///
public override void Train()
{
most_popular.Feedback = Feedback;
most_popular.Train();
attribute_count_by_user = new IDictionary <int, int> [MaxUserID + 1];
for (int u = 0; u < attribute_count_by_user.Count; u++)
{
attribute_count_by_user[u] = new Dictionary <int, int>();
}
for (int index = 0; index < Feedback.Count; index++)
{
int user_id = Feedback.Users[index];
int item_id = Feedback.Items[index];
foreach (int a in item_attributes[item_id]) // TODO speed up
{
if (attribute_count_by_user[user_id].ContainsKey(a))
{
attribute_count_by_user[user_id][a]++;
}
else
{
attribute_count_by_user[user_id][a] = 1;
}
}
}
}
private static void startMostPopular(ITimedRatings all_data)
{
removeUserThreshold(ref all_data);
Console.WriteLine("Start iteration Test Most Popular ");
//for (int i = 0; i < 5; i++) {
ITimedRatings validation_data = new TimedRatings(); // 10%
ITimedRatings test_data = new TimedRatings(); // 20%
ITimedRatings training_data = new TimedRatings(); // 70%
readAndSplitData(all_data, ref test_data, ref training_data, ref validation_data);
IPosOnlyFeedback training_data_pos = new PosOnlyFeedback <SparseBooleanMatrix> (); // 80%
for (int index = 0; index < training_data.Users.Count; index++)
{
training_data_pos.Add(training_data.Users [index], training_data.Items [index]);
}
MyMediaLite.ItemRecommendation.MostPopular recommender = new MyMediaLite.ItemRecommendation.MostPopular();
recommender.Feedback = training_data_pos;
DateTime start_time = DateTime.Now;
recommender.Train();
Console.Write("Total Training time needed:");
Console.WriteLine(((TimeSpan)(DateTime.Now - start_time)).TotalMilliseconds);
Console.WriteLine("Final results in this iteration:");
var results = MyMediaLite.Eval.ItemsWeatherItemRecommender.EvaluateTime(recommender, validation_data, training_data, "VALIDATION ", false);
results = MyMediaLite.Eval.ItemsWeatherItemRecommender.EvaluateTime(recommender, test_data, training_data, "TEST ", false);
//}
}
public static void Main(string[] args)
{
// load the data
var training_data = ItemData.Read(args[0]);
var test_data = ItemData.Read(args[1]);
// set up the recommender
var recommender = new MostPopular();
recommender.Feedback = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = recommender.Evaluate(test_data, training_data);
foreach (var key in results.Keys)
Console.WriteLine("{0}={1}", key, results[key]);
Console.WriteLine(results);
// make a score prediction for a certain user and item
Console.WriteLine(recommender.Predict(1, 1));
}
public static void Main(string[] args)
{
// load the data
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
var training_data = ItemRecommendation.Read(args[0], user_mapping, item_mapping);
var relevant_users = training_data.AllUsers; // users that will be taken into account in the evaluation
var relevant_items = training_data.AllItems; // items that will be taken into account in the evaluation
var test_data = ItemRecommendation.Read(args[1], user_mapping, item_mapping);
// set up the recommender
var recommender = new MostPopular();
recommender.Feedback = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = ItemPredictionEval.Evaluate(recommender, test_data, training_data, relevant_users, relevant_items);
foreach (var key in results.Keys)
Console.WriteLine("{0}={1}", key, results[key]);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(user_mapping.ToInternalID(1), item_mapping.ToInternalID(1)));
}
