Height

        /// <summary>
        /// 높이
        /// </summary>
        public override int Height { get; set; } = 640;

        #endregion
        #region 깊이 - Depth

        /// <summary>
        /// 깊이
        /// </summary>
        public override int Depth { get; set; } = 3;

        #endregion
        #region 차원 - Dimension

        /// <summary>
        /// 차원
        /// </summary>
        public override int Dimension { get; set; } = 85;

        #endregion
        #region 스트라이드 배열 - StrideArray

        /// <summary>
        /// 스트라이드 배열
        /// </summary>
        public override float[] StrideArray { get; set; } = new float[] { 8, 16, 32 };

        #endregion
        #region 앵커 배열 - AnchorArray

        /// <summary>
        /// 앵커 배열
        /// </summary>
        public override float[][][] AnchorArray { get; set; } = new float[][][]
        {
            new float[][] { new float[] { 010, 13 }, new float[] { 016, 030 }, new float[] { 033, 023 } },
            new float[][] { new float[] { 030, 61 }, new float[] { 062, 045 }, new float[] { 059, 119 } },
            new float[][] { new float[] { 116, 90 }, new float[] { 156, 198 }, new float[] { 373, 326 } }
        };

        #endregion
        #region 도형 배열 - ShapeArray

        /// <summary>
        /// 도형 배열
        /// </summary>
        public override int[] ShapeArray { get; set; } = new int[] { 80, 40, 20 };

        #endregion
        #region 신뢰도 - Confidence

        /// <summary>
        /// 신뢰도
        /// </summary>
        public override float Confidence { get; set; } = 0.20f;

        #endregion
        #region 다중 신뢰도 - MultipleConfidence

        /// <summary>
        /// 다중 신뢰도
        /// </summary>
        public override float MultipleConfidence { get; set; } = 0.25f;

        #endregion
        #region 오버랩 - Overlap

        /// <summary>
        /// 오버랩
        /// </summary>
        public override float Overlap { get; set; } = 0.45f;

        #endregion
        #region 출력 배열 - OutputArray

        /// <summary>
        /// 출력 배열
        /// </summary>
        public override string[] OutputArray { get; set; } = new[] { "output" };

        #endregion
        #region 레이블 리스트 - LabelList

        /// <summary>
        /// 레이블 리스트
        /// </summary>
        public override List<YOLOLabel> LabelList { get; set; } = new List<YOLOLabel>()
        {
            new YOLOLabel { ID = 1,  Name = "person"         },
            new YOLOLabel { ID = 2,  Name = "bicycle"        },
            new YOLOLabel { ID = 3,  Name = "car"            },
            new YOLOLabel { ID = 4,  Name = "motorcycle"     },
            new YOLOLabel { ID = 5,  Name = "airplane"       },
            new YOLOLabel { ID = 6,  Name = "bus"            },
            new YOLOLabel { ID = 7,  Name = "train"          },
            new YOLOLabel { ID = 8,  Name = "truck"          },
            new YOLOLabel { ID = 9,  Name = "boat"           },
            new YOLOLabel { ID = 10, Name = "traffic light"  },
            new YOLOLabel { ID = 11, Name = "fire hydrant"   },
            new YOLOLabel { ID = 12, Name = "stop sign"      },
            new YOLOLabel { ID = 13, Name = "parking meter"  },
            new YOLOLabel { ID = 14, Name = "bench"          },
            new YOLOLabel { ID = 15, Name = "bird"           },
            new YOLOLabel { ID = 16, Name = "cat"            },
            new YOLOLabel { ID = 17, Name = "dog"            },
            new YOLOLabel { ID = 18, Name = "horse"          },
            new YOLOLabel { ID = 19, Name = "sheep"          },
            new YOLOLabel { ID = 20, Name = "cow"            },
            new YOLOLabel { ID = 21, Name = "elephant"       },
            new YOLOLabel { ID = 22, Name = "bear"           },
            new YOLOLabel { ID = 23, Name = "zebra"          },
            new YOLOLabel { ID = 24, Name = "giraffe"        },
            new YOLOLabel { ID = 25, Name = "backpack"       },
            new YOLOLabel { ID = 26, Name = "umbrella"       },
            new YOLOLabel { ID = 27, Name = "handbag"        },
            new YOLOLabel { ID = 28, Name = "tie"            },
            new YOLOLabel { ID = 29, Name = "suitcase"       },
            new YOLOLabel { ID = 30, Name = "frisbee"        },
            new YOLOLabel { ID = 31, Name = "skis"           },
            new YOLOLabel { ID = 32, Name = "snowboard"      },
            new YOLOLabel { ID = 33, Name = "sports ball"    },
            new YOLOLabel { ID = 34, Name = "kite"           },
            new YOLOLabel { ID = 35, Name = "baseball bat"   },
            new YOLOLabel { ID = 36, Name = "baseball glove" },
            new YOLOLabel { ID = 37, Name = "skateboard"     },
            new YOLOLabel { ID = 38, Name = "surfboard"      },
            new YOLOLabel { ID = 39, Name = "tennis racket"  },
            new YOLOLabel { ID = 40, Name = "bottle"         },
            new YOLOLabel { ID = 41, Name = "wine glass"     },
            new YOLOLabel { ID = 42, Name = "cup"            },
            new YOLOLabel { ID = 43, Name = "fork"           },
            new YOLOLabel { ID = 44, Name = "knife"          },
            new YOLOLabel { ID = 45, Name = "spoon"          },
            new YOLOLabel { ID = 46, Name = "bowl"           },
            new YOLOLabel { ID = 47, Name = "banana"         },
            new YOLOLabel { ID = 48, Name = "apple"          },
            new YOLOLabel { ID = 49, Name = "sandwich"       },
            new YOLOLabel { ID = 50, Name = "orange"         },
            new YOLOLabel { ID = 51, Name = "broccoli"       },
            new YOLOLabel { ID = 52, Name = "carrot"         },
            new YOLOLabel { ID = 53, Name = "hot dog"        },
            new YOLOLabel { ID = 54, Name = "pizza"          },
            new YOLOLabel { ID = 55, Name = "donut"          },
            new YOLOLabel { ID = 56, Name = "cake"           },
            new YOLOLabel { ID = 57, Name = "chair"          },
            new YOLOLabel { ID = 58, Name = "couch"          },
            new YOLOLabel { ID = 59, Name = "potted plant"   },
            new YOLOLabel { ID = 60, Name = "bed"            },
            new YOLOLabel { ID = 61, Name = "dining table"   },
            new YOLOLabel { ID = 62, Name = "toilet"         },
            new YOLOLabel { ID = 63, Name = "tv"             },
            new YOLOLabel { ID = 64, Name = "laptop"         },
            new YOLOLabel { ID = 65, Name = "mouse"          },
            new YOLOLabel { ID = 66, Name = "remote"         },
            new YOLOLabel { ID = 67, Name = "keyboard"       },
            new YOLOLabel { ID = 68, Name = "cell phone"     },
            new YOLOLabel { ID = 69, Name = "microwave"      },
            new YOLOLabel { ID = 70, Name = "oven"           },
            new YOLOLabel { ID = 71, Name = "toaster"        },
            new YOLOLabel { ID = 72, Name = "sink"           },
            new YOLOLabel { ID = 73, Name = "refrigerator"   },
            new YOLOLabel { ID = 74, Name = "book"           },
            new YOLOLabel { ID = 75, Name = "clock"          },
            new YOLOLabel { ID = 76, Name = "vase"           },
            new YOLOLabel { ID = 77, Name = "scissors"       },
            new YOLOLabel { ID = 78, Name = "teddy bear"     },
            new YOLOLabel { ID = 79, Name = "hair drier"     },
            new YOLOLabel { ID = 80, Name = "toothbrush"     }
        };

