/// <summary>
/// Executes the test for a given recommender and a test dataset.
/// </summary>
/// <param name="recommender">The recommender to evaluate.</param>
/// <param name="evaluator">The evaluator.</param>
/// <param name="testDataset">The test dataset.</param>
/// <param name="predictionTime">When the method returns, this parameter contains the total prediction time.</param>
/// <param name="evaluationTime">When the method returns, this parameter contains the total evaluation time.</param>
/// <param name="metrics">When the method returns, this parameter contains names and values of the computed metrics.</param>
public override void Execute(
Recommender recommender,
Evaluator evaluator,
SplitInstanceSource <RecommenderDataset> testDataset,
out TimeSpan predictionTime,
out TimeSpan evaluationTime,
out MetricValueDistributionCollection metrics)
{
Stopwatch predictionTimer = Stopwatch.StartNew();
IDictionary <User, IDictionary <Item, int> > predictions = recommender.Predict(testDataset);
IDictionary <User, IDictionary <Item, RatingDistribution> > uncertainPredictions =
this.computeUncertainPredictionMetrics ? recommender.PredictDistribution(testDataset) : null;
predictionTime = predictionTimer.Elapsed;
var evaluationTimer = Stopwatch.StartNew();
metrics = new MetricValueDistributionCollection();
metrics.Add("MAE", new MetricValueDistribution(evaluator.ModelDomainRatingPredictionMetric(testDataset, predictions, Metrics.AbsoluteError)));
metrics.Add("Per-user MAE", new MetricValueDistribution(evaluator.ModelDomainRatingPredictionMetric(testDataset, predictions, Metrics.AbsoluteError, RecommenderMetricAggregationMethod.PerUserFirst)));
metrics.Add("RMSE", new MetricValueDistribution(Math.Sqrt(evaluator.ModelDomainRatingPredictionMetric(testDataset, predictions, Metrics.SquaredError))));
metrics.Add("Per-user RMSE", new MetricValueDistribution(Math.Sqrt(evaluator.ModelDomainRatingPredictionMetric(testDataset, predictions, Metrics.SquaredError, RecommenderMetricAggregationMethod.PerUserFirst))));
if (this.computeUncertainPredictionMetrics)
{
metrics.Add("Expected MAE", new MetricValueDistribution(evaluator.ModelDomainRatingPredictionMetricExpectation(testDataset, uncertainPredictions, Metrics.AbsoluteError)));
}
evaluationTime = evaluationTimer.Elapsed;
}
/// <summary>
/// Executes the test for a given recommender and a test dataset.
/// </summary>
/// <param name="recommender">The recommender to evaluate.</param>
/// <param name="evaluator">The evaluator.</param>
/// <param name="testDataset">The test dataset.</param>
/// <param name="predictionTime">When the method returns, this parameter contains the total prediction time.</param>
/// <param name="evaluationTime">When the method returns, this parameter contains the total evaluation time.</param>
/// <param name="metrics">When the method returns, this parameter contains names and values of the computed metrics.</param>
public override void Execute(
Recommender recommender,
Evaluator evaluator,
SplitInstanceSource <RecommenderDataset> testDataset,
out TimeSpan predictionTime,
out TimeSpan evaluationTime,
out MetricValueDistributionCollection metrics)
{
Stopwatch predictionTimer = Stopwatch.StartNew();
IDictionary <User, IEnumerable <User> > predictions = evaluator.FindRelatedUsersWhoRatedSameItems(
recommender, testDataset, this.maxRelatedUserCount, this.minCommonRatingCount, this.minRelatedUserPoolSize);
predictionTime = predictionTimer.Elapsed;
Stopwatch evaluationTimer = Stopwatch.StartNew();
metrics = new MetricValueDistributionCollection();
metrics.Add(
"Related users L1 Sim NDCG",
new MetricValueDistribution(evaluator.RelatedUsersMetric(testDataset, predictions, this.minCommonRatingCount, Metrics.Ndcg, Metrics.NormalizedManhattanSimilarity)));
metrics.Add(
"Related users L2 Sim NDCG",
new MetricValueDistribution(evaluator.RelatedUsersMetric(testDataset, predictions, this.minCommonRatingCount, Metrics.Ndcg, Metrics.NormalizedEuclideanSimilarity)));
evaluationTime = evaluationTimer.Elapsed;
}
/// <summary>
/// Executes the test for a given recommender and a test dataset.
/// </summary>
/// <param name="recommender">The recommender to evaluate.</param>
/// <param name="evaluator">The evaluator.</param>
/// <param name="testDataset">The test dataset.</param>
/// <param name="predictionTime">When the method returns, this parameter contains the total prediction time.</param>
/// <param name="evaluationTime">When the method returns, this parameter contains the total evaluation time.</param>
/// <param name="metrics">When the method returns, this parameter contains names and values of the computed metrics.</param>
public override void Execute(
Recommender recommender,
Evaluator evaluator,
SplitInstanceSource <RecommenderDataset> testDataset,
out TimeSpan predictionTime,
out TimeSpan evaluationTime,
out MetricValueDistributionCollection metrics)
{
var predictionTimer = Stopwatch.StartNew();
IDictionary <User, IEnumerable <Item> > recommendations = evaluator.RecommendRatedItems(
recommender, testDataset, this.maxRecommendedItemCount, this.minRecommendationPoolSize);
predictionTime = predictionTimer.Elapsed;
var evaluationTimer = Stopwatch.StartNew();
metrics = new MetricValueDistributionCollection();
Func <int, double> ratingToGainConverter = r => r - testDataset.InstanceSource.StarRatingInfo.MinStarRating + 1; // Prevent non-positive gains
metrics.Add("Recommendation NDCG", new MetricValueDistribution(evaluator.ItemRecommendationMetric(testDataset, recommendations, Metrics.Ndcg, ratingToGainConverter)));
metrics.Add("Recommendation GAP", new MetricValueDistribution(evaluator.ItemRecommendationMetric(testDataset, recommendations, Metrics.GradedAveragePrecision, ratingToGainConverter)));
evaluationTime = evaluationTimer.Elapsed;
}
