StandardTrainersCatalog.AveragedPerceptron 메서드
정의
중요
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오버로드
| AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options) |
AveragedPerceptronTrainer 부울 레이블 데이터를 통해 학습된 선형 이진 분류 모델을 사용하여 대상을 예측하는 고급 옵션을 만듭니다. |
| AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32) |
AveragedPerceptronTrainer부울 레이블 데이터를 통해 학습된 선형 이진 분류 모델을 사용하여 대상을 예측하는 를 만듭니다. |
AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)
AveragedPerceptronTrainer 부울 레이블 데이터를 통해 학습된 선형 이진 분류 모델을 사용하여 대상을 예측하는 고급 옵션을 만듭니다.
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 AveragedPerceptronTrainer매개 변수
이진 분류 카탈로그 트레이너 개체입니다.
트레이너 옵션.
반환
예제
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());
}
}
}
적용 대상
AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)
AveragedPerceptronTrainer부울 레이블 데이터를 통해 학습된 선형 이진 분류 모델을 사용하여 대상을 예측하는 를 만듭니다.
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 AveragedPerceptronTrainer매개 변수
이진 분류 카탈로그 트레이너 개체입니다.
- lossFunction
- IClassificationLoss
학습 프로세스에서 최소화된 손실 함수입니다. 경우 null, HingeLoss 사용되며 최대 마진 평균 퍼셉트론 트레이너로 이어질 것입니다.
- learningRate
- Single
SGD에서 사용하는 초기 학습 속도입니다.
- decreaseLearningRate
- Boolean
true to decrease the learningRate as iterations progress; otherwise, false.
기본값은 false입니다.
- numberOfIterations
- Int32
학습 데이터 세트를 통과하는 패스 수입니다.
반환
예제
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());
}
}
}