        #endregion
        #region 탐지 사용 여부 - UseDetect

        /// <summary>
        /// 탐지 사용 여부
        /// </summary>
        public override bool UseDetect { get; set; } = true;

        #endregion

        //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 생성자 - YOLOCOCOP5Model()

        /// <summary>
        /// 생성자
        /// </summary>
        public YOLOCOCOP5Model()
        {
        }

        #endregion
    }
}

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using System.Collections.Generic;

namespace TestProject

{

/// <summary>

/// YOLO COCO P5 모델

/// </summary>

public class YOLOCOCOP5Model : YOLOModel

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 너비 - Width

/// <summary>

/// 너비

/// </summary>

public override int Width { get; set; } = 640;

#endregion

#region 높이 - Height

/// <summary>

/// 높이

/// </summary>

public override int Height { get; set; } = 640;

#endregion

#region 깊이 - Depth

/// <summary>

/// 깊이

/// </summary>

public override int Depth { get; set; } = 3;

#endregion

#region 차원 - Dimension

/// <summary>

/// 차원

/// </summary>

public override int Dimension { get; set; } = 85;

#endregion

#region 스트라이드 배열 - StrideArray

/// <summary>

/// 스트라이드 배열

/// </summary>

public override float[] StrideArray { get; set; } = new float[] { 8, 16, 32 };

#endregion

#region 앵커 배열 - AnchorArray

/// <summary>

/// 앵커 배열

/// </summary>

public override float[][][] AnchorArray { get; set; } = new float[][][]

{

new float[][] { new float[] { 010, 13 }, new float[] { 016, 030 }, new float[] { 033, 023 } },

new float[][] { new float[] { 030, 61 }, new float[] { 062, 045 }, new float[] { 059, 119 } },

new float[][] { new float[] { 116, 90 }, new float[] { 156, 198 }, new float[] { 373, 326 } }

};

#endregion

#region 도형 배열 - ShapeArray

/// <summary>

/// 도형 배열

/// </summary>

public override int[] ShapeArray { get; set; } = new int[] { 80, 40, 20 };

#endregion

#region 신뢰도 - Confidence

/// <summary>

/// 신뢰도

/// </summary>

public override float Confidence { get; set; } = 0.20f;

#endregion

#region 다중 신뢰도 - MultipleConfidence

/// <summary>

/// 다중 신뢰도

/// </summary>

public override float MultipleConfidence { get; set; } = 0.25f;

#endregion

#region 오버랩 - Overlap

/// <summary>

/// 오버랩

/// </summary>

public override float Overlap { get; set; } = 0.45f;

#endregion

#region 출력 배열 - OutputArray

/// <summary>

/// 출력 배열

/// </summary>

public override string[] OutputArray { get; set; } = new[] { "output" };

#endregion

#region 레이블 리스트 - LabelList

/// <summary>

/// 레이블 리스트

/// </summary>

public override List<YOLOLabel> LabelList { get; set; } = new List<YOLOLabel>()

