private static void getSubset(ITimedRatings all_data, ref ITimedRatings sub_all_data)
{
List <int> sub_list_item = new List <int>();
List <DateTime> sub_list_time = new List <DateTime>();
List <int> sub_list_user = new List <int>();
System.Random gen = new System.Random();
IList <int> all_users = all_data.AllUsers;
IList <int> users = new List <int> ();
for (int i = 0; i < all_users.Count; i++)
{
int rnd = gen.Next(100);
if (rnd <= 2)
{
users.Add(all_users[i]);
}
}
for (int i = 0; i < all_data.Users.Count; i++)
{
if (users.Contains(all_data.Users [i]))
{
sub_list_item.Add(all_data.Items [i]);
sub_list_time.Add(all_data.Times [i]);
sub_list_user.Add(all_data.Users [i]);
}
}
create_data(sub_list_user, sub_list_item, sub_list_time, ref sub_all_data);
Console.Write(sub_all_data.Statistics());
Console.WriteLine("finished creating subset:");
Console.WriteLine(DateTime.Now);
}
private static void startBPRMF(ITimedRatings all_data)
{
removeUserThreshold(ref all_data);
Console.WriteLine("Start iteration Test BPRMF");
//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.BPRMF recommender = new MyMediaLite.ItemRecommendation.BPRMF();
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 removeUserThreshold(ref ITimedRatings all_data)
{
IList <int> items_to_delete = new List <int> ();
Dictionary <int, IList <DateTime> > itemsTimes = all_data.getTimesItemDict();
foreach (int item in all_data.AllItems)
{
if (itemsTimes [item].Count < 20)
{
items_to_delete.Add(item);
}
}
foreach (int item in items_to_delete)
{
all_data.RemoveItem(item);
}
IList <int> user_to_delete = new List <int>();
Dictionary <int, IList <int> > userItems = all_data.getItemsUserDict();
foreach (int user in all_data.AllUsers)
{
if (userItems[user].Count < 20)
{
user_to_delete.Add(user);
}
}
foreach (int user in user_to_delete)
{
all_data.RemoveUser(user);
}
Console.Write(all_data.Statistics());
Console.Write("Finished removing thresholds");
}
// private static void readAndSplitDataRandomly(ITimedRatings all_data, ref ITimedRatings validation_data, ref ITimedRatings test_data, ref ITimedRatings training_data){
//
//
// Console.WriteLine (all_data.Statistics ());
//
// List<int> validation_list_item = new List<int>();
// List<int> test_list_item = new List<int>();
// List<int> training_list_item = new List<int>();
//
// List<DateTime> validation_list_time = new List<DateTime>();
// List<DateTime> test_list_time = new List<DateTime>();
// List<DateTime> training_list_time = new List<DateTime>();
//
// List<int> validation_list_user = new List<int>();
// List<int> test_list_user = new List<int>();
// List<int> training_list_user = new List<int>();
//
// System.Random gen = new System.Random();
// for (int i = 0; i < all_data.Users.Count; i++) {
// int rnd = gen.Next(100);
// if(rnd <= 10){
// validation_list_item.Add(all_data.Items[i]);
// validation_list_time.Add(all_data.Times[i]);
// validation_list_user.Add(all_data.Users[i]);
// }else if(rnd <=30){
// test_list_item.Add(all_data.Items[i]);
// test_list_time.Add(all_data.Times[i]);
// test_list_user.Add(all_data.Users[i]);
// }else{
// training_list_item.Add(all_data.Items[i]);
// training_list_time.Add(all_data.Times[i]);
// training_list_user.Add(all_data.Users[i]);
// }
// }
// create_data(validation_list_user, validation_list_item, validation_list_time, ref validation_data);
// create_data(test_list_user, test_list_item, test_list_time, ref test_data);
// create_data(training_list_user, training_list_item, training_list_time, ref training_data);
//
//
//
// Console.Write(validation_data.Statistics ());
// Console.Write(test_data.Statistics ());
// Console.Write(training_data.Statistics());
// Console.WriteLine ("finished creating datasets:");
// Console.WriteLine (DateTime.Now);
// }
//
private static void readAndSplitData(ITimedRatings all_data, ref ITimedRatings test_data, ref ITimedRatings training_data, ref ITimedRatings validation_data)
{
Dictionary <int, IList <DateTime> > user_times = all_data.getTimesUserDict();
