/// <summary>
/// Calculates MAE on 10-star rating input data with feature info
/// </summary>
/// <returns> MAE of movies grouped by number of ratings given for them </returns>
public Dictionary <string, double> GetRatingsToMaeOnFeaturePredictions()
{
var starRatingTrainTestSplittingMapping = RecommenderMappingFactory.GetStarsMapping(false);
var trainSource = SplitInstanceSource.Training(RatingsPath);
var testSource = SplitInstanceSource.Test(RatingsPath);
Console.WriteLine($"Calculation of mean absolute error for movies with different numbers of ratings in the training set for data with feature info.");
Rand.Restart(RandomSeed);
var recommender = GetRecommender(starRatingTrainTestSplittingMapping, 16);
recommender.Settings.Training.UseItemFeatures = true;
recommender.Settings.Training.UseSharedUserThresholds = true;
recommender.Settings.Training.Advanced.UserThresholdPriorVariance = 10;
recommender.Train(trainSource);
var distribution = recommender.PredictDistribution(testSource);
var predictionError = PredictionError(testSource, starRatingTrainTestSplittingMapping, distribution);
var ratingsNumToMae = CreateItemPopularityPredictions(trainSource, starRatingTrainTestSplittingMapping, predictionError);
return(ratingsNumToMae);
}
/// <summary>
/// Predictions based on 10-star rating input data and features
/// </summary>
/// <param name="traitsCounts"> Number of item traits </param>
/// <returns> Metrics </returns>
public MetricValues PredictionsOnDataWithFeatures(IList <int> traitsCounts)
{
var starRatingTrainTestSplittingMapping = RecommenderMappingFactory.GetStarsMapping(true);
var binaryRatingTrainTestSplittingMapping = RecommenderMappingFactory.BinarizeMapping(starRatingTrainTestSplittingMapping);
var trainSource = SplitInstanceSource.Training(RatingsPath);
var testSource = SplitInstanceSource.Test(RatingsPath);
var binaryRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(binaryRatingTrainTestSplittingMapping.ForEvaluation());
var starsRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(starRatingTrainTestSplittingMapping.ForEvaluation());
var correctFractions = new Dictionary <string, double>();
var ndcgs = new Dictionary <string, double>();
var maes = new Dictionary <string, double>();
foreach (var traitCount in traitsCounts)
{
Console.WriteLine($"Running metrics calculation for data with features and a model with {traitCount} traits.");
Rand.Restart(RandomSeed);
var recommender = GetRecommender(starRatingTrainTestSplittingMapping, traitCount);
recommender.Settings.Training.UseItemFeatures = true;
recommender.Settings.Training.UseSharedUserThresholds = true;
recommender.Settings.Training.Advanced.UserThresholdPriorVariance = 10;
recommender.Train(trainSource);
var distribution = recommender.PredictDistribution(testSource);
var binarizedPredictions = BinarizePredictions(distribution);
var predictions = recommender.Predict(testSource);
var correctFraction = 1.0 - binaryRatingEvaluator.RatingPredictionMetric(testSource, binarizedPredictions, Metrics.ZeroOneError);
correctFractions.Add(traitCount.ToString(), correctFraction);
var itemRecommendationsForEvaluation = starsRatingEvaluator.RecommendRatedItems(recommender, testSource, 5, 5);
var ndcg = starsRatingEvaluator.ItemRecommendationMetric(testSource, itemRecommendationsForEvaluation, Metrics.Ndcg);
ndcgs.Add(traitCount.ToString(), ndcg);
var mae = starsRatingEvaluator.RatingPredictionMetric(testSource, predictions, Metrics.AbsoluteError);
//Divide maes by 2 to convert 10-star rating to 5-star rating
maes.Add(traitCount.ToString(), mae / 2.0);
}
return(new MetricValues(correctFractions, ndcgs, maes));
}
/// <summary>
/// Predictions based on like/dislike input data
/// </summary>
/// <param name="traitsCounts"> Number of item traits </param>
/// <returns>A tuple of probability of like and metrics </returns>
public (Dictionary <string, double[][]> likeProbability, MetricValues metricValues) PredictionsOnBinaryData(
IList <int> traitsCounts
)
{
var starRatingTrainTestSplittingMapping = RecommenderMappingFactory.GetStarsMapping(true);
var binaryRatingTrainTestSplittingMapping = RecommenderMappingFactory.BinarizeMapping(starRatingTrainTestSplittingMapping);
var trainSource = SplitInstanceSource.Training(RatingsPath);
var testSource = SplitInstanceSource.Test(RatingsPath);
var binaryRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(binaryRatingTrainTestSplittingMapping.ForEvaluation());
var starsRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(starRatingTrainTestSplittingMapping.ForEvaluation());
var correctFractions = new Dictionary <string, double>();
var ndcgs = new Dictionary <string, double>();
var likeProbability = new Dictionary <string, double[][]>();