{

new YOLOLabel { ID = 1, Name = "person" },

new YOLOLabel { ID = 2, Name = "bicycle" },

new YOLOLabel { ID = 3, Name = "car" },

new YOLOLabel { ID = 4, Name = "motorcycle" },

new YOLOLabel { ID = 5, Name = "airplane" },

new YOLOLabel { ID = 6, Name = "bus" },

new YOLOLabel { ID = 7, Name = "train" },

new YOLOLabel { ID = 8, Name = "truck" },

new YOLOLabel { ID = 9, Name = "boat" },

new YOLOLabel { ID = 10, Name = "traffic light" },

new YOLOLabel { ID = 11, Name = "fire hydrant" },

new YOLOLabel { ID = 12, Name = "stop sign" },

new YOLOLabel { ID = 13, Name = "parking meter" },

new YOLOLabel { ID = 14, Name = "bench" },

new YOLOLabel { ID = 15, Name = "bird" },

new YOLOLabel { ID = 16, Name = "cat" },

new YOLOLabel { ID = 17, Name = "dog" },

new YOLOLabel { ID = 18, Name = "horse" },

new YOLOLabel { ID = 19, Name = "sheep" },

new YOLOLabel { ID = 20, Name = "cow" },

new YOLOLabel { ID = 21, Name = "elephant" },

new YOLOLabel { ID = 22, Name = "bear" },

new YOLOLabel { ID = 23, Name = "zebra" },

new YOLOLabel { ID = 24, Name = "giraffe" },

new YOLOLabel { ID = 25, Name = "backpack" },

new YOLOLabel { ID = 26, Name = "umbrella" },

new YOLOLabel { ID = 27, Name = "handbag" },

new YOLOLabel { ID = 28, Name = "tie" },

new YOLOLabel { ID = 29, Name = "suitcase" },

new YOLOLabel { ID = 30, Name = "frisbee" },

new YOLOLabel { ID = 31, Name = "skis" },

new YOLOLabel { ID = 32, Name = "snowboard" },

new YOLOLabel { ID = 33, Name = "sports ball" },

new YOLOLabel { ID = 34, Name = "kite" },

new YOLOLabel { ID = 35, Name = "baseball bat" },

new YOLOLabel { ID = 36, Name = "baseball glove" },

new YOLOLabel { ID = 37, Name = "skateboard" },

new YOLOLabel { ID = 38, Name = "surfboard" },

new YOLOLabel { ID = 39, Name = "tennis racket" },

new YOLOLabel { ID = 40, Name = "bottle" },

new YOLOLabel { ID = 41, Name = "wine glass" },

new YOLOLabel { ID = 42, Name = "cup" },

new YOLOLabel { ID = 43, Name = "fork" },

new YOLOLabel { ID = 44, Name = "knife" },

new YOLOLabel { ID = 45, Name = "spoon" },

new YOLOLabel { ID = 46, Name = "bowl" },

new YOLOLabel { ID = 47, Name = "banana" },

new YOLOLabel { ID = 48, Name = "apple" },

new YOLOLabel { ID = 49, Name = "sandwich" },

new YOLOLabel { ID = 50, Name = "orange" },

new YOLOLabel { ID = 51, Name = "broccoli" },

new YOLOLabel { ID = 52, Name = "carrot" },

new YOLOLabel { ID = 53, Name = "hot dog" },

new YOLOLabel { ID = 54, Name = "pizza" },

new YOLOLabel { ID = 55, Name = "donut" },

new YOLOLabel { ID = 56, Name = "cake" },

new YOLOLabel { ID = 57, Name = "chair" },

new YOLOLabel { ID = 58, Name = "couch" },

new YOLOLabel { ID = 59, Name = "potted plant" },

new YOLOLabel { ID = 60, Name = "bed" },

new YOLOLabel { ID = 61, Name = "dining table" },

new YOLOLabel { ID = 62, Name = "toilet" },

new YOLOLabel { ID = 63, Name = "tv" },

new YOLOLabel { ID = 64, Name = "laptop" },

new YOLOLabel { ID = 65, Name = "mouse" },

new YOLOLabel { ID = 66, Name = "remote" },

new YOLOLabel { ID = 67, Name = "keyboard" },

new YOLOLabel { ID = 68, Name = "cell phone" },

new YOLOLabel { ID = 69, Name = "microwave" },

new YOLOLabel { ID = 70, Name = "oven" },

new YOLOLabel { ID = 71, Name = "toaster" },

new YOLOLabel { ID = 72, Name = "sink" },

new YOLOLabel { ID = 73, Name = "refrigerator" },

new YOLOLabel { ID = 74, Name = "book" },

new YOLOLabel { ID = 75, Name = "clock" },

new YOLOLabel { ID = 76, Name = "vase" },

new YOLOLabel { ID = 77, Name = "scissors" },

new YOLOLabel { ID = 78, Name = "teddy bear" },

new YOLOLabel { ID = 79, Name = "hair drier" },

new YOLOLabel { ID = 80, Name = "toothbrush" }

};

#endregion

#region 탐지 사용 여부 - UseDetect

/// <summary>

/// 탐지 사용 여부

/// </summary>

public override bool UseDetect { get; set; } = true;

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Constructor

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 생성자 - YOLOCOCOP5Model()

/// <summary>

/// 생성자

/// </summary>

public YOLOCOCOP5Model()

{

}

#endregion

}

[C#/ML.NET] 다중 클래스 분류 모델 사용하기

■ 다중 클래스 분류 모델을 사용하는 방법을 보여준다. ▶ IssueData.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 이슈 데이터
    /// </summary>
    public class IssueData
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region ID - ID

        /// <summary>
        /// ID
        /// </summary>
        [LoadColumn(0)]
        public string ID { get; set; }

        #endregion
        #region 영역 - Area

        /// <summary>
        /// 영역
        /// </summary>
        [LoadColumn(1)]
        public string Area { get; set; }

        #endregion
        #region 제목 - Title

        /// <summary>
        /// 제목
        /// </summary>
        [LoadColumn(2)]
        public string Title { get; set; }

        #endregion
        #region 설명 - Description

        /// <summary>
        /// 설명
        /// </summary>
        [LoadColumn(3)]
        public string Description { get; set; }

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 이슈 데이터

/// </summary>

public class IssueData

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region ID - ID

/// <summary>

/// ID

/// </summary>

[LoadColumn(0)]

public string ID { get; set; }

#endregion

#region 영역 - Area

/// <summary>

/// 영역

/// </summary>

[LoadColumn(1)]

public string Area { get; set; }

#endregion

#region 제목 - Title

/// <summary>

/// 제목

/// </summary>

[LoadColumn(2)]

public string Title { get; set; }

#endregion

#region 설명 - Description

/// <summary>

/// 설명

/// </summary>

[LoadColumn(3)]

public string Description { get; set; }

#endregion

}

▶ IssuePrediction.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 이슈 예측
    /// </summary>
    public class IssuePrediction
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 영역 - Area

        /// <summary>
        /// 영역
        /// </summary>
        [ColumnName("PredictedLabel")]
        public string Area;

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 이슈 예측

/// </summary>

public class IssuePrediction

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 영역 - Area

/// <summary>

/// 영역

/// </summary>

[ColumnName("PredictedLabel")]

public string Area;

#endregion

}

▶ Program.cs


using System;
using System.IO;

using Microsoft.ML;
using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 프로그램
    /// </summary>
    class Program
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Field
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region Field

        /// <summary>
        /// 애플리케이션 경로
        /// </summary>
        private static string _applicationPath => Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]);

        /// <summary>
        /// 훈련 데이터 파일 경로
        /// </summary>
        private static string _trainingDataFilePath => Path.Combine(_applicationPath, "Data", "issues_train.tsv");

        /// <summary>
        /// 테스트 데이터 파일 경로
        /// </summary>
        private static string _testDataFilePath => Path.Combine(_applicationPath, "Data", "issues_test.tsv");

        /// <summary>
        /// 모델 파일 경로
        /// </summary>
        private static string _modelFilePath => Path.Combine(_applicationPath, "Data", "model.zip");

        /// <summary>
        /// 모델 컨텍스트
        /// </summary>
        private static MLContext _context;

        /// <summary>
        /// 예측 엔진
        /// </summary>
        private static PredictionEngine<IssueData, IssuePrediction> _predictionEngine;

        /// <summary>
        /// 모델
        /// </summary>
        private static ITransformer _model;

        /// <summary>
        /// 훈련 데이터 뷰
        /// </summary>
        private static IDataView _trainingDataView;

        #endregion

        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region 프로그램 시작하기 - Main()

        /// <summary>
        /// 프로그램 시작하기
        /// </summary>
        private static void Main()
        {
            Console.WriteLine("BEGIN MAIN FUNCTION");

            _context = new MLContext(seed: 0);

            Console.WriteLine("BEGIN SET TRAINING DATA VIEW");

            _trainingDataView = _context.Data.LoadFromTextFile<IssueData>(_trainingDataFilePath, hasHeader: true);

            Console.WriteLine("END SET TRAINING DATA VIEW");

            Console.WriteLine("BEGIN SET PIPE LINE");

            var pipeline = GetPipeLine();

            Console.WriteLine("END SET PIPE LINE");

            _model = GetModel(_trainingDataView, pipeline);

            Console.WriteLine("BEGIN PREDICT SINGLE ITEM");

            _predictionEngine = _context.Model.CreatePredictionEngine<IssueData, IssuePrediction>(_model);

            IssueData issueData = new IssueData()
            {
                Title       = "WebSockets communication is slow in my machine",
                Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
            };

