StandardTrainersCatalog.PairwiseCoupling<TModel> 메서드

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.StandardTrainers.dll

패키지:

Microsoft.ML v3.0.1

PairwiseCouplingTrainer쌍으로 결합 전략을 사용하여 다중 클래스 대상을 예측하는 이진 분류 추정기를 binaryEstimator만듭니다.

public static Microsoft.ML.Trainers.PairwiseCouplingTrainer PairwiseCoupling<TModel> (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.ITrainerEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<TModel>,TModel> binaryEstimator, string labelColumnName = "Label", bool imputeMissingLabelsAsNegative = false, Microsoft.ML.IEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.ICalibrator>> calibrator = default, int maximumCalibrationExampleCount = 1000000000) where TModel : class;

static member PairwiseCoupling : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.ITrainerEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<'Model>, 'Model (requires 'Model : null)> * string * bool * Microsoft.ML.IEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.ICalibrator>> * int -> Microsoft.ML.Trainers.PairwiseCouplingTrainer (requires 'Model : null)

<Extension()>
Public Function PairwiseCoupling(Of TModel As Class) (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, binaryEstimator As ITrainerEstimator(Of ISingleFeaturePredictionTransformer(Of TModel), TModel), Optional labelColumnName As String = "Label", Optional imputeMissingLabelsAsNegative As Boolean = false, Optional calibrator As IEstimator(Of ISingleFeaturePredictionTransformer(Of ICalibrator)) = Nothing, Optional maximumCalibrationExampleCount As Integer = 1000000000) As PairwiseCouplingTrainer

형식 매개 변수

TModel

모델의 형식입니다. 이 형식 매개 변수는 일반적으로 .에서 binaryEstimator자동으로 유추됩니다.

매개 변수

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

다중 클래스 분류 카탈로그 트레이너 개체입니다.

binaryEstimator

ITrainerEstimator<ISingleFeaturePredictionTransformer<TModel>,TModel>

기본 트레이너로 사용되는 이진 파일 ITrainerEstimator<TTransformer,TModel> 의 인스턴스입니다.

labelColumnName

String

레이블 열의 이름입니다.

imputeMissingLabelsAsNegative

Boolean

누락된 레이블을 누락된 상태로 유지하는 대신 음수 레이블이 있는 것으로 처리할지 여부입니다.

calibrator

IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>

보정기입니다. If a calibrator is not explicitly provided, it will default to Microsoft.ML.Calibrators.PlattCalibratorTrainer

maximumCalibrationExampleCount

Int32

보정기를 학습시킬 인스턴스 수입니다.

반환

PairwiseCouplingTrainer

예제

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class PairwiseCoupling
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define the trainer.
            var pipeline =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion.MapValueToKey("Label")
                // Apply PairwiseCoupling multiclass meta trainer on top of
                // binary trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .PairwiseCoupling(
                mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data
                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data
                .CreateEnumerable<Prediction>(transformedTestData,
                reuseRowObject: false).ToList();

            // Look at 5 predictions
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, " +
                    $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2

            // Evaluate the overall metrics
            var metrics = mlContext.MulticlassClassification
                .Evaluate(transformedTestData);

            PrintMetrics(metrics);

            // Expected output:
            //   Micro Accuracy: 0.90
            //   Macro Accuracy: 0.90
            //   Log Loss: 0.36
            //   Log Loss Reduction: 0.67

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   150 |     0 |    10 | 0.9375
            //           1 ||     0 |   166 |    11 | 0.9379
            //           2 ||    15 |    15 |   133 | 0.8160
            //             ||========================
            //   Precision ||0.9091 |0.9171 |0.8636 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 4);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public uint Label { get; set; }
            // Predicted label from the trainer.
            public uint PredictedLabel { get; set; }
        }

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

설명

PKPD(Pairwise 결합) 전략에서 이진 분류 알고리즘은 각 클래스 쌍에 대해 하나의 분류자를 학습시키는 데 사용됩니다. 그런 다음 예측은 이러한 이진 분류자를 실행하고 예측된 이진 분류자 수를 계산하여 각 클래스에 대한 점수를 계산하여 수행됩니다. 예측은 점수가 가장 높은 클래스입니다.