/// <summary>For each item, get the users who rated it, both from the training and the test data</summary>
/// <returns>array of array of user IDs</returns>
/// <param name='recommender'>the recommender to retrieve the data from</param>
public static int[][] UsersWhoRated(this ITransductiveRatingPredictor recommender)
{
var ratings = recommender.Ratings;
var additional_feedback = recommender.AdditionalFeedback;
int max_item_id = Math.Max(ratings.MaxItemID, additional_feedback.MaxItemID);
var users_who_rated_the_item = new int[max_item_id + 1][];
for (int item_id = 0; item_id <= max_item_id; item_id++)
{
var training_users = item_id <= ratings.MaxItemID ? from index in ratings.ByItem[item_id] select ratings.Users[index] : new int[0];
var test_users = item_id <= additional_feedback.MaxItemID ? from index in additional_feedback.ByItem[item_id] select additional_feedback.Users[index] : new int[0];
users_who_rated_the_item[item_id] = training_users.Union(test_users).ToArray();
}
return(users_who_rated_the_item);
}
/// <summary>For each user, get the items they rated, both from the training and the test data</summary>
/// <returns>array of array of item IDs</returns>
/// <param name='recommender'>the recommender to retrieve the data from</param>
public static int[][] ItemsRatedByUser(this ITransductiveRatingPredictor recommender)
{
var ratings = recommender.Ratings;
var additional_feedback = recommender.AdditionalFeedback;
int max_user_id = Math.Max(ratings.MaxUserID, additional_feedback.MaxUserID);
var items_rated_by_user = new int[max_user_id + 1][];
for (int user_id = 0; user_id <= max_user_id; user_id++)
{
var training_items = user_id <= ratings.MaxUserID ? from index in ratings.ByUser[user_id] select ratings.Items[index] : new int[0];
var test_items = user_id <= additional_feedback.MaxUserID ? from index in additional_feedback.ByUser[user_id] select additional_feedback.Items[index] : new int[0];
items_rated_by_user[user_id] = training_items.Union(test_items).ToArray();
}
return(items_rated_by_user);
}
/// <summary>Compute the number of feedback events per user</summary>
/// <returns>number of feedback events in both the training and tests data sets, per user</returns>
/// <param name='recommender'>the recommender to get the data from</param>
public static int[] UserFeedbackCounts(this ITransductiveRatingPredictor recommender)
{
int max_user_id = Math.Max(recommender.Ratings.MaxUserID, recommender.AdditionalFeedback.MaxUserID);
var result = new int[max_user_id + 1];
for (int user_id = 0; user_id <= max_user_id; user_id++)
{
if (user_id <= recommender.Ratings.MaxUserID)
{
result[user_id] += recommender.Ratings.CountByUser[user_id];
}
if (user_id <= recommender.AdditionalFeedback.MaxUserID)
{
result[user_id] += recommender.AdditionalFeedback.CountByUser[user_id];
}
}
return(result);
}
/// <summary>Compute the number of feedback events per item</summary>
/// <returns>number of feedback events in both the training and tests data sets, per item</returns>
/// <param name='recommender'>the recommender to get the data from</param>
public static int[] ItemFeedbackCounts(this ITransductiveRatingPredictor recommender)
{
int max_item_id = Math.Max(recommender.Ratings.MaxItemID, recommender.AdditionalFeedback.MaxItemID);
var result = new int[max_item_id + 1];
for (int item_id = 0; item_id <= max_item_id; item_id++)
{
if (item_id <= recommender.Ratings.MaxItemID)
{
result[item_id] += recommender.Ratings.CountByItem[item_id];
}
if (item_id <= recommender.AdditionalFeedback.MaxItemID)
{
result[item_id] += recommender.AdditionalFeedback.CountByItem[item_id];
}
}
return(result);
}