Dictionary <int, IList <int> > user_items = all_data.getItemsUserDict();
foreach (int user_id in all_data.AllUsers)
{
List <DateTime> timesOfUser = (List <DateTime>)user_times [user_id];
List <int> itemsOfUser = (List <int>)user_items[user_id];
int amountCheckIns = timesOfUser.Count;
//int validation = (int)(amountCheckIns * 0.1);
int test = (int)(amountCheckIns * 0.2);
int training = (int)(amountCheckIns * 0.7);
List <int> training_list_item = itemsOfUser.GetRange(0, training);
List <int> test_list_item = itemsOfUser.GetRange(training, amountCheckIns - (training));
List <int> validation_list_item = itemsOfUser.GetRange(training + test, amountCheckIns - (training + test));
List <DateTime> training_list_time = timesOfUser.GetRange(0, training);
List <DateTime> test_list_time = timesOfUser.GetRange(training, amountCheckIns - (training));
List <DateTime> validation_list_time = timesOfUser.GetRange(training + test, amountCheckIns - (training + test));
create_data(user_id, validation_list_item, validation_list_time, ref validation_data);
create_data(user_id, test_list_item, test_list_time, ref test_data);
create_data(user_id, training_list_item, training_list_time, ref training_data);
}
Console.Write(validation_data.Statistics());
Console.Write(test_data.Statistics());
Console.Write(training_data.Statistics());
Console.WriteLine("finished creating datasets:");
Console.WriteLine(DateTime.Now);
}
private static void startIterationTestNew(ITimedRatings all_data, bool weather_aware, double beta, double mu, int city, int iterations, string feature = "")
{
removeUserThreshold(ref all_data);
Console.WriteLine("Start iteration test");
ITimedRatings validation_data = new TimedRatings(); // 10%
ITimedRatings test_data = new TimedRatings(); // 30%
ITimedRatings training_data = new TimedRatings(); // 70%
readAndSplitData(all_data, ref test_data, ref training_data, ref validation_data);
int K = 100;
int rangeSize = 10;
int user_count = all_data.AllUsers.Count;
int location_count = all_data.AllItems.Count();
double[,] U1 = new double[user_count, K];
double[,] U2 = new double[user_count, K];
double[,] L1 = new double[location_count, K];
double[,] L2 = new double[location_count, K];
double[,] L3 = new double[location_count, K];
double[,] F = new double[rangeSize, K];
Dictionary <int, int> idMapperCategories = new Dictionary <int, int> ();
Dictionary <int, int> idMapperLocations = new Dictionary <int, int> ();
Dictionary <int, int> idMapperUser = new Dictionary <int, int> ();
initMatrixNormal(all_data.AllUsers, ref U1, ref idMapperUser, K);
initMatrixNormal(all_data.AllUsers, ref U2, ref idMapperUser, K);
initMatrixNormal(all_data.AllItems, ref L1, ref idMapperLocations, K);
initMatrixNormal(all_data.AllItems, ref L2, ref idMapperLocations, K);
initMatrixNormal(all_data.AllItems, ref L3, ref idMapperLocations, K);
initMatrixNormal(rangeSize, ref F, K);
WeatherContextAwareItemRecommender recommender = new WeatherContextAwareItemRecommender(U1, U2, L1, L2, L3, F, idMapperLocations, idMapperUser, city, feature);
recommender.connection_string = connection;
recommender.Ratings = training_data;
recommender.Validation = validation_data;
recommender.Test = test_data;
recommender.weather_aware = weather_aware;
recommender.rangeSize = rangeSize;
recommender.max_iter = iterations;
recommender.evaluation_at = 20;
recommender.beta = beta;
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:");
//}
}
/// <summary>Create a TimedRatingsProxy object</summary>
/// <param name="ratings">a ratings data structure</param>
/// <param name="indices">an index list pointing to entries in the ratings</param>
public TimedRatingsProxy(ITimedRatings ratings, IList<int> indices)
{
Users = new ListProxy<int>(ratings.Users, indices);
Items = new ListProxy<int>(ratings.Items, indices);
Values = new ListProxy<float>(ratings, indices);
Times = new ListProxy<DateTime>(ratings.Times, indices);
MaxUserID = ratings.MaxUserID;
MaxItemID = ratings.MaxItemID;
Scale = ratings.Scale;
EarliestTime = Count > 0 ? Times.Min() : DateTime.MaxValue;
