/// <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));
}
