/// <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>
public static 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);
}
/// <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));
}
/// <summary>Evaluate an iterative recommender on the folds of a dataset split, display results on STDOUT</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="repeated_events">allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list)</param>
/// <param name="max_iter">the maximum number of iterations</param>
/// <param name="find_iter">the report interval</param>
/// <param name="show_fold_results">if set to true to print per-fold results to STDERR</param>
static public void DoIterativeCrossValidation(
this IRecommender recommender,
uint num_folds,
IList <int> test_users,
IList <int> candidate_items,
CandidateItems candidate_item_mode,
RepeatedEvents repeated_events,
uint max_iter,
uint find_iter = 1,
bool show_fold_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);
recommender.DoIterativeCrossValidation(split, test_users, candidate_items, candidate_item_mode, repeated_events, max_iter, find_iter);
}
/// <summary>Evaluate an iterative recommender on the folds of a dataset split, display results on STDOUT</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="repeated_events">allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list)</param>
/// <param name="max_iter">the maximum number of iterations</param>
/// <param name="find_iter">the report interval</param>
/// <param name="show_fold_results">if set to true to print per-fold results to STDERR</param>
public static void DoIterativeCrossValidation(
this IRecommender recommender,
uint num_folds,
IList<int> test_users,
IList<int> candidate_items,
CandidateItems candidate_item_mode,
RepeatedEvents repeated_events,
uint max_iter,
uint find_iter = 1,
bool show_fold_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);
recommender.DoIterativeCrossValidation(split, test_users, candidate_items, candidate_item_mode, repeated_events, max_iter, find_iter);
}