LatestTime = Count > 0 ? Times.Max() : DateTime.MinValue;
}
/// <summary>Create a TimedRatingsProxy object</summary>
/// <param name="ratings">a ratings data structure</param>
/// <param name="indices">an index list pointing to entries in the ratings</param>
public TimedRatingsProxy(ITimedRatings ratings, IList <int> indices)
{
Users = new ListProxy <int>(ratings.Users, indices);
Items = new ListProxy <int>(ratings.Items, indices);
Values = new ListProxy <float>(ratings, indices);
Times = new ListProxy <DateTime>(ratings.Times, indices);
MaxUserID = ratings.MaxUserID;
MaxItemID = ratings.MaxItemID;
Scale = ratings.Scale;
EarliestTime = Count > 0 ? Times.Min() : DateTime.MaxValue;
LatestTime = Count > 0 ? Times.Max() : DateTime.MinValue;
}
private static void startIterationTest(ITimedRatings all_data, bool weather_aware, double beta, double mu, int city, int iterations)
{
removeUserThreshold(ref all_data);
Console.WriteLine("Start iteration test");
ITimedRatings validation_data = new TimedRatings(); // 10%
ITimedRatings test_data = new TimedRatings(); // 30%
ITimedRatings training_data = new TimedRatings(); // 70%
readAndSplitData(all_data, ref test_data, ref training_data, ref validation_data);
int K = 100;
int rangeSize = 10;
int user_count = all_data.AllUsers.Count;
int location_count = all_data.AllItems.Count();
double[,] U1 = new double[user_count, K];
double[,] U2 = new double[user_count, K];
double[,] U3 = new double[user_count, K];
double[,] L1 = new double[location_count, K];
Dictionary <int, int> idMapperLocations = new Dictionary <int, int> ();
Dictionary <int, int> idMapperUser = new Dictionary <int, int> ();
initMatrixNormal(all_data.AllUsers, ref U1, ref idMapperUser, K);
initMatrixNormal(all_data.AllUsers, ref U2, ref idMapperUser, K);
initMatrixNormal(all_data.AllUsers, ref U3, ref idMapperUser, K);
initMatrixNormal(all_data.AllItems, ref L1, ref idMapperLocations, K);
WeatherItemRecommender recommender = new WeatherItemRecommender(U1, U2, U3, L1, idMapperLocations, idMapperUser);
recommender.connection_string = connection;
recommender.Ratings = training_data;
recommender.Validation = validation_data;
recommender.Test = test_data;
recommender.weather_aware = weather_aware;
recommender.max_iter = iterations;
recommender.evaluation_at = 20;
recommender.beta = beta;
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);
}
/// <summary>
/// Gets string for subselecting all id's used from database
/// </summary>
/// <returns>The all identifiers string for database.</returns>
public static string getAllIdsStringForDatabase(ITimedRatings ratings)
{
string all_ids = "(";
bool first = true;
foreach (int id in ratings.AllItems)
{
if (first)
{
all_ids += id.ToString();
first = false;
}
else
{
all_ids += "," + id.ToString();
}
}
all_ids += ")";
return(all_ids);
}
private static void startUserKNN(string data)
{
MyMediaLite.Data.Mapping user_mapping = new MyMediaLite.Data.Mapping();
MyMediaLite.Data.Mapping item_mapping = new MyMediaLite.Data.Mapping();
ITimedRatings all_data = readDataMapped(data, ref user_mapping, ref item_mapping);
removeUserThreshold(ref all_data);
Console.WriteLine("Start iteration Test UserKNN");
//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.UserKNN recommender = new MyMediaLite.ItemRecommendation.UserKNN();
recommender.K = 80;
recommender.Q = 1;
recommender.Weighted = false;
recommender.Alpha = 0.5f;
recommender.Correlation = MyMediaLite.Correlation.BinaryCorrelationType.Jaccard;
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 Dictionary <int, int> getCategories(ITimedRatings ratings)
{
TestMediaLite.DBConnect conn = new TestMediaLite.DBConnect(connection);
string all_ids = getAllIdsStringForDatabase(ratings);
Dictionary <int, int> venueCategoryMapper = new Dictionary <int, int>();
//select suca.id, v.id_int from VENUE v inner join CATEGORY ca on(v.category_id = ca.id) inner join CATEGORY suca on(ca.parent_category = suca.foursquare_id)
