MulticlassClassificationExperiment Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
AutoML experiment on multiclass classification datasets.
public sealed class MulticlassClassificationExperiment : Microsoft.ML.AutoML.ExperimentBase<Microsoft.ML.Data.MulticlassClassificationMetrics,Microsoft.ML.AutoML.MulticlassExperimentSettings>type MulticlassClassificationExperiment = class
inherit ExperimentBase<MulticlassClassificationMetrics, MulticlassExperimentSettings>Public NotInheritable Class MulticlassClassificationExperiment
Inherits ExperimentBase(Of MulticlassClassificationMetrics, MulticlassExperimentSettings)- Inheritance
-
MulticlassClassificationExperiment
Examples
using System;
using System.IO;
using System.Linq;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
namespace Microsoft.ML.AutoML.Samples
{
public static class MulticlassClassificationExperiment
{
private static string TrainDataPath = "<Path to your train dataset goes here>";
private static string TestDataPath = "<Path to your test dataset goes here>";
private static string ModelPath = @"<Desired model output directory goes here>\OptDigitsModel.zip";
private static string LabelColumnName = "Number";
private static uint ExperimentTime = 60;
public static void Run()
{
MLContext mlContext = new MLContext();
// STEP 1: Load data
IDataView trainDataView = mlContext.Data.LoadFromTextFile<PixelData>(TrainDataPath, separatorChar: ',');
IDataView testDataView = mlContext.Data.LoadFromTextFile<PixelData>(TestDataPath, separatorChar: ',');
// STEP 2: Run AutoML experiment
Console.WriteLine($"Running AutoML multiclass classification experiment for {ExperimentTime} seconds...");
ExperimentResult<MulticlassClassificationMetrics> experimentResult = mlContext.Auto()
.CreateMulticlassClassificationExperiment(ExperimentTime)
.Execute(trainDataView, LabelColumnName);
// STEP 3: Print metric from the best model
RunDetail<MulticlassClassificationMetrics> bestRun = experimentResult.BestRun;
Console.WriteLine($"Total models produced: {experimentResult.RunDetails.Count()}");
Console.WriteLine($"Best model's trainer: {bestRun.TrainerName}");
Console.WriteLine($"Metrics of best model from validation data --");
PrintMetrics(bestRun.ValidationMetrics);
// STEP 4: Evaluate test data
IDataView testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
MulticlassClassificationMetrics testMetrics = mlContext.MulticlassClassification.Evaluate(testDataViewWithBestScore, labelColumnName: LabelColumnName);
Console.WriteLine($"Metrics of best model on test data --");
PrintMetrics(testMetrics);
// STEP 5: Save the best model for later deployment and inferencing
using (FileStream fs = File.Create(ModelPath))
mlContext.Model.Save(bestRun.Model, trainDataView.Schema, fs);
// STEP 6: Create prediction engine from the best trained model
var predictionEngine = mlContext.Model.CreatePredictionEngine<PixelData, PixelPrediction>(bestRun.Model);
// STEP 7: Initialize new pixel data, and get the predicted number
var testPixelData = new PixelData
{
PixelValues = new float[] { 0, 0, 1, 8, 15, 10, 0, 0, 0, 3, 13, 15, 14, 14, 0, 0, 0, 5, 10, 0, 10, 12, 0, 0, 0, 0, 3, 5, 15, 10, 2, 0, 0, 0, 16, 16, 16, 16, 12, 0, 0, 1, 8, 12, 14, 8, 3, 0, 0, 0, 0, 10, 13, 0, 0, 0, 0, 0, 0, 11, 9, 0, 0, 0 }
};
var prediction = predictionEngine.Predict(testPixelData);
Console.WriteLine($"Predicted number for test pixels: {prediction.Prediction}");
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
private static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"LogLoss: {metrics.LogLoss}");
Console.WriteLine($"LogLossReduction: {metrics.LogLossReduction}");
Console.WriteLine($"MacroAccuracy: {metrics.MacroAccuracy}");
Console.WriteLine($"MicroAccuracy: {metrics.MicroAccuracy}");
}
}
}
Methods
Applies to
Feedback
Submit and view feedback for