StandardTrainersCatalog.LdSvm メソッド

リファレンス

定義

名前空間:: Microsoft.ML

アセンブリ:: Microsoft.ML.StandardTrainers.dll

パッケージ:: Microsoft.ML v3.0.1

パッケージ:: Microsoft.ML v1.5.5

パッケージ:: Microsoft.ML v1.6.0

パッケージ:: Microsoft.ML v1.7.0

パッケージ:: Microsoft.ML v2.0.0

重要

一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。

オーバーロード

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)	ローカルディープ SVM 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 LdSvmTrainer します。
LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)	ローカルディープ SVM モデルを使用してターゲットを予測する Create LdSvmTrainer。

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

ローカルディープ SVM 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 LdSvmTrainer します。

public static Microsoft.ML.Trainers.LdSvmTrainer LdSvm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.LdSvmTrainer.Options options);

static member LdSvm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.LdSvmTrainer.Options -> Microsoft.ML.Trainers.LdSvmTrainer

<Extension()>
Public Function LdSvm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As LdSvmTrainer.Options) As LdSvmTrainer

パラメーター

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

BinaryClassificationCatalog。

options: LdSvmTrainer.Options

トレーナーのオプション。

戻り値

LdSvmTrainer

例

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class LdSvmWithOptions
    {
        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 LdSvmTrainer.Options
            {
                TreeDepth = 5,
                NumberOfIterations = 10000,
                Sigma = 0.1f,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .LdSvm(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: True
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.80
            //   AUC: 0.89
            //   F1 Score: 0.79
            //   Negative Precision: 0.81
            //   Negative Recall: 0.81
            //   Positive Precision: 0.79
            //   Positive Recall: 0.79

            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      189 |       49 | 0.7941
            //    negative ||       50 |      212 | 0.8092
            //             ||======================
            //   Precision ||   0.7908 |   0.8123 |
        }

        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() +
                        0.1f).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());
        }
    }
}

適用対象

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)

ローカルディープ SVM モデルを使用してターゲットを予測する Create LdSvmTrainer。

public static Microsoft.ML.Trainers.LdSvmTrainer LdSvm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfIterations = 15000, int treeDepth = 3, bool useBias = true, bool useCachedData = true);

static member LdSvm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * int * bool * bool -> Microsoft.ML.Trainers.LdSvmTrainer

<Extension()>
Public Function LdSvm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfIterations As Integer = 15000, Optional treeDepth As Integer = 3, Optional useBias As Boolean = true, Optional useCachedData As Boolean = true) As LdSvmTrainer

パラメーター

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

BinaryClassificationCatalog。

labelColumnName: String

ラベル列の名前。

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)。

numberOfIterations: Int32

イテレーションの数。

treeDepth: Int32

ローカルディープ SVM ツリーの深さ。

useBias: Boolean

モデルにバイアス項が必要かどうかを示します。

useCachedData: Boolean

キャッシュを使用してデータを反復処理するかどうかを示します。

戻り値

LdSvmTrainer

例

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class LdSvm
    {
        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
                .LdSvm();

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

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

            PrintMetrics(metrics);

            // Expected output:
            // Accuracy: 0.82
            // AUC: 0.85
            // F1 Score: 0.81
            // Negative Precision: 0.82
            // Negative Recall: 0.82
            // Positive Precision: 0.81
            // Positive Recall: 0.81

            // TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            // Confusion table
            //           ||======================
            // PREDICTED || positive | negative | Recall
            // TRUTH     ||======================
            //  positive ||      192 |       46 | 0.8067
            //  negative ||       46 |      216 | 0.8244
            //           ||======================
            // Precision ||   0.8067 |   0.8244 |
        }

        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() +
                        0.1f).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());
        }
    }
}

適用対象

次の方法で共有

StandardTrainersCatalog.LdSvm メソッド

定義

オーバーロード

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

パラメーター

戻り値

例

適用対象

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)

パラメーター

戻り値

例

適用対象

その他のリソース