List <string>[] res = conn.Select(" select ca.id, v.id_int from VENUE v inner join CATEGORY ca on(v.category_id = ca.id) where v.id_int in " + all_ids, 2);
List <string> ca_ids = res[0];
List <string> venue_ids = res[1];
int i = 0;
foreach (string venue_id in venue_ids)
{
int v_id = int.Parse(venue_id);
int ca_id = int.Parse(ca_ids[i]);
venueCategoryMapper.Add(v_id, ca_id);
i++;
}
return(venueCategoryMapper);
}
//
private static void readAndSplitDataRandomly(ITimedRatings all_data, ref ITimedRatings test_data, ref ITimedRatings training_data)
{
Dictionary <int, IList <DateTime> > user_times = all_data.getTimesUserDict();
Dictionary <int, IList <int> > user_items = all_data.getItemsUserDict();
List <int> test_list_item = new List <int>();
List <int> training_list_item = new List <int>();
List <DateTime> test_list_time = new List <DateTime>();
List <DateTime> training_list_time = new List <DateTime>();
List <int> test_list_user = new List <int>();
List <int> training_list_user = new List <int>();
System.Random gen = new System.Random();
for (int i = 0; i < all_data.Users.Count; i++)
{
int rnd = gen.Next(100);
if (rnd <= 30)
{
test_list_item.Add(all_data.Items[i]);
test_list_time.Add(all_data.Times[i]);
test_list_user.Add(all_data.Users[i]);
}
else
{
training_list_item.Add(all_data.Items[i]);
training_list_time.Add(all_data.Times[i]);
training_list_user.Add(all_data.Users[i]);
}
}
create_data(test_list_user, test_list_item, test_list_time, ref test_data);
create_data(training_list_user, training_list_item, training_list_time, ref training_data);
Console.Write(test_data.Statistics());
Console.Write(training_data.Statistics());
Console.WriteLine("finished creating datasets:");
Console.WriteLine(DateTime.Now);
}
// /// <summary>
// /// Gets string for subselecting all id's used from database
// /// </summary>
// /// <returns>The all identifiers string for database.</returns>
// static private string getAllIdsStringForDatabase(IList<int> allItems){
//
// string all_ids = "(";
// bool first = true;
// foreach (int id in allItems) {
// if (first) {
// all_ids += id.ToString ();
// first = false;
// } else
// all_ids += "," + id.ToString ();
// }
// all_ids += ")";
// return all_ids;
// }
//
//// static public void getWeatherVectorLocation(IList<int> items, string connection_string, ref Dictionary<int,IList<double>> venueWeatherVectors){
//// DBConnect conn = new DBConnect (connection_string);
//// List<string>[] res;
//// res = conn.Select ("select * " +
//// " from weather_avgs_per_venue where id_int in "+getAllIdsStringForDatabase(items), 9);
//// List<string> all_ids = res [0];
//// List<string> temperature = res [1];
//// List<string> precip_intensity = res [2];
//// List<string> wind_speed = res [3];
//// List<string> humidity = res [4];
//// List<string> cloud_cover = res [5];
//// List<string> pressure = res [6];
//// List<string> visibility = res [7];
//// List<string> moonphase = res [8];
//// int i = 0;
//// foreach(string id in all_ids){
//// venueWeatherVectors.Add(int.Parse (id),new List<double> { double.Parse(temperature [i]), double.Parse(precip_intensity [i]), double.Parse(wind_speed [i]), double.Parse(humidity [i]),
//// double.Parse(cloud_cover [i])});
//// i++;
//// }
//// }
/// <summary>Evaluation for rankings of items</summary>
/// <remarks>
/// User-item combinations that appear in both sets are ignored for the test set, and thus in the evaluation,
/// except the boolean argument repeated_events is set.
///
/// The evaluation measures are listed in the Measures property.
/// Additionally, 'num_users' and 'num_items' report the number of users that were used to compute the results
/// and the number of items that were taken into account.
///
/// Literature:
/// <list type="bullet">
/// <item><description>
/// C. Manning, P. Raghavan, H. Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008
/// </description></item>
/// </list>
///
/// On multi-core/multi-processor systems, the routine tries to use as many cores as possible,
/// which should to an almost linear speed-up.