            IssuePrediction issuePrediction = _predictionEngine.Predict(issueData);

            Console.WriteLine("--------------------------------------------------");
            Console.WriteLine($"AREA : {issuePrediction.Area}");
            Console.WriteLine("--------------------------------------------------");

            Evaluate(_trainingDataView.Schema);

            SaveModel(_context, _trainingDataView.Schema, _model);

            PredictIssue();

            Console.WriteLine("END MAIN FUNCTION");
        }

        #endregion

        #region 파이프 라인 구하기 - GetPipeLine()

        /// <summary>
        /// 파이프 라인 구하기
        /// </summary>
        /// <returns>파이프 라인</returns>
        private static IEstimator<ITransformer> GetPipeLine()
        {
            Console.WriteLine("BEGIN GET PIPE LINE FUNCTION");

            var pipeline = _context.Transforms.Conversion.MapValueToKey(inputColumnName : "Area", outputColumnName : "Label")
                .Append(_context.Transforms.Text.FeaturizeText(inputColumnName : "Title"      , outputColumnName : "TitleFeaturized"      ))
                .Append(_context.Transforms.Text.FeaturizeText(inputColumnName : "Description", outputColumnName : "DescriptionFeaturized"))
                .Append(_context.Transforms.Concatenate("Features", "TitleFeaturized", "DescriptionFeaturized"))
                .AppendCacheCheckpoint(_context)
                .Append(_context.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
                .Append(_context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

            Console.WriteLine("END GET PIPE LINE FUNCTION");

            return pipeline;
        }

        #endregion
        #region 모델 구하기 - GetModel(trainingDataView, pipeline)

        /// <summary>
        /// 모델 구하기
        /// </summary>
        /// <param name="trainingDataView">훈련 데이터 뷰</param>
        /// <param name="pipeline">파이프 라인</param>
        /// <returns>모델</returns>
        private static ITransformer GetModel(IDataView trainingDataView, IEstimator<ITransformer> pipeline)
        {
            Console.WriteLine("BEGIN GET MODEL FUNCTION");

            var model = pipeline.Fit(trainingDataView);

            Console.WriteLine("END GET MODEL FUNCTION");

            return model;
        }

        #endregion
        #region 평가하기 - Evaluate(trainingDataViewSchema)

        /// <summary>
        /// 평가하기
        /// </summary>
        /// <param name="trainingDataViewSchema">훈련 데이터 뷰 스키마</param>
        private static void Evaluate(DataViewSchema trainingDataViewSchema)
        {
            Console.WriteLine("BEGIN EVALUATE FUNCTION");

            IDataView testDataView = _context.Data.LoadFromTextFile<IssueData>(_testDataFilePath, hasHeader : true);

            MulticlassClassificationMetrics metrics = _context.MulticlassClassification.Evaluate(_model.Transform(testDataView));

            Console.WriteLine("--------------------------------------------------"      );
            Console.WriteLine("METRICS FOR MULTI-CLASS CLASSIFICATION MODEL - TEST DATA");
            Console.WriteLine("--------------------------------------------------"      );
            Console.WriteLine($"MICRO ACCURACY     : {metrics.MicroAccuracy:0.###}"     );
            Console.WriteLine($"MACRO ACCURACY     : {metrics.MacroAccuracy:0.###}"     );
            Console.WriteLine($"LOG LOSS           : {metrics.LogLoss:#.###}"           );
            Console.WriteLine($"LOG LOSS REDUCTION : {metrics.LogLossReduction:#.###}"  );
            Console.WriteLine("--------------------------------------------------"      );

            Console.WriteLine("END EVALUATE FUNCTION");
        }

        #endregion
        #region 모델 저장하기 - SaveModel(context, trainingDataViewSchema, model)

        /// <summary>
        /// 모델 저장하기
        /// </summary>
        /// <param name="context">ML 컨텍스트</param>
        /// <param name="trainingDataViewSchema">훈련 데이터 뷰 스키마</param>
        /// <param name="model">모델</param>
        private static void SaveModel(MLContext context, DataViewSchema trainingDataViewSchema, ITransformer model)
        {
            Console.WriteLine("BEGIN SAVE MODEL FUNCTION");

            context.Model.Save(model, trainingDataViewSchema, _modelFilePath);

            Console.WriteLine("END SAVE MODEL FUNCTION");
        }

        #endregion
        #region 이슈 예측하기 - PredictIssue()

        /// <summary>
        /// 이슈 예측하기
        /// </summary>
        private static void PredictIssue()
        {
            Console.WriteLine("BEGIN PREDICT ISSUE FUNCTION");

            ITransformer model = _context.Model.Load(_modelFilePath, out var inputSchema);

            IssueData issueData = new IssueData()
            {
                Title       = "Entity Framework crashes",
                Description = "When connecting to the database, EF is crashing"
            };

            _predictionEngine = _context.Model.CreatePredictionEngine<IssueData, IssuePrediction>(model);

            IssuePrediction issuePrediction = _predictionEngine.Predict(issueData);

            Console.WriteLine("--------------------------------------------------");
            Console.WriteLine($"AREA : {issuePrediction.Area}");
            Console.WriteLine("--------------------------------------------------");

            Console.WriteLine("END PREDICT ISSUE FUNCTION");
        }

        #endregion
    }
}

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using System;

using System.IO;

using Microsoft.ML;

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 프로그램

/// </summary>

class Program

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Field

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region Field

/// <summary>

/// 애플리케이션 경로

/// </summary>

private static string _applicationPath => Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]);

/// <summary>

/// 훈련 데이터 파일 경로

/// </summary>

private static string _trainingDataFilePath => Path.Combine(_applicationPath, "Data", "issues_train.tsv");

/// <summary>

/// 테스트 데이터 파일 경로

/// </summary>

private static string _testDataFilePath => Path.Combine(_applicationPath, "Data", "issues_test.tsv");

/// <summary>

/// 모델 파일 경로

/// </summary>

private static string _modelFilePath => Path.Combine(_applicationPath, "Data", "model.zip");

/// <summary>

/// 모델 컨텍스트

/// </summary>

private static MLContext _context;

/// <summary>

/// 예측 엔진

/// </summary>

private static PredictionEngine<IssueData, IssuePrediction> _predictionEngine;

/// <summary>

/// 모델

/// </summary>

private static ITransformer _model;

/// <summary>

/// 훈련 데이터 뷰

/// </summary>

private static IDataView _trainingDataView;

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region 프로그램 시작하기 - Main()

/// <summary>

/// 프로그램 시작하기

/// </summary>

private static void Main()

{

Console.WriteLine("BEGIN MAIN FUNCTION");

_context = new MLContext(seed: 0);

Console.WriteLine("BEGIN SET TRAINING DATA VIEW");

_trainingDataView = _context.Data.LoadFromTextFile<IssueData>(_trainingDataFilePath, hasHeader: true);

Console.WriteLine("END SET TRAINING DATA VIEW");

Console.WriteLine("BEGIN SET PIPE LINE");

var pipeline = GetPipeLine();

Console.WriteLine("END SET PIPE LINE");

_model = GetModel(_trainingDataView, pipeline);

Console.WriteLine("BEGIN PREDICT SINGLE ITEM");

_predictionEngine = _context.Model.CreatePredictionEngine<IssueData, IssuePrediction>(_model);

IssueData issueData = new IssueData()

{

Title = "WebSockets communication is slow in my machine",

Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."

