MklComponentsCatalog.SymbolicSgdLogisticRegression Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Sobrecargas
| SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options) |
Cree SymbolicSgdLogisticRegressionBinaryTrainer con opciones avanzadas, que predice un destino mediante un modelo de clasificación binaria lineal entrenado a través de datos de etiquetas booleanas. El descenso de degradado estocástico (SGD) es un algoritmo iterativo que optimiza una función objetivo diferente. Paraleliza SymbolicSgdLogisticRegressionBinaryTrainer SGD mediante la ejecución simbólica. |
| SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32) |
Cree SymbolicSgdLogisticRegressionBinaryTrainer, que predice un destino mediante un modelo de clasificación binaria lineal entrenado a través de datos de etiquetas booleanas. El descenso de degradado estocástico (SGD) es un algoritmo iterativo que optimiza una función objetivo diferente. Paraleliza SymbolicSgdLogisticRegressionBinaryTrainer SGD mediante la ejecución simbólica. |
SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)
Cree SymbolicSgdLogisticRegressionBinaryTrainer con opciones avanzadas, que predice un destino mediante un modelo de clasificación binaria lineal entrenado a través de datos de etiquetas booleanas. El descenso de degradado estocástico (SGD) es un algoritmo iterativo que optimiza una función objetivo diferente. Paraleliza SymbolicSgdLogisticRegressionBinaryTrainer SGD mediante la ejecución simbólica.
public static Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options options);static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SymbolicSgdLogisticRegressionBinaryTrainer.Options) As SymbolicSgdLogisticRegressionBinaryTrainerParámetros
El parámetro de cadena de consulta BinaryClassificationCatalog.
Opciones avanzadas del algoritmo. Vea SymbolicSgdLogisticRegressionBinaryTrainer.Options.
Devoluciones
Ejemplos
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 SymbolicSgdLogisticRegressionWithOptions
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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 SymbolicSgdLogisticRegressionBinaryTrainer.Options()
{
LearningRate = 0.2f,
NumberOfIterations = 10,
NumberOfThreads = 1,
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SymbolicSgdLogisticRegression(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}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.72
// AUC: 0.81
// F1 Score: 0.66
// Negative Precision: 0.68
// Negative Recall: 0.87
// Positive Precision: 0.80
// Positive Recall: 0.56
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 133 | 105 | 0.5588
// negative || 34 | 228 | 0.8702
// ||======================
// Precision || 0.7964 | 0.6847 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.1f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"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());
}
}
}
Se aplica a
SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)
Cree SymbolicSgdLogisticRegressionBinaryTrainer, que predice un destino mediante un modelo de clasificación binaria lineal entrenado a través de datos de etiquetas booleanas. El descenso de degradado estocástico (SGD) es un algoritmo iterativo que optimiza una función objetivo diferente. Paraleliza SymbolicSgdLogisticRegressionBinaryTrainer SGD mediante la ejecución simbólica.
public static Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", int numberOfIterations = 50);static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * int -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional numberOfIterations As Integer = 50) As SymbolicSgdLogisticRegressionBinaryTrainerParámetros
El parámetro de cadena de consulta BinaryClassificationCatalog.
- featureColumnName
- String
Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single.
- numberOfIterations
- Int32
Número de iteraciones de entrenamiento.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SymbolicSgdLogisticRegression
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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
.SymbolicSgdLogisticRegression();
// 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}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.69
// AUC: 0.76
// F1 Score: 0.68
// Negative Precision: 0.72
// Negative Recall: 0.66
// Positive Precision: 0.66
// Positive Recall: 0.71
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 196 | 42 | 0.8235
// negative || 42 | 220 | 0.8397
// ||======================
// Precision || 0.8235 | 0.8397 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.1f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"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());
}
}
}
Se aplica a
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