StandardTrainersCatalog.Prior Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.StandardTrainers.dll

Paquete:

Microsoft.ML v3.0.1

Cree PriorTrainer, que predice un destino mediante un modelo de clasificación binaria.

public static Microsoft.ML.Trainers.PriorTrainer Prior (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string exampleWeightColumnName = default);

static member Prior : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string -> Microsoft.ML.Trainers.PriorTrainer

<Extension()>
Public Function Prior (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional exampleWeightColumnName As String = Nothing) As PriorTrainer

Parámetros

catalog

BinaryClassificationCatalog.BinaryClassificationTrainers

El parámetro de cadena de consulta BinaryClassificationCatalog.

labelColumnName

String

Nombre de la columna de etiquetas.

exampleWeightColumnName

String

Nombre de la columna de peso de ejemplo (opcional).

Devoluciones

PriorTrainer

Ejemplos

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class Prior
    {
        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 = mlContext.BinaryClassification.Trainers
                .Prior();

            // 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();

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

            // Expected output:
            //   Label: True, Prediction: True
            //   Label: False, Prediction: True
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.68
            //   AUC: 0.50  (this is expected for Prior trainer)
            //   F1 Score: 0.81
            //   Negative Precision: 0.00
            //   Negative Recall: 0.00
            //   Positive Precision: 0.68
            //   Positive Recall: 1.00
            //
            //   TEST POSITIVE RATIO:    0.6840 (342.0/(342.0+158.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      342 |        0 | 1.0000
            //    negative ||      158 |        0 | 0.0000
            //             ||======================
            //   Precision ||   0.6840 |   0.0000 |
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = randomFloat() > 0.3f;
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    // For data points with false label, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(x => x ? randomFloat() : randomFloat() +
                        0.3f).ToArray()

                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public bool Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print BinaryClassificationMetrics objects.
        private static void PrintMetrics(BinaryClassificationMetrics metrics)
        {
            Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
            Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
            Console.WriteLine($"Negative Precision: " +
                $"{metrics.NegativePrecision:F2}");

            Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
            Console.WriteLine($"Positive Precision: " +
                $"{metrics.PositivePrecision:F2}");

            Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

Comentarios

Este instructor usa la proporción de una etiqueta en el conjunto de entrenamiento como probabilidad de esa etiqueta. Este entrenador se usa a menudo como línea base para otros mdels más sofisticados.