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StandardTrainersCatalog.SdcaLogisticRegression Methode

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Definition

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.StandardTrainers.dll
Paket:
Microsoft.ML v3.0.1
Paket:
Microsoft.ML v1.0.0
Paket:
Microsoft.ML v1.1.0
Paket:
Microsoft.ML v1.2.0
Paket:
Microsoft.ML v1.3.1
Paket:
Microsoft.ML v1.4.0
Paket:
Microsoft.ML v1.5.5
Paket:
Microsoft.ML v1.6.0
Paket:
Microsoft.ML v1.7.0
Paket:
Microsoft.ML v2.0.0

Überlädt

SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer+Options)

Erstellen Sie SdcaLogisticRegressionBinaryTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines linearen Klassifizierungsmodells vorhersagt.
SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Erstellen Sie SdcaLogisticRegressionBinaryTrainerein Ziel, das ein Ziel mithilfe eines linearen Klassifizierungsmodells vorhersagt.

SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer+Options)

Erstellen Sie SdcaLogisticRegressionBinaryTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines linearen Klassifizierungsmodells vorhersagt.

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

Parameter

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Das Binärklassifizierungskatalogobjekt.

options
SdcaLogisticRegressionBinaryTrainer.Options

Traineroptionen.

Gibt zurück

SdcaLogisticRegressionBinaryTrainer

Beispiele

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 SdcaLogisticRegressionWithOptions
    {
        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);

            // ML.NET doesn't cache data set by default. Therefore, if one reads a
            // data set from a file and accesses it many times, it can be slow due
            // to expensive featurization and disk operations. When the considered
            // data can fit into memory, a solution is to cache the data in memory.
            // Caching is especially helpful when working with iterative algorithms 
            // which needs many data passes.
            trainingData = mlContext.Data.Cache(trainingData);

            // Define trainer options.
            var options = new SdcaLogisticRegressionBinaryTrainer.Options()
            {
                // Make the convergence tolerance tighter.
                ConvergenceTolerance = 0.05f,
                // Increase the maximum number of passes over training data.
                MaximumNumberOfIterations = 30,
                // Give the instances of the positive class slightly more weight.
                PositiveInstanceWeight = 1.2f,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SdcaLogisticRegression(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: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.60
            //   AUC: 0.67
            //   F1 Score: 0.65
            //   Negative Precision: 0.69
            //   Negative Recall: 0.45
            //   Positive Precision: 0.56
            //   Positive Recall: 0.77
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      165 |       73 | 0.6933
            //    negative ||      112 |      150 | 0.5725
            //             ||======================
            //   Precision ||   0.5957 |   0.6726 |
        }

        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());
        }
    }
}

Gilt für:

SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Erstellen Sie SdcaLogisticRegressionBinaryTrainerein Ziel, das ein Ziel mithilfe eines linearen Klassifizierungsmodells vorhersagt.

public static Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer SdcaLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);
static member SdcaLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer
<Extension()>
Public Function SdcaLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaLogisticRegressionBinaryTrainer

Parameter

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Das Binärklassifizierungskatalogobjekt.

labelColumnName
String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen Singlesein.

featureColumnName
String

Der Name der Featurespalte. Die Spaltendaten müssen ein bekannter Vektor von Single.

exampleWeightColumnName
String

Der Name der Beispielgewichtsspalte (optional).

l2Regularization
Nullable<Single>

Die L2-Gewichtung für die Regularisierung.

l1Regularization
Nullable<Single>

Der L1-Normalisierungs-Hyperparameter . Höhere Werte werden dazu führen, dass das Modell sparsamer wird.

maximumNumberOfIterations
Nullable<Int32>

Die maximale Anzahl der Übergebenen, die über die Daten ausgeführt werden sollen.

Gibt zurück

SdcaLogisticRegressionBinaryTrainer

Beispiele

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

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

            // ML.NET doesn't cache data set by default. Therefore, if one reads a
            // data set from a file and accesses it many times, it can be slow due
            // to expensive featurization and disk operations. When the considered
            // data can fit into memory, a solution is to cache the data in memory.
            // Caching is especially helpful when working with iterative algorithms 
            // which needs many data passes.
            trainingData = mlContext.Data.Cache(trainingData);

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SdcaLogisticRegression();

            // 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: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.63
            //   AUC: 0.70
            //   F1 Score: 0.64
            //   Negative Precision: 0.67
            //   Negative Recall: 0.60
            //   Positive Precision: 0.60
            //   Positive Recall: 0.68
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      154 |       84 | 0.6471
            //    negative ||       94 |      168 | 0.6412
            //             ||======================
            //   Precision ||   0.6210 |   0.6667 |
        }

        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());
        }
    }
}

Gilt für: