/// <summary>Evaluate on the folds of a dataset split</summary> /// <param name="recommender">an item recommender</param> /// <param name="num_folds">the number of folds</param> /// <param name="test_users">a collection of integers with all test users</param> /// <param name="candidate_items">a collection of integers with all candidate items</param> /// <param name="candidate_item_mode">the mode used to determine the candidate items</param> /// <param name="compute_fit">if set to true measure fit on the training data as well</param> /// <param name="show_results">set to true to print results to STDERR</param> /// <returns>a dictionary containing the average results over the different folds of the split</returns> static public ItemRecommendationEvaluationResults DoCrossValidation( this IRecommender recommender, uint num_folds, IList <int> test_users, IList <int> candidate_items, CandidateItems candidate_item_mode = CandidateItems.OVERLAP, bool compute_fit = false, bool show_results = false) { if (!(recommender is ItemRecommender)) { throw new ArgumentException("recommender must be of type ItemRecommender"); } var split = new PosOnlyFeedbackCrossValidationSplit <PosOnlyFeedback <SparseBooleanMatrix> >(((ItemRecommender)recommender).Feedback, num_folds); return(recommender.DoCrossValidation(split, test_users, candidate_items, candidate_item_mode, compute_fit, show_results)); }