StandardTrainersCatalog.Prior Yöntem
Tanım
Önemli
Bazı bilgiler ürünün ön sürümüyle ilgilidir ve sürüm öncesinde önemli değişiklikler yapılmış olabilir. Burada verilen bilgilerle ilgili olarak Microsoft açık veya zımni hiçbir garanti vermez.
İkili sınıflandırma modeli kullanarak hedefi tahmin eden öğesini oluşturun PriorTrainer.
public static Microsoft.ML.Trainers.PriorTrainer Prior (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string exampleWeightColumnName = default);
static member Prior : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string -> Microsoft.ML.Trainers.PriorTrainer
<Extension()>
Public Function Prior (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional exampleWeightColumnName As String = Nothing) As PriorTrainer
Parametreler
- labelColumnName
- String
Etiket sütununun adı.
- exampleWeightColumnName
- String
Örnek ağırlık sütununun adı (isteğe bağlı).
Döndürülenler
Örnekler
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class Prior
{
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
.Prior();
// 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}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.68
// AUC: 0.50 (this is expected for Prior trainer)
// F1 Score: 0.81
// Negative Precision: 0.00
// Negative Recall: 0.00
// Positive Precision: 0.68
// Positive Recall: 1.00
//
// TEST POSITIVE RATIO: 0.6840 (342.0/(342.0+158.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 342 | 0 | 1.0000
// negative || 158 | 0 | 0.0000
// ||======================
// Precision || 0.6840 | 0.0000 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.3f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.3f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"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());
}
}
}
Açıklamalar
Bu eğitmen, eğitim kümesindeki bir etiketin oranını bu etiketin olasılığı olarak kullanır. Bu eğitmen genellikle diğer daha gelişmiş mdel'ler için bir temel olarak kullanılır.