};

IssuePrediction issuePrediction = _predictionEngine.Predict(issueData);

Console.WriteLine("--------------------------------------------------");

Console.WriteLine($"AREA : {issuePrediction.Area}");

Console.WriteLine("--------------------------------------------------");

Evaluate(_trainingDataView.Schema);

SaveModel(_context, _trainingDataView.Schema, _model);

PredictIssue();

Console.WriteLine("END MAIN FUNCTION");

}

#endregion

#region 파이프 라인 구하기 - GetPipeLine()

/// <summary>

/// 파이프 라인 구하기

/// </summary>

/// <returns>파이프 라인</returns>

private static IEstimator<ITransformer> GetPipeLine()

{

Console.WriteLine("BEGIN GET PIPE LINE FUNCTION");

var pipeline = _context.Transforms.Conversion.MapValueToKey(inputColumnName : "Area", outputColumnName : "Label")

.Append(_context.Transforms.Text.FeaturizeText(inputColumnName : "Title" , outputColumnName : "TitleFeaturized" ))

.Append(_context.Transforms.Text.FeaturizeText(inputColumnName : "Description", outputColumnName : "DescriptionFeaturized"))

.Append(_context.Transforms.Concatenate("Features", "TitleFeaturized", "DescriptionFeaturized"))

.AppendCacheCheckpoint(_context)

.Append(_context.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))

.Append(_context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

Console.WriteLine("END GET PIPE LINE FUNCTION");

return pipeline;

}

#endregion

#region 모델 구하기 - GetModel(trainingDataView, pipeline)

/// <summary>

/// 모델 구하기

/// </summary>

/// <param name="trainingDataView">훈련 데이터 뷰</param>

/// <param name="pipeline">파이프 라인</param>

/// <returns>모델</returns>

private static ITransformer GetModel(IDataView trainingDataView, IEstimator<ITransformer> pipeline)

{

Console.WriteLine("BEGIN GET MODEL FUNCTION");

var model = pipeline.Fit(trainingDataView);

Console.WriteLine("END GET MODEL FUNCTION");

return model;

}

#endregion

#region 평가하기 - Evaluate(trainingDataViewSchema)

/// <summary>

/// 평가하기

/// </summary>

/// <param name="trainingDataViewSchema">훈련 데이터 뷰 스키마</param>

private static void Evaluate(DataViewSchema trainingDataViewSchema)

{

Console.WriteLine("BEGIN EVALUATE FUNCTION");

IDataView testDataView = _context.Data.LoadFromTextFile<IssueData>(_testDataFilePath, hasHeader : true);

MulticlassClassificationMetrics metrics = _context.MulticlassClassification.Evaluate(_model.Transform(testDataView));

Console.WriteLine("--------------------------------------------------" );

Console.WriteLine("METRICS FOR MULTI-CLASS CLASSIFICATION MODEL - TEST DATA");

Console.WriteLine("--------------------------------------------------" );

Console.WriteLine($"MICRO ACCURACY : {metrics.MicroAccuracy:0.###}" );

Console.WriteLine($"MACRO ACCURACY : {metrics.MacroAccuracy:0.###}" );

Console.WriteLine($"LOG LOSS : {metrics.LogLoss:#.###}" );

Console.WriteLine($"LOG LOSS REDUCTION : {metrics.LogLossReduction:#.###}" );

Console.WriteLine("--------------------------------------------------" );

Console.WriteLine("END EVALUATE FUNCTION");

}

#endregion

#region 모델 저장하기 - SaveModel(context, trainingDataViewSchema, model)

/// <summary>

/// 모델 저장하기

/// </summary>

/// <param name="context">ML 컨텍스트</param>

/// <param name="trainingDataViewSchema">훈련 데이터 뷰 스키마</param>

/// <param name="model">모델</param>

private static void SaveModel(MLContext context, DataViewSchema trainingDataViewSchema, ITransformer model)

{

Console.WriteLine("BEGIN SAVE MODEL FUNCTION");

context.Model.Save(model, trainingDataViewSchema, _modelFilePath);

Console.WriteLine("END SAVE MODEL FUNCTION");

}

#endregion

#region 이슈 예측하기 - PredictIssue()

/// <summary>

/// 이슈 예측하기

/// </summary>

private static void PredictIssue()

{

Console.WriteLine("BEGIN PREDICT ISSUE FUNCTION");

ITransformer model = _context.Model.Load(_modelFilePath, out var inputSchema);

IssueData issueData = new IssueData()

{

Title = "Entity Framework crashes",

Description = "When connecting to the database, EF is crashing"

};

_predictionEngine = _context.Model.CreatePredictionEngine<IssueData, IssuePrediction>(model);

IssuePrediction issuePrediction = _predictionEngine.Predict(issueData);

Console.WriteLine("--------------------------------------------------");

Console.WriteLine($"AREA : {issuePrediction.Area}");

Console.WriteLine("--------------------------------------------------");

Console.WriteLine("END PREDICT ISSUE FUNCTION");

}

#endregion

}

TestProject.zip

[C#/ML.NET] 이진 클래스 분류 모델 사용하기

■ 이진 클래스 분류 모델을 사용하는 방법을 보여준다. ▶ SentimentData.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 감정 데이터
    /// </summary>
    public class SentimentData
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Field
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region Field

        /// <summary>
        /// 감정 텍스트
        /// </summary>
        [LoadColumn(0)]
        public string SentimentText;

        /// <summary>
        /// 감정
        /// </summary>
        [LoadColumn(1)]
        [ColumnName("Label")]
        public bool Sentiment;

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 감정 데이터

/// </summary>

public class SentimentData

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Field

////////////////////////////////////////////////////////////////////////////////////////// Public

#region Field

/// <summary>

/// 감정 텍스트

/// </summary>

[LoadColumn(0)]

public string SentimentText;

/// <summary>

/// 감정

/// </summary>

[LoadColumn(1)]

[ColumnName("Label")]

public bool Sentiment;

#endregion

}

▶ SentimentPrediction.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 감정 예측
    /// </summary>
    public class SentimentPrediction : SentimentData
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 예측 - Prediction

        /// <summary>
        /// 예측
        /// </summary>
        [ColumnName("PredictedLabel")]
        public bool Prediction { get; set; }