/// </remarks>
/// <param name="recommender">item recommender</param>
/// <param name="test">test cases</param>
/// <param name="training">training data</param>
/// <param name="n">length of the item list to evaluate -- if set to -1 (default), use the complete list, otherwise compute evaluation measures on the top n items</param>
/// <returns>a dictionary containing the evaluation results (default is false)</returns>
// static public ItemRecommendationEvaluationResults Evaluate(
// this IRecommender recommender,
// ITimedRatings test,
// ITimedRatings training,
// string connection_string = "",
// int n = -1,double alpha = 0.1)
// {
//
// var result = new ItemRecommendationEvaluationResults();
// var candidates = test.AllItems.Intersect(training.AllItems).ToList();
// int num_users = 0;
// ThreadPool.SetMinThreads(test.AllUsers.Count, test.AllUsers.Count);
// Dictionary<int,IList<int>> user_items = test.getItemsUserDict ();
// ParallelOptions po = new ParallelOptions{
// MaxDegreeOfParallelism = Environment.ProcessorCount
// };
//
// //foreach(int user_id in test.AllUsers){
// Parallel.ForEach (test.AllUsers, po, user_id => {
// try {
// n = user_items [user_id].Count;
// IList<Tuple<int,float>> prediction;
// prediction = recommender.Recommend (user_id, candidate_items: candidates, n: n);
// var prediction_list = (from t in prediction select t.Item1).ToArray ();
// int num_candidates_for_this_user = candidates.Count ();
// int num_dropped_items = num_candidates_for_this_user - prediction.Count;
// var correct_items = user_items [user_id].Intersect (candidates).ToList ();
// if (correct_items.Count () == 0)
// return;
//
// double auc = AUC.Compute (prediction_list, correct_items, num_dropped_items);
// double map = PrecisionAndRecall.AP (prediction_list, correct_items);
// double ndcg = NDCG.Compute (prediction_list, correct_items);
// double rr = ReciprocalRank.Compute (prediction_list, correct_items);
// var positions = new int[] { 5, 10 };
// var prec = PrecisionAndRecall.PrecisionAt (prediction_list, correct_items, positions);
// var recall = PrecisionAndRecall.RecallAt (prediction_list, correct_items, positions);
//
// // thread-safe incrementing
// lock (result) {
// num_users++;
// result ["AUC"] += (float)auc;
// result ["MAP"] += (float)map;
// result ["NDCG"] += (float)ndcg;
// result ["MRR"] += (float)rr;
// result ["prec@5"] += (float)prec [5];
// result ["prec@10"] += (float)prec [10];
// result ["recall@5"] += (float)recall [5];
// result ["recall@10"] += (float)recall [10];
// }
//
// if (num_users % 1000 == 0)
// Console.Error.Write (".");
// if (num_users % 60000 == 0)
// Console.Error.WriteLine ();
// } catch (Exception e) {
// Console.Error.WriteLine ("===> ERROR: " + e.Message + e.StackTrace);
// throw;
// }
// });
//
// foreach (string measure in Measures)
// result[measure] /= num_users;
// result["num_users"] = num_users;
// result["num_lists"] = num_users;
// result["num_items"] = candidates.Count();
//
// return result;
// }
static public double EvaluateTime(
this IRecommender recommender,
ITimedRatings test,
ITimedRatings training,
string dataset,
bool time_aware,
int n = -1, double alpha = 0.1)
{
Dictionary <int, ItemRecommendationEvaluationResults> userRecommendationResults = new Dictionary <int, ItemRecommendationEvaluationResults> ();
foreach (int user in test.AllUsers)
{
userRecommendationResults.Add(user, new ItemRecommendationEvaluationResults());
}
var candidates = test.AllItems.Intersect(training.AllItems).ToList();
ParallelOptions po = new ParallelOptions {
MaxDegreeOfParallelism = Environment.ProcessorCount
};
bool init = true;
Dictionary <int, IList <int> > trainingUserItems = training.getItemsUserDict();
Parallel.For(0, test.Users.Count - 1, po, index => {
try{
DateTime time = test.Times[index];
int user = test.Users[index];
int item = test.Items[index];
if (trainingUserItems[user].Contains(item))
