TreeExtensions.FastTreeTweedie Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v3.0.1

Overloads

FastTreeTweedie(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)	Create FastTreeTweedieTrainer, which predicts a target using a decision tree regression model.
FastTreeTweedie(RegressionCatalog+RegressionTrainers, FastTreeTweedieTrainer+Options)	Create FastTreeTweedieTrainer using advanced options, which predicts a target using a decision tree regression model.

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

Create FastTreeTweedieTrainer, which predicts a target using a decision tree regression model.

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

Parameters

catalog

RegressionCatalog.RegressionTrainers

The RegressionCatalog.

labelColumnName

String

The name of the label column. The column data must be Single.

featureColumnName

String

The name of the feature column. The column data must be a known-sized vector of Single.

exampleWeightColumnName

String

The name of the example weight column (optional).

numberOfLeaves

Int32

The maximum number of leaves per decision tree.

numberOfTrees

Int32

Total number of decision trees to create in the ensemble.

minimumExampleCountPerLeaf

Int32

The minimal number of data points required to form a new tree leaf.

learningRate

Double

The learning rate.

Returns

FastTreeTweedieTrainer

Examples

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)

Create FastTreeTweedieTrainer using advanced options, which predicts a target using a decision tree regression model.

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

Parameters

catalog

RegressionCatalog.RegressionTrainers

The RegressionCatalog.

options

FastTreeTweedieTrainer.Options

Trainer options.

Returns

FastTreeTweedieTrainer

Examples

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);
        }
    }
}