        #endregion
        #region 확률 - Probability

        /// <summary>
        /// 확률
        /// </summary>
        public float Probability { get; set; }

        #endregion
        #region 점수 - Score

        /// <summary>
        /// 점수
        /// </summary>
        public float Score { get; set; }

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 감정 예측

/// </summary>

public class SentimentPrediction : SentimentData

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 예측 - Prediction

/// <summary>

/// 예측

/// </summary>

[ColumnName("PredictedLabel")]

public bool Prediction { get; set; }

#endregion

#region 확률 - Probability

/// <summary>

/// 확률

/// </summary>

public float Probability { get; set; }

#endregion

#region 점수 - Score

/// <summary>

/// 점수

/// </summary>

public float Score { get; set; }

#endregion

}

▶ Program.cs


using System;
using System.Collections.Generic;
using System.IO;

using Microsoft.ML;
using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 프로그램
    /// </summary>
    class Program
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Field
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region Field

        /// <summary>
        /// 레이블 파일 경로
        /// </summary>
        private static readonly string _labelFilePath = Path.Combine(Environment.CurrentDirectory, "Data", "label.txt");

        #endregion

        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region 프로그램 시작하기 - Main()

        /// <summary>
        /// 프로그램 시작하기
        /// </summary>
        private static void Main()
        {
            Console.WriteLine("BEGIN MAIN FUNCTION");

            MLContext context = new MLContext();

            DataOperationsCatalog.TrainTestData trainTestData = GetTrainTestData(context);

            ITransformer model = GetModel(context, trainTestData.TrainSet);

            Evaluate(context, model, trainTestData.TestSet);

            PredictSingleItem(context, model);

            Predict(context, model);

            Console.WriteLine("END MAIN FUNCTION");
        }

        #endregion

        #region 훈련 테스트 데이터 구하기 - GetTrainTestData(context)

        /// <summary>
        /// 훈련 테스트 데이터 구하기
        /// </summary>
        /// <param name="context">ML 컨텍스트</param>
        /// <returns>훈련 테스트 데이터</returns>
        public static DataOperationsCatalog.TrainTestData GetTrainTestData(MLContext context)
        {
            Console.WriteLine("BEGIN GET TRAIN TEST DATA");

            IDataView dataView = context.Data.LoadFromTextFile<SentimentData>(_labelFilePath, hasHeader : false);

            DataOperationsCatalog.TrainTestData trainTestData = context.Data.TrainTestSplit(dataView, testFraction : 0.2);

            Console.WriteLine("END GET TRAIN TEST DATA");

            return trainTestData;
        }

        #endregion
        #region 모델 구하기 - GetModel(context, trainingDataView)

        /// <summary>
        /// 모델 구하기
        /// </summary>
        /// <param name="context">컨텍스트</param>
        /// <param name="trainingDataView">훈련 데이터 뷰</param>
        /// <returns>모델</returns>
        public static ITransformer GetModel(MLContext context, IDataView trainingDataView)
        {
            Console.WriteLine("BEGIN GET MODEL FUNCTION");

            var estimator = context.Transforms.Text.FeaturizeText
            (
                outputColumnName : "Features",
                inputColumnName  : nameof(SentimentData.SentimentText)
            )
            .Append
            (
                context.BinaryClassification.Trainers.SdcaLogisticRegression
                (
                    labelColumnName   : "Label",
                    featureColumnName : "Features"
                )
            );

            Console.WriteLine("BEGIN CREATE AND TRAIN THE MODEL");

            var model = estimator.Fit(trainingDataView);

            Console.WriteLine("END CREATE AND TRAIN THE MODEL");

            Console.WriteLine("END GET MODEL FUNCTION");

            return model;
        }

        #endregion
        #region 평가하기 - Evaluate(context, model, testDataView)

        /// <summary>
        /// 평가하기
        /// </summary>
        /// <param name="context">ML 컨텍스트</param>
        /// <param name="model">모델</param>
        /// <param name="testDataView">테스트 데이터 뷰</param>
        public static void Evaluate(MLContext context, ITransformer model, IDataView testDataView)
        {
            Console.WriteLine("BEGIN EVALUATE FUNCTION");

            IDataView predictions = model.Transform(testDataView);

            CalibratedBinaryClassificationMetrics metrics = context.BinaryClassification.Evaluate(predictions, "Label");

            Console.WriteLine("MODEL QUALITY METRICS EVALUATION");
            Console.WriteLine("--------------------------------------------------");
            Console.WriteLine($"ACCURACY : {metrics.Accuracy:P2}"         );
            Console.WriteLine($"AUC      : {metrics.AreaUnderRocCurve:P2}");
            Console.WriteLine($"F1SCORE  : {metrics.F1Score:P2}"          );
            Console.WriteLine("--------------------------------------------------");

            Console.WriteLine("END EVALUATE FUNCTION");
        }

        #endregion
        #region 단일 항목 예측하기 - PredictSingleItem(context, model)

        /// <summary>
        /// 단일 항목 예측하기
        /// </summary>
        /// <param name="context">컨텍스트</param>
        /// <param name="model">모델</param>
        private static void PredictSingleItem(MLContext context, ITransformer model)
        {
            Console.WriteLine("BEGIN PREDICT SINGLE ITEM FUNCTION");

            PredictionEngine<SentimentData, SentimentPrediction> predictionEngine = context.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);

            SentimentData sentimentData = new SentimentData { SentimentText = "This was a very bad steak" };

            SentimentPrediction sentimentPrediction = predictionEngine.Predict(sentimentData);

            Console.WriteLine("--------------------------------------------------");
            Console.WriteLine($"SENTIMENT   : {sentimentPrediction.SentimentText}"                                            );
            Console.WriteLine($"PREDICTION  : {(Convert.ToBoolean(sentimentPrediction.Prediction) ? "Positive" : "Negative")}");
            Console.WriteLine($"PROBABILITY : {sentimentPrediction.Probability}"                                              );
            Console.WriteLine("--------------------------------------------------");

            Console.WriteLine("END PREDICT SINGLE ITEM FUNCTION");
        }

        #endregion
        #region 예측하기 - Predict(context, model)

        /// <summary>
        /// 예측하기
        /// </summary>
        /// <param name="context">ML 컨텍스트</param>
        /// <param name="model">모델</param>
        public static void Predict(MLContext context, ITransformer model)
        {
            Console.WriteLine("BEGIN PREDICT FUNCTION");

            IEnumerable<SentimentData> sentimentDataEnumerable = new[]
            {
                new SentimentData { SentimentText = "This was a horrible meal" },
                new SentimentData { SentimentText = "I love this spaghetti."   }
            };

            IDataView sentimentDataView = model.Transform(context.Data.LoadFromEnumerable(sentimentDataEnumerable));

