MLNetAutoMLRanking.cs

MLNetAutoMLRanking.cs

using System;
using System.Linq;
using System.Net.Http.Headers;
using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;

namespace ConsoleApp1
{
    public class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Start ...");
            Run();
            Console.WriteLine("End");
        }

        private static string TrainDataPath = @"data\train.txt";
        private static string TestDataPath = @"data\test.txt";
        private static string ModelPath = @"Model.zip";
        private static string LabelColumnName = "Label";
        private static string GroupColumnName = "GroupId";
        private static uint ExperimentTime = 600;

        public static void Run()
        {
            var mlContext = new MLContext();

            // STEP 1: Load data
            var trainDataView = mlContext.Data.LoadFromTextFile<SearchData>(TrainDataPath, hasHeader: false, separatorChar: '\t');
            var testDataView = mlContext.Data.LoadFromTextFile<SearchData>(TestDataPath, hasHeader: false, separatorChar: '\t');

            // STEP 2: Run AutoML experiment
            Console.WriteLine($"Running AutoML recommendation experiment for {ExperimentTime} seconds...");
            var experimentResult = mlContext.Auto()
                .CreateRankingExperiment(new RankingExperimentSettings() { MaxExperimentTimeInSeconds = ExperimentTime })
                .Execute(trainDataView, testDataView,
                    new ColumnInformation()
                    {
                        LabelColumnName = LabelColumnName,
                        GroupIdColumnName = GroupColumnName
                    });

            // STEP 3: Print metric from best model
            var bestRun = experimentResult.BestRun;
            Console.WriteLine($"=====================================================");
            Console.WriteLine($"Total models produced: {experimentResult.RunDetails.Count()}");

            var i = 0;
            foreach (var experimentResultRunDetail in experimentResult.RunDetails)
            {
                i++;
                Console.WriteLine($"  {i} - TrainerName: {experimentResultRunDetail.TrainerName}");
                Console.WriteLine($"      Runtime In Seconds: {experimentResultRunDetail.RuntimeInSeconds}");
                Console.WriteLine($"");
                //PrintMetrics(experimentResultRunDetail.ValidationMetrics);
            }

            Console.WriteLine($"");
            Console.WriteLine($"=====================================================");
            Console.WriteLine($"Best model's trainer: {bestRun.TrainerName}");

            // STEP 5: Evaluate test data
            var testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
            var 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)
        {
            if (metrics is null)
            {
                Console.WriteLine($"  No metrics");
                return;
            }
            var ndcg = metrics.NormalizedDiscountedCumulativeGains.Aggregate("", (current, p) => current + p + " - ");
            var dcg = metrics.DiscountedCumulativeGains.Aggregate("", (current, p) => current + p + " - ");
            Console.WriteLine($"  Normalized Discounted Cumulative Gains: {ndcg}");
            Console.WriteLine($"  Discounted Cumulative Gains: {dcg}");
        }
    }

    class SearchData
    {
        [LoadColumn(0)]
        public string GroupId;

        [LoadColumn(1)]
        public float Features;

        [LoadColumn(2)]
        public float Label;
    }

    class SearchDataPrediction
    {
        [ColumnName("PredictedLabel")]
        public float Prediction;

        public float Score { get; set; }
    }
}