StandardTrainersCatalog.OneVersusAll<TModel> Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.StandardTrainers.dll

Paket:

Microsoft.ML v2.0.0

OneVersusAllTrainerBuat , yang memprediksi target multikelas menggunakan strategi satu versus-semua dengan estimator klasifikasi biner yang ditentukan oleh binaryEstimator.

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

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

<Extension()>
Public Function OneVersusAll(Of TModel As Class) (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, binaryEstimator As ITrainerEstimator(Of BinaryPredictionTransformer(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, Optional useProbabilities As Boolean = true) As OneVersusAllTrainer

Jenis parameter

TModel

Jenis model. Parameter jenis ini biasanya akan disimpulkan secara otomatis dari binaryEstimator.

Parameter

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

Objek pelatih katalog klasifikasi multikelas.

binaryEstimator

ITrainerEstimator<BinaryPredictionTransformer<TModel>,TModel>

Instans biner ITrainerEstimator<TTransformer,TModel> yang digunakan sebagai pelatih dasar.

labelColumnName

String

Nama kolom label.

imputeMissingLabelsAsNegative

Boolean

Apakah memperlakukan label yang hilang sebagai memiliki label negatif, alih-alih membuatnya hilang.

calibrator

IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>

Kalibrator. Jika kalibrator tidak disediakan secara eksplisit, ini akan default ke Microsoft.ML.Calibrators.PlattCalibratorTrainer

maximumCalibrationExampleCount

Int32

Jumlah instans untuk melatih kalibrator.

useProbabilities

Boolean

Gunakan probabilitas (vs. output mentah) untuk mengidentifikasi kategori skor teratas.

Mengembalikan

OneVersusAllTrainer

Contoh

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class OneVersusAll
    {
        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 OneVersusAll multiclass meta trainer on top of
                // binary trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .OneVersusAll(
                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: 0.68

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   152 |     0 |     8 | 0.9500
            //           1 ||     0 |   168 |     9 | 0.9492
            //           2 ||    17 |    15 |   131 | 0.8037
            //             ||========================
            //   Precision ||0.8994 |0.9180 |0.8851 |
        }

        // 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());
        }
    }
}

Keterangan

Dalam strategi satu versus-semua, algoritma klasifikasi biner digunakan untuk melatih satu pengklasifikasi untuk setiap kelas, yang membedakan kelas tersebut dari semua kelas lainnya. Prediksi kemudian dilakukan dengan menjalankan pengklasifikasi biner ini, dan memilih prediksi dengan skor keyakinan tertinggi.