            IEnumerable<SentimentPrediction> sentimentPredictionEnumerable = context.Data.CreateEnumerable<SentimentPrediction>(sentimentDataView, reuseRowObject : false);

            Console.WriteLine("--------------------------------------------------");

            foreach(SentimentPrediction sentimentPrediction  in sentimentPredictionEnumerable)
            {
                Console.WriteLine($"SENTIMENT   : {sentimentPrediction.SentimentText}");
                Console.WriteLine($"PREDICTION  : {(Convert.ToBoolean(sentimentPrediction.Prediction) ? "Positive" : "Negative")}");
                Console.WriteLine($"PROBABILITY : {sentimentPrediction.Probability}");
                Console.WriteLine("--------------------------------------------------");
            }

            Console.WriteLine("END PREDICT FUNCTION");
        }

        #endregion
    }
}

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using System;

using System.Collections.Generic;

using System.IO;

using Microsoft.ML;

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 프로그램

/// </summary>

class Program

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Field

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region Field

/// <summary>

/// 레이블 파일 경로

/// </summary>

private static readonly string _labelFilePath = Path.Combine(Environment.CurrentDirectory, "Data", "label.txt");

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region 프로그램 시작하기 - Main()

/// <summary>

/// 프로그램 시작하기

/// </summary>

private static void Main()

{

Console.WriteLine("BEGIN MAIN FUNCTION");

MLContext context = new MLContext();

DataOperationsCatalog.TrainTestData trainTestData = GetTrainTestData(context);

ITransformer model = GetModel(context, trainTestData.TrainSet);

Evaluate(context, model, trainTestData.TestSet);

PredictSingleItem(context, model);

Predict(context, model);

Console.WriteLine("END MAIN FUNCTION");

}

#endregion

#region 훈련 테스트 데이터 구하기 - GetTrainTestData(context)

/// <summary>

/// 훈련 테스트 데이터 구하기

/// </summary>

/// <param name="context">ML 컨텍스트</param>

/// <returns>훈련 테스트 데이터</returns>

public static DataOperationsCatalog.TrainTestData GetTrainTestData(MLContext context)

{

Console.WriteLine("BEGIN GET TRAIN TEST DATA");

IDataView dataView = context.Data.LoadFromTextFile<SentimentData>(_labelFilePath, hasHeader : false);

DataOperationsCatalog.TrainTestData trainTestData = context.Data.TrainTestSplit(dataView, testFraction : 0.2);

Console.WriteLine("END GET TRAIN TEST DATA");

return trainTestData;

}

#endregion

#region 모델 구하기 - GetModel(context, trainingDataView)

/// <summary>

/// 모델 구하기

/// </summary>

/// <param name="context">컨텍스트</param>

/// <param name="trainingDataView">훈련 데이터 뷰</param>

/// <returns>모델</returns>

public static ITransformer GetModel(MLContext context, IDataView trainingDataView)

{

Console.WriteLine("BEGIN GET MODEL FUNCTION");

var estimator = context.Transforms.Text.FeaturizeText

(

outputColumnName : "Features",

inputColumnName : nameof(SentimentData.SentimentText)

)

.Append

(

context.BinaryClassification.Trainers.SdcaLogisticRegression

(

labelColumnName : "Label",

featureColumnName : "Features"

)

);

Console.WriteLine("BEGIN CREATE AND TRAIN THE MODEL");

var model = estimator.Fit(trainingDataView);

Console.WriteLine("END CREATE AND TRAIN THE MODEL");

Console.WriteLine("END GET MODEL FUNCTION");

return model;

}

#endregion

#region 평가하기 - Evaluate(context, model, testDataView)

/// <summary>

/// 평가하기

/// </summary>

/// <param name="context">ML 컨텍스트</param>

/// <param name="model">모델</param>

/// <param name="testDataView">테스트 데이터 뷰</param>

public static void Evaluate(MLContext context, ITransformer model, IDataView testDataView)

{

Console.WriteLine("BEGIN EVALUATE FUNCTION");

IDataView predictions = model.Transform(testDataView);

CalibratedBinaryClassificationMetrics metrics = context.BinaryClassification.Evaluate(predictions, "Label");

Console.WriteLine("MODEL QUALITY METRICS EVALUATION");

Console.WriteLine("--------------------------------------------------");

Console.WriteLine($"ACCURACY : {metrics.Accuracy:P2}" );

Console.WriteLine($"AUC : {metrics.AreaUnderRocCurve:P2}");

Console.WriteLine($"F1SCORE : {metrics.F1Score:P2}" );

Console.WriteLine("--------------------------------------------------");

Console.WriteLine("END EVALUATE FUNCTION");

}

#endregion

#region 단일 항목 예측하기 - PredictSingleItem(context, model)

/// <summary>

/// 단일 항목 예측하기

/// </summary>

/// <param name="context">컨텍스트</param>

/// <param name="model">모델</param>

private static void PredictSingleItem(MLContext context, ITransformer model)

{

Console.WriteLine("BEGIN PREDICT SINGLE ITEM FUNCTION");

PredictionEngine<SentimentData, SentimentPrediction> predictionEngine = context.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);

SentimentData sentimentData = new SentimentData { SentimentText = "This was a very bad steak" };

SentimentPrediction sentimentPrediction = predictionEngine.Predict(sentimentData);

Console.WriteLine("--------------------------------------------------");

Console.WriteLine($"SENTIMENT : {sentimentPrediction.SentimentText}" );

Console.WriteLine($"PREDICTION : {(Convert.ToBoolean(sentimentPrediction.Prediction) ? "Positive" : "Negative")}");

Console.WriteLine($"PROBABILITY : {sentimentPrediction.Probability}" );

Console.WriteLine("--------------------------------------------------");

Console.WriteLine("END PREDICT SINGLE ITEM FUNCTION");

}

#endregion

#region 예측하기 - Predict(context, model)

/// <summary>

/// 예측하기

/// </summary>

/// <param name="context">ML 컨텍스트</param>

/// <param name="model">모델</param>

public static void Predict(MLContext context, ITransformer model)

{

Console.WriteLine("BEGIN PREDICT FUNCTION");

IEnumerable<SentimentData> sentimentDataEnumerable = new[]

{

new SentimentData { SentimentText = "This was a horrible meal" },

new SentimentData { SentimentText = "I love this spaghetti." }

};

IDataView sentimentDataView = model.Transform(context.Data.LoadFromEnumerable(sentimentDataEnumerable));

IEnumerable<SentimentPrediction> sentimentPredictionEnumerable = context.Data.CreateEnumerable<SentimentPrediction>(sentimentDataView, reuseRowObject : false);

Console.WriteLine("--------------------------------------------------");

foreach(SentimentPrediction sentimentPrediction in sentimentPredictionEnumerable)

{

Console.WriteLine($"SENTIMENT : {sentimentPrediction.SentimentText}");

