StandardTrainersCatalog.SdcaMaximumEntropy Methode
Definition
Wichtig
Einige Informationen beziehen sich auf Vorabversionen, die vor dem Release ggf. grundlegend überarbeitet werden. Microsoft übernimmt hinsichtlich der hier bereitgestellten Informationen keine Gewährleistungen, seien sie ausdrücklich oder konkludent.
Überlädt
| SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>) |
Erstellen Sie SdcaMaximumEntropyMulticlassTrainerein Ziel, das ein Ziel mithilfe eines maximalen Entropyklassifizierungsmodells angibt, das mit einer Koordinatenabstiegsmethode trainiert wurde. |
| SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaMaximumEntropyMulticlassTrainer+Options) |
Erstellen Sie SdcaMaximumEntropyMulticlassTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines maximalen Entropyklassifizierungsmodells, das mit einer Koordinatenabstiegsmethode trainiert wird. |
SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
Erstellen Sie SdcaMaximumEntropyMulticlassTrainerein Ziel, das ein Ziel mithilfe eines maximalen Entropyklassifizierungsmodells angibt, das mit einer Koordinatenabstiegsmethode trainiert wurde.
public static Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer SdcaMaximumEntropy (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaMaximumEntropy : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer<Extension()>
Public Function SdcaMaximumEntropy (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, 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 SdcaMaximumEntropyMulticlassTrainerParameter
Das Objekt des Katalogkatalogs mit mehreren Klassen.
- labelColumnName
- String
Der Name der Bezeichnungsspalte. Die Spaltendaten müssen KeyDataViewTypesein.
- featureColumnName
- String
Der Name der Featurespalte. Die Spaltendaten müssen ein bekannter Vektor von Single.
- exampleWeightColumnName
- String
Der Name der Beispielgewichtsspalte (optional).
Die L2-Gewichtung für die Regularisierung.
Der L1-Normalisierungs-Hyperparameter . Höhere Werte werden dazu führen, dass das Modell sparsamer wird.
Die maximale Anzahl der Übergebenen, die über die Daten ausgeführt werden sollen.
Gibt zurück
Beispiele
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class SdcaMaximumEntropy
{
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 =
// Convert the string labels into key types.
mlContext.Transforms.Conversion
.MapValueToKey(nameof(DataPoint.Label))
// Apply SdcaMaximumEntropy multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy());
// 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();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.22
// Log Loss Reduction: 0.80
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 147 | 0 | 13 | 0.9188
// 1 || 0 | 165 | 12 | 0.9322
// 2 || 14 | 8 | 141 | 0.8650
// ||========================
// Precision ||0.9130 |0.9538 |0.8494 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Gilt für:
SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaMaximumEntropyMulticlassTrainer+Options)
Erstellen Sie SdcaMaximumEntropyMulticlassTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines maximalen Entropyklassifizierungsmodells, das mit einer Koordinatenabstiegsmethode trainiert wird.
public static Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer SdcaMaximumEntropy (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer.Options options);static member SdcaMaximumEntropy : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer.Options -> Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer<Extension()>
Public Function SdcaMaximumEntropy (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As SdcaMaximumEntropyMulticlassTrainer.Options) As SdcaMaximumEntropyMulticlassTrainerParameter
Das Objekt des Katalogkatalogs mit mehreren Klassen.
Traineroptionen.
Gibt zurück
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.MulticlassClassification
{
public static class SdcaMaximumEntropyWithOptions
{
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 SdcaMaximumEntropyMulticlassTrainer.Options
{
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
MaximumNumberOfIterations = 30,
};
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply SdcaMaximumEntropy multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy(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();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.92
// Macro Accuracy: 0.92
// Log Loss: 0.31
// Log Loss Reduction: 0.72
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 147 | 0 | 13 | 0.9188
// 1 || 0 | 164 | 13 | 0.9266
// 2 || 10 | 6 | 147 | 0.9018
// ||========================
// Precision ||0.9363 |0.9647 |0.8497 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}