通过

StandardTrainersCatalog.AveragedPerceptron 方法

  • 参考

定义

命名空间:
Microsoft.ML
程序集:
Microsoft.ML.StandardTrainers.dll
包:
Microsoft.ML v3.0.1
包:
Microsoft.ML v1.0.0
包:
Microsoft.ML v1.1.0
包:
Microsoft.ML v1.2.0
包:
Microsoft.ML v1.3.1
包:
Microsoft.ML v1.4.0
包:
Microsoft.ML v1.5.5
包:
Microsoft.ML v1.6.0
包:
Microsoft.ML v1.7.0
包:
Microsoft.ML v2.0.0

重载

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)

使用高级选项创建一个 AveragedPerceptronTrainer ，该模型使用通过布尔标签数据训练的线性二元分类模型预测目标。
AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)

创建一个 AveragedPerceptronTrainer，该模型使用通过布尔标签数据训练的线性二元分类模型预测目标。

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)

使用高级选项创建一个 AveragedPerceptronTrainer ，该模型使用通过布尔标签数据训练的线性二元分类模型预测目标。

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

参数

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

二元分类目录训练器对象。

options
AveragedPerceptronTrainer.Options

教练选项。

返回

AveragedPerceptronTrainer

示例

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 AveragedPerceptronWithOptions
    {
        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 AveragedPerceptronTrainer.Options
            {
                LossFunction = new SmoothedHingeLoss(),
                LearningRate = 0.1f,
                LazyUpdate = false,
                RecencyGain = 0.1f,
                NumberOfIterations = 10
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .AveragedPerceptron(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
                .EvaluateNonCalibrated(transformedTestData);

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.89
            //   AUC: 0.96
            //   F1 Score: 0.88
            //   Negative Precision: 0.87
            //   Negative Recall: 0.92
            //   Positive Precision: 0.91
            //   Positive Recall: 0.85
            //
            // TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      151 |       87 | 0.6345
            //    negative ||       53 |      209 | 0.7977
            //             ||======================
            //   Precision ||   0.7402 |   0.7061 |
        }

        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());
        }
    }
}

适用于

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)

创建一个 AveragedPerceptronTrainer，该模型使用通过布尔标签数据训练的线性二元分类模型预测目标。

public static Microsoft.ML.Trainers.AveragedPerceptronTrainer AveragedPerceptron (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", Microsoft.ML.Trainers.IClassificationLoss lossFunction = default, float learningRate = 1, bool decreaseLearningRate = false, float l2Regularization = 0, int numberOfIterations = 10);
public static Microsoft.ML.Trainers.AveragedPerceptronTrainer AveragedPerceptron (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", Microsoft.ML.Trainers.IClassificationLoss lossFunction = default, float learningRate = 1, bool decreaseLearningRate = false, float l2Regularization = 0, int numberOfIterations = 1);
static member AveragedPerceptron : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * Microsoft.ML.Trainers.IClassificationLoss * single * bool * single * int -> Microsoft.ML.Trainers.AveragedPerceptronTrainer
<Extension()>
Public Function AveragedPerceptron (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional lossFunction As IClassificationLoss = Nothing, Optional learningRate As Single = 1, Optional decreaseLearningRate As Boolean = false, Optional l2Regularization As Single = 0, Optional numberOfIterations As Integer = 10) As AveragedPerceptronTrainer
<Extension()>
Public Function AveragedPerceptron (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional lossFunction As IClassificationLoss = Nothing, Optional learningRate As Single = 1, Optional decreaseLearningRate As Boolean = false, Optional l2Regularization As Single = 0, Optional numberOfIterations As Integer = 1) As AveragedPerceptronTrainer

参数

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

二元分类目录训练器对象。

labelColumnName
String

标签列的名称。 列数据必须是 Boolean。

featureColumnName
String

功能列的名称。 列数据必须是已知大小的向量 Single。

lossFunction
IClassificationLoss

训练过程中最小化的 损失 函数。 如果使用 null， HingeLoss 并导致最大边距平均感知器训练器。

learningRate
Single

由 SGD 使用的初始学习速率。

decreaseLearningRate
Boolean

true 若要减少 learningRate 迭代进度，则为 。否则为 false。 默认值为 false。

l2Regularization
Single

正则化的 L2 权重。

numberOfIterations
Int32

通过训练数据集的传递数。

返回

AveragedPerceptronTrainer

示例

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

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

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

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.72
            //   AUC: 0.79
            //   F1 Score: 0.68
            //   Negative Precision: 0.71
            //   Negative Recall: 0.80
            //   Positive Precision: 0.74
            //   Positive Recall: 0.63
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      151 |       87 | 0.6345
            //    negative ||       53 |      209 | 0.7977
            //             ||======================
            //   Precision ||   0.7402 |   0.7061 |
        }

        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());
        }
    }
}

适用于