StandardTrainersCatalog.OneVersusAll<TModel> Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

Create a OneVersusAllTrainer, which predicts a multiclass target using one-versus-all strategy with the binary classification estimator specified by 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

Type Parameters

TModel

The type of the model. This type parameter will usually be inferred automatically from binaryEstimator.

Parameters

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

The multiclass classification catalog trainer object.

binaryEstimator

ITrainerEstimator<BinaryPredictionTransformer<TModel>,TModel>

An instance of a binary ITrainerEstimator<TTransformer,TModel> used as the base trainer.

labelColumnName

String

The name of the label column.

imputeMissingLabelsAsNegative

Boolean

Whether to treat missing labels as having negative labels, instead of keeping them missing.

calibrator

IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>

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

maximumCalibrationExampleCount

Int32

Number of instances to train the calibrator.

useProbabilities

Boolean

Use probabilities (vs. raw outputs) to identify top-score category.

Returns

OneVersusAllTrainer

Examples

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());
        }
    }
}

Remarks

In one-versus-all strategy, a binary classification algorithm is used to train one classifier for each class, which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, and choosing the prediction with the highest confidence score.