Sdílet prostřednictvím

TreeExtensions.FastTree Metoda

  • Reference

Definice

Obor názvů:
Microsoft.ML
Sestavení:
Microsoft.ML.FastTree.dll
Balíček:
Microsoft.ML.FastTree v3.0.1
Balíček:
Microsoft.ML.FastTree v1.0.0
Balíček:
Microsoft.ML.FastTree v1.1.0
Balíček:
Microsoft.ML.FastTree v1.2.0
Balíček:
Microsoft.ML.FastTree v1.3.1
Balíček:
Microsoft.ML.FastTree v1.4.0
Balíček:
Microsoft.ML.FastTree v1.5.5
Balíček:
Microsoft.ML.FastTree v1.6.0
Balíček:
Microsoft.ML.FastTree v1.7.0
Balíček:
Microsoft.ML.FastTree v2.0.0

Přetížení

FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, FastTreeBinaryTrainer+Options)

Vytvořte FastTreeBinaryTrainer s pokročilými možnostmi, které predikují cíl pomocí modelu binární klasifikace rozhodovacího stromu.
FastTree(RankingCatalog+RankingTrainers, FastTreeRankingTrainer+Options)

FastTreeRankingTrainer Vytvořte s pokročilými možnostmi, které řadí řadu vstupů na základě jejich relevance pomocí modelu řazení rozhodovacího stromu.
FastTree(RegressionCatalog+RegressionTrainers, FastTreeRegressionTrainer+Options)

Vytvořte FastTreeRegressionTrainer s pokročilými možnostmi, které predikují cíl pomocí regresního modelu rozhodovacího stromu.
FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Int32, Double)

Vytvořit FastTreeBinaryTrainer, který predikuje cíl pomocí modelu binární klasifikace rozhodovacího stromu.
FastTree(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)

Vytvořit FastTreeRegressionTrainer, který predikuje cíl pomocí regresního modelu rozhodovacího stromu.
FastTree(RankingCatalog+RankingTrainers, String, String, String, String, Int32, Int32, Int32, Double)

FastTreeRankingTrainerVytvořte , která seřadí řadu vstupů na základě jejich relevance pomocí modelu řazení rozhodovacího stromu.

FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, FastTreeBinaryTrainer+Options)

Vytvořte FastTreeBinaryTrainer s pokročilými možnostmi, které predikují cíl pomocí modelu binární klasifikace rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer FastTree (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options options);
static member FastTree : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer
<Extension()>
Public Function FastTree (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As FastTreeBinaryTrainer.Options) As FastTreeBinaryTrainer

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Hodnota BinaryClassificationCatalog

options
FastTreeBinaryTrainer.Options

Možnosti trenéra.

Návraty

FastTreeBinaryTrainer

Příklady

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.BinaryClassification
{
    public static class FastTreeWithOptions
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree 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 FastTreeBinaryTrainer.Options
            {
                // Use L2Norm for early stopping.
                EarlyStoppingMetric = 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.BinaryClassification.Trainers
                .FastTree(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(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: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.78
            //   AUC: 0.88
            //   F1 Score: 0.79
            //   Negative Precision: 0.83
            //   Negative Recall: 0.74
            //   Positive Precision: 0.74
            //   Positive Recall: 0.84
            //   Log Loss: 0.62
            //   Log Loss Reduction: 37.77
            //   Entropy: 1.00
            //
            //  TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //  Confusion table
            //            ||======================
            //  PREDICTED || positive | negative | Recall
            //  TRUTH     ||======================
            //   positive ||      185 |       53 | 0.7773
            //   negative ||       83 |      179 | 0.6832
            //            ||======================
            //  Precision ||   0.6903 |   0.7716 |
        }

        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() > 0.5f;
                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() +
                        0.03f).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());
        }
    }
}

Platí pro

FastTree(RankingCatalog+RankingTrainers, FastTreeRankingTrainer+Options)

FastTreeRankingTrainer Vytvořte s pokročilými možnostmi, které řadí řadu vstupů na základě jejich relevance pomocí modelu řazení rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer FastTree (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options options);
static member FastTree : Microsoft.ML.RankingCatalog.RankingTrainers * Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer
<Extension()>
Public Function FastTree (catalog As RankingCatalog.RankingTrainers, options As FastTreeRankingTrainer.Options) As FastTreeRankingTrainer

Parametry

catalog
RankingCatalog.RankingTrainers

Hodnota RankingCatalog

options
FastTreeRankingTrainer.Options

Možnosti trenéra.

