RankingExperiment Class
Definition
Important
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AutoML experiment on ranking datasets.
public sealed class RankingExperiment : Microsoft.ML.AutoML.ExperimentBase<Microsoft.ML.Data.RankingMetrics,Microsoft.ML.AutoML.RankingExperimentSettings>type RankingExperiment = class
inherit ExperimentBase<RankingMetrics, RankingExperimentSettings>Public NotInheritable Class RankingExperiment
Inherits ExperimentBase(Of RankingMetrics, RankingExperimentSettings)- Inheritance
Examples
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using Microsoft.ML.AutoML.Samples.DataStructures;
using Microsoft.ML.Data;
namespace Microsoft.ML.AutoML.Samples
{
public static class RankingExperiment
{
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>\Model.zip";
private static string LabelColumnName = "Label";
private static string GroupColumnName = "GroupId";
private static uint ExperimentTime = 60;
public static void Run()
{
MLContext mlContext = new MLContext();
// STEP 1: Load data
IDataView trainDataView = mlContext.Data.LoadFromTextFile<SearchData>(TrainDataPath, hasHeader: true, separatorChar: ',');
IDataView testDataView = mlContext.Data.LoadFromTextFile<SearchData>(TestDataPath, hasHeader: true, separatorChar: ',');
// STEP 2: Run AutoML experiment
Console.WriteLine($"Running AutoML recommendation experiment for {ExperimentTime} seconds...");
ExperimentResult<RankingMetrics> experimentResult = mlContext.Auto()
.CreateRankingExperiment(new RankingExperimentSettings() { MaxExperimentTimeInSeconds = ExperimentTime })
.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = LabelColumnName,
GroupIdColumnName = GroupColumnName
});
// STEP 3: Print metric from best model
RunDetail<RankingMetrics> bestRun = experimentResult.BestRun;
Console.WriteLine($"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 5: Evaluate test data
IDataView testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
RankingMetrics testMetrics = mlContext.Ranking.Evaluate(testDataViewWithBestScore, labelColumnName: LabelColumnName);
Console.WriteLine($"Metrics of best model on test data --");
PrintMetrics(testMetrics);
// STEP 6: Save the best model for later deployment and inferencing
mlContext.Model.Save(bestRun.Model, trainDataView.Schema, ModelPath);
// STEP 7: Create prediction engine from the best trained model
var predictionEngine = mlContext.Model.CreatePredictionEngine<SearchData, SearchDataPrediction>(bestRun.Model);
// STEP 8: Initialize a new test, and get the prediction
var testPage = new SearchData
{
GroupId = "1",
Features = 9,
Label = 1
};
var prediction = predictionEngine.Predict(testPage);
Console.WriteLine($"Predicted rating for: {prediction.Prediction}");
// New Page
testPage = new SearchData
{
GroupId = "2",
Features = 2,
Label = 9
};
prediction = predictionEngine.Predict(testPage);
Console.WriteLine($"Predicted: {prediction.Prediction}");
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
private static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine($"NormalizedDiscountedCumulativeGains: {metrics.NormalizedDiscountedCumulativeGains}");
Console.WriteLine($"DiscountedCumulativeGains: {metrics.DiscountedCumulativeGains}");
}
}
}
Methods
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