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)));
}
public IHttpActionResult GetRecommendation(int id)
{
//var product = products.FirstOrDefault((p) => p.Id == id);
//if (product == null)
//{
// return NotFound();
//}
return(Ok(ItemRecommendation.Recommend(id)));
}
static void LoadData()
{
TimeSpan loading_time = Utils.MeasureTime(delegate() {
// training data
training_data = ItemRecommendation.Read(Path.Combine(data_dir, training_file), user_mapping, item_mapping);
// relevant users and items
if (relevant_users_file != null)
{
relevant_users = new HashSet <int>(user_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_users_file))));
}
else
{
relevant_users = training_data.AllUsers;
}
if (relevant_items_file != null)
{
relevant_items = new HashSet <int>(item_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_items_file))));
}
else
{
relevant_items = training_data.AllItems;
}
if (!(recommender is MyMediaLite.ItemRecommendation.Random))
{
((ItemRecommender)recommender).Feedback = training_data;
}
// user attributes
if (recommender is IUserAttributeAwareRecommender)
{
if (user_attributes_file == null)
{
Usage("Recommender expects --user-attributes=FILE.");
}
else
{
((IUserAttributeAwareRecommender)recommender).UserAttributes = AttributeData.Read(Path.Combine(data_dir, user_attributes_file), user_mapping);
}
}
// item attributes
if (recommender is IItemAttributeAwareRecommender)
{
if (item_attributes_file == null)
{
Usage("Recommender expects --item-attributes=FILE.");
}
else
{
((IItemAttributeAwareRecommender)recommender).ItemAttributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
if (filtered_eval)
{
if (item_attributes_file == null)
{
Usage("--filtered-evaluation expects --item-attributes=FILE.");
}
else
{
item_attributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
// user relation
if (recommender is IUserRelationAwareRecommender)
{
if (user_relations_file == null)
{
Usage("Recommender expects --user-relation=FILE.");
}
else
{
((IUserRelationAwareRecommender)recommender).UserRelation = RelationData.Read(Path.Combine(data_dir, user_relations_file), user_mapping);
Console.WriteLine("relation over {0} users", ((IUserRelationAwareRecommender)recommender).NumUsers); // TODO move to DisplayDataStats
}
}
// item relation
if (recommender is IItemRelationAwareRecommender)
{
if (user_relations_file == null)
{
Usage("Recommender expects --item-relation=FILE.");
}
else
{
((IItemRelationAwareRecommender)recommender).ItemRelation = RelationData.Read(Path.Combine(data_dir, item_relations_file), item_mapping);
Console.WriteLine("relation over {0} items", ((IItemRelationAwareRecommender)recommender).NumItems); // TODO move to DisplayDataStats
}
}
// test data
if (test_ratio == 0)
{
if (test_file != null)
{
test_data = ItemRecommendation.Read(Path.Combine(data_dir, test_file), user_mapping, item_mapping);
}
}
else
{
var split = new PosOnlyFeedbackSimpleSplit <PosOnlyFeedback <SparseBooleanMatrix> >(training_data, test_ratio);
training_data = split.Train[0];
test_data = split.Test[0];
}
});
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
}
static void Main(string[] args)
{
var program = new ItemRecommendation();
program.Run(args);
}
public static void Main(string[] args)
{
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
// check number of command line parameters
if (args.Length < 4)
{
Usage("Not enough arguments.");
}
// read command line parameters
RecommenderParameters parameters = null;
try { parameters = new RecommenderParameters(args, 4); }
catch (ArgumentException e) { Usage(e.Message); }
// other parameters
string data_dir = parameters.GetRemoveString("data_dir");
string relevant_items_file = parameters.GetRemoveString("relevant_items");
string item_attributes_file = parameters.GetRemoveString("item_attributes");
string user_attributes_file = parameters.GetRemoveString("user_attributes");
//string save_mapping_file = parameters.GetRemoveString( "save_model");
