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StandardTrainersCatalog.LbfgsMaximumEntropy メソッド

定義

オーバーロード

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LbfgsMaximumEntropyMulticlassTrainer+Options)

L-BFGS メソッドでトレーニングされた最大エントロピー分類モデルを使用してターゲットを予測する高度なオプションを使用して作成 LbfgsMaximumEntropyMulticlassTrainer します。

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Create LbfgsMaximumEntropyMulticlassTrainer。L-BFGS メソッドでトレーニングされた最大エントロピー分類モデルを使用してターゲットを予測します。

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LbfgsMaximumEntropyMulticlassTrainer+Options)

L-BFGS メソッドでトレーニングされた最大エントロピー分類モデルを使用してターゲットを予測する高度なオプションを使用して作成 LbfgsMaximumEntropyMulticlassTrainer します。

public static Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer LbfgsMaximumEntropy (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer.Options options);
static member LbfgsMaximumEntropy : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer.Options -> Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer
<Extension()>
Public Function LbfgsMaximumEntropy (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As LbfgsMaximumEntropyMulticlassTrainer.Options) As LbfgsMaximumEntropyMulticlassTrainer

パラメーター

options
LbfgsMaximumEntropyMulticlassTrainer.Options

アルゴリズムに対する高度な引数。

戻り値

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class LbfgsMaximumEntropyWithOptions
    {
        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 LbfgsMaximumEntropyMulticlassTrainer.Options
            {
                HistorySize = 50,
                L1Regularization = 0.1f,
                NumberOfThreads = 1
            };

            // Define the trainer.
            var pipeline =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion.MapValueToKey("Label")
                // Apply LbfgsMaximumEntropy multiclass trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .LbfgsMaximumEntropy(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();

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

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3

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

            PrintMetrics(metrics);

            // Expected output:
            //   Micro Accuracy: 0.91
            //   Macro Accuracy: 0.91
            //   Log Loss: 0.22
            //   Log Loss Reduction: 0.80

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   147 |     0 |    13 | 0.9188
            //           1 ||     0 |   165 |    12 | 0.9322
            //           2 ||    11 |     7 |   145 | 0.8896
            //             ||========================
            //   Precision ||0.9304 |0.9593 |0.8529 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 4);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

適用対象

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Create LbfgsMaximumEntropyMulticlassTrainer。L-BFGS メソッドでトレーニングされた最大エントロピー分類モデルを使用してターゲットを予測します。

public static Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer LbfgsMaximumEntropy (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, float l1Regularization = 1, float l2Regularization = 1, float optimizationTolerance = 1E-07, int historySize = 20, bool enforceNonNegativity = false);
static member LbfgsMaximumEntropy : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * single * single * single * int * bool -> Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer
<Extension()>
Public Function LbfgsMaximumEntropy (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional l1Regularization As Single = 1, Optional l2Regularization As Single = 1, Optional optimizationTolerance As Single = 1E-07, Optional historySize As Integer = 20, Optional enforceNonNegativity As Boolean = false) As LbfgsMaximumEntropyMulticlassTrainer

パラメーター

labelColumnName
String

ラベル列の名前。 列データは次の値にする KeyDataViewType必要があります。

featureColumnName
String

フィーチャー列の名前。 列データは既知のサイズの Singleベクトルである必要があります。

exampleWeightColumnName
String

例の重み付け列の名前 (省略可能)。

l1Regularization
Single

L1 正則化 ハイパーパラメーター。 値が大きいほど、モデルがスパースになる傾向があります。

l2Regularization
Single

正則化の L2 重み。

optimizationTolerance
Single

オプティマイザーの収束のしきい値。

historySize
Int32

のメモリ サイズ LbfgsMaximumEntropyMulticlassTrainer。 Low=faster、精度が低い。

enforceNonNegativity
Boolean

負以外の重みを適用します。

戻り値

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class LbfgsMaximumEntropy
    {
        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 =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion
                .MapValueToKey(nameof(DataPoint.Label))
                // Apply LbfgsMaximumEntropy multiclass trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .LbfgsMaximumEntropy());

            // 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();

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

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3

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

            PrintMetrics(metrics);

            // Expected output:
            //  Micro Accuracy: 0.91
            //  Macro Accuracy: 0.91
            //  Log Loss: 0.24
            //  Log Loss Reduction: 0.79

            //  Confusion table
            //            ||========================
            //  PREDICTED ||     0 |     1 |     2 | Recall
            //  TRUTH     ||========================
            //          0 ||   148 |     0 |    12 | 0.9250
            //          1 ||     0 |   165 |    12 | 0.9322
            //          2 ||    11 |     7 |   145 | 0.8896
            //            ||========================
            //  Precision ||0.9308 |0.9593 |0.8580 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 4);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

適用対象