Console.WriteLine($"PREDICTION : {(Convert.ToBoolean(sentimentPrediction.Prediction) ? "Positive" : "Negative")}");

Console.WriteLine($"PROBABILITY : {sentimentPrediction.Probability}");

Console.WriteLine("--------------------------------------------------");

}

Console.WriteLine("END PREDICT FUNCTION");

}

#endregion

}

TestProject.zip

[C#/ML.NET] 선형 회귀 모델 사용하기

■ 선형 회귀 모델을 사용하는 방법을 보여준다. ▶ HouseData.cs


namespace TestProject
{
    /// <summary>
    /// 주택 데이터
    /// </summary>
    public class HouseData
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 크기 - Size

        /// <summary>
        /// 크기
        /// </summary>
        public float Size { get; set; }

        #endregion
        #region 가격 - Price

        /// <summary>
        /// 가격
        /// </summary>
        public float Price { get; set; }

        #endregion
    }
}

namespace TestProject

{

/// <summary>

/// 주택 데이터

/// </summary>

public class HouseData

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 크기 - Size

/// <summary>

/// 크기

/// </summary>

public float Size { get; set; }

#endregion

#region 가격 - Price

/// <summary>

/// 가격

/// </summary>

public float Price { get; set; }

#endregion

}

▶ Prediction.cs


using Microsoft.ML.Data;

namespace TestProject
{
    /// <summary>
    /// 예측
    /// </summary>
    public class Prediction
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Property
        ////////////////////////////////////////////////////////////////////////////////////////// Public

        #region 가격 - Price

        /// <summary>
        /// 가격
        /// </summary>
        [ColumnName("Score")]
        public float Price { get; set; }

        #endregion
    }
}

using Microsoft.ML.Data;

namespace TestProject

{

/// <summary>

/// 예측

/// </summary>

public class Prediction

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Property

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 가격 - Price

/// <summary>

/// 가격

/// </summary>

[ColumnName("Score")]

public float Price { get; set; }

#endregion

}

▶ Program.cs


using System;
using System.Globalization;
using System.Threading;

using Microsoft.ML;

namespace TestProject
{
    /// <summary>
    /// 프로그램
    /// </summary>
    class Program
    {
        //////////////////////////////////////////////////////////////////////////////////////////////////// Method
        ////////////////////////////////////////////////////////////////////////////////////////// Static
        //////////////////////////////////////////////////////////////////////////////// Private

        #region 프로그램 시작하기 - Main()

        /// <summary>
        /// 프로그램 시작하기
        /// </summary>
        private static void Main()
        {
            Console.WriteLine("BEGIN MAIN FUNCTION");

            CultureInfo cultureInfo = new CultureInfo("en-us");

            Thread.CurrentThread.CurrentCulture   = cultureInfo;
            Thread.CurrentThread.CurrentUICulture = cultureInfo;

            MLContext context = new MLContext();

            Console.WriteLine("BEGIN SET TRAINING DATA VIEW");

            HouseData[] trainingArray =
            {
                new HouseData() { Size = 1.1f, Price = 1.2f },
                new HouseData() { Size = 1.9f, Price = 2.3f },
                new HouseData() { Size = 2.8f, Price = 3.0f },
                new HouseData() { Size = 3.4f, Price = 3.7f }
            };

            IDataView trainingDataView = context.Data.LoadFromEnumerable(trainingArray);

            Console.WriteLine("END SET TRAINING DATA VIEW");

            Console.WriteLine("BEGIN SET PIPELINE");

            var pipeline = context.Transforms.Concatenate("Features", new[] { "Size" })
                .Append(context.Regression.Trainers.Sdca(labelColumnName : "Price", maximumNumberOfIterations : 1000));

            Console.WriteLine("END SET PIPELINE");

            Console.WriteLine("BEGIN SET MODEL");

            var model = pipeline.Fit(trainingDataView);

            Console.WriteLine("END SET MODEL");

            Console.WriteLine("BEGIN PREDICT");

            HouseData houseData = new HouseData() { Size = 2.5f };

            Prediction predition = context.Model.CreatePredictionEngine<HouseData, Prediction>(model).Predict(houseData);

            Console.WriteLine("--------------------------------------------------");
            Console.WriteLine($"SIZE  : {houseData.Size * 1000} SQ FT");
            Console.WriteLine($"PRICE : {predition.Price * 100000:C}");
            Console.WriteLine("--------------------------------------------------");

            Console.WriteLine("END PREDICT");

            Console.WriteLine("END MAIN FUNCTION");
        }

        #endregion
    }
}

using System;

using System.Globalization;

using System.Threading;

using Microsoft.ML;

namespace TestProject

{

/// <summary>

/// 프로그램

/// </summary>

class Program

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Static

//////////////////////////////////////////////////////////////////////////////// Private

#region 프로그램 시작하기 - Main()

/// <summary>

/// 프로그램 시작하기

/// </summary>

private static void Main()

{

Console.WriteLine("BEGIN MAIN FUNCTION");

CultureInfo cultureInfo = new CultureInfo("en-us");

Thread.CurrentThread.CurrentCulture = cultureInfo;

Thread.CurrentThread.CurrentUICulture = cultureInfo;

MLContext context = new MLContext();

Console.WriteLine("BEGIN SET TRAINING DATA VIEW");

HouseData[] trainingArray =

{

new HouseData() { Size = 1.1f, Price = 1.2f },

new HouseData() { Size = 1.9f, Price = 2.3f },

new HouseData() { Size = 2.8f, Price = 3.0f },

new HouseData() { Size = 3.4f, Price = 3.7f }

};

IDataView trainingDataView = context.Data.LoadFromEnumerable(trainingArray);

Console.WriteLine("END SET TRAINING DATA VIEW");

Console.WriteLine("BEGIN SET PIPELINE");

var pipeline = context.Transforms.Concatenate("Features", new[] { "Size" })

.Append(context.Regression.Trainers.Sdca(labelColumnName : "Price", maximumNumberOfIterations : 1000));

Console.WriteLine("END SET PIPELINE");

Console.WriteLine("BEGIN SET MODEL");

var model = pipeline.Fit(trainingDataView);

Console.WriteLine("END SET MODEL");

Console.WriteLine("BEGIN PREDICT");

HouseData houseData = new HouseData() { Size = 2.5f };

Prediction predition = context.Model.CreatePredictionEngine<HouseData, Prediction>(model).Predict(houseData);

Console.WriteLine("--------------------------------------------------");

Console.WriteLine($"SIZE : {houseData.Size * 1000} SQ FT");

Console.WriteLine($"PRICE : {predition.Price * 100000:C}");

Console.WriteLine("--------------------------------------------------");

Console.WriteLine("END PREDICT");

Console.WriteLine("END MAIN FUNCTION");

}

#endregion

}

TestProject.zip