TreeExtensions.FastTreeTweedie Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.FastTree.dll

Paquete:

Microsoft.ML.FastTree v3.0.1

Sobrecargas

FastTreeTweedie(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)	Cree FastTreeTweedieTrainer, que predice un destino mediante un modelo de regresión de árbol de decisión.
FastTreeTweedie(RegressionCatalog+RegressionTrainers, FastTreeTweedieTrainer+Options)	Cree FastTreeTweedieTrainer mediante opciones avanzadas, que predice un destino mediante un modelo de regresión de árbol de decisión.

FastTreeTweedie(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)

Cree FastTreeTweedieTrainer, que predice un destino mediante un modelo de regresión de árbol de decisión.

public static Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer FastTreeTweedie (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfLeaves = 20, int numberOfTrees = 100, int minimumExampleCountPerLeaf = 10, double learningRate = 0.2);

static member FastTreeTweedie : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer

<Extension()>
Public Function FastTreeTweedie (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Integer = 20, Optional numberOfTrees As Integer = 100, Optional minimumExampleCountPerLeaf As Integer = 10, Optional learningRate As Double = 0.2) As FastTreeTweedieTrainer

Parámetros

catalog

RegressionCatalog.RegressionTrainers

El parámetro de cadena de consulta RegressionCatalog.

labelColumnName

String

Nombre de la columna de etiquetas. Los datos de columna deben ser Single.

featureColumnName

String

Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single.

exampleWeightColumnName

String

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

numberOfLeaves

Int32

Número máximo de hojas por árbol de decisión.

numberOfTrees

Int32

Número total de árboles de decisión que se van a crear en el conjunto.

minimumExampleCountPerLeaf

Int32

Número mínimo de puntos de datos necesarios para formar una nueva hoja de árbol.

learningRate

Double

Velocidad de aprendizaje.

Devoluciones

FastTreeTweedieTrainer

Ejemplos

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class FastTreeTweedieRegression
    {
        // This example requires installation of additional NuGet
        // package for Microsoft.ML.FastTree found at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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.Regression.Trainers.FastTreeTweedie(
                labelColumnName: nameof(DataPoint.Label),
                featureColumnName: nameof(DataPoint.Features));

            // 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(5, 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 for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // Expected output:
            //   Label: 0.985, Prediction: 0.945
            //   Label: 0.155, Prediction: 0.104
            //   Label: 0.515, Prediction: 0.515
            //   Label: 0.566, Prediction: 0.448
            //   Label: 0.096, Prediction: 0.082

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

            // Expected output:
            //   Mean Absolute Error: 0.04
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.06
            //   RSquared: 0.96 (closer to 1 is better. The worst case is 0)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

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

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

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}

FastTreeTweedie(RegressionCatalog+RegressionTrainers, FastTreeTweedieTrainer+Options)

Cree FastTreeTweedieTrainer mediante opciones avanzadas, que predice un destino mediante un modelo de regresión de árbol de decisión.

public static Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer FastTreeTweedie (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options options);

static member FastTreeTweedie : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer

<Extension()>
Public Function FastTreeTweedie (catalog As RegressionCatalog.RegressionTrainers, options As FastTreeTweedieTrainer.Options) As FastTreeTweedieTrainer

Parámetros

catalog

RegressionCatalog.RegressionTrainers

El parámetro de cadena de consulta RegressionCatalog.

options

FastTreeTweedieTrainer.Options

Opciones del instructor.

Devoluciones

FastTreeTweedieTrainer

Ejemplos

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class FastTreeTweedieWithOptionsRegression
    {
        // This example requires installation of additional NuGet
        // package for Microsoft.ML.FastTree found at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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 trainer options.
            var options = new FastTreeTweedieTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Use L2Norm for early stopping.
                EarlyStoppingMetric =
                    Microsoft.ML.Trainers.FastTree.EarlyStoppingMetric.L2Norm,

                // Create a simpler model by penalizing usage of new features.
                FeatureFirstUsePenalty = 0.1,
                // Reduce the number of trees to 50.
                NumberOfTrees = 50
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.FastTreeTweedie(options);

            // 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(5, 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 for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // Expected output:
            //   Label: 0.985, Prediction: 0.954
            //   Label: 0.155, Prediction: 0.103
            //   Label: 0.515, Prediction: 0.450
            //   Label: 0.566, Prediction: 0.515
            //   Label: 0.096, Prediction: 0.078

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

            // Expected output:
            //   Mean Absolute Error: 0.04
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.05
            //   RSquared: 0.98 (closer to 1 is better. The worst case is 0)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

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

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

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}