AutoMLExperiment 類別
定義
重要
部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。
AutoML 實驗的類別
public class AutoMLExperimenttype AutoMLExperiment = classPublic Class AutoMLExperiment- 繼承
-
AutoMLExperiment
範例
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML.Data;
namespace Microsoft.ML.AutoML.Samples
{
public static class AutoMLExperiment
{
public static async Task RunAsync()
{
var seed = 0;
// 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 context = new MLContext(seed);
// Create a list of training data points and convert it to IDataView.
var data = GenerateRandomBinaryClassificationDataPoints(100, seed);
var dataView = context.Data.LoadFromEnumerable(data);
var trainTestSplit = context.Data.TrainTestSplit(dataView);
// Define the sweepable pipeline using predefined binary trainers and search space.
var pipeline = context.Auto().BinaryClassification(labelColumnName: "Label", featureColumnName: "Features");
// Create an AutoML experiment
var experiment = context.Auto().CreateExperiment();
// Redirect AutoML log to console
context.Log += (object o, LoggingEventArgs e) =>
{
if (e.Source == nameof(AutoMLExperiment) && e.Kind > Runtime.ChannelMessageKind.Trace)
{
Console.WriteLine(e.RawMessage);
}
};
// Config experiment to optimize "Accuracy" metric on given dataset.
// This experiment will run hyper-parameter optimization on given pipeline
experiment.SetPipeline(pipeline)
.SetDataset(trainTestSplit.TrainSet, fold: 5) // use 5-fold cross validation to evaluate each trial
.SetBinaryClassificationMetric(BinaryClassificationMetric.Accuracy, "Label")
.SetMaxModelToExplore(100); // explore 100 trials
// start automl experiment
var result = await experiment.RunAsync();
// Expected output samples during training:
// Update Running Trial - Id: 0
// Update Completed Trial - Id: 0 - Metric: 0.5536912515402218 - Pipeline: FastTreeBinary - Duration: 595 - Peak CPU: 0.00 % -Peak Memory in MB: 35.81
// Update Best Trial - Id: 0 - Metric: 0.5536912515402218 - Pipeline: FastTreeBinary
// evaluate test dataset on best model.
var bestModel = result.Model;
var eval = bestModel.Transform(trainTestSplit.TestSet);
var metrics = context.BinaryClassification.Evaluate(eval);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.67
// AUC: 0.75
// F1 Score: 0.33
// Negative Precision: 0.88
// Negative Recall: 0.70
// Positive Precision: 0.25
// Positive Recall: 0.50
// TEST POSITIVE RATIO: 0.1667(2.0 / (2.0 + 10.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 1 | 1 | 0.5000
// negative || 3 | 7 | 0.7000
// ||======================
// Precision || 0.2500 | 0.8750 |
}
private static IEnumerable<BinaryClassificationDataPoint> GenerateRandomBinaryClassificationDataPoints(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 BinaryClassificationDataPoint
{
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 BinaryClassificationDataPoint
{
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());
}
}
}
建構函式
| AutoMLExperiment(MLContext, AutoMLExperiment+AutoMLExperimentSettings) |
AutoML 實驗的類別 |
方法
| AddSearchSpace(String, SearchSpace) |
AutoML 實驗的類別 |
| Run() |
執行實驗並同步傳回最佳試用結果。 |
| RunAsync(CancellationToken) |
執行實驗，並以異步方式傳回最佳試用結果。 如果取消時 |
| SetMaximumMemoryUsageInMegaByte(Double) |
AutoML 實驗的類別 |
| SetMaxModelToExplore(Int32) |
AutoML 實驗的類別 |
| SetMonitor<TMonitor>() |
AutoML 實驗的類別 |
| SetMonitor<TMonitor>(Func<IServiceProvider,TMonitor>) |
AutoML 實驗的類別 |
| SetMonitor<TMonitor>(TMonitor) |
AutoML 實驗的類別 |
| SetTrainingTimeInSeconds(UInt32) |
AutoML 實驗的類別 |
| SetTrialRunner<TTrialRunner>() |
AutoML 實驗的類別 |
| SetTrialRunner<TTrialRunner>(Func<IServiceProvider,TTrialRunner>) |
AutoML 實驗的類別 |
| SetTrialRunner<TTrialRunner>(TTrialRunner) |
AutoML 實驗的類別 |
| SetTuner<TTuner>() |
AutoML 實驗的類別 |
| SetTuner<TTuner>(Func<IServiceProvider,TTuner>) |
AutoML 實驗的類別 |
| SetTuner<TTuner>(TTuner) |
AutoML 實驗的類別 |