Návraty

FastTreeRankingTrainer

Příklady

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.Ranking
{
    public static class FastTreeWithOptions
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree 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 FastTreeRankingTrainer.Options
            {
                // Use NdcgAt3 for early stopping.
                EarlyStoppingMetric = EarlyStoppingRankingMetric.NdcgAt3,
                // Create a simpler model by penalizing usage of new features.
                FeatureFirstUsePenalty = 0.1,
                // Reduce the number of trees to 50.
                NumberOfTrees = 50,
                // Specify the row group column name.
                RowGroupColumnName = "GroupId"
            };

            // Define the trainer.
            var pipeline = mlContext.Ranking.Trainers.FastTree(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(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Take the top 5 rows.
            var topTransformedTestData = mlContext.Data.TakeRows(
                transformedTestData, 5);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                topTransformedTestData, reuseRowObject: false).ToList();

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

            // Expected output:
            //   Label: 5, Score: 8.807633
            //   Label: 1, Score: -10.71331
            //   Label: 3, Score: -8.134147
            //   Label: 3, Score: -6.545538
            //   Label: 1, Score: -10.27982

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

            // Expected output:
            //   DCG: @1:40.57, @2:61.21, @3:74.11
            //   NDCG: @1:0.96, @2:0.95, @3:0.97
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0, int groupSize = 10)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = random.Next(0, 5);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    GroupId = (uint)(i / groupSize),
                    // Create random features that are correlated with the label.
                    // For data points with larger labels, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => randomFloat() + x * 0.1f).ToArray()
                };
            }
        }

        // Example with label, groupId, and 50 feature values. A data set is a
        // collection of such examples.
        private class DataPoint
        {
            [KeyType(5)]
            public uint Label { get; set; }
            [KeyType(100)]
            public uint GroupId { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print RankerMetrics objects.
        public static void PrintMetrics(RankingMetrics metrics)
        {
            Console.WriteLine("DCG: " + string.Join(", ",
                metrics.DiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
            Console.WriteLine("NDCG: " + string.Join(", ",
                metrics.NormalizedDiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
        }
    }
}

Platí pro

FastTree(RegressionCatalog+RegressionTrainers, FastTreeRegressionTrainer+Options)

Vytvořte FastTreeRegressionTrainer s pokročilými možnostmi, které predikují cíl pomocí regresního modelu rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer FastTree (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options options);
static member FastTree : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer
<Extension()>
Public Function FastTree (catalog As RegressionCatalog.RegressionTrainers, options As FastTreeRegressionTrainer.Options) As FastTreeRegressionTrainer

Parametry

catalog
RegressionCatalog.RegressionTrainers

Hodnota RegressionCatalog

options
FastTreeRegressionTrainer.Options

Možnosti trenéra.

Návraty

FastTreeRegressionTrainer

Příklady

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 FastTreeWithOptionsRegression
    {
        // 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 FastTreeRegressionTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Use L2-norm for early stopping. If the gradient's L2-norm is
                // smaller than an auto-computed value, training process will stop.
                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.FastTree(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: 0.950
            //   Label: 0.155, Prediction: 0.111
            //   Label: 0.515, Prediction: 0.475
            //   Label: 0.566, Prediction: 0.575
            //   Label: 0.096, Prediction: 0.093

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

            // Expected output:
            //   Mean Absolute Error: 0.03
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.03
            //   RSquared: 0.99 (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);
        }
    }
}

Platí pro

FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Int32, Double)

Vytvořit FastTreeBinaryTrainer, který predikuje cíl pomocí modelu binární klasifikace rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer FastTree (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers 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 FastTree : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer
<Extension()>
Public Function FastTree (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, 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 FastTreeBinaryTrainer

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Hodnota BinaryClassificationCatalog

labelColumnName
String

Název sloupce popisku. Data sloupce musí být Boolean.

featureColumnName
String

Název sloupce funkce. Data sloupce musí být vektorem známé velikosti Single.

exampleWeightColumnName
String

Název ukázkového sloupce hmotnosti (volitelné).

numberOfLeaves
Int32

Maximální počet listů na rozhodovací strom.

numberOfTrees
Int32

Celkový počet rozhodovacích stromů, které se mají vytvořit v souboru.

minimumExampleCountPerLeaf
Int32

Minimální počet datových bodů potřebných k vytvoření nového listu stromu

learningRate
Double

Míra učení.

Návraty

FastTreeBinaryTrainer

Příklady

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class FastTree
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree 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.BinaryClassification.Trainers
                .FastTree();

            // 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: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.81
            //   AUC: 0.91
            //   F1 Score: 0.80
            //   Negative Precision: 0.82
            //   Negative Recall: 0.80
            //   Positive Precision: 0.79
            //   Positive Recall: 0.81
            //   Log Loss: 0.59
            //   Log Loss Reduction: 41.04
            //   Entropy: 1.00
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      185 |       53 | 0.7773
            //    negative ||       83 |      179 | 0.6832
            //             ||======================
            //   Precision ||   0.6903 |   0.7716 |
        }

        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() > 0.5f;
                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() +
                        0.03f).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());
        }
    }
}