int random_seed = parameters.GetRemoveInt32("random_seed", -1);
bool no_eval = parameters.GetRemoveBool("no_eval", false);
bool compute_fit = parameters.GetRemoveBool("compute_fit", false);
if (random_seed != -1)
{
MyMediaLite.Util.Random.InitInstance(random_seed);
}
// main data files and method
string trainfile = args[0].Equals("-") ? "-" : Path.Combine(data_dir, args[0]);
string testfile = args[1].Equals("-") ? "-" : Path.Combine(data_dir, args[1]);
string load_model_file = args[2];
string method = args[3];
// set correct recommender
switch (method)
{
case "BPR-MF-ItemMapping":
recommender = Recommender.Configure(bprmf_map, parameters, Usage);
break;
case "BPR-MF-ItemMapping-Optimal":
recommender = Recommender.Configure(bprmf_map_bpr, parameters, Usage);
break;
case "BPR-MF-ItemMapping-Complex":
recommender = Recommender.Configure(bprmf_map_com, parameters, Usage);
break;
case "BPR-MF-ItemMapping-kNN":
recommender = Recommender.Configure(bprmf_map_knn, parameters, Usage);
break;
case "BPR-MF-ItemMapping-SVR":
recommender = Recommender.Configure(bprmf_map_svr, parameters, Usage);
break;
case "BPR-MF-UserMapping":
recommender = Recommender.Configure(bprmf_user_map, parameters, Usage);
break;
case "BPR-MF-UserMapping-Optimal":
recommender = Recommender.Configure(bprmf_user_map_bpr, parameters, Usage);
break;
default:
Usage(string.Format("Unknown method: '{0}'", method));
break;
}
if (parameters.CheckForLeftovers())
{
Usage(-1);
}
// ID mapping objects
var user_mapping = new EntityMapping();
var item_mapping = new EntityMapping();
// training data
training_data = ItemRecommendation.Read(Path.Combine(data_dir, trainfile), user_mapping, item_mapping);
recommender.Feedback = training_data;
// relevant items
if (!relevant_items_file.Equals(string.Empty))
{
relevant_items = new HashSet <int>(item_mapping.ToInternalID(Utils.ReadIntegers(Path.Combine(data_dir, relevant_items_file))));
}
else
{
relevant_items = training_data.AllItems;
}
// user attributes
if (recommender is IUserAttributeAwareRecommender)
{
if (user_attributes_file.Equals(string.Empty))
{
Usage("Recommender expects user_attributes=FILE.");
}
else
{
((IUserAttributeAwareRecommender)recommender).UserAttributes = AttributeData.Read(Path.Combine(data_dir, user_attributes_file), user_mapping);
}
}
// item attributes
if (recommender is IItemAttributeAwareRecommender)
{
if (item_attributes_file.Equals(string.Empty))
{
Usage("Recommender expects item_attributes=FILE.");
}
else
{
((IItemAttributeAwareRecommender)recommender).ItemAttributes = AttributeData.Read(Path.Combine(data_dir, item_attributes_file), item_mapping);
}
}
// test data
test_data = ItemRecommendation.Read(Path.Combine(data_dir, testfile), user_mapping, item_mapping);
TimeSpan seconds;
Recommender.LoadModel(recommender, load_model_file);
// set the maximum user and item IDs in the recommender - this is important for the cold start use case
recommender.MaxUserID = user_mapping.InternalIDs.Max();
recommender.MaxItemID = item_mapping.InternalIDs.Max();
DisplayDataStats();
Console.Write(recommender.ToString() + " ");
if (compute_fit)
{
seconds = Utils.MeasureTime(delegate() {
int num_iter = recommender.NumIterMapping;
recommender.NumIterMapping = 0;
recommender.LearnAttributeToFactorMapping();
Console.Error.WriteLine();
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "iteration {0} fit {1}", -1, recommender.ComputeFit()));
recommender.NumIterMapping = 1;
for (int i = 0; i < num_iter; i++, i++)
{
recommender.IterateMapping();
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "iteration {0} fit {1}", i, recommender.ComputeFit()));
}
recommender.NumIterMapping = num_iter; // restore
});
}
else
{
seconds = Utils.MeasureTime(delegate() {
recommender.LearnAttributeToFactorMapping();
});
}
Console.Write("mapping_time " + seconds + " ");
if (!no_eval)
{
seconds = EvaluateRecommender(recommender, test_data, training_data);
}
Console.WriteLine();
}
