TreeExtensions.FastTreeTweedie Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.FastTree.dll

Pacchetto:

Microsoft.ML.FastTree v2.0.0

Overload

FastTreeTweedie(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)	Creare FastTreeTweedieTrainer, che stima una destinazione usando un modello di regressione dell'albero delle decisioni.
FastTreeTweedie(RegressionCatalog+RegressionTrainers, FastTreeTweedieTrainer+Options)	Creare FastTreeTweedieTrainer usando le opzioni avanzate, che stimano una destinazione usando un modello di regressione dell'albero delle decisioni.

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

Creare FastTreeTweedieTrainer, che stima una destinazione usando un modello di regressione dell'albero delle decisioni.

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

Parametri

catalog

RegressionCatalog.RegressionTrainers

Oggetto RegressionCatalog.

labelColumnName

String

Nome della colonna dell'etichetta. I dati della colonna devono essere Single.

featureColumnName

String

Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note di Single.

exampleWeightColumnName

String

Nome della colonna peso di esempio (facoltativo).

numberOfLeaves

Int32

Numero massimo di foglie per albero delle decisioni.

numberOfTrees

Int32

Numero totale di alberi delle decisioni da creare nell'ensemble.

minimumExampleCountPerLeaf

Int32

Numero minimo di punti dati necessari per formare una nuova foglia dell'albero.

learningRate

Double

Frequenza di apprendimento.

Restituisce

FastTreeTweedieTrainer

Esempio

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)

Creare FastTreeTweedieTrainer usando le opzioni avanzate, che stimano una destinazione usando un modello di regressione dell'albero delle decisioni.

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

Parametri

catalog

RegressionCatalog.RegressionTrainers

Oggetto RegressionCatalog.

options

FastTreeTweedieTrainer.Options

Opzioni del formatore.

Restituisce

FastTreeTweedieTrainer

Esempio

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);
        }
    }
}