Platí pro

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

Vytvořit FastTreeRegressionTrainer, který predikuje cíl pomocí regresního modelu rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer FastTree (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 FastTree : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer
<Extension()>
Public Function FastTree (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 FastTreeRegressionTrainer

Parametry

catalog
RegressionCatalog.RegressionTrainers

Hodnota RegressionCatalog

labelColumnName
String

Název sloupce popisku. Data sloupce musí být Single.

featureColumnName
String

Název sloupce funkce. Data sloupce musí být vektorem známé velikosti Single.

exampleWeightColumnName
String

Název ukázkového sloupce hmotnosti (volitelné).

numberOfLeaves
Int32

Maximální počet listů na rozhodovací strom.

numberOfTrees
Int32

Celkový počet rozhodovacích stromů, které se mají vytvořit v souboru.

minimumExampleCountPerLeaf
Int32

Minimální počet datových bodů potřebných k vytvoření nového listu stromu

learningRate
Double

Míra učení.

Návraty

FastTreeRegressionTrainer

Příklady

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class FastTreeRegression
    {
        // 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.FastTree(
                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: 0.938
            //   Label: 0.155, Prediction: 0.131
            //   Label: 0.515, Prediction: 0.517
            //   Label: 0.566, Prediction: 0.519
            //   Label: 0.096, Prediction: 0.089

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

            // Expected output:
            //   Mean Absolute Error: 0.03
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.03
            //   RSquared: 0.99 (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);
        }
    }
}

Platí pro

FastTree(RankingCatalog+RankingTrainers, String, String, String, String, Int32, Int32, Int32, Double)

FastTreeRankingTrainerVytvořte , která seřadí řadu vstupů na základě jejich relevance pomocí modelu řazení rozhodovacího stromu.

public static Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer FastTree (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string rowGroupColumnName = "GroupId", string exampleWeightColumnName = default, int numberOfLeaves = 20, int numberOfTrees = 100, int minimumExampleCountPerLeaf = 10, double learningRate = 0.2);
static member FastTree : Microsoft.ML.RankingCatalog.RankingTrainers * string * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer
<Extension()>
Public Function FastTree (catalog As RankingCatalog.RankingTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional rowGroupColumnName As String = "GroupId", 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 FastTreeRankingTrainer

Parametry

catalog
RankingCatalog.RankingTrainers

Hodnota RankingCatalog

labelColumnName
String

Název sloupce popisku. Data sloupce musí být Single nebo KeyDataViewType.

featureColumnName
String

Název sloupce funkce. Data sloupce musí být vektorem známé velikosti Single.

rowGroupColumnName
String

Název sloupce skupiny.

exampleWeightColumnName
String

Název ukázkového sloupce hmotnosti (volitelné).

numberOfLeaves
Int32

Maximální počet listů na rozhodovací strom.

numberOfTrees
Int32

Celkový počet rozhodovacích stromů, které se mají vytvořit v souboru.

minimumExampleCountPerLeaf
Int32

Minimální počet datových bodů potřebných k vytvoření nového listu stromu

learningRate
Double

Míra učení.

Návraty

FastTreeRankingTrainer

Příklady

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

namespace Samples.Dynamic.Trainers.Ranking
{
    public static class FastTree
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree 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.Ranking.Trainers.FastTree();

            // 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);

            // Take the top 5 rows.
            var topTransformedTestData = mlContext.Data.TakeRows(
                transformedTestData, 5);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                topTransformedTestData, reuseRowObject: false).ToList();

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

            // Expected output:
            //   Label: 5, Score: 13.0154
            //   Label: 1, Score: -19.27798
            //   Label: 3, Score: -12.43686
            //   Label: 3, Score: -8.178633
            //   Label: 1, Score: -17.09313

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

            // Expected output:
            //   DCG: @1:41.95, @2:63.33, @3:75.65
            //   NDCG: @1:0.99, @2:0.98, @3:0.99
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0, int groupSize = 10)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = random.Next(0, 5);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    GroupId = (uint)(i / groupSize),
                    // Create random features that are correlated with the label.
                    // For data points with larger labels, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50).Select(
                           x => randomFloat() + x * 0.1f).ToArray()
                };
            }
        }

        // Example with label, groupId, and 50 feature values. A data set is a
        // collection of such examples.
        private class DataPoint
        {
            [KeyType(5)]
            public uint Label { get; set; }
            [KeyType(100)]
            public uint GroupId { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print RankerMetrics objects.
        public static void PrintMetrics(RankingMetrics metrics)
        {
            Console.WriteLine("DCG: " + string.Join(", ",
                metrics.DiscountedCumulativeGains.Select(
                (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));

            Console.WriteLine("NDCG: " + string.Join(", ",
                metrics.NormalizedDiscountedCumulativeGains.Select(
                (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
        }
    }
}

Platí pro