foreach (var traitCount in traitsCounts)
{
Console.WriteLine($"Running metrics calculation for binarized data and a model with {traitCount} traits.");
Rand.Restart(RandomSeed);
var recommender = GetRecommender(binaryRatingTrainTestSplittingMapping, traitCount);
recommender.Settings.Training.Advanced.UserThresholdPriorVariance = EpsilonPriorVariance;
recommender.Train(trainSource);
var predictions = recommender.Predict(testSource);
likeProbability.Add(traitCount.ToString(), GetLikeProbability(recommender.PredictDistribution(testSource)));
var correctFraction = 1.0 - binaryRatingEvaluator.RatingPredictionMetric(testSource, predictions, Metrics.ZeroOneError);
correctFractions.Add(traitCount.ToString(), correctFraction);
var itemRecommendationsForEvaluation = starsRatingEvaluator.RecommendRatedItems(recommender, testSource, 5, 5);
var ndcg = starsRatingEvaluator.ItemRecommendationMetric(testSource, itemRecommendationsForEvaluation, Metrics.Ndcg);
ndcgs.Add(traitCount.ToString(), ndcg);
}
return(likeProbability, new MetricValues(correctFractions, ndcgs));
}
public ModelRunner(RecommenderMappingFactory recommenderMappingFactory, string ratingsPath)
{
RatingsPath = ratingsPath;
RecommenderMappingFactory = recommenderMappingFactory;
}
/// <summary>
/// Predictions based on 10-star rating input data
/// </summary>
/// <param name="traitsCounts"> Number of item traits </param>
/// <returns>A tuple of probability of thresholds posterior distributions, most probable ratings and metrics </returns>
public (Dictionary <string, IDictionary <string, Gaussian> > posteriorDistributionsOfThresholds, Dictionary <string, double[][]> mostProbableRatings, MetricValues metricValues) PredictionsOnStarRatings(
IList <int> traitsCounts
)
{
var starRatingTrainTestSplittingMapping = RecommenderMappingFactory.GetStarsMapping(true);
var binaryRatingTrainTestSplittingMapping = RecommenderMappingFactory.BinarizeMapping(starRatingTrainTestSplittingMapping);
var trainSource = SplitInstanceSource.Training(RatingsPath);
var testSource = SplitInstanceSource.Test(RatingsPath);
var binaryRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(binaryRatingTrainTestSplittingMapping.ForEvaluation());
var starsRatingEvaluator = new RecommenderEvaluator <SplitInstanceSource <string>, string, Movie, int, int, IDictionary <int, double> >(starRatingTrainTestSplittingMapping.ForEvaluation());
var correctFractions = new Dictionary <string, double>();
var ndcgs = new Dictionary <string, double>();
var maes = new Dictionary <string, double>();
var mostProbableRatings = new Dictionary <string, double[][]>();
var posteriorDistributionsOfThresholds = new Dictionary <string, IDictionary <string, Gaussian> >();
foreach (var traitCount in traitsCounts)
{
Console.WriteLine($"Running metrics calculation for 10-star data and a model with {traitCount} traits.");
Rand.Restart(RandomSeed);
var recommender = GetRecommender(starRatingTrainTestSplittingMapping, traitCount);
recommender.Settings.Training.UseSharedUserThresholds = true;
recommender.Settings.Training.Advanced.UserThresholdPriorVariance = 10;
recommender.Train(trainSource);
var distributions = recommender.PredictDistribution(testSource);
var predictions = recommender.Predict(testSource);
mostProbableRatings.Add(traitCount.ToString(),
GetJaggedDoubles(predictions.Select(userRating =>
userRating.Value.Select(movieRating => (double)movieRating.Value))));
var posteriorDistributionOfThresholds = recommender.GetPosteriorDistributions().Users.First().Value.Thresholds.ToList();
var posteriorDistributionOfThresholdsDict = BeautifyPosteriorDistribution(posteriorDistributionOfThresholds);
posteriorDistributionsOfThresholds.Add(traitCount.ToString(), posteriorDistributionOfThresholdsDict);
var binarizedPredictions = BinarizePredictions(distributions);
var correctFraction = 1.0 - binaryRatingEvaluator.RatingPredictionMetric(testSource, binarizedPredictions, Metrics.ZeroOneError);
correctFractions.Add(traitCount.ToString(), correctFraction);
var itemRecommendationsForEvaluation = starsRatingEvaluator.RecommendRatedItems(recommender, testSource, 5, 5);
var ndcg = starsRatingEvaluator.ItemRecommendationMetric(testSource, itemRecommendationsForEvaluation, Metrics.Ndcg);
ndcgs.Add(traitCount.ToString(), ndcg);
var mae = starsRatingEvaluator.RatingPredictionMetric(testSource, predictions, Metrics.AbsoluteError);
//Divide maes by 2 to convert 10-star rating to 5-star rating
maes.Add(traitCount.ToString(), mae / 2.0);
}
return(posteriorDistributionsOfThresholds, mostProbableRatings,
new MetricValues(correctFractions, ndcgs, maes));
}
/// <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.");
}
