/// <summary>
/// The main.
/// </summary>
public static void Main(string[] args)
{
InitializeUI();
Outputter outputter = Outputter.GetOutputter(Contents.ChapterName);
// To get results exactly like in the book set it to FullRun.
ExperimentRunType runType = ExperimentRunType.FastRun;
try
{
RunExperiments(outputter, runType);
}
catch (Exception e)
{
Console.WriteLine($"\nAn unhandled exception was thrown:\n{e}");
}
finally
{
if (args.Length == 1)
{
Console.WriteLine("\n\nSaving outputs...");
outputter.SaveOutputAsProducedFlattening(args[0]);
Console.WriteLine("Done saving.");
}
}
}
/// <summary>
/// Forces ModelRunner to run experiments, takes its results and show them via outputter.
/// </summary>
/// <param name="outputter">A container for experiments output.</param>
/// <param name="experimentRunType">
/// When set to <see cref="ExperimentRunType.FullRun"/>, inference is run to convergence, which gives the metrics shown in the book.
/// When set to <see cref="ExperimentRunType.FastRun"/>, the number of iterations in inference is reduced to improve execution time, while still achieving reasonable accuracy numbers, and some of the trait counts are omitted.
/// When set to <see cref="ExperimentRunType.TestRun"/>, the number of iterations in inference is reduced still, and even more of the trait counts are omitted.
/// </param>
public static void RunExperiments(Outputter outputter, ExperimentRunType experimentRunType)
{
// List containing numbers of traits to use in experiments. A separate set of experiments will be run for each number in the list.
var traitCounts =
experimentRunType == ExperimentRunType.FullRun ? new int[] { 0, 1, 2, 4, 8, 16 }
: experimentRunType == ExperimentRunType.FastRun ? new int[] { 0, 1, 2, 4 }
: new int[] { 0, 4 }; // experimentRunType == ExperimentRunType.TestRun
var movies = GetMovies();
var recommenderMappingFactory = new RecommenderMappingFactory(movies);
var modelRunner = new ModelRunner(recommenderMappingFactory, RatingsPath)
{
IterationCount = experimentRunType == ExperimentRunType.FullRun ? 200 : 30
};
#region Section3
Console.WriteLine($"\n{Contents.S3TrainingOurRecommender.NumberedName}.\n");
var(ratings, ratingsToStarsDistribution, rankToRatingsDistributions) = PriorRatings(movies);
outputter.Out(ratings, Contents.S3TrainingOurRecommender.NumberedName, "Ratings");
outputter.Out(ratingsToStarsDistribution,
Contents.S3TrainingOurRecommender.NumberedName, "The number of ratings given for each possible number of stars");
#endregion
#region Section4
Console.WriteLine($"\n{Contents.S4OurFirstRecommendations.NumberedName}.\n");
outputter.Out(modelRunner.GetGroundTruth(recommenderMappingFactory.GetBinaryMapping(true)),
Contents.S4OurFirstRecommendations.NumberedName, "Ground truth");
var(predictions, metricsOfPredictionsOnBinary) = modelRunner.PredictionsOnBinaryData(traitCounts);
outputter.Out(predictions, Contents.S4OurFirstRecommendations.NumberedName, "Predictions");
outputter.Out(metricsOfPredictionsOnBinary.CorrectFractions,
Contents.S4OurFirstRecommendations.NumberedName,
"Fraction of predictions correct");
outputter.Out(metricsOfPredictionsOnBinary.Ndcgs,
Contents.S4OurFirstRecommendations.NumberedName,
"Average NDCG@5");
#endregion
#region Section5
Console.WriteLine($"\n{Contents.S5ModellingStarRatings.NumberedName}.\n");
outputter.Out(modelRunner.GetGroundTruth(recommenderMappingFactory.GetStarsMapping(true)),
Contents.S5ModellingStarRatings.NumberedName, "Ground truth");
var(posteriorDistributionsOfThresholds, predictionsOnStars, metricsOfPredictionsWithStars) =
modelRunner.PredictionsOnStarRatings(traitCounts);
var ratingsNumToMaeStars = modelRunner.GetRatingsNumToMaeOnStarsPredictions();
outputter.Out(posteriorDistributionsOfThresholds, Contents.S5ModellingStarRatings.NumberedName, "Posterior distributions for star ratings thresholds");
outputter.Out(predictionsOnStars, Contents.S5ModellingStarRatings.NumberedName, "Predictions");
var traitsToCorrectFractionSection5 = new Dictionary <string, IDictionary <string, double> >()
{
{ "Initial", metricsOfPredictionsOnBinary.CorrectFractions },
{ "With stars", metricsOfPredictionsWithStars.CorrectFractions }
};
var traitCountToMaeSection5 = new Dictionary <string, IDictionary <string, double> >()
{
{ "Initial", metricsOfPredictionsOnBinary.Ndcgs },
{ "With stars", metricsOfPredictionsWithStars.Ndcgs }
};
outputter.Out(traitsToCorrectFractionSection5, Contents.S5ModellingStarRatings.NumberedName,
"Fraction of predictions correct");
outputter.Out(traitCountToMaeSection5, Contents.S5ModellingStarRatings.NumberedName,
"Average NDCG@5");
outputter.Out(metricsOfPredictionsWithStars.Maes, Contents.S5ModellingStarRatings.NumberedName,
"Mean absolute error (MAE)");
#endregion
#region Section6
Console.WriteLine($"\n{Contents.S6AnotherColdStartProblem.NumberedName}.\n");
outputter.Out(rankToRatingsDistributions,
Contents.S6AnotherColdStartProblem.NumberedName, "The number of ratings given for each movie in the data set as a whole. ");
var metricsOfPredictionsWithFeatures = modelRunner.PredictionsOnDataWithFeatures(traitCounts);
var ratingsNumToMaeFeatures = modelRunner.GetRatingsToMaeOnFeaturePredictions();
outputter.Out(ratingsNumToMaeStars, Contents.S6AnotherColdStartProblem.NumberedName,
"MAE for movies with different numbers of ratings.");
var ratingsNumToMae = new Dictionary <string, Dictionary <string, double> >
{
{ "With stars", ratingsNumToMaeStars },
{ "With stars and features", ratingsNumToMaeFeatures }
};
outputter.Out(ratingsNumToMae, Contents.S6AnotherColdStartProblem.NumberedName,
"MAE for movies with different numbers of ratings. A model including feature information.");
var traitCountToMae = new Dictionary <string, IDictionary <string, double> >()
{
{ "With stars", metricsOfPredictionsWithStars.Maes },
{ "With stars and features", metricsOfPredictionsWithFeatures.Maes },
};
outputter.Out(traitCountToMae, Contents.S6AnotherColdStartProblem.NumberedName,
"Mean absolute error (MAE)");
var traitCountToNdcg = new Dictionary <string, IDictionary <string, double> >()
{
{ "Initial", metricsOfPredictionsOnBinary.Ndcgs },
{ "With stars", metricsOfPredictionsWithStars.Ndcgs },
{ "With stars and features", metricsOfPredictionsWithFeatures.Ndcgs },
};
outputter.Out(traitCountToNdcg, Contents.S6AnotherColdStartProblem.NumberedName,
"Average NDCG@5");
#endregion
Console.WriteLine("\nCompleted all experiments.");
}
