StandardTrainersCatalog.LbfgsLogisticRegression Méthode
Définition
Important
Certaines informations portent sur la préversion du produit qui est susceptible d’être en grande partie modifiée avant sa publication. Microsoft exclut toute garantie, expresse ou implicite, concernant les informations fournies ici.
Surcharges
| LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options) |
Créez LbfgsLogisticRegressionBinaryTrainer avec des options avancées, ce qui prédit une cible à l’aide d’un modèle de classification binaire linéaire entraîné sur les données d’étiquette booléenne. |
| LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean) |
Créez LbfgsLogisticRegressionBinaryTrainer, qui prédit une cible à l’aide d’un modèle de classification binaire linéaire entraîné sur les données d’étiquette booléenne. |
LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)
Créez LbfgsLogisticRegressionBinaryTrainer avec des options avancées, ce qui prédit une cible à l’aide d’un modèle de classification binaire linéaire entraîné sur les données d’étiquette booléenne.
public static Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer LbfgsLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer.Options options);static member LbfgsLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer.Options -> Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer<Extension()>
Public Function LbfgsLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As LbfgsLogisticRegressionBinaryTrainer.Options) As LbfgsLogisticRegressionBinaryTrainerParamètres
Objet de formateur de catalogue de classification binaire.
Arguments avancés à l’algorithme.
Retours
Exemples
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 LbfgsLogisticRegressionWithOptions
{
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 LbfgsLogisticRegressionBinaryTrainer.Options()
{
MaximumNumberOfIterations = 100,
OptimizationTolerance = 1e-8f,
L2Regularization = 0.01f
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.LbfgsLogisticRegression(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
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.87
// AUC: 0.96
// F1 Score: 0.87
// Negative Precision: 0.89
// Negative Recall: 0.87
// Positive Precision: 0.86
// Positive Recall: 0.88
// Log Loss: 0.37
// Log Loss Reduction: 0.63
// Entropy: 1.00
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 210 | 28 | 0.8824
// negative || 35 | 227 | 0.8664
// ||======================
// Precision || 0.8571 | 0.8902 |
}
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());
}
}
}
S’applique à
LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)
Créez LbfgsLogisticRegressionBinaryTrainer, qui prédit une cible à l’aide d’un modèle de classification binaire linéaire entraîné sur les données d’étiquette booléenne.
public static Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer LbfgsLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers 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 LbfgsLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * single * single * single * int * bool -> Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer<Extension()>
Public Function LbfgsLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, 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 LbfgsLogisticRegressionBinaryTrainerParamètres
Objet de formateur de catalogue de classification binaire.
- featureColumnName
- String
Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.
- exampleWeightColumnName
- String
Nom de l’exemple de colonne de poids (facultatif).
- l1Regularization
- Single
Hyperparamètre de normalisation L1. Les valeurs plus élevées ont tendance à conduire à un modèle plus éparse.
- l2Regularization
- Single
Poids L2 pour la normalisation.
- optimizationTolerance
- Single
Seuil de convergence d’optimiseur.
- historySize
- Int32
Taille de la mémoire pour LbfgsLogisticRegressionBinaryTrainer. Low=plus rapide, moins précis.
- enforceNonNegativity
- Boolean
Appliquer des poids non négatifs.
Retours
Exemples
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class LbfgsLogisticRegression
{
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
.LbfgsLogisticRegression();
// 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
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.88
// AUC: 0.96
// F1 Score: 0.87
// Negative Precision: 0.90
// Negative Recall: 0.87
// Positive Precision: 0.86
// Positive Recall: 0.89
// Log Loss: 0.38
// Log Loss Reduction: 0.62
// Entropy: 1.00
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 212 | 26 | 0.8908
// negative || 35 | 227 | 0.8664
// ||======================
// Precision || 0.8583 | 0.8972 |
}
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());
}
}
}
S’applique à
Commentaires
Bientôt disponible : Tout au long de 2024, nous allons supprimer progressivement GitHub Issues comme mécanisme de commentaires pour le contenu et le remplacer par un nouveau système de commentaires. Pour plus d’informations, consultez https://aka.ms/ContentUserFeedback.
Envoyer et afficher des commentaires pour