{
return;
}
IList <int> correct_items = new List <int>();
correct_items.Add(item);
correct_items = correct_items.Intersect(candidates).ToList();
if (correct_items.Count() == 0)
{
return;
}
IList <Tuple <int, float> > prediction;
if (time_aware)
{
prediction = ((ITimeAwareRatingPredictor)recommender).RecommendTime(user, time, candidate_items: candidates, n: 20);
}
else
{
prediction = recommender.Recommend(user, candidate_items: candidates, n: 20);
}
var prediction_list = (from t in prediction select t.Item1).ToArray();
double auc = AUC.Compute(prediction_list, correct_items, 0);
double map = PrecisionAndRecall.AP(prediction_list, correct_items);
double ndcg = NDCG.Compute(prediction_list, correct_items);
double rr = ReciprocalRank.Compute(prediction_list, correct_items);
var positions = new int[] { 5, 10 };
var prec = PrecisionAndRecall.PrecisionAt(prediction_list, correct_items, positions);
var recall = PrecisionAndRecall.RecallAt(prediction_list, correct_items, positions);
lock (userRecommendationResults){
ItemRecommendationEvaluationResults res = userRecommendationResults[user];
res["AUC"] += (float)auc;
res["MAP"] += (float)map;
res["NDCG"] += (float)ndcg;
res["MRR"] += (float)rr;
res["prec@5"] += (float)prec [5];
res["prec@10"] += (float)prec [10];
res["recall@5"] += (float)recall [5];
res["recall@10"] += (float)recall [10];
if (!init)
{
res["AUC"] /= 2;
res["MAP"] /= 2;
res["NDCG"] /= 2;
res["MRR"] /= 2;
res["prec@5"] /= 2;
res["prec@10"] /= 2;
res["recall@5"] /= 2;
res["recall@10"] /= 2;
}
init = false;
userRecommendationResults[user] = res;
}
} catch (Exception e) {
Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace);
throw;
}
});
ItemRecommendationEvaluationResults avg_res = new ItemRecommendationEvaluationResults();
int num_users = 0;
Console.WriteLine("Detailed user results:");
foreach (int user in userRecommendationResults.Keys)
{
Console.Write("User: "******"{0}={1}", key, userRecommendationResults [user] [key]);
}
num_users++;
}
foreach (string measure in Measures)
{
avg_res[measure] /= num_users;
}
Console.WriteLine(dataset + " Avg results:");
foreach (var key in avg_res.Keys)
{
Console.WriteLine("{0}={1}", key, avg_res[key]);
}
return(avg_res["prec@5"]);
}
// public static double startMuTuning(ITimedRatings all_data){
//
// ITimedRatings validation_data = new TimedRatings (); // 10%
// ITimedRatings test_data = new TimedRatings (); // 20%
// ITimedRatings training_data = new TimedRatings (); // 70%
// ITimedRatings sub_all_data = new TimedRatings();
//
// getSubset (all_data, ref sub_all_data);
// removeUserThreshold (ref sub_all_data);
// readAndSplitData (sub_all_data, ref validation_data, ref test_data, ref training_data);
// Dictionary<int,int> venueCategoryMapper = getCategories(sub_all_data);
//
// int K = 100;
// int user_count = sub_all_data.AllUsers.Count;
// int location_count = sub_all_data.AllItems.Count ();
// Console.WriteLine (location_count);
// int category_count = venueCategoryMapper.Values.ToList ().Distinct ().Count();
//
// double[,] U1 = new double[user_count,K];
// double[,] U2 = new double[user_count,K];
// double[,] U3 = new double[user_count,K];
// double[,] U4 = new double[user_count,K];
// double[,] L1 = new double[location_count,K];
// double[,] CA1 = new double[category_count,K];
// Dictionary<int,int> idMapperCategories = new Dictionary<int,int>();
// Dictionary<int,int> idMapperLocations =new Dictionary<int,int>();
// Dictionary<int,int> idMapperUser = new Dictionary<int,int>();
//
//
// initMatrixNormal (sub_all_data.AllUsers, ref U3, ref idMapperUser, K);
// initMatrixNormal (sub_all_data.AllUsers, ref U4, ref idMapperUser, K);
// initMatrixNormal (sub_all_data.AllUsers, ref U1, ref idMapperUser, K);
// initMatrixNormal (sub_all_data.AllUsers, ref U2, ref idMapperUser, K);
// initMatrixNormal(sub_all_data.AllItems, ref L1,ref idMapperLocations, K);
// initMatrixNormal(venueCategoryMapper.Values.ToList ().Distinct ().ToList(), ref CA1,ref idMapperCategories ,K);
// Console.WriteLine (idMapperCategories.Keys.ToList ().Count);
// for (int i = 0; i < idMapperCategories.Keys.ToList ().Count; i++) {
// Console.Write (idMapperCategories.Keys.ToList () [i]);
// Console.Write (", ");
// Console.Write (idMapperCategories.Values.ToList () [i]);
// }
// List<MyMediaLite.Eval.ItemRecommendationEvaluationResults> result_list = new List<MyMediaLite.Eval.ItemRecommendationEvaluationResults> ();
//
// double mu = 0.1;
// double best_mu = 0;
// double best_mu_value = 0;
// while (mu <= 1+0.01) { // +0.01 because of double rounding error
// Console.WriteLine ("Start with mu = " + mu.ToString ());
// WeatherItemRecommender recommender = new WeatherItemRecommender (U1, U2, U3, U4, CA1, L1,idMapperLocations, idMapperCategories, idMapperUser);
// recommender.connection_string = connection;
// recommender.Ratings = training_data;
// recommender.Validation = validation_data;
// recommender.weather_category_aware = true;
// recommender.weather_aware = false;
// recommender.max_iter = 250;
// recommender.evaluation_at = 251;
// recommender.beta = 0f;
// recommender.mu = mu;
// 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:");
// MyMediaLite.Eval.ItemRecommendationEvaluationResults results = MyMediaLite.Eval.ItemsWeatherItemRecommender.EvaluateTime (recommender, test_data, training_data);
// foreach (var key in results.Keys)
// Console.WriteLine ("{0}={1}", key, results [key]);
// if (results ["prec@5"] > best_mu_value) {
// best_mu = mu;
// best_mu_value = results ["prec@5"];
// }
// Console.WriteLine ("Finished mu = " + mu.ToString ());
// mu += 0.1;
//
// }
// return best_mu;
// }
public static void totalTest(string data_file, int mode, int city, int iterations, string feature)
{
ITimedRatings validation_data = new TimedRatings(); // 10%
ITimedRatings test_data = new TimedRatings(); // 20%
ITimedRatings training_data = new TimedRatings(); // 70%
ITimedRatings all_data = readData(data_file);
if (mode == 1)
{
// Console.WriteLine ("Start beta tuning");
// double beta = 0.1;
// for (int i = 0; i < 5; i++) {
// Console.WriteLine ("Start beta total iteration " + i.ToString ());
// beta = startBetaTuning (all_data);
// Console.WriteLine ("Finished beta total iteration " + i.ToString ());
// Console.WriteLine ("Best beta = " + beta.ToString ());
// }
// Console.WriteLine ("End beta tuning");
}
if (mode == 2)
{
double beta = 0.2;
Console.WriteLine("Start geo base-line algo");
startIterationTestNew(all_data, false, beta, 0f, city, iterations);
Console.WriteLine("End geo base-line algo");
}
if (mode == 3)
{
double beta = 0.2;
Console.WriteLine("Start geo weather aware algo");
startIterationTest(all_data, true, beta, 0f, city, iterations);
Console.WriteLine("End geo base-line algo");
}
if (mode == 4)
{
double beta = 0.2;
Console.WriteLine("Start weather context aware algo");
startIterationTestNew(all_data, true, beta, 0f, city, iterations, feature);
Console.WriteLine("End weather context aware algo");
}
if (mode == 5)
{
Console.WriteLine("Start most popular algo");
startMostPopular(all_data);
Console.WriteLine("End most popular algo");
}
if (mode == 6)
{
Console.WriteLine("Start ItemKNN algo");
startItemKNN(data_file);
Console.WriteLine("End ItemKNN algo");
}
if (mode == 7)
{
Console.WriteLine("Start UserKNN algo");
startUserKNN(data_file);
Console.WriteLine("End UserKNN algo");
}
if (mode == 8)
{
Console.WriteLine("Start WRMF algo");
startWRMF(all_data);
Console.WriteLine("End WRMF algo");
}
if (mode == 9)
{
Console.WriteLine("Start BPRMF algo");
startBPRMF(all_data);
Console.WriteLine("End BPRMF algo");
}
}
private static void create_data(List <int> user_list, List <int> item_list, List <DateTime> time_list, ref ITimedRatings data)
{
for (int i = 0; i < item_list.Count; i++)
{
data.Add(user_list[i], item_list [i], 0, time